Sta9000

Baruch College/Mount Sinai
School of Medicine
Program in Health Care
Administration and Policy

Health Data Analysis and
®
Statistics Using SAS

Course Notes
STA9000
Fall 2009

Health Care Data Analysis and Statistics Using SAS® 2

Health Data Analysis and Statistics Using SAS® Course Notes was developed by Raymond R. Arons.
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of
SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product
names are trademarks of their respective companies.
Health Data Analysis and Statistics Using SAS® Course Notes

Copyright © 2009 Raymond R. Arons. All rights reserved. Printed in the United States of America. No
part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by
any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of
the publisher, Raymond R. Arons, Teaneck, New Jersey.

Acknowledgement: I wish to acknowledge the contribution of Victoria L Franke to this manuscript. She
is a published poet, a retired high school English teacher, and a close personal friend. Her generosity of
talent and spirit helped me edit this text and provided me with a sounding board for many of the concepts
presented. Her dedication, combined with an intense interest in the subject matter, resulted in what I hope
to be the quality and accuracy of this manuscript.

Prepared date: 21August09.

Course Description
The course focuses on the applied analysis of public access health care data. The first data set studied is
from the National Center for Health Statistics, The National Hospital Discharge Survey from 2006. With
the permission of the Department of Health and Human Services, Organ Procurement and Transplantation
Network (OPTN), students will have the opportunity to study the nation’s liver transplantation data from
1984 through 2007. From the Office of Statewide Health Planning and Development (OSPHD), students
will have the opportunity to study the 2007 California Hospital Emergency Department data. The course
will apply SAS best practices in the analysis of these data aimed at producing descriptive statistics,
exploratory data analysis (EDA), linear model building, linear model assessment, linear model
interpretation, logistic model building, logistic model assessment, and logistic model interpretation.

Copyright © 2009 Raymond R. Arons, Teaneck, NJ, USA

Health Care Data Analysis and Statistics Using SAS® 3

Table of Contents

Chapter 1 –Navigating SAS Screens,Functions,Icons and Libraries

Chapter 2-Health Data and SAS Functions

Chapter 3-National Hospital Discharge Survey (NHDS

Chapter 4-Organ Procurement Transplantation Network OPTN- Liver Transplants

Chapter 5- Office of Statewide Health Planning & Development (OSHPD) California Emergency
Department Data


School of Medicine

®
Course Notes
STA9000

Chapter 1 –Navigating
SAS
Screens,Functions,Icons
and Libraries

Navigating SAS Screens, Functions, Icons, and Libraries - Lecture 1 2

Health Data Analysis and Statistics Using SAS® Course Note


Prepared date: 24June09.

Course Description
The course focuses on the applied analysis of public access health care data. The first data set studied is
from the National Center for Health Statistics, The National Hospital Discharge Survey from 2006. With
the permission of the Department of Health and Human Services, Organ Procurement and Transplantation
Network (OPTN), students will have the opportunity to study the nation’s liver transplantation data from
1984 through 2007. From the Office of Statewide Health Planning and Development (OSPHD), students
will have the opportunity to study the 2007 California Hospital Emergency Department data. The course
will apply SAS best practices in the analysis of these data aimed at producing descriptive statistics,
exploratory data analysis (EDA), linear model building, linear model assessment, linear model
interpretation, logistic model building, logistic model assessment, and logistic model interpretation.

Prerequisites
Before attending this course, your skills should include:

• An understanding of statistical methods obtained in your first semester course STA9307,
Introduction to Statistics.

• Knowledge of clinical coding methods such as the ICD-9-CM diagnosis and procedures along
with the Medicare and all payer diagnosis-related grouping system (DRGs).

• It would be helpful, but not necessary, if you had previous experience using a statistical program
such as SPSS and/or the Excel Statistical Application.


Chapter 1 - Navigating SAS Screens, Functions, and Libraries 3

Navigating SAS Screens,
Functions, Icons and
Libraries
Lecture 1

Objectives

Learn the housekeeping functions of SAS.
How to move around screens, there functions and
importance.
– editor
– log
– output
Identify the drop down icons and what are there
functions.
Create SAS libraries


Chapter 1-Navigating SAS Screens, Functions, Icons and Libraries 4

Double Click on SAS 9.2

SAS Loading

This is the first event that indicates your SAS 9.2 is booting up. It can vary in real time from 6 seconds to
3 minutes depending upon your computer speed and RAM.



SAS Loaded

SAS Log Screen

SAS Editor Screen

This is the first set of screens when you open SAS. The upper screen is the log screen which tells you
how your program ran and if there are any errors. It will indicate the data read, and the SAS functions
performed along with the duration for each. It is one of the most important screens since it identifies your
errors and provides you with suggested tools to fix your code. Always check your log after you run any
program. The initial log screen, which is presented above, indicates the time needed to boot-up SAS,
information such as site license (City University of New York – T/R, Site 0070007378), and what PC
platform you are operating on. You are asked your site name and site license from SAS Tech-Support to
confirm you as a CUNY user. They will help on line or over the phone to solve coding or any SAS
question. The number of SAS tech support in North America is 919-677-8008.



Log Screen

Editor Screen

The Editor Screen is where you write your SAS code. Make sure you are using the Enhanced Editor,
which will provide you with prompts if your code is in error by turning red. The colors of black, blue,
green and violet tend to be good signs that your coding is OK. .

Output Screen

Log Screen

Editor Screen



You can view all three screens-Output, Log and Editor- in a horizontal or vertical window. The above is
horizontal. This gives you the ability to watch your program execute in the log, and view the output. To
select horizontal versus vertical, you select the Window drop-down menu.

The above is the vertical screen configuration. Notice the three tabs at the bottom of the SAS screen
which reflect the three windows that are open. The highlighted blue above the window indicates you are
ready to write some code.



File Drop Down Menu

The above is the file drop-down menu that has a number of functions. They will be discussed and used
throughout the course.

There are eight (8) drop
down, File, Edit, Tools,
Run, Solutions, Window
and Help.
There are 15 icons in the following order:
Erase Page, Select Folder for Saving, Save,
Print, Print Preview, Cut, Copy, Paste, Undo,
Create Library, SAS Explorer, Run, Clear All
Screens, Break, and Help

...



It should be noted that your professor has not used many of these functions. However, this has
been a useful exercise to review what I know and what I do not know.

1. Similar to the Word, the File drop down menu includes New Program, Open
Program, Close a Screen, Append, Open Object, Save, Save As, Save As Object,
Import Data Export Data, Page Setup, Print, Print Setup, Print Preview, Print,
Send Mail and a list of the last four (4) SAS programs you used.
2. The Edit drop down menu has Undo Cut, Copy, Paste, Delete, Rename, Select
All, Deselect All, Copy Item, and Move Item.
3. The View menu has Large Icons, Small Icons, List Details, Show Tree, Up One
Level, Refresh, Reorder Columns, Enhanced Editor, Program Editor, Log, Output,
Graph, Results, Explorer, and Contents Only.
4. The Tools menu has Query, Table Editor, Graphics Editor, Image Editor, Text
Editor, New Library, New File Shortcut, Customize and Options. Options offer
Log, System Keys Color, Fonts, Titles, Footnotes, Preferences, and Change
Current Folder. I have used the options – Fonts to make my editor fonts larger for
the class to read.
5. Solutions have many functions also that I have yet to learn and they include
Analysis, Development and Programming, Reporting, Accessories, ASSIST,
Desktop, and EIS/OLAP Application Builder.
6. Windows contains Minimize All Windows, Cascade, Tile Vertically, Tile
Horizontally, Resize, Size Docking, Docked, Log, and Editor, which includes
Untitled, Explorer, Output, and Result.
7. Lastly, the Help drop down menu offers off-line help. Using this Window, there is
a range of SAS help and documentation functions. This includes, Getting Started
with SAS, Learning SAS Programming, SAS on the Web and About SAS 9.2
(This allows you to access your site license computer specifications etc.)



Creating a SAS Library
A SAS Library is similar to a folder in a Windows PC.
It allows SAS data sets to be stored and accessed.
Two of our data sets are SAS data sets.
– OPTN Liver Transplants
– California ED Visits
The following series of slides will demonstrate how to
create libraries for these data sets.

Creating a SAS Library –optn.liver

continued...



Creating a SAS Library - optn.liver

This is the ICON SAS
Explorer which will show
you the newly created
data set optn.liver. Left
click it.

continued...


continued...




This is the file
called optn.liver

continued...

If you left click on the file, you will be able to see your SAS data set optn.liver.


Variable name, values
and observation

Above are the first 17 variables and 28 observations of the SAS data set optn.liver



Creating a SAS Library –osphd.cal2007

The New Library Wizard again has three necessary entries. They are:
1. Name – This in our case is OSPHD for the 2007 California ED visit data.
2. Check the box enable at start up so it always is available fir each SAS session.
3. Identify the location of the file trough the brows option. In our case, the data is on the C drive in
folder DATA9000.
4. When all of the above is completed, left click OK.




This is the file called
osphd.cal2007


Variable name, values
and observation


School of Medicine

®

Course Notes
STA9000
Chapter 2-Health Data
and SAS Functions

2

Health Data Analysis and Statistics Using SAS® Course Notes was developed by Raymond R. Arons.SAS
and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS
Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product


Prepared date 15August09

Chapter 2-Health Data and SAS Functions 3

Health Data and SAS
Functions
Lecture 2

The Forms of Raw Data
Magnetic Tape
Cartridge
CD and DVD ROM
File Transport (FTP) over the Internet

40-160 MB

10-60 GB

...


Chapter 2 - Health Data and SAS Functions 4

Data and Definitions
An observation (OBS) in a string of letters and/or
numbers with what is known as a logical record
length (LRECL).
A variable is located in positions along the continuum
of these strings as either individual or a group of
numbers and/or characters.
Variables with values that are pre-identified by their
names are found in SAS data sets.
Observations (OBS) reflect the number of strings of
data, also known as cases, patients, subjects, and
visits.

What are Character, Numeric, Continuous
and Qualitative Variables?
Examples of Character Values are: 01, 022, 001,
0003, E234, V30.1, 00222, 499_broadway, 07666
and t2000.
Numeric Values: 1, 230, 12.1, 0, 3245, 6890,
1000000, 200, 20, 2 and 0.
Continuous Variables: age 0-100 years, length of stay
1-1000 days, height 50-80 inches, weight 100-350 lbs,
temperature 0-110 ºF.
Qualitative Variables : race (01, 02, 03, 04, 05, sex
(M,F), ethnicity (1, 2), payer (01,02,04,06,07), hospital
disposition (01, 02, 03, 04, 05, 20).



What Does Some Data Look Like?
Flat Fixed Length ASCII File
OSRA D D D D D P A P P
BEAG X X X R I A D R R
SXCE 1 2 3 G S Y M 1 2
1234567890123456789012345678901234567
1112447234234338888812703020321303456
2112447234234338888812703020321303456
3223455558312121222222201020145674567
4131122221212123334543003080477745123
5143374551434341212301405090473847778
------Logical Record Length----(LRECL=37 OBS

ASCII Data Set OBS

LRECL

Most data sets such as NHDS06 can be opened in Notepad or Word and you can see the observations,
string of numbers, and logical record length.



SAS Data Set Variables

obs

SAS has become the standard of data sets such as the OPTN Liver transplant data in which are seen the
variable names and their values. They only can be opened in SAS.

Binary Data OBS

10000010000111010100000100010010010
20000010000111010100000100010010010
30000010000111010100000100010010010
40000010000111010100000100010010010
50000010000111010100000100010010010
60000010000111010100000100010010010
70010101010101010101000000101010101
LRECL



SAS Statements to be Covered
Data
Infile
Input
Keep
Indicator Variables
Truth Logic
Class
Where
Procs

The Data Step
data nhds06;
data caid2008;
data care2004;
data sta9000;
data caled2007;

Limit data name to 32 characteristics
Do not forget semicolon;;;;;;;;;;;;;;;;;;;;;

The data step is the beginning of your program. It is like the first bookend on a bookshelf and all that
follows are the books that contain the functions, modifications and preparations that you desire prior to
beginning your data analysis. The second bookend is the SAS procedures or PROCS which range from
basic data housekeeping to complex statistical analysis.



Example INFILE Statements

Infile 'C:data9000nhds06.pu.txt‘ lrecl=88;
Infile 'C:data90test.txt‘lrecl=250 obs=10;
Infile 'C:data9000cal2007 lrecl=999;
Infile 'C:data2000osphd.dat‘ lrecl=200;
Infile 'C:data3000nhds06.pu.txt‘ lrecl=88;

The Infile points to where your raw data set is located. It is like your compass (GPS) to identify the
location of your data, name, observation length (LRECL) and the opportunity to select the number of
observations you choose to study. Note: quotations are placed from the beginning of the location to the
end of the name of the data set. Most errors begin with not correctly identifying the location of your data
set.

The IF Statement
Located within the data statement and in some instances
after the input statement. The if statement selects specific
observations in your data that meet the selected criteria
and ignores all other obs.
IF drg=127;
IF drg=95 or drg=96;
IF drg=121 and age GT 80;
IF dx1=‘4101’;
IF dx1=‘042’ and age GT 70;
IF sex=‘F’ and drg=483;



SAS Program Outline
Data Statement – Name data set data=nhds;

Infile Statement – Where is C:STA9000NHDS06.PU.TXT;
the data located?
input @ @1 SVYEAR $2.
@3 EWBORN 1.
@4 AGEUNITS 1.
@5AGE 2.

Input Statements

white = (race=‘w’);
Change variables in data set –i.e.,
black = (race=‘b’);
indicator variables, etc. asian = (race=‘a’);

proc means data=nhds;
PROC Means - Example var white black asian;
SAS analysis of data title ‘mean analysis’;
run;

The Indicator Variable
The indicator variable takes a qualitative
measurement such as male (M), female (F), white
(W), black (B), death (20), discharges home (1),
physician referral (2) , admitted from a clinic (3),
transferred from a nursing home (4), etc., and converts
it to a quantitative (numeric) value equal to 1 if it is true
and 0 if it did not occur.
This then allows the SAS PROCS to perform statistical
analysis of the data.
For example, you cannot get a mean value from five
Ms and seven Fs. You can get means from five ones
(1) and seven zeros (0).


Chapter 2 - Health Data and SAS Functions
10

Indicator Variables
/****ethnic indicator variables ***/
hispanic = (eth='E1');
non_hispanic = (eth='E2');
hispanic_unk = (eth='99');
hispanic_blnk = (eth='*')

/****gender indicator variables ***/
male = (sex='M');
female = (sex='F');
othgen = (sex='U');
unkgen = (sex='*');

continued...

Indicator Variables
/****race indicator variables ***/

native_american = (race='R1');
asian = (race='R2');
black = (race='R3');
hawaiian = (race='R4');
white = (race='R5');
othrace = (race='R9');
unkrace = (race='99');
race_blk = (race='*');



Creating New Variables from Old
If age=1 then age_group=‘agelt25’;
If age=2 then age_group=‘age25_44’;
If age=9 then age_group=‘agegt_90’;

SAS Print Procedure
proc print data=nhds06;
title ‘print out all variables and observations’;
run;
will print all variables and all observations
proc print data=nhds06 (obs=1000);
title ‘print out all variables and just 1000
observations’;
run;
will print all variables and only 1000 observations
proc print data=nhds06 data=nhds06;;
var age sex;
will print only age and sex and all observations


12

PROC Freq
proc freq data=nhds06;
title ‘Frequency Distribution of All data in nhds06’;
run;
Will perform a frequency on all data in
input statement.
tables age sex race;
title ‘Frequency Distribution of age sex and race in nhds06’;
run;
Will do frequency distribution on only age,sex & race.
tables age*sex*race;
title ‘Frequency Distribution of age by sex by race in nhds06’;
run;
Will perform combined table of age,sex and race

Example of a PROC Freq for a range of demographic variables.
Cumulative Cumulative
agecat5 Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
masked age group 207909 4.76 207909 4.76
0 198 0.00 208107 4.77
Under 1 year 161311 3.70 369418 8.46
1-17 years 908157 20.81 1277575 29.27
18-34 years 1111672 25.47 2389247 54.74
35-64 years 1465023 33.57 3854270 88.31
65 years & over 510278 11.69 4364548 100.00

Sex

sex Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
masked sex 442466 10.14 442466 10.14
female 2130477 48.81 2572943 58.95
male 1791507 41.05 4364450 100.00
unknown sex 98 0.00 4364548 100.00

Ethnicity



eth Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
masked ethnic 846250 19.39 846250 19.39
ukn_ethnic 168051 3.85 1014301 23.24
Hispanic 1190871 27.29 2205172 50.52
non_Hispanic 2159376 49.48 4364548 100.00

Race

race Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
masked race 710284 16.27 710284 16.27
unknown race 126835 2.91 837119 19.18
native american 11001 0.25 848120 19.43
asian 115621 2.65 963741 22.08
black 379891 8.70 1343632 30.79
hawaiian 14802 0.34 1358434 31.12
white 2311007 52.95 3669441 84.07
other race 695107 15.93 4364548 100.00

PROC Tabulate SAS code
The below example of a proc tabulate allows for
tabulating data and simultaneously doing mean values of
los within the analysis age_group, and sex.

proc tabulate data=nhds06;
class age_group male;
var los;
tables age_group all,
(age_group, all*(los*(n*f=5. mean*f=4.1))
/RTS=10;


14
This is a partial example of as Proc Tabulate of rank order by county deaths.
Example of Proc Tabulate Rank order of the County ED Deaths

„ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ†
‚ ‚ died ‚ ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ‰ ‚
‚ ‚ 0 ‚ 1 ‚ All ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒ‰
‚ ‚ age_yrs ‚ age_yrs ‚ age_yrs ‚
‚ ‡ƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒ‰
‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒ‰
‚patco ‚ ‚ ‚ ‚ ‚ ‚ ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚ ‚ ‚ ‚ ‚ ‚
‚Los Angeles ‚632660‚ 31‚ 904‚ 62‚633564‚ 31‚
‚San Diego ‚199414‚ 36‚ 246‚ 64‚199660‚ 36‚
‚Orange ‚174899‚ 34‚ 252‚ 66‚175151‚ 34‚
‚San Bernardino ‚186261‚ 29‚ 261‚ 59‚186522‚ 29‚
‚Riverside ‚177735‚ 32‚ 252‚ 62‚177987‚ 32‚
‚Alameda ‚121411‚ 35‚ 184‚ 62‚121595‚ 35‚
‚Sacramento ‚ 98329‚ 37‚ 242‚ 60‚ 98571‚ 37‚
‚Santa Clara ‚ 91068‚ 34‚ 126‚ 66‚ 91194‚ 34‚

PROC Means with Indicator Variables
proc means data=nhds06;
var
hispanic non_hispanic hispanic_unk hispanic_blnk
male female othgen unkgen native_american asian
black hawaiian white othrace unkrace race_blk
;
title ‘Analysis of NHDS data’;
run;



This is an example of a PROC Means of the homeless in California ED visits.
Means of homeless Variable in the Last Half 2007 California EDs
1 46368 age_yrs 23643 39.0247431 922662.00
agelt1 46368 0.0124871 579.0000000
age1to17 46368 0.0972222 4508.00
age18to34 46368 0.2917745 13529.00
age35to64 46368 0.5240252 24298.00
agege65 46368 0.0540675 2507.00
aknagecat 46368 0.0176415 818.0000000
male 46368 0.5776398 26784.00
female 46368 0.2785542 12916.00
unkgen 46368 0.1431159 6636.00
hispanic 46368 0.1674646 7765.00
non_hispanic 46368 0.4510870 20916.00
hispanic_unk 46368 0.0838941 3890.00
hispanic_blnk 46368 0.2975543 13797.00
white 46368 0.4684265 21720.00
black 46368 0.1289683 5980.00
native_american 46368 0.0037957 176.0000000
asian 46368 0.0097481 452.0000000
hawaiian 46368 0.000905797 42.0000000
othrace 46368 0.0896955 4159.00
unkrace 46368 0.0464329 2153.00
race_blk 46368 0.2520273 11686.00
selfpay 46368 0.4831134 22401.00
othnonfed 46368 0.0948283 4397.00
ppo 46368 0.0333420 1546.00
pos 46368 0.0016606 77.0000000
epo 46368 0.000560732 26.0000000
carehmo 46368 0.0087560 406.0000000

PROC Reg SAS Code
The below is an example of a multivariate regression
analysis. This analysis measures the influence of the
effect variables (independent) of age, race, sex and
outcome upon a response (dependent) variable length of
hospital stay (los).
proc reg data=nhds06;
model los=age male black death;
title ‘Regression analysis of response
variable los and demographic effects’;
run;
quit;


16
Linear regression for age of ED patients in California in 2007

The REG Procedure
Model: MODEL1
Dependent Variable: age_yrs

Number of Observations Read 4364548
Number of Observations Used 2887049
Number of Observations with Missing Values 1477499

Analysis of Variance

Sum of Mean
Source DF Squares Square F Value Pr > F

Model 6 182211742 30368624 61709.6 <.0001
Error 2.89E6 1420775878 492.12165
Corrected Total 2.89E6 1602987621

Root MSE 22.18382 R-Square 0.1137
Dependent Mean 33.59664 Adj R-Sq 0.1137
Coeff Var 66.02987

Parameter Estimates

Parameter Standard
Variable DF Estimate Error t Value Pr > |t|

Intercept 1 36.80474 0.02970 1239.05 <.0001
male 1 -3.76998 0.02631 -143.28 <.0001
white 1 5.00609 0.02814 177.87 <.0001
hispanic 1 -13.43844 0.02848 -471.90 <.0001
selfpay 1 -1.99993 0.03499 -57.16 <.0001
snf 1 37.18328 0.24254 153.31 <.0001
LosAngeles 1 0.52051 0.03214 16.19 <.0001



PROC LOGIST SAS Code
Below is an example of a SAS PROC LOGIST measuring a
qualitative response variable death and the influence of effects of
age, race, and sex.

PROC LOGIST data=nhds06 des;
model death=age male black;
Title ‘The demographic effects that influence death
at discharge’;
run;
quit;

Logistic Regression for in Homeless Visits to California EDs in 2007

The LOGISTIC Procedure

Model Information

Data Set WORK.ED2007
Response Variable homeless
Number of Response Levels 2
Model binary logit
Optimization Technique Fisher's scoring

Type 3 Analysis of Effects

Wald
Effect DF Chi-Square Pr > ChiSq

agecat5 6 101425783 <.0001
sex 3 63393608.4 <.0001
race 7 32233931.4 <.0001
eth 3 46386687.8 <.0001

Analysis of Maximum Likelihood Estimates

Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq


18

Intercept 1 -5.2457 0.000097 2925199665 <.0001
agecat5 * 1 -1.2673 0.000714 3149519.81 <.0001
agecat5 0 1 59.1960 1.3203E9 0.0000 1.0000
agecat5 2 1 0.0727 0.000291 62445.0935 <.0001
agecat5 3 1 0.8342 0.000187 19798648.4 <.0001
agecat5 4 1 1.1877 0.000134 78301886.1 <.0001
agecat5 5 1 0.1294 0.000384 113313.412 <.0001
sex * 1 0.9567 0.000235 16634980.2 <.0001
sex M 1 0.8979 0.000131 46758637.3 <.0001
sex U 1 20.4739 6.8995 8.8059 0.0030
race * 1 -0.3412 0.000182 3504440.60 <.0001
race 99 1 -1.0485 0.000437 5768647.87 <.0001
race R1 1 -0.0646 0.00155 1741.0541 <.0001
race R2 1 -1.0437 0.000844 1528065.56 <.0001
race R4 1 -1.0865 0.00253 184309.705 <.0001
race R5 1 -0.5595 0.000148 14306875.6 <.0001
race R9 1 -0.8255 0.000313 6939869.22 <.0001
eth * 1 0.7271 0.000170 18353359.9 <.0001
eth 99 1 1.6103 0.000304 28029136.3 <.0001
eth E1 1 0.0155 0.000240 4199.1113 <.0001

Odds Ratio Estimates

Point 95% Wald
Effect Estimate Confidence Limits

agecat5 * vs 1 0.282 0.281 0.282
agecat5 0 vs 1 >999.999 <0.001 >999.999

WARNING: The validity of the model fit is questionable.


Point 95% Wald

agecat5 2 vs 1 1.075 1.075 1.076
agecat5 3 vs 1 2.303 2.302 2.304
agecat5 4 vs 1 3.280 3.279 3.280
agecat5 5 vs 1 1.138 1.137 1.139
sex * vs F 2.603 2.602 2.604
sex M vs F 2.455 2.454 2.455
sex U vs F >999.999 >999.999 >999.999
race * vs R3 0.711 0.711 0.711
race 99 vs R3 0.350 0.350 0.351
race R1 vs R3 0.937 0.935 0.940
race R2 vs R3 0.352 0.352 0.353
race R4 vs R3 0.337 0.336 0.339
race R5 vs R3 0.571 0.571 0.572
race R9 vs R3 0.438 0.438 0.438
eth * vs E2 2.069 2.068 2.070
eth 99 vs E2 5.004 5.001 5.007
eth E1 vs E2 1.016 1.015 1.016



Association of Predicted Probabilities and Observed Responses

Percent Concordant 69.1 Somers' D 0.451
Percent Discordant 24.0 Gamma 0.484
Percent Tied 6.9 Tau-a 0.009
Pairs 200225370240 c 0.725

Some of my SAS Rules
Save your work after each minute of coding.
The number one error is a missing semicolon;;;;;
The number two error is a missing semicolon;;;;;
After you submit your work, make it a habit to look at
your log for errors.
SAS is not case sensitive, but using lower case is a
good practice.
A little OCD is very helpful to diagnosing your code for
errors.
Relax and enjoy learning. Your PC will never break.
If it does not run, ask for help.


20


School of Medicine

®

Course Notes
STA9000
Chapter 3-National
Hospital Discharge
Survey (NHDS)

2



Prepared date 29June09.

TABLE OF CONTENTS

Lecture 3-NHDS 3

1. National Hospital Discharge Survey Description 4

2. Downloading the NHDS from the NCHS Web Site 10
3. NHDS Sample Data File Documentation 13
4. NHDS Regulations 23
5. The NHDS SAS Input and Labels Statement 25

6. The NHDS Data, Infile, Input, Labels, PROC Contents, and PROC Freq Statements 27
7. SAS Format Statements for NHDS 35
8. Proc Frequency with Formats Statements 43
9. Exercise 3.1 46
10. SAS Code for NHDS Indicator and Truth Logic Variables 47
11. Exercise 3.2 52
12 Multivariate Linear Regression (Proc Reg) Model of Days of Care (DOC) 53
13. Exercise 3.3 55
14. Logistic Regression Model for the Uninsured (self pay) 56
15. Exercise 3.4 61
16. Proc Tabulate to Identify the Dx Differences between the Uninsured and Insured 62
17. Exercise 3.5 64

APPENDEX 1 Exercise Answers 65
Exercise 3.1 65
Exercise 3,2 74
Exercise 3.3 82
Exercise 3.4 86
Exercise 3.5 93
APPENDIX 2 NHDS Published Paper with Multivariate Analysis 99


Lecture 3-NHDS 4

The National Hospital
Discharge Survey
(NHDS)
Lecture 3

Objectives
How to Download National Hospital Discharge Survey
(NHDS) from the CDCs National Center for Health
Statistics (NCHS).
Create a file on your C drive to store all data and
documentation identified as C:Data9000.
Download via FTP 2006 NHDS Data and
Documentation.
Describe the NHDS public-use data files.
Pose the range of potential study questions.
Write all of the SAS code to analyze the NHDS data.


Lecture 3-NHDS 5

History of NHDS
In 1962 the National Center for Health Statistics
began exploring the possibilities for surveying
hospitals nationally to measure the morbidity and
demographics of patients cared for in the nation’s
hospitals.
A national advisory committee was established and in
1963 the School of Public Health of the University of
Pittsburgh demonstrated the feasibility of such a
program.
In 1964, with the support of the American Hospital
Association and the American Medical Association
and elements of the US Public Health Service, the
first NHDS was initiated.

History of NHDS – Data Source
The National Hospital Discharge Survey (NHDS)
covers discharges from noninstitutional hospitals,
excluding Federal, military, and Veterans
Administration hospitals, located in the 50 States and
the District of Columbia. Only short-stay hospitals with
an average length of stay for all patients of less than
30 days are included in the survey.
In 2006, the sample consisted of 501 hospitals. Of
these hospitals, 23 were found to be out-of-scope
(ineligible) because they went out of business or
otherwise failed to meet the criteria for the NHDS
universe. Of the 478 in-scope (eligible) hospitals, 438
hospitals (92%) responded to the survey.


Lecture 3-NHDS 6

History of NHDS
NCHS has conducted the NHDS continuously since
1965. The original sample was selected in 1964 from
a frame of short-stay hospitals listed in the National
Master Facility Inventory (NMFI).
In 1988, the NHDS was redesigned to provide
geographic sampling comparable to other surveys
conducted by the NCHS; to update the sample of
hospitals selected into the survey; and to maximize
the use of data collected through automated systems.

Sample Design Benefits
The unique stratified sample design allows
researchers for 2006 to study 376,328 hospital
discharges to measure the clinical and demographic
characteristics of the estimated 6,000 short term
hospitals and 34.8 million discharges.
For example, in 2006 the estimated number of
discharges from short-stay hospitals who were women
was 20,864,000. This is 59.9 percent of the estimated
34,854,000 total discharges for that year.


Lecture 3-NHDS 7

Example Analysis Questions from NHDS
What are the differences in diseases treated by acute-
care hospitals for patients with and without insurance?
How does breast surgery vary by region, age, race, and
marital status?
How does radical prostectomies differ by region, age,
race, and payer over the past decade?
Across the nation, and by hospital ownership, are there
significant differences between Medicaid and non-
Medicaid patients having interventional cardiac
procedures and cardiac surgery?

continued...

Example Analysis Questions from NHDS
How does back surgery vary by region, age, race, and
marital status?
How has acute myocardial infarction differ by region,
age, race, and payer over the past decade?
Does low cesarean rates correlate with increase in
birth trauma when region, race, age, and payer are
considered?


Lecture 3-NHDS 8

NHDS Specifications
Data Set Name: NHDS06.PU.TXT
Record Length 88
Number of Records 376,328
ASCII Format

NHDS Variables
SVYEAR = 'Last two digits of survey year'
NEWBORN = 'Newborn infant flag'
AGEUNITS = 'Units for age'
AGE = 'Age in years, months, or days'
SEX = 'Patient sex'
RACE = 'Patient race'
MARSTAT = 'Marital status of patient'
DISC_MON = 'Month of discharge'
DISCSTAT = 'Status at discharge‘ N
DOC = 'Number of days of care' u
m
b
er
continued...
of


Lecture 3-NHDS 9

NHDS Variables
LOSFLAG = 'Zero length of stay flag'
REGION = 'Geographic region of hospital'
BEDSIZE = 'Bedsize grouping for hospital'
OWNER = 'Ownership of hospital'
WEIGHT = 'Analysis weight'
CENTURY = 'First two digits of survey year'
DX1 = 'ICD-9-CM diagnosis code - first'
DX2 = 'ICD-9-CM diagnosis code – second'
DX3 = 'ICD-9-CM diagnosis code - third'
DX4 = 'ICD-9-CM diagnosis code - fourth‘
DX5 = 'ICD-9-CM diagnosis code - fifth'

continued...

NHDS Variables
DX6 = 'ICD-9-CM diagnosis code - sixth'
DX7 = 'ICD-9-CM diagnosis code - seventh'
PD1 = 'ICD-9-CM procedure code - first'
PD2 = 'ICD-9-CM procedure code - second'
PD3 = 'ICD-9-CM procedure code - third'
PD4 = 'ICD-9-CM procedure code - fourth'
ESOP1 = 'Principal expected source of payment'
ESOP2 = 'Secondary expected source of payment'
DRG = 'Diagnosis-related group'
ADM_TYPE = 'Type of admission'
ASOURCE = 'Source of admission'

Before you download files, create on your C drive a new file called Data9000. This is where to store all of
the data and documentation for the course. (C:DATA9000)


Lecture 3-NHDS 10

2. Downloading The NHDS from the NCHS Web Site

Go to the Google site and enter NHDS. This will send you to the following URL
www.cdc.gov/nchs/about/major/hdashd/nhds.htm. The site contains data for the National Hospital
Discharge (NHDS) and Ambulatory Surgery Data (NHAS). We are interested in obtaining the NHDS data
and documentations identified as the survey description. The latest year available at printing was 2006.

Data Files

Documentaion Files


Lecture 3-NHDS 11

Scroll down to the location above that is identified as Public Use Data File and left click on
NHDS. The following screen will appear.

Survey Documentation

Agreement File

SAS Input Code

This screen above contains the (3) PDF documentation files:
(NHDS_2006_Documentation.PDF); the confidentiality agreement file (NHDS06readme.txt);
and SAS Input Code. The documentation file will contain the survey description and methods. It
also identifies all of the variables, the number of observations in the data set and the location of
the variables in each observation. In addition, the length of each observation (LRECL) can be
determined in this documentation either in the narrative or from position of the end of the last
variable. As shown, the data for years 1996 through 2006 are available for analysis of disease,
insurance, and mortality trends. Upon downloading, transfer all three files to C:STA9000 files.


Lecture 3-NHDS 12

The name of the NHDS 2006 file when downloaded is NHDS06.PU.TXT.

Below are eleven (11) selected pages out of the 71 pages of NHDS_2006_Documentation.PDF containing
all of the documentation associated with the survey.


Lecture 3-NHDS 13

3. NHDS SAMPLE DATA FILE DOCUMENTATION

NHDS is systematic random sample of discharges selected from each sampled hospital.
A report on the design and development of the original NHDS has been published (3).

In 1988, the NHDS was redesigned to provide geographic sampling comparability with other
surveys conducted by the NCHS; to update the sample of hospitals selected into the survey;
and to maximize the use of data collected through automated systems. The 1988 hospital
sample was drawn from a sampling frame that consisted of hospitals that were listed in the
April 1987 SMG Hospital Market Database (2), met the above criteria, and began accepting
patients by August 1987. This sampling frame was used until 2003. In 2003 and 2006, the
sampling frame was constructed from the “Healthcare Market Index” and the “Hospital
Market Profiling Solution”, both formerly known as the SMG Hospital Market Database, and
both produced by Verispan, L.L.C. The hospital sample is updated every three years to allow
for hospitals that opened later or changed their eligibility status since the previous sample
update. Updates were performed in 1991, 1994, 1997, 2000, 2003 and 2006.
When the survey was redesigned in 1988, a modified, three-stage design was implemented.
Units selected at the first stage of sampling consisted of either hospitals or geographic
areas, such as counties, groups of counties, or metropolitan statistical areas in the 50 states
and the District of Columbia. Within sampled geographic areas, additional hospitals were
selected. Finally at the last stage, discharges were selected within the sampled hospitals
using systematic random sampling.

These changes in the survey may affect trend data. That is, some of the differences
between NHDS statistics based on the 1965-87 sample and statistics based on the sample
drawn in 1988 may be due to sampling error rather than actual changes in hospital
utilization.

Two data collection procedures were used for the survey. The first was a manual system of
sample selection and data abstraction, used for approximately 55 percent of the responding
hospitals. The second was an automated method, used for approximately 45 percent of the
responding hospitals. The automated method involved the purchase of computerized data
files from abstracting service organizations, state data systems, or from the hospitals
themselves.
In the manual system, the sample selection and the transcription of information from the
hospital records to abstract forms were performed at the hospitals. Of the hospitals using
this system in 2006, about 25 percent had the work performed by their own medical records
staff. In the remaining hospitals using the manual system, personnel of the U.S. Bureau of
the Census did the work on behalf of NCHS. The completed forms, along with sample
selection control sheets, were forwarded to NCHS for editing, and weighting.
For the automated system, NCHS purchased files containing machine-readable medical
record data from which records were systematically sampled by NCHS.


Lecture 3-NHDS 14

3. NHDS SAMPLE DATA FILE DOCUMENTATION (continued)

Admission and Source of Admission. The coding of all variables can be found in section III of
this document which describes the record layout.

Medical Coding and Edits. The medical information that was recorded manually on the
sample patient abstracts was coded centrally by NCHS staff. A maximum of seven
diagnostic codes was assigned for each sample abstract. In addition, if the medical
information included surgical or nonsurgical procedures, a maximum of four codes for these
procedures was assigned. The system currently used for coding the diagnoses and
procedures on the medical abstract forms as well as on the commercial abstracting services
data files is the International Classification of Diseases, 9th Revision, Clinical Modification, or
ICD-9-CM (4).
NHDS usually presents diagnoses and procedures in the order they are listed on the abstract
form or obtained from abstract services; however, there are exceptions. For women
discharged after a delivery, a code of V27 from the supplemental classification is entered as
the first-listed code, with a code designating either normal or abnormal delivery in the
second-listed position. In another exception, a decision was made to reorder some acute
myocardial infarction diagnoses. If an acute myocardial infarction is listed with other
circulatory diagnoses and is other than the first entry, it is reordered to the first position. If
a symptom appears as a first-listed code and a diagnosis appears as a secondary code, the
diagnosis replaces the symptom which is moved back.
Following conversion of the data on the medical abstract to a computer file and combining it
with the automated data files, a final medical edit was accomplished by computer inspection
and by a manual review of rejected records. Priority was given to medical information in the
editing decision.

The methodology for editing the NHDS was revised in the 1996 data year. As before, the
updated edit program was designed to make as few changes as possible in the data, while
following the same general specifications as the previous edit program,. However, there
may be some minor anomalies which would be apparent when examining data over time,
performing trend analyses, or examining combinations of variables. Particular features of
the updated edit program which may affect certain variables are:

When imputation for missing age and sex data is performed, the known distribution of
these variables is maintained, according to categories of the First-Listed Diagnosis.
Procedure codes are no longer reordered. However, if the length of stay is missing for
a discharge, it is imputed based on the first-listed procedure. Principal and additional
expected sources of payment are no longer re-ordered, with
one exception: Self-Pay is listed as the principal source only if there are no other

The Medical Abstract Form (Appendix E) and the automated data contain items relating to
the personal characteristics of the patient, including birth date or age, sex, race, and marital
status, but not name and address; administrative information, including admission and
discharge dates, and discharge status; and medical information, including diagnoses and
surgical and nonsurgical procedures. Since 1977, patient zip code, expected source of
payment, and dates of surgery have also been collected. (Patient date of birth and zip code
are confidential information and are not available to the public). Beginning in the 2001
survey year, two additional items were included in the medical abstract form: Type of


Lecture 3-NHDS 15


How to Use the Data File. The NHDS records are weighted to allow inflation to national or
regional estimates. The weight applied to each record is found in location 21-25. To produce
an estimate of the number of discharges, the weights for the desired records must be
summed. To produce an estimate for number of days of care, the weight must be multiplied
by the days of care (location 13-16) and these products are summed. Average length of
stay data can be obtained by dividing the days of care by the number of discharges as
calculated above.
Appendix D contains weighted and unweighted frequencies for selected variables. These
may be used as a cross-check when processing NHDS data. Please note that, beginning in
2003, Chapter 00 – Procedures and Interventions, Not Elsewhere Classified – was added to
the list of frequencies for all-listed procedures on page 58.

Diagnosis-Related Groups (DRGs). Many users of the NHDS data have expressed an
interest in converting the medical data to DRGs. This has been done using DRG Grouper
Programs obtained from the Centers for Medicare and Medicaid Services (formerly HCFA).
The DRGs and the DRG Grouper Programs were developed outside of the National Center for
Health Statistics; any questions about DRGs, other than specific questions about how they
relate to NHDS data, should be addressed elsewhere.

Questions. Questions concerning NHDS data should be directed to:
Centers for Disease Control and Prevention
National Center for Health Statistics Division of
Health Care Statistics Ambulatory and Hospital
Care Statistics Branch 3311 Toledo Road
Hyattsville, Maryland 20782 Phone:
301.458.4321 Fax: 301.458.4032 email:
NHDS@cdc.gov

For more information about the NHDS, visit our website:
http://www.cdc.gov/nchs/about/major/hdasd/nhds.htm


Lecture 3-NHDS 16

REFERENCES
1
Dennison C, Pokras R. Plan and Operation of the National Hospital Discharge Survey. National Center
for Health Statistics. Vital Health Stat 1 (39). 2000.
http://www.cdc.gov/nchs/data/series/sr_01/sr01_039.pdf
2
SMG Marketing Group, Inc. Hospital Market Database. Chicago: Healthcare Information Specialists,
Chicago, IL. April 1987, April 1991, April 1994, April 1997, April 2000; Verispan, L.L.C. Healthcare
Market Index and Hospital Market Profiling Solution, 2003 and 2006.
3
Simmons WR, Schnack GA. Development of the Design of the NCHS Hospital Discharge Survey.
National Center for Health Statistics. Vital Health Stat 2(39). 1977.
4
International Classification of Diseases, 9th Revision, Clinical Modification, 6th edition. U.S. Department
of Health and Humans Services, National Center for Health Statistics, Health Care Financing
Administration. 2004.
5
Office of the Secretary, Department of Health and Human Services: Health Information Policy Council:
1984 Revision of the Uniform Hospital Discharge Data Set. Federal Register, Volume 50, No. 147. July
31, 1985.
6
Bieler GS, Williams RL. Analyzing Survey Data Using SUDAAN Release 7.5. Research Triangle
Institute: Research Triangle Park, N.C. 1997.



Lecture 3-NHDS 17

II. TECHNICAL DESCRIPTION OF DATA FILE

Data Set Name NHDS06.PU.TXT
Record Length 88

III. RECORD LAYOUT: Location and Coding of Data Elements

This section provides detailed information for each sampled record on the file, with a
description of each item included on the record. Data elements are arranged
sequentially according to their physical location on the file. Unless otherwise stated in
the Item Description, the data are derived from the abstract form or from automated
sources. The SMG Hospital Market Database file, Verispan’s data products, and the
hospital interview are alternate sources of data; some other items are computer
generated.



Lecture 3-NHDS 18

Item Number of
Location Item description Code description
Number Positions

1 1-2 2 Survey Year 06
2 3 1 Newborn status
1=Newborn 2=Not newborn

3 4 1 Units for age
1=Years 2=Months 3=Days

4 5-6 2 Age in years, If units=years: 00-99* If units=months: 01-
months, or days 11 If units=days: 00-28 *Ages 100 and
over were recoded to 99

5 7 1 Sex
1=Male 2=Female

6 8 1 Race 1=White 2=Black/African American
3=American Indian/Alaskan Native
4=Asian 5=Native Hawaiian or other
Pacific Isldr 6=Other 8=Multiple race
indicated 9=Not stated

7 9 1 Marital status 1=Married 2=Single 3=Widowed
4=Divorced 5=Separated 9=Not stated

8 10-11 2 Discharge month 01-12=January to December
9 12 1 Discharge Status

1=Routine/discharged home 2=Left
against medical advice
3=Discharged/transferred to short-term
facility 4=Discharged/transferred to long-
term care institution 5=Alive, disposition
not stated 6=Dead 9=Not stated or not
reported
10 13-16 4 Days of care Use to calculate number of days of care.
Values of zero generated by the computer
from admission and discharge dates were
changed to one. (Discharges for which
dates of admission and discharge are the



Lecture 3-NHDS 19

Item Number of
Number Positions
same are identified in Item Number 11)
11 17 1 Length of stay flag
0=Less than 1 day 1=One day or more

12 18 1 Geographic region 1=Northeast 2=Midwest 3=South 4=West

13 19 1 Number of beds, 1=6-99 2=100-199 3=200-299 4=300-499
recode 5=500 and over

14 20 1 Hospital ownership
1=Proprietary 2=Government 3=Nonprofit,
including church

15 21-25 5 Analysis weight Use to obtain weighted estimates
16 26-27 2 First two digits of 20
survey year

17 28-32 5 Diagnosis code #1 *







24 63-66 4 Procedure code#1 *



28 79-80 2 Principal expected
source of payment

01=Worker’s compensation 02=Medicare
03=Medicaid 04=Other government
05=Blue Cross/Blue Shield 06=HMO/PPO


Lecture 3-NHDS 20


Item Number of
Number Positions
07=Other private insurance 08=Self-pay
09=No charge 10=Other 99=Not stated

29 81-82 2 Secondary Same coding as item 28 above, except
expected source of Not Stated left blank (not coded to 99)
payment
30 83-85 3 Diagnosis-Related Grouper version 23.0
Groups (DRG)
31 86 1 Type of Admission 1 = Emergency 2 = Urgent 3 = Elective 4
= Newborn 9 = Not available

32 87-88 2 Source of 01 = Physician referral 02 = Clinical
Admission referral 03 = HMO referral 04 = Transfer
from a hospital 05 = Transfer from skilled
nursing facility 06 = Transfer from other
health facility 07 = Emergency room 08 =
Court/law enforcement 09 = Other 99 =
Not available

*Diagnosis and procedure codes are in compliance with the International Classification of Diseases, 9th
Revision, Clinical Modification, (ICD-9-CM). For diagnosis codes, there is an implied decimal between
positions 3 and 4. For E-codes, the implied decimal is between the 4th and 5th position. For inapplicable
4th or 5th digits, a dash is inserted. For procedure codes, there is an implied decimal between positions 2
and 3. For inapplicable 3rd or 4th digits, a dash is inserted


Lecture 3-NHDS 21


APPENDIX A

DEFINITION OF TERMS

Terms relating to hospitals and hospitalization

Hospitals: Short stay hospitals or hospitals whose specialty is general (medical or surgical),
or children's general. Hospitals must have 6 beds or more staffed for patients use. Federal
hospitals and hospital units of institutions are not included.

Type of ownership of hospital: The type of organization that controls and operates the
hospital. Hospitals are grouped as follows:

Not for Profit: Hospitals operated by a church or another not for profit organization.

Government: Hospitals operated by State and local government.

Proprietary: Hospitals operated by individuals, partnerships, or corporations for profit.

Patient: A person who is formally admitted to the inpatient service of a short-stay hospital
for observation, care, diagnosis, or treatment, or by birth.

Discharge: The formal release of a patient by a hospital; that is, the termination of a period
of hospitalization by death or by disposition to place of residence, nursing home, or another
hospital. The terms "discharges" and "patients discharged" are used synonymously.

Discharge rate: The ratio of the number of hospital discharges during the year to the
number of persons in the civilian population on July 1 of that year.

Days of care: The total number of patient days accumulated at time of discharge by
patients discharged from short stay hospitals during a year. A stay of less than 1 day
(patient admission and discharge on the same day) is counted as 1 day in the summation
of total days of care. For patients admitted and discharged on different days, the number of
days of care is computed by counting all days from (and including) the date of admission to
(but not including) the date of discharge.

Rate of days of care: The ratio of the number of patient days accumulated at time of
discharge to the number of persons in the civilian population on July 1 of that year.

Average length of stay: The total number of days of care accumulated at time of
discharge by patients discharged during the year, divided by the number of patients
discharged.

Discharge diagnoses: One or more diseases or injuries (or some factor that influences
health status and contact with health services that is not itself a current illness or injury)
listed by the attending physician on the medical record of a patient. In the NHDS,
discharge (or final) diagnoses listed on the face sheet (summary sheet) of the medical
record are transcribed in the order listed. Each sample discharge is assigned a maximum of
seven five-digit codes according to ICD-9-CM (4).


Lecture 3-NHDS 22


Principal diagnosis: The condition established after study to be chiefly responsible for
occasioning the admission of the patient to the hospital for care.

First-listed diagnosis: The coded diagnosis identified as the principal diagnosis or listed
first on the face sheet of the medical record if the principal diagnosis cannot be identified.
The number of first-listed diagnoses is equivalent to the number of discharges.

Procedure: One or more surgical or nonsurgical operations, procedures, or special
treatments listed by the physician on the medical record. In the NHDS, all terms listed on
the face sheet (summary sheet) of the medical record under the caption "operation,"
"operative procedures," "operations and/or special treatment," and the like are transcribed
in the order listed. A maximum of four procedures are coded.

Rate of procedures: The ratio of the number of all-listed procedures during a year to the
number of persons in the civilian population on July 1 of that year determines the rate of
procedures.

Demographic terms

Age: Refers to the age of the patient on the birthday prior to admission to the hospital
inpatient service.

Population: Civilian population is the resident population excluding members of the Armed
Forces.

Geographic regions: Hospitals are classified by location in one of the four geographic
regions of the United States corresponding to those used by the U.S. Bureau of the
Census:

NORTHEAST MIDWEST SOUTH WEST
Maine Michigan Delaware Montana
New Hampshire Ohio Maryland Idaho
Vermont Illinois District of Columbia Wyoming
Massachusetts Indiana Virginia Colorado
Connecticut Wisconsin West Virginia New Mexico
Rhode Island Minnesota North Carolina Arizona
New York Iowa South Carolina Utah
New Jersey Missouri Georgia Nevada
Pennsylvania North Dakota Florida Washington
South Dakota Kentucky Oregon
Nebraska Tennessee California
Kansas Alabama Hawaii
Mississippi Alaska
Arkansas
Louisiana
Oklahoma


Lecture 3-NHDS 23

4. NHDS REGULATIONS
Below is the NHDS06readme.txt containing the Public Health Laws that govern the use of this data.

!WARNING -- DATA USE RESTRICTIONS!
READ CAREFULLY BEFORE USING

The Public Health Service Act (Section 308(d)) provides that the data
collected by the National Center for Health Statistics (NCHS), Centers
for Disease Control and Prevention (CDC), may be used only for the
purpose of health statistical reporting and analysis. Any effort to
determine the identity of any reported case is prohibited by this law.

NCHS does all it can to assure that the identity of data subjects
cannot be disclosed. All direct identifiers, as well as any
characteristics that might lead to identification, are omitted from the
dataset. Any intentional identification or disclosure of a person or
establishment violates the assurances of confidentiality given to the
providers of the information. Therefore, users will:

1. Use the data in this dataset for statistical reporting and analysis
only.
2. Make no use of the identity of any person or establishment
discovered inadvertently and advise the Director, NCHS, of any
such discovery.
3. Not link this dataset with individually identifiable data from other
NCHS or non-NCHS datasets.

BY USING THESE DATA, YOU SIGNIFY YOUR AGREEMENT TO COMPLY WITH
THE ABOVE-STATED STATUTORILY-BASED AGREEMENTS.

*************************************************************************

The following is a list of files needed to use the 2006 NHDS:

File Name File Description

NHDS06.PU.TXT NHDS 2006 ASCII dataset

nhds06.pdf NHDS 2006 documentation in
Adobe Acrobat PDF format.
This file also contains all
necessary population spreadsheets
(for rate calculations) and
standard error tables.

*NOTE: You will need Adobe Acrobat Reader software to view the
documentation file. The Reader software can be downloaded for
free from: http://www.adobe.com/products/acrobat/readstep2.html

-----------------------------------------------------------------------


Lecture 3-NHDS 24

CAUTION - Because the NHDS is a sample survey, the application
of weights to the sample data is needed in order to produce national
estimates of inpatient hospital utilization statistics.

-----------------------------------------------------------------------

For questions concerning NHDS data, please contact:

Ambulatory and Hospital Care Statistics Branch
Division of Health Care Statistics
National Center for Health Statistics
Centers for Disease Control and Prevention
3311 Toledo Road
Hyattsville, Maryland 20782
Phone: 301.458.4321
Fax: 301.458.4032
Email: NHDS@cdc.gov

For more information about the NHDS, visit our website:
http://www.cdc.gov/nchs/about/major/hdasd/nhds.htm

For email discussions and dissemination of NHDS data, join our
Hospital Discharge and Ambulatory Surgery Data listserv (HDAS-DATA).
In the body of an email message (leaving the subject line blank), type:
subscribe hdas-data Your Name

Send this message to:
listserv@cdc.gov


Lecture 3-NHDS 25

5. SAS DATA INPUT FILE
DATA ANYNAME ;
INFILE 'refer to location of NHDS datafile by drive and directory'
;
INPUT @ 1 SVYEAR $2.
@ 3 NEWBORN 1.
@ 4 AGEUNITS 1.
@ 5 AGE 2.
@ 7 SEX 1.
@ 8 RACE 1.
@ 9 MARSTAT 1.
@ 10 DISC_MON $2.
@ 12 DISCSTAT 1.
@ 13 DOC 4.
@ 17 LOSFLAG 1.
@ 18 REGION 1.
@ 19 BEDSIZE 1.
@ 20 OWNER 1.
@ 21 WEIGHT 5.
@ 26 CENTURY $2.
@ 28 DX1 $5.
@ 33 DX2 $5.
@ 38 DX3 $5.
@ 43 DX4 $5.
@ 48 DX5 $5.
@ 53 DX6 $5.
@ 58 DX7 $5.
@ 63 PD1 $4.
@ 67 PD2 $4.
@ 71 PD3 $4.
@ 75 PD4 $4.
@ 79 ESOP1 2.
@ 81 ESOP2 2.
@ 83 DRG $3.
@ 86 ADM_TYPE 1.
@ 87 ASOURCE 2.
;
LABEL SVYEAR = 'Last two digits of survey year'
SEX = 'Patient sex'
DISCSTAT = 'Status at discharge'
DOC = 'Number of days of care'


Lecture 3-NHDS 26

DX2 = 'ICD-9-CM diagnosis code - second'
DX4 = 'ICD-9-CM diagnosis code - fourth'
;


Lecture 3-NHDS 27

6. The NHDS Data, Infile, Input, Labels, PROC Contents, and
PROC Freq Statements.
nhds02d.sas

The program below contains the basic structure for a SAS analysis of a data set. The data is
identified along with its location. The input statement presents the variable and its location
within each observation. Labels give names to each variable; PROC Contents yields the
specifications of your data set, and PROC Freq provides the frequency distributions of each of
the variables both unweighted and weighted.
/****nhds02d.sas**********/
data nhds2006 ;
Infile 'C:DATA9000NHDS06.PU.TXT' LRECL=88;
input
@ 1 SVYEAR $2.
@ 3 NEWBORN 1.
@ 4 AGEUNITS 1.
@ 5 AGE 2.
@ 7 SEX 1.
@ 8 RACE 1.
@ 9 MARSTAT 1.
@ 10 DISC_MON $2.
@ 12 DISCSTAT 1.
@ 13 DOC 4.
@ 17 LOSFLAG 1.
@ 18 REGION 1.
@ 19 BEDSIZE 1.
@ 20 OWNER 1.
@ 21 WEIGHT 5.
@ 26 CENTURY $2.
@ 28 DX1 $5.
@ 33 DX2 $5.
@ 38 DX3 $5.
@ 43 DX4 $5.
@ 48 DX5 $5.
@ 53 DX6 $5.
@ 58 DX7 $5.
@ 63 PD1 $4.
@ 67 PD2 $4.
@ 71 PD3 $4.
@ 75 PD4 $4.
@ 79 ESOP1 2.
@ 81 ESOP2 2.
@ 83 DRG $3.
@ 86 ADM_TYPE 1.
@ 87 ASOURCE 2.
;


Lecture 3-NHDS 28

/*****Labels are used to identify input variable names****/

LABEL SVYEAR = 'Last two digits of survey year'
SEX = 'Patient sex'
DISCSTAT = 'Status at discharge'
DOC = 'Number of days of care'
DX2 = 'ICD-9-CM diagnosis code - second'
DX4 = 'ICD-9-CM diagnosis code - fourth'
;
/**********Identify the Variables in the Data Set*******/
proc contents data=nhds2006;
run;
/**********Frequency Distribution of Selected**********/

tables sex race marstat disc_mon discstat
losflag region bedsize owner;
title 'frequency distribution of selected variable';
run;

/******Weighted Frequency Distribution of Selected*****/
weight weight; /***For all other PROC’s the syntax is freq weight**/


Lecture 3-NHDS 29

title 'frequency distribution of selected variable';
run;

Below is the output of the Proc Contents for the 2006 NHDS data set.
The SAS System

The CONTENTS Procedure

Data Set Name WORK.NHDS2006 Observations 376328
Member Type DATA Variables 32
Engine V9 Indexes 0
Created Tuesday, June 30, 2009 12:23:42 PM Observation Length 200
Last Modified Tuesday, June 30, 2009 12:23:42 PM Deleted Observations 0
Protection Compressed NO
Data Set Type Sorted NO
Label
Data Representation WINDOWS_32
Encoding wlatin1 Western (Windows)

Engine/Host Dependent Information

Data Set Page Size 16384
Number of Data Set Pages 4647
First Data Page 1
Max Obs per Page 81
Obs in First Data Page 57
Number of Data Set Repairs 0
Filename C:DOCUME~1DR0E98~1.RAYLOCALS~1TempSAS
Temporary Files_TD5532nhds2006.sas7bdat
Release Created 9.0201M0
Host Created XP_PRO

Alphabetic List of Variables and Attributes

# Variable Type Len Label

31 ADM_TYPE Num 8 Type of admission
4 AGE Num 8 Age in years, months, or days
3 AGEUNITS Num 8 Units for age
32 ASOURCE Num 8 Source of admission
13 BEDSIZE Num 8 Bedsize grouping for hospital
16 CENTURY Char 2 First two digits of survey year
9 DISCSTAT Num 8 Status at discharge
8 DISC_MON Char 2 Month of discharge
10 DOC Num 8 Number of days of care
30 DRG Char 3 Diagnosis-related group
17 DX1 Char 5 ICD-9-CM diagnosis code - first
18 DX2 Char 5 ICD-9-CM diagnosis code - second
19 DX3 Char 5 ICD-9-CM diagnosis code - third
20 DX4 Char 5 ICD-9-CM diagnosis code - fourth
21 DX5 Char 5 ICD-9-CM diagnosis code - fifth
22 DX6 Char 5 ICD-9-CM diagnosis code - sixth
23 DX7 Char 5 ICD-9-CM diagnosis code - seventh


Lecture 3-NHDS 30

28 ESOP1 Num 8 Principal expected source of payment
29 ESOP2 Num 8 Secondary expected source of payment
11 LOSFLAG Num 8 Zero length of stay flag
7 MARSTAT Num 8 Marital status of patient
2 NEWBORN Num 8 Newborn infant flag
14 OWNER Num 8 Ownership of hospital
24 PD1 Char 4 ICD-9-CM procedure code - first
25 PD2 Char 4 ICD-9-CM procedure code - second
26 PD3 Char 4 ICD-9-CM procedure code - third
27 PD4 Char 4 ICD-9-CM procedure code - fourth
6 RACE Num 8 Patient race
12 REGION Num 8 Geographic region of hospital
5 SEX Num 8 Patient sex
1 SVYEAR Char 2 Last two digits of survey year
15 WEIGHT Num 8 Analysis weight

Below is the output of the Proc Freq unweighted for the selected variables of 2006 NHDS.
Frequency Distribution of Selected Variable

The FREQ Procedure

Patient sex

SEX Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 154677 41.10 154677 41.10
2 221651 58.90 376328 100.00

Patient race

RACE Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 200368 53.24 200368 53.24
2 51195 13.60 251563 66.85
3 1025 0.27 252588 67.12
4 3018 0.80 255606 67.92
5 405 0.11 256011 68.03
6 15322 4.07 271333 72.10
8 108 0.03 271441 72.13
9 104887 27.87 376328 100.00

Marital status of patient

MARSTAT Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 55046 14.63 55046 14.63
2 78704 20.91 133750 35.54
3 18224 4.84 151974 40.38
4 8622 2.29 160596 42.67
5 1565 0.42 162161 43.09
9 214167 56.91 376328 100.00


Lecture 3-NHDS 31

Month of discharge

DISC_MON Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
01 31343 8.33 31343 8.33
02 30172 8.02 61515 16.35
03 33780 8.98 95295 25.32
04 30732 8.17 126027 33.49
05 31668 8.41 157695 41.90
06 31658 8.41 189353 50.32
07 31537 8.38 220890 58.70
08 32323 8.59 253213 67.29
09 31415 8.35 284628 75.63
10 30798 8.18 315426 83.82
11 29874 7.94 345300 91.76
12 31028 8.24 376328 100.00

Status at discharge

DISCSTAT Frequency Percent Frequency Percent
1 300393 79.82 300393 79.82
2 3545 0.94 303938 80.76
3 9534 2.53 313472 83.30
4 32007 8.51 345479 91.80
5 20944 5.57 366423 97.37
6 7336 1.95 373759 99.32
9 2569 0.68 376328 100.00

Zero length of stay flag

LOSFLAG Frequency Percent Frequency Percent
0 6360 1.69 6360 1.69
1 369968 98.31 376328 100.00

Geographic region of hospital

REGION Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 90192 23.97 90192 23.97
2 97656 25.95 187848 49.92
3 141839 37.69 329687 87.61
4 46641 12.39 376328 100.00

Bedsize grouping for hospital


Lecture 3-NHDS 32

BEDSIZE Frequency Percent Frequency Percent
1 41089 10.92 41089 10.92
2 85716 22.78 126805 33.70
3 82411 21.90 209216 55.59
4 119903 31.86 329119 87.46
5 47209 12.54 376328 100.00

Ownership of hospital

OWNER Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 38869 10.33 38869 10.33
2 32052 8.52 70921 18.85
3 305407 81.15 376328 100.00

Below is the output of the Proc Freq weighted for the selected variables of 2006 NHDS.
Weighted Frequency Distribution of Selected NHDS Variable

The FREQ Procedure

Patient sex

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 16030906 41.24 16030906 41.24
2 22842871 58.76 38873777 100.00

Patient race

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 23116012 59.46 23116012 59.46
2 4685938 12.05 27801950 71.52
3 118912 0.31 27920862 71.82
4 649604 1.67 28570466 73.50
5 95043 0.24 28665509 73.74
6 711022 1.83 29376531 75.57
8 33376 0.09 29409907 75.65
9 9463870 24.35 38873777 100.00


1 9336056 24.02 9336056 24.02
2 10931362 28.12 20267418 52.14


Lecture 3-NHDS 33

3 3083076 7.93 23350494 60.07
4 1471056 3.78 24821550 63.85
5 234303 0.60 25055853 64.45
9 13817924 35.55 38873777 100.00

Month of discharge

01 3326380 8.56 3326380 8.56
02 3127993 8.05 6454373 16.60
03 3475747 8.94 9930120 25.54
04 3189227 8.20 13119347 33.75
05 3329115 8.56 16448462 42.31
06 3292765 8.47 19741227 50.78
07 3240989 8.34 22982216 59.12
08 3342033 8.60 26324249 67.72
09 3221755 8.29 29546004 76.00
10 3129742 8.05 32675746 84.06
11 3068077 7.89 35743823 91.95
12 3129954 8.05 38873777 100.00

Status at discharge

1 30736926 79.07 30736926 79.07
2 332166 0.85 31069092 79.92
3 1595663 4.10 32664755 84.03
4 3164590 8.14 35829345 92.17
5 1724117 4.44 37553462 96.60
6 743475 1.91 38296937 98.52
9 576840 1.48 38873777 100.00


0 755008 1.94 755008 1.94
1 38118769 98.06 38873777 100.00


1 7998781 20.58 7998781 20.58
2 8784236 22.60 16783017 43.17
3 14603982 37.57 31386999 80.74
4 7486778 19.26 38873777 100.00


Lecture 3-NHDS 34


1 8727912 22.45 8727912 22.45
2 8171471 21.02 16899383 43.47
3 7475041 19.23 24374424 62.70
4 9141896 23.52 33516320 86.22
5 5357457 13.78 38873777 100.00


ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
1 4679522 12.04 4679522 12.04
2 4762338 12.25 9441860 24.29
3 29431917 75.71 38873777 100.00

The number of observations for the unweighted observations is 376,328 versus 38,873,777 when
weighted. Note that the name of the variable values is not shown. To obtain this we need to use the PROC
Format statements presented below.


Lecture 3-NHDS 35

7. SAS FORMATS FOR NHDS
nhds01d.sas

Below are the SAS codes that create the names associated with the values of each NHDS
variable. This code should be executed first in the series of NHDS programs.
/*nhds01d.sas*/

proc format;

value newbornf
1="Newborn"
2="Not Newborn"

;
value ageunitf
1='Years'
2='Months'
3='Days'
;

value sexf
1='Male'
2='Female'
;

value racef

1='White'
2='Black'
3='Native American'
4='Asian'
5='Native Hawaiian or PI'
6='Other Race'
8='Multiple Race'
9='Race Not Stated'
;

value maritialf

1='Married'
2='Single'
3='Widowed'
4='Divorced'
5='Separated'
9='MS Not Stated'


Lecture 3-NHDS 36

;

value $dismonthf

'01' ='January'
'02' ='February'
'03' ='March'
'04' ='April'
'05' ='May'
'06' ='June'
'07' ='July'
'07' ='August'
'09' ='September'
'10' ='October'
'11' ='November'
'12' ='December'
;

value $disstatf

1="Home"
2="Left Against Medical Advice"
3="Dischaged to Acute"
4="Discharged to LTC"
5="Alive Status not Stated"
6="Diseased"
9="Status not Reported"
;

value losflagf

0="less than 1 day"
1="One day or More"
;
value regionf

1="Northeast"
2="Midwest"
3="South"
4="West"
;

value numbedsf

1="6-99"
2="100-199"
3="200-299"
4="300-499"
5="500 and Over"
;


Lecture 3-NHDS 37

value ownerf

1="Proprietary"
2="Governmental"
3="Non-Profit"
;
value payer1f

1="Worker's Comp"
2="Medicare"
3="Medicaid"
4="Other Govnt"
5="Blue Cross"
6="HMO/PPO"
7="Other Private"
8="Self Pay"
9="No Charge"
10="Other Ins"
99="Payer Not Stated"
;

value payer2f

1="Worker's Comp"
2="Medicare"
3="Medicaid"
4="Other Govnt"
5="Blue Cross"
6="HMO/PPO"
7="Other Private"
8="Self Pay"
9="No Charge"
10="Other Ins"
.="Payer Not Stated"
;

value admitypef

1="Emergency"
2="Urgent"
3="Elective"
4="New Born"
9="admit NA"
;

value adsourcef


Lecture 3-NHDS 38

1="Physican Referral"
2="Clinical Referral"
3="HMO Referral"
4="Acute Transfer"
5="SNF Transfer"
6="Other Tranfer"
7="Emergency Room"
8="Court/Law Enforcement"
9="Source_other"
99="Sourse N/A"
;

value $diag3df /***Partial List***/

'001'='(001) Cholera'
'002'='(002) Typhoid and paratyphoid fevers'
'003'='(003) Other salmonella infections'
'004'='(004) Shigellosis'
'005'='(005) Other food poisoning (bacterial)'
'006'='(006) Amebiasis'
'007'='(007) Other protozoal intestinal dise...'
'008'='(008) Intestinal infections due to ot...'
'009'='(009) Ill-defined intestinal infections'
'010'='(010) Primary tuberculous infection'
'011'='(011) Pulmonary tuberculosis'
'012'='(012) Other respiratory tuberculosis'
'013'='(013) Tuberculosis of meninges and ce...'
'014'='(014) Tuberculosis of intestine/perit...'
'015'='(015) Tuberculosis of bones and joints'
'016'='(016) Tuberculosis of genitourinary s...'
'017'='(017) Tuberculosis of other organs'
'018'='(018) Miliary tuberculosis'
'020'='(020) Plague'
'021'='(021) Tularemia'
'022'='(022) Anthrax'
'023'='(023) Brucellosis'
'024'='(024) Glanders'
'025'='(025) Melioidosis'
'026'='(026) Rat-bite fever'
'027'='(027) Other zoonotic bacterial diseases'
'030'='(030) Leprosy'
'031'='(031) Diseases due to other mycobacteria'
'032'='(032) Diphtheria'
'033'='(033) Whooping cough'
'034'='(034) Streptococcal sore throat and s...'
'035'='(035) Erysipelas'
'036'='(036) Meningococcal infection'
'037'='(037) Tetanus'
'038'='(038) Septicemia'
'039'='(039) Actinomycotic infections'


Lecture 3-NHDS 39

'040'='(040) Other bacterial diseases'
'041'='(041) Bacterial infec in conditns cla...'
'042'='(042) Human immunodeficiency virus in...'
'044'='(044) Other human immunodeficiency vi...'
'045'='(045) Acute poliomyelitis'
'046'='(046) Slow virus infection of central...'
'047'='(047) Meningitis due to enterovirus'
'048'='(048) Other enterovirus diseases of c...'
'049'='(049) Oth non-arthropod-borne viral d...'
'050'='(050) Smallpox'
'051'='(051) Cowpox and paravaccinia'
'052'='(052) Chickenpox'
'053'='(053) Herpes zoster'
'054'='(054) Herpes simplex'
'055'='(055) Measles'
'056'='(056) Rubella'
'057'='(057) Other viral exanthemata'
'060'='(060) Yellow fever'
'061'='(061) Dengue'
'062'='(062) Mosquito-borne viral encephalitis'
'063'='(063) Tick-borne viral encephalitis'
'064'='(064) Viral encephalitis transmitted ...'
'065'='(065) Arthropod-borne hemorrhagic fever'
'066'='(066) Other arthropod-borne viral dis...'
'070'='(070) Viral hepatitis'
'071'='(071) Rabies'
'072'='(072) Mumps'
'073'='(073) Ornithosis'
'074'='(074) Specific diseases due to Coxsac...'
'075'='(075) Infectious mononucleosis'
'076'='(076) Trachoma'
'077'='(077) Other diseases of conjunctiva d...'
'078'='(078) Other diseases due to viruses a...'
'079'='(079) Viral infection in conditns cla...'
'080'='(080) Louse-borne [epidemic] typhus'
'081'='(081) Other typhus'
'082'='(082) Tick-borne rickettsioses'
'083'='(083) Other rickettsioses'
'084'='(084) Malaria'
'085'='(085) Leishmaniasis'
'086'='(086) Trypanosomiasis'
'087'='(087) Relapsing fever'
'088'='(088) Other arthropod-borne diseases'
'090'='(090) Congenital syphilis'
'091'='(091) Early syphilis, symptomatic'
'092'='(092) Early syphilis, latent'
'093'='(093) Cardiovascular syphilis'
'094'='(094) Neurosyphilis'
'095'='(095) Other forms of late syphilis, w...'
'096'='(096) Late syphilis, latent'


Lecture 3-NHDS 40

'097'='(097) Other and unspecified syphilis'
'098'='(098) Gonococcal infections'
'099'='(099) Other venereal diseases'
'100'='(100) Leptospirosis'
'101'='(101) Vincent`s angina'
'102'='(102) Yaws'
'103'='(103) Pinta'
'104'='(104) Other spirochetal infection'
'110'='(110) Dermatophytosis'
'111'='(111) Dermatomycosis, other and unspe...'
'112'='(112) Candidiasis'
'114'='(114) Coccidioidomycosis'
'115'='(115) Histoplasmosis'
'116'='(116) Blastomycotic infection'
'117'='(117) Other mycoses'
'118'='(118) Opportunistic mycoses

/* The following format is provided in case you wish to use only the
first two columns of the various fields which display Procedures
Classification codes from Vol. 3 of the ICD-9-CM, for broader
groupings of this item. */

VALUE $PROC2DF
'00'='Blank/00:Procedures and interventns, NEC'
'01'='01:Incision and excision of skull, br...'
'02'='02:Other operations on skull, brain, ...'
'03'='03:Operations on spinal cord and spin...'
'04'='04:Operations on cranial and peripher...'
'05'='05:Operations on sympathetic nerves o...'
'06'='06:Operations on thyroid and parathyr...'
'07'='07:Operations on other endocrine glands'
'08'='08:Operations on eyelids'
'09'='09:Operations on lacrimal system'
'10'='10:Operations on conjunctiva'
'11'='11:Operations on cornea'
'12'='12:Operations on iris, ciliary body, ...'
'13'='13:Operations on lens'
'14'='14:Operations on retina, choroid, vit...'
'15'='15:Operations on extraocular muscles'
'16'='16:Operations on orbit and eyeball'
'18'='18:Operations on external ear'
'19'='19:Reconstructive operations on middl...'
'20'='20:Other operations on middle and inn...'
'21'='21:Operations on nose'
'22'='22:Operations on nasal sinuses'
'23'='23:Removal and restoration of teeth'
'24'='24:Other operations on teeth, gums, a...'
'25'='25:Operations on tongue'
'26'='26:Operations on salivary glands and ...'
'27'='27:Other operations on mouth and face'
'28'='28:Operations on tonsils and adenoids'


Lecture 3-NHDS 41

'29'='29:Operations on pharynx'
'30'='30:Excision of larynx'
'31'='31:Other operations on larynx and tra...'
'32'='32:Excision of lung and bronchus'
'33'='33:Other operations on lung and bronchus'
'34'='34:Operations on chest wall, pleura, ...'
'35'='35:Operations on valves and septa of ...'
'36'='36:Operations on vessels of heart'
'37'='37:Other operations on heart and peri...'
'38'='38:Incision, excision, and occlusion ...'
'39'='39:Other operations on vessels'
'40'='40:Operations on lymphatic system'
'41'='41:Operations on bone marrow and spleen'
'42'='42:Operations on esophagus'
'43'='43:Incision and excision of stomach'
'44'='44:Other operations on stomach'
'45'='45:Incision, excision, and anastomosi...'
'46'='46:Other operations on intestine'
'47'='47:Operations on appendix'
'48'='48:Operations on rectum, rectosigmoid...'
'49'='49:Operations on anus'
'50'='50:Operations on liver'
'51'='51:Operations on gallbladder and bili...'
'52'='52:Operations on pancreas'
'53'='53:Repair of hernia'
'54'='54:Other operations on abdominal region'
'55'='55:Operations on kidney'
'56'='56:Operations on ureter'
'57'='57:Operations on urinary bladder'
'58'='58:Operations on urethra'
'59'='59:Other operations on urinary tract'
'60'='60:Operations on prostate and seminal...'
'61'='61:Operations on scrotum and tunica v...'
'62'='62:Operations on testes'
'63'='63:Operations on spermatic cord, epid...'
'64'='64:Operations on penis'
'65'='65:Operations on ovary'
'66'='66:Operations on fallopian tubes'
'67'='67:Operations on cervix'
'68'='68:Other incision and excision of uterus'
'69'='69:Other operations on uterus and sup...'
'70'='70:Operations on vagina and cul-de-sac'
'71'='71:Operations on vulva and perineum'
'72'='72:Forceps, vacuum, and breech delivery'
'73'='73:Other procedures inducing or assis...'
'74'='74:Cesarean section and removal of fetus'
'75'='75:Other obstetric operations'
'76'='76:Operations on facial bones and joints'
'77'='77:Incision, excision, and division o...'
'78'='78:Other operations on bones, except ...'
'79'='79:Reduction of fracture and dislocation'


Lecture 3-NHDS 42

'80'='80:Incision and excision of joint str...'
'81'='81:Repair and plastic operations on j...'
'82'='82:Operations on muscle, tendon, and ...'
'83'='83:Operations on muscle, tendon, fasc...'
'84'='84:Other procedures on musculoskeleta...'
'85'='85:Operations on the breast'
'86'='86:Operations on skin and subcutaneou...'
'87'='87:Diagnostic radiology'
'88'='88:Other diagnostic radiology and rel...'
'89'='89:Interview, evaluation, consultatio...'
'90'='90:Microscopic examination I'
'91'='91:Microscopic examination II'
'92'='92:Nuclear medicine'
'93'='93:Physical therapy/respiratory thera...'
'94'='94:Procedures related to the psyche'
'95'='95:Ophthalmologic and otologic diagno...'
'96'='96:Nonoperative intubation and irriga...'
'97'='97:Replacement and removal of therape...'
'98'='98:Nonoperative removal of foreign body'
'99'='99:Other nonoperative procedures'
;


Lecture 3-NHDS 43

8. Proc Frequency with Format Statements

Below is the weighted Proc Frequency SAS code.
weight weight;

format sex sexf. race racef. marstat marstatf. disc_mon $dismonthf.
discstat discstatf. losflag losflagf. region regionf.
bedsize bedsizef. owner ownerf.;

title 'Weighted Frequency Distribution with
Formats for Selected Variable';
run;

Below is the weighted Proc Frequency SAS output.
Weighted Frequency Distribution with Formats for Selected Variable

The FREQ Procedure

Patient sex

Male 16030906 41.24 16030906 41.24
Female 22842871 58.76 38873777 100.00

Patient race

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
White 23116012 59.46 23116012 59.46
Black 4685938 12.05 27801950 71.52
Native American 118912 0.31 27920862 71.82
Asian 649604 1.67 28570466 73.50
Native Hawaiian or PI 95043 0.24 28665509 73.74
Other Race 711022 1.83 29376531 75.57
Multiple Race 33376 0.09 29409907 75.65
Race Not Stated 9463870 24.35 38873777 100.00


Married 9336056 24.02 9336056 24.02
Single 10931362 28.12 20267418 52.14


Lecture 3-NHDS 44

Widowed 3083076 7.93 23350494 60.07
Divorced 1471056 3.78 24821550 63.85
Separated 234303 0.60 25055853 64.45
MS Not Stated 13817924 35.55 38873777 100.00

Month of discharge

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
January 3326380 8.56 3326380 8.56
February 3127993 8.05 6454373 16.60
March 3475747 8.94 9930120 25.54
April 3189227 8.20 13119347 33.75
May 3329115 8.56 16448462 42.31
June 3292765 8.47 19741227 50.78
July 3240989 8.34 22982216 59.12
August 3342033 8.60 26324249 67.72
September 3221755 8.29 29546004 76.00
October 3129742 8.05 32675746 84.06
November 3068077 7.89 35743823 91.95
December 3129954 8.05 38873777 100.00

Status at discharge

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Home 30736926 79.07 30736926 79.07
Left Against Medical Advice 332166 0.85 31069092 79.92
Dischaged to Acute 1595663 4.10 32664755 84.03
Discharged to LTC 3164590 8.14 35829345 92.17
Alive Status not Stated 1724117 4.44 37553462 96.60
Diseased 743475 1.91 38296937 98.52
Status not Reported 576840 1.48 38873777 100.00


less than 1 day 755008 1.94 755008 1.94
One day or More 38118769 98.06 38873777 100.00


Northeast 7998781 20.58 7998781 20.58
Midwest 8784236 22.60 16783017 43.17
South 14603982 37.57 31386999 80.74
West 7486778 19.26 38873777 100.00


Lecture 3-NHDS 45


ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
6-99 8727912 22.45 8727912 22.45
100-199 8171471 21.02 16899383 43.47
200-299 7475041 19.23 24374424 62.70
300-499 9141896 23.52 33516320 86.22
500 and Over 5357457 13.78 38873777 100.00


ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Proprietary 4679522 12.04 4679522 12.04
Governmental 4762338 12.25 9441860 24.29
Non-Profit 29431917 75.71 38873777 100.00

Below is the log associated with the above output.
5344 proc freq data=nhds2006;
5345 weight weight;
5346 tables sex race marstat disc_mon discstat
5347 losflag region bedsize owner;
5348 format sex sexf. race racef. marstat marstatf. disc_mon $dismonthf.
5349 discstat discstatf. losflag losflagf. region regionf.
5350 bedsize bedsizef. owner ownerf.;
5351 title 'Weighted Frequency Distribution with
5352 Formats for Selected Variable';
5353 run;

NOTE: There were 376328 observations read from the data set WORK.NHDS2006.
NOTE: PROCEDURE FREQ used (Total process time):
real time 0.40 seconds
cpu time 0.40 second


Lecture 3-NHDS 46

9. Exercises 3.1

1. Prepare and run weighted and formatted PROC FREQ for variables ESOP1, ADM_TYPE,
and ADM_SOURCE.
2. Add a variable in the input code for DX13 to read $3 (first 3 characters of the principal
diagnosis) and produce the frequency distribution for PROC FREQ and a weighted and
formatted output.
3. Add a variable in the input code for PD12 to read $2 (first 2 characters of the principal
procedure) and produce the procedure distribution using PROC FREQ and a weighted and
formatted output.
4. What are the top 5 most frequent procedures for NHDS 2006?
5. What are the top 5 most frequent diagnoses for NHDS 2006?
6. Write the format statements for the Medicare DRGs which are found in C:DATA9000DRG
Medicare FY07.


Lecture 3-NHDS 47

10. SAS Code for NHDS Indicator and Truth Logic Variables
nhds03d1.sas

Below is the SAS code that creates indicator and truth logic variables.
/****nhds03d.sas**********/
data nhds06;
set work.nhds2006;
/*************************************************/
/*Indicator variables were previously identified */
/*as dummy variables equal to 1 or 0 when they */
/*existed or did not exist in an observation. */
/*They are used in statistical analysis of data. */
/*************************************************/

/****Indicator Variable for Gender*****/

male = (sex=1);
female = (sex=2);

/******************************************************/
/*Truth Logic is used to create a continuous variable */
/*that corresponds to the values (1-3, 1-5, etc.)*/
/*assigned to a variable in an observation and */
/*is used in regression and logistic analysis. */
/******************************************************/

/*****Truth Logic for Gender*************/

gendercat= 1*(sex=1) + 2*(sex=2);

/****Indicator Variable for Race*****/

white = (race=1);
black = (race=2);
nativeam = (race=3);
asian = (race=4);
hawaiianpi = (race=5);
othrace = (race=6);
multirace = (race=8);
racenotstat = (race=9);

;
/*****Truth Logic For Race*************/

racecat = 1*(race=1) + 2*(race=2) + 3*(race=3)+
4*(race=4) + 5*(race=5) + 6*(race=6)+
7*(race=8) + 8*(race=9);


Lecture 3-NHDS 48

/****Indicator Variable for Marital Status*****/

married = (marstat=1);
single = (marstat=2);
widowed = (marstat=3);
divorced = (marstat=4);
separated = (marstat=5);
msnotstat = (marstat=9);

/*****Truth Logic For Marital Status************/

marstatcat= 1*(marstat=1) + 2*(marstat=2) + 3*(marstat=3)+
4*(marstat=4) + 5*(marstat=5) + 6*(marstat=9);

/****Indicator Variable for Discharge Status*****/

home = (discstat=1);
dislma = (discstat=2);
disacute = (discstat=3);
disltc = (discstat=4);
alivens = (discstat=5);
disceased = (discstat=6);
disstatna = (discstat=9);

/*****Truth Logic For Marital Status************/

discstatcat= 1*(discstat=1) + 2*(discstat=2) + 3*(discstat=3) +
4*(discstat=4) + 5*(discstat=5) + 6*(discstat=6) +
7*(discstat=9);

/****Indicator Variable for Region*****/

northeast = (region=1);
midwest = (region=2);
south = (region=3);
west = (region=4);

/*****Truth Logic For Region************/

regioncat= 1*(region=1) + 2*(region=2) + 3*(region=3) +
4*(region=4);

/****Indicator Variable for Ownership*****/

private = (owner=1);
government = (owner=2);
nonprofit = (owner=3);


Lecture 3-NHDS 49

/*****Truth Logic For Ownership************/

ownercat= 1*(owner=1) + 2*(owner=2) + 3*(owner=3);

/****Indicator Variable for Payer*****/

workercomp = (esop1=1);
medicare = (esop1=2);
medicaid = (esop1=3);
othergvmt = (esop1=4);
bluecross = (esop1=5);
hmoppo = (esop1=6);
othprivate = (esop1=7);
selfpay = (esop1=8);
nocharge = (esop1=9);
othinsure = (esop1=10);
paynotstated = (esop1=99);

/*****Truth Logic For Payer1*************/

payercat = 1*(esop1=1) + 2*(esop1=2) + 3*(esop1=3)+
4*(esop1=4) + 5*(esop1=5) + 6*(esop1=6)+
7*(esop1=7) + 8*(esop1=8) + 9*(esop1=9)+
10*(esop1=10) +11*(esop1=99);

/****Indicator Variable for Admit Source*****/
docreferal = (asource=1);
clinreferal = (asource=2);
hmoreferal = (asource=3);
hospreferal = (asource=4);
snftransfer = (asource=5);
othtransfer = (asource=6);
edsource = (asource=7);
legalsource = (asource=8);
othsource = (asource=9);
sourcena = (asource=99);

/*****Truth Logic for Admit Source*************/

sourcecat = 1*(asource=1) + 2*(asource=2) + 3*(asource=3)+
4*(asource=4) + 5*(asource=5) + 6*(asource=6)+
7*(asource=7) + 8*(asource=8) + 9*(asource=9)+
10*(asource=99);

Below is the PROC MEANS code for the unweighted NHDS06 indicator and truth logic
variables.

Lecture 3-NHDS 50

proc means n mean sum min max data=nhds06;
var male female gendercat white black nativeam
asian hawaiianpi othrace multirace racenotstat
racecat married single widowed divorced separated
msnotstat home dislma disacute disltc alivens
disceased disstatna discstatcat
northeast midwest south west regioncat
private government nonprofit ownercat
workercomp medicare medicaid othergvmt
bluecross hmoppo othprivate selfpay
nocharge othinsure paynotstated payercat
docreferal clinreferal hmoreferal hospreferal
snftransfer othtransfer edsource legalsource
othsource sourcena sourcecat
;
title 'Means Procedure for NHDS2006 Variables Unweighted';
run;

Below is the output from the above PROC Means.
The MEANS Procedure

Variable N Mean Sum Minimum Maximum
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
male 376328 0.4110165 154677.00 0 1.0000000
female 376328 0.5889835 221651.00 0 1.0000000
gendercat 376328 1.5889835 597979.00 1.0000000 2.0000000
white 376328 0.5324292 200368.00 0 1.0000000
black 376328 0.1360382 51195.00 0 1.0000000
nativeam 376328 0.0027237 1025.00 0 1.0000000
asian 376328 0.0080196 3018.00 0 1.0000000
hawaiianpi 376328 0.0010762 405.0000000 0 1.0000000
othrace 376328 0.0407145 15322.00 0 1.0000000
multirace 376328 0.000286984 108.0000000 0 1.0000000
racenotstat 376328 0.2787117 104887.00 0 1.0000000
racecat 376328 3.3261251 1251714.00 1.0000000 8.0000000
married 376328 0.1462713 55046.00 0 1.0000000
single 376328 0.2091367 78704.00 0 1.0000000
widowed 376328 0.0484258 18224.00 0 1.0000000
divorced 376328 0.0229109 8622.00 0 1.0000000
separated 376328 0.0041586 1565.00 0 1.0000000
msnotstat 376328 0.5690966 214167.00 0 1.0000000
home 376328 0.7982212 300393.00 0 1.0000000
dislma 376328 0.0094200 3545.00 0 1.0000000
disacute 376328 0.0253343 9534.00 0 1.0000000
disltc 376328 0.0850508 32007.00 0 1.0000000
alivens 376328 0.0556536 20944.00 0 1.0000000
disceased 376328 0.0194936 7336.00 0 1.0000000
disstatna 376328 0.0068265 2569.00 0 1.0000000
discstatcat 376328 1.6762824 630832.00 1.0000000 7.0000000
northeast 376328 0.2396633 90192.00 0 1.0000000
midwest 376328 0.2594970 97656.00 0 1.0000000
south 376328 0.3769026 141839.00 0 1.0000000
west 376328 0.1239371 46641.00 0 1.0000000


Lecture 3-NHDS 51

regioncat 376328 2.3851135 897585.00 1.0000000 4.0000000
private 376328 0.1032849 38869.00 0 1.0000000
government 376328 0.0851704 32052.00 0 1.0000000
nonprofit 376328 0.8115447 305407.00 0 1.0000000
ownercat 376328 2.7082598 1019194.00 1.0000000 3.0000000
workercomp 376328 0.0039487 1486.00 0 1.0000000
medicare 376328 0.3477950 130885.00 0 1.0000000
medicaid 376328 0.1831408 68921.00 0 1.0000000
othergvmt 376328 0.0116202 4373.00 0 1.0000000
bluecross 376328 0.1028172 38693.00 0 1.0000000
hmoppo 376328 0.1666950 62732.00 0 1.0000000
othprivate 376328 0.0922759 34726.00 0 1.0000000
selfpay 376328 0.0422716 15908.00 0 1.0000000
nocharge 376328 0.0054607 2055.00 0 1.0000000
othinsure 376328 0.0317569 11951.00 0 1.0000000
paynotstated 376328 0.0122181 4598.00 0 1.0000000
payercat 376328 4.2949156 1616297.00 1.0000000 11.0000000
docreferal 376328 0.3001743 112964.00 0 1.0000000
clinreferal 376328 0.0135547 5101.00 0 1.0000000
hmoreferal 376328 0.0045997 1731.00 0 1.0000000
hospreferal 376328 0.0285070 10728.00 0 1.0000000
snftransfer 376328 0.0048335 1819.00 0 1.0000000
othtransfer 376328 0.0065050 2448.00 0 1.0000000
edsource 376328 0.4169288 156902.00 0 1.0000000
legalsource 376328 0.0019850 747.0000000 0 1.0000000
othsource 376328 0.1151256 43325.00 0 1.0000000
sourcena 376328 0.1077863 40563.00 0 1.0000000
sourcecat 376328 5.5666839 2094899.00 1.0000000 10.0000000

A method to validate your indicator variables consists of confirming that each has a minimum
value of 0 and a maximum value of 1. If this does not exist, check your code. For truth logic
variables, the minimum is usually 1 and the maximum equals the total number of values assigned
to the variable. Again, if this does not occur, check your logic code. For example, paynotstated
has a minimum of 1 and a maximum of 0 while payercat has a minimum of 1 and a maximum of
10 that corresponds to the number of payer categories.
The N=376,328 is the total observation in the data set and also reflects the completeness of the
data for each variable. In the above case, there are no missing values in any observation. The
mean value is the percentage of each variable within a category. For payer, the percentage of
Medicare patients in the sample is 34.8%. The sum column of 130,885 equals the number of
Medicare observation sampled in the NHDS survey in 2006.


Lecture 3-NHDS 52

11. Exercises 3.2

1. Using nhds03d, write the indicator and truth logic code for the variables admitype, bedsize,
dismonth, and los flag.
2. Submit a weighted version of the nhds03d Prod Means with these variables plus days of care
(DOC) and age.
3. Using the above output and weighted means. Present a narrative of the presentage distribution
of admitype, bedsize, dismonth, and losflag.
4. Using nhds03d insert the [class selfpay] into the proc mean. The output will yield the
differences between the selfpay (uninsured) and non-selfpay (insured). Prepare a narrative that
compares the demographic differences between the uninsured and insured populations.


Lecture 3-NHDS 53

12. Multiple Linear Regression Model of Days of Care
nhds04d.sas

Below is the linear regression model (Proc Reg) of days of care and the effects of gender, race,
marital status, payer, disposition and region. The NHDS was not designed for multivariate
analysis and is principally used for descriptive statistics. However, this demonstration yields
reasonable findings and creates templates for use with other data sets which are designed for
multivariate analysis with a multi-stage probability sample design. Nevertheless, it should be
noted that a substantial number of studies are published yearly using multivariate analysis of
NHDS of the unweighted annual sample. Appendix 2 presents a sample of these studies.

/***nhds04d.sas***/

Proc Reg data=nhds06;
model doc=male white married medicare disceased south;
run;
title 'Linear regression of days of care as the dependent variable and
the selected effects’
quit;

Below is the output of the linear regression.
The REG Procedure
Model: MODEL1
Dependent Variable: DOC Number of days of care



Sum of Mean

Model 6 390901 65150 1428.16 <.0001
Error 376321 17167112 45.61827
Corrected Total 376327 17558013

Coeff Var 143.27828

Parameter Estimates

Parameter Standard
Variable Label DF Estimate Error t Value Pr > |t|


Lecture 3-NHDS 54

Intercept Intercept 1 3.82799 0.02146 178.41 <.0001
male 1 0.53717 0.02239 23.99 <.0001
white 1 -0.05574 0.02248 -2.48 0.0132
married 1 -0.52875 0.03218 -16.43 <.0001
medicare 1 1.55716 0.02325 66.98 <.0001
disceased 1 3.84237 0.07999 48.04 <.0001
south 1 0.41328 0.02312 17.88 <.0001

As seen above in the PROC REG output of discharges in 2006 across the nation, the model of
days of care (DOC) , all of the effects are significant at p<.01. Controlling for sex, race, marital
status, payer, disposition and region, the findings are as follows:
1. Males will stay 0.5 days longer than females. p<.0001
2. Whites stay 0.05 days less than non-whites. p<.0001
3. Those married stay 0.5 fewer days than those not married. p<.0001
4. Medicare payers stay 1.6 days longer than non-Medicare discharges. p<.0001
5. Those who die compared to those discharged alive have 3.8 additional days of care before
death. p<.0001
6. Those living in the South compared to other national regions have 0.41 additional days of
care. p<.0001


Lecture 3-NHDS 55

13. Exercises 3.3

1. Run the model with the freq weight statement after PROC REG and determine if the findings
are significantly different.
2. Add to the model the additional effects of hospital ownership and source of admission using
the indicator variables of private and snftransfer and interpret these two effects.
3. Write the regression equation of this first model using the intercept and effect coefficients,
and using the following format:
DOC = β0 + β1male+ β2white + β3married + β4Medicare + β5diseased + β6south +ε


Lecture 3-NHDS 56

14. A Logistic Regression Model for the Uninsured (selfpay).
nhds05d.sas

Below is the logistic regression model (proc logistic) of the uninsured and the effects of age
gender, race, admission type and region. As previously discussed, NHDS was not designed for
multivariate analysis and is principally used for descriptive statistics. However, this
demonstration yields reasonable findings and creates templates for use with other data sets which
are designed for multivariate analysis. Nevertheless, it should be noted that a substantial number
of studies are published yearly using multivariate analysis of NHDS of the unweighted annual
sample. Appendix 2 presents a sample of these studies.
/*nhds05d.sas*/
options nolabel nodate nonumber;
proc logistic data=nhds06 des;
class gendercat (param=ref ref='2') /*female**/
racecat (param=ref ref='1') /*white**/
admitcat (param=ref ref='3') /*elective*/
regioncat (param=ref ref='2'); /*midwest*/
model selfpay=age gendercat racecat admitcat regioncat;

;

units age=10;
title 'Logistic Regression for NHDS Selfpay (uninsured)';
run;
quit;
options label;
title;

Logistic Regression for NHDS Selfpay (uninsured)


Model Information

Data Set WORK.NHDS06
Response Variable selfpay
Model binary logit


Response Profile


Lecture 3-NHDS 57

Ordered Total
Value selfpay Frequency

1 1 15908
2 0 360420

Probability modeled is selfpay=1.

Class Level Information

Class Value Design Variables

gendercat 1 1
2 0

racecat 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0
3 0 1 0 0 0 0 0
4 0 0 1 0 0 0 0
5 0 0 0 1 0 0 0
6 0 0 0 0 1 0 0
7 0 0 0 0 0 1 0
8 0 0 0 0 0 0 1

admitcat 1 1 0 0 0
2 0 1 0 0
3 0 0 0 0
4 0 0 1 0
5 0 0 0 1

regioncat 1 1 0 0
2 0 0 0
3 0 1 0
4 0 0 1


Model Convergence Status

Convergence criterion (GCONV=1E-8) satisfied.

Model Fit Statistics

Intercept
Intercept and
Criterion Only Covariates

AIC 131790.20 123843.34
SC 131801.04 124027.59
-2 Log L 131788.20 123809.34

Testing Global Null Hypothesis: BETA=0


Lecture 3-NHDS 58

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 7978.8568 16 <.0001
Score 8272.5095 16 <.0001
Wald 7829.0388 16 <.0001


Wald

AGE 1 4518.7861 <.0001
gendercat 1 662.1638 <.0001
racecat 7 378.8640 <.0001
admitcat 4 3360.3676 <.0001
regioncat 3 466.3526 <.0001


Standard Wald

Intercept 1 -3.0982 0.0353 7701.4191 <.0001
AGE 1 -0.0240 0.000357 4518.7861 <.0001
gendercat 1 1 0.4287 0.0167 662.1638 <.0001
racecat 2 1 0.2042 0.0233 76.7092 <.0001
racecat 3 1 0.7448 0.1139 42.7874 <.0001
racecat 4 1 0.1118 0.0968 1.3341 0.2481
racecat 5 1 0.0760 0.2444 0.0967 0.7558
racecat 6 1 0.2805 0.0370 57.3473 <.0001
racecat 7 1 1.2076 0.3236 13.9272 0.0002



Standard Wald

racecat 8 1 0.3655 0.0208 308.1565 <.0001
admitcat 1 1 0.8955 0.0253 1248.2156 <.0001
admitcat 2 1 0.1894 0.0312 36.7482 <.0001
admitcat 4 1 -0.7057 0.0372 360.5407 <.0001
admitcat 5 1 0.4933 0.0416 140.3279 <.0001
regioncat 1 1 0.0155 0.0272 0.3221 0.5703
regioncat 3 1 0.3876 0.0230 282.7929 <.0001
regioncat 4 1 0.1302 0.0311 17.5216 <.0001


Point 95% Wald


Lecture 3-NHDS 59


AGE 0.976 0.976 0.977
gendercat 1 vs 2 1.535 1.486 1.586
racecat 2 vs 1 1.227 1.172 1.284
racecat 3 vs 1 2.106 1.685 2.632
racecat 4 vs 1 1.118 0.925 1.352
racecat 5 vs 1 1.079 0.668 1.742
racecat 6 vs 1 1.324 1.231 1.423
racecat 7 vs 1 3.346 1.774 6.308
racecat 8 vs 1 1.441 1.384 1.501
admitcat 1 vs 3 2.449 2.330 2.573
admitcat 2 vs 3 1.209 1.137 1.285
admitcat 4 vs 3 0.494 0.459 0.531
admitcat 5 vs 3 1.638 1.509 1.777
regioncat 1 vs 2 1.016 0.963 1.071
regioncat 3 vs 2 1.473 1.408 1.542
regioncat 4 vs 2 1.139 1.072 1.211


Pairs 5733561360 c 0.716



Odds Ratios

Effect Unit Estimate

AGE 10.0000 0.786

As seen above in the proc logistic output of discharges in 2006 across the nation, the model of
days of care uninsured (selfpay), all of the effects are significant at p<.0001. Controlling for age,
gender, race, admit type, payer, and region, the findings are as follows:
1. All else being equal, males are 1.535 times more likely to be uninsured than females,
p<.0001[CI 1.486, 1.586].
2. All else being equal, blacks compared to whites are 1.227 times more likely to be
uninsured, p<.0001[CI 1.172 , 1.284].
3. All else being equal, multiple races compared to whites are 3.346 times more likely to be
uninsured, p<.0001[CI 1.774, 6.308].
4. All else being equal, Native Americans compared to whites were 2.106 times more likely to
be uninsured, p<.0001[CI 1.685, 2.632].


Lecture 3-NHDS 60

5. All else being equal, emergencies compared to electives were 2.449 times more likely to be
uninsured, p<.0001[CI 2.330, 2.573].
6. All else being equal, newborn compared to electives were 50.6 percent less likely to be
uninsured, p<.0001[CI 0.459 , 0.531].
7. All else being equal, those from the South compared to the Midwest were 1.473 times more
likely to be uninsured, p<.0001[CI 1.408, 1.542].
8. All else being equal, those from the West compared to the Midwest were 1.139 times more


Lecture 3-NHDS 61

15. Exercises 3.4

1. Add to the logistic model the effects of marital status (msnotstat), hospital ownership
(ownercat), admission source (asource), discharge disposition (discstatcat), and days of care
(DOC) to the first model.
2. In a narrative, describe the contribution of these additional effects upon the outcome variable.


Lecture 3-NHDS 62

16. Proc Tabulate to Identify the Differences of the Principal
Diagnosis of the Uninsured (self pay) and Insured.
nhdso3d

The Proc Tabulate below, found in nhds03d, will produce a table comparing the uninsured (self
pay) and insured, the principal diagnoses, and days of care.
proc tabulate data=nhds06 order=freq; /* formchar=' '; */
freq weight;
class selfpay dx13;
var doc;
tables dx13 all,
(selfpay all)*(doc*(n*f=8.0 mean*f=3.2)) /rts=50;
format dx13 $diag3df.;
run;
title 'Distribution in Rank order of the Selfpay Diagnosis';

Partial output of Proc Tabulate
Distribution in Rank order of the Selfpay Diagnosis

„ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒ†
‚ ‚ selfpay ‚ ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚
‚ ‚ 0 ‚ 1 ‚ All ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒƒƒ‰
‚ ‚ DOC ‚ DOC ‚ DOC ‚
‚ ‡ƒƒƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒƒƒ…ƒƒƒ‰
‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒ‰
‚dx13 ‚ ‚ ‚ ‚ ‚ ‚ ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚ ‚ ‚ ‚ ‚ ‚
‚(V27) Outcome of delivery ‚ 3971889‚2.6‚ 155602‚2.4‚ 4127491‚2.6‚
‚(V30) Singleton ‚ 3718260‚3.2‚ 184180‚2.6‚ 3902440‚3.2‚
‚(428) Heart failure ‚ 1078964‚5.2‚ 27442‚3.8‚ 1106406‚5.1‚
‚(486) Pneumonia, organism unspecified ‚ 1024185‚4.9‚ 28995‚3.5‚ 1053180‚4.9‚
‚(414) Other forms of chronic ischemic... ‚ 942554‚3.2‚ 36606‚3.2‚ 979160‚3.2‚
‚(296) Affective psychoses ‚ 915191‚7.3‚ 55940‚5.5‚ 971131‚7.2‚
‚(427) Cardiac dysrhythmias ‚ 756333‚3.4‚ 15192‚2.6‚ 771525‚3.4‚
‚(715) Osteoarthrosis and allied disor... ‚ 748890‚3.8‚ 4426‚3.5‚ 753316‚3.8‚
‚(276) Disorders of fluid, electrolyte... ‚ 701855‚3.5‚ 21792‚2.8‚ 723647‚3.5‚
‚(410) Acute myocardial infarction ‚ 618843‚5.5‚ 27807‚3.9‚ 646650‚5.4‚


Lecture 3-NHDS 63

‚(250) Diabetes mellitus ‚ 533206‚4.8‚ 50927‚3.8‚ 584133‚4.7‚
‚(682) Other cellulitis and abscess ‚ 501709‚4.5‚ 52639‚3.8‚ 554348‚4.5‚
‚(038) Septicemia ‚ 515467‚8.6‚ 14967‚9.4‚ 530434‚8.7‚
‚(996) Complications peculiar to certa... ‚ 515933‚6.2‚ 6609‚5.3‚ 522542‚6.2‚
‚(599) Other disorders of urethra and ... ‚ 515112‚4.6‚ 6658‚3.5‚ 521770‚4.6‚
‚(491) Chronic bronchitis ‚ 495774‚4.7‚ 16889‚3.7‚ 512663‚4.7‚
‚(V57) Care involving use of rehabilit... ‚ 470596‚ 13‚ 6475‚ 18‚ 477071‚ 13‚
‚(493) Asthma ‚ 414525‚3.2‚ 29044‚2.8‚ 443569‚3.2‚
‚(434) Occlusion of cerebral arteries ‚ 368478‚5.4‚ 20163‚4.3‚ 388641‚5.4‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚ ‚ ‚ ‚ ‚
‚(518) Other diseases of lung ‚ 354158‚8.7‚ 11618‚7.4‚ 365776‚8.7‚
‚(574) Cholelithiasis ‚ 308150‚3.9‚ 27210‚3.2‚ 335360‚3.8‚
‚(295) Schizophrenic psychoses ‚ 319453‚ 12‚ 13647‚7.7‚ 333100‚ 12‚
‚(820) Fracture of neck of femur ‚ 323278‚6.1‚ 6551‚6.8‚ 329829‚6.2‚
‚(722) Intervertebral disc disorders ‚ 316081‚3.1‚ 7727‚2.8‚ 323808‚3.1‚
‚(560) Intestinal obstruction without ... ‚ 313044‚6.2‚ 9717‚4.4‚ 322761‚6.2‚
‚(584) Acute renal failure ‚ 305059‚6.6‚ 10275‚4.3‚ 315334‚6.6‚
‚(562) Diverticula of intestine ‚ 302204‚4.8‚ 12237‚4.4‚ 314441‚4.8‚
‚(540) Acute appendicitis ‚ 270725‚3.2‚ 28807‚3.2‚ 299532‚3.2‚
‚(401) Essential hypertension ‚ 269301‚2.3‚ 24027‚1.9‚ 293328‚2.2‚
‚(577) Diseases of pancreas ‚ 240159‚5.7‚ 32723‚4.4‚ 272882‚5.5‚
‚(998) Other complications of procedur... ‚ 236957‚6.1‚ 9409‚5.8‚ 246366‚6.1‚
‚(530) Diseases of esophagus ‚ 206088‚3.5‚ 11484‚2.5‚ 217572‚3.4‚
‚(558) Other noninfective gastroenteri... ‚ 205865‚3.1‚ 11678‚2.3‚ 217543‚3.1‚
‚(466) Acute bronchitis and bronchiolitis ‚ 208706‚3.2‚ 8140‚2.4‚ 216846‚3.2‚
‚(218) Uterine leiomyoma ‚ 201650‚2.4‚ 8331‚3.0‚ 209981‚2.4‚
‚(435) Transient cerebral ischemia ‚ 182669‚3.0‚ 4635‚3.2‚ 187304‚3.0‚
All ‚37060872‚4.7‚ 1812905‚3.8‚38873777‚4.6‚


Lecture 3-NHDS 64

17. Exercises 3.5

Using the Proc Tabulate above, substitute the principal procedure (pr21) and var age to produce a table
comparing the uninsured (self pay) and insured, the principal procedure and corresponding mean age.


Lecture 3-NHDS 65

APPENDIX 1: Exercise Answers

Exercises 3.1
Question 3.1-1
1. Prepare and run weighted and formatted PROC FREQ for variables ESOP1, ADM_TYPE, and
ADM_SOURCE.

Answer 3.1-1
The below Proc Freq is the code for question 1
weight weight;
tables esop1 adm_type asource;
format esop1 payer1f. adm_type admitypef.
asource adsourcef.

;
title1 'Weighted Frequency Distribution with';
title2 'Formats for Selected NHDS Variables';
run;

Exercises 3.1
Output for the above PROC Freq

Weighted Frequency Distribution with
Formats for Selected NHDS Variables

The FREQ Procedure

Principal expected source of payment

ESOP1 Frequency Percent Frequency Percent
Worker's Comp 157957 0.41 157957 0.41
Medicare 13523750 34.79 13681707 35.20
Medicaid 7526704 19.36 21208411 54.56
Other Govnt 616773 1.59 21825184 56.14
Blue Cross 3770005 9.70 25595189 65.84
HMO/PPO 5802453 14.93 31397642 80.77
Other Private 3733869 9.61 35131511 90.37
Self Pay 1812905 4.66 36944416 95.04
No Charge 179347 0.46 37123763 95.50
Other Ins 1125661 2.90 38249424 98.39
Payer Not Stated 624353 1.61 38873777 100.00

Type of admission


Lecture 3-NHDS 66

ADM_TYPE Frequency Percent Frequency Percent
Emergency 14781383 38.02 14781383 38.02
Urgent 8499636 21.86 23281019 59.89
Elective 8382888 21.56 31663907 81.45
New Born 4019881 10.34 35683788 91.79
admit NA 3189989 8.21 38873777 100.00

Source of admission

ASOURCE Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Physican Referral 12728421 32.74 12728421 32.74
Clinical Referral 861246 2.22 13589667 34.96
HMO Referral 169737 0.44 13759404 35.40
Acute Transfer 1069107 2.75 14828511 38.15
SNF Transfer 218427 0.56 15046938 38.71
Other Tranfer 210381 0.54 15257319 39.25
Emergency Room 14921494 38.38 30178813 77.63
Court/Law Enforcement 102001 0.26 30280814 77.90
Source_other 4531547 11.66 34812361 89.55
Sourse N/A 4061416 10.45 38873777 100.00

Exercises 3.1 (continued)
Question 3.1-2
2. Add a variable in the input code for DX13 to read $3 (first 3 characters of the principal diagnosis) in
nhds02d and produce the frequency distribution for PROC FREQ and a weighted and formatted
output.
Answer 3.1-2
The updated nhds02d that contains the new variable dx13

data nhds2006 ;
input
@ 1 svyear $2.
@ 3 newborn 1.
@ 4 ageunits 1.
@ 5 age 2.
@ 7 sex 1.
@ 8 race 1.
@ 9 marstat 1.
@ 10 disc_mon $2.
@ 12 discstat 1.
@ 13 doc 4.
@ 17 losflag 1.
@ 18 region 1.


Lecture 3-NHDS 67

@ 19 bedsize 1.
@ 20 owner 1.
@ 21 weight 5.
@ 26 century $2.
@ 28 dx1 $5.
@ 28 dx13 $3.
The proc freq code used in nhds02d used to obtain principal diagnoses distribution of NHDS.
weight weight;
tables dx13;
format dx13 $diag3df.

;
title2 'Formats for NHDS Principal Diagnosis';
run;
Partial output of above principal diagnoses distribution of NHDS
Formats for NHDS Principal Diagnosis

The FREQ Procedure

dx13 Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
(002) Typhoid and paratyphoid fevers 357 0.00 357 0.00
(003) Other salmonella infections 6705 0.02 7062 0.02
(004) Shigellosis 1010 0.00 8072 0.02
(005) Other food poisoning (bacterial) 3617 0.01 11689 0.03
(006) Amebiasis 708 0.00 12397 0.03
(007) Other protozoal intestinal dise... 818 0.00 13215 0.03
(008) Intestinal infections due to ot... 186110 0.48 199325 0.51
(009) Ill-defined intestinal infections 21050 0.05 220375 0.57
(010) Primary tuberculous infection 119 0.00 220494 0.57
(011) Pulmonary tuberculosis 7703 0.02 228197 0.59
(013) Tuberculosis of meninges and ce... 63 0.00 228260 0.59
(015) Tuberculosis of bones and joints 827 0.00 229087 0.59
(017) Tuberculosis of other organs 979 0.00 230066 0.59
(018) Miliary tuberculosis 41 0.00 230107 0.59
(023) Brucellosis 133 0.00 230240 0.59
(027) Other zoonotic bacterial diseases 89 0.00 230329 0.59
(031) Diseases due to other mycobacteria 1344 0.00 231673 0.60
(033) Whooping cough 3168 0.01 234841 0.60
(034) Streptococcal sore throat and s... 9920 0.03 244761 0.63
(035) Erysipelas 2894 0.01 247655 0.64
(036) Meningococcal infection 1019 0.00 248674 0.64
(037) Tetanus 14 0.00 248688 0.64
(038) Septicemia 530434 1.36 779122 2.00


Lecture 3-NHDS 68

Question 3.1-3
3. Add a variable in the input code for PD12 to read $2 (first 2 characters of the principal procedure)
and produce the procedure distribution using PROC FREQ and a weighted and formatted output.
Answer 3.1-3
The partial nhds02d that contains the new variable principal procedure code (pdx12) with two characters
/****nhds02d.sas**********/
data nhds2006 ;
input
@ 1 svyear $2.
@ 3 newborn 1.
@ 4 ageunits 1.
@ 5 age 2.
@ 7 sex 1.
@ 8 race 1.
@ 9 marstat 1.
@ 10 disc_mon $2.
@ 12 discstat 1.
@ 13 doc 4.
@ 17 losflag 1.
@ 18 region 1.
@ 19 bedsize 1.
@ 20 owner 1.
@ 21 weight 5.
@ 26 century $2.
@ 28 dx1 $5.
@ 28 dx13 $3.
@ 33 DX2 $5.
@ 38 DX3 $5.
@ 43 DX4 $5.
@ 48 DX5 $5.
@ 53 DX6 $5.
@ 58 DX7 $5.
@ 63 PD1 $4.
@ 63 pdx2 $2.

The proc freq code, used in nhds02d, used to obtain principal procedure distribution of NHDS.
weight weight;
tables pd12;
format pd12 $proc2df.
;
title2 'Formats for NHDS Principal Procedure';
run;


Lecture 3-NHDS 69

Output of proc freq code, used in nhds02d, used to obtain principal procedure distribution of
NHDS
Formats for NHDS Principal Procedure
The FREQ Procedure
ICD-9-CM procedure code - 2 position
pd12 Frequency Percent Frequency Percent
Blank/00:Procedures and interventns, NEC 750131 3.13 750131 3.13
01:Incision and excision of skull, br... 108128 0.45 858259 3.58
02:Other operations on skull, brain, ... 63580 0.27 921839 3.84
03:Operations on spinal cord and spin... 415543 1.73 1337382 5.58
04:Operations on cranial and peripher... 15619 0.07 1353001 5.64
05:Operations on sympathetic nerves o... 841 0.00 1353842 5.64
06:Operations on thyroid and parathyr... 64094 0.27 1417936 5.91
07:Operations on other endocrine glands 14458 0.06 1432394 5.97
08:Operations on eyelids 17617 0.07 1450011 6.05
09:Operations on lacrimal system 1240 0.01 1451251 6.05
10:Operations on conjunctiva 256 0.00 1451507 6.05
11:Operations on cornea 852 0.00 1452359 6.06
12:Operations on iris, ciliary body, ... 1162 0.00 1453521 6.06
13:Operations on lens 3525 0.01 1457046 6.08
14:Operations on retina, choroid, vit... 8387 0.03 1465433 6.11
15:Operations on extraocular muscles 901 0.00 1466334 6.11
16:Operations on orbit and eyeball 4285 0.02 1470619 6.13
18:Operations on external ear 12618 0.05 1483237 6.18
19:Reconstructive operations on middl... 354 0.00 1483591 6.19
20:Other operations on middle and inn... 10286 0.04 1493877 6.23
21:Operations on nose 39267 0.16 1533144 6.39
22:Operations on nasal sinuses 8745 0.04 1541889 6.43
23:Removal and restoration of teeth 5846 0.02 1547735 6.45
24:Other operations on teeth, gums, a... 3566 0.01 1551301 6.47
25:Operations on tongue 9460 0.04 1560761 6.51
26:Operations on salivary glands and ... 7813 0.03 1568574 6.54
27:Other operations on mouth and face 41362 0.17 1609936 6.71
28:Operations on tonsils and adenoids 32508 0.14 1642444 6.85
29:Operations on pharynx 6601 0.03 1649045 6.88
30:Excision of larynx 6894 0.03 1655939 6.90
31:Other operations on larynx and tra... 92709 0.39 1748648 7.29
32:Excision of lung and bronchus 70556 0.29 1819204 7.59
33:Other operations on lung and bronchus 156952 0.65 1976156 8.24
34:Operations on chest wall, pleura, ... 241419 1.01 2217575 9.25
35:Operations on valves and septa of ... 119009 0.50 2336584 9.74
36:Operations on vessels of heart 271042 1.13 2607626 10.87
37:Other operations on heart and peri... 868062 3.62 3475688 14.49
38:Incision, excision, and occlusion ... 818977 3.41 4294665 17.91
39:Other operations on vessels 619691 2.58 4914356 20.49
40:Operations on lymphatic system 41724 0.17 4956080 20.66
41:Operations on bone marrow and spleen 67897 0.28 5023977 20.95
42:Operations on esophagus 50015 0.21 5073992 21.16
43:Incision and excision of stomach 125793 0.52 5199785 21.68
44:Other operations on stomach 206256 0.86 5406041 22.54
45:Incision, excision, and anastomosi... 1278566 5.33 6684607 27.87


Lecture 3-NHDS 70

46:Other operations on intestine 84498 0.35 6769105 28.22
47:Operations on appendix 315299 1.31 7084404 29.54
48:Operations on rectum, rectosigmoid... 82768 0.35 7167172 29.88
49:Operations on anus 29066 0.12 7196238 30.01
50:Operations on liver 55959 0.23 7252197 30.24
51:Operations on gallbladder and bili... 445406 1.86 7697603 32.10
52:Operations on pancreas 24454 0.10 7722057 32.20
53:Repair of hernia 139507 0.58 7861564 32.78
54:Other operations on abdominal region 302987 1.26 8164551 34.04
55:Operations on kidney 144371 0.60 8308922 34.64
56:Operations on ureter 57733 0.24 8366655 34.89
57:Operations on urinary bladder 155450 0.65 8522105 35.53
58:Operations on urethra 11696 0.05 8533801 35.58
59:Other operations on urinary tract 87477 0.36 8621278 35.95
60:Operations on prostate and seminal... 165092 0.69 8786370 36.64
61:Operations on scrotum and tunica v... 6498 0.03 8792868 36.66
62:Operations on testes 7291 0.03 8800159 36.69
63:Operations on spermatic cord, epid... 1348 0.01 8801507 36.70
64:Operations on penis 1145843 4.78 9947350 41.48
65:Operations on ovary 97301 0.41 10044651 41.88
66:Operations on fallopian tubes 114660 0.48 10159311 42.36
67:Operations on cervix 14019 0.06 10173330 42.42
68:Other incision and excision of uterus 592406 2.47 10765736 44.89
69:Other operations on uterus and sup... 43649 0.18 10809385 45.07
70:Operations on vagina and cul-de-sac 75770 0.32 10885155 45.39
71:Operations on vulva and perineum 20373 0.08 10905528 45.47
72:Forceps, vacuum, and breech delivery 212179 0.88 11117707 46.36
73:Other procedures inducing or assis... 1597333 6.66 12715040 53.02
74:Cesarean section and removal of fetus 1292562 5.39 14007602 58.41
75:Other obstetric operations 938482 3.91 14946084 62.32
76:Operations on facial bones and joints 38324 0.16 14984408 62.48
77:Incision, excision, and division o... 77579 0.32 15061987 62.80
78:Other operations on bones, except ... 93376 0.39 15155363 63.19
79:Reduction of fracture and dislocation 559179 2.33 15714542 65.52
80:Incision and excision of joint str... 167779 0.70 15882321 66.22
81:Repair and plastic operations on j... 1350214 5.63 17232535 71.85
82:Operations on muscle, tendon, and ... 12153 0.05 17244688 71.90
83:Operations on muscle, tendon, fasc... 120425 0.50 17365113 72.40
84:Other procedures on musculoskeleta... 111220 0.46 17476333 72.87
85:Operations on the breast 97026 0.40 17573359 73.27
86:Operations on skin and subcutaneou... 761241 3.17 18334600 76.45
87:Diagnostic radiology 283951 1.18 18618551 77.63
88:Other diagnostic radiology and rel... 736008 3.07 19354559 80.70
89:Interview, evaluation, consultatio... 291331 1.21 19645890 81.91
90:Microscopic examination I 9886 0.04 19655776 81.96
91:Microscopic examination II 1993 0.01 19657769 81.96
92:Nuclear medicine 74211 0.31 19731980 82.27
93:Physical therapy/respiratory thera... 705245 2.94 20437225 85.21
94:Procedures related to the psyche 498209 2.08 20935434 87.29
95:Ophthalmologic and otologic diagno... 157521 0.66 21092955 87.95
96:Nonoperative intubation and irriga... 667657 2.78 21760612 90.73
97:Replacement and removal of therape... 38220 0.16 21798832 90.89
98:Nonoperative removal of foreign body 12924 0.05 21811756 90.95
99:Other nonoperative procedures 2171673 9.05 23983429 100.00
Frequency Missing = 14890348


Lecture 3-NHDS 71

Question 3.1-4
4. Name the top five 2006 NHDS procedure codes along with their volumes.
Answer 3.1-4
First Two I9 codes and Procedure Name and Number of Discharges

45:Incision, excision, and anastomosi.. 1,278,566

73:Other procedures inducing or assis.. 1,597,333

74:Cesarean section and removal of fetus 1.292,562
81: Repair and plastic operations on j... 1,350.214

64:Operations on penis 1,145,843

Question 3.1-5
5. Name the top five 2006 NHDS I9 diagnoses codes along with their volumes.
Answer 3.1-5

First Three I9 codes and Diagnosis Name Discharges

(296) Affective psychoses 971, 131

428) Heart failure 1,106,406

(486) Pneumonia, organism unspecified 1,053,180

(V27) Outcome of delivery 4,127,491

(V30) Singleton (New Born) 3,902,440


Lecture 3-NHDS 72

Question 3.1-6
6.Write the format statements for the first 100 Medicare DRGs which are found in
C:DATA9000DRG Medicare FY07.
Answer 3.1-6
Format statement for the first 100 Medicare DRG
value drgf
1 = "(1) CRANIOTOMY AGE >17 W CC"
2 = "(2) CRANIOTOMY AGE >17 W/O CC"
3 = "(3) CRANIOTOMY AGE 0-17"
4 = "(4) NO LONGER VALID"
5 = "(5) NO LONGER VALID"
6 = "(6) CARPAL TUNNEL RELEASE"
7 = "(7) PERIPH & CRANIAL NERVE & OTHER NERV SYST PROC W CC"
8 = "(8) PERIPH & CRANIAL NERVE & OTHER NERV SYST PROC W/O CC"
9 = "(9) SPINAL DISORDERS & INJURIES"
10 = "(10) NERVOUS SYSTEM NEOPLASMS W CC"
11 = "(11) NERVOUS SYSTEM NEOPLASMS W/O CC"
12 = "(12) DEGENERATIVE NERVOUS SYSTEM DISORDERS"
13 = "(13) MULTIPLE SCLEROSIS & CEREBELLAR ATAXIA"
14 = "(14) INTRACRANIAL HEMORRHAGE OR CEREBRAL INFARCTION"
15 = "(15) NONSPECIFIC CVA & PRECEREBRAL OCCLUSION W/O INFARCT"
16 = "(16) NONSPECIFIC CEREBROVASCULAR DISORDERS W CC"
17 = "(17) NONSPECIFIC CEREBROVASCULAR DISORDERS W/O CC"
18 = "(18) CRANIAL & PERIPHERAL NERVE DISORDERS W CC"
19 = "(19) CRANIAL & PERIPHERAL NERVE DISORDERS W/O CC"
20 = "(20) NERVOUS SYSTEM INFECTION EXCEPT VIRAL MENINGITIS"
21 = "(21) VIRAL MENINGITIS"
22 = "(22) HYPERTENSIVE ENCEPHALOPATHY"
23 = "(23) NONTRAUMATIC STUPOR & COMA"
24 = "(24) SEIZURE & HEADACHE AGE >17 W CC"
25 = "(25) SEIZURE & HEADACHE AGE >17 W/O CC"
26 = "(26) SEIZURE & HEADACHE AGE 0-17"
27 = "(27) TRAUMATIC STUPOR & COMA, COMA >1 HR"
28 = "(28) TRAUMATIC STUPOR & COMA, COMA <1 HR AGE >17 W CC"
29 = "(29) TRAUMATIC STUPOR & COMA, COMA <1 HR AGE >17 W/O CC"
30 = "(30) TRAUMATIC STUPOR & COMA, COMA <1 HR AGE 0-17"
31 = "(31) CONCUSSION AGE >17 W CC"
32 = "(32) CONCUSSION AGE >17 W/O CC"
33 = "(33) CONCUSSION AGE 0-17"
34 = "(34) OTHER DISORDERS OF NERVOUS SYSTEM W CC"
35 = "(35) OTHER DISORDERS OF NERVOUS SYSTEM W/O CC"
36 = "(36) RETINAL PROCEDURES"
37 = "(37) ORBITAL PROCEDURES"
38 = "(38) PRIMARY IRIS PROCEDURES"
39 = "(39) LENS PROCEDURES WITH OR WITHOUT VITRECTOMY"
40 = "(40) EXTRAOCULAR PROCEDURES EXCEPT ORBIT AGE >17"
41 = "(41) EXTRAOCULAR PROCEDURES EXCEPT ORBIT AGE 0-17"
42 = "(42) INTRAOCULAR PROCEDURES EXCEPT RETINA, IRIS & LENS"


Lecture 3-NHDS 73

43 = "(43) HYPHEMA"
44 = "(44) ACUTE MAJOR EYE INFECTIONS"
45 = "(45) NEUROLOGICAL EYE DISORDERS"
46 = "(46) OTHER DISORDERS OF THE EYE AGE >17 W CC"
47 = "(47) OTHER DISORDERS OF THE EYE AGE >17 W/O CC"
48 = "(48) OTHER DISORDERS OF THE EYE AGE 0-17"
49 = "(49) MAJOR HEAD & NECK PROCEDURES"
50 = "(50) SIALOADENECTOMY"
51 = "(51) SALIVARY GLAND PROCEDURES EXCEPT SIALOADENECTOMY"
52 = "(52) CLEFT LIP & PALATE REPAIR"
53 = "(53) SINUS & MASTOID PROCEDURES AGE >17"
54 = "(54) SINUS & MASTOID PROCEDURES AGE 0-17"
55 = "(55) MISCELLANEOUS EAR, NOSE, MOUTH & THROAT PROCEDURES"
56 = "(56) RHINOPLASTY"
57 = "(57) T&A PROC, EXCEPT TONSILLECTOMY &/OR ADEN AGE >17"
58 = "(58) T&A PROC, EXCEPT TONSILLECTOMY &/OR ADENO AGE 0-17"
59 = "(59) TONSILLECTOMY &/OR ADENOIDECTOMY ONLY, AGE >17"
60 = "(60) TONSILLECTOMY &/OR ADENOIDECTOMY ONLY, AGE 0-17"
61 = "(61) MYRINGOTOMY W TUBE INSERTION AGE >17"
62 = "(62) MYRINGOTOMY W TUBE INSERTION AGE 0-17"
63 = "(63) OTHER EAR, NOSE, MOUTH & THROAT O.R. PROCEDURES"
64 = "(64) EAR, NOSE, MOUTH & THROAT MALIGNANCY"
65 = "(65) DYSEQUILIBRIUM"
66 = "(66) EPISTAXIS"
67 = "(67) EPIGLOTTITIS"
68 = "(68) OTITIS MEDIA & URI AGE >17 W CC"
69 = "(69) OTITIS MEDIA & URI AGE >17 W/O CC"
70 = "(70) OTITIS MEDIA & URI AGE 0-17"
71 = "(71) LARYNGOTRACHEITIS"
72 = "(72) NASAL TRAUMA & DEFORMITY"
73 = "(73) OTHER EAR, NOSE, MOUTH & THROAT DIAGNOSES AGE >17"
74 = "(74) OTHER EAR, NOSE, MOUTH & THROAT DIAGNOSES AGE 0-17"
75 = " 75) MAJOR CHEST PROCEDURES"
76 = "(76) OTHER RESP SYSTEM O.R. PROCEDURES W CC"
77 = "(77) OTHER RESP SYSTEM O.R. PROCEDURES W/O CC"
78 = "(78) PULMONARY EMBOLISM"
79 = "(79) RESPIRATORY INFECTIONS & INFLAMMATIONS AGE >17 W CC"
80 = "(80) RESPIRATORY INFECTIONS & INFLAMMATIONS AGE >17 W/O CC"
81 = "(81) RESPIRATORY INFECTIONS & INFLAMMATIONS AGE 0-17"
82 = "(82) RESPIRATORY NEOPLASMS"
83 = "(83) MAJOR CHEST TRAUMA W CC "
84 = "(84) MAJOR CHEST TRAUMA W/O CC"
85 = "(85) PLEURAL EFFUSION W CC"
86 = "(86) PLEURAL EFFUSION W/O CC"

87 = "(87) PULMONARY EDEMA & RESPIRATORY FAILURE"
88 = "(88) CHRONIC OBSTRUCTIVE PULMONARY DISEASE"
89 = "(89) SIMPLE PNEUMONIA & PLEURISY AGE >17 W CC"

Lecture 3-NHDS 74

90 = "(90) SIMPLE PNEUMONIA & PLEURISY AGE >17 W/O CC"
91 = "(91) SIMPLE PNEUMONIA & PLEURISY AGE 0-17"
92 = "(92) INTERSTITIAL LUNG DISEASE W CC"
93 = "(93) INTERSTITIAL LUNG DISEASE W/O CC"
94 = "(94) PNEUMOTHORAX W CC"
95 = "(95) PNEUMOTHORAX W/O CC"
96 = "(96) BRONCHITIS & ASTHMA AGE >17 W CC"
97 = "(97) BRONCHITIS & ASTHMA AGE >17 W/O CC"
98 = "(98) BRONCHITIS & ASTHMA AGE 0-17"
99 = "(99) RESPIRATORY SIGNS & SYMPTOMS W CC"
100 = "(100) RESPIRATORY SIGNS & SYMPTOMS W/O CC"

Exercises 3.2
Question 3.2-1
1. Write the indicator and truth logic code for the variables: admitype, bedsize, dismonth and
losflag and include the data and set statement from nhds03d.
Answer 3.2-1
The code below is the answer to question 1.
data nhds06;
set work.nhds2006;
/****************Answers to questions 3.2**************/

/***********Indicator Variables for Admit Type******/

emergency = (adm_type=1);
urgent = (adm_type=2);
elective = (adm_type=3);
new_born = (adm_type=4);
admit_na = (adm_type=9);

/***********Truth Logic for for Admit Type******/

admitcat= 1*(adm_type=1) + 2*(adm_type=2) + 3*(adm_type=3)+
4*(adm_type=4) + 5*(adm_type=9);

/***********Indicator Variables for Bedsize******/

beds6_99 = (bedsize=1);

Exercises 3.2 continued)


Lecture 3-NHDS 75

beds500plus = (bedsize=5);

/***********Truth Logic for Bedsize******/

bedcat = 1*(bedsize=1) + 2*(bedsize=2) + 3*(bedsize=3) +
4*(bedsize=4) + 5*(bedsize=5);

/***********Indicator Variables for Discharge Month******/

January = (disc_mon='01');
February = (disc_mon='02');
March = (disc_mon='03');
April = (disc_mon='04');
May = (disc_mon='05');
June = (disc_mon='06');
July = (disc_mon='07');
August = (disc_mon='08');

September = (disc_mon='09');
October = (disc_mon='10');
November = (disc_mon='11');
December = (disc_mon='12');

/***********Truth Logic for Discharge Month******/

monthcat = 1*(disc_mon='01')+ 2*(disc_mon='02') +3*(disc_mon='03') +
4*(disc_mon='04')+ 5*(disc_mon='05') +6*(disc_mon='06') +
7*(disc_mon='07')+ 8*(disc_mon='08') +9*(disc_mon='09') +
10*(disc_mon='10')+ 11*(disc_mon='11') +12*(disc_mon='12');

/***********Indicator Variables for Los Flag******/

ltoneday = (losflag =0);
geoneday = (losflag =1);

/***********Truth Logic for LOS Flag******/

losflagcat=1*(losflag =0)+ 2*(losflag=1);

weight weight;
var

emergency urgent elective new_born admit_na
admitcat beds6_99 beds100_199 beds200_299
beds300_499 beds500plus bedcat
january february march april may june


Lecture 3-NHDS 76

july august september october november
december monthcat ltoneday geoneday doc age;
;
run;
title 'Proc Means of Additional NHDS Variables';

Question 3.2-2
2. Using the above code, submit a weighted version of the above Proc Means with these
additional variables plus days of care (DOC) and age.
Answer 3.2-2
Below is the PROC Means Output that answers question 2.
Proc Means of Additional NHDS Variables
The MEANS Procedure
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
emergency 38873777 0.3802405 14781383.00 0 1.0000000
urgent 38873777 0.2186470 8499636.00 0 1.0000000
elective 38873777 0.2156438 8382888.00 0 1.0000000
new_born 38873777 0.1034086 4019881.00 0 1.0000000
admit_na 38873777 0.0820602 3189989.00 0 1.0000000
admitcat 38873777 2.2884009 88958788.00 1.0000000 5.0000000
beds6_99 38873777 0.2245193 8727912.00 0 1.0000000
beds100_199 38873777 0.2102052 8171471.00 0 1.0000000
beds200_299 38873777 0.1922901 7475041.00 0 1.0000000
beds300_499 38873777 0.2351687 9141896.00 0 1.0000000
beds500plus 38873777 0.1378167 5357457.00 0 1.0000000
bedcat 38873777 2.8515584 110850846 1.0000000 5.0000000
January 38873777 0.0855687 3326380.00 0 1.0000000
February 38873777 0.0804654 3127993.00 0 1.0000000
March 38873777 0.0894111 3475747.00 0 1.0000000
April 38873777 0.0820406 3189227.00 0 1.0000000
May 38873777 0.0856391 3329115.00 0 1.0000000
June 38873777 0.0847040 3292765.00 0 1.0000000
July 38873777 0.0833721 3240989.00 0 1.0000000
August 38873777 0.0859714 3342033.00 0 1.0000000
September 38873777 0.0828773 3221755.00 0 1.0000000
October 38873777 0.0805104 3129742.00 0 1.0000000
November 38873777 0.0789241 3068077.00 0 1.0000000
December 38873777 0.0805158 3129954.00 0 1.0000000
monthcat 38873777 6.4360450 250193377 1.0000000 12.0000000
ltoneday 38873777 0.0194220 755008.00 0 1.0000000
geoneday 38873777 0.9805780 38118769.00 0 1.0000000
DOC 38873777 4.6309689 180023252 1.0000000 381.0000000
AGE 38873777 47.2108638 1835264591 0 99.0000000
losflagcat 38873777 1.9805780 76992546.00 1.0000000 2.0000000

Question 3.2-3
3. Using the above, present a narrative of the descriptive statistics associated of this proc mean
output.

Lecture 3-NHDS 77

Answer 3.2-3
When weighted, there were 38.9 million discharges in the 2006 NHDS. The mean days of care
were 4.63 days with a range of 1 to 381 days. The mean age was 47.2 with a range of 0 to 99
years. Admitting type indicated 38.0% were from the emergency department, 21.9% were urgent,
21.5% were elective, 10.3% were newborns and 8.2% were not reported.
The distribution of hospital bed size across the nation were: 22.4 percent for 6 to 99 beds, 21.0%
for 100 to 199 beds, 19.2% for 200 to 299 beds, 23.5% for 300 to 499 beds and lastly 13.8
percent for 500 or more beds.
The 2006 monthly discharge distribution from January through October ranged from a low of
8.0% in October to a high of 8.9% in March 2006. The lowest month was November 2006 with
7.8%. The length of stay flag indicated that 1.9% of the 2006 discharges were less than 1 day
and 98.0% were one day or more.
Question 3.2-4
4. Using nhds03d, insert the [class selfpay] into the proc mean. The output will yield the
differences between the selfpay (uninsured) and non-selfpay (insured). Prepare a narrative that
compares the demographic differences between the uninsured and insured populations.
Answer 3.2-4

Below is the PROC Means code added to nhds03d that answers question 3.2- 4.
freq weight;
class selfpay;
var doc age male female gendercat white black nativeam
asian hawaiianpi othrace multirace racenotstat
racecat married single widowed divorced separated
msnotstat home dislma disacute disltc alivens
disceased disstatna discstatcat northeast midwest
south west regioncat private government nonprofit
ownercat workercomp medicare medicaid othergvmt
bluecross hmoppo othprivate selfpay
nocharge othinsure paynotstated payercat
docreferal clinreferal hmoreferal hospreferal
snftransfer othtransfer edsource legalsource
othsource sourcena sourcecat emergency urgent
elective new_born admit_na admitcat
;
run;

Below is the PROC Means output that assists in completing question 3.2- 4.
Proc Means Comparing Insured to Uninsured Variables

The MEANS Procedure


Lecture 3-NHDS 78

selfpay N Obs Variable N Mean Sum
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
0 37060872 AGE 37060872 47.7673372 1770299170
DOC 37060872 4.6694280 173053072
male 37060872 0.4078268 15114418.00
female 37060872 0.5921732 21946454.00
gendercat 37060872 1.5921732 59007326.00
white 37060872 0.5993990 22214248.00
black 37060872 0.1178947 4369280.00
nativeam 37060872 0.0029098 107840.00
asian 37060872 0.0167593 621114.00
hawaiianpi 37060872 0.0024822 91993.00
othrace 37060872 0.0178340 660943.00
multirace 37060872 0.000812879 30126.00
racenotstat 37060872 0.2419082 8965328.00
racecat 37060872 2.9713255 110119913
married 37060872 0.2421331 8973663.00
single 37060872 0.2760046 10228970.00
widowed 37060872 0.0821900 3046033.00
divorced 37060872 0.0369322 1368741.00
separated 37060872 0.0058082 215258.00
msnotstat 37060872 0.3569319 13228207.00
home 37060872 0.7856002 29115030.00
dislma 37060872 0.0077943 288862.00
disacute 37060872 0.0416858 1544911.00
disltc 37060872 0.0846993 3139029.00
alivens 37060872 0.0455597 1688483.00
disceased 37060872 0.0193410 716793.00
disstatna 37060872 0.0153198 567764.00
discstatcat 37060872 1.7161259 63601124.00
northeast 37060872 0.2077585 7699713.00
midwest 37060872 0.2275832 8434432.00
south 37060872 0.3705125 13731515.00
west 37060872 0.1941458 7195212.00
regioncat 37060872 2.5510455 94543970.00
private 37060872 0.1223865 4535750.00
government 37060872 0.1166868 4324516.00
nonprofit 37060872 0.7609267 28200606.00
ownercat 37060872 2.6385402 97786600.00
workercomp 37060872 0.0042621 157957.00
medicare 37060872 0.3649064 13523750.00
medicaid 37060872 0.2030903 7526704.00
othergvmt 37060872 0.0166422 616773.00
bluecross 37060872 0.1017247 3770005.00
hmoppo 37060872 0.1565655 5802453.00
othprivate 37060872 0.1007496 3733869.00
selfpay 37060872 0 0
nocharge 37060872 0.0048393 179347.00
othinsure 37060872 0.0303733 1125661.00
paynotstated 37060872 0.0168467 624353.00
0 37060872 payercat 37060872 4.0957780 151793103
docreferal 37060872 0.3337514 12369117.00
clinreferal 37060872 0.0220258 816297.00
hmoreferal 37060872 0.0042765 158491.00
hospreferal 37060872 0.0278450 1031960.00
snftransfer 37060872 0.0057882 214516.00


Lecture 3-NHDS 79

othtransfer 37060872 0.0055308 204977.00
edsource 37060872 0.3770255 13972892.00
legalsource 37060872 0.0026930 99806.00
othsource 37060872 0.1167582 4327160.00
sourcena 37060872 0.1043056 3865656.00
sourcecat 37060872 5.3187404 197117158
emergency 37060872 0.3738683 13855887.00
urgent 37060872 0.2210097 8190813.00
elective 37060872 0.2198195 8146704.00
new_born 37060872 0.1034431 3833692.00
admit_na 37060872 0.0818593 3033776.00
admitcat 37060872 2.2984152 85181273.00

1 1812905 AGE 1812905 35.8349836 64965421.00
DOC 1812905 3.8447574 6970180.00
male 1812905 0.5055356 916488.00
female 1812905 0.4944644 896417.00
gendercat 1812905 1.4944644 2709322.00
white 1812905 0.4974138 901764.00
black 1812905 0.1746688 316658.00
nativeam 1812905 0.0061073 11072.00
asian 1812905 0.0157151 28490.00
hawaiianpi 1812905 0.0016824 3050.00
othrace 1812905 0.0276236 50079.00
multirace 1812905 0.0017927 3250.00
racenotstat 1812905 0.2749962 498542.00
racecat 1812905 3.3146061 6009066.00
married 1812905 0.1998963 362393.00
single 1812905 0.3874400 702392.00
widowed 1812905 0.0204330 37043.00
divorced 1812905 0.0564370 102315.00
separated 1812905 0.0105052 19045.00
msnotstat 1812905 0.3252884 589717.00
home 1812905 0.8946393 1621896.00
dislma 1812905 0.0238865 43304.00
disacute 1812905 0.0279948 50752.00
disltc 1812905 0.0140995 25561.00
alivens 1812905 0.0196557 35634.00
disceased 1812905 0.0147178 26682.00
disstatna 1812905 0.0050063 9076.00
discstatcat 1812905 1.3044247 2364798.00
northeast 1812905 0.1649662 299068.00
midwest 1812905 0.1929522 349804.00
south 1812905 0.4812536 872467.00
west 1812905 0.1608281 291566.00
regioncat 1812905 2.6379435 4782341.00
private 1812905 0.0793048 143772.00
government 1812905 0.2415030 437822.00
nonprofit 1812905 0.6791922 1231311.00
ownercat 1812905 2.5998875 4713349.00
workercomp 1812905 0 0
medicare 1812905 0 0
medicaid 1812905 0 0
othergvmt 1812905 0 0
bluecross 1812905 0 0
hmoppo 1812905 0 0
othprivate 1812905 0 0


Lecture 3-NHDS 80

selfpay 1812905 1.0000000 1812905.00
nocharge 1812905 0 0
othinsure 1812905 0 0
paynotstated 1812905 0 0
payercat 1812905 8.0000000 14503240.00
docreferal 1812905 0.1981924 359304.00
clinreferal 1812905 0.0247939 44949.00
hmoreferal 1812905 0.0062033 11246.00
hospreferal 1812905 0.0204903 37147.00
snftransfer 1812905 0.0021573 3911.00
othtransfer 1812905 0.0029809 5404.00
edsource 1812905 0.5232497 948602.00
legalsource 1812905 0.0012108 2195.00
othsource 1812905 0.1127400 204387.00
sourcena 1812905 0.1079814 195760.00
sourcecat 1812905 6.1439314 11138364.00
emergency 1812905 0.5105044 925496.00
urgent 1812905 0.1703470 308823.00
elective 1812905 0.1302793 236184.00
new_born 1812905 0.1027020 186189.00
admit_na 1812905 0.0861672 156213.00
admitcat 1812905 2.0836806 3777515.00

From the above, prepare a descriptive statistic narrative that compares the demographic
differences between the uninsured and insured populations.
In the nation in 2006, the NHDS indicated there were 1.8 million uninsured (selfpay) discharges
compared to 37.1 million discharges with insurance (non-selfpay). The uninsured were younger,
with a mean age of 35.8, compared to 47.7 years for the insured. The uninsured included more
males than females (50.5% versus 49.4%) which was the reverse for those insured (40.7%
versus 59.2%). They also had fewer days of care, (3.8 versus 4.7 days ).
Regarding the uninsured, fewer were whites (49.7% versus 59.9%), and more blacks (17.5%
versus 11.8% ). Less were married (19.9% versus 24.2%) and more single (38.7% versus
27.6%). More were discharges home (89.4% versus 78.6%) and less were discharges to a
skilled nursing facility (1.4% versus 8.5%) or dead on discharge (1.5% versus 1.9%). Less were
from the Northeast (16.5% versus 20.8%), Midwest (19.3% versus 22.7%) , and West (16.0%
versus 19.4%) while more were from the South (48.1% versus 37.0%). More were treated in
government hospitals (24% versus 11.6%) and less were treated in nonprofit (67.9%
versus76.1%) and private hospitals (7.9% versus 12.2%). Less were referred by physicians
(19.8% versus 33.4%) and more than half came through the emergency department (52.3%
versus 37.7%). More were emergent (51.0% versus 37.3%), less were urgent (17.0% versus

22.1%) less were elective (13.0% versus 21.9%) and lastly newborns were equal (10.2% versus
10.3%).

Hospital Discharge Survey: 2006 (Press Release)


Lecture 3-NHDS 81

In 2006, there were an estimated 34.9 million hospital discharges. This number does not include
newborn infants. Fifty-eight percent of all discharges were hospitalized 3 days or less. The rate
of coronary hospitalizations for coronary atherosclerosis for all age groups, especially those aged
65 years and over, has declined since 2002.

http://nchspressroom.wordpress.com/2008/08/01/hospital-discharge-survey-2006/

Exercise 3.3
Question 3.3-1
1. Run the model with the freq weight statement after PROC REG and determine if the findings
are significantly different.
Answer 3.3-1
The code below produces the answer to question 1
/***nhds04d***/


Lecture 3-NHDS 82

freq weight;
model doc=male white married medicare disceased south;
run;
title 'Linear regression of days of care as the dependent
variable and the effects of sex, race, martial status
diseased and region'
quit;

PROC REG Output
The REG Procedure (UNWEIGHTED)
Model: MODEL1



Sum of Mean

Model 6 390901 65150 1428.16 <.0001
Error 376321 17167112 45.61827

Coeff Var 143.27828

Parameter Estimates

Parameter Standard

male 1 0.53717 0.02239 23.99 <.0001
white 1 -0.05574 0.02248 -2.48 0.0132

Exercise 3.3 (continued)
married 1 -0.52875 0.03218 -16.43 <.0001
medicare 1 1.55716 0.02325 66.98 <.0001
disceased 1 3.84237 0.07999 48.04 <.0001
south 1 0.41328 0.02312 17.88 <.0001
The REG Procedure (WEIGHTED)
Model: MODEL1

Sum of Frequencies Read 38873777


Lecture 3-NHDS 83

Sum of Frequencies Used 38873777

Frequency: WEIGHT Analysis weight


Sum of Mean

Model 6 33795032 5632505 124977 <.0001
Error 3.89E7 1751981138 45.06846

Coeff Var 144.96545

Parameter Estimates

Parameter Standard

male 1 0.55769 0.00219 254.71 <.0001
white 1 -0.13288 0.00224 -59.24 <.0001
married 1 -0.44510 0.00259 -171.68 <.0001
medicare 1 1.46303 0.00228 642.47 <.0001
disceased 1 3.24600 0.00789 411.18 <.0001
south 1 0.35608 0.00225 158.19 <.0001

Answer 3.3 - 1
1. When comparing both unweighted and weighted DOC models, the intercept is slightly higher,
and four out of six coefficients are slightly lower. Overall, these differences were not substantial
and in all cases, the effects remained significant. The coefficient of determination R2 was less in
the weighted model.

Question 3.3 - 2
2. Add to the model the additional effects of hospital ownership and source of admission using
the indicator variables of private and snftransfer, and interpret these two effects.
Answer 3.3-Number 2
The model below includes the two effects of ownership and source of admission.

Lecture 3-NHDS 84

freq weight;
model doc=male white married medicare disceased south
private snftransfer
;
run;
title 'Linear regression of doc as the dependent and with Added
Effects';

quit;
Proc REG Output with additional effects.
The REG Procedure
Model: MODEL1

Sum of Frequencies Read 38873777
Sum of Frequencies Used 38873777

Frequency: WEIGHT Analysis weight

Sum of Mean

Model 8 34833079 4354135 96668.8 <.0001
Error 3.89E7 1750943091 45.04176

Coeff Var 144.92251

Parameter Estimates

Parameter Standard

male 1 0.56261 0.00219 256.98 <.0001
white 1 -0.15352 0.00225 -68.11 <.0001
married 1 -0.44955 0.00259 -173.37 <.0001
medicare 1 1.45204 0.00228 636.29 <.0001
disceased 1 3.20176 0.00790 405.34 <.0001
south 1 0.30039 0.00234 128.57 <.0001


Lecture 3-NHDS 85

private 1 0.33262 0.00347 95.85 <.0001
snftransfer 1 1.69864 0.01445 117.56 <.0001

Answer 3.3 -2
From the above PROC REG output, controlling for sex, race, marital status, payer, disposition
and region, the findings are as follows;
1. Private ownership compared to non-private results in 0.33 days of additional care. P<.0001
2. Patients transferred from skilled nursing facilities compared to all other admission sources
have 1.7 additional days of care. P<.0001
Question 3.3-3
3. Write the regression equation of this first model using the intercept and effect coefficients
and using the following format:
DOC = β0 + β1male+ β2white + β3married + β4Medicare + β5diseased + β6south +ε

Answer 3.3 Number 3

DOC = 3.82799 + 0.53717*male -0.05574*white -0.52875*married + 1.55716*
Medicare + 3.84237*disceased + 0.41328*south +Question 3.4-1

Exercise 3.4
Exercise 3.4-1
1. Add to the logistic model the effects of marital status (msnotstat), hospital ownership
(ownercat), admission source (asource) discharge disposition (discstatcat), and days of care
(DOC) to the first model.
Answer 3.4-1
The model below adds the effects of maritial status, disposition, ownership and source of
admission.

Lecture 3-NHDS 86

proc logistic data=nhds06 des;
class gendercat (param=ref ref='2') /*female**/
racecat (param=ref ref='1') /*white**/
admitcat (param=ref ref='3') /*elctive*/
regioncat (param=ref ref='2') /*midwest*/
discstatcat (param=ref ref='5') /*disposition-SNF*/
ownercat (param=ref ref='1') /*hospital ownership-private*/
marstatcat (param=ref ref='4') /*maritial status -divorced*/
sourcecat (param=ref ref='2') /*admission source-clinic*/
;
model selfpay=age doc gendercat racecat marstatcat admitcat
regioncat sourcecat discstatcat ownercat

;

units age=10 doc=1;
title 'Added Effects to Logistic Regression for NHDS Selfpay
(uninsured)';
run;
quit;
options label;
title;

Proc Logistic Output with additional four effects.
Added Effects to Logistic Regression for NHDS Selfpay (uninsured)


Model Information

Data Set WORK.NHDS06
Model binary logit


Response Profile

Ordered Total

1 1 15908
2 0 360420



Lecture 3-NHDS 87



gendercat 1 1
2 0

racecat 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0
3 0 1 0 0 0 0 0
4 0 0 1 0 0 0 0
5 0 0 0 1 0 0 0
6 0 0 0 0 1 0 0
7 0 0 0 0 0 1 0
8 0 0 0 0 0 0 1

admitcat 1 1 0 0 0
2 0 1 0 0
3 0 0 0 0
4 0 0 1 0
5 0 0 0 1

regioncat 1 1 0 0
2 0 0 0
3 0 1 0
4 0 0 1




discstatcat 1 1 0 0 0 0 0
2 0 1 0 0 0 0
3 0 0 1 0 0 0
4 0 0 0 1 0 0
5 0 0 0 0 0 0
6 0 0 0 0 1 0
7 0 0 0 0 0 1

ownercat 1 0 0
2 1 0
3 0 1

marstatcat 1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 0 0
5 0 0 0 1 0
6 0 0 0 0 1

sourcecat 1 1 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0
3 0 1 0 0 0 0 0 0 0
4 0 0 1 0 0 0 0 0 0


Lecture 3-NHDS 88

5 0 0 0 1 0 0 0 0 0
6 0 0 0 0 1 0 0 0 0
7 0 0 0 0 0 1 0 0 0
8 0 0 0 0 0 0 1 0 0
9 0 0 0 0 0 0 0 1 0
10 0 0 0 0 0 0 0 0 1




Intercept
Intercept and

AIC 131790.20 120070.38
SC 131801.04 120503.91
-2 Log L 131788.20 119990.38




Score 12161.9462 39 <.0001
Wald 10542.9534 39 <.0001


Wald

AGE 1 2171.0882 <.0001
DOC 1 71.8449 <.0001
gendercat 1 504.9573 <.0001
racecat 7 518.2272 <.0001
marstatcat 5 568.4548 <.0001
admitcat 4 246.7438 <.0001
regioncat 3 454.6374 <.0001
sourcecat 9 571.4626 <.0001
discstatcat 6 910.6489 <.0001
ownercat 2 1382.8221 <.0001


Standard Wald


Lecture 3-NHDS 89

Intercept 1 -3.1460 0.1066 870.7149 <.0001
AGE 1 -0.0195 0.000419 2171.0882 <.0001
DOC 1 -0.0145 0.00171 71.8449 <.0001
gendercat 1 1 0.3809 0.0169 504.9573 <.0001
racecat 2 1 0.1034 0.0238 18.9090 <.0001
racecat 3 1 0.6091 0.1154 27.8421 <.0001
racecat 4 1 0.1332 0.0977 1.8604 0.1726
racecat 5 1 0.0454 0.2468 0.0339 0.8539
racecat 6 1 0.3374 0.0378 79.5576 <.0001
racecat 7 1 1.2308 0.3271 14.1601 0.0002
racecat 8 1 0.4595 0.0214 460.0285 <.0001
marstatcat 1 1 -0.6913 0.0510 183.4291 <.0001
marstatcat 2 1 -0.3266 0.0501 42.4854 <.0001
marstatcat 3 1 -1.2620 0.0865 212.6395 <.0001
marstatcat 5 1 0.1756 0.1012 3.0151 0.0825
marstatcat 6 1 -0.7135 0.0477 223.3982 <.0001
admitcat 1 1 0.4003 0.0341 138.0887 <.0001
admitcat 2 1 0.0346 0.0324 1.1434 0.2849
admitcat 4 1 -0.5028 0.1181 18.1113 <.0001
admitcat 5 1 0.0567 0.0471 1.4501 0.2285
regioncat 1 1 0.0249 0.0279 0.7956 0.3724
regioncat 3 1 0.4124 0.0247 279.8162 <.0001
regioncat 4 1 0.0850 0.0321 7.0097 0.0081
sourcecat 1 1 -0.3904 0.0700 31.1472 <.0001
sourcecat 3 1 0.4527 0.1120 16.3323 <.0001
sourcecat 4 1 -0.0541 0.0856 0.3995 0.5273
sourcecat 5 1 -0.4951 0.3007 2.7109 0.0997
sourcecat 6 1 -0.0318 0.1394 0.0519 0.8197
sourcecat 7 1 0.3107 0.0702 19.5796 <.0001
sourcecat 8 1 -1.3142 0.3000 19.1864 <.0001
sourcecat 9 1 -0.6493 0.1297 25.0451 <.0001
sourcecat 10 1 0.0950 0.0723 1.7267 0.1888
discstatcat 1 1 0.5885 0.0519 128.3456 <.0001
discstatcat 2 1 1.5070 0.0709 452.0134 <.0001
discstatcat 3 1 0.3682 0.0765 23.1889 <.0001
discstatcat 4 1 -0.8618 0.0868 98.4910 <.0001
discstatcat 6 1 0.4747 0.0855 30.8496 <.0001
discstatcat 7 1 -0.0345 0.1272 0.0735 0.7863
ownercat 2 1 1.0423 0.0364 821.5382 <.0001
ownercat 3 1 0.2144 0.0313 46.9432 <.0001


Point 95% Wald

AGE 0.981 0.980 0.981
DOC 0.986 0.982 0.989
gendercat 1 vs 2 1.464 1.416 1.513
racecat 2 vs 1 1.109 1.058 1.162
racecat 3 vs 1 1.839 1.466 2.306
racecat 4 vs 1 1.143 0.943 1.384
racecat 5 vs 1 1.046 0.645 1.698
racecat 6 vs 1 1.401 1.301 1.509
racecat 7 vs 1 3.424 1.804 6.501
racecat 8 vs 1 1.583 1.518 1.651


Lecture 3-NHDS 90

marstatcat 1 vs 4 0.501 0.453 0.554
marstatcat 2 vs 4 0.721 0.654 0.796
marstatcat 3 vs 4 0.283 0.239 0.335
marstatcat 5 vs 4 1.192 0.978 1.453
marstatcat 6 vs 4 0.490 0.446 0.538
admitcat 1 vs 3 1.492 1.396 1.595
admitcat 2 vs 3 1.035 0.972 1.103
admitcat 4 vs 3 0.605 0.480 0.762
admitcat 5 vs 3 1.058 0.965 1.161
regioncat 1 vs 2 1.025 0.971 1.083
regioncat 3 vs 2 1.510 1.439 1.585
regioncat 4 vs 2 1.089 1.022 1.159
sourcecat 1 vs 2 0.677 0.590 0.776
sourcecat 3 vs 2 1.572 1.263 1.959
sourcecat 4 vs 2 0.947 0.801 1.120
sourcecat 5 vs 2 0.610 0.338 1.099
sourcecat 6 vs 2 0.969 0.737 1.273
sourcecat 7 vs 2 1.364 1.189 1.566
sourcecat 8 vs 2 0.269 0.149 0.484
sourcecat 9 vs 2 0.522 0.405 0.674
sourcecat 10 vs 2 1.100 0.954 1.267
discstatcat 1 vs 5 1.801 1.627 1.994
discstatcat 2 vs 5 4.513 3.928 5.186
discstatcat 3 vs 5 1.445 1.244 1.679
discstatcat 4 vs 5 0.422 0.356 0.501
discstatcat 6 vs 5 1.608 1.360 1.901
discstatcat 7 vs 5 0.966 0.753 1.240
ownercat 2 vs 1 2.836 2.641 3.045
ownercat 3 vs 1 1.239 1.165 1.318


Pairs 5733561360 c 0.744

Odds Ratios


AGE 10.0000 0.823
DOC 1.0000 0.986

2. In a narrative, describe the contribution of these additional effects upon the outcome variable.
As seen above in the proc logistic model output with the five additional effects, all of the effects
were significant with the following findings:


Lecture 3-NHDS 91

1. All else being equal, males are 1.464 times more likely to be uninsured than females,
p<.0001[CI 1.416, 1.5131]. These odds are slightly lower in the expanded model (1.464
versus 1.535).
2. All else being equal, blacks compared to whites are 1.109 times more likely to be uninsured,
p<.0001[CI 1.058, 1.162]. These odds are slightly lower in the expanded model (1.227
versus 1.109).
3. All else being equal, multiple races compared to whites are 3.424 times more likely to be
uninsured, p<.0001[CI 1.804, 6.501]. These odds are slightly higher in the expanded model
(3.424 versus 3.346).
4. All else being equal, Native Americans compared to whites were 1.839 times more likely to
be uninsured, p<.0001[CI 1.466, 2.306]. These odds are slightly lower in the expanded
model (1.839 versus 2.106).
5. All else being equal, emergencies compared to electives were 1.492 times more likely to be
uninsured, p<.0001[CI 1.396, 1.595]. These odds are significantly lower in the expanded
model (1.492 versus 2.449).
6. All else being equal, newborns compared to electives were 39.5 percent less likely to be
uninsured, p<.0001[CI 0.480 , 0.762]. These odds are slightly lower in the expanded model
(0.605 versus 0.506).
7. All else being equal, those from the South compared to the Midwest were 1.510 times more
likely to be uninsured, p<.0001[CI 1.439, 1.585]. These odds are slightly higher in the
expanded model (1.510 versus 1.473).
8. All else being equal, those from the West compared to the Midwest were 1.089 times more
likely to be uninsured, p<.0001[CI 1.022, 1.159]. These odds are slightly lower in the
expanded model (1.089 versus 1.139).
9. All else being equal, those who are married compared to those divorced were 49.9 percent
less likely to be uninsured, p<.0001[CI 0.501, 0.554].
10. All else being equal, those who are widowed compared to those divorced were 27.9 percent
less likely to be uninsured, p<.0001[CI 0.239, 0.335].
11. All else being equal, those who are single compared to those divorced were 49.9 percent less
12. All else being equal, those who enter the hospital through the emergency department
compared to a clinic referral are 1.36 times more likely to be uninsured, p<.0001[CI 1.189,
1.566].
13. All else being equal, those who enter the hospital by physician compared to a clinic referral
are 32.3 percent less likely to be uninsured, p<.0001[CI 0.590, 0.776].
14. All else being equal, those who enter the hospital through the legal system compared to a
clinic referral are 73.1 percent less likely to be uninsured, p<.0001[CI 0.590, 0.776].


Lecture 3-NHDS 92

15. All else being equal, those who left the hospital against medical advice compared to those
alive without discharge status were 4.5 times more likely to be uninsured, p<.0001[CI 3.982,
5.186].
16. All else being equal, those who are discharged dead compared to those alive without
discharge status were 1.6 times more likely to be uninsured, p<.0001[CI 1.360, 1.901].
17. All else being equal, those who are discharged to a long term care facility compared to those
alive without discharge status were 67.8 percent less likely to be uninsured, p<.0001[CI
0.356, 0.501].
18. All else being equal, those who are discharged home compared to those alive without
discharge status were 1.8 times more likely to be uninsured, p<.0001[CI 1.627, 1.994].
19. All else being equal, those who are discharged from a governmental hospital compared to
for- profit hospital were 2.8 times more likely to be uninsured, p<.0001[CI 2.641, 3.045].
20. All else being equal, those who are discharged from a not-for-profit hospital compared to a
for-profit hospital were 1.24 times more likely to be uninsured, p<.0001[CI 1.165, 1.318].
21. For every decade of age, a patient is 17.7 percent less likely to be uninsured, p<.0001.
22. For ever additional day of care, a patient is 1.4 percent less likely to be uninsured, p<.0001.

Exercise 3.5
Using the Proc Tabulate above substitute the principal procedure (pr21) and var age to produce a table
comparing the uninsured (self pay) and insured, the principal procedure, and corresponding mean age.
The code below will produce the rank order distribution of procedures associated with the selfpay (1) and
non-selfpay (0) population.
proc tabulate data=nhds06 order=freq; /* formchar=' '; */
freq weight;
class selfpay pd12;
var age;
tables pd12 all,
(selfpay all)*(age*(n*f=8.0 mean*f=3.2)) /rts=50;
format pd12 $proc2df.;
run;


Lecture 3-NHDS 93

title 'Distribution in Rank order of the Selfpay Procedure';

Output of Proc Tabulate showing the rank order distribution of procedures associated with the selfpay (1)
and non-selfpay (0) population.

Distribution in Rank order of the Selfpay Procedure

„ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒ†
‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚
‚ ‚ 0 ‚ 1 ‚ All ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒƒƒ‰
‚ ‚ AGE ‚ AGE ‚ AGE ‚
‚ ‡ƒƒƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒƒƒ…ƒƒƒ‰
‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‚pd12 ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚99:Other nonoperative procedures ‚ 2070497‚ 36‚ 101176‚ 22‚ 2171673‚ 36‚
‚73:Other procedures inducing or assis... ‚ 1530492‚ 27‚ 66841‚ 27‚ 1597333‚ 27‚
‚81:Repair and plastic operations on j... ‚ 1336131‚ 64‚ 14083‚ 49‚ 1350214‚ 64‚
‚74:Cesarean section and removal of fetus ‚ 1252479‚ 29‚ 40083‚ 28‚ 1292562‚ 29‚
‚45:Incision, excision, and anastomosi... ‚ 1230025‚ 65‚ 48541‚ 45‚ 1278566‚ 64‚
‚64:Operations on penis ‚ 1103692‚.64‚ 42151‚.67‚ 1145843‚.64‚
‚75:Other obstetric operations ‚ 903994‚ 27‚ 34488‚ 26‚ 938482‚ 27‚
‚37:Other operations on heart and peri... ‚ 834035‚ 65‚ 34027‚ 50‚ 868062‚ 64‚
‚38:Incision, excision, and occlusion ... ‚ 790064‚ 60‚ 28913‚ 42‚ 818977‚ 59‚
‚86:Operations on skin and subcutaneou... ‚ 704522‚ 47‚ 56719‚ 38‚ 761241‚ 47‚
‚Blank/00:Procedures and interventns, NEC ‚ 721001‚ 66‚ 29130‚ 53‚ 750131‚ 65‚
‚88:Other diagnostic radiology and rel... ‚ 687087‚ 59‚ 48921‚ 45‚ 736008‚ 58‚
‚93:Physical therapy/respiratory thera... ‚ 685791‚ 58‚ 19454‚ 37‚ 705245‚ 57‚
‚96:Nonoperative intubation and irriga... ‚ 630843‚ 51‚ 36814‚ 38‚ 667657‚ 50‚
‚39:Other operations on vessels ‚ 608045‚ 63‚ 11646‚ 42‚ 619691‚ 62‚
‚68:Other incision and excision of uterus ‚ 575106‚ 46‚ 17300‚ 44‚ 592406‚ 46‚
‚79:Reduction of fracture and dislocation ‚ 522517‚ 58‚ 36662‚ 39‚ 559179‚ 57‚
‚94:Procedures related to the psyche ‚ 443144‚ 40‚ 55065‚ 38‚ 498209‚ 40‚


Lecture 3-NHDS 94

‚51:Operations on gallbladder and bili... ‚ 411028‚ 54‚ 34378‚ 42‚ 445406‚ 53‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒ
‚03:Operations on spinal cord and spin... ‚ 399301‚ 42‚ 16242‚ 37‚ 415543‚ 42‚
‚47:Operations on appendix ‚ 286785‚ 31‚ 28514‚ 30‚ 315299‚ 31‚
‚54:Other operations on abdominal region ‚ 283608‚ 52‚ 19379‚ 41‚ 302987‚ 52‚
‚89:Interview, evaluation, consultatio... ‚ 272446‚ 54‚ 18885‚ 49‚ 291331‚ 53‚
‚87:Diagnostic radiology ‚ 257469‚ 58‚ 26482‚ 45‚ 283951‚ 57‚
‚36:Operations on vessels of heart ‚ 264174‚ 65‚ 6868‚ 54‚ 271042‚ 65‚
‚34:Operations on chest wall, pleura, ... ‚ 224149‚ 62‚ 17270‚ 41‚ 241419‚ 61‚
‚72:Forceps, vacuum, and breech delivery ‚ 200739‚ 26‚ 11440‚ 28‚ 212179‚ 27‚
‚44:Other operations on stomach ‚ 194872‚ 51‚ 11384‚ 52‚ 206256‚ 51‚
‚80:Incision and excision of joint str... ‚ 162147‚ 53‚ 5632‚ 40‚ 167779‚ 52‚
‚60:Operations on prostate and seminal... ‚ 162856‚ 69‚ 2236‚ 58‚ 165092‚ 68‚
‚95:Ophthalmologic and otologic diagno... ‚ 149094‚.35‚ 8427‚.29‚ 157521‚.35‚
‚33:Other operations on lung and bronchus ‚ 150780‚ 60‚ 6172‚ 41‚ 156952‚ 60‚
‚57:Operations on urinary bladder ‚ 150825‚ 71‚ 4625‚ 52‚ 155450‚ 71‚
‚55:Operations on kidney ‚ 137542‚ 53‚ 6829‚ 41‚ 144371‚ 52‚
‚53:Repair of hernia ‚ 135019‚ 56‚ 4488‚ 50‚ 139507‚ 55‚
‚43:Incision and excision of stomach ‚ 124603‚ 59‚ 1190‚ 50‚ 125793‚ 59‚
‚83:Operations on muscle, tendon, fasc... ‚ 112733‚ 51‚ 7692‚ 36‚ 120425‚ 50‚
‚35:Operations on valves and septa of ... ‚ 116690‚ 55‚ 2319‚ 51‚ 119009‚ 55‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒƒƒ‹ƒƒƒŒ
‚66:Operations on fallopian tubes ‚ 106087‚ 29‚ 8573‚ 34‚ 114660‚ 29‚
‚84:Other procedures on musculoskeleta... ‚ 106932‚ 63‚ 4288‚ 50‚ 111220‚ 63‚
‚01:Incision and excision of skull, br... ‚ 103852‚ 52‚ 4276‚ 37‚ 108128‚ 52‚
‚65:Operations on ovary ‚ 88645‚ 43‚ 8656‚ 35‚ 97301‚ 42‚
‚85:Operations on the breast ‚ 95105‚ 55‚ 1921‚ 41‚ 97026‚ 55‚
‚78:Other operations on bones, except ... ‚ 88667‚ 51‚ 4709‚ 51‚ 93376‚ 51‚
‚31:Other operations on larynx and tra... ‚ 87235‚ 54‚ 5474‚ 49‚ 92709‚ 54‚
‚59:Other operations on urinary tract ‚ 82846‚ 56‚ 4631‚ 44‚ 87477‚ 56‚


Lecture 3-NHDS 95

‚46:Other operations on intestine ‚ 80751‚ 55‚ 3747‚ 34‚ 84498‚ 54‚
‚48:Operations on rectum, rectosigmoid... ‚ 76688‚ 60‚ 6080‚ 44‚ 82768‚ 59‚
‚77:Incision, excision, and division o... ‚ 75099‚ 54‚ 2480‚ 36‚ 77579‚ 53‚
‚70:Operations on vagina and cul-de-sac ‚ 74468‚ 61‚ 1302‚ 46‚ 75770‚ 61‚
‚92:Nuclear medicine ‚ 72788‚ 61‚ 1423‚ 50‚ 74211‚ 61‚
‚32:Excision of lung and bronchus ‚ 68886‚ 62‚ 1670‚ 45‚ 70556‚ 62‚
‚41:Operations on bone marrow and spleen ‚ 64837‚ 52‚ 3060‚ 41‚ 67897‚ 51‚
‚06:Operations on thyroid and parathyr... ‚ 61306‚ 52‚ 2788‚ 44‚ 64094‚ 52‚
‚02:Other operations on skull, brain, ... ‚ 60960‚ 41‚ 2620‚ 50‚ 63580‚ 41‚
‚56:Operations on ureter ‚ 54484‚ 49‚ 3249‚ 37‚ 57733‚ 48‚
‚50:Operations on liver ‚ 51995‚ 55‚ 3964‚ 41‚ 55959‚ 54‚
‚42:Operations on esophagus ‚ 47398‚ 60‚ 2617‚ 44‚ 50015‚ 59‚
‚69:Other operations on uterus and sup... ‚ 39942‚ 34‚ 3707‚ 28‚ 43649‚ 33‚
‚40:Operations on lymphatic system ‚ 38119‚ 53‚ 3605‚ 51‚ 41724‚ 53‚
‚27:Other operations on mouth and face ‚ 37741‚ 29‚ 3621‚ 31‚ 41362‚ 29‚
‚21:Operations on nose ‚ 38265‚ 55‚ 1002‚ 46‚ 39267‚ 54‚
‚76:Operations on facial bones and joints ‚ 32867‚ 33‚ 5457‚ 33‚ 38324‚ 33‚
‚97:Replacement and removal of therape... ‚ 37135‚ 56‚ 1085‚ 36‚ 38220‚ 55‚
‚28:Operations on tonsils and adenoids ‚ 30024‚ 21‚ 2484‚ 21‚ 32508‚ 21‚
‚49:Operations on anus ‚ 26654‚ 47‚ 2412‚ 33‚ 29066‚ 46‚
‚52:Operations on pancreas ‚ 22760‚ 58‚ 1694‚ 44‚ 24454‚ 57‚
‚71:Operations on vulva and perineum ‚ 18759‚ 43‚ 1614‚ 27‚ 20373‚ 42‚
‚08:Operations on eyelids ‚ 16025‚ 46‚ 1592‚ 24‚ 17617‚ 44‚
‚04:Operations on cranial and peripher... ‚ 14983‚ 54‚ 636‚ 38‚ 15619‚ 54‚
‚07:Operations on other endocrine glands ‚ 13308‚ 47‚ 1150‚ 42‚ 14458‚ 46‚
‚67:Operations on cervix ‚ 12830‚ 38‚ 1189‚ 43‚ 14019‚ 39‚
‚98:Nonoperative removal of foreign body ‚ 12206‚ 46‚ 718‚ 26‚ 12924‚ 45‚
‚18:Operations on external ear ‚ 12096‚ 28‚ 522‚ 39‚ 12618‚ 28‚


Lecture 3-NHDS 96

‚82:Operations on muscle, tendon, and ... ‚ 11268‚ 42‚ 885‚ 32‚ 12153‚ 41‚
‚58:Operations on urethra ‚ 11127‚ 61‚ 569‚ 54‚ 11696‚ 61‚
‚20:Other operations on middle and inn... ‚ 10105‚ 28‚ 181‚1.5‚ 10286‚ 28‚
‚90:Microscopic examination I ‚ 9886‚ 25‚ .‚ .‚ 9886‚ 25‚
‚25:Operations on tongue ‚ 9460‚ 37‚ .‚ .‚ 9460‚ 37‚
‚22:Operations on nasal sinuses ‚ 8637‚ 43‚ 108‚ 23‚ 8745‚ 42‚
‚14:Operations on retina, choroid, vit... ‚ 8369‚ 55‚ 18‚ 66‚ 8387‚ 55‚
‚26:Operations on salivary glands and ... ‚ 7576‚ 55‚ 237‚ 60‚ 7813‚ 55‚
‚62:Operations on testes ‚ 6313‚ 36‚ 978‚ 39‚ 7291‚ 37‚
‚30:Excision of larynx ‚ 6839‚ 47‚ 55‚ 49‚ 6894‚ 47‚
‚29:Operations on pharynx ‚ 6559‚ 47‚ 42‚2.0‚ 6601‚ 47‚
‚61:Operations on scrotum and tunica v... ‚ 6240‚ 47‚ 258‚ 40‚ 6498‚ 47‚
‚23:Removal and restoration of teeth ‚ 5562‚ 28‚ 284‚ 36‚ 5846‚ 29‚
‚16:Operations on orbit and eyeball ‚ 4154‚ 41‚ 131‚ 20‚ 4285‚ 40‚
‚24:Other operations on teeth, gums, a... ‚ 3566‚ 48‚ .‚ .‚ 3566‚ 48‚
‚13:Operations on lens ‚ 3525‚ 62‚ .‚ .‚ 3525‚ 62‚
‚91:Microscopic examination II ‚ 1760‚ 27‚ 233‚ 41‚ 1993‚ 28‚
‚63:Operations on spermatic cord, epid... ‚ 1348‚ 39‚ .‚ .‚ 1348‚ 39‚
‚09:Operations on lacrimal system ‚ 730‚ 26‚ 510‚ 40‚ 1240‚ 32‚
‚12:Operations on iris, ciliary body, ... ‚ 1162‚ 55‚ .‚ .‚ 1162‚ 55‚
‚15:Operations on extraocular muscles ‚ 901‚ 52‚ .‚ .‚ 901‚ 52‚
‚11:Operations on cornea ‚ 779‚ 34‚ 73‚ 48‚ 852‚ 35‚
‚05:Operations on sympathetic nerves o... ‚ 841‚ 49‚ .‚ .‚ 841‚ 49‚
‚19:Reconstructive operations on middl... ‚ 354‚ 50‚ .‚ .‚ 354‚ 50‚
‚10:Operations on conjunctiva ‚ 228‚ 66‚ 28‚ 54‚ 256‚ 65‚
‚All ‚22906357‚ 47‚ 1077072‚ 36‚23983429‚ 46‚

The table above shows there were 22.9 million procedures performed on the nation’s 37.0
million patients discharged from hospitals in 2006. This equals 648 procedures per 1000
discharges. For the uninsured, there were 1.07 million procedures performed which equals 590


Lecture 3-NHDS 97

per 1000, which is not significantly different. However, reviewing the ranking of the top 10
procedures below, there are differences. Seven out of the ten procedures exist for both groupings.
However, 81: Repair & plastic Joint Operations, 37: Other operations on heart, and 38: Incision,
excision vessels are not found in the top ten uninsured. The uninsured have in the top 10, 96:
Non-operative intubations, 94: Procedures related to psych, and 88: Other diagnostic radiology,
which ranks for the insured 14th, 18th and 12th, respectively.
Rank Insured Discharges Uninsured Discharges

1 99: Nonoperative Procedures 2,070,497 99: Nonoperative Procedures 101,176

2 73: Assisting or Inducing delivery 1,530,492 73: Assisting or Inducing delivery 66,841

3 81:Repair & plastic Joint Operations 1,336,131 86:Skin and subcutaneous tissue 56,719

4 74:Cesarean section fetus remove 1,252,479 94:Procedures related to psych 55,065

5 45:Incision, excision of intestines 1,230,025 88:Other diagnostic radiology 48,921

6 64:Operations on penis 1,103,692 45:Incision, excision of intestines 48,541

7 75:Other obstetric operations 903,994 64:Operations on penis 42,151

8 37: Other operations on heart 834,035 74:Cesarean section & fetus rem 40,083

9 38:Incision,excision vessels 790,064 96: Nonoperative intubation’s 36,814

10 86:Skin and subcutaneous tissue 704,522 75:Other obstetric operations 34.448


Lecture 3-NHDS 98

APPENDIX 2: NHDS Published Papers Applying
Multivariate Analysis

1. S. Tahir, L. Price, P. Shah, F. Welt, Eighteen Year (1985–2002) Analysis of Incidence,
Mortality, and Cardiac Procedure Outcomes of Acute Myocardial Infarction in Patients ≥ 65
Years of AgeThe American Journal of Cardiology, Volume 101, Issue 7, Pages 930-936

The National Hospital Discharge Survey (NHDS), a nationally representative sample of acute
care hospitals in the United States, was used for analysis. …..A multivariate logistic regression
model was developed to identify predictors of mortality in these patients. The logit of propensity
score was used as an adjuster for reducing the bias of nonrandom assignment of cardiovascular
procedures…… Multivariate analysis suggests that a lack of the use of procedures in these
patients may at least partially explain their higher mortality.
2. Edward H. Livingston, MD; Joshua Langert, BA, The Impact of Age and Medicare Status on
Bariatric Surgical Outcomes, Arch Surg. 2006;141:1115-1120
We assessed 25 428 bariatric procedures with logistic regression, finding that age (odds ratio,
1.04; 95% confidence interval, 1.02-1.07), male sex (odds ratio, 2.45; 95% confidence interval,
1.48-4.03), electrolyte disorders (odds ratio, 13.91; 95% confidence interval, 8.29-23.33), and
congestive heart failure (odds ratio, 4.96; 95% confidence interval, 2.52-9.77) were independent
risk factors for bariatric surgery mortality. Adverse outcomes increased as a function of age in a
nearly linear fashion, with a steep increase after the age of 65 years. Most Medicare patients
undergoing these operations were younger than 65 years and had a much greater disease burden
than non-Medicare patients.
3. Jutta M. Joesch, Ginger L. Gossman, Koray Tanfer Primary Cesarean Deliveries Prior to
Labor in the United States, 1979-2004: Discussion, Maternal and Child Health
Journal. 2008;12(3):323-331.
Analyses were conducted with 1979-2004 National Hospital Discharge Survey (NHDS) public
use files. The NHDS public use data do not include direct identifiers. The study protocol was
therefore exempt from institutional review board approval……. We used logistic regression with
pooled 1979-2004 data to describe how the odds of delivering by primary cesarean prior to labor
changed between 1979 and 2004. We estimated 5 different logistic regression models…….. We
used Wald tests to determine which of the five models provides the best fit for describing the
odds of delivering by primary cesarean prior to labor over time. Based on these tests, the model
with a cubic time trend was the preferred model. We refer to this model as the "unadjusted"
model in the remainder of the text…….


School of Medicine

®

Course Notes
STA9000
Lecture 4-Organ
Procurement
Transplantation Network
OPTN- Liver Transplants

2



Prepared date 29July09.

TABLE OF CONTENTS

Lecture 4-OPTN Liver Transplants 3

Organ Procurement Transplantation Network Description 4

Objectives of Chapter 4
Section – 1 The Federal and Non-Profit Agencies Associated with
the U.S. Liver Transplantation 5
Budget Breakdown FY 2010 12
Section 2 - Liver Transplantation Figures Published In the 2007 Annual Report
of the Organ Procurement and Transplantation Network (OPTN) 16
Section 3 – OTPN/UNOS Liver Transplantation Data Tables from 2007 Annual Report 26
Section 4 – OTPN/UNOS Liver Transplantation Public Use Data Set Variables 40
Demonstration 1: OPTN/UNOS Liver Data, PROC Format, Labels, PROC
Contents, and PROC Freq Statements 46
Exercise 4.1 65
Demonstration 2: SAS Code for OPTN/UNOS Liver Indicator and Truth Logic Variables 78
Exercise 4.2 85

Demonstration 3: Multiple Linear Regression Model on OPTN Liver Transplant
Patient Survival Time in Days (PTIME) 93

Exercise 4.3 95
Demonstration 4: Logistic Regression Model of Liver Transplant Patient Death. 99
Exercise 4.4 103
Demonstration 5: Survival Analysis of Liver Transplants Using Kaplan-Meier Methods 115
Exercise 4.5 119



Organ Procurement
OPTN- Liver Transplants
Data
Lecture 4

Objectives
Provide an overview of U.S Organ Transplantation
System from the Health and Human Services, Organ
Procurement and Transplantation Network (OPTN),
through United Network Organ System (UNOS) to
Liver Transplants and estimated annual costs.
Review the available descriptive data that is provided
in the annual 2007 OPTN reports.
Review the variables and their definitions that exist on
the OPTN Liver transplantation data files.
Identify the additional information that can be obtained
from the raw data.
Propose a range of potential study questions.
Write SAS code to analyze the OPTN data.



Section – 1: The Federal and Non-
Profit Agencies Associated with U.S.
Liver Transplantation

The U.S. Transplantation System



Health Resources and Service
Administration (HRSA)
The Health Resources and Services Administration
(HRSA), an agency of the U.S. Department of Health
and Human Services, is the primary Federal agency
for improving access to health care services for people
who are uninsured, isolated or medically vulnerable.
Comprising six bureaus and 13 offices, HRSA
provides leadership and financial support to health
care providers in every state and U.S. territory. HRSA
grantees provide health care to uninsured people,
people living with HIV/AIDS, and pregnant women,
mothers and children. They train health professionals
and improve systems of care in rural communities.

continued...

Health Resources and Service Administration
(HRSA)
HRSA oversees organ, bone marrow and cord blood
donation. It supports programs that prepare against
bioterrorism, compensate individuals harmed by
vaccination, and maintains databases that protect
against health care malpractice and health care waste,
fraud and abuse.
Since 1943 the agencies that were HRSA precursors
have worked to improve the health of needy people.
HRSA was created in 1982, when the Health
Resources Administration and the Health Services
Administration

continued...



HRSA Organ Transplantation Legislation

Authorizing Legislation: Sections 371 - 378 of the
Public Health Service Act, (P.L. 98-507 and
P.L. 108-216), as amended.
The National Organ Transplant Act of 1984 (NOTA),
as amended, provides the authorities for the Program.
The primary purpose of the Program is to extend and
enhance the lives of individuals with end-stage organ
failure for whom an organ transplant is the most
appropriate therapeutic treatment.
The Program works towards achieving this goal by
providing for a national system, the Organ
Procurement and Transplantation Network (OPTN), to
allocate and distribute donor organs to individuals
waiting for an organ transplant.

OPTN Program Description and
Accomplishments
The allocation of organs is guided by organ allocation
policies developed by the OPTN with analytic support
provided by the Scientific Registry of Transplant
Recipients (SRTR).
In addition to the efficient and effective allocation of
donor organs through the OPTN, the Program also
supports efforts to increase the supply of donor organs
made available for transplantation.
Ideally, an organ would be available for every
transplant candidate at the time the procedure would
provide maximum benefit to the patient. Unfortunately,
the demand for organ transplantation greatly exceeds
the available supply of organs from deceased and
living donors combined (see Figure 1).

continued...



Accomplishments
This trend is anticipated to continue, unless there is a
major breakthrough in transplantation technology that
will obviate the need for donor organs or the incidence
of end-stage organ failure in the U.S. dramatically
declines.
This supply and demand imbalance is vividly
evidenced by the 94,000 patients who were waiting for
an organ transplant at the end of 2006. This number
continues to increase as almost 98,000 patients were
waiting for an organ transplant as of January 2008.
Tragically, 6,700 individuals died, approximately 18
per day, in 2006 while waiting for a donor organ.

continued...

Accomplishments
The below graph shows trends from 1993 to 2006 in
the number of patients on the transplant waitlist, the
number of transplants performed from deceased
donors only, and the number of transplants from living
as well as deceased donors.
The number of patients on the transplant waitlist has
grown steadily from about 30,000 in 1993 to over
94,000 in 2006.
While the number of transplants performed has also
grown over this time period, it has not grown as
rapidly as the waitlist, leading to what is characterized
on the graph as an “organ gap”.

continued...



Accomplishments

United Network for Organ Sharing
A Virginia nonstock, not-for-profit corporation. The articles of
incorporation establish the legal standing of the corporation (as in
effect November 13, 1998).
The name of the corporation is: United Network for Organ
Sharing.
Article II - Purpose and Powers
(a) To establish a national Organ Procurement and
Transplantation Network under the Public Health Service Act, in
order to improve the effectiveness of the nation's renal and
extrarenal organ procurement, distribution, and transplantation
systems by increasing the availability of, and access to, donor
organs for patients with end-stage organ failure; to develop,
implement, and maintain quality assurance activities; and to
systematically gather and analyze data and regularly publish the
results of the national experience in organ procurement and
preservation, tissue typing, and clinical organ transplantation. The
Corporation is organized exclusively for charitable, educational,
and scientific purposes related to organ procurement and
transplantation.

continued...




(b) To have all the powers provided for in Section 13.1-826,
Code of Virginia 1950, as amended, as at any time amended;
provided, however,
– (1) all of the assets, earnings and income of the
Corporation shall be used exclusively for the purposes set
forth above, including the payment of proper expenses
incidental thereto, and
– (2) no part of the net earnings of the Corporation shall inure
to the benefit of or be distributable to its members,
directors, officers, or other private persons, except that the
Corporation shall be authorized and empowered to pay
reasonable compensation for services rendered and to
make payments and distributions in furtherance of the
purposes set forth above, and

continued...

– (3) no substantial part of the activities of the
Corporation shall consist of carrying on
propaganda, or otherwise attempting to influence
legislation, nor shall it in any manner or to any
extent participate in, or intervene in (including the
publishing or distributing of statements), any
political campaign on behalf of any candidate for
public office; nor shall the Corporation engage in
any activities that are unlawful under applicable
federal, state or local laws, and
– (4) the Corporation shall not operate for the
purposes of carrying on a trade or business for
profit.

continued...



c) The Corporation shall neither have nor exercise any
power, nor shall it engage directly or indirectly in any
activity, that would invalidate its status as a
corporation which is exempt from federal income
taxation as any organization described in IRC Section
501(c)(3) or invalidate its status as a corporation,
contributions to which are deductible under IRC
Section 170(c)(2).

UNOS staff prepared preliminary OPTN financial
statements for the period October 1, 2006, through
September 30, 2007. OPTN expenditures were
$177,000 less than the budget of $26,711,000. In
addition to this budget variance, the OPTN expects to
recover costs of $127,000 for peer review activities
from two OPTN members. In following current
practices, the $127,000 will be credited to the OPTN
contract upon receipt from the member
The 2008 budget exceeds the estimated OPTN
contract expenditures in the OPTN contract UNOS
signed with HRSA in 2005.

http://www.unos.org/articles.asp



The Board approved modifications to 3.6.4.5 (Liver Candidates with
Exceptional Cases), which will provide standardized criteria and
MELD/PELD scores for six diagnoses. The Board specified that
candidates meeting the exceptional case criteria are eligible for
additional MELD/PELD exception points. Unless the applicable RRB
has a pre-existing agreement regarding point assignment for these
diagnoses, an initial MELD score of 22/PELD score of 28 shall be
assigned. For candidates with Primary Hyperoxaluria meeting the
criteria in 3.6.4.5.5, an initial MELD score of 28/PELD score of 41
shall be assigned
http://www.unos.org/SharedContentDocuments/Executive_Summary_-_June_2009.pdf

FY 2010 Budget

HHS -$879 Billion

Health Resources & Services Administration -
$7.25 Billion

OTPN- $60 million

UNOS-$30 million (est.)

http://www.hhs.gov/asrt/ob/docbudget/2010budgetinbrief.pdf



Organ Procurement and
Supporting Transplantation: The FY 2010 Budget continues
support for activities in organ, bone marrow, and cord blood stem
cell transplantation through a combined investment of $60 million.
Through a national system, the Organ Transplantation program
allocates and distributes donor organs to individuals waiting for
an organ transplant and supports efforts to increase the supply of
donor organs. Similarly, the C.W. “Bill” Young Cell
Transplantation
Program provides support to patients who need a potentially life-
saving marrow or cord blood transplant. In FY 2007 these
programs helped to facilitate the donation of over 27,877 organs,
and in FY 2008 increased the number of potential ethic and racial
minority bone marrow donors to over 2 million.
The Budget request also includes $12 million for the National
Cord Blood Inventory program which will be used to support the
collection and purchase of approximately 8,500 new cord blood
units.

Estimated U.S. Average 2008 First-Year Billed Charges Per Transplant

30 Days 180 Days
Pre- Hospital Physician Post- Immuno-
transplan Procureme Transplant During transplant suppressant
Transplant t nt Admission Transplant Admission s Total

Heart Only $34,200 $94,300 $486,400 $50,800 $99,700 $22,300 $787,700

Single Lung $7,500 $53,600 $256,600 $27,900 $84,300 $20,500 $450,400
Only

Double Lung $20,700 $96,500 $344,700 $59,300 $113,800 $22,800 $657,800
Only

Heart-Lung $49,100 $151,900 $682,500 $73,000 $143,300 $24,700 $1,123,800

Liver Only $21,200 $73,600 $286,100 $44,100 $77,800 $20,600 $523,400

Kidney Only $16,700 $67,500 $92,700 $17,500 $47,400 $17,200 $259,000

Pancreas $16,500 $68,400 $93,400 $16,300 $58,700 $22,200 $275,200
Only

Intestine Only $48,400 $77,200 $743,800 $100,600 $124,300 $27,500 $1,121,800

http://www.transplantliving.org/beforethetransplant/finance/costs.aspx

http://www.milliman.com/expertise/healthcare/publications/rr/pdfs/2008-us-organ-tisse-RR4-1-08.pdf



Estimated U.S. 2008 First-Year Total
Charges Per Transplant
Estimated 2008 Transplantation Costs
Transplant N Costs Total

Heart Only 2247 $787,700 $1,769,961,900

Single Lung Only 802 $450,400 $361,220,800

Double Lung Only 764 $657,800 $502,559,200

Heart-Lung 31 $1,123,800 $34,837,800

Liver-Only 6550 $523,400 $3,428,270,000

Kidney-Only 17447 $259,000 $4,518,773,000

Pancreas Only 399 $275,200 $109,804,800

Intestine Only 70 $1,121,800 $78,526,000

Grand Total $10,803,953,500


2008 Estimated Liver Transplantation Costs

Livers - $3.4 Billion
All Transplants - $10.8 Billion




Section 2 - Liver Transplantation Figures
Published In the 2007 Annual Report of the
Organ Procurement and Transplantation
Network (OPTN).



Section 3 - Liver Transplantation Data Tables
Published in the 2007 Annual Report of the
Organ Procurement and Transplantation
Network (OPTN).



Section 4 – OTPN/UNOS Liver
Transplantation Public Use Data Set
Variables

Introduction to liver blood test
An initial step in detecting liver damage is a simple blood test to determine the presence of certain liver
enzymes in the blood. Under normal circumstances, these enzymes reside within the cells of the liver. But
when the liver is injured for any reason, these enzymes are spilled into the blood stream. Enzymes are
proteins that are present throughout the body, each with a unique function. Enzymes help to speed up
(catalyze) routine and necessary chemical reactions in the body.
Among the most sensitive and widely used of these liver enzymes are the aminotransferases. They include
aspartate aminotransferase (AST or SGOT) and alanine aminotransferase (ALT or SGPT). These enzymes
are normally contained within liver cells. If the liver is injured, the liver cells spill the enzymes into
blood, raising the enzyme levels in the blood and signaling the liver damage.
The MELD score
The MELD/PELD Calculator provided on this Web site uses the specific formulas approved by the
OPTN/UNOS Board of Directors and allocation of livers by the OPTN match system. The MELD/PELD
calculator collects data elements used in both the MELD and PELD score calculations. Please note the
following:
Serum Creatinine (mg/dl)* ,Bilirubin (mg/dl), INR *For patients who have had dialysis twice within the
last week, or 24 hours of CVVHD, the creatinine value will be automatically set to 4 mg/dl.
The PELD score calculation uses:
Albumin (g/dl) , Bilirubin (mg/dl) and INR. INR, Growth failure (based on gender, height and weight)
Age at listing

http//www.unos.org/resources/MeldPeldCalculator asp?index=97



Functions of the Liver

The liver is one of the largest and most complex organs in the body. It weighs approximately 1,500-1,800
grams (or about three to four pounds) and is made up of a spongy mass of wedge-shaped lobes. The liver
has numerous functions that are necessary for life. The liver helps process carbohydrates, fats, and
proteins, and stores vitamins. It processes nutrients absorbed from food in the intestines and turns them
into materials that the body needs for life. For example, it makes the factors that the blood needs for
clotting. It also secretes bile to help digest fats, and breaks down toxic substances in the blood such as
drugs and alcohol.

Liver Transplant Procedures

A liver transplant may involve the whole liver, a reduced liver, or a liver segment. Most transplants
involve the whole organ but segmental transplants have been performed with increasing frequency in
recent years. This would allow two liver recipients to be transplanted from one cadaveric donor or to
allow for living donor liver donation. A reduced liver transplant may result if the donor liver is too large
for the recipient.
Introduction to liver blood test
An initial step in detecting liver damage is a simple blood test to determine the presence of certain liver
enzymes in the blood. Under normal circumstances, these enzymes reside within the cells of the liver. But
when the liver is injured for any reason, these enzymes are spilled into the blood stream. Enzymes are
proteins that are present throughout the body, each with a unique function. Enzymes help to speed up
(catalyze) routine and necessary chemical reactions in the body.
Among the most sensitive and widely used of these liver enzymes are the aminotransferases. They include
aspartate aminotransferase (AST or SGOT) and alanine aminotransferase (ALT or SGPT). These enzymes
are normally contained within liver cells. If the liver is injured, the liver cells spill the enzymes into
blood, raising the enzyme levels in the blood and signaling the liver damage.

http//www.unos.org/resources/MeldPeldCalculator asp?index=97



OPTN/UNOS Public Use Liver Data Set
Specifications
Data Set Name UNOS.LIVER
Number of Variables-27
SAS data set

Creatinine: A chemical waste molecule that is generated from muscle metabolism. Creatinine is
produced from creatine, a molecule of major importance for energy production in muscles. Approximately
2% of the body's creatine is converted to creatinine every day. Creatinine is transported through the
bloodstream to the kidneys. The kidneys filter out most of the creatinine and dispose of it in the urine.
Although it is a waste, creatinine serves a vital diagnostic function
Total Bilirubin (TBIL)
Bilirubin is a breakdown product of heme (a part of haemoglobin in red blood cells). The liver is
responsible for clearing this, excreting it out throughbile into the instestine. Problems with the liver or
blockage of the drainage of bile will cause increased levels of bilirubin, as will increased haemolysis of
red cells.
INR
The liver is responsible for the production of coagulation factors. The INR measures the speed of a
particular pathway of coagulation, comparing it to normal. If the INR is increased, it means it is taking
longer than usual for blood to clot. The INR will only be increased if the liver is so damaged that
synthesis of vitamin K-dependent coagulation factors has been impaired: it is not a sensitive measure of
liver function. It is very important to normalize the INR before operating on people with liver problems,
(usually by transfusion with blood plasma containing the deficient factors), as they could bleed
excessively.



OPTN/UNOS Public Use Data Variables
cod = 'Cause of death'
diag = 'Diagnosis at transplantation'
px_stat = 'Patient status pre-transplant'
end_stat = 'Patient status post-transplant'
hgt_cm_trr = 'Patient height (cm)at transplant'
wgt_kg_trr = 'Patient weight (kg)at transplant'
sgpt_tx = 'Blood (Serum) Glutamic- Oxaloacetic
Transaminase (SGOT) Test‘
px_stat = 'Current status of the patient'

continued...

Development of the MELD/PELD Allocation System
Today, the model for end-stage liver disease (MELD)/pediatric end-stage liver disease (PELD) model is
the disease severity scoring system used to determine the allocation of donor livers in the US (see "MELD
and PELD Equations for Disease Severity").
In response to the US government's Final Rule Mandate, which clearly stated that waiting time must be
de-emphasized as a major determinant of organ allocation, the UNOS Liver and Intestinal Transplantation
Committee decided to further assess the Mayo end-stage liver disease model -- later renamed as the model
for end-stage liver disease -- as a potential basis for liver allocation. MELD, which was developed to
assess short-term prognosis of patients undergoing transjugular intrahepatic portal systemic shunt
procedures, was based on three simple biochemical variables -- serum creatinine, serum bilirubin, and the
international normalized ratio of prothrombin time (INR) In addition, the etiology of liver disease was
initially used in this model.
The advantage of MELD is that it relies on objective and standardized laboratory tests that are readily
available and reproducible throughout the world. None of the parameters in the model are subjective or
have political overtones, such as age, gender, race, or transplant center. Evaluation and validation of
MELD on the basis of both retrospective and prospective data was encouraging. The most recent and
important study to validate MELD was its application to the national UNOS waiting list. This study
confirmed that the MELD score, determined at the time of listing for liver transplantation, accurately
estimates liver disease severity and predicts the 3-month mortality rate among patients with chronic liver
disease, and could be applied for the allocation of donor livers. The conclusion was that MELD could be
applied to allocation of donor livers on the basis of disease severity.



abo = 'Blood type of patient'
gender = 'Gender of patient'
tx_date = 'Transplant date'
prev_tx = 'Previous transplant'
end_stat = 'Status at transplant '
hgt_cm_don = 'Donor height(cm)at
harvesting‘
wgt_kg_don = Donor weight (kg) at
harvesting

continued...

The PELD committee developed a similar disease severity scoring system to meet the characteristics of
children with chronic liver disease. As serum creatinine levels are not predictive of early mortality in the
pediatric population, other variables had to be included. Using multivariate analysis, significant clinical
factors incorporated into the PELD score included serum bilirubin levels, international ratio of
prothrombin time, serum albumin levels, age <1 year, and growth failure defined as height or weight more
than two standard deviations below the normal for age and gender. It was recognized that the MELD and
PELD scores would not serve all liver transplant candidates equally well, and the most important
exceptions included patients with HCC. It was noted that patients with HCC often had very low MELD or
PELD scores, and thus would be disadvantaged by the new system. Therefore, it was proposed that an
estimate of risk for tumor progression beyond stage 2 disease, rather than risk of death, was necessary to
incorporate patients with HCC into the MELD/PELD allocation system. By recording the risk of tumor
progression beyond stage 2 disease with the risk of death as defined by the MELD score in patients with
chronic liver disease, a priority score could potentially be assigned to patients with HCC. Initially, and
somewhat arbitrarily, the allocation policy estimated the risk of tumor progression beyond stage 2 disease
or mortality at 15%, corresponding to a MELD score of 24 for patients meeting stage 1 HCC criteria. A
risk of 30%, a MELD score of 29, was estimated for patients with stage 2 HCC disease.
A regional review board has been appointed to review urgent cases, and those in which patients are
exempted from or disadvantaged by the MELD/PELD allocation system, such as patients with primary
familial amyloidosis, polycystic liver disease, and hepatopulmonary syndrome. It is at the discretion of
this regional review board that additional MELD points can be given to patients for whom the risk of
dying is considered to be above and beyond that estimated by the MELD/PELD system. Such cases are
considered on an individual basis



age_don = 'Donor age'
ethcat_don = 'Donor ethnicity'
don_ty = 'Donor source'
gender_don = 'Donor gender'
abo_don = 'Doner blood type'
creat_tx = 'Patient creatinine at transplant‘
tbili_tx = 'Patient billirubin at transplant'
ethcat = 'Patient ethnicity'
N
u
m
b
er
continued...
of
MELD and PELD Equations for Disease Severity
MELDa = (0.957 x log[creatinine mg/dl]) + (0.378 x log[bilirubin mg/dl]) + (1.12 x log[INR]) + 0.643 x
10
PELD = (0.436 x ageb) - (0.687 x log[albumin g/dl]) + (0.480 x log[bilirubin mg/dl]) + (1.857 x
log[INR]) + (0.0667 x growth failurec) x 10
aCapped at 40 points.bAge <1 year = 1 MELD point; age >1 year = 0 MELD points.cGrowth failure = 1
MELD point; no growth failure = 0 MELD points. INR, international normalized ratio of prothrombin
time.
Russell H Wiesner, Mayo Clinic Transplant Center, Rochester, MN ,Patient Selection in an Era of Donor
Liver Shortage: US Policy: Sidebar: MELD and PELD Equations for Disease Severity
http://www.medscape.com/viewarticle/497528_3

Pre-MELD/PELD Liver Allocation Policy Criteria
Sickest First and Waiting Time
Liver allocation was initially based on a patient's level of care. Patients requiring continuous care in the
intensive care unit (ICU) -- including patients with acute esophageal variceal bleeding not responding to
endoscopic therapy, patients who developed hepatorenal syndrome, and patients with intractable ascites or
portosystemic encephalopathy -- received first priority. Patients requiring continuous hospitalization were
the next priority for allocation, followed by patients cared for at home. As the waiting list grew, however,
waiting time became the major factor in determining who received a donor liver. This allocation system
led to the establishment of many makeshift ICUs specifically for patients waiting for liver transplantation,
and many patients were added to the waiting list years before they actually needed a liver transplant, so



they would be at the top of the list by the time they required a transplant. This led to numerous turned
down offers for deceased donor livers

px_stat_date = 'Date patient graft status was
identified'
grf_stat = 'Status of liver graft'
age = ‘Patient age at transplant'
diag = 'Diagnosis at transplant'
cold_isch = 'Duration of cold ischemia-
preserved donor liver’
gtime = 'Graph survival time in days'
ptime = 'Patient survival time in days'

continued...

The Child-Turcotte-Pugh Scoring System
In 1996, the Child-Turcotte-Pugh (CTP) scoring system was adopted as the measure of liver disease
severity to prioritize liver allocation, and a separate 'Status 1' category was created for patients with
fulminant hepatic failure, primary nonfunction of the liver, hepatic artery thrombosis diagnosed within 7
days of transplantion, or decompensated Wilson's Disease. These candidates were given the highest
priority and this remains unchanged. Patients with chronic liver disease were grouped into three
categories: status 2a (CTP ≥ 10, admission to the ICU, and estimated <7 days to survive); status 2b (CTP
≥ 10 or a CTP ≥ 7 in patients with one or more major complications of portal hypertension, and patients
with stage 1 and 2 hepatocellular carcinoma [HCC]); and status 3 (CTP score of ≥ 7), the minimal listing
criteria. The CTP score was considered a shortcoming of the overall allocation system because it was
never evaluated for the prediction of mortality over time in patients with chronic liver disease.
Furthermore, the CTP score had two variables that were subjective in nature, namely ascites and
encephalopathy, which could easily be overestimated. Ultimately, the CTP score failed to prioritize
numerous patients waiting for deceased donor livers on the basis of disease severity.
Waiting Time
In 2000, the waiting list for liver transplantation grew to 20,000 patients, leading to longer waiting times
and more patient deaths on the waiting list. Waiting time became the dominant factor for determining
deceased donor liver allocation. This became less acceptable when two studies documented that time
spent on the waiting list was not associated with an increased death rate. Waiting time was also perceived



as a shortcoming of the allocation system,[6] because it did not reflect the medical need for liver
transplantation and was used arbitrarily to determine who received a donor organ. Ultimately waiting time
also contributed to the failure of the allocation system because it did not prioritize patients waiting for
deceased donor livers.
Russell H Wiesner, Mayo Clinic Transplant Center, Rochester, MN Patient Selection in an Era of Donor
Liver Shortage: US Policy: Pre-MELD/PELD Liver Allocation Policy Criteria.
http://www.medscape.com/viewarticle/497528_8

Example Analysis of Liver Transplant Data
What are the predictors of surviving a liver
transplantation considering the effects of gender, race,
age, blood type, status pre-transplant, principal
diagnosis and donor type?
All else being equal, have the odds of receiving a liver
transplant improved between blacks and whites?
How does the MELD/PELD scoring system affect the
likelihood of surviving a liver transplant when principal
diagnosis categories and other clinical and demographic
factors are considered?
How do liver transplant survival rates vary across
clinical and demographic effects?



1. OPTN/UNOS Liver Data, PROC Format, Labels, PROC
Contents, and PROC Freq Statements.
unos01d.sas

The program below contains the basic structure for a SAS analysis of the UNOS Liver data set.
PROC Format gives names to variable values, PROC Contents yields the specifications of your
data set, and PROC Freq provides the frequency distributions of each of the variables
/*unos01d.sas*/

Proc format;

Value li_cod /**Causes of Death of Liver Transplant Recipients**/

998 = '998 Unknown'
999 = '999 Other Specify'
4246='4246 Cardiovascular - Arterial Embolism '
4247='4247 Cardiovascular - Pulmonary Embolism'
4600='4600 Graft Failure:Primary '
4601='4601 Graft Failure:Vascular Thrombosis '
4602='4602 Graft Failure:Biliary Tract Complication '
4603='4603 Graft Failure:Hepatitis '
4604='4604 Graft Failure:Recurrent (Non-Hepatitis)'
4605='4605 Graft Failure:Rejection '
4606='4606 Graft Failure:Infection (Non-Hepatitis) '
4610='4610 Graft Failure:Other Specify'
4615='4615 Graft Vs. Host Disease'
4620='4620 Cardio: Arrythmia '
4621='4621 Cardio: Arterial Or Pulmonary Embolism'
4622='4622 Cardio: Hyperkalemic Arrest '
4623='4623 Cardio: Congestive Failure (Chf)'
4624='4624 Cardio: Myocardial Infarction '
4625='4625 Cardio: Other Specify '
4626='4626 Cardiac Arrest '
4630='4630 Cerebrovascular: Embolic Stroke'
4631='4631 Cerebrovascular: Hemorrhagic Stroke'
4635='4635 Cerebrovascular: Other Specify'
4640='4640 Pulm Insuff Or Edema (Exc Pneumonia) (Ards)'
4645='4645 Respiratory Failure: Other Specify Cause '
4650='4650 Renal Failure'
4660='4660 Multiple Organ System Failure'
4700='4700 Hemorrhage: Lower Gastrointestinal'
4701='4701 Hemorrhage: Neurological (Brain)'
4702='4702 Hemorrhage: Variceal '
4703='4703 Hemorrhage: Disseminated Intravascular..(Dic)'
4705='4705 Hemorrhage: Other Specify '
4706='4706 Hemorrhage '


4800='4800 Inf: Bacterial Peritonitis'
4801='4801 Inf: Pneumonia Legionella Pneumocystosis'
4802='4802 Inf: Generalized Sepsis'
4803='4803 Inf: Fungal Aspergillus Cryptococcal'
4804='4804 Inf: Mixed Other Specify'
4805='4805 Inf: Opportunistic'
4806='4806 Inf: Viral'
4810='4810 Inf: Other Specify'
4811='4811 Infection'
4850='4850 Malignancy: Primary Other Specify'
4851='4851 Malignancy: Metastatic Other Specify'
4855='4855 Malignancy: Other Specify'
4856='4856 Malignancy'
4860='4860 Post-Tx Lymphoproliferative Disorder'
4900='4900 Operative: Other Specify'
4910='4910 Brain Dead:Never Recovered From Surgery'
4920='4920 Suicide:Attempted Suicide - Died Later'
4930='4930 Trauma: Motor Vehicle'
4935='4935 Trauma: Other Specify'
4940='4940 Diabetes Mellitus'
4941='4941 Fluid/Electrolyte Disorder'
4942='4942 Acid/Base Disorder'
4945='4945 Acute Pancreatitis'
4950='4950 Aids'
4951='4951 Immunosuppressive Drug Related - Hematologic'
4952='4952 Immunosuppressive Drug Related - Non-Hematologic'
4953='4953 Non-Immuno Drug Related - Hematologic'
4954='4954 Non-Immuno Drug Related - Non-Hematolo.....Drug'

;

Value li_dgn /***Primary diagnosis at Transplantation**/

999 ='999 Other Specify '
4100='4100 Ahn: Drug Other Specify '
4101='4101 Ahn: Type A '
4102='4102 Ahn: Type B- Hbsag+ '
4103='4103 Ahn: Non A- Non B '
4104='4104 Ahn: Type C '
4105='4105 Ahn: Type D '
4106='4106 Ahn: Type B And C '
4107='4107 Ahn: Type B And D '
4108='4108 Ahn: Etiology Unknown '
4110='4110 Ahn: Other, Specify (E.G.Acute Viral Infection..'
4200='4200 Cirrhosis: Drug/Indust Exposure Other Specify'
4201='4201 Cirrhosis: Type A '
4202='4202 Cirrhosis: Type B- Hbsag+ '
4203='4203 Cirrhosis: Type Non A, Non B '
4204='4204 Cirrhosis: Type C '
4102='4102 Ahn: Type B- Hbsag+ '



4103='4103 Ahn: Non A- Non B '
4104='4104 Ahn: Type C '
4105='4105 Ahn: Type D '
4106='4106 Ahn: Type B And C '
4107='4107 Ahn: Type B And D '
4108='4108 Ahn: Etiology Unknown '
4110='4110 Ahn: Other, Specify (E.G.Acute Viral Infection..'
4200='4200 Cirrhosis: Drug/Indust Exposure Other Specify'
4201='4201 Cirrhosis: Type A '
4202='4202 Cirrhosis: Type B- Hbsag+ '
4203='4203 Cirrhosis: Type Non A, Non B '
4204='4204 Cirrhosis: Type C '
4205='4205 Cirrhosis: Type D '
4206='4206 Cirrhosis: Type B And C '
4207='4207 Cirrhosis: Type B And D '
4208='4208 Cirrhosis: Cryptogenic- Idiopathic '
4209='4209 Cirrhosis: Chronic Active Hepatitis:........'
4210='4210 Cirrhosis: Other, Specify (E.G., Histiocy...'
4212='4212 Cirrhosis: Autoimmune'
4213='4213 Cirrhosis: Cryptogenic (Idiopathic)'
4214='4214 Cirrhosis: Fatty Liver (Nash)'
4215='4215 Alcoholic Cirrhosis'
4216='4216 Alcoholic Cirrhosis With Hepatitis C'
4217='4217 Acute Alcoholic Hepatitis '
4220='4220 Primary Biliary Cirrhosis (Pbc)'
4230='4230 Sec Biliary Cirrhosis: Carolis Disease'
4231='4231 Sec Biliary Cirrhosis: Choledochol Cyst'
4235='4235 Sec Biliary Cirrhosis: Other Specify'
4240='4240 Psc: Crohns Disease'
4241='4241 Psc: Ulcerative Colitis'
4242='4242 Psc: No Bowel Disease'
4245='4245 Psc: Other Specify'
4250='4250 Familial Cholestasis: Bylers Disease'
4255='4255 Familial Cholestasis: Other Specify'
4260='4260 Choles Liver Disease: Other Specify'
4264='4264 Neonatal Cholestatic Liver Disease'
4265='4265 Neonatal Hepatitis Other Specify'
4270='4270 Biliary Atresia: Extrahepatic'
4271='4271 Biliary Hypoplasia: Nonsyndromic Paucity....'
4272='4272 Biliary Hypoplasia: Alagille’s Syndrome.....'
4275='4275 Biliary Atresia Or Hypoplasia: Other,Specify'
4280='4280 Congenital Hepatic Fibrosis'
4285='4285 Cystic Fibrosis'
4290='4290 Budd-Chiari Syndrome'
4300='4300 Metdis: Alpha-1-Antitrypsin Defic A-1-A'
4301='4301 Metdis: Wilsons Disease'
4302='4302 Metdis: Hemochromatosis - Hemosiderosis'
4303='4303 Metdis: Glyc Stor Dis Type I (Gsd-I)'
4304='4304 Metdis: Glyc Stor Dis Type Ii (Gsd-Iv)'
4305='4305 Metdis: Hyperlipidemia-Ii- Homozgyous Hy'
4306='4306 Metdis: Tyrosinemia'



4307='4307 Metdis: Primary Oxalosis/Oxaluria- Hyper'
4308='4308 Metdis: Maple Syrup Urine Disease'
4315='4315 Metdis: Other Specify'
4400='4400 Plm: Hepatoma - Hepatocellular Carcinoma'
4401='4401 Plm: Hepatoma (Hcc) And Cirrhosis'
4402='4402 Plm: Fibrolamellar (Fl-Hc)'
4403='4403 Plm: Cholangiocarcinoma (Ch-Ca)'
4404='4404 Plm: Hepatoblastoma (Hbl)'
4405='4405 Plm: Hemangioendothelioma-Hemangiosarcom '
4410='4410 Plm: Other Specify '
4420='4420 Bile Duct Cancer (Cholangioma-Biliary Tr'
4430='4430 Secondary Hepatic Malignancy Other Specify'
4450='4450 Benign Tumor: Hepatic Adenoma '
4451='4451 Benign Tumor: Polycystic Liver Disease'
4455='4455 Benign Tumor: Other Specify'
4500='4500 Tpn/Hyperalimentation Ind Liver Disease'
4510='4510 Graft Vs. Host Dis Sec To Non-Li Tx'
4520='4520 Trauma Other Specify'
4592='4592 Hepatitis B: Chronic Or Acute'
4593='4593 Hepatitis C: Chronic Or Actue'
4597='4597 Na: Non-Hd Followups Only'

;

value status /***Transplantation Status on UNOS National List****/

1010 = '1010 HL: Status 1A'
1020 = '1020 HL: Status 1B'
1030 = '1030 HL: Status 2'
1090 = '1090 HL: Old Status 1'
1999 = '1999 HL: Temporarily Inactive '
2010 = '2010 HR: Status 1A '
2020 = '2020 HR: Status 1B '
2030 = '2030 HR: Status 2 '
2090 = '2090 HR: Old Status 1'
2999 = '2999 HR: Temporarily inactive'
3010 = '3010 IN: Status 1'
3020 = '3020 IN: Non-urgent'
3999 = '3999 IN: Temporarily Inactive'
4010 = '4010 KI: Active (1)'
4050 = '4050 KI: Active - Medically urgent (5)'
4060 = '4060 KI: Active - Critical Status (6)'
4099 = '4099 KI: Temporarily Inactive (7)'
4999 = '4999 KI: Old Temporarily inactive (7)'
5010 = '5010 KP: Active'
5099 = '5099 KP: Temporarily Inactive '
5999 = '5999 KP: Old Temporarily Inactive'
6002 = '6002 LI: Old status 2'
6004 = '6004 LI: Old status 4'
6010 = '6010 LI: Status 1'
6011 = '6011 LI: Status 1A'



6012 = '6012 LI: Status 1B'
6020 = '6020 LI: Status 2A'
6025 = '6025 LI: Region 7 Calculated 25'
6029 = '6029 LI: Region 8 Lab Score 29'
6030 = '6030LI: Status 2B'
6040 = '6040LI: Status 3 '
6050 = '6050LI: MELD/PELD'
6101 = '6101LI: MELD/PELD -99'
6102 = '6102LI: MELD/PELD -98'
6103 = '6103LI: MELD/PELD -97'
6104 = '6104LI: MELD/PELD -96'
6105 = '6105LI: MELD/PELD -95'
6106 = '6106LI: MELD/PELD -94'
6107 = '6107LI: MELD/PELD -93'
6108 = '6108LI: MELD/PELD -92'
6109 = '6109LI: MELD/PELD -91'
6110 = '6110LI: MELD/PELD -90'
6111 = '6111LI: MELD/PELD -89'
6112 = '6112LI: MELD/PELD -88'
6113 = '6113LI: MELD/PELD -87'
6114 = '6114LI: MELD/PELD -86'
6115 = '6115LI: MELD/PELD -85'
6116 = '6116LI: MELD/PELD -84'
6117 = '6117LI: MELD/PELD -83'
6118 = '6118LI: MELD/PELD -82'
6119 = '6119LI: MELD/PELD -81'
6120 = '6120LI: MELD/PELD -80'
6121 = '6121LI: MELD/PELD -79'
6122 = '6122LI: MELD/PELD -78'
6123 = '6123LI: MELD/PELD -77'
6124 = '6124LI: MELD/PELD -76'
6125 = '6125LI: MELD/PELD -75'
6126 = '6126LI: MELD/PELD -74'
6127 = '6127LI: MELD/PELD -73'
6128 = '6128LI: MELD/PELD -72'
6129 = '6129LI: MELD/PELD -71'
6130 = '6130LI: MELD/PELD -70'
6131 = '6131LI: MELD/PELD -69'
6132 = '6132LI: MELD/PELD -68'
6133 = '6133LI: MELD/PELD -67'
6134 = '6134LI: MELD/PELD -66'
6135 = '6135LI: MELD/PELD -65'
6136 = '6136LI: MELD/PELD -64'
6137 = '6137LI: MELD/PELD -63'
6138 = '6138LI: MELD/PELD -62'
6139 = '6139LI: MELD/PELD -61'
6140 = '6140LI: MELD/PELD -60'
6141 = '6141LI: MELD/PELD -59'
6142 = '6142LI: MELD/PELD -58'
6143 = '6143LI: MELD/PELD -57'
6144 = '6144LI: MELD/PELD -56'



6145 = '6145LI: MELD/PELD -55'
6146 = '6146LI: MELD/PELD -54'
6147 = '6147LI: MELD/PELD -53'
6148 = '6148LI: MELD/PELD -52'
6149 = '6149LI: MELD/PELD -51'
6150 = '6150LI: MELD/PELD -50'
6151 = '6151LI: MELD/PELD -49'
6152 = '6152LI: MELD/PELD -48'
6153 = '6153LI: MELD/PELD -47'
6154 = '6154LI: MELD/PELD -46'
6155 = '6155LI: MELD/PELD -45'
6156 = '6156LI: MELD/PELD -44'
6157 = '6157LI: MELD/PELD -43'
6158 = '6158LI: MELD/PELD -42'
6159 = '6159LI: MELD/PELD -41'
6160 = '6160LI: MELD/PELD -40'
6161 = '6161LI: MELD/PELD -39'
6162 = '6162LI: MELD/PELD -38'
6163 = '6163LI: MELD/PELD -37'
6164 = '6164LI: MELD/PELD -36'
6165 = '6165LI: MELD/PELD -35'
6166 = '6166LI: MELD/PELD -34'
6167 = '6167LI: MELD/PELD -33'
6168 = '6168LI: MELD/PELD -32'
6169 = '6169LI: MELD/PELD -31'
6170 = '6170LI: MELD/PELD -30'
6171 = '6171LI: MELD/PELD -29'
6172 = '6172LI: MELD/PELD -28'
6173 = '6173LI: MELD/PELD -27'
6174 = '6174LI: MELD/PELD -26'
6175 = '6175LI: MELD/PELD -25'
6176 = '6176LI: MELD/PELD -24'
6177 = '6177LI: MELD/PELD -23'
6178 = '6178LI: MELD/PELD -22'
6179 = '6179LI: MELD/PELD -21'
6180 = '6180LI: MELD/PELD -20'
6181 = '6181LI: MELD/PELD -19'
6182 = '6182LI: MELD/PELD -18'
6183 = '6183LI: MELD/PELD -17'
6184 = '6184LI: MELD/PELD -16'
6185 = '6185LI: MELD/PELD -15'
6186 = '6186LI: MELD/PELD -14'
6187 = '6187LI: MELD/PELD -13'
6188 = '6188LI: MELD/PELD -12'
6189 = '6189LI: MELD/PELD -11'
6190 = '6190LI: MELD/PELD -10'
6191 = '6191LI: MELD/PELD -9'
6192 = '6192LI: MELD/PELD -8'
6193 = '6193LI: MELD/PELD -7'
6194 = '6194LI: MELD/PELD -6'
6195 = '6195LI: MELD/PELD -5'



6196 = '6196LI: MELD/PELD -4'
6197 = '6197LI: MELD/PELD -3'
6198 = '6198LI: MELD/PELD -2'
6199 = '6199LI: MELD/PELD -1'
6200 = '6200LI: MELD/PELD 0'
6201 = '6201LI: MELD/PELD 1'
6202 = '6202LI: MELD/PELD 2'
6203 = '6203LI: MELD/PELD 3'
6204 = '6204LI: MELD/PELD 4'
6205 = '6205LI: MELD/PELD 5'
6206 = '6206LI: MELD/PELD 6'
6207 = '6207LI: MELD/PELD 7'
6208 = '6208LI: MELD/PELD 8'
6209 = '6209LI: MELD/PELD 9'
6210 = '6210LI: MELD/PELD 10'
6211 = '6211LI: MELD/PELD 11'
6212 = '6212LI: MELD/PELD 12'
6213 = '6213LI: MELD/PELD 13'
6214 = '6214LI: MELD/PELD 14'
6215 = '6215LI: MELD/PELD 15'
6216 = '6216LI: MELD/PELD 16'
6217 = '6217LI: MELD/PELD 17'
6218 = '6218LI: MELD/PELD 18'
6219 = '6219LI: MELD/PELD 19'
6220 = '6220LI: MELD/PELD 20'
6221 = '6221LI: MELD/PELD 21'
6222 = '6222LI: MELD/PELD 22'
6223 = '6223LI: MELD/PELD 23'
6224 = '6224LI: MELD/PELD 24'
6225 = '6225LI: MELD/PELD 25'
6226 = '6226LI: MELD/PELD 26'
6227 = '6227LI: MELD/PELD 27'
6228 = '6228LI: MELD/PELD 28'
6229 = '6229LI: MELD/PELD 29'
6230 = '6230LI: MELD/PELD 30'
6232 = '6232LI: MELD/PELD 32'
6233 = '6233LI: MELD/PELD 33'
6234 = '6234LI: MELD/PELD 34'
6235 = '6235LI: MELD/PELD 35'
6231 = '6231LI: MELD/PELD 31'
6236 = '6236LI: MELD/PELD 36'
6237 = '6237LI: MELD/PELD 37'
6238 = '6238LI: MELD/PELD 38'
6239 = '6239LI: MELD/PELD 39'
6240 = '6240LI: MELD/PELD 40'
6241 = '6241LI: MELD/PELD 41'
6242 = '6242LI: MELD/PELD 42'
6243 = '6243LI: MELD/PELD 43'
6244 = '6244LI: MELD/PELD 44'
6245 = '6245LI: MELD/PELD 45'
6246 = '6246LI: MELD/PELD 46'



6247 = '6247LI: MELD/PELD 47'
6248 = '6248LI: MELD/PELD 48'
6249 = '6249LI: MELD/PELD 49'
6250 = '6250LI: MELD/PELD 50'
6251 = '6251LI: MELD/PELD 51'
6252 = '6252LI: MELD/PELD 52'
6253 = '6253LI: MELD/PELD 53'
6254 = '6254LI: MELD/PELD 54'
6255 = '6255LI: MELD/PELD 55'
6256 = '6256LI: MELD/PELD 56'
6257 = '6257LI: MELD/PELD 57'
6258 = '6258LI: MELD/PELD 58'
6259 = '6259LI: MELD/PELD 59'
6260 = '6260LI: MELD/PELD 60'
6261 = '6261LI: MELD/PELD 61'
6262 = '6262LI: MELD/PELD 62'
6263 = '6263LI: MELD/PELD 63'
6264 = '6264LI: MELD/PELD 64'
6265 = '6265LI: MELD/PELD 65'
6266 = '6266LI: MELD/PELD 66'
6267 = '6267LI: MELD/PELD 67'
6268 = '6268LI: MELD/PELD 68'
6269 = '6269LI: MELD/PELD 69'
6270 = '6270LI: MELD/PELD 70'
6271 = '6271LI: MELD/PELD 71'
6272 = '6272LI: MELD/PELD 72'
6273 = '6273LI: MELD/PELD 73'
6274 = '6274LI: MELD/PELD 74'
6275 = '6275LI: MELD/PELD 75'
6276 = '6276LI: MELD/PELD 76'
6277 = '6277LI: MELD/PELD 77'
6278 = '6278LI: MELD/PELD 78'
6279 = '6279LI: MELD/PELD 79'
6280 = '6280LI: MELD/PELD 80'
6281 = '6281LI: MELD/PELD 81'
6282 = '6282LI: MELD/PELD 82'
6283 = '6283LI: MELD/PELD 83'
6284 = '6284LI: MELD/PELD 84'
6285 = '6285LI: MELD/PELD 85'
6286 = '6286LI: MELD/PELD 86'
6287 = '6287LI: MELD/PELD 87'
6288 = '6288LI: MELD/PELD 88'
6289 = '6289LI: MELD/PELD 89'
6290 = '6290LI: MELD/PELD 90'
6291 = '6291LI: MELD/PELD 91'
6292 = '6292LI: MELD/PELD 92'
6293 = '6293LI: MELD/PELD 93'
6294 = '6294LI: MELD/PELD 94'
6295 = '6295LI: MELD/PELD 95'
6296 = '6296LI: MELD/PELD 96'
6297 = '6297LI: MELD/PELD 97'



6298 = '6298LI: MELD/PELD 98'
6299 = '6299LI: MELD/PELD 99'
6999 = '6999LI: Temporarily Inactive'
7010 = '7010LU: Active'
7999 = '7999LU: Temporarily Inactive'
8010 = '8010PA: Active'
8099 = '8099PA: Temporarily Inactive'
8999 = '8999PA: Old Temporarily Inactive'
9010 = '9010PI: Status 1'
9020 = '9020PI: Status 2'
9030 = '9030PI: Active'
9099 = '9099PI: Temporarily Inactive'
9999 = '9999PI: Old Temporarily Inactive'

;
value $pxstat
'A' = 'A Living'
'D' = 'D Dead'
'L' = 'L Lost to Follow up'
'N' = 'N Not Seen'
'R' = 'R Retransplanted'
;

value $graph_stat

'.' = 'Not reported'
'N' = 'N Failed'
'U' = 'U Unknown'
'Y' = 'Y Functioning'

;

value ethcat

1 = '1 White'
2 = '2 Black'
4 = '4 Hispanic'
5 = '5 Asian'
6 = '6 Amer Ind/Alaska Native'
7 = '7 Native Hawaiian/other Pacific Islander'
9 = '9 Multiracial'
998 = '998 Unknown'
;

value $don_type

'C' = 'Deceased Donor'
'F' = 'Foreign Donor'
'L' = 'Living Donor'

;



value $prev_tx

'Y' = 'Yes Previous transplant'
'N' = 'No Previous transplant'
;

value $abotype

'A' = 'Type A'
'A1' = 'Type A1'
'A1B' = 'Type A1B'
'A2' = 'Type A2'
'A2B' = 'Type A2B'
'AB' = 'Type AB'
'B' = 'Type B'
'O' = 'Type O'
'UNK' = 'Type UNK'

;

value $gender

'M' = 'Male'
'F' = 'Female'
;
data liver;
label cod = 'Cause of death'
diag = 'Diagnosis at transplantation'
px_stat = 'Patient stautus pre-transplant'
end_stat = 'Patient status post-transplant'
hgt_cm_trr = 'Patient height (cm)at transplant'
wgt_kg_trr = 'Patient weight (kg)at transplant'
sgpt_tx = 'Blood(Serum)Glutamic-Oxaloacetic
Transaminase(SGOT)Test'
px_stat = 'Current status of the patient'
abo = 'Blood type of patient'
gender = 'Gender of patient'
tx_date = 'Transplant date'
prev_tx = 'Previous transplant'
end_stat = 'Status at transplant '
hgt_cm_don = 'Donor height(cm)at harvesting'
wgt_kg_don = 'Donor weight(kg)at harvesting'
age_don = 'Donor age'
ethcat_don = 'Donor ethnicity'
don_ty = 'Donor source '
gender_don = 'Donor gender'
abo_don = 'Donor blood type'
creat_tx = 'Patient creatinine at transplant'
tbili_tx = 'Patient billirubin at transplant'
ethcat = 'Patient ethnicity'



px_stat_date = 'Date patient graft status was identified'
grf_stat = 'Status of liver graft'
age = 'Patient age at transplant'
diag = 'Diagnosis at transplant'
cold_isch = 'Duration of cold ischemia-preserved donor
liver'
;
set unos.liver;

options label nodate nonumber;
proc contents data=liver varnum;
run;

proc freq data=liver ;
tables gender gender_don cod diag end_stat grf_stat
px_stat ethcat ethcat_don px_stat don_ty abo abo_don
;
format cod li_cod. diag li_dgn. grf_stat $graph_stat.
ethcat ethcat. ethcat_don ethcat. px_stat $pxstat. end_stat status.
don_ty $don_type. abo $abotype. abo_don $abotype. gender $gender.
gender_don $gender.
;
run;

Below is the output of the Proc Contents for the Liver Transplantation data set.
The SAS System


Data Set Name WORK.LIVER Observations 88636
Engine V9 Indexes 0
Created Sunday, July 19, 2009 06:51:21 PM Observation Length 160
Last Modified Sunday, July 19, 2009 06:51:21 PM Deleted Observations 0



Label


First Data Page 1
Max Obs per Page 102
Filename C:DOCUME~1DR0E98~1.RAYLOCALS~1TempSAS
Temporary Files_TD3752liver.sas7bdat
Host Created XP_PRO
The SAS System


Variables in Creation Order

# Variable Type Len Format Informat Label

1 cod Num 8 6. 6. Cause of death
2 diag Num 8 Diagnosis at transplant
3 px_stat Char 1 $1. $1. Current status of the patient
4 end_stat Num 8 6. Status at transplant
5 hgt_cm_trr Num 8 9.4 9.4 Patient height (cm)at transplant
6 wgt_kg_trr Num 8 9.4 9.4 Patient weight (kg)at transplant
7 sgpt_tx Num 8 10.2 10.2 Blood(Serum)Glutamic-Oxaloacetic
Transaminase(SGOT)Test
8 abo Char 3 $3. $3. Blood type of patient
9 gender Char 1 $1. $1. Gender of patient
10 tx_date Num 8 MMDDYY10. Transplant date
11 prev_tx Char 1 Previous transplant
12 hgt_cm_don Num 8 8. 9.4 Doners height(cm)at harvesting
13 wgt_kg_don Num 8 9.4 9.4 Doners weight(kg)at harvesting
14 age_don Num 8 Doners age
15 ethcat_don Num 8 Doners ethnicity
16 don_ty Char 3 $3. $3. Doner source
17 gender_don Char 1 $1. $1. Doners gender
18 abo_don Char 3 $3. $3. Doners blood type
19 creat_tx Num 8 9.4 9.4 Patients Creatinine at transplant
20 tbili_tx Num 8 9.4 9.4 Patient billirubin at transplant
21 ethcat Num 8 Patints ethnicity
22 px_stat_ Num 8 MMDDYY10. Date patient graft status was identified
date
23 grf_stat Char 1 Status of liver graft
24 age Num 8 patient age at transplant
25 cold_isch Num 8 Duration of cold ischemia-preserved donor liver
26 GTIME Num 5 Graph survival time in days
27 PTIME Num 5 Patient survival time in days



Below is the partial output of the Proc Freq for the selected variables of the UNOS Liver
Transplants with and without the cumulative frequencies.

The SAS System

The FREQ Procedure

Gender of patient

gender Frequency Percent Frequency Percent
Female 34629 39.07 34629 39.07
Male 54007 60.93 88636 100.00

Doners gender

gender_ Cumulative Cumulative
don Frequency Percent Frequency Percent
Female 35486 40.04 35486 40.04
Male 53134 59.96 88620 100.00

The SAS System

The FREQ Procedure

Cause of death

cod Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
998 Unknown 2936 11.52
999 Other Specify 2163 8.49
4246 Cardiovascular - Arterial Embolism 7 0.03
4247 Cardiovascular - Pulmonary Embolism 43 0.17
4600 Graft Failure:Primary 685 2.69
4601 Graft Failure:Vascular Thrombosis 277 1.09
4602 Graft Failure:Biliary Tract Complication 66 0.26
4603 Graft Failure:Hepatitis 1205 4.73
4604 Graft Failure:Recurrent (Non-Hepatitis) 336 1.32
4605 Graft Failure:Rejection 515 2.02
4606 Graft Failure:Infection (Non-Hepatitis) 105 0.41
4610 Graft Failure:Other Specify 296 1.16
4615 Graft Vs. Host Disease 90 0.35
4620 Cardio: Arrythmia 204 0.80
4621 Cardio: Arterial Or Pulmonary Embolism 103 0.40
4622 Cardio: Hyperkalemic Arrest 169 0.66
4623 Cardio: Congestive Failure (Chf) 248 0.97
4624 Cardio: Myocardial Infarction 726 2.85
4625 Cardio: Other Specify 449 1.76
4626 Cardiac Arrest 838 3.29
4630 Cerebrovascular: Embolic Stroke 105 0.41



4631 Cerebrovascular: Hemorrhagic Stroke 293 1.15
4635 Cerebrovascular: Other Specify 364 1.43
4640 Pulm Insuff Or Edema (Exc Pneumonia) (Ards) 319 1.25
4645 Respiratory Failure: Other Specify Cause 540 2.12
4650 Renal Failure 516 2.03
4660 Multiple Organ System Failure 2225 8.73
4700 Hemorrhage: Lower Gastrointestinal 276 1.08
4701 Hemorrhage: Neurological (Brain) 301 1.18
4702 Hemorrhage: Variceal 59 0.23
4703 Hemorrhage: Disseminated Intravascular..(Dic) 6 0.02
4705 Hemorrhage: Other Specify 295 1.16
4706 Hemorrhage 129 0.51
4800 Inf: Bacterial Peritonitis 221 0.87
4801 Inf: Pneumonia Legionella Pneumocystosis 331 1.30
4802 Inf: Generalized Sepsis 3179 12.48
4803 Inf: Fungal Aspergillus Cryptococcal 432 1.70
4804 Inf: Mixed Other Specify 89 0.35
4805 Inf: Opportunistic 38 0.15
4806 Inf: Viral 204 0.80
4810 Inf: Other Specify 334 1.31
4811 Infection 233 0.91
4850 Malignancy: Primary Other Specify 590 2.32
4851 Malignancy: Metastatic Other Specify 1176 4.62
4855 Malignancy: Other Specify 382 1.50
4856 Malignancy 365 1.43
4860 Post-Tx Lymphoproliferative Disorder 267 1.05
4900 Operative: Other Specify 351 1.38
4910 Brain Dead:Never Recovered From Surgery 99 0.39
4920 Suicide:Attempted Suicide - Died Later 62 0.24
4930 Trauma: Motor Vehicle 92 0.36
4935 Trauma: Other Specify 42 0.16
4940 Diabetes Mellitus 21 0.08
4941 Fluid/Electrolyte Disorder 6 0.02
4942 Acid/Base Disorder 1 0.00
4945 Acute Pancreatitis 67 0.26
4950 Aids 1 0.00
4953 Non-Immuno Drug Related - Hematologic 2 0.01
4954 Non-Immuno Drug Related - Non-Hematolo.....Drug 2 0.01

Diagnosis at transplant

diag Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
999 Other Specify 3440 3.89
4100 Ahn: Drug Other Specify 784 0.89
4101 Ahn: Type A 187 0.21
4102 Ahn: Type B- Hbsag+ 785 0.89
4104 Ahn: Type C 1321 1.49
4105 Ahn: Type D 5 0.01
4106 Ahn: Type B And C 80 0.09
4107 Ahn: Type B And D 10 0.01
4108 Ahn: Etiology Unknown 2355 2.66



4110 Ahn: Other, Specify (E.G.Acute Viral Infection.. 1122 1.27
4200 Cirrhosis: Drug/Indust Exposure Other Specify 149 0.17
4201 Cirrhosis: Type A 47 0.05
4202 Cirrhosis: Type B- Hbsag+ 2914 3.29
4204 Cirrhosis: Type C 18998 21.46
4205 Cirrhosis: Type D 23 0.03
4206 Cirrhosis: Type B And C 510 0.58
4207 Cirrhosis: Type B And D 47 0.05
4208 Cirrhosis: Cryptogenic- Idiopathic 2979 3.37
4209 Cirrhosis: Chronic Active Hepatitis:........ 175 0.20
4210 Cirrhosis: Other, Specify (E.G., Histiocy... 1073 1.21
4212 Cirrhosis: Autoimmune 3110 3.51
4213 Cirrhosis: Cryptogenic (Idiopathic) 4517 5.10
4214 Cirrhosis: Fatty Liver (Nash) 960 1.08
4215 Alcoholic Cirrhosis 10623 12.00
4216 Alcoholic Cirrhosis With Hepatitis C 4350 4.91
4217 Acute Alcoholic Hepatitis 68 0.08
4220 Primary Biliary Cirrhosis (Pbc) 4696 5.30
4230 Sec Biliary Cirrhosis: Carolis Disease 118 0.13
4231 Sec Biliary Cirrhosis: Choledochol Cyst 61 0.07
4235 Sec Biliary Cirrhosis: Other Specify 261 0.29
4240 Psc: Crohns Disease 725 0.82
4241 Psc: Ulcerative Colitis 2137 2.41
4242 Psc: No Bowel Disease 1495 1.69
4245 Psc: Other Specify 1176 1.33
4250 Familial Cholestasis: Bylers Disease 74 0.08
4255 Familial Cholestasis: Other Specify 115 0.13
4260 Choles Liver Disease: Other Specify 241 0.27
4264 Neonatal Cholestatic Liver Disease 12 0.01
4265 Neonatal Hepatitis Other Specify 219 0.25
4270 Biliary Atresia: Extrahepatic 3098 3.50
4271 Biliary Hypoplasia: Nonsyndromic Paucity.... 119 0.13
4272 Biliary Hypoplasia: Alagille’s Syndrome..... 381 0.43
4275 Biliary Atresia Or Hypoplasia: Other,Specify 1125 1.27
4280 Congenital Hepatic Fibrosis 199 0.22
4285 Cystic Fibrosis 252 0.28
4290 Budd-Chiari Syndrome 558 0.63
4300 Metdis: Alpha-1-Antitrypsin Defic A-1-A 1470 1.66
4301 Metdis: Wilsons Disease 549 0.62
4302 Metdis: Hemochromatosis - Hemosiderosis 615 0.69
4303 Metdis: Glyc Stor Dis Type I (Gsd-I) 62 0.07
4304 Metdis: Glyc Stor Dis Type Ii (Gsd-Iv) 33 0.04
4305 Metdis: Hyperlipidemia-Ii- Homozgyous Hy 10 0.01
4306 Metdis: Tyrosinemia 124 0.14
4307 Metdis: Primary Oxalosis/Oxaluria- Hyper 195 0.22
4308 Metdis: Maple Syrup Urine Disease 38 0.04
4315 Metdis: Other Specify 573 0.65
4400 Plm: Hepatoma - Hepatocellular Carcinoma 1644 1.86
4401 Plm: Hepatoma (Hcc) And Cirrhosis 3061 3.46
4402 Plm: Fibrolamellar (Fl-Hc) 44 0.05
4403 Plm: Cholangiocarcinoma (Ch-Ca) 276 0.31
4404 Plm: Hepatoblastoma (Hbl) 269 0.30
4405 Plm: Hemangioendothelioma-Hemangiosarcom 136 0.15
4410 Plm: Other Specify 190 0.21
4420 Bile Duct Cancer (Cholangioma-Biliary Tr 45 0.05
4430 Secondary Hepatic Malignancy Other Specify 138 0.16
4450 Benign Tumor: Hepatic Adenoma 40 0.05



4451 Benign Tumor: Polycystic Liver Disease 293 0.33
4455 Benign Tumor: Other Specify 53 0.06
4500 Tpn/Hyperalimentation Ind Liver Disease 800 0.90
4510 Graft Vs. Host Dis Sec To Non-Li Tx 90 0.10
4520 Trauma Other Specify 71 0.08
4593 Hepatitis C: Chronic Or Actue 8 0.01

Status at transplant

end_stat Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
4010 KI: Active (1) 1 0.00 1 0.00
6002 LI: Old status 2 7483 8.55 7484 8.56
6004 LI: Old status 4 1433 1.64 8917 10.19
6010 LI: Status 1 11115 12.71 20032 22.90
6011 LI: Status 1A 927 1.06 20959 23.96
6012 LI: Status 1B 142 0.16 21101 24.12
6020 LI: Status 2A 4199 4.80 25300 28.92
6030LI: Status 2B 11786 13.47 37086 42.39
6040LI: Status 3 15177 17.35 52263 59.74
6189LI: MELD/PELD -11 1 0.00 52264 59.74
6190LI: MELD/PELD -10 9 0.01 52273 59.75
6191LI: MELD/PELD -9 11 0.01 52284 59.77
6192LI: MELD/PELD -8 13 0.01 52297 59.78
6193LI: MELD/PELD -7 11 0.01 52308 59.79
6194LI: MELD/PELD -6 14 0.02 52322 59.81
6195LI: MELD/PELD -5 11 0.01 52333 59.82
6196LI: MELD/PELD -4 14 0.02 52347 59.84
6197LI: MELD/PELD -3 25 0.03 52372 59.87
6198LI: MELD/PELD -2 21 0.02 52393 59.89
6199LI: MELD/PELD -1 23 0.03 52416 59.92
6200LI: MELD/PELD 0 21 0.02 52437 59.94
6201LI: MELD/PELD 1 30 0.03 52467 59.98
6202LI: MELD/PELD 2 34 0.04 52501 60.01
6203LI: MELD/PELD 3 22 0.03 52523 60.04
6204LI: MELD/PELD 4 28 0.03 52551 60.07
6205LI: MELD/PELD 5 28 0.03 52579 60.10
6206LI: MELD/PELD 6 278 0.32 52857 60.42
6207LI: MELD/PELD 7 200 0.23 53057 60.65
6208LI: MELD/PELD 8 217 0.25 53274 60.90
6209LI: MELD/PELD 9 284 0.32 53558 61.22
6210LI: MELD/PELD 10 457 0.52 54015 61.75
6211LI: MELD/PELD 11 396 0.45 54411 62.20
6212LI: MELD/PELD 12 505 0.58 54916 62.78
6213LI: MELD/PELD 13 594 0.68 55510 63.45
6214LI: MELD/PELD 14 710 0.81 56220 64.27
6215LI: MELD/PELD 15 1129 1.29 57349 65.56
6216LI: MELD/PELD 16 1212 1.39 58561 66.94
6217LI: MELD/PELD 17 1274 1.46 59835 68.40
6218LI: MELD/PELD 18 1532 1.75 61367 70.15
6219LI: MELD/PELD 19 933 1.07 62300 71.22
6220LI: MELD/PELD 20 1325 1.51 63625 72.73
6221LI: MELD/PELD 21 1045 1.19 64670 73.93
6222LI: MELD/PELD 22 3355 3.84 68025 77.76
6223LI: MELD/PELD 23 1263 1.44 69288 79.20
6224LI: MELD/PELD 24 3011 3.44 72299 82.65



6225LI: MELD/PELD 25 1621 1.85 73920 84.50
6226LI: MELD/PELD 26 767 0.88 74687 85.38
6227LI: MELD/PELD 27 716 0.82 75403 86.19
6228LI: MELD/PELD 28 1144 1.31 76547 87.50
6229LI: MELD/PELD 29 1691 1.93 78238 89.44
6230LI: MELD/PELD 30 1014 1.16 79252 90.59
6231LI: MELD/PELD 31 733 0.84 79985 91.43
6232LI: MELD/PELD 32 633 0.72 80618 92.16
6233LI: MELD/PELD 33 499 0.57 81117 92.73
6234LI: MELD/PELD 34 455 0.52 81572 93.25
6235LI: MELD/PELD 35 462 0.53 82034 93.77
6236LI: MELD/PELD 36 389 0.44 82423 94.22
6237LI: MELD/PELD 37 343 0.39 82766 94.61
6238LI: MELD/PELD 38 316 0.36 83082 94.97
6239LI: MELD/PELD 39 313 0.36 83395 95.33
6240LI: MELD/PELD 40 1760 2.01 85155 97.34
6241LI: MELD/PELD 41 27 0.03 85182 97.37
6242LI: MELD/PELD 42 5 0.01 85187 97.38
6243LI: MELD/PELD 43 12 0.01 85199 97.39
6244LI: MELD/PELD 44 16 0.02 85215 97.41
6245LI: MELD/PELD 45 19 0.02 85234 97.43
6246LI: MELD/PELD 46 18 0.02 85252 97.45
6247LI: MELD/PELD 47 4 0.00 85256 97.46
6248LI: MELD/PELD 48 9 0.01 85265 97.47
6249LI: MELD/PELD 49 2 0.00 85267 97.47
6250LI: MELD/PELD 50 6 0.01 85273 97.48
6251LI: MELD/PELD 51 2 0.00 85275 97.48
6252LI: MELD/PELD 52 3 0.00 85278 97.48
6253LI: MELD/PELD 53 1 0.00 85279 97.48
6255LI: MELD/PELD 55 4 0.00 85283 97.49
6256LI: MELD/PELD 56 2 0.00 85285 97.49
6258LI: MELD/PELD 58 1 0.00 85286 97.49
6260LI: MELD/PELD 60 1 0.00 85287 97.49
6261LI: MELD/PELD 61 1 0.00 85288 97.49
6263LI: MELD/PELD 63 1 0.00 85289 97.50
6264LI: MELD/PELD 64 1 0.00 85290 97.50
6265LI: MELD/PELD 65 1 0.00 85291 97.50
6266LI: MELD/PELD 66 1 0.00 85292 97.50
6267LI: MELD/PELD 67 1 0.00 85293 97.50
6269LI: MELD/PELD 69 1 0.00 85294 97.50
6299LI: MELD/PELD 99 1 0.00 85295 97.50
6999LI: Temporarily Inactive 2185 2.50 87480 100.00


Status of liver graft

grf_stat Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
N Failed 17030 21.06 17030 21.06
U Unknown 709 0.88 17739 21.94
Y Functioning 63129 78.06 80868 100.00


Current status of the patient



px_stat Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
A Living 46128 52.53 46128 52.53
D dead 25470 29.00 71598 81.53
L Lost to Follow up 7565 8.61 79163 90.14
R Retransplanted 8658 9.86 87821 100.00


Patints ethnicity

ethcat Frequency Percent Frequency Percent
1 White 66103 74.58 66103 74.58
2 Black 7882 8.89 73985 83.47
4 Hispanic 10315 11.64 84300 95.11
5 Asian 3272 3.69 87572 98.80
6 Amer Ind/Alaska Native 462 0.52 88034 99.32
7 Native Hawaiian/other Pacific Islander 152 0.17 88186 99.49
9 Multiracial 363 0.41 88549 99.90
998 Unknown 87 0.10 88636 100.00

Doners ethnicity

ethcat_don Frequency Percent Frequency Percent
1 White 65430 73.82 65430 73.82
2 Black 11012 12.42 76442 86.24
4 Hispanic 9500 10.72 85942 96.96
5 Asian 1576 1.78 87518 98.74
6 Amer Ind/Alaska Native 268 0.30 87786 99.04
7 Native Hawaiian/other Pacific Islander 186 0.21 87972 99.25
9 Multiracial 422 0.48 88394 99.73
998 Unknown 242 0.27 88636 100.00
Current status of the patient

px_stat Frequency Percent Frequency Percent
A Living 46128 52.53 46128 52.53
D dead 25470 29.00 71598 81.53
L Lost to Follow up 7565 8.61 79163 90.14
R Retransplanted 8658 9.86 87821 100.00


Doner source

don_ty Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ



Deceased Donor 84910 95.80 84910 95.80
Foreign Donor 196 0.22 85106 96.02
Living Donor 3530 3.98 88636 100.00

Blood type of patient

abo Frequency Percent Frequency Percent
Type A 34339 38.74 34339 38.74
Type A1 225 0.25 34564 39.00
Type A1B 12 0.01 34576 39.01
Type A2 46 0.05 34622 39.06
Type A2B 12 0.01 34634 39.07
Type AB 4226 4.77 38860 43.84
Type B 11286 12.73 50146 56.58
Type O 38488 43.42 88634 100.00
Type UNK 2 0.00 88636 100.00

Doners blood type

abo_don Frequency Percent Frequency Percent
Type A 17119 19.32 17119 19.32
Type A1 14527 16.39 31646 35.71
Type A1B 270 0.30 31916 36.02
Type A2 1145 1.29 33061 37.31
Type A2B 81 0.09 33142 37.40
Type AB 2184 2.46 35326 39.87
Type B 9546 10.77 44872 50.64
Type O 43718 49.34 88590 99.98
Type UNK 20 0.02 88610 100.00




Exercises 4.1

Using UNOS01d.sas, write the SAS code to produce the following:
1. PROC FREQ of blood type of donor by recipient.
2. PROC FREQ of principal diagnosis in rank order by race.
3. PROC FREQ of status transplant in rank order by race.
4. PROC FREQ of cause of death by race.
5. PROC FREQ of race by patient status.
6. PROC FREQ of donor gender by patient gender.

Below is SAS code for exercise 4.1-1.
proc freq data=liver;
tables abo*abo_don;
gender_don $gender.
;
title 'Blood type of donor by recipient';
run;

Below is the PROC FREQ output for exercise 4.1-1.
Blood type of donor by recipient

The FREQ Procedure

Table of abo by abo_don

abo(Blood type of patient) abo_don(Doners blood type)

Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚Type A ‚Type A1 ‚Type A1B‚Type A2 ‚Type A2B‚Type AB ‚Type B ‚Type O ‚Type UNK‚
Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Type A ‚ 16072 ‚ 13759 ‚ 0 ‚ 877 ‚ 0 ‚ 49 ‚ 66 ‚ 3492 ‚ 8 ‚
34323
‚ 18.14 ‚ 15.53 ‚ 0.00 ‚ 0.99 ‚ 0.00 ‚ 0.06 ‚ 0.07 ‚ 3.94 ‚ 0.01 ‚
38.73
‚ 46.83 ‚ 40.09 ‚ 0.00 ‚ 2.56 ‚ 0.00 ‚ 0.14 ‚ 0.19 ‚ 10.17 ‚ 0.02 ‚



‚ 93.88 ‚ 94.71 ‚ 0.00 ‚ 76.59 ‚ 0.00 ‚ 2.24 ‚ 0.69 ‚ 7.99 ‚ 40.00 ‚
Type A1 ‚ 126 ‚ 74 ‚ 0 ‚ 16 ‚ 0 ‚ 0 ‚ 0 ‚ 9 ‚ 0 ‚
225
‚ 0.14 ‚ 0.08 ‚ 0.00 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.01 ‚ 0.00 ‚
0.25
‚ 56.00 ‚ 32.89 ‚ 0.00 ‚ 7.11 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 4.00 ‚ 0.00 ‚
‚ 0.74 ‚ 0.51 ‚ 0.00 ‚ 1.40 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.02 ‚ 0.00 ‚
abo(Blood type of patient) abo_don(Doners blood type)

Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚Type A ‚Type A1 ‚Type A1B‚Type A2 ‚Type A2B‚Type AB ‚Type B ‚Type O ‚Type UNK‚
Total
Type A1B ‚ 2 ‚ 0 ‚ 2 ‚ 1 ‚ 0 ‚ 4 ‚ 2 ‚ 1 ‚ 0 ‚
12
‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
0.01
‚ 16.67 ‚ 0.00 ‚ 16.67 ‚ 8.33 ‚ 0.00 ‚ 33.33 ‚ 16.67 ‚ 8.33 ‚ 0.00 ‚
‚ 0.01 ‚ 0.00 ‚ 0.74 ‚ 0.09 ‚ 0.00 ‚ 0.18 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚
Type A2 ‚ 25 ‚ 17 ‚ 0 ‚ 2 ‚ 0 ‚ 0 ‚ 0 ‚ 2 ‚ 0 ‚
46
‚ 0.03 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
0.05
‚ 54.35 ‚ 36.96 ‚ 0.00 ‚ 4.35 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 4.35 ‚ 0.00 ‚
‚ 0.15 ‚ 0.12 ‚ 0.00 ‚ 0.17 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
Type A2B ‚ 3 ‚ 1 ‚ 2 ‚ 0 ‚ 1 ‚ 2 ‚ 1 ‚ 2 ‚ 0 ‚
12
‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
0.01
‚ 25.00 ‚ 8.33 ‚ 16.67 ‚ 0.00 ‚ 8.33 ‚ 16.67 ‚ 8.33 ‚ 16.67 ‚ 0.00 ‚
‚ 0.02 ‚ 0.01 ‚ 0.74 ‚ 0.00 ‚ 1.23 ‚ 0.09 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
Type AB ‚ 478 ‚ 373 ‚ 262 ‚ 24 ‚ 79 ‚ 2046 ‚ 690 ‚ 271 ‚ 3 ‚
4226
‚ 0.54 ‚ 0.42 ‚ 0.30 ‚ 0.03 ‚ 0.09 ‚ 2.31 ‚ 0.78 ‚ 0.31 ‚ 0.00 ‚
4.77
‚ 11.31 ‚ 8.83 ‚ 6.20 ‚ 0.57 ‚ 1.87 ‚ 48.41 ‚ 16.33 ‚ 6.41 ‚ 0.07 ‚
‚ 2.79 ‚ 2.57 ‚ 97.04 ‚ 2.10 ‚ 97.53 ‚ 93.68 ‚ 7.23 ‚ 0.62 ‚ 15.00 ‚
Type B ‚ 68 ‚ 45 ‚ 2 ‚ 7 ‚ 1 ‚ 33 ‚ 8670 ‚ 2457 ‚ 2 ‚
11285
‚ 0.08 ‚ 0.05 ‚ 0.00 ‚ 0.01 ‚ 0.00 ‚ 0.04 ‚ 9.78 ‚ 2.77 ‚ 0.00 ‚
12.74
‚ 0.60 ‚ 0.40 ‚ 0.02 ‚ 0.06 ‚ 0.01 ‚ 0.29 ‚ 76.83 ‚ 21.77 ‚ 0.02 ‚
‚ 0.40 ‚ 0.31 ‚ 0.74 ‚ 0.61 ‚ 1.23 ‚ 1.51 ‚ 90.82 ‚ 5.62 ‚ 10.00 ‚
Type O ‚ 344 ‚ 258 ‚ 2 ‚ 218 ‚ 0 ‚ 50 ‚ 117 ‚ 37483 ‚ 7 ‚
38479
‚ 0.39 ‚ 0.29 ‚ 0.00 ‚ 0.25 ‚ 0.00 ‚ 0.06 ‚ 0.13 ‚ 42.30 ‚ 0.01 ‚
43.43
‚ 0.89 ‚ 0.67 ‚ 0.01 ‚ 0.57 ‚ 0.00 ‚ 0.13 ‚ 0.30 ‚ 97.41 ‚ 0.02 ‚
‚ 2.01 ‚ 1.78 ‚ 0.74 ‚ 19.04 ‚ 0.00 ‚ 2.29 ‚ 1.23 ‚ 85.74 ‚ 35.00 ‚



Type UNK ‚ 1 ‚ 0 ‚ 0 ‚ 0 ‚ 0 ‚ 0 ‚ 0 ‚ 1 ‚ 0 ‚
2
‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
0.00
‚ 50.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 50.00 ‚ 0.00 ‚
‚ 0.01 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
Total 17119 14527 270 1145 81 2184 9546 43718 20
88610
19.32 16.39 0.30 1.29 0.09 2.46 10.77 49.34 0.02
100.00


proc freq data=liver order=freq;
tables diag*ethcat;
gender_don $gender.
;
title 'Principal diagnosis in rank order by race';
run;

Below is the partial PROC FREQ output for exercise 4.1-2.

Principal Diagnosis by Race

The FREQ Procedure

Table of diag by ethcat

diag(Diagnosis at transplant) ethcat(Patints ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚1 White ‚4 Hispan‚2 Black ‚5 Asian ‚6 Amer I‚9 Multir‚7 Native‚998 Unkn‚
Total
‚ ‚ic ‚ ‚ ‚nd/Alask‚acial ‚ Hawaiia‚own ‚
‚ ‚ ‚ ‚ ‚a Native‚ ‚n/other ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Pacific ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Islander‚ ‚
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
4204 Cirrhosis: ‚ 14115 ‚ 2611 ‚ 1572 ‚ 535 ‚ 64 ‚ 71 ‚ 24 ‚ 6 ‚
18998



Type C ‚ 15.95 ‚ 2.95 ‚ 1.78 ‚ 0.60 ‚ 0.07 ‚ 0.08 ‚ 0.03 ‚ 0.01 ‚
21.46
‚ 74.30 ‚ 13.74 ‚ 8.27 ‚ 2.82 ‚ 0.34 ‚ 0.37 ‚ 0.13 ‚ 0.03 ‚
‚ 21.38 ‚ 25.35 ‚ 19.96 ‚ 16.40 ‚ 13.85 ‚ 19.56 ‚ 15.79 ‚ 6.98 ‚
4215 Alcoholic C ‚ 8949 ‚ 1102 ‚ 380 ‚ 81 ‚ 76 ‚ 16 ‚ 6 ‚ 13 ‚
10623
irrhosis ‚ 10.11 ‚ 1.24 ‚ 0.43 ‚ 0.09 ‚ 0.09 ‚ 0.02 ‚ 0.01 ‚ 0.01 ‚
12.00
‚ 84.24 ‚ 10.37 ‚ 3.58 ‚ 0.76 ‚ 0.72 ‚ 0.15 ‚ 0.06 ‚ 0.12 ‚
‚ 13.56 ‚ 10.70 ‚ 4.82 ‚ 2.48 ‚ 16.45 ‚ 4.41 ‚ 3.95 ‚ 15.12 ‚
4220 Primary Bil ‚ 3896 ‚ 475 ‚ 192 ‚ 68 ‚ 33 ‚ 17 ‚ 7 ‚ 8 ‚
4696
iary Cirrhosis ( ‚ 4.40 ‚ 0.54 ‚ 0.22 ‚ 0.08 ‚ 0.04 ‚ 0.02 ‚ 0.01 ‚ 0.01 ‚
5.30
Pbc) ‚ 82.96 ‚ 10.11 ‚ 4.09 ‚ 1.45 ‚ 0.70 ‚ 0.36 ‚ 0.15 ‚ 0.17 ‚
‚ 5.90 ‚ 4.61 ‚ 2.44 ‚ 2.08 ‚ 7.14 ‚ 4.68 ‚ 4.61 ‚ 9.30 ‚
4213 Cirrhosis: ‚ 3578 ‚ 558 ‚ 260 ‚ 90 ‚ 16 ‚ 6 ‚ 4 ‚ 5 ‚
4517
Cryptogenic (Idi ‚ 4.04 ‚ 0.63 ‚ 0.29 ‚ 0.10 ‚ 0.02 ‚ 0.01 ‚ 0.00 ‚ 0.01 ‚
5.10
opathic) ‚ 79.21 ‚ 12.35 ‚ 5.76 ‚ 1.99 ‚ 0.35 ‚ 0.13 ‚ 0.09 ‚ 0.11 ‚
‚ 5.42 ‚ 5.42 ‚ 3.30 ‚ 2.76 ‚ 3.46 ‚ 1.65 ‚ 2.63 ‚ 5.81 ‚
4216 Alcoholic C ‚ 3346 ‚ 583 ‚ 341 ‚ 31 ‚ 32 ‚ 14 ‚ 3 ‚ 0 ‚
4350
irrhosis With He ‚ 3.78 ‚ 0.66 ‚ 0.39 ‚ 0.04 ‚ 0.04 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚
4.91
patitis C ‚ 76.92 ‚ 13.40 ‚ 7.84 ‚ 0.71 ‚ 0.74 ‚ 0.32 ‚ 0.07 ‚ 0.00 ‚
‚ 5.07 ‚ 5.66 ‚ 4.33 ‚ 0.95 ‚ 6.93 ‚ 3.86 ‚ 1.97 ‚ 0.00 ‚
999 Other Speci ‚ 2380 ‚ 432 ‚ 427 ‚ 155 ‚ 13 ‚ 25 ‚ 8 ‚ 0 ‚
3440
fy ‚ 2.69 ‚ 0.49 ‚ 0.48 ‚ 0.18 ‚ 0.01 ‚ 0.03 ‚ 0.01 ‚ 0.00 ‚
3.89
‚ 69.19 ‚ 12.56 ‚ 12.41 ‚ 4.51 ‚ 0.38 ‚ 0.73 ‚ 0.23 ‚ 0.00 ‚
‚ 3.61 ‚ 4.19 ‚ 5.42 ‚ 4.75 ‚ 2.81 ‚ 6.89 ‚ 5.26 ‚ 0.00 ‚
4212 Cirrhosis: ‚ 2087 ‚ 390 ‚ 546 ‚ 49 ‚ 20 ‚ 11 ‚ 6 ‚ 1 ‚
3110
Autoimmune ‚ 2.36 ‚ 0.44 ‚ 0.62 ‚ 0.06 ‚ 0.02 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚
3.51
‚ 67.11 ‚ 12.54 ‚ 17.56 ‚ 1.58 ‚ 0.64 ‚ 0.35 ‚ 0.19 ‚ 0.03 ‚
‚ 3.16 ‚ 3.79 ‚ 6.93 ‚ 1.50 ‚ 4.33 ‚ 3.03 ‚ 3.95 ‚ 1.16 ‚
4270 Biliary Atr ‚ 1703 ‚ 500 ‚ 640 ‚ 158 ‚ 23 ‚ 54 ‚ 17 ‚ 3 ‚
3098
esia: Extrahepat ‚ 1.92 ‚ 0.56 ‚ 0.72 ‚ 0.18 ‚ 0.03 ‚ 0.06 ‚ 0.02 ‚ 0.00 ‚
3.50
ic ‚ 54.97 ‚ 16.14 ‚ 20.66 ‚ 5.10 ‚ 0.74 ‚ 1.74 ‚ 0.55 ‚ 0.10 ‚
‚ 2.58 ‚ 4.85 ‚ 8.13 ‚ 4.84 ‚ 4.98 ‚ 14.88 ‚ 11.18 ‚ 3.49 ‚
4401 Plm: Hepato ‚ 2020 ‚ 498 ‚ 214 ‚ 284 ‚ 21 ‚ 16 ‚ 4 ‚ 4 ‚
3061



ma (Hcc) And Cir ‚ 2.28 ‚ 0.56 ‚ 0.24 ‚ 0.32 ‚ 0.02 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚
3.46
rhosis ‚ 65.99 ‚ 16.27 ‚ 6.99 ‚ 9.28 ‚ 0.69 ‚ 0.52 ‚ 0.13 ‚ 0.13 ‚
‚ 3.06 ‚ 4.83 ‚ 2.72 ‚ 8.70 ‚ 4.55 ‚ 4.41 ‚ 2.63 ‚ 4.65 ‚
4208 Cirrhosis: ‚ 2354 ‚ 358 ‚ 169 ‚ 54 ‚ 22 ‚ 13 ‚ 4 ‚ 5 ‚
2979
Cryptogenic- Idi ‚ 2.66 ‚ 0.40 ‚ 0.19 ‚ 0.06 ‚ 0.02 ‚ 0.01 ‚ 0.00 ‚ 0.01 ‚
3.37
opathic ‚ 79.02 ‚ 12.02 ‚ 5.67 ‚ 1.81 ‚ 0.74 ‚ 0.44 ‚ 0.13 ‚ 0.17 ‚
‚ 3.57 ‚ 3.48 ‚ 2.15 ‚ 1.65 ‚ 4.76 ‚ 3.58 ‚ 2.63 ‚ 5.81 ‚
4202 Cirrhosis: ‚ 1661 ‚ 175 ‚ 209 ‚ 816 ‚ 9 ‚ 14 ‚ 23 ‚ 7 ‚
2914
Type B- Hbsag+ ‚ 1.88 ‚ 0.20 ‚ 0.24 ‚ 0.92 ‚ 0.01 ‚ 0.02 ‚ 0.03 ‚ 0.01 ‚
3.29
‚ 57.00 ‚ 6.01 ‚ 7.17 ‚ 28.00 ‚ 0.31 ‚ 0.48 ‚ 0.79 ‚ 0.24 ‚
‚ 2.52 ‚ 1.70 ‚ 2.65 ‚ 25.01 ‚ 1.95 ‚ 3.86 ‚ 15.13 ‚ 8.14 ‚
4108 Ahn: Etiolo ‚ 1464 ‚ 390 ‚ 391 ‚ 69 ‚ 17 ‚ 17 ‚ 4 ‚ 3 ‚
2355
gy Unknown ‚ 1.65 ‚ 0.44 ‚ 0.44 ‚ 0.08 ‚ 0.02 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚
2.66
‚ 62.17 ‚ 16.56 ‚ 16.60 ‚ 2.93 ‚ 0.72 ‚ 0.72 ‚ 0.17 ‚ 0.13 ‚
‚ 2.22 ‚ 3.79 ‚ 4.96 ‚ 2.11 ‚ 3.68 ‚ 4.68 ‚ 2.63 ‚ 3.49 ‚
Total 66019 10300 7876 3263 462 363 152 86
88521
74.58 11.64 8.90 3.69 0.52 0.41 0.17 0.10
100.00

tables end_stat*ethcat;
gender_don $gender.
;
title 'Status at transplant in rank order(order=freq) by race';
run;

Status at transplant in rank order(order=freq) by race



The FREQ Procedure

Table of end_stat by ethcat

end_stat(Status at transplant) ethcat(Patints ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Total
‚ ‚ ‚ ‚ ‚ ‚ ‚Pacific ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Islander‚ ‚
6040LI: Status 3 ‚ 12022 ‚ 1463 ‚ 1067 ‚ 441 ‚ 88 ‚ 46 ‚ 25 ‚ 25 ‚
15177
‚ 13.74 ‚ 1.67 ‚ 1.22 ‚ 0.50 ‚ 0.10 ‚ 0.05 ‚ 0.03 ‚ 0.03 ‚
17.35
‚ 79.21 ‚ 9.64 ‚ 7.03 ‚ 2.91 ‚ 0.58 ‚ 0.30 ‚ 0.16 ‚ 0.16 ‚
‚ 18.43 ‚ 14.34 ‚ 13.71 ‚ 13.64 ‚ 19.34 ‚ 12.85 ‚ 16.67 ‚ 30.12 ‚
6030LI: Status 2 ‚ 9101 ‚ 1269 ‚ 942 ‚ 351 ‚ 55 ‚ 36 ‚ 32 ‚ 0 ‚
11786
B ‚ 10.40 ‚ 1.45 ‚ 1.08 ‚ 0.40 ‚ 0.06 ‚ 0.04 ‚ 0.04 ‚ 0.00 ‚
13.47
‚ 77.22 ‚ 10.77 ‚ 7.99 ‚ 2.98 ‚ 0.47 ‚ 0.31 ‚ 0.27 ‚ 0.00 ‚
‚ 13.96 ‚ 12.44 ‚ 12.10 ‚ 10.86 ‚ 12.09 ‚ 10.06 ‚ 21.33 ‚ 0.00 ‚
6010 LI: Status ‚ 7652 ‚ 1434 ‚ 1377 ‚ 473 ‚ 67 ‚ 63 ‚ 29 ‚ 20 ‚
11115
1 ‚ 8.75 ‚ 1.64 ‚ 1.57 ‚ 0.54 ‚ 0.08 ‚ 0.07 ‚ 0.03 ‚ 0.02 ‚
12.71
‚ 68.84 ‚ 12.90 ‚ 12.39 ‚ 4.26 ‚ 0.60 ‚ 0.57 ‚ 0.26 ‚ 0.18 ‚
‚ 11.73 ‚ 14.05 ‚ 17.69 ‚ 14.63 ‚ 14.73 ‚ 17.60 ‚ 19.33 ‚ 24.10 ‚
6002 LI: Old sta ‚ 5816 ‚ 721 ‚ 617 ‚ 229 ‚ 40 ‚ 34 ‚ 6 ‚ 20 ‚
7483
tus 2 ‚ 6.65 ‚ 0.82 ‚ 0.71 ‚ 0.26 ‚ 0.05 ‚ 0.04 ‚ 0.01 ‚ 0.02 ‚
8.55
‚ 77.72 ‚ 9.64 ‚ 8.25 ‚ 3.06 ‚ 0.53 ‚ 0.45 ‚ 0.08 ‚ 0.27 ‚
‚ 8.92 ‚ 7.07 ‚ 7.93 ‚ 7.09 ‚ 8.79 ‚ 9.50 ‚ 4.00 ‚ 24.10 ‚
6020 LI: Status ‚ 3084 ‚ 610 ‚ 297 ‚ 162 ‚ 27 ‚ 11 ‚ 8 ‚ 0 ‚
4199
2A ‚ 3.53 ‚ 0.70 ‚ 0.34 ‚ 0.19 ‚ 0.03 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚
4.80
‚ 73.45 ‚ 14.53 ‚ 7.07 ‚ 3.86 ‚ 0.64 ‚ 0.26 ‚ 0.19 ‚ 0.00 ‚
end_stat(Status at transplant) ethcat(Patints ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Total



‚ ‚ ‚ ‚ ‚ ‚ ‚Pacific ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Islander‚ ‚
‚ 4.73 ‚ 5.98 ‚ 3.82 ‚ 5.01 ‚ 5.93 ‚ 3.07 ‚ 5.33 ‚ 0.00 ‚
6222LI: MELD/PEL ‚ 2491 ‚ 348 ‚ 305 ‚ 190 ‚ 10 ‚ 6 ‚ 5 ‚ 0 ‚
3355
D 22 ‚ 2.85 ‚ 0.40 ‚ 0.35 ‚ 0.22 ‚ 0.01 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚
3.84
‚ 74.25 ‚ 10.37 ‚ 9.09 ‚ 5.66 ‚ 0.30 ‚ 0.18 ‚ 0.15 ‚ 0.00 ‚
‚ 3.82 ‚ 3.41 ‚ 3.92 ‚ 5.88 ‚ 2.20 ‚ 1.68 ‚ 3.33 ‚ 0.00 ‚
6224LI: MELD/PEL ‚ 2205 ‚ 335 ‚ 302 ‚ 138 ‚ 11 ‚ 15 ‚ 5 ‚ 0 ‚
3011
D 24 ‚ 2.52 ‚ 0.38 ‚ 0.35 ‚ 0.16 ‚ 0.01 ‚ 0.02 ‚ 0.01 ‚ 0.00 ‚
3.44
‚ 73.23 ‚ 11.13 ‚ 10.03 ‚ 4.58 ‚ 0.37 ‚ 0.50 ‚ 0.17 ‚ 0.00 ‚
‚ 3.38 ‚ 3.28 ‚ 3.88 ‚ 4.27 ‚ 2.42 ‚ 4.19 ‚ 3.33 ‚ 0.00 ‚
6999LI: Temporar ‚ 1682 ‚ 199 ‚ 188 ‚ 72 ‚ 20 ‚ 7 ‚ 0 ‚ 17 ‚
2185
ily Inactive ‚ 1.92 ‚ 0.23 ‚ 0.21 ‚ 0.08 ‚ 0.02 ‚ 0.01 ‚ 0.00 ‚ 0.02 ‚
2.50
‚ 76.98 ‚ 9.11 ‚ 8.60 ‚ 3.30 ‚ 0.92 ‚ 0.32 ‚ 0.00 ‚ 0.78 ‚
‚ 2.58 ‚ 1.95 ‚ 2.42 ‚ 2.23 ‚ 4.40 ‚ 1.96 ‚ 0.00 ‚ 20.48 ‚
6240LI: MELD/PEL ‚ 1090 ‚ 342 ‚ 202 ‚ 102 ‚ 13 ‚ 8 ‚ 3 ‚ 0 ‚
1760
D 40 ‚ 1.25 ‚ 0.39 ‚ 0.23 ‚ 0.12 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
2.01
‚ 61.93 ‚ 19.43 ‚ 11.48 ‚ 5.80 ‚ 0.74 ‚ 0.45 ‚ 0.17 ‚ 0.00 ‚
‚ 1.67 ‚ 3.35 ‚ 2.60 ‚ 3.16 ‚ 2.86 ‚ 2.23 ‚ 2.00 ‚ 0.00 ‚
6229LI: MELD/PEL ‚ 1168 ‚ 233 ‚ 148 ‚ 122 ‚ 6 ‚ 12 ‚ 2 ‚ 0 ‚
1691
D 29 ‚ 1.34 ‚ 0.27 ‚ 0.17 ‚ 0.14 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
1.93
‚ 69.07 ‚ 13.78 ‚ 8.75 ‚ 7.21 ‚ 0.35 ‚ 0.71 ‚ 0.12 ‚ 0.00 ‚
‚ 1.79 ‚ 2.28 ‚ 1.90 ‚ 3.77 ‚ 1.32 ‚ 3.35 ‚ 1.33 ‚ 0.00 ‚
6225LI: MELD/PEL ‚ 1094 ‚ 245 ‚ 139 ‚ 123 ‚ 13 ‚ 6 ‚ 1 ‚ 0 ‚
1621
D 25 ‚ 1.25 ‚ 0.28 ‚ 0.16 ‚ 0.14 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
1.85
‚ 67.49 ‚ 15.11 ‚ 8.57 ‚ 7.59 ‚ 0.80 ‚ 0.37 ‚ 0.06 ‚ 0.00 ‚
‚ 1.68 ‚ 2.40 ‚ 1.79 ‚ 3.81 ‚ 2.86 ‚ 1.68 ‚ 0.67 ‚ 0.00 ‚
6218LI: MELD/PEL ‚ 1208 ‚ 173 ‚ 105 ‚ 27 ‚ 10 ‚ 7 ‚ 2 ‚ 0 ‚
1532
D 18 ‚ 1.38 ‚ 0.20 ‚ 0.12 ‚ 0.03 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
1.75
‚ 78.85 ‚ 11.29 ‚ 6.85 ‚ 1.76 ‚ 0.65 ‚ 0.46 ‚ 0.13 ‚ 0.00 ‚
‚ 1.85 ‚ 1.70 ‚ 1.35 ‚ 0.84 ‚ 2.20 ‚ 1.96 ‚ 1.33 ‚ 0.00 ‚
Total 65216 10203 7783 3232 455 358 150 83
87480



74.55 11.66 8.90 3.69 0.52 0.41 0.17 0.09
100.00

tables cod*ethcat;
gender_don $gender.
;
title 'Cause of death in rank order(order=freq) by race';
run;

Cause of death in rank order(order=freq) by race
The FREQ Procedure
Table of cod by ethcat

cod(Cause of death) ethcat(Patints ethnicity)
Frequency ‚
Percent ‚
Row Pct ‚
Total
‚ ‚ ‚ ‚ ‚ ‚ ‚Pacific ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Islander‚ ‚
4802 Inf: Gener ‚ 2437 ‚ 297 ‚ 323 ‚ 77 ‚ 22 ‚ 7 ‚ 5 ‚ 11 ‚
3179
alized Sepsis ‚ 9.57 ‚ 1.17 ‚ 1.27 ‚ 0.30 ‚ 0.09 ‚ 0.03 ‚ 0.02 ‚ 0.04 ‚
12.48
‚ 76.66 ‚ 9.34 ‚ 10.16 ‚ 2.42 ‚ 0.69 ‚ 0.22 ‚ 0.16 ‚ 0.35 ‚
‚ 12.52 ‚ 12.02 ‚ 13.40 ‚ 9.60 ‚ 14.47 ‚ 8.05 ‚ 10.64 ‚ 31.43 ‚
998 Unknown ‚ 2244 ‚ 327 ‚ 253 ‚ 85 ‚ 11 ‚ 11 ‚ 4 ‚ 1 ‚
2936
‚ 8.81 ‚ 1.28 ‚ 0.99 ‚ 0.33 ‚ 0.04 ‚ 0.04 ‚ 0.02 ‚ 0.00 ‚
11.52
‚ 76.43 ‚ 11.14 ‚ 8.62 ‚ 2.90 ‚ 0.37 ‚ 0.37 ‚ 0.14 ‚ 0.03 ‚
‚ 11.52 ‚ 13.23 ‚ 10.50 ‚ 10.60 ‚ 7.24 ‚ 12.64 ‚ 8.51 ‚ 2.86 ‚
4660 Multiple O ‚ 1639 ‚ 217 ‚ 265 ‚ 71 ‚ 15 ‚ 10 ‚ 7 ‚ 1 ‚
2225
rgan System Fail ‚ 6.43 ‚ 0.85 ‚ 1.04 ‚ 0.28 ‚ 0.06 ‚ 0.04 ‚ 0.03 ‚ 0.00 ‚
8.73
ure ‚ 73.66 ‚ 9.75 ‚ 11.91 ‚ 3.19 ‚ 0.67 ‚ 0.45 ‚ 0.31 ‚ 0.04 ‚
‚ 8.42 ‚ 8.78 ‚ 11.00 ‚ 8.85 ‚ 9.87 ‚ 11.49 ‚ 14.89 ‚ 2.86 ‚



999 Other Specif ‚ 1583 ‚ 244 ‚ 233 ‚ 71 ‚ 16 ‚ 9 ‚ 6 ‚ 1 ‚
2163
y ‚ 6.21 ‚ 0.96 ‚ 0.91 ‚ 0.28 ‚ 0.06 ‚ 0.04 ‚ 0.02 ‚ 0.00 ‚
8.49
‚ 73.19 ‚ 11.28 ‚ 10.77 ‚ 3.28 ‚ 0.74 ‚ 0.42 ‚ 0.28 ‚ 0.05 ‚
‚ 8.13 ‚ 9.87 ‚ 9.67 ‚ 8.85 ‚ 10.53 ‚ 10.34 ‚ 12.77 ‚ 2.86 ‚
4603 Graft Fail ‚ 864 ‚ 150 ‚ 127 ‚ 52 ‚ 5 ‚ 4 ‚ 1 ‚ 2 ‚
1205
ure:Hepatitis ‚ 3.39 ‚ 0.59 ‚ 0.50 ‚ 0.20 ‚ 0.02 ‚ 0.02 ‚ 0.00 ‚ 0.01 ‚
4.73
‚ 71.70 ‚ 12.45 ‚ 10.54 ‚ 4.32 ‚ 0.41 ‚ 0.33 ‚ 0.08 ‚ 0.17 ‚
‚ 4.44 ‚ 6.07 ‚ 5.27 ‚ 6.48 ‚ 3.29 ‚ 4.60 ‚ 2.13 ‚ 5.71 ‚
cod(Cause of death) ethcat(Patints ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Total
‚ ‚ ‚ ‚ ‚ ‚ ‚Pacific ‚ ‚
‚ ‚ ‚ ‚ ‚ ‚ ‚Islander‚ ‚
4851 Malignancy ‚ 958 ‚ 100 ‚ 55 ‚ 54 ‚ 6 ‚ 0 ‚ 1 ‚ 2 ‚
1176
: Metastatic Oth ‚ 3.76 ‚ 0.39 ‚ 0.22 ‚ 0.21 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚ 0.01 ‚
4.62
er Specify ‚ 81.46 ‚ 8.50 ‚ 4.68 ‚ 4.59 ‚ 0.51 ‚ 0.00 ‚ 0.09 ‚ 0.17 ‚
‚ 4.92 ‚ 4.05 ‚ 2.28 ‚ 6.73 ‚ 3.95 ‚ 0.00 ‚ 2.13 ‚ 5.71 ‚
4626 Cardiac Ar ‚ 618 ‚ 93 ‚ 91 ‚ 29 ‚ 1 ‚ 4 ‚ 2 ‚ 0 ‚
838
rest ‚ 2.43 ‚ 0.37 ‚ 0.36 ‚ 0.11 ‚ 0.00 ‚ 0.02 ‚ 0.01 ‚ 0.00 ‚
3.29
‚ 73.75 ‚ 11.10 ‚ 10.86 ‚ 3.46 ‚ 0.12 ‚ 0.48 ‚ 0.24 ‚ 0.00 ‚
‚ 3.17 ‚ 3.76 ‚ 3.78 ‚ 3.62 ‚ 0.66 ‚ 4.60 ‚ 4.26 ‚ 0.00 ‚
4624 Cardio: My ‚ 606 ‚ 64 ‚ 35 ‚ 13 ‚ 3 ‚ 4 ‚ 0 ‚ 1 ‚
726
ocardial Infarct ‚ 2.38 ‚ 0.25 ‚ 0.14 ‚ 0.05 ‚ 0.01 ‚ 0.02 ‚ 0.00 ‚ 0.00 ‚
2.85
ion ‚ 83.47 ‚ 8.82 ‚ 4.82 ‚ 1.79 ‚ 0.41 ‚ 0.55 ‚ 0.00 ‚ 0.14 ‚
‚ 3.11 ‚ 2.59 ‚ 1.45 ‚ 1.62 ‚ 1.97 ‚ 4.60 ‚ 0.00 ‚ 2.86 ‚
4600 Graft Fail ‚ 486 ‚ 71 ‚ 89 ‚ 28 ‚ 4 ‚ 4 ‚ 1 ‚ 2 ‚
685
ure:Primary ‚ 1.91 ‚ 0.28 ‚ 0.35 ‚ 0.11 ‚ 0.02 ‚ 0.02 ‚ 0.00 ‚ 0.01 ‚
2.69
‚ 70.95 ‚ 10.36 ‚ 12.99 ‚ 4.09 ‚ 0.58 ‚ 0.58 ‚ 0.15 ‚ 0.29 ‚
‚ 2.50 ‚ 2.87 ‚ 3.69 ‚ 3.49 ‚ 2.63 ‚ 4.60 ‚ 2.13 ‚ 5.71 ‚
4850 Malignancy ‚ 499 ‚ 33 ‚ 32 ‚ 18 ‚ 5 ‚ 0 ‚ 2 ‚ 1 ‚
590



: Primary Other ‚ 1.96 ‚ 0.13 ‚ 0.13 ‚ 0.07 ‚ 0.02 ‚ 0.00 ‚ 0.01 ‚ 0.00 ‚
2.32
Specify ‚ 84.58 ‚ 5.59 ‚ 5.42 ‚ 3.05 ‚ 0.85 ‚ 0.00 ‚ 0.34 ‚ 0.17 ‚
‚ 2.56 ‚ 1.34 ‚ 1.33 ‚ 2.24 ‚ 3.29 ‚ 0.00 ‚ 4.26 ‚ 2.86 ‚
4645 Respirator ‚ 429 ‚ 49 ‚ 43 ‚ 14 ‚ 2 ‚ 2 ‚ 0 ‚ 1 ‚
540
y Failure: Other ‚ 1.68 ‚ 0.19 ‚ 0.17 ‚ 0.05 ‚ 0.01 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚
2.12
Specify Cause ‚ 79.44 ‚ 9.07 ‚ 7.96 ‚ 2.59 ‚ 0.37 ‚ 0.37 ‚ 0.00 ‚ 0.19 ‚
‚ 2.20 ‚ 1.98 ‚ 1.78 ‚ 1.75 ‚ 1.32 ‚ 2.30 ‚ 0.00 ‚ 2.86 ‚
4650 Renal Fail ‚ 418 ‚ 40 ‚ 43 ‚ 9 ‚ 3 ‚ 1 ‚ 1 ‚ 1 ‚
516
ure ‚ 1.64 ‚ 0.16 ‚ 0.17 ‚ 0.04 ‚ 0.01 ‚ 0.00 ‚ 0.00 ‚ 0.00 ‚
2.03
‚ 81.01 ‚ 7.75 ‚ 8.33 ‚ 1.74 ‚ 0.58 ‚ 0.19 ‚ 0.19 ‚ 0.19 ‚
‚ 2.15 ‚ 1.62 ‚ 1.78 ‚ 1.12 ‚ 1.97 ‚ 1.15 ‚ 2.13 ‚ 2.86 ‚
Total 19472 2471 2410 802 152 87 47 35
25476
76.43 9.70 9.46 3.15 0.60 0.34 0.18 0.14
100.00

tables ethcat*px_stat;
gender_don $gender.
;
title 'Race by patient status in rank order(order=freq)';
run;

Race by patient status in rank order(order=freq)

The FREQ Procedure

Table of ethcat by px_stat



ethcat(Patints ethnicity)
px_stat(Current status of the patient)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚A Living‚Dead ‚R Retran‚L Lost t‚ Total
‚ ‚ ‚splanted‚o Follow‚
‚ ‚ ‚ ‚ up ‚
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1 White ‚ 34249 ‚ 19472 ‚ 6367 ‚ 5448 ‚ 65536
‚ 39.00 ‚ 22.17 ‚ 7.25 ‚ 6.20 ‚ 74.62
‚ 52.26 ‚ 29.71 ‚ 9.72 ‚ 8.31 ‚
‚ 74.25 ‚ 76.45 ‚ 73.54 ‚ 72.02 ‚
4 Hispanic ‚ 5575 ‚ 2467 ‚ 982 ‚ 1164 ‚ 10188
‚ 6.35 ‚ 2.81 ‚ 1.12 ‚ 1.33 ‚ 11.60
‚ 54.72 ‚ 24.21 ‚ 9.64 ‚ 11.43 ‚
‚ 12.09 ‚ 9.69 ‚ 11.34 ‚ 15.39 ‚
2 Black ‚ 3917 ‚ 2411 ‚ 949 ‚ 533 ‚ 7810
‚ 4.46 ‚ 2.75 ‚ 1.08 ‚ 0.61 ‚ 8.89
‚ 50.15 ‚ 30.87 ‚ 12.15 ‚ 6.82 ‚
‚ 8.49 ‚ 9.47 ‚ 10.96 ‚ 7.05 ‚
5 Asian ‚ 1868 ‚ 801 ‚ 247 ‚ 315 ‚ 3231
‚ 2.13 ‚ 0.91 ‚ 0.28 ‚ 0.36 ‚ 3.68
‚ 57.81 ‚ 24.79 ‚ 7.64 ‚ 9.75 ‚
‚ 4.05 ‚ 3.14 ‚ 2.85 ‚ 4.16 ‚
Total 46128 25470 8658 7565 87821
52.53 29.00 9.86 8.61 100.00

(Continued)
Race by patient status in rank order(order=freq)

The FREQ Procedure

Table of ethcat by px_stat

ethcat(Patints ethnicity)
px_stat(Current status of the patient)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚A Living‚Dead ‚R Retran‚L Lost t‚ Total
‚ ‚ ‚splanted‚o Follow‚
‚ ‚ ‚ ‚ up ‚
6 Amer Ind/Alask ‚ 224 ‚ 151 ‚ 47 ‚ 37 ‚ 459
a Native ‚ 0.26 ‚ 0.17 ‚ 0.05 ‚ 0.04 ‚ 0.52
‚ 48.80 ‚ 32.90 ‚ 10.24 ‚ 8.06 ‚
‚ 0.49 ‚ 0.59 ‚ 0.54 ‚ 0.49 ‚



9 Multiracial ‚ 198 ‚ 87 ‚ 47 ‚ 28 ‚ 360
‚ 0.23 ‚ 0.10 ‚ 0.05 ‚ 0.03 ‚ 0.41
‚ 55.00 ‚ 24.17 ‚ 13.06 ‚ 7.78 ‚
‚ 0.43 ‚ 0.34 ‚ 0.54 ‚ 0.37 ‚
7 Native Hawaiia ‚ 80 ‚ 46 ‚ 10 ‚ 14 ‚ 150
n/other Pacific ‚ 0.09 ‚ 0.05 ‚ 0.01 ‚ 0.02 ‚ 0.17
Islander ‚ 53.33 ‚ 30.67 ‚ 6.67 ‚ 9.33 ‚
‚ 0.17 ‚ 0.18 ‚ 0.12 ‚ 0.19 ‚
998 Unknown ‚ 17 ‚ 35 ‚ 9 ‚ 26 ‚ 87
‚ 0.02 ‚ 0.04 ‚ 0.01 ‚ 0.03 ‚ 0.10
‚ 19.54 ‚ 40.23 ‚ 10.34 ‚ 29.89 ‚
‚ 0.04 ‚ 0.14 ‚ 0.10 ‚ 0.34 ‚
Total 46128 25470 8658 7565 87821
52.53 29.00 9.86 8.61 100.00


proc freq data=liver;
tables gender*gender_don;
gender_don $gender.
;
title 'patient gender by donor gender)';
run;

Below is PROC FREQ output for exercise 4.1-6.

patient gender by doner gender)

The FREQ Procedure

Table of gender by gender_don

gender(Gender of patient)
gender_don(Doners gender)

Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚Female ‚Male ‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Female ‚ 16150 ‚ 18469 ‚ 34619
‚ 18.22 ‚ 20.84 ‚ 39.06
‚ 46.65 ‚ 53.35 ‚
‚ 45.51 ‚ 34.76 ‚
Male ‚ 19336 ‚ 34665 ‚ 54001



‚ 21.82 ‚ 39.12 ‚ 60.94
‚ 35.81 ‚ 64.19 ‚
‚ 54.49 ‚ 65.24 ‚
Total 35486 53134 88620
40.04 59.96 100.00




2. SAS Code for OPTN/UNOS Liver Indicator and Truth
Logic Variables
unos02d1.sas
/****unos02d.sas**********/
data liver;
set unos.liver;
/*************************************************/
/*Indicator variables were previously identified */
/*as dummy variables equal to 1 or 0 when they */
/*existed or did not exist in an observation. */
/*They are used in statistical analysis of data. */
/*************************************************/

/****Indicator Variable for Gender*****/

male = (gender='M');
female = (gender='F');

/******************************************************/
/*Truth Logic is used to create a continuous variable */
/*that corresponds to the values (1-3, 1-5, etc.) */
/*assigned to a variable in an observation and */
/*is used in regression and logistic analysis. */
/******************************************************/

/*****Truth Logic for Patient Gender*************/

patgendercat= 1*(gender='M') + 2*(gender='F');

/****Indicator variable for donor gender*****/

maledon = (gender_don='M');
femaledon = (gender_don='F');

/*****Truth Logic for donor gender*************/

dongendercat= 1*(gender_don='M') + 2*(gender_don='F');

/****Indicator Variable for Race*****/

white = (ethcat=1);
black = (ethcat=2);
hispanic = (ethcat=4);



asian = (ethcat=5);
ameralaska = (ethcat=6);
hawaiianpi = (ethcat=7);
multirace = (ethcat=9);
raceunknown = (ethcat=998);

/*****Truth Logic For Race*************/

racecat = 1*(ethcat=1) + 2*(ethcat=2) + 3*(ethcat=4)+
4*(ethcat=5) + 5*(ethcat=6) + 6*(ethcat=7)+
7*(ethcat=9) + 8*(ethcat=998);
/*****Truth Logic For Donor Race*************/

racecat = 1*(ethcat_don=1) + 2*(ethcat_don=2) + 3*(ethcat_don=4)+
4*(ethcat_don=5) + 5*(ethcat_don=6) + 6*(ethcat_don=7)+
7*(ethcat_don=9) + 8*(ethcat_don=998);

/****Indicator Variable for Donor Race*****/

donwhite = (ethcat_don=1);
donblack = (ethcat_don=2);
donhispanic = (ethcat_don=4);
donasian = (ethcat_don=5);
donameralaska = (ethcat_don=6);
donhawaiianpi = (ethcat_don=7);
donmultirace = (ethcat_don=9);
donraceunknown = (ethcat_don=998);

/*****Truth Logic For Patient Race*************/

donracecat = 1*(ethcat_don=1) + 2*(ethcat_don=2) +
3*(ethcat_don=4)+ 4*(ethcat_don=5) +
5*(ethcat_don=6) + 6*(ethcat_don=7)+
7*(ethcat_don=9) + 8*(ethcat_don=998);

/****Indicator Variable for Patient Status*****/

living = (px_stat='A');
deceased = (px_stat='D');
lostfollowup = (px_stat='L');
notseen = (px_stat='N');
retrans = (px_stat='R');

/*****Truth Logic For Patient Status************/



patstatcat= 1*(px_stat='A') + 2*(px_stat='D') + 3*(px_stat='L')+
4*(px_stat='N') + 5*(px_stat='R');

/****Indicator Variable for Doner Blood Type*****/

donA =(abo_don='A');
donA1 =(abo_don='A1');
donA1B =(abo_don='A1B');
donA2 =(abo_don='A2');
donA2B =(abo_don='A2B');
donAB =(abo_don='AB');
donB =(abo_don='B');
donO =(abo_don='O');
donunk =(abo_don='UNK');

/*****Truth Logic For Doner Blood Type************/

abodoncat= 1*(abo_don='A') + 2*(abo_don='A1') +
3*(abo_don='A1B') + 4*(abo_don='A2') +
5*(abo_don='A2B') + 6*(abo_don='AB') +
7*(abo_don='B') + 8*(abo_don='O') +
9*(abo_don='UNK');

/****Indicator Variable for Patient Blood Type*****/

patA = (abo='A');
patA1 = (abo='A1');
patA1B = (abo='A1B');
patA2 = (abo='A2');
patA2B = (abo='A2B');
patAB = (abo='AB');
patB = (abo='B');
patO = (abo='O');
patunk = (abo='UNK');

/*****Truth Logic For Patient Blood Type************/

abopatcat = 1*(abo='A') + 2*(abo='A1') + 3*(abo='A1B') +
4*(abo='A2') + 5*(abo='A2B') + 6*(abo='AB') +
7*(abo='B') + 8*(abo='O') + 9*(abo='UNK');

/****Indicator Variable for Graph Status*****/

graftnoreport = grf_stat='.';
graftfailed = grf_stat='N';
graftunknown = grf_stat='U';
graftworking = grf_stat='Y';



/*****Truth Logic For Patient Graph Status ************/

graftcat = 1*(grf_stat='.') + 2*(grf_stat='N') +
3*(grf_stat='U') + 4*(grf_stat='Y');

/****Indicator Variable for Donor Type*****/

deceased_donor = (don_ty='C');
foreign_donor = (don_ty='F');
living_donor = (don_ty='L');


dontypecat = 1*(don_ty='C') + 2*(don_ty='F') +3*(don_ty='L');

/****Indicator Variable for Previous Transplant*****/

yes_previous = (prev_tx='Y');
no_previous = (prev_tx='N');

previoustxcat= 1*(prev_tx='Y') + 2*(prev_tx='N');

/****Indicator Variable for Age Groupings*****/

agelt1 = (age < 1);
age1_5 = (1=<age<=5);
age6_10 = ( 6=<age<=10);
age11_17 = (11=<age<=17);
age18_34 = (18=<age<=34);
age35_49 = (35=<age<=49);
age50_64 = (50=<age<=64);
agegt65 = (age >65);

/*****Truth Logic for Patient Age Gouping ************/

agecat = 1*(age < 1) + 2*(1=<age<=5) + 3*(6=<age<=10) +
4*(11=<age<=17)+ 5*(18=<age<=34) + 6*(35=<age<=49) +
7*(50=<age<=64)+ 8*(age >=65);

proc means n mean sum min max data=liver;
var male female patgendercat maledon femaledon
dongendercat white black hispanic asian
ameralaska hawaiianpi multirace raceunknown



racecat donwhite donblack donhispanic donasian
donameralaska donhawaiianpi donmultirace
donraceunknown donracecat living deceased
lostfollowup notseen retrans patstatcat donA donA1
donA1B donA2 donA2B donAB donB donO donunk abodoncat
patA patA1 patA1B patA2 patA2B patAB patB
patO patunk abopatcat graftnoreport
graftfailed graftunknown graftworking graftcat
deceased_donor foreign_donor living_donor dontypecat
yes_previous no_previous previoustxcat agelt1 age1_5
age6_10 age11_17 age18_34 age35_49 age50_64 agegt65
agecat
;
run;
title 'Proc Means for Liver Tx Indicators and Truth Logic Variables';

Below is the output from the above PROC Means.
Proc Means for Liver Tx Indicators and Truth Logic Variables

The MEANS Procedure

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
male 88636 0.6093122 54007.00 0 1.0000000
female 88636 0.3906878 34629.00 0 1.0000000
patgendercat 88636 1.3906878 123265.00 1.0000000 2.0000000
maledon 88636 0.5994630 53134.00 0 1.0000000
femaledon 88636 0.4003565 35486.00 0 1.0000000
dongendercat 88636 1.4001760 124106.00 0 2.0000000
white 88636 0.7457805 66103.00 0 1.0000000
black 88636 0.0889255 7882.00 0 1.0000000
hispanic 88636 0.1163748 10315.00 0 1.0000000
asian 88636 0.0369150 3272.00 0 1.0000000
ameralaska 88636 0.0052123 462.0000000 0 1.0000000
hawaiianpi 88636 0.0017149 152.0000000 0 1.0000000
multirace 88636 0.0040954 363.0000000 0 1.0000000
raceunknown 88636 0.000981542 87.0000000 0 1.0000000
racecat 88636 1.4622050 129604.00 1.0000000 8.0000000
donwhite 88636 0.7381876 65430.00 0 1.0000000
donblack 88636 0.1242385 11012.00 0 1.0000000
donhispanic 88636 0.1071799 9500.00 0 1.0000000
donasian 88636 0.0177806 1576.00 0 1.0000000
donameralaska 88636 0.0030236 268.0000000 0 1.0000000
donhawaiianpi 88636 0.0020985 186.0000000 0 1.0000000
donmultirace 88636 0.0047610 422.0000000 0 1.0000000
donraceunknown 88636 0.0027303 242.0000000 0 1.0000000
donracecat 88636 1.4622050 129604.00 1.0000000 8.0000000
living 88636 0.5204206 46128.00 0 1.0000000
deceased 88636 0.2873550 25470.00 0 1.0000000
lostfollowup 88636 0.0853491 7565.00 0 1.0000000
notseen 88636 0 0 0 0
retrans 88636 0.0976804 8658.00 0 1.0000000



patstatcat 88636 1.8395799 163053.00 0 5.0000000
donA 88636 0.1931382 17119.00 0 1.0000000
donA1 88636 0.1638950 14527.00 0 1.0000000
donA1B 88636 0.0030462 270.0000000 0 1.0000000
donA2 88636 0.0129180 1145.00 0 1.0000000
donA2B 88636 0.000913850 81.0000000 0 1.0000000
donAB 88636 0.0246401 2184.00 0 1.0000000
donB 88636 0.1076989 9546.00 0 1.0000000
donO 88636 0.4932307 43718.00 0 1.0000000
donunk 88636 0.000225642 20.0000000 0 1.0000000
abodoncat 88636 5.4359177 481818.00 0 9.0000000
patA 88636 0.3874159 34339.00 0 1.0000000
patA1 88636 0.0025385 225.0000000 0 1.0000000
patA1B 88636 0.000135385 12.0000000 0 1.0000000
patA2 88636 0.000518976 46.0000000 0 1.0000000
patA2B 88636 0.000135385 12.0000000 0 1.0000000
patAB 88636 0.0476781 4226.00 0 1.0000000
patB 88636 0.1273298 11286.00 0 1.0000000
patO 88636 0.4342254 38488.00 0 1.0000000
Proc Means for Liver Tx Indicators and Truth Logic Variables

The MEANS Procedure

patunk 88636 0.000022564 2.0000000 0 1.0000000
abopatcat 88636 5.0470351 447349.00 1.0000000 9.0000000
graftnoreport 88636 0 0 0 0
graftfailed 88636 0.1921341 17030.00 0 1.0000000
graftunknown 88636 0.0079990 709.0000000 0 1.0000000
graftworking 88636 0.7122275 63129.00 0 1.0000000
graftcat 88636 3.2571754 288703.00 0 4.0000000
deceased_donor 88636 0.9579629 84910.00 0 1.0000000
foreign_donor 88636 0.0022113 196.0000000 0 1.0000000
living_donor 88636 0.0398258 3530.00 0 1.0000000
dontypecat 88636 1.0818629 95892.00 1.0000000 3.0000000
yes_previous 88636 0.1145810 10156.00 0 1.0000000
no_previous 88636 0.8854190 78480.00 0 1.0000000
previoustxcat 88636 1.8854190 167116.00 1.0000000 2.0000000
agelt1 88636 0.0346360 3070.00 0 1.0000000
age1_5 88636 0.0457715 4057.00 0 1.0000000
age6_10 88636 0.0170698 1513.00 0 1.0000000
age11_17 88636 0.0256216 2271.00 0 1.0000000
age18_34 88636 0.0753418 6678.00 0 1.0000000
age35_49 88636 0.3057900 27104.00 0 1.0000000
age50_64 88636 0.4248387 37656.00 0 1.0000000
agegt65 88636 0.0548874 4865.00 0 1.0000000
agecat 88636 6.0326391 534709.00 1.0000000 8.0000000

The method to validate your indicator variables consists of confirming that each has a minimum
value of zero and a maximum value of one. If this does not exist, check your code. For truth
logic variables, the minimum is usually 1 and the maximum equals the total number of values
assigned to the variable. Again, if this does not occur, check your logic code. For example,
previous transplant (yes_previous) has a minimum of 0 and a maximum of 1 while agecat has a
minimum of 1 and a maximum of 8 that corresponds to the number of age categories.


The N=88,636 is the total observation of liver transplants in the data set and for each variable
reflects the completeness of the data. In the above case with the exception of patient status=
notseen and graft status= noreport, there are no missing values in any observation. The mean
value is the percentage of each variable within a category. For those age50_64, they were 42.4
percent of the liver transplants or 37,656 between 1987 and 2008.



Exercises 4.2

1. Using unos02d, write the indicator and truth logic code for the donor age grouping using the
same ranges for patient age.
2. Using unos02d, write the indicator and truth logic code for the end status using Meld/Peld
scores of (-11 to -1), (0 to 10), (11 to 20), (21 to 30), (31 to 40), (41 to 50) and (51 to 99).
3. Using unos02d, write the indicator and truth logic code for the end status using Status Scores
of 1, 1A, 1B, 2A, 2B, and 3.
4. Using unos02d, write the indicator and truth logic code for Cold Ischemic time with the ranges
of 0-5, 6-10, 11-16, 16-20, and 21.
5. Insert into a separate proc means the above variables plus all of the continuous variables such
as patient height, weight, age, donor height, weight, age, patient sgot, creatinine, graft and
patient survival time.
6. Convert days to years for graft and patient survival time and produce year from transplant
date.
7. From the proc means output, prepare a descriptive statistics narrative of the findings.

Below is SAS code for exercise 4.2-1 to 5
data liver;
set unos.liver;
/****Indicator Variable for Donor Age Groupings*****/

donagelt1 = (age_don < 1);
donage1_5 = (1=<age_don<=5);
donage6_10 = ( 6=<age_don<=10);
donage11_17 = (11=<age_don<=17);
donage18_34 = (18=<age_don<=34);
donage35_49 = (35=<age_don<=49);
donage50_64 = (50=<age_don<=64);
donagegt65 = (age_don >65);

/*****Truth Logic for Patient Age Gouping ************/

donoragecat = 1*(age_don < 1) + 2*(1=<age_don<=5) +
3*(6=<age_don<=10) + 4*(11=<age_don<=17)+ 5*(18=<age_don<=34) +
6*(35=<age_don<=49)+
7*(50=<age_don<=64)+ 8*(age_don >=65);



/*Answers to exercise 4.2-2*/
/*2. Using unos02d, write the indicator and truth logic code for the
/*end status using Meld/Peld scores of (-11 to -1), (0 to 10), (11 to
20), (21 to 30), (31 to 40), (41 to 50) and (51 to 99).*/

/****Indicator Variable for Meld/Peld Status Groupings*****/

meld_peld_neg11_neg1 = (6189=<end_stat<=6199);
meld_peld_pos0_10 = (6200=<end_stat<=6210);

/*****Truth Logic for Patient Meldpelcat ************/

meldpelcat = 1*(6189=<end_stat<=6199) + 2*(6200=<end_stat<=6210) +
3*(6211=<end_stat<=6220) + 4*(6221=<end_stat<=6230) +
5*(6231=<end_stat<=6240) + 6*(6241=<end_stat<=6250) +
7*(6251=<end_stat<=6299);

/*Answers to exercise 4.2-3*/
/*3. Using unos02d, write the indicator and truth logic code for the
end status */
/*using Status Scores of 1, 1A, 1B, 2A, 2B, and 3.*/

/****Indicator Variable for Non-Meld/Peld Status Groupings*****/

statold2 = (end_stat=6002);
statold4 = (end_stat=6004);
statscore1 = (end_stat=6010);
statscore1A = (end_stat=6011);
statscore1B = (end_stat=6012);
statscore2A = (end_stat=6020);
statscore2B = (end_stat=6030);
statscore3 = (end_stat=6040);

/*****Truth Logic for Patient Non-Meld/Peld Status Groupings****/

oldstatuscat = 1*(end_stat=6002) + 2*(end_stat=6004) +
3*(end_stat=6010) + 4*(end_stat=6011)+
5*(end_stat=6012) + 6*(end_stat=6020)+
7*(end_stat=6030) + 8*(end_stat=6040);



/*4. Using unos02d, write the indicator and truth logic code for Cold
Ischemic time in hours the with the ranges of 0-5, 6-10, 11-16, 16-20,
and 21+. */

/****Indicator Variable for Liver Cold Ischemic in Hours Prior to
Transplant***/

cold0_5 = (0=<cold_isch<=5);
cold6_10 = (6=<cold_isch<=10);
cold11_16 = (11=<cold_isch<=16);
cold17_20 = (17=<cold_isch<=20);
coldGE21 = (cold_isch>=21);

/****Truth Logic Variable for Liver Cold Ischemic in Hours Prior to
Transplant***/

cold_ischcat = 1*(0=<cold_isch<=5) + 2*(6=<cold_isch<=10) +
3*(11=<cold_isch<=16) + 4*(17=<cold_isch<=20)+
5*(cold_isch>=21);

;

/*5. Insert into a separate proc means the above variables plus all of
the continuous variables, such as patient height, weight, age, donor
height weight, age, patient sgot, creatinine, graft survival time,
and patient survival time.*/

var donagelt1 donage1_5 donage6_10 donage11_17
donage18_34 donage35_49 donage50_64 donagegt65
donoragecat meld_peld_neg11_neg1 meld_peld_pos0_10
meld_peld_pos11_20 meld_peld_pos21_30 meld_peld_pos31_40
meld_peld_pos41_50 meld_peld_pos51_99 meldpelcat statold2
statold4 statscore1 statscore1A statscore1B statscore2A
statscore2B statscore3 oldstatuscat cold0_5 cold6_10 cold11_16
cold17_20 coldGE21 cold_ischcat hgt_cm_trr wgt_kg_trr hgt_cm_don
wgt_kg_don sgpt_tx age_don creat_tx tbili_tx age gtime ptime
;
run;
title 'Proc Means for Additional Liver Tx Indicators, Truth Logic
Variables and'
' all Continious Variables';
options nolabel nodate nonumber



Below is the output for exercise 4.2-1 to 5
Proc Means for All and Additional Liver Tx Indicators, Truth Logic Variables and all Continuous
Variables
male 88636 0.6093122 54007.00 0 1.0000000
female 88636 0.3906878 34629.00 0 1.0000000
patgendercat 88636 1.3906878 123265.00 1.0000000 2.0000000
maledon 88636 0.5994630 53134.00 0 1.0000000
femaledon 88636 0.4003565 35486.00 0 1.0000000
dongendercat 88636 1.4001760 124106.00 0 2.0000000
white 88636 0.7457805 66103.00 0 1.0000000
black 88636 0.0889255 7882.00 0 1.0000000
hispanic 88636 0.1163748 10315.00 0 1.0000000
asian 88636 0.0369150 3272.00 0 1.0000000
ameralaska 88636 0.0052123 462.0000000 0 1.0000000
hawaiianpi 88636 0.0017149 152.0000000 0 1.0000000
multirace 88636 0.0040954 363.0000000 0 1.0000000
raceunknown 88636 0.000981542 87.0000000 0 1.0000000
racecat 88636 1.4932872 132359.00 1.0000000 8.0000000
living 88636 0.5204206 46128.00 0 1.0000000
deceased 88636 0.2873550 25470.00 0 1.0000000
lostfollowup 88636 0.0853491 7565.00 0 1.0000000
notseen 88636 0 0 0 0
retrans 88636 0.0976804 8658.00 0 1.0000000
patstatcat 88636 1.8395799 163053.00 0 5.0000000
donA 88636 0.1931382 17119.00 0 1.0000000
donA1 88636 0.1638950 14527.00 0 1.0000000
donA1B 88636 0.0030462 270.0000000 0 1.0000000
donA2 88636 0.0129180 1145.00 0 1.0000000
donA2B 88636 0.000913850 81.0000000 0 1.0000000
donAB 88636 0.0246401 2184.00 0 1.0000000
donB 88636 0.1076989 9546.00 0 1.0000000
donO 88636 0.4932307 43718.00 0 1.0000000
donunk 88636 0.000225642 20.0000000 0 1.0000000
abodoncat 88636 5.4359177 481818.00 0 9.0000000
patA 88636 0.3874159 34339.00 0 1.0000000
patA1 88636 0.0025385 225.0000000 0 1.0000000
patA1B 88636 0.000135385 12.0000000 0 1.0000000
patA2 88636 0.000518976 46.0000000 0 1.0000000
patA2B 88636 0.000135385 12.0000000 0 1.0000000
patAB 88636 0.0476781 4226.00 0 1.0000000
patB 88636 0.1273298 11286.00 0 1.0000000
patO 88636 0.4342254 38488.00 0 1.0000000
patunk 88636 0.000022564 2.0000000 0 1.0000000
abopatcat 88636 5.0470351 447349.00 1.0000000 9.0000000
graftnoreport 88636 0 0 0 0
graftfailed 88636 0.1921341 17030.00 0 1.0000000
graftunknown 88636 0.0079990 709.0000000 0 1.0000000
graftworking 88636 0.7122275 63129.00 0 1.0000000
graftcat 88636 3.2571754 288703.00 0 4.0000000
deceased_donor 88636 0.9579629 84910.00 0 1.0000000
foreign_donor 88636 0.0022113 196.0000000 0 1.0000000
living_donor 88636 0.0398258 3530.00 0 1.0000000
dontypecat 88636 1.0818629 95892.00 1.0000000 3.0000000
yes_previous 88636 0.1145810 10156.00 0 1.0000000



no_previous 88636 0.8854190 78480.00 0 1.0000000
previoustxcat 88636 1.8854190 167116.00 1.0000000 2.0000000
agelt1 88636 0.0346360 3070.00 0 1.0000000
age1_5 88636 0.0457715 4057.00 0 1.0000000
age6_10 88636 0.0170698 1513.00 0 1.0000000
age11_17 88636 0.0256216 2271.00 0 1.0000000
age18_34 88636 0.0753418 6678.00 0 1.0000000
age35_49 88636 0.3057900 27104.00 0 1.0000000
age50_64 88636 0.4248387 37656.00 0 1.0000000
agegt65 88636 0.0548874 4865.00 0 1.0000000
agecat 88636 6.0326391 534709.00 1.0000000 8.0000000
donagelt1 88636 0.0165734 1469.00 0 1.0000000
donage1_5 88636 0.0369150 3272.00 0 1.0000000
donage6_10 88636 0.0282729 2506.00 0 1.0000000
donage11_17 88636 0.1119184 9920.00 0 1.0000000
donage18_34 88636 0.3227808 28610.00 0 1.0000000
donage35_49 88636 0.2437948 21609.00 0 1.0000000
donage50_64 88636 0.1797802 15935.00 0 1.0000000
donagegt65 88636 0.0530484 4702.00 0 1.0000000
donoragecat 88636 5.4377454 481980.00 1.0000000 8.0000000
meld_peld_neg11_neg1 88636 0.0017262 153.0000000 0 1.0000000
meld_peld_pos0_10 88636 0.0180401 1599.00 0 1.0000000
meld_peld_pos11_20 88636 0.1084210 9610.00 0 1.0000000
meld_peld_pos21_30 88636 0.1763053 15627.00 0 1.0000000
meld_peld_pos31_40 88636 0.0665982 5903.00 0 1.0000000
meld_peld_pos41_50 88636 0.0013313 118.0000000 0 1.0000000
meld_peld_pos51_99 88636 0.000248206 22.0000000 0 1.0000000
meldpelcat 88636 1.4110068 125066.00 0 7.0000000
statold2 88636 0.0844239 7483.00 0 1.0000000
statold4 88636 0.0161672 1433.00 0 1.0000000
statscore1 88636 0.1254005 11115.00 0 1.0000000
statscore1A 88636 0.0104585 927.0000000 0 1.0000000
statscore1B 88636 0.0016021 142.0000000 0 1.0000000
statscore2A 88636 0.0473735 4199.00 0 1.0000000
statscore2B 88636 0.1329708 11786.00 0 1.0000000
statscore3 88636 0.1712284 15177.00 0 1.0000000
oldstatuscat 88636 3.1276682 277224.00 0 8.0000000
cold0_5 88636 0.1622591 14382.00 0 1.0000000
cold6_10 88636 0.4278059 37919.00 0 1.0000000
cold11_16 88636 0.1670089 14803.00 0 1.0000000
cold17_20 88636 0.0196196 1739.00 0 1.0000000
coldGE21 88636 0.0167652 1486.00 0 1.0000000
cold_ischcat 88636 1.5973758 141585.00 0 4.0000000
HGT_CM_TRR 79064 163.4329425 12921662.16 4.0000000 225.0000000
WGT_KG_TRR 82104 73.4018192 6026582.97 1.3608000 200.0000000
HGT_CM_DON 75411 165.2848126 12464293.00 1.0000000 251.0000000
WGT_KG_DON 81778 71.0079675 5806889.57 0.4535924 453.1387776
SGPT_TX 59219 224.2110228 13277552.56 0.1000000 20000.00
AGE_DON 88552 34.6171967 3065422.00 0 120.0000000
CREAT_TX 87490 1.3690477 119777.98 0 21.0000000
TBILI_TX 86426 8.5326685 737444.41 0 118.9000000
AGE 88636 44.6404621 3956752.00 0 87.0000000
GTIME 87773 1547.89 135862846 0 7327.00
PTIME 87773 1547.84 135858253 0 7327.00




/*6. Convert days to years for graft and patient survival time and
produce year from transplant date.*/

data liver;
set unos.liver;

gtimeyrs=gtime/365.25;
ptimeyrs=ptime/365.25;

txyear=year(tx_date);

var gtimeyrs ptimeyrs txyear;
run;
run;
title 'Proc Means for Additional Liver Years,patent and graft survival
time';
Below is SAS output for exercise 4.3-6.

The MEANS Procedure

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gtimeyrs 87773 4.2378886 371972.20 0 20.0602327
ptimeyrs 87773 4.2377454 371959.62 0 20.0602327
txyear 88636 1999.34 177213242 1987.00 2008.00
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7. From the proc means output, prepare a descriptive statistics narrative of the findings.
Descriptive Statistics Summary
Between 1987 and the first few months of 2008, over this 22 year period, there were 88,636 liver
transplantations performed in the U.S. The demographics above indicated that Male transplant
patients were 60.9%, female transplant patients were 39.0%. Male donors contributed 59.9% of
the organs while female donors 40%. White patients comprised 74.5%, blacks 8.9%, Hispanics
11.6% and the remaining 5% spread across Asian, Native American and Hawaiian, Pacific
Islander and multiple races. White donors equaled 73.8%, blacks 12.4%, Hispanics 10.7%, and
the remaining 3.1% divided across Asian, Native American and Hawaiian, Pacific Islander and
multiple races.
Over this 22-year period, of the 88,636 transplants, 52.0% were living, 28.7% died and 8.5%
were lost for follow-up. Of the transplantations, 9.7% were re-transplanted.
The donor and patient blood were as follows: For patients, 43.4% Type O, 38.7% Type A, 16.4%
Type A1, 12.7% Type B, 4.7% Type AB and the remaining 0.5% distributed among type A1,



A1B, A2, and A2B. For donors, 49.3% type O, 19.3% type A, 16.3% type A1, 10.7% Type B and
the remaining 4.4% distributed among type, A1B, A2, and A2B. For further explanation of blood
types see http://en.wikipeda.org/wki/ABO_blood_group_system.
When follow-up was performed at various times post transplant, 19.2 percent of the liver
transplant grafts had failed, 71.2 were working and 9.6% of the graph status was unknown.
Deceased donors equaled 95.8%, living donors 3.9% and foreign donors 0.2%. Previous
transplantation occurred in 10,156 patients or 11.4%.
Selected patient age groupings in rank order were 42.4% from 50 to 64 years, 30.5% from 35 to
49 years 11.1% from11 to 17, 8.1% from 0 to 10, 7.5% from 18 to 34 and 5.4% greater to or
equal to 65. Selected donor age groups in rank order were 32.2% from 18 to 34 years, 24,4%
from 35 to 49 years 17.9% from 50 to 64, and 5.3% greater to or equal to 65.
The Meld/Peld liver disease severity score used beginning in 2002 to determine the likelihood
of death of the patient prior to transplant in rank order was 17.6%, 21 to 30; 10.8%, 11 to 20;
6.6% 31 to 40. Prior to 2002 the status of the transplant patient measuring severity in rank order
was 17.1%, Score 3; 13.3%, Score 2B; 12.5%, Score 1; 8.8%, Old Status; 2 4.7%, Score 2A.
The Meld/Peld and all of the status scores are mutually exclusive and should add up to 100%.
The time the liver is preserved cold after it is harvested from a donor is known as cold ischemic
time. The cold ischemic time in rank order for the transplantation patients as 42.8% from 6 to 10
hours, 16.7% from 11 to 16 hours, 16.2% from 0 to 5 hours, 1.9% from 17 to 20 hours and 1.6%
greater than or equal to 21 hours.
The mean height of the patient was 163.4 cm or 5.4 feet.. The mean height of the donor was
165.3 cm or 5.5 feet. The low mean height of the donor and patient is attributed to the number of
youngsters who are both patients and donors. The mean weight of the patient prior to
transplantation was 73.4 kg (161 lbs.) and the donor mean weight equaled 71.0 kg. (156.5lbs.).
The mean age of the donor was 34.6 years compared to the patient at 44.6.
The mean transplant patient blood(serum)glutamic-oxaloacetic transaminase(SGOT) test was
224.21. The NIH normal adult range 10 to 34 IU/L. The mean transplant patients Creatinine was
1.36 mg/l with a range of 0 to 21mg/l. The NIH normal ranges are 0.8 to 1.4 mg/l. The mean
transplant patient’s Bilirubin was 8.53 with a range of 0 to 18. The NIH normal high ranges for
male are 0.3 to 1.9 mg/dl. www.nlm.nih.gov/midlineplus.ency/003479.htm#normal
Lastly the mean time before liver graft failing was 1,547 days (4.2 years) with a range of 0 to
7,327 days (20 years). The mean patient survival time was 1547 (4.2 years) days with a range of
0 to 7,327 days (20 years). This does not reflect the improved survival rates that have occurred
since 1987 , which will be measured in a later section. However, the sum of years of life for
these 88,636 patients over this 22-year period is 371,972 years.
In summary of the 88,636 liver transplantations performed between 1987 and 2008, 52.0% were
remained alive. More transplants were males from male donors. Recipients and donors were
more likely to be white. Type O was the dominant blood type for both patients and donors. The
largest age groups for patients were 50 to 64 years and for donors 18 to 34 years. The most likely
Meld/Peld disease severity score was 21 to 30 and the older severity score prior to 2002 was



Score 3. Six to 10 hours was the more common cold ischemic time. The mean height and weight
of patients was 5.4 feet and 161 pounds, respectively. The mean height and weight of donors
was 5.5 feet and 156 pounds, respectively. The clinical chemistry markers indicated the mean
SGOT was 224.2 IU/L, bilirubin was 8.53mg/dl, and creatinine was 1.36mg/l. Lastly, over this
22-year period, the mean number of years before graft and patient death was 4.2 years.



3. Multiple Linear Regression Model on OPTN Liver
Transplant Patient Survival Time in Days (PTIME)
unos03d.sas

Below is the linear regression model (Proc Reg) of the patient survival time in days of care and the
effects of age, gender, race, transplant year, donor source, donor age, Meld/Peld and older
severity score, and cold ischemic time.
/****unos03d.sas**********/
/*Below is the linear regression model (Proc Reg) of the patient */
/*survival time in days of care and the effects of age, gender, */
/*race, transplant year, donor source, donor age, donor race */
/*Meld/Peld and older severity score, and cold ischemic time*/

Proc Reg data=liver;
model ptime=age age_don white donwhite male maledon txyearcat
deceased_donor meld_peld_pos21_30 statscore3 cold6_10;
run;
title 'linear regression model of the patient survival time in days';

linear regression model of the patient survival time in days

The REG Procedure
Model: MODEL1
Dependent Variable: PTIME



Sum of Mean

Model 11 60031556770 5457414252 2946.15 <.0001
Error 87677 1.624117E11 1852386
Corrected Total 87688 2.224432E11

Coeff Var 87.98748

Parameter Estimates



Parameter Standard

Intercept 1 3244.98436 31.49376 103.04 <.0001
AGE 1 0.11743 0.28768 0.41 0.6831
AGE_DON 1 -5.79403 0.28321 -20.46 <.0001
white 1 93.62677 10.82212 8.65 <.0001
donwhite 1 109.95161 10.67399 10.30 <.0001
male 1 -65.45225 9.59968 -6.82 <.0001
maledon 1 32.34375 9.63490 3.36 0.0008
txyearcat 1 -126.55629 1.05015 -120.51 <.0001
deceased_donor 1 -21.46791 23.84214 -0.90 0.3679
meld_peld_pos21_30 1 -150.27926 13.86360 -10.84 <.0001
statscore3 1 381.04028 13.21076 28.84 <.0001
cold6_10 1 59.61080 9.46631 6.30 <.0001

As seen above in the PROC REG models output of patient survival time in days (ptime) for
liver transplants from 1987-2008, with the exception of patient age and donor type, all of the
effects are significant at p<.0001. Controlling for donor and patient age, donor and patient race,
donor and patient gender, year of transplant, type of donor, severity scores and cold ischemic
time the following are the findings:
1. All else being equal, as the age of the donor increases by one year the patient will lose 5.7
days of life. p<.0001
2. All else being equal, white patients will have 93 more days of life compared to non-whites.
p<.0001
3. All else being equal, those obtaining white donor livers will have 109 more days of life
compared to those obtaining non-whites donor livers. p<.0001
4. All else being equal, male liver recipients will have 65 fewer days of life than females.
p<.0001
5. All else being equal, patients receiving male donor hearts will have 32 more days of life
compared to those receiving female donor hearts. p<.001
6. All else being equal, for every year increase in age, a liver recipient will loose 126 days of
life. p<.0001
7. All else being equal, a patient with a meld/peld score of 21 to 30 compared to all other scores
will have 150 fewer days of life. p<.0001
8. All else being equal, those with older severity scores of three (3) will have 381 additional
days of life. p<.0001
9. All else being equal, those with cold ischemic times of 6-20 hours compared to longer or
shorter times will have 59.6 additional days of life . p<.0001
10. The coefficient of determination R-Square is 0.2699, which indicates that 73.1 percent of
patient survival time is explained by other effects not included in the model.



Exercise 4.3

Using unos03d.sas, perform the exercises below and interpret the findings:
1. Remove patient age and donor type from the model to improve parsimony (simplicity) and
compare it to original model.
2. Substitute patient liver graft failure for patent survival time in the model.
3. Create a model with only patient height and weight and other clinical chemistry measurement.
/*Answer to Exercise 4.3 */

model ptime=age_don white donwhite male maledon txyearcat
meld_peld_pos21_30 statscore3 cold6_10;
run;
title 'linear regression with age and donor type removed from'
' model of the patient survival time in days';

Proc Reg output for exercise 4.3.
Linear regression model of the patient survival time in days

The REG Procedure
Model: MODEL1



Sum of Mean

Model 9 60029892715 6669988079 3600.80 <.0001
Error 87679 1.624133E11 1852363

Coeff Var 87.98692

Parameter Estimates



Parameter Standard

Intercept 1 3226.07993 20.81544 154.98 <.0001
AGE_DON 1 -5.75274 0.26082 -22.06 <.0001
white 1 94.35660 10.71380 8.81 <.0001
donwhite 1 110.17522 10.67101 10.32 <.0001
male 1 -65.52955 9.59093 -6.83 <.0001
maledon 1 32.32149 9.59802 3.37 0.0008
txyearcat 1 -126.39437 1.03600 -122.00 <.0001
meld_peld_pos21_30 1 -151.03028 13.74371 -10.99 <.0001
statscore3 1 381.71376 13.18509 28.95 <.0001
cold6_10 1 58.12153 9.31513 6.24 <.0001

Comparing the first model with the effects of age and donor type, there is no significant differences
observed between this model with of age and donor type removed.

model gtime=age_don white donwhite male maledon txyearcat
meld_peld_pos21_30 statscore3 cold6_10;
run;
title 'linear regression model of the graph survival time'

linear regression model of the graph survival time'

The REG Procedure
Model: MODEL1
Dependent Variable: GTIME



Sum of Mean

Model 9 60025383060 6669487007 3600.70 <.0001
Error 87679 1.624054E11 1852273

Coeff Var 87.98181

Parameter Estimates



Parameter Standard

Intercept 1 3226.12595 20.81493 154.99 <.0001
AGE_DON 1 -5.75297 0.26082 -22.06 <.0001
white 1 94.33729 10.71354 8.81 <.0001
donwhite 1 110.16816 10.67075 10.32 <.0001
male 1 -65.49751 9.59069 -6.83 <.0001
maledon 1 32.26677 9.59779 3.36 0.0008
txyearcat 1 -126.38813 1.03597 -122.00 <.0001
meld_peld_pos21_30 1 -151.09602 13.74337 -10.99 <.0001
statscore3 1 381.67376 13.18477 28.95 <.0001
cold6_10 1 58.08216 9.31490 6.24 <.0001

There appears to be no difference between the model of graph survival time and patient survival
time. A look at the raw OPTN/UNOS data (unos.liver) shows these values are the same for each
observation.

3. Create a model with only patient height and weight and other clinical chemistry measurement.

model ptime=sgpt_tx creat_tx tbili_tx hgt_cm_trr
wgt_kg_trr hgt_cm_don wgt_kg_don
;
title 'linear regression model of graph survival time with height
weight and chemistry';
run;

Proc Reg Output for exercise 4.3
linear regression model of graph survival time with height weight and chemistry

The REG Procedure
Model: MODEL1



Sum of Mean

Model 7 1278220766 182602967 110.98 <.0001
Error 42241 69501466235 1645356



Coeff Var 100.15787

Parameter Estimates

Parameter Standard

Intercept 1 1369.28647 53.28463 25.70 <.0001
SGPT_TX 1 -0.00145 0.00903 -0.16 0.8721
CREAT_TX 1 -74.76085 5.04766 -14.81 <.0001
TBILI_TX 1 -2.87170 0.60351 -4.76 <.0001
HGT_CM_TRR 1 2.05532 0.39878 5.15 <.0001
WGT_KG_TRR 1 -3.48845 0.36902 -9.45 <.0001
HGT_CM_DON 1 2.11001 0.41087 5.14 <.0001
WGT_KG_DON 1 -5.33215 0.38082 -14.00 <.0001

1. All else being equal, SGPT values of the patient before transplant alone are not significant
predictors of patient survival time in days.. p=.8721.
2. All else being equal, every unit increase in creatinine level before transplant will result in
74.7 fewer days of life. p<.0001.
3. All else being equal, every unit increase in bilirubin level before transplant will result in 2.87
fewer days of life. p<.0001.
4. All else being equal, each centimeter increase in height of the transplant patient will result in
2.05 additional days of life. p<.0001.
5. All else being equal, every kilogram increase in weight of the transplant patient will result in
3.48 fewer days of life. p<.0001.
6. All else being equal, each centimeter increase in height of the liver donor will result in 2.11
additional days of life. p<.0001.
7. All else being equal, every kilogram increase in weight of the liver donor will result in 5.33
fewer days of life. p<.0001.



4. Logistic Regression Model of Patient of Liver Transplant
Patient Death
unos04d.sas

Below is the logistic regression model of death following a liver transplantation using the effects
of patient age, donor age and gender, and patient race.
/*unos04d.sas*/
proc logistic data=liver des;
class patgendercat (param=ref ref='1') /*ref patient female**/
dongendercat (param=ref ref='1') /*ref donor female**/
racecat (param=ref ref='1') /*ref patient race white*/

;
model deceased=age age_don patgendercat dongendercat racecat

;

units age=10 age_don=10;
title 'logistic regression model of the patient status of death';
run;
quit;
;

Below is the logistic regression model of death output.
Logistic regression model of the patient status of death
Model Information

Data Set WORK.LIVER
Response Variable deceased
Model binary logit

Response Profile

Ordered Total
Value deceased Frequency

1 1 25450
2 0 63102

Probability modeled is deceased=1.



NOTE: 84 observations were deleted due to missing values for the response or explanatory
variables.



patgendercat 1 0
2 1

dongendercat 1 0
2 1

racecat 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 0
3 0 1 0 0 0 0 0
4 0 0 1 0 0 0 0
5 0 0 0 1 0 0 0
6 0 0 0 0 1 0 0
7 0 0 0 0 0 1 0
8 0 0 0 0 0 0 1


logistic regression model of the patient status of death



Intercept
Intercept and

AIC 106230.54 105454.56
SC 106239.93 105567.25
-2 Log L 106228.54 105430.56



Score 769.8822 11 <.0001
Wald 759.6023 11 <.0001




Wald

AGE 1 589.2217 <.0001
AGE_DON 1 0.2799 0.5968
patgendercat 1 19.9454 <.0001
dongendercat 1 0.0303 0.8617
racecat 7 69.1360 <.0001


Standard Wald

Intercept 1 -1.4176 0.0252 3175.6624 <.0001
AGE 1 0.0115 0.000475 589.2217 <.0001
AGE_DON 1 -0.00024 0.000451 0.2799 0.5968
patgendercat 2 1 0.0691 0.0155 19.9454 <.0001
dongendercat 2 1 -0.00272 0.0156 0.0303 0.8617
racecat 2 1 -0.1078 0.0233 21.4977 <.0001
racecat 3 1 -0.1593 0.0251 40.2186 <.0001
racecat 4 1 -0.0818 0.0570 2.0564 0.1516
racecat 5 1 -0.3244 0.1456 4.9646 0.0259
racecat 6 1 0.2508 0.1547 2.6294 0.1049
racecat 7 1 -0.2207 0.1142 3.7342 0.0533
racecat 8 1 0.2358 0.1485 2.5221 0.1123



Point 95% Wald

AGE 1.012 1.011 1.013
AGE_DON 1.000 0.999 1.001
patgendercat 2 vs 1 1.072 1.040 1.105
dongendercat 2 vs 1 0.997 0.967 1.028
racecat 2 vs 1 0.898 0.858 0.940
racecat 3 vs 1 0.853 0.812 0.896
racecat 4 vs 1 0.921 0.824 1.030
racecat 5 vs 1 0.723 0.543 0.962
racecat 6 vs 1 1.285 0.949 1.740
racecat 7 vs 1 0.802 0.641 1.003
racecat 8 vs 1 1.266 0.946 1.693


Pairs 1605945900 c 0.558



Odds Ratios


AGE 10.0000 1.122
AGE_DON 10.0000 0.998

As seen above in the proc logistic output of death after transplant considering the effects of
patient age, donor age and gender, and patient race, the findings are as follows:
1. All else being equal, for every additional year of age at transplant the likelihood of death
increases by 1.2 percent, p<.0001[CI 1.011, 1.0131].
2. All else being equal, donor age does not have a significant effect upon patient death after
transplant., p=.5968[CI 0.999 , 1.0011].
3. All else being equal, females compared to males are 7.2 percent more likely to die after
transplant, p<.0001[CI 1.040, 1.1050].
4. All else being equal, donor gender does not have a significant effect upon patient death after
transplant., p=.8617[CI 0.967, 1.0280].
5. All else being equal, blacks compared to white are 10.2 less likely to die after transplant,
p<.0001[CI 0.858, 0.940].
6. All else being equal, Hispanics compared to whites are 13.7 less likely to die after transplant,
p<.0001[CI 0.812, 0.896].



Exercises 4.4

1. Add to the logistic model unos04d.sas, the effects of year of transplant on dying using 1996 as
the reference year and interpret the findings.
2. Change the logistic model of exercise 4.4-1 to consider survival rather than death..
Answer to exercise 4.4-1.
/*1. Add to the logistic model unos04d.sas, the effects of year of
transplant using 1966 as*/
/*the reference year and interpret the findings. */
txyearcat (param=ref ref='10') /*ref tx year 1996*/
;
model deceased=age age_don patgendercat dongendercat racecat
txyearcat
;

title 'logistic regression model of the patient status of death';
run;
quit;

Proc logistic output for exercise 4.4-1.



Model Information

Data Set WORK.LIVER
Response Variable deceased
Model binary logit


Response Profile



Ordered Total
Value deceased Frequency

1 1 25450
2 0 63102

Probability modeled is deceased=1.

variables.


Class Value

patgendercat 1
2

dongendercat 1
2

racecat 1
2
3
4
5
6
7
8

txyearcat 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22




Design Variables

0
1

0
1

0 0 0 0 0 0 0
1 0 0 0 0 0 0
0 1 0 0 0 0 0
0 0 1 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1




Intercept
Intercept and

AIC 106230.54 97754.883
SC 106239.93 98064.798
-2 Log L 106228.54 97688.883





Score 7849.3305 32 <.0001
Wald 6924.0794 32 <.0001


Wald

AGE 1 1133.8575 <.0001
AGE_DON 1 197.6462 <.0001
patgendercat 1 2.6282 0.1050
racecat 7 21.4843 0.0031
txyearcat 21 6328.2080 <.0001


Standard Wald

Intercept 1 -1.5634 0.0422 1375.1982 <.0001
AGE 1 0.0171 0.000509 1133.8575 <.0001
AGE_DON 1 0.00678 0.000482 197.6462 <.0001
patgendercat 2 1 -0.0263 0.0162 2.6282 0.1050
dongendercat 2 1 0.0166 0.0164 1.0245 0.3115
racecat 2 1 0.0692 0.0244 8.0469 0.0046
racecat 3 1 0.0761 0.0264 8.2701 0.0040
racecat 4 1 0.0883 0.0596 2.1947 0.1385
racecat 5 1 -0.0562 0.1515 0.1378 0.7105
racecat 6 1 0.3620 0.1600 5.1213 0.0236
racecat 7 1 0.0873 0.1188 0.5404 0.4622
racecat 8 1 -0.0900 0.1513 0.3535 0.5522
txyearcat 1 1 0.9286 0.1188 61.0672 <.0001
txyearcat 2 1 0.6491 0.0600 117.0390 <.0001
txyearcat 3 1 0.7031 0.0549 164.0513 <.0001
txyearcat 4 1 0.5534 0.0515 115.3177 <.0001
txyearcat 5 1 0.5584 0.0501 124.2864 <.0001
txyearcat 6 1 0.4099 0.0496 68.2059 <.0001
txyearcat 7 1 0.3027 0.0482 39.4757 <.0001
txyearcat 8 1 0.1406 0.0478 8.6573 0.0033
txyearcat 9 1 0.0861 0.0469 3.3710 0.0664
txyearcat 11 1 -0.0475 0.0465 1.0412 0.3076
txyearcat 12 1 -0.1645 0.0460 12.7835 0.0003
txyearcat 13 1 -0.2520 0.0457 30.4572 <.0001
txyearcat 14 1 -0.4253 0.0458 86.1805 <.0001
txyearcat 15 1 -0.4780 0.0456 109.8532 <.0001
txyearcat 16 1 -0.6581 0.0462 203.2506 <.0001
txyearcat 17 1 -0.8089 0.0462 306.3459 <.0001
txyearcat 18 1 -0.9278 0.0460 406.9406 <.0001
txyearcat 19 1 -1.1043 0.0466 560.5756 <.0001



txyearcat 20 1 -1.4722 0.0493 892.8329 <.0001
txyearcat 21 1 -2.2320 0.0599 1389.9345 <.0001
txyearcat 22 1 -4.7871 1.0027 22.7937 <.0001


Point 95% Wald

AGE 1.017 1.016 1.018
AGE_DON 1.007 1.006 1.008
patgendercat 2 vs 1 0.974 0.944 1.006
dongendercat 2 vs 1 1.017 0.985 1.050
racecat 2 vs 1 1.072 1.022 1.124
racecat 3 vs 1 1.079 1.025 1.136
racecat 4 vs 1 1.092 0.972 1.228
racecat 5 vs 1 0.945 0.702 1.272
racecat 6 vs 1 1.436 1.050 1.965
racecat 7 vs 1 1.091 0.865 1.377
racecat 8 vs 1 0.914 0.679 1.230
txyearcat 1 vs 10 2.531 2.005 3.195
txyearcat 2 vs 10 1.914 1.701 2.153
txyearcat 3 vs 10 2.020 1.814 2.249
txyearcat 4 vs 10 1.739 1.572 1.924
txyearcat 5 vs 10 1.748 1.584 1.928
txyearcat 6 vs 10 1.507 1.367 1.661
txyearcat 7 vs 10 1.354 1.232 1.488
txyearcat 8 vs 10 1.151 1.048 1.264
txyearcat 9 vs 10 1.090 0.994 1.195
txyearcat 11 vs 10 0.954 0.870 1.045
txyearcat 12 vs 10 0.848 0.775 0.928
txyearcat 13 vs 10 0.777 0.711 0.850
txyearcat 14 vs 10 0.654 0.597 0.715
txyearcat 15 vs 10 0.620 0.567 0.678
txyearcat 16 vs 10 0.518 0.473 0.567
txyearcat 17 vs 10 0.445 0.407 0.488
txyearcat 18 vs 10 0.395 0.361 0.433
txyearcat 19 vs 10 0.331 0.302 0.363
txyearcat 20 vs 10 0.229 0.208 0.253
txyearcat 21 vs 10 0.107 0.095 0.121
txyearcat 22 vs 10 0.008 0.001 0.059


Pairs 1605945900 c 0.692



Odds Ratios




AGE 10.0000 1.187
AGE_DON 10.0000 1.070

As seen above in the proc logistic output of death after transplant considering the effects of
patient age, donor age and gender, patient race and year of transplant , the findings are as
follows:
1. All else being equal, for every additional year of age at transplant the likelihood of death
increases by 1.7 percent, p<.0001[CI 1.016, 1.0181].
2. All else being equal, for every year of the age of the donor at transplant the likelihood of
death increases by 0.7 percent, p<.0001[CI 1.016, 1.0181].
3. All else being equal, both patient and donor gender are insignificant effects upon those likely
to die after transplant,.
4. All else being equal, blacks compared to whites are 7.2 percent more likely to die after
transplant, p<.0.01[CI 1.022, 1.124].
5. All else being equal, Hispanics compared to whites are 7.9 percent more likely to die after
transplant, p<.0.01[CI 1.025, 1.136].
6. All else being equal, patients transplanted in 1989 compared to 1996 were twice (2.020) as
likely to die after transplant, p<.0.0001[CI 1.814, 2.249].
7. All else being equal, patients transplanted in 2006 compared to 1996 were 87.1 percent less
likely to die after transplant, p<.0.0001[CI 0.208, 0.253].

/*2. Change the logistic model of exercise 4.4-1 to consider survival
rather than death*/
/*options nolabel nodate nonumber; */



txyearcat (param=ref ref='10') /*ref tx year 1996*/
;
model living=age age_don patgendercat dongendercat racecat
txyearcat
;

title 'logistic regression model of the patient status of alive';
run;
quit;

Proc logistic output for exercise 4.4-2.
logistic regression model of the patient status of being alive


Model Information

Data Set WORK.LIVER
Response Variable living
Model binary logit


Response Profile

Ordered Total
Value living Frequency

1 1 46093
2 0 42459

Probability modeled is living=1.

variables.


Class Value

patgendercat 1
2



dongendercat 1
2

racecat 1
2
3
4
5
6
7
8

txyearcat 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22


Design Variables

0
1

0
1

0 0 0 0 0 0 0
1 0 0 0 0 0 0
0 1 0 0 0 0 0
0 0 1 0 0 0 0
0 0 0 1 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0



0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1




Intercept
Intercept and

AIC 122611.96 105637.71
SC 122621.36 105947.62
-2 Log L 122609.96 105571.71



Score 15989.6946 32 <.0001
Wald 13860.1677 32 <.0001


Wald

AGE 1 34.8506 <.0001
AGE_DON 1 669.7725 <.0001
patgendercat 1 31.3904 <.0001



racecat 7 162.1487 <.0001
txyearcat 21 13730.8457 <.0001


Standard Wald

Intercept 1 0.00424 0.0396 0.0115 0.9147
AGE 1 -0.00270 0.000458 34.8506 <.0001
AGE_DON 1 -0.0119 0.000458 669.7725 <.0001
patgendercat 2 1 0.0869 0.0155 31.3904 <.0001
dongendercat 2 1 -0.0245 0.0156 2.4775 0.1155
racecat 2 1 -0.2093 0.0229 83.5753 <.0001
racecat 3 1 -0.2162 0.0246 77.4271 <.0001
racecat 4 1 -0.2879 0.0558 26.6660 <.0001
racecat 5 1 0.0578 0.1373 0.1771 0.6739
racecat 6 1 -0.3304 0.1560 4.4862 0.0342
racecat 7 1 -0.1205 0.1076 1.2547 0.2627
racecat 8 1 -0.0383 0.1489 0.0663 0.7968
txyearcat 1 1 -1.6463 0.1726 91.0171 <.0001
txyearcat 2 1 -1.3304 0.0741 322.0483 <.0001
txyearcat 3 1 -1.1792 0.0653 325.7096 <.0001
txyearcat 4 1 -1.0185 0.0590 298.1991 <.0001
txyearcat 5 1 -0.8943 0.0559 255.5065 <.0001
txyearcat 6 1 -0.6650 0.0535 154.7815 <.0001
txyearcat 7 1 -0.4891 0.0504 94.1616 <.0001
txyearcat 8 1 -0.2369 0.0483 24.1021 <.0001
txyearcat 9 1 -0.1437 0.0470 9.3478 0.0022
txyearcat 11 1 0.1959 0.0454 18.5915 <.0001
txyearcat 12 1 0.3626 0.0445 66.4972 <.0001
txyearcat 13 1 0.5233 0.0439 142.2907 <.0001
txyearcat 14 1 0.7127 0.0434 269.1792 <.0001
txyearcat 15 1 0.8313 0.0432 370.4007 <.0001
txyearcat 16 1 1.1341 0.0435 679.9726 <.0001
txyearcat 17 1 1.2992 0.0433 898.7326 <.0001
txyearcat 18 1 1.5013 0.0433 1204.7821 <.0001
txyearcat 19 1 1.6946 0.0436 1508.6824 <.0001
txyearcat 20 1 2.1153 0.0456 2148.5186 <.0001
txyearcat 21 1 2.0269 0.0453 1998.1635 <.0001
txyearcat 22 1 -1.0982 0.2092 27.5634 <.0001


Point 95% Wald

AGE 0.997 0.996 0.998
AGE_DON 0.988 0.987 0.989
patgendercat 2 vs 1 1.091 1.058 1.124
dongendercat 2 vs 1 0.976 0.947 1.006



racecat 2 vs 1 0.811 0.776 0.848
racecat 3 vs 1 0.806 0.768 0.845
racecat 4 vs 1 0.750 0.672 0.836
racecat 5 vs 1 1.059 0.810 1.386
racecat 6 vs 1 0.719 0.529 0.976
racecat 7 vs 1 0.886 0.718 1.095
racecat 8 vs 1 0.962 0.719 1.289
txyearcat 1 vs 10 0.193 0.137 0.270
txyearcat 2 vs 10 0.264 0.229 0.306
txyearcat 3 vs 10 0.308 0.271 0.350
txyearcat 4 vs 10 0.361 0.322 0.405
txyearcat 5 vs 10 0.409 0.366 0.456
txyearcat 6 vs 10 0.514 0.463 0.571
txyearcat 7 vs 10 0.613 0.556 0.677
txyearcat 8 vs 10 0.789 0.718 0.867
txyearcat 9 vs 10 0.866 0.790 0.950
txyearcat 11 vs 10 1.216 1.113 1.330
txyearcat 12 vs 10 1.437 1.317 1.568
txyearcat 13 vs 10 1.688 1.549 1.839
txyearcat 14 vs 10 2.040 1.873 2.221
txyearcat 15 vs 10 2.296 2.110 2.499
txyearcat 16 vs 10 3.108 2.854 3.385
txyearcat 17 vs 10 3.666 3.368 3.991
txyearcat 18 vs 10 4.488 4.123 4.885
txyearcat 19 vs 10 5.445 4.998 5.931
txyearcat 20 vs 10 8.292 7.583 9.068
txyearcat 21 vs 10 7.590 6.945 8.296
txyearcat 22 vs 10 0.333 0.221 0.502


Pairs 1957062687 c 0.745

Odds Ratios


AGE 10.0000 0.973
AGE_DON 10.0000 0.888

As seen above in the proc logistic output of living after transplant considering the effects of
patient age, donor age and gender, patient race and year of transplant , the findings are as
follows:
1. All else being equal, each additional year in patient age at transplantation, the likelihood of
surviving decreases by 0.3 percent. p<.0001[CI 0.996, 0.998]
2. All else being equal, for each year increase in donor age at transplantation, the patient’s
likelihood of being alive decreases by 0.2 percent. p<.0001[CI 0.987, 0.989]


3. All else being equal, female patients compared to males are 9.1 percent more likely of
surviving after transplant. p<.0.001[CI 1.058, 1.1240]
4. All else being equal, patients having female donor compared to male donor has no significant
effect upon survival after transplant.
5. All else being equal, blacks compared to whites are 18.9 percent less likely to survive after
transplant p<.0.001[CI 0.776, 0.848]
6. All else being equal, Hispanics compared to whites are 19.1 percent less likely to survive
after transplant. p<.0.001[CI0.768, 0.845]
7. All else being equal, patients transplanted in the year 1989 compared to 1996 were 69.2
percent less likely to survive. p<.0.0001[CI 0.271, 0.350
8. All else being equal, patients transplanted in 2000 compared to 1996 were twice as likely to
survive. p<.0.0001[CI 1.873, 2.221]



5. Survival Analysis

“Survival analysis is a collection of specialized methods used to analyze data in which time until
an event occurs in the response variable of interest. The response variable, often called in
survival analysis, a failure time, survival time, or event time, and is usually continuous and can
be measured in weeks, months, years etc. . Events can be death, onset of disease, marriages,
arrests, etc.. What is unique about survival analysis is that even if the subject did not experience
the event (death), the subject’s survival time or length of time is taken into account”
Survival Analysis Using the Proportional Hazard Model Course Notes, 2006 SAS institute, Cary
NC, pp 1-3, ISBN 978-1-59994-306.

Below is the SAS code for a Life Test Procedure Survival Analysis.
*unos05d.sas*/

proc lifetest data=liver plots =(s,c,ls, lls) cs=none;
time txyearcat*deceased(0);
strata gender / test=(all);

title 'Liver Transplant Survival Rates';
run;
quit;

Partial Output of the Life Test Procedure of Liver transplants .
The LIFETEST Procedure

Stratum 2: GENDER = M

Product-Limit Survival Estimates

Survival
Standard Number Number
txyearcat Survival Failure Error Failed Left

19.0000* . . . 14551 11867
19.0000* . . . 14551 11866
19.0000* . . . 14551 11865
19.0000* . . . 14551 11864
19.0000* . . . 14551 11863
19.0000* . . . 14551 11862
19.0000* . . . 14551 11861
19.0000* . . . 14551 11860
19.0000* . . . 14551 11859
19.0000* . . . 14551 11858
19.0000* . . . 14551 11857
19.0000* . . . 14551 11856



19.0000* . . . 14551 11855
19.0000* . . . 14551 11854
19.0000* . . . 14551 11853
19.0000* . . . 14551 11852
19.0000* . . . 14551 11851
19.0000* . . . 14551 11850
19.0000* . . . 14551 11849
19.0000* . . . 14551 11848
19.0000* . . . 14551 11847
19.0000* . . . 14551 11846
19.0000* . . . 14551 11845
19.0000* . . . 14551 11844
Liver Transplant Survival Rates


Stratum 2: GENDER = M

NOTE: The marked survival times are censored observations.

Summary Statistics for Time Variable txyearcat

Quartile Estimates

Point 95% Confidence Interval
Percent Estimate Transform [Lower Upper)

75 . LOGLOG . .
50 . LOGLOG . .
25 16.0000 LOGLOG 15.0000 16.0000

Mean Standard Error

17.6839 0.0241

NOTE: The mean survival time and its standard error were underestimated because the largest
observation was censored and the estimation was restricted to the largest event time.

Summary of the Number of Censored and Uncensored Values

Percent
Stratum GENDER Total Failed Censored Censored

1 F 34629 10078 24551 70.90
2 M 54007 15392 38615 71.50
-------------------------------------------------------------
Total 88636 25470 63166 71.26


Testing Homogeneity of Survival Curves for txyearcat over Strata



Rank Statistics

Modified
GENDER Log-Rank Wilcoxon Tarone Peto Peto Fleming

F 797.25 54404932 205342.7 697.88 697.87 712.69
M -797.25 -5.44E7 -205343 -697.88 -697.87 -712.69

Covariance Matrix for the Log-Rank Statistics

GENDER F M

F 5725.14 -5725.14
M -5725.14 5725.14

Covariance Matrix for the Wilcoxon Statistics

GENDER F M

F 2.187E13 -2.19E13
M -2.19E13 2.187E13

Covariance Matrix for the Tarone Statistics

GENDER F M

F 3.2996E8 -3.3E8
M -3.3E8 3.2996E8

Covariance Matrix for the Peto Statistics

GENDER F M

F 3919.25 -3919.25
M -3919.25 3919.25

Covariance Matrix for the Modified Peto Statistics

GENDER F M

F 3919.10 -3919.10
M -3919.10 3919.10

Covariance Matrix for the Fleming Statistics

GENDER F M

F 4119.94 -4119.94
M -4119.94 4119.94
Test of Equality over Strata



Pr >
Test Chi-Square DF Chi-Square

Log-Rank 111.0194 1 <.0001
Wilcoxon 135.3590 1 <.0001
Tarone 127.7890 1 <.0001
Peto 124.2695 1 <.0001
Modified Peto 124.2694 1 <.0001
Fleming(1) 123.2863 1 <.0001

The above results of the nonparametric tests show there is a significant difference in the survival
functions between males and females

The plot of survival function shows that over 22 years males have had longer survival rates
compared to females.



Exercise 4.5

1. Produce a survival analysis using patient race - white, black and Hispanic - as the strata and
determine which race has the highest survival rate.
2. Produce a survival analysis using donor race - white, black and Hispanic -as the strata and
determine which race has the highest survival rate
3. Produce a survival analysis using patient age groups as the strata.

/*2. Produce a survival analysis using race white, black and Hispanic
as the strata and determine which race has the highest survival rate.
.*/

where ethcat=1 or ethcat=2 or ethcat=4;
strata ethcat / test=(all);

run;
quit;

Partial output of Proc Lifetest for Exercise 4.5-1



Stratum 3: ETHCAT = 4


Survival



21.0000* . . . 2466 38
21.0000* . . . 2466 37
21.0000* . . . 2466 36
21.0000* . . . 2466 35
21.0000* . . . 2466 34
21.0000* . . . 2466 33
21.0000* . . . 2466 32
21.0000* . . . 2466 31
21.0000* . . . 2466 30
22.0000 0.5803 0.4197 0.0211 2467 29
22.0000* . . . 2467 28
22.0000* . . . 2467 27
22.0000* . . . 2467 26
22.0000* . . . 2467 25
22.0000* . . . 2467 24
22.0000* . . . 2467 23
22.0000* . . . 2467 22
22.0000* . . . 2467 21
22.0000* . . . 2467 20
22.0000* . . . 2467 19
22.0000* . . . 2467 18
22.0000* . . . 2467 17
22.0000* . . . 2467 16
22.0000* . . . 2467 15
22.0000* . . . 2467 14
22.0000* . . . 2467 13
22.0000* . . . 2467 12
22.0000* . . . 2467 11
22.0000* . . . 2467 10
22.0000* . . . 2467 9
22.0000* . . . 2467 8
22.0000* . . . 2467 7
22.0000* . . . 2467 6
22.0000* . . . 2467 5
22.0000* . . . 2467 4
22.0000* . . . 2467 3
22.0000* . . . 2467 2
22.0000* . . . 2467 1
22.0000* . . . 2467 0


Stratum 3: ETHCAT = 4


Quartile Estimates





75 . LOGLOG . .
50 . LOGLOG . .
25 17.0000 LOGLOG 17.0000 18.0000

Mean Standard Error

18.9788 0.0538



Percent
Stratum ETHCAT Total Failed Censored Censored

1 1 66103 19472 46631 70.54
2 2 7882 2411 5471 69.41
3 4 10315 2467 7848 76.08
-------------------------------------------------------------------
Total 84300 24350 59950 71.12


Rank Statistics

Modified
ETHCAT Log-Rank Wilcoxon Tarone Peto Peto Fleming

1 767.31 54511758 203704.2 698.36 698.35 711.22
2 14.87 -4190729 -8535.46 -16.17 -16.17 -14.15
4 -782.18 -5.032E7 -195169 -682.19 -682.18 -697.06


ETHCAT 1 2 4

1 4207.29 -1785.89 -2421.40
2 -1785.89 2096.54 -310.65
4 -2421.40 -310.65 2732.05


ETHCAT 1 2 4

1 1.406E13 -5.98E12 -8.08E12
2 -5.98E12 6.984E12 -1E12
4 -8.08E12 -1E12 9.08E12




ETHCAT 1 2 4

1 2.2595E8 -9.602E7 -1.299E8
2 -9.602E7 1.1234E8 -1.632E7
4 -1.299E8 -1.632E7 1.4625E8


ETHCAT 1 2 4

1 2834.79 -1204.39 -1630.40
2 -1204.39 1410.48 -206.09
4 -1630.40 -206.09 1836.49



ETHCAT 1 2 4

1 2834.67 -1204.34 -1630.33
2 -1204.34 1410.42 -206.08
4 -1630.33 -206.08 1836.41


ETHCAT 1 2 4

1 2984.23 -1267.78 -1716.45
2 -1267.78 1485.01 -217.23
4 -1716.45 -217.23 1933.67

Pr >

Log-Rank 226.5981 2 <.0001
Wilcoxon 292.7058 2 <.0001
Tarone 268.7596 2 <.0001
Peto 259.6070 2 <.0001
Modified Peto 259.6106 2 <.0001
Fleming(1) 257.1350 2 <.0001
The results of the above non-parametric tests show that there is a significant difference between
the survival functions between whites, blacks and Hispanics.



This plot of survival function shows that over 22 years whites (ethcat=1) have a longer liver
transplant survival rate when compared to Hispanics (ethcat=4) and blacks (ethcat=2). It also
should be noted survival rates, over in the past five years, between blacks and whites have
almost become equal.



/*2. Produce a survival analysis using doner race white, black and
Hispanic as the strata.*/

where ethcat_don=1 or ethcat_don=2 or ethcat_don=4;
strata ethcat_don / test=(all);

run;
quit;

Partial output of Proc Lifetest for Donor Race, Exercise 4.5-2.

Stratum 3: ETHCAT_DON = 4


Survival

21.0000* . . . 2473 40
21.0000* . . . 2473 39
21.0000* . . . 2473 38
21.0000* . . . 2473 37
21.0000* . . . 2473 36
21.0000* . . . 2473 35
21.0000* . . . 2473 34
21.0000* . . . 2473 33
21.0000* . . . 2473 32
21.0000* . . . 2473 31
21.0000* . . . 2473 30
21.0000* . . . 2473 29
21.0000* . . . 2473 28
22.0000* . . . 2473 27
22.0000* . . . 2473 26
22.0000* . . . 2473 25
22.0000* . . . 2473 24
22.0000* . . . 2473 23
22.0000* . . . 2473 22
22.0000* . . . 2473 21
22.0000* . . . 2473 20
22.0000* . . . 2473 19
22.0000* . . . 2473 18
22.0000* . . . 2473 17
22.0000* . . . 2473 16
22.0000* . . . 2473 15
22.0000* . . . 2473 14



22.0000* . . . 2473 13
22.0000* . . . 2473 12
22.0000* . . . 2473 11
22.0000* . . . 2473 10
22.0000* . . . 2473 9
22.0000* . . . 2473 8
22.0000* . . . 2473 7
22.0000* . . . 2473 6
22.0000* . . . 2473 5
22.0000* . . . 2473 4
22.0000* . . . 2473 3
22.0000* . . . 2473 2
22.0000* . . . 2473 1
22.0000* . . . 2473 0



Stratum 3: ETHCAT_DON = 4


Quartile Estimates


75 . LOGLOG . .
50 . LOGLOG . .
25 17.0000 LOGLOG 17.0000 18.0000

Mean Standard Error

18.3210 0.0510



Percent
Stratum ETHCAT_DON Total Failed Censored Censored

1 1 65430 19275 46155 70.54
2 2 11012 2973 8039 73.00
3 4 9500 2473 7027 73.97
-------------------------------------------------------------------
Total 85942 24721 61221 71.24





Rank Statistics

ETHCAT_ Modified
DON Log-Rank Wilcoxon Tarone Peto Peto Fleming

1 1151.8 78602574 299192.2 1022.0 1022.0 1042.3
2 -496.7 -3.326E7 -127436 -437.6 -437.6 -446.4
4 -655.1 -4.534E7 -171756 -584.3 -584.3 -595.8


ETHCAT_DON 1 2 4

1 4685.12 -2464.48 -2220.63
2 -2464.48 2892.59 -428.11
4 -2220.63 -428.11 2648.74


ETHCAT_DON 1 2 4

1 1.616E13 -8.55E12 -7.61E12
2 -8.55E12 9.927E12 -1.38E12
4 -7.61E12 -1.38E12 8.986E12


ETHCAT_DON 1 2 4

1 2.5531E8 -1.347E8 -1.206E8
2 -1.347E8 1.57E8 -2.23E7
4 -1.206E8 -2.23E7 1.4291E8


ETHCAT_DON 1 2 4

1 3147.89 -1659.62 -1488.27
2 -1659.62 1938.37 -278.75
4 -1488.27 -278.75 1767.01



ETHCAT_DON 1 2 4

1 3147.76 -1659.55 -1488.20
2 -1659.55 1938.29 -278.74
4 -1488.20 -278.74 1766.94




ETHCAT_DON 1 2 4

1 3313.76 -1746.76 -1567.00
2 -1746.76 2040.94 -294.18
4 -1567.00 -294.18 1861.18


Pr >

Log-Rank 290.6337 2 <.0001
Wilcoxon 395.1013 2 <.0001
Tarone 361.3777 2 <.0001
Peto 341.4192 2 <.0001
Modified Peto 341.4225 2 <.0001
Fleming(1) 337.2794 2 <.0001

The results of the above non-parametric tests show that there is a significant difference between the
survival functions between recipients of whites, blacks and Hispanics donors.



This plot of survival function shows that over 22 years those receiving livers from white donors
(ethcat=1) have a longer liver transplant survival rate when compared to Hispanics (ethcat=4)
and blacks (ethcat=2). This has been consistent for 22 years.

/*3. Produce a survival analysis using patient age groups as the
strata. */

strata agecat / test=(all);

run;
quit;



Stratum 8: agecat = 8


Survival



22.0000* . . . 2325 9
22.0000* . . . 2325 8
22.0000* . . . 2325 7
22.0000* . . . 2325 6
22.0000* . . . 2325 5
22.0000* . . . 2325 4
22.0000* . . . 2325 3
22.0000* . . . 2325 2
22.0000* . . . 2325 1
22.0000* . . . 2325 0



Quartile Estimates


75 . LOGLOG . .
50 21.0000 LOGLOG . .
25 15.0000 LOGLOG 14.0000 15.0000

Mean Standard Error

17.4546 0.0663



Percent
Stratum agecat Total Failed Censored Censored

1 1 3070 627 2443 79.58
2 2 4057 847 3210 79.12
3 3 1513 251 1262 83.41
4 4 2271 463 1808 79.61



Percent
Stratum agecat Total Failed Censored Censored

5 5 6678 1748 4930 73.82
6 6 27104 7795 19309 71.24
7 7 37656 11414 26242 69.69
8 8 6287 2325 3962 63.02



-------------------------------------------------------------------
Total 88636 25470 63166 71.26



Rank Statistics

Modified
agecat Log-Rank Wilcoxon Tarone Peto Peto Fleming

1 -148.01 -8326456 -34721.7 -120.19 -120.19 -123.53
2 -105.90 -4610268 -21788.7 -78.51 -78.51 -81.48
3 -102.57 -5779113 -23844.8 -83.30 -83.30 -85.60
4 -110.01 -5898304 -24821.2 -87.72 -87.72 -90.34
5 139.66 16723985 51116.82 161.32 161.32 161.48
6 806.92 52487545 207508.4 693.69 693.69 709.07
7 -689.48 -4.725E7 -181334 -605.47 -605.46 -617.86
8 209.39 2656151 27884.9 120.18 120.17 128.25


agecat 1 2 3 4 5 6 7 8

1 730.48 -28.46 -10.58 -17.09 -47.90 -208.78 -355.62 -62.05
2 -28.46 891.74 -13.05 -21.06 -59.05 -257.30 -436.66 -76.16
3 -10.58 -13.05 339.15 -7.83 -21.94 -95.76 -161.78 -28.21
4 -17.09 -21.06 -7.83 544.66 -35.44 -154.66 -262.75 -45.83
5 -47.90 -59.05 -21.94 -35.44 1464.24 -432.59 -738.45 -128.87
6 -208.78 -257.30 -95.76 -154.66 -432.59 4907.42 -3200.32 -558.02
7 -355.62 -436.66 -161.78 -262.75 -738.45 -3200.32 6136.81 -981.23
8 -62.05 -76.16 -28.21 -45.83 -128.87 -558.02 -981.23 1880.37


agecat 1 2 3 4 5 6 7 8

1 2.89E12 -1.2E11 -4.5E10 -7.12E10 -1.98E11 -8.77E11 -1.35E12 -2.32E11
2 -1.2E11 3.577E12 -5.64E10 -8.9E10 -2.48E11 -1.1E12 -1.68E12 -2.89E11
3 -4.5E10 -5.64E10 1.379E12 -3.35E10 -9.33E10 -4.12E11 -6.3E11 -1.09E11
4 -7.12E10 -8.9E10 -3.35E10 2.167E12 -1.47E11 -6.52E11 -1E12 -1.73E11
5 -1.98E11 -2.48E11 -9.33E10 -1.47E11 5.766E12 -1.82E12 -2.78E12 -4.8E11
6 -8.77E11 -1.1E12 -4.12E11 -6.52E11 -1.82E12 1.931E13 -1.23E13 -2.13E12
7 -1.35E12 -1.68E12 -6.3E11 -1E12 -2.78E12 -1.23E13 2.308E13 -3.31E12
8 -2.32E11 -2.89E11 -1.09E11 -1.73E11 -4.8E11 -2.13E12 -3.31E12 6.718E12



agecat 1 2 3 4 5 6 7 8



1 42999350 -1738916 -651605 -1040061 -2895291 -1.279E7 -2.036E7 -3525283
2 -1738916 52912751 -810452 -1292224 -3600003 -1.589E7 -2.522E7 -4364853
3 -651605 -810452 20313772 -484178 -1348360 -5956796 -9430101 -1632280
4 -1040061 -1292224 -484178 32224989 -2151333 -9509395 -1.513E7 -2619655
5 -2895291 -3600003 -1348360 -2151333 85844237 -2.645E7 -4.211E7 -7291945
6 -1.279E7 -1.589E7 -5956796 -9509395 -2.645E7 2.8866E8 -1.859E8 -3.219E7
7 -2.036E7 -2.522E7 -9430101 -1.513E7 -4.211E7 -1.859E8 3.5044E8 -5.233E7
8 -3525283 -4364853 -1632280 -2619655 -7291945 -3.219E7 -5.233E7 1.0395E8


agecat 1 2 3 4 5 6 7 8

1 507.83 -20.34 -7.60 -12.17 -33.97 -149.37 -242.31 -42.05
2 -20.34 623.72 -9.44 -15.10 -42.16 -185.23 -299.51 -51.95
3 -7.60 -9.44 238.78 -5.64 -15.75 -69.28 -111.68 -19.37
4 -12.17 -15.10 -5.64 379.91 -25.20 -110.90 -179.71 -31.18
5 -33.97 -42.16 -15.75 -25.20 1015.40 -309.15 -502.04 -87.13
6 -149.37 -185.23 -69.28 -110.90 -309.15 3407.66 -2201.73 -382.00
7 -242.31 -299.51 -111.68 -179.71 -502.04 -2201.73 4172.97 -635.99
8 -42.05 -51.95 -19.37 -31.18 -87.13 -382.00 -635.99 1249.68


agecat 1 2 3 4 5 6 7 8

1 507.81 -20.34 -7.60 -12.17 -33.97 -149.36 -242.30 -42.05
2 -20.34 623.70 -9.44 -15.09 -42.16 -185.22 -299.49 -51.95
3 -7.60 -9.44 238.77 -5.64 -15.75 -69.28 -111.68 -19.37
4 -12.17 -15.09 -5.64 379.90 -25.20 -110.90 -179.71 -31.18
5 -33.97 -42.16 -15.75 -25.20 1015.36 -309.14 -502.02 -87.13
6 -149.36 -185.22 -69.28 -110.90 -309.14 3407.53 -2201.65 -381.98
7 -242.30 -299.49 -111.68 -179.71 -502.02 -2201.65 4172.80 -635.96
8 -42.05 -51.95 -19.37 -31.18 -87.13 -381.98 -635.96 1249.62




agecat 1 2 3 4 5 6 7 8

1 533.17 -21.31 -7.96 -12.76 -35.61 -156.48 -254.81 -44.24
2 -21.31 654.52 -9.88 -15.81 -44.16 -193.95 -314.77 -54.63
3 -7.96 -9.88 250.45 -5.91 -16.50 -72.52 -117.33 -20.36
4 -12.76 -15.81 -5.91 398.77 -26.41 -116.16 -188.93 -32.80
5 -35.61 -44.16 -16.50 -26.41 1066.24 -323.88 -528.00 -91.68
6 -156.48 -193.95 -72.52 -116.16 -323.88 3578.20 -2313.63 -401.58
7 -254.81 -314.77 -117.33 -188.93 -528.00 -2313.63 4389.00 -671.53
8 -44.24 -54.63 -20.36 -32.80 -91.68 -401.58 -671.53 1316.81




Pr >

Log-Rank 263.8732 7 <.0001
Wilcoxon 268.5349 7 <.0001
Tarone 274.2439 7 <.0001
Peto 268.1175 7 <.0001
Modified Peto 268.1196 7 <.0001
Fleming(1) 267.7420 7 <.0001

The results of the above non-parametric tests show that there is a significant difference between the
survival functions between the various age groups of patients.

This plot of survival function shows that those receiving livers in age group 6-10 years old
(agecat=3) have a longer liver transplant survival rate when compared to all other age groups.



The age group greater than 65 (agecat=8) has the lowest survival rate and appears to be
consistent over the 22 years.


School of Medicine

®

Course Notes
STA9000
Chapter 5- Office of Statewide
Health Planning & Development
(OSHPD) California Emergency
Department Data

2



Prepared date 7Aug09.

TABLE OF CONTENTS

Chapter 5-OSPHD Emergency Department 3

Objectives of Chapter 4

Section – 1 The Office of Statewide Health Planning and Development (OSPHD) – Putting
the Pieces Together 5
Section 2 - Using OSHPD Data to Understand Health and Healthcare Patterns:
Descriptive Reports and Research Briefs 14
Section 3 – Using the National Hospital Ambulatory Medical Care Survey (NHAMCS)
data for injury analysis, 2004 18
Section 4 – OSPHD Public Use Data Variables 22
Section 5 - Emergency Department and Ambulatory Surgery Center File Documentation 23
Demonstration 1 OSPHD California 2007 Emergency Department Data PROC Format,
Labels, PROC Contents, and PROC Freq Statements 33
Exercise 5.1 51
Demonstration 2 SAS Code for OSPHD Indicator and Truth Logic Variables with
PROC MEANS and PROC TABULATE 66
Exercise 5.2 73

Demonstration 3 Multiple Linear Regression Model of California ED visits with the response
variable of Patient Age (age_years) 87

Exercise 5.3 89
Demonstration 4 Logistic Regression Model of California Self Pay Emergency Department
Visits 93
Exercise 5.4 98



Office of Statewide Health
Planning & Development
(OSHPD) California
Emergency Department
Data

Lecture 5

Objectives
Provide an overview of Office of Statewide Health
Planning & Development (OSHPD) California
Emergency Department Data
Review the available descriptive data that is provided
in the annual 2007 OSPHD reports.
Review the variables and their definitions that exist on
the (OSHPD) California Emergency Department Data
OPTN Liver transplantation data files.
Identify the additional information that can be obtained
from the raw data.
Pose the range of potential study questions.
Write SAS code to analyze the OSPHD data.



The Office of
Statewide Health
Planning and
Development
David M. Carlisle, MD,
PhD
Director, OSHPD

Ron Spingarn
Deputy Director,
Healthcare Information Division

Office of Statewide Health
Planning and Development
456 Employees
Annual Budget of $88.9 million
Offices in Sacramento and Los Angeles
Five Divisions and Five Boards/Commissions



OSHPD in the California
Government Hierarchy
Governor

Health & Human
CPHS Services

Health
Social Mental Public
Care
Services Health OSHPD
Services Health

Other CHHS Departments: Aging, Alcohol and Drug Programs, Child Support Services,
Community Services and Development, Developmental Services, Emergency Medical
Services Authority, Managed Risk Medical Insurance Board, and Rehabilitation

OSHPD History

Created as a result of the break up of the
Department of Public Health in 1978.
Responsible for:
Hospital construction and plan review.
Collection and dissemination of healthcare
information.
Collection and reporting of outcome data on selected
medical conditions and procedures.



Origin of OSHPD’s Data Collection

OSHPD data and quality programs have evolved over
more than three decades.
1971 -- SB 283 established California Hospital Disclosure
Act, created California Hospital Commission (CHC)
To set standards for hospital uniform accounting &
reporting
To prepare for hospital rate setting as means of
health care cost control
Allow scrutiny of financial aspects of CA hospitals
Data collection began in July 1974.

Thanks to Michael Kassis for compiling this historical information!

Data Collection (continued)
1974 -- CHC’s jurisdiction expanded, mandating uniform accounting
and reporting system for long-term care (LTC) facilities
CHC renamed the California Health Facilities Commission
(CHFC) reflecting broadened responsibilities
LTC data collection began for FYs starting on or after 1/1/1977
1980 -- SB 1370 added responsibilities:
Collect quarterly financial and utilization data to assess success
of hospital industry’s voluntary effort to contain costs
Integrate CHFC’s LTC disclosure report with Medi-Cal cost
report to reduce reporting burden on health facilities
Collect 12 discharge data elements on hospital inpatients to
inform understanding of the characteristics of care rendered by
hospitals
Submission of quarterly financial, patient-level data began in
1981



Data Collection (continued)

1982 -- AB 3480 expanded inpatient discharge data
elements
Total charges, other diagnoses, other procedures and
dates, date of principal procedure, starting 1/1/1983.
Option to report the Abstract (Medical) Record
Number.
1984 -- SB 181: Health Data and Advisory Council
Consolidation Act
Transferred functions of CHFC to OSHPD, eliminated
State Advisory Health Council and formed advisory
body (California Health Policy and Data Advisory
Commission -- CHPDAC)

OSHPD Data and Quality Measurement History
1985 -- AB 2011 (Chapter 1021) required hospitals to submit hospital inpatient discharge data
semiannually, not later than six months after the end of each semiannual period commencing six months
after January 1, 1986.
1988 -- SB 2398 (Chapter 1140) added external cause of injury and patient social security number
effective with discharges on July 1, 1990.
1991 -- AB 524 - Bronzan (Chapter 1075) established the California Hospital Outcomes Project (CHOP)
in order to promote and conduct risk adjusted outcome studies of hospitals and strengthen patient
discharge data through additions or changes. The bill also created the Technical Advisory Committee
(TAC) within CHPDAC.
SB 697 - Torres (Chapter 812) required private not-for-profit hospitals to develop and annually submit
community benefits plans that include a description of the activities that the hospital has undertaken in
order to address identified community needs within its mission and financial capacity, and the process by
which the hospital developed the plan in consultation with the community. Submission of plans began
with fiscal years ending in 1995.



Our Vision:
“Equitable Healthcare
Accessibility for California”

OSHPD’s Divisions

Workforce
Facilities Development
Development
Healthcare
Professions
Education
Foundation
OSHPD
Cal-Mortgage Healthcare
Loan Information
Insurance

Administration
Plus Six Advisory Bodies



Major Challenges – Policy:
Ensuring Patient Confidentiality and Secure Data
Informing the Public and Policy-Makers to Help
Ensure Access to Healthcare
Assessing Quality of Care
Enforcing Seismic Safety

Shifting Landscape
New Legislators
Hospital Construction
Healthcare Reform (state & federal)
New Federal Administration
Economic Stimulus Package for CA
Healthcare Information Technology



Facilities Development Division

Reviews plans and
inspects health facility
construction projects to
ensure compliance
with building
standards.

Ensures patient safety in
these facilities in the
event of an earthquake
or other disaster.

The 1971 San Fernando, California, earthquake (magnitude 6.7) severely damaged the recently built
Olive View Hospital. This building was not instrumented with seismic sensors. Accordingly, no data were
obtained to understand how the damage initiated and progressed during the intense shaking. The building
was razed and replaced with a stronger structure that survived the 1994 Northridge earthquake.

Most loss of life and property in earthquakes is the result of damage to or collapse of buildings or other
structures from strong shaking. Key to reducing such losses are recordings of structural response to
damaging levels of shaking. Using these recordings, engineers can better design new buildings and
strengthen existing buildings to survive future quakes. The U.S. Geological Survey (USGS) and
cooperators are engaged in a national effort to acquire these critically needed strong-motion
measurements in earthquake-prone urban areas.

http://pubs.usgs.gov/fs/2003/fs068-03/



Collects and maintains data
from California- licensed:
Hospitals
Long-term care facilities
Home health agencies
Hospices, and
Primary care and specialty
clinics


Workforce
Facilities Development
Development
Healthcare
Professions
Education Healthcare
Foundation
OSHPD
Outcomes
Center
Cal-Mortgage Healthcare
Loan Information
Insurance
ARSS

Administration
PDS
DMO HIRC



Healthcare Outcomes Center

Produces:
Risk-adjusted outcome reports, assessing quality of care of
hospitals and surgeons for Coronary Artery Bypass Graft
(CABG) Surgery and Community-Acquired Pneumonia
Inpatient Mortality Indicators (currently on 8 conditions or
procedures)

OSPHD Contacts

Visit our Web site at:
www.oshpd.ca.gov
or contact us at:
Ombudsman@oshpd.ca.gov



Using OSHPD Data to
Understand Health and
Healthcare Patterns:
Descriptive Reports and
Research Briefs

Presenter:
Mary Tran, PhD,
MPH



Kinds of healthcare information you can find in
the data
Patient information:
Demographics, source of admission, and area of residence
Diagnoses, procedures, type of discharge
Source of payment, length of stay, charges

Facility information:
Hospital type of ownership, capacity, financing
Staffing ratios, time on diversion
Area of location

Examples of possible linkages, with IRB approvals:
Hospitalization records with death certificates
Hospitalization records with outpatient visits
Multiple hospitalizations over time for the same patient

Examples of subjects you can address:

Specific types of illness or injury that are leading to
hospitalization—trends, demographics, geography
How patients are utilizing the healthcare system (who
goes where for what kind of care?)
Reflections of public health trends (as revealed by
patterns of hospitalization or ED visits)
Trends in sources of payment for healthcare
Tracking capacity of the healthcare system



Examples of analyses from
OSHPD reports

How many episodes of care happen in ED,
compared to hospitalizations?
Patient Profile Report, 2005

ED-Visits only 8,556,699

ASC-Hospital associated 1,738,755

ASC-Free standing 1,052,398

Inpatient-Physical Rehab 36,309

Inpatient-Chemical
13,538
Dependency

Inpatient-Psychiatric 197,822 Total Inpatient: 3,990,255
Total ASC Free-standing + Hospital-associated: 2,831,212
Total ED Visits + Admissions: 10,182,025
Inpatient-SNF 68,714

Inpatient-GAC 3,673,824

0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 9,000,000



Do urban areas make
more use of emergency
rooms for medical care?

Are the same types of procedures routinely
performed in inpatient vs. outpatient settings?
Top 10 Procedures for Inpatient and Outpatient Encounters. California, 2005
GENERAL ACUTE ASC FREE- ASC HOSPITAL EMERGENCY
CARE STANDING BASED DEPARTMENT

Principal Procedure (CCS Code)
F M F M F M F M
Other procedures to assist delivery 239,767
Cesarean section 163,197
Repair of current obstetric laceration 55,651
Hysterectomy, abdominal and vaginal 49,647
Respiratory intubation, mechanical ventilation 35,436 39,091
Blood transfusion 34,224 25,586
Upper gastrointestinal endoscopy, biopsy 34,025 30,266 54,451 38,031 64,570 46,700
Prophylactic vaccinations and inoculations 31,789 23,531
Forceps, vacuum, and breech delivery 30,211
Episiotomy 29,136
Circumcision 55,094
PTCA 38,471
Appendectomy 23,304
Cardiac catheterization 23,116 22,632
Other vascular catheterization, not heart 20,415
Colonoscopy and biopsy 151,050 130,478 124,217 107,128
Lens and cataract procedures 80,454 52,988 65,994 44,443
Insertion of catheter, injection to spinal canal 73,888 57,198 37,955 26,574
OR procedures on skin and breast 27,242 18,177
Procedures on muscles and tendons 14,104 13,879 17,542
Excision of semilunar cartilage of knee 13,776 18,385 20,742
Procedures on eyelids, conjunctiva, cornea 13,163 8,254
Decompression peripheral nerve 11,781
Other OR therapeutic procedures on joints 10,348 12,405
Inguinal and femoral hernia repair 8,544 36,896
Arthroplasty other than hip or knee 6,177
Excision of skin lesion 15,268
Lumpectomy, quadrantectomy of breast 30,977
Cholecystectomy 22,449
D and C after delivery 18,617
Pathology 18,450
Interview, evaluation, consultation 1,181,059 1,034,969
Laboratory - Chemistry and Hematology 142,021 99,541
Suture of skin and subcutaneous tissue 92,509 185,634
Traction, splints, and other wound care 78,742 95,264
Diagnostic radiology 78,115 80,593
Routine chest X-ray 49,284 45,036
Electrocardiogram 39,638 30,630
Microscopic examination 38,500 22,573
Incision and drainage (skin, subQ) 23,512 28,540
Total number of encounters 1,477,340 908,067 590,471 454,982 968,245 761,625 2,209,650 2,015,345



Other OSPHD reports in the pipeline

HIV/AIDS: Trends in Hospital and Emergency Room
Patients
Gunshot Wounds: Trends in Hospital and Emergency
Room Patients
Emergency Rooms: Patterns of Utilization
Status of California’s Safety Net

Section 3- Using the National Hospital Ambulatory Medical Care Survey (NHAMCS)
data for injury analysis, 2004 l



Linda McCaig, Ambulatory Care Statistics Branch, Division of Health Care Statistics, Using National
Hospital Ambulatory Medical Care Survey (NHAMCS) data for injury analysis, 2004



Highlights From the 1994-2004 National Hospital
Ambulatory Surgery Survey (NHAMCS)

continued...

Highlights From the 2004 National Hospital

continued...




continued...




OSPHD Public Use Data Variables
Facility Identification Number
Patient Type (Am-Surg. or ED)
License Type
– Free standing
– Hospital Based
Age in Years (at admission)
Age Range (20 Categories)
Age Range (5 Categories)
Gender N
Ethnicity u
m
Race
b
er
continued...
of

OSPHD Public Use Data Variables
Patient Zip Code (first 3 digits)
Patient County
Quarter of Year Service
Patient Disposition
Expected Source of Payment
Principal External Cause of Injury –E-Code
Other External Cause of Injury - E-Code
Principal Diagnosis
Other Diagnosis
Principal Procedure
Other Procedures



INTRODUCTION

Emergency Department and Ambulatory Surgery Center

File Documentation

Public Version July – December 2007

SAS (version 9.1) File
Comma-Delimited Text File



INTRODUCTION
General Information:
The California Office of Statewide Health Planning and Development (OSHPD) provides public
datasets of data collected from licensed emergency departments and ambulatory surgery
centers in California. The ambulatory surgery data includes both hospital-based and
freestanding clinics. Each record within the dataset consists of one outpatient encounter, also
known as a service visit, for each time a patient is treated. Data collected for these encounters
include demographic, clinical, payment, and facility information.
The public data is released twice a year by OSHPD once it has been screened by the automated
reporting software (MIRCal) and corrected by the individual facilities. Separate public files are
available for emergency departments and ambulatory surgery center encounters. Because of its
size, the emergency department data is divided into three separate files based on the geographic
location of the facility as indicated below:
• Los Angeles County
• Southern California (seven counties, not including Los Angeles)
• Northern California (50 counties)

Masked Variables:
To protect patient confidentiality, records with unique combinations of certain demographic
variables will have one or more of those variables masked to make sure the files are de-
identified. In most cases, masking involves defaulting the variable to blank or missing. Each
unique record will have the minimum number of fields masked to ensure it is no longer unique.

The variable masking occurs in the following order:
ORDER OF
DATA FIELDS SUBJECT TO MASKING
MASKING
1st Age in years (on service date)
2nd Ethnicity
3rd Race
4th Sex
5th Age Category 20 (20 Age Categories)
6th Age Category 5 (5 Age Categories)
7th Service Quarter
8th Patient ZIP Code (5-digit)*
9th Small County Groups**
10th Patient ZIP Code (3-digit)* *Five-digit ZIP will be masked to three-digits; if record is
still unique, ZIP will be totally masked with an asterisk.

**Small counties with total populations of 30,000 or less are grouped into 3 categories:
Central (CE), Northeastern (NE), and Northwestern (NW). Listings of small counties
for each reporting year are provided in Appendix A along with the number of records
that were masked by variable.

Copyright © 2009 Raymond R. Arons, Teaneck, NJ, USA 10
3 of


Modifications and Variant Action Reports:
Some facilities have applied for and been granted "modifications" to standard emergency
department or ambulatory surgery data reporting requirements. Other facilities were unable to
complete specific fields as required and were deemed "non-compliant" at the time of reporting.
See Appendix B (Data Exceptions and Modifications) for a listing of all non-compliant facilities
and those with approved modifications and their affected variables.

Importing Notes:
There are several fields that although they appear to contain numeric data, should be treated as
text. This is particularly important when working with diagnosis and procedure codes. These
fields are comprised of ICD-9-CM (diagnosis) and CPT (procedure) codes. Diagnosis and
procedure codes are stored without decimals and many contain leading zeros. For example,
the ICD-9-CM code for Salmonella Gastroenteritis is “003.0” (implied decimal following the third
digit from the left). If it is not formatted as text, the leading zeros may be dropped and the code
will appear as “30”, an invalid diagnosis code.

File Format:
In the comma-delimited set, the length of each field and the length of each record will vary
according to the data reported. To assist you in using the comma delimited patient-level
datasets, a header row identifying each data element is provided in the position of the first
record. The SAS data set was created using SAS version 9.1 for Windows.

The attributes for each data field is provided below.



File Documentation
Facility Identification Number
Field Name: Fac_ID Definition: A unique six-digit identifier assigned to each
facility by the Office of Statewide Health Planning and Development. The first two digits
indicate the county in which the facility is located. The last four digits are unique within
each county. A list of facility numbers and their names are provided in Appendices C
(emergency departments) and D (ambulatory Surgery centers). Variable Type:
Character Variable Length: 6

Patient Type

Field Name: Pat_Type Definition: A one character filed that indicates the type of facility
where a particular patient encounter occurred. A represents Ambulatory Surgery Center
and E represents Emergency Department. Variable Type: Character Variable Length: 1

License Type

Field Name: Lic_Type Definition: The license type of the reporting facility where C=Clinic and
H=Hospital. For Ambulatory Surgery Centers, this variable can be used to distinguish
between freestanding surgery centers and hospital based clinics. Variable Type: Character
Variable Length: 1

Age in Years (at Admission)
Field Name: Age_Yrs Definition: Age of the patient at the time of service. This is based on
the reported service data and patient’s date of birth. If the date of birth is unknown or invalid,
the age in years is set to “0”. Variable Type: Numeric Variable Length: 3

Age Range (20 categories)
Field Name: agecat20 Definition: Age range (based on 20 categories) of the patient at the time of
service. 01 = Under 1 year 11 = 45–49 years 02 = 1–4 years 12 = 50–54 years 03 = 5–9 years 13 =
55–59 years



04 = 10–14 years 14 = 60–64 years 05 = 15–19 years
18 = 80–84 years 09 = 35–39 years 19 = 85 years or
greater 10 = 40–44 years 00 = Unknown age

Variable Type: Character
Variable Length: 2

Age Range (5 categories)
Field Name: agecat20 Definition: Age range (based on 5 categories) of the patient at the
time of service. 1 = Under 1 year 4 = 35–64 years 2 = 1–17 years 5 = 65 years or greater 3
= 18–34 years 0 = Unknown Age Variable Type: Character Variable Length: 1

Gender
Field Name: Sex Definition: Gender of the patient at time of service. M = Male F =
Female U = Unknown / Invalid Variable Type: Character Variable Length: 1

Ethnicity
Field Name: Eth Definition: Ethnicity (self reported) of the patient. E1 = Hispanic E2 =
Non-Hispanic 99 = Unknown / Invalid / Blank Variable Type: Character Variable
Length: 2

Race
Field Name: Race Definition: Patient’s racial background (self-
reported). R1 = American Indian / Alaskan Native



R2 = Asian R3 = Black / African American R4 = Native Hawaiian / Other Pacific
Islander R5 = White R9 = Other Race 99 = Unknown / Invalid / Blank Variable
Type Character Variable Length 2

Patient Zip Code
Field Name: Patzip Definition: The patient’s 5-digit Zip Code of residence. If the Zip Code
is unknown it is assigned a value of 99999. In the masking process the Zip Code may be
masked at the 3-digit level (i.e. first 3 digits) Variable Type: Character Variable Length: 5

Patient County
Field Name: Patco Definition: The patient’s county of residence. OSHPD assigns the county of
residence based on the patient’s reported Zip Code. Invalid, blank and unknown zip codes are
assigned a county code value of 00. Counties with populations of less than 30,000 residents
are assigned to one of three small county codes. 01 = Alameda 21 = Marin 42 = Santa Barbara
03 = Amador 23 = Mendocino 43 = Santa Clara 04 = Butte 24 = Merced 44 = Santa Cruz 05 =
Calaveras 27 = Monterey 45 = Shasta 06 = Colusa 28 = Napa 47 = Siskiyou 07 = Contra Costa
29 = Nevada 48 = Solano 09 = El Dorado 30 = Orange 49 = Sonoma 10 = Fresno 31 = Placer
50 = Stanislaus 11 = Glenn 33 = Riverside 51 = Sutter 12 = Humboldt 34 = Sacramento 52 =
Tehama 13 = Imperial 35 = San Benito 53 = Trinity 15 = Kern 36 = San Bernardino 54 = Tulare
16 = Kings 37 = San Diego 55 = Tuolumne 17 = Lake 38 = San Francisco 56 = Ventura 18 =
Lassen 39 = San Joaquin 57 = Yolo 19 = Los Angeles 40 = San Luis Obispo 58 = Yuba 20 =
Madera 41 = San Mateo 00 = Unknown / Invalid CE = Alpine, Inyo, Mariposa & Mono Counties
combined NE = Del Norte, Modoc, Plumas & Sierra Counties combined NW = Colusa, Glenn &
Trinity Counties combined Variable Type: Character Variable Length: 2



Quarter of Service
Field Name: Serv_Q Definition: The calendar quarter when service
was provided.
1 = January - March
2 = April - June
3 = July - September
4 = October - December
Variable Type: Character
Variable Length: 1

Disposition
Field Name: Dispn Definition: The consequent arrangement or event ending a patient’s
encounter in the reporting facility. 01 = Discharged to home of self care (routine discharge) 02
= Discharged/Transferred to a short term general care hospital or inpatient care 03 =
Discharged/Transferred to a skilled nursing facility with Medicare certification 04 =
Discharged/Transferred to an intermediate care facility 05 = Discharged/Transferred to
another type of institution not on this list 06 = Discharged/Transferred home under the care of
a home health service organization 07 = Left or discontinued care against medical advice 20 =
Died 43 = Discharged/Transferred to a federal health care facility 50 = Discharged home with
hospice care 51 = Discharged to a medical facility with hospice care 61 =
Discharged/Transferred to a hospital-based Medicare approved swing bed 62 =
Discharged/Transferred to an inpatient rehabilitation facility or unit of a hospital 63 =
Discharged/Transferred to a Medicare certified long term care hospital 64 =
Discharged/Transferred to a nursing facility certified under Medicaid but not Medicare 65 =
Discharged/Transferred to a psychiatric hospital or unit of a hospital 66 =
Discharged/Transferred to a critical access hospital 00 = Other 99 = Invalid / Blank Variable
Type: Character Variable Length: 2

Field Name: payer Definition: The type of entity or organization expected to pay
the greatest share of the
patient’s bill. For a complete list of definitions for these payers see
Appendix E.
09 = Self Pay
11 = Other Non-federal Programs
12 = Preferred Provider Organization (PPO)


13 = Point of Service (POS) 14 = Exclusive Provider Organization (EPO) 16 = Health
Maintenance Organization (HMO) Medicare Risk AM = Automobile Medical BL = Blue Cross
/ Blue Shield CH = CHAMPUS (TRICARE) CI = Commercial Insurance Company DS =
Disability HM = Health Maintenance Organization MA = Medicare Part A MB = Medicare
Part B MC = Medi-Cal (California’s Medicaid program) OF = Other Federal Program TV =
Title V VA = Veterans Affairs Plan WC = Workers’ Compensation Health Claim 00 = Other
99 = Invalid / Unknown Variable Type: Vharacter, Variable Length 2

External Cause of Injury - Principal E-Code
Field Name: EC_Prin Definition: The external cause of injury or poisoning or adverse effect
code (E800E999) which describes the mechanism that resulted in the most severe injury,
poisoning, or adverse effect related to the encounter. An E-code is to be included for the first
reported encounter during which the injury, poisoning, or adverse effect was first diagnosed
and/or treated. If a patient was first diagnosed in a doctor’s office and then sent to an ED or AS
facility, the E-code is to be reported on the ED or AS record. They are coded according to the
rd
ICD-9-CM. Variable Type: Character (implied decimal after the 3 character from the left)
Variable Length: 5 (7 for SAS variables)

External Cause of Injury - Other E-Code (up to 4)

Field Name(s): EC1 – EC4 Definition: The additional external cause of injury or poisoning or
adverse effect codes (E800-E999) that completely describe the mechanisms that contributed to,
or the causal events surrounding, any injury, poisoning, or adverse effect. Up to 4 other E-
codes should be included for the first reported encounter during which the injury, poisoning, or
adverse effect was first diagnosed and/or treated. If a patient was first diagnosed in a doctor’s
office and then sent to an ED or AS facility, the E-code is to be reported on the ED or AS



Principal Diagnosis
Field Name(s): Dx_Prin
Definition: The condition, problem, or other reason established to be the chief cause of the
encounter. Procedures are coded according to the Current Procedural Terminology (CPT). The
version used depends on the year the encounter was reported. They are coded according to the
rd
ICD-9-CM Variable Type: Character (implied decimal after the 3 character from the Variable
Length: 5 (7 for SAS variables)

Other Diagnoses (up to 24)
Field Name(s): Odx1-Odx24
Definition: All conditions that coexist at the time of the encounter for emergency or
ambulatory surgery care, that develop subsequently during the encounter, or that affect the
treatment received. They are coded according to the ICD9-CM. Variable Type: Character
rd
(implied decimal after the 3 character from the left)

Principal Procedure
Field Name(s): Pr_Prin
Definition: The procedure that is surgical in nature, or carries a procedural risk, or carries
an anesthetic risk and is most closely related to the principal diagnosis, as the chief reason for
the encounter. Procedures are coded according to the Current Procedural Terminology (CPT).
The version used depends nd the year the encounter was reported Variable Type: Character
on
.
(implied decimal after the 2 character from the left)

Other Procedures (up to 20)
Field Name(s): Opr1-Opr20
Definition: All other procedures, related to the encounter, which are surgical in nature, carry
a procedural risk, or carry an anesthetic risk. Procedures are coded according to the Current
Procedural Terminology (CPT). The version used depends on the year the encounter was
nd
reported Variable Type: Character (implied decimal after the 2 character from the left)



Modifications and Variant Action Reports:
Some facilities have applied for and been granted "modifications" to standard emergency
department or ambulatory surgery data reporting requirements. Other facilities were unable to
complete specific fields as required and were deemed "non-compliant" at the time of reporting.
See Appendix B (Data Exceptions and Modifications) for a listing of all non-compliant facilities
and those with approved modifications and their affected variables.

Importing Notes:
There are several fields that although they appear to contain numeric data, should be treated as
text. This is particularly important when working with diagnosis and procedure codes. These
fields are comprised of ICD-9-CM (diagnosis) and CPT (procedure) codes. Diagnosis and
procedure codes are stored without decimals and many contain leading zeros. For example,
the ICD-9-CM code for Salmonella Gastroenteritis is “003.0” (implied decimal following the third
digit from the left). If it is not formatted as text, the leading zeros may be dropped and the code
will appear as “30”, an invalid diagnosis code.

File Format:
In the comma-delimited set, the length of each field and the length of each record will vary
according to the data reported. To assist you in using the comma delimited patient-level
datasets, a header row identifying each data element is provided in the position of the first
record. The SAS data set was created using SAS version 9.1 for Windows.

The attributes for each data field is provided below.

.

Copyright © 2009 Raymond R. Arons, Teaneck, NJ, USA 10
3 of


1. OSPHD California 2007 Emergency Department Data
PROC Format, Labels, PROC Contents, and PROC Freq
Statements
oshpd01.sas

The program below contains the basic structure for a SAS analysis of the OSPHD Emergency
Department. PROC Format provides the names of the values of the variables, PROC Contents
yields the specifications of your data set, and PROC Freq provides the frequency distributions of
each of the variables. In most instances, the PROC Formats are partial lists of the actual code
due to their size.

/*oshpd01.sas*/

proc format;

value $diag3df

'001'='(001) Cholera'
'002'='(002) Typhoid and paratyphoid fevers'
'003'='(003) Other salmonella infections'
'004'='(004) Shigellosis'
'005'='(005) Other food poisoning (bacterial)'
'006'='(006) Amebiasis'
'007'='(007) Other protozoal intestinal dise...'
'008'='(008) Intestinal infections due to ot...'
'009'='(009) Ill-defined intestinal infections'
'010'='(010) Primary tuberculous infection'
'011'='(011) Pulmonary tuberculosis'
'012'='(012) Other respiratory tuberculosis'
'013'='(013) Tuberculosis of meninges and ce...'
'014'='(014) Tuberculosis of intestine/perit...'
'015'='(015) Tuberculosis of bones and joints'
'016'='(016) Tuberculosis of genitourinary s...'
'017'='(017) Tuberculosis of other organs'
'018'='(018) Miliary tuberculosis'
'020'='(020) Plague'
'021'='(021) Tularemia'
'022'='(022) Anthrax'
'023'='(023) Brucellosis'
'024'='(024) Glanders'
'025'='(025) Melioidosis'
'026'='(026) Rat-bite fever'
'027'='(027) Other zoonotic bacterial diseases'
'030'='(030) Leprosy'
'031'='(031) Diseases due to other mycobacteria'
'032'='(032) Diphtheria'
'033'='(033) Whooping cough'



'034'='(034) Streptococcal sore throat and s...'
'035'='(035) Erysipelas'
'036'='(036) Meningococcal infection'
'037'='(037) Tetanus'
'038'='(038) Septicemia'
'039'='(039) Actinomycotic infections'
'040'='(040) Other bacterial diseases'
'041'='(041) Bacterial infec in conditns cla...'
'044'='(044) Other human immunodeficiency vi...'
'045'='(045) Acute poliomyelitis'
'046'='(046) Slow virus infection of central...'
'047'='(047) Meningitis due to enterovirus'
'048'='(048) Other enterovirus diseases of c...'
'049'='(049) Oth non-arthropod-borne viral d...'
'050'='(050) Smallpox'
'051'='(051) Cowpox and paravaccinia'
'052'='(052) Chickenpox'
'053'='(053) Herpes zoster'
'054'='(054) Herpes simplex'
'055'='(055) Measles'
'056'='(056) Rubella'
'057'='(057) Other viral exanthemata'
'060'='(060) Yellow fever'
'061'='(061) Dengue'
'062'='(062) Mosquito-borne viral encephalitis'
'063'='(063) Tick-borne viral encephalitis'
'064'='(064) Viral encephalitis transmitted ...'
'065'='(065) Arthropod-borne hemorrhagic fever'
'066'='(066) Other arthropod-borne viral dis...'
'070'='(070) Viral hepatitis'
'071'='(071) Rabies'
'072'='(072) Mumps'
'073'='(073) Ornithosis'
'074'='(074) Specific diseases due to Coxsac...'
'075'='(075) Infectious mononucleosis'
'076'='(076) Trachoma'
'077'='(077) Other diseases of conjunctiva d...'
'078'='(078) Other diseases due to viruses a...'
'079'='(079) Viral infection in conditns cla...'
'080'='(080) Louse-borne [epidemic] typhus'
'081'='(081) Other typhus'
'082'='(082) Tick-borne rickettsioses'
'083'='(083) Other rickettsioses'
'084'='(084) Malaria'
'085'='(085) Leishmaniasis'
'086'='(086) Trypanosomiasis'
'087'='(087) Relapsing fever'
'088'='(088) Other arthropod-borne diseases'
;



value $ecodef

'E8000'='E8000 RR COLLISION NOS-EMPLOY'
'E8001'='E8001 RR COLL NOS-PASSENGER'
'E8002'='E8002 RR COLL NOS-PEDESTRIAN'
'E8003'='E8003 RR COLL NOS-PED CYCLIST'
'E8008'='E8008 RR COLL NOS-PERSON NEC'
'E8009'='E8009 RR COLL NOS-PERSON NOS'
'E8010'='E8010 RR COLL W OTH OBJ-EMPLOY'
'E8011'='E8011 RR COLL W OTH OBJ-PASNGR'
'E8012'='E8012 RR COLL W OTH OBJ-PEDEST'
'E8013'='E8013 RR COLL W OTH OBJ-CYCL'
'E8018'='E8018 RR COL W OTH OBJ-PER NEC'
'E8019'='E8019 RR COL W OTH OBJ-PER NOS'
'E8020'='E8020 RR ACC W DERAIL-EMPLOYEE'
'E8021'='E8021 RR ACC W DERAIL-PASSENG'
'E8022'='E8022 RR ACC W DERAIL-PEDEST'
'E8023'='E8023 RR ACC W DERAIL-PED CYCL'
'E8028'='E8028 RR ACC W DERAIL-PERS NEC'
'E8029'='E8029 RR ACC W DERAIL-PERS NOS'
'E8030'='E8030 RR ACC W EXPLOSION-EMPL'
'E8031'='E8031 RR ACC W EXPLOS-PASNGR'
'E8032'='E8032 RR ACC W EXPLOS-PEDEST'
'E8033'='E8033 RR ACC W EXPLOS-PED CYCL'
'E8038'='E8038 RR ACC W EXPLOS-PERS NEC'
'E8039'='E8039 RR ACC W EXPLOS-PERS NOS'
'E8040'='E8040 FALL ON/FROM TRAIN-EMPL'
'E8041'='E8041 FALL FROM TRAIN-PASSENGR'
'E8042'='E8042 FALL FROM TRAIN-PEDEST'
'E8043'='E8043 FALL FROM TRAIN-PED CYCL'
'E8048'='E8048 FALL FROM TRAIN-PERS NEC'
'E8049'='E8049 FALL FROM TRAIN-PERS NOS'
'E8050'='E8050 HIT BY TRAIN-EMPLOYEE'
'E8051'='E8051 HIT BY TRAIN-PASSENGER'
'E8052'='E8052 HIT BY TRAIN-PEDESTRIAN'
'E8053'='E8053 HIT BY TRAIN-PED CYCLIST'
'E8058'='E8058 HIT BY TRAIN-PERSON NEC'
'E8059'='E8059 HIT BY TRAIN-PERSON NOS'
'E8060'='E8060 RR ACC NEC-EMPLOYEE'
'E8061'='E8061 RR ACC NEC-PASSENGER'
'E8062'='E8062 RR ACC NEC-PEDESTRIAN'
'E8063'='E8063 RR ACC NEC-PED CYCLIST'
'E8068'='E8068 RR ACC NEC-PERSON NEC'
'E8069'='E8069 RR ACC NEC-PERSON NOS'
'E8070'='E8070 RR ACCIDENT NOS-EMPLOYEE'
'E8071'='E8071 RR ACC NOS-PASSENGER'
'E8072'='E8072 RR ACC NOS-PEDESTRIAN'
'E8073'='E8073 RR ACC NOS-PED CYCLIST'
'E8078'='E8078 RR ACC NOS-PERSON NEC'
'E8079'='E8079 RR ACC NOS-PERSON NOS'



'E8100'='E8100 MV-TRAIN COLL-DRIVER'
'E8101'='E8101 MV-TRAIN COLL-PASNGR'
'E8102'='E8102 MV-TRAIN COLL-MOTORCYCL'
'E8103'='E8103 MV-TRAIN COLL-MCYCL PSGR'
'E8104'='E8104 MV-TRAIN COLL-ST CAR'
'E8105'='E8105 MV-TRAIN COLL-ANIM RID'
'E8106'='E8106 MV-TRAIN COLL-PED CYCL'
'E8107'='E8107 MV-TRAIN COLL-PEDEST'
'E8108'='E8108 MV-TRAIN COLL-PERS NEC'
'E8109'='E8109 MV-TRAIN COLL-PERS NOS'
'E8110'='E8110 REENTRANT MV COLL-DRIVER'
'E8111'='E8111 REENTRANT MV COLL-PASNGR'
'E8112'='E8112 REENTRANT COLL-MOTCYCL'
'E8113'='E8113 REENTRANT COLL-MCYC PSGR'
'E8114'='E8114 REENTRANT COLL-ST CAR'
'E8115'='E8115 REENTRANT COLL-ANIM RID'
'E8116'='E8116 REENTRANT COLL-PED CYCL'
'E8117'='E8117 REENTRANT COLL-PEDEST'
'E8118'='E8118 REENTRANT COLL-PERS NEC'
'E8119'='E8119 REENTRANT COLL-PERS NOS'
'E8120'='E8120 MV COLLISION NOS-DRIVER'
'E8121'='E8121 MV COLLISION NOS-PASNGR'
'E8122'='E8122 MV COLLIS NOS-MOTORCYCL'
'E8123'='E8123 MV COLL NOS-MCYCL PSNGR'
'E8124'='E8124 MV COLLISION NOS-ST CAR'
'E8125'='E8125 MV COLL NOS-ANIM RID'
'E8126'='E8126 MV COLL NOS-PED CYCL'
'E8127'='E8127 MV COLLISION NOS-PEDEST'
'E8128'='E8128 MV COLLIS NOS-PERS NEC'
'E8129'='E8129 MV COLLIS NOS-PERS NOS'
'E8130'='E8130 MV-OTH VEH COLL-DRIVER'
'E8131'='E8131 MV-OTH VEH COLL-PASNGR'
'E8132'='E8132 MV-OTH VEH COLL-MOTCYCL'
'E8133'='E8133 MV-OTH VEH COLL-MCYC PSG'
'E8134'='E8134 MV-OTH VEH COLL-ST CAR'
'E8135'='E8135 MV-OTH VEH COLL-ANIM RID'
'E8136'='E8136 MV-OTH VEH COLL-PED CYCL'
'E8137'='E8137 MV-OTH VEH COLL-PEDEST'
'E8138'='E8138 MV-OTH VEH COLL-PERS NEC'
'E8139'='E8139 MV-OTH VEH COLL-PERS NOS'
'E8140'='E8140 MV COLL W PEDEST-DRIVER'
'E8141'='E8141 MV COLL W PEDEST-PASNGR'
'E8142'='E8142 MV COLL W PEDEST-MOTCYCL'
'E8143'='E8143 MV COLL W PED-MCYCL PSGR'
'E8144'='E8144 MV COLL W PEDEST-ST CAR'
'E8145'='E8145 MV COLL W PED-ANIM RID'
'E8146'='E8146 MV COLL W PED-PED CYCL'
'E8147'='E8147 MV COLL W PEDEST-PEDEST'

VALUE $proc2df



'00'='Blank/00:Procedures and interventns, NEC'
'01'='01:Incision and excision of skull, br...'
'02'='02:Other operations on skull, brain, ...'
'03'='03:Operations on spinal cord and spin...'
'04'='04:Operations on cranial and peripher...'
'05'='05:Operations on sympathetic nerves o...'
'06'='06:Operations on thyroid and parathyr...'
'07'='07:Operations on other endocrine glands'
'08'='08:Operations on eyelids'
'09'='09:Operations on lacrimal system'
'10'='10:Operations on conjunctiva'
'11'='11:Operations on cornea'
'12'='12:Operations on iris, ciliary body, ...'
'13'='13:Operations on lens'
'14'='14:Operations on retina, choroid, vit...'
'15'='15:Operations on extraocular muscles'
'16'='16:Operations on orbit and eyeball'
'18'='18:Operations on external ear'
'19'='19:Reconstructive operations on middl...'
'20'='20:Other operations on middle and inn...'
'21'='21:Operations on nose'
'22'='22:Operations on nasal sinuses'
'23'='23:Removal and restoration of teeth'
'24'='24:Other operations on teeth, gums, a...'
'25'='25:Operations on tongue'
'26'='26:Operations on salivary glands and ...'
'27'='27:Other operations on mouth and face'
'28'='28:Operations on tonsils and adenoids'
'29'='29:Operations on pharynx'
'30'='30:Excision of larynx'
'31'='31:Other operations on larynx and tra...'
'32'='32:Excision of lung and bronchus'
'33'='33:Other operations on lung and bronchus'
'34'='34:Operations on chest wall, pleura, ...'
'35'='35:Operations on valves and septa of ...'
'36'='36:Operations on vessels of heart'
'37'='37:Other operations on heart and peri...'
'38'='38:Incision, excision, and occlusion ...'
'39'='39:Other operations on vessels'
'40'='40:Operations on lymphatic system'
'41'='41:Operations on bone marrow and spleen'
'42'='42:Operations on esophagus'
'43'='43:Incision and excision of stomach'
'44'='44:Other operations on stomach'
'45'='45:Incision, excision, and anastomosi...'
'46'='46:Other operations on intestine'
'47'='47:Operations on appendix'
'48'='48:Operations on rectum, rectosigmoid...'
'49'='49:Operations on anus'
'50'='50:Operations on liver'



'51'='51:Operations on gallbladder and bili...'
'52'='52:Operations on pancreas'
'53'='53:Repair of hernia'
'54'='54:Other operations on abdominal region'
'55'='55:Operations on kidney'
'56'='56:Operations on ureter'
'57'='57:Operations on urinary bladder'
'58'='58:Operations on urethra'
'59'='59:Other operations on urinary tract'
'60'='60:Operations on prostate and seminal...'
'61'='61:Operations on scrotum and tunica v...'
'62'='62:Operations on testes'
'63'='63:Operations on spermatic cord, epid...'
'64'='64:Operations on penis'
'65'='65:Operations on ovary'
'66'='66:Operations on fallopian tubes'
'67'='67:Operations on cervix'
'68'='68:Other incision and excision of uterus'
'69'='69:Other operations on uterus and sup...'
'70'='70:Operations on vagina and cul-de-sac'
'71'='71:Operations on vulva and perineum'
'72'='72:Forceps, vacuum, and breech delivery'
'73'='73:Other procedures inducing or assis...'
'74'='74:Cesarean section and removal of fetus'
'75'='75:Other obstetric operations'
'76'='76:Operations on facial bones and joints'
'77'='77:Incision, excision, and division o...'
'78'='78:Other operations on bones, except ...'
'79'='79:Reduction of fracture and dislocation'
'80'='80:Incision and excision of joint str...'
'81'='81:Repair and plastic operations on j...'
'82'='82:Operations on muscle, tendon, and ...'
'83'='83:Operations on muscle, tendon, fasc...'
'84'='84:Other procedures on musculoskeleta...'
'85'='85:Operations on the breast'
'86'='86:Operations on skin and subcutaneou...'
'87'='87:Diagnostic radiology'
'88'='88:Other diagnostic radiology and rel...'
'89'='89:Interview, evaluation, consultatio...'
'90'='90:Microscopic examination I'
'91'='91:Microscopic examination II'
'92'='92:Nuclear medicine'
'93'='93:Physical therapy/respiratory thera...'
'94'='94:Procedures related to the psyche'
'95'='95:Ophthalmologic and otologic diagno...'
'96'='96:Nonoperative intubation and irriga...'
'97'='97:Replacement and removal of therape...'
'98'='98:Nonoperative removal of foreign body'
'99'='99:Other nonoperative procedures'
;



value $hospitalf

'010735'='ALAMEDA HOSPITAL'
'010739'='ALTA BATES SUMMIT MED CTR-ALTA BATES CAMPUS
'
'010776'='CHILDRENS HOSPITAL AND RESEARCH CTR AT
OAKLAND '
'010805'='EDEN MEDICAL CENTER '
'010846'='ALAMEDA CO MED CTR - HIGHLAND CAMPUS '
'010856'='KAISER FND HOSP - OAKLAND CAMPUS '
'010858'='KAISER FND HOSP - HAYWARD '
'010937'='ALTA BATES SUMMIT MED CTR-SUMMIT CAMPUS-
HAWTHORNE '
'010967'='ST. ROSE HOSPITAL '
'010983'='VALLEY MEMORIAL HOSPITAL - LIVERMORE '
'010987'='WASHINGTON HOSPITAL - FREMONT '
'013619'='SAN LEANDRO HOSPITAL '
'014132'='KAISER FND HOSP - FREMONT '
'034002'='SUTTER AMADOR HOSPITAL '
'040802'='BIGGS GRIDLEY MEMORIAL HOSPITAL '
'040875'='FEATHER RIVER HOSPITAL '
'040937'='OROVILLE HOSPITAL '
'040962'='ENLOE MEDICAL CENTER- ESPLANADE CAMPUS '
'050932'='MARK TWAIN ST. JOSEPHS HOSPITAL '
'060870'='COLUSA REGIONAL MEDICAL CENTER '
'070904'='DOCTORS MEDICAL CENTER - SAN PABLO '
'070924'='CONTRA COSTA REGIONAL MEDICAL CENTER '
'070934'='SUTTER DELTA MEDICAL CENTER '
'070988'='JOHN MUIR MEDICAL CENTER-WALNUT CREEK CAMPUS
'070990'='KAISER FND HOSP - WALNUT CREEK '
'071018'='JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS '
'074017'='SAN RAMON REGIONAL MEDICAL CENTER '
'074093'='KAISER FND HOSP - RICHMOND CAMPUS '
'074097'='KAISER FOUND HSP-ANTIOCH '
'084001'='SUTTER COAST HOSPITAL '
'090793'='BARTON MEMORIAL HOSPITAL '
'090933'='MARSHALL MEDICAL CENTER (1-RH) '
'100005'='COMMUNITY MEDICAL CENTER - CLOVIS '
'100697'='COALINGA REGIONAL MEDICAL CENTER '
'100717'='COMMUNITY REGIONAL MEDICAL CENTER-FRESNO '
'100745'='KINGSBURG MEDICAL CENTER '
'100797'='SIERRA KINGS DISTRICT HOSPITAL '
'100899'='ST. AGNES MEDICAL CENTER '
'104062'='KAISER FND HOSP - FRESNO '
'110889'='GLENN MEDICAL CENTER '
'121002'='MAD RIVER COMMUNITY HOSPITAL '
'121031'='JEROLD PHELPS COMMUNITY HOSPITAL '
'121051'='REDWOOD MEMORIAL HOSPITAL '
'121080'='ST JOSEPH HOSPITAL EUREKA '



'130699'='EL CENTRO REGIONAL MEDICAL CENTER '
'130760'=' PIONEERS MEMORIAL HOSPITAL '
'141273'='NORTHERN INYO HOSPITAL '
'141338'='SOUTHERN INYO HOSPITAL '
'150706'='DELANO REGIONAL MEDICAL CENTER '
'150722'='BAKERSFIELD MEMORIAL HOSPITAL- 34TH STREET '
'150736'='KERN MEDICAL CENTER'
'150737'='KERN VALLEY HEALTHCARE DISTRICT'
'150761'='MERCY HOSPITAL - BAKERSFIELD'
'150782'='RIDGECREST REGIONAL HOSPITAL '
'190148'='CENTINELA HOSPITAL MEDICAL CENTER'
'190155'='CENTURY CITY DOCTORS HOSPITAL'
'190159'='TRI-CITY REGIONAL MEDICAL CENTER'
'190170'='CHILDRENS HOSPITAL OF LOS ANGELES'
'190197'='COMMUNITY AND MISSION HSP OF HNTG PK -
SLAUSON'
'190198'='LOS ANGELES COMMUNITY HOSPITAL'
'190200'='SAN GABRIEL VALLEY MEDICAL CENTER'
'190240'='LAKEWOOD REGIONAL MEDICAL CENTER'
'190243'='DOWNEY REGIONAL MEDICAL CENTER'
'190256'='EAST LOS ANGELES DOCTORS HOSPITAL'
'190280'='ENCINO-TARZANA REGIONAL MED CTR-ENCINO'
'190298'='FOOTHILL PRESBYTERIAN HOSPITAL-JOHNSTON
MEMORIAL'
'190315'='GARFIELD MEDICAL CENTER'
'190323'='GLENDALE ADVENTIST MEDICAL CENTER - WILSON
TERRACE'
'190328'='EAST VALLEY HOSPITAL MEDICAL CENTER'
'190352'='GREATER EL MONTE COMMUNITY HOSPITAL'
'190382'='HOLLYWOOD PRESBYTERIAN MEDICAL CENTER'
'190385'='PROVIDENCE HOLY CROSS MEDICAL CENTER'
'190392'='GOOD SAMARITAN HOSPITAL-LOS ANGELES'
'190400'='HUNTINGTON MEMORIAL HOSPITAL'
'190413'='CITRUS VALLEY MEDICAL CENTER - IC CAMPUS'
'190422'='TORRANCE MEMORIAL MEDICAL CENTER'
'190429'='KAISER FND HOSP - SUNSET'
'190430'='KAISER FND HOSP - BELLFLOWER'
'190431'='KAISER FND HOSP - HARBOR CITY'
'190432'='KAISER FND HOSP - PANORAMA CITY'
'190434'='KAISER FND HOSP - WEST LA'
'190455'='LANCASTER COMMUNITY HOSPITAL'
'190470'='LITTLE COMPANY OF MARY HOSPITAL'
'190475'='COMMUNITY HOSPITAL OF LONG BEACH'
'190500'='CENTINELA FREEMAN REG MED CTR-MARINA CAMPUS'
'190517'='ENCINO-TARZANA REGIONAL MED CTR-TARZANA'
'190521'='MEMORIAL HOSPITAL OF GARDENA'
'190522'='GLENDALE MEMORIAL HOSPITAL AND HEALTH
CENTER'
'190524'='MISSION COMMUNITY HOSPITAL - PANORAMA
CAMPUS'
'190525'='LONG BEACH MEMORIAL MEDICAL CENTER'



value $countyf

'01' = 'Alameda'
'03' = 'Amador'
'04' = 'Butte'
'05' = 'Calaveras'
'06' = 'Colusa'
'07' = 'Contra Costa'
'09' = 'El Dorado'
'10' = 'Fresno'
'11' = 'Glenn'
'12' = 'Humboldt'
'13' = 'Imperial'
'15' = 'Kern'
'16' = 'Kings'
'17' = 'Lake'
'18' = 'Lassen'
'19' = 'Los Angeles'
'20' = 'Madera'
'21' = 'Marin'
'23' = 'Mendocino'
'24' = 'Merced'
'27' = 'Monterey'
'28' = 'Napa'
'29' = 'Nevada'
'30' = 'Orange'
'31' = 'Placer'
'33' = 'Riverside'
'34' = 'Sacramento'
'35' = 'San Benito'
'36' = 'San Bernardino'
'37' = 'San Diego'
'38' = 'San Francisco'
'39' = 'San Joaquin'
'40' = 'San Luis Obispo'
'41' = 'San Mateo'
'42' = 'Santa Barbara'
'43' = 'Santa Clara'
'44' = 'Santa Cruz'
'45' = 'Shasta'
'47' = 'Siskiyou'
'48' = 'Solano'
'49' = 'Sonoma'
'50' = 'Stanislaus'
'51' = 'Sutter'
'52' = 'Tehama'
'53' = 'Trinity'
'54' = 'Tulare'
'55' = 'Tuolumne'
'56' = 'Ventura'
'57' = 'Yolo'



'58' = 'Yuba'
'00' = 'County Unknown'
'CE' = 'Alpine, Inyo, Mariposa & Mono'
'NE' = 'Del Norte, Modoc, Plumas & Sierra'
'NW' = 'Colusa, Glenn & Trinity'

;

value $licencef
'C'='free standing'
'H'='hospital based'
;
value $agecat5f
'1' ='Under 1 year'
'2' ='1-17 years'
'3' ='18-34 years'
'4' ='35-64 years'
'5' ='65 years & over'
'*' ='masked age group'

;

value $sexf
'F' ='female'
'M' ='male'
'U' ='unknown sex'
'*' ='masked sex'
;

value $ethf

'E1'='Hispanic'
'E2'='non_Hispanic'
'99'='ukn_ethnic'
'*' ='masked ethnic'

;

value $racef

'R1' ='native american'
'R2' ='asian'
'R3' ='black'
'R4' ='hawaiian'
'R5' ='white'
'R9' ='other race'
'99' ='unknown race'
'*' ='masked race'
value $payerf



'09' ='selfpay'
'11' ='other non-federal'
'12' ='prefered provided org (PPO)'
'13' ='point of service (POS)'
'14' ='Exclusive Provider Organization (EPO)'
'16' ='Medicare HMO'
'AM' ='automible med'
'BL' ='Bluecross'
'CH' ='Tricare'
'CI' ='commercial'
'DS' ='disability'
'HM' ='other HMO'
'MA' ='Medicare Part A'
'MB' ='Medicare Part B'
'MC' ='Medical-Cal'
'OF' ='Other Federal'
'TV' ='Title V'
'VA' ='Veterans Adm'
'WC' ='Worker Comp'
'00' ='Other payer'
'99' ='Payer Blank'

;
value $dispnf

'01' = '(01)Sent home or self care'
'02' = '(02)Sent to a short term general care hospital or
inpatient care'
'03' = '(03)Sent to a skilled nursing facility with Medicare
certification'
'04' = '(04)Sent to an intermediate care facility'
'05' = '(05)Sent to another type of institution not on this
list'
'06' = '(06)Sent home under the care of a home health
service organization'
'07' = '(07)Left or discontinued care against medical
advice'
'20' = '(20)Died'
'43' = '(43)Sent to a federal health care facility'
'50' = '(50)Sent home with hospice care'
'51' = '(51)Sent to a medical facility with hospice care'
'61' = '(61)Sent to a hospital-based Medicare approved swing
bed'
'62' = '(62)Sent to an inpatient rehabilitation facility or
unit of a hospital'
'63' = '(63)Sent to a Medicare certified long term care
hospital'



'64' = '(64)Sent to a nursing facility certified under
Medicaid but not Medicare'
'65' = '(65)Sent to a psychiatric hospital or unit of a
hospital'
'66' = '(66)Sent to a critical access hospital'
'00' = '(00)Disposition Other'
'99' = '(99)Disposition Invalid_Blank'
;

value $serv_q

3 = 'Third Quarter 2007'
4 = 'Fourth Quarter 2007'
;
run;

data ed2007;
set osphd.caled2007 (keep=fac_id age_yrs agecat5 sex eth race
patzip patco serv_q dispn payer ec_prin
dx_prin odx1 odx2 pr_prin opr1);

label fac_id = 'Facility Number (6-digit)'
age_yrs = 'Age in Years at Service Date'
agecat5 = 'Age Categories 5'
sex = 'Sex'
eth = 'Ethnicity'
race = 'Race'
patzip = 'Patient Zip Code'
patco = 'Patient County of Residenc'
serv_q = 'Quarter of Service'
dispn = 'Disposition of the Patient'
payer = 'Expected Source of Payment'
ec_prin = 'Principal E-code'
dx_prin = 'Principal Diagnosis'
odx1 = 'Other Diagnosis 1'
odx2 = 'Other Diagnosis 2'
pr_prin = 'Principal Procedure'
opr1 = 'Other Procedure 1'

;
;
/* Substrings functions to select the*/
/* first N characters of a variable */
/* Must have length statement */
/* dx_prin is 5 characters long –we need first 3*/
/* pr_prin is 4 characters long –we need first 2*/

length dx_prin3 $3;
length pr_prin2 $2;



dx_prin3=substr(dx_prin,1,3);
pr_prin2=substr(pr_prin,1,2);

proc contents data=ed2007 varnum;
run;
proc freq data=ed2007;
tables agecat5 sex eth race patco
serv_q dispn payer
;
format agecat5 $agecat5f. sex $sexf. eth $ethf. race $racef.
patco $countyf. serv_q dispn $dispnf. payer $payerf.
;
run;

Below is the output of the Proc Contents for the California OSPHD data .
The SAS System


Data Set Name WORK.ED2007 Observations 4364548
Engine V9 Indexes 0
Created Saturday, August 08, 2009 06:29:19 PM Observation Length 65
Last Modified Saturday, August 08, 2009 06:29:19 PM Deleted Observations 0
Label


First Data Page 1
Max Obs per Page 125
Filename C:DOCUME~1RAYMON~1.AROLOCALS~1TempSAS
Temporary Files_TD2820ed2007.sas7bdat
Host Created XP_PRO

Variables in Creation Order

# Variable Type Len Label

1 fac_id Char 6 Facility Number (6-digit)
2 age_yrs Num 3 Age in Years at Service Date



3 agecat5 Char 1 Age Categories 5
4 sex Char 1 Sex
5 eth Char 2 Ethnicity
6 race Char 2 Race
7 patzip Char 5 Patient Zip Code
8 patco Char 2 Patient County of Residenc
9 serv_q Char 1 Quarter of Service
10 dispn Char 2 Disposition of the Patient
11 payer Char 2 Expected Source of Payment
12 ec_prin Char 7 Principal E-code
13 dx_prin Char 7 Principal Diagnosis
14 odx1 Char 7 Other Diagnosis 1
15 odx2 Char 7 Other Diagnosis 2
16 pr_prin Char 5 Principal Procedure
17 opr1 Char 5 Other Procedure 1

Below is the output of the Proc Freq for the California OSPHD data.
The SAS System

The FREQ Procedure

Age Categories 5

agecat5 Frequency Percent Frequency Percent
masked age group 207909 4.76 207909 4.76
0 198 0.00 208107 4.77
Under 1 year 161311 3.70 369418 8.46
1-17 years 908157 20.81 1277575 29.27
18-34 years 1111672 25.47 2389247 54.74
35-64 years 1465023 33.57 3854270 88.31
65 years & over 510278 11.69 4364548 100.00

Sex

sex Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
masked sex 442466 10.14 442466 10.14
female 2130477 48.81 2572943 58.95
male 1791507 41.05 4364450 100.00
unknown sex 98 0.00 4364548 100.00

Ethnicity

eth Frequency Percent Frequency Percent
masked ethnic 846250 19.39 846250 19.39
ukn_ethnic 168051 3.85 1014301 23.24
Hispanic 1190871 27.29 2205172 50.52



non_Hispanic 2159376 49.48 4364548 100.00

Race

race Frequency Percent Frequency Percent
masked race 710284 16.27 710284 16.27
unknown race 126835 2.91 837119 19.18
native american 11001 0.25 848120 19.43
asian 115621 2.65 963741 22.08
black 379891 8.70 1343632 30.79
hawaiian 14802 0.34 1358434 31.12
white 2311007 52.95 3669441 84.07
other race 695107 15.93 4364548 100.00
The SAS System

The FREQ Procedure

Patient County of Residence

patco Frequency Percent Frequency Percent
* 382 0.01 382 0.01
County Unknown 115702 2.65 116084 2.66
Alameda 186248 4.27 302332 6.93
Amador 5966 0.14 308298 7.06
Butte 29674 0.68 337972 7.74
Calaveras 6380 0.15 344352 7.89
Contra Costa 137924 3.16 482276 11.05
El Dorado 21978 0.50 504254 11.55
Fresno 115053 2.64 619307 14.19
Humboldt 23977 0.55 643284 14.74
Imperial 34185 0.78 677469 15.52
Kern 100344 2.30 777813 17.82
Kings 21597 0.49 799410 18.32
Lake 14922 0.34 814332 18.66
Lassen 4995 0.11 819327 18.77
Los Angeles 1051560 24.09 1870887 42.87
Madera 22816 0.52 1893703 43.39
Marin 29658 0.68 1923361 44.07
Mendocino 18836 0.43 1942197 44.50
Merced 35855 0.82 1978052 45.32
Monterey 55059 1.26 2033111 46.58
Napa 16339 0.37 2049450 46.96
Nevada 12242 0.28 2061692 47.24
Orange 281870 6.46 2343562 53.70
Placer 32879 0.75 2376441 54.45
Riverside 246686 5.65 2623127 60.10
Sacramento 164159 3.76 2787286 63.86
San Benito 8025 0.18 2795311 64.05
San Bernardino 267990 6.14 3063301 70.19
San Diego 302019 6.92 3365320 77.11
San Francisco 82529 1.89 3447849 79.00
San Joaquin 88816 2.03 3536665 81.03



San Luis Obispo 37074 0.85 3573739 81.88
San Mateo 74571 1.71 3648310 83.59
Santa Barbara 43457 1.00 3691767 84.59
Santa Clara 157136 3.60 3848903 88.19
Santa Cruz 29036 0.67 3877939 88.85
Shasta 35393 0.81 3913332 89.66
Siskiyou 8297 0.19 3921629 89.85
Solano 51526 1.18 3973155 91.03
Sonoma 54896 1.26 4028051 92.29
Stanislaus 85193 1.95 4113244 94.24
Sutter 12697 0.29 4125941 94.53
Tehama 13499 0.31 4139440 94.84
Tulare 67073 1.54 4206513 96.38
Tuolumne 9219 0.21 4215732 96.59
The SAS System

The FREQ Procedure

Patient County of Residenc

patco Frequency Percent Frequency Percent
Ventura 86190 1.97 4301922 98.57
Yolo 20390 0.47 4322312 99.03
Yuba 13573 0.31 4335885 99.34
Alpine, Inyo, Mariposa & Mono 7194 0.16 4343079 99.51
Del Norte, Modoc, Plumas & Sierra 13102 0.30 4356181 99.81
Colusa, Glenn & Trinity 8367 0.19 4364548 100.00

Quarter of Service

serv_q Frequency Percent Frequency Percent
* 133987 3.07 133987 3.07
Third Quarter 2007 2113448 48.42 2247435 51.49
Fourth Quarter 2007 2117113 48.51 4364548 100.00

Disposition of the Patient

dispn Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
(00)Disposition Other 35391 0.81
(01)Sent home or self care 4107639 94.11
(02)Sent to a short term general care hospital or inpatient care 56573 1.30
(03)Sent to a skilled nursing facility with Medicare certification 12374 0.28
(04)Sent to an intermediate care facility 2862 0.07
(05)Sent to another type of institution not on this list 17537 0.40
(06)Sent home under the care of a home health service organization 1978 0.05
(07)Left or discontinued care against medical advice 91514 2.10
(20)Died 7993 0.18
(43)Sent to a federal health care facility 351 0.01
(50)Sent home with hospice care 476 0.01
(51)Sent to a medical facility with hospice care 164 0.00



(61)Sent to a hospital-based Medicare approved swing bed 25 0.00
(62)Sent to an inpatient rehabilitation facility or unit of a hospital 690 0.02
(63)Sent to a Medicare certified long term care hospital 1621 0.04
(64)Sent to a nursing facility certified under Medicaid but not Medicare 266 0.01
(65)Sent to a psychiatric hospital or unit of a hospital 25800 0.59
(66)Sent to a critical access hospital 637 0.01
(99)Disposition Invalid_Blank 657 0.02

Disposition of the Patient

Cumulative
Cumulative
dispn Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
ƒ
(00)Disposition Other 35391 0.81
(01)Sent home or self care 4143030 94.92
(02)Sent to a short term general care hospital or inpatient care 4199603 96.22
(03)Sent to a skilled nursing facility with Medicare certification 4211977 96.50
(04)Sent to an intermediate care facility 4214839 96.57
(05)Sent to another type of institution not on this list 4232376 96.97
(06)Sent home under the care of a home health service organization 4234354 97.02
(07)Left or discontinued care against medical advice 4325868 99.11
(20)Died 4333861 99.30
(43)Sent to a federal health care facility 4334212 99.30
(50)Sent home with hospice care 4334688 99.32
(51)Sent to a medical facility with hospice care 4334852 99.32
(61)Sent to a hospital-based Medicare approved swing bed 4334877 99.32
(62)Sent to an inpatient rehabilitation facility or unit of a hospital 4335567 99.34
(63)Sent to a Medicare certified long term care hospital 4337188 99.37
(64)Sent to a nursing facility certified under Medicaid but not Medicare 4337454 99.38
(65)Sent to a psychiatric hospital or unit of a hospital 4363254 99.97
(66)Sent to a critical access hospital 4363891 99.98
(99)Disposition Invalid_Blank 4364548 100.00


payer Frequency Percent Frequency Percent
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Other payer 64563 1.48 64563 1.48
selfpay 763197 17.49 827760 18.97
other non-federal 102253 2.34 930013 21.31
prefered provided org (PPO) 264994 6.07 1195007 27.38
point of service (POS) 16455 0.38 1211462 27.76
Exclusive Provider Organization (EPO) 12904 0.30 1224366 28.05
Medicare HMO 181625 4.16 1405991 32.21
Payer Blank 1554 0.04 1407545 32.25
automible med 2660 0.06 1410205 32.31
Bluecross 240964 5.52 1651169 37.83
Tricare 31592 0.72 1682761 38.56
commercial 155151 3.55 1837912 42.11
disability 11 0.00 1837923 42.11
other HMO 886968 20.32 2724891 62.43
Medicare Part A 289889 6.64 3014780 69.07
Medicare Part B 159774 3.66 3174554 72.74



Medical-Cal 1064731 24.39 4239285 97.13
Other Federal 38440 0.88 4277725 98.01
Title V 3027 0.07 4280752 98.08
Veterans Adm 2754 0.06 4283506 98.14
Worker Comp 81042 1.86 4364548 100.00



Exercises 5.1

1. Produce a PROC FREQ in rank order of the emergency department principal diagnoses by
gender in the last half of 2007.
death in the last half of 2007.
3. Produce a PROC FREQ in rank order of the emergency department principal procedures by
ethnicity in the last half of 2007.
4. Produce a PROC FREQ in rank order of the emergency department principal injuries by
5. Produce a PROC FREQ in rank order of California EDs principal injuries by age groupings
in the last half of 2007.
6. Produce a PROC FREQ in rank order of California EDs principal injuries by death and
7. Briefly describe the findings in a paragraph for each exercise.

proc freq data=ed2007 order=freq;
tables dx_prin3*sex
;
patco $countyf. serv_q $serv_q. dispn $dispnf. payer $payerf.
dx_prin3 $diag3df.
;
title 'California EDs Principal Diagnoses by Gender';
run;

California EDs Principal Diagnoses by Gender

The FREQ Procedure

Table of dx_prin3 by sex

dx_prin3 sex(Sex)



Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚female ‚male ‚masked s‚unknown ‚ Total
‚ ‚ ‚ex ‚sex ‚
(789) Other symp ‚ 129614 ‚ 69805 ‚ 18853 ‚ 4 ‚ 218276
toms involving a ‚ 2.97 ‚ 1.60 ‚ 0.43 ‚ 0.00 ‚ 5.00
bdome... ‚ 59.38 ‚ 31.98 ‚ 8.64 ‚ 0.00 ‚
‚ 6.08 ‚ 3.90 ‚ 4.26 ‚ 4.08 ‚
(780) General sy ‚ 100612 ‚ 88454 ‚ 22413 ‚ 1 ‚ 211480
mptoms ‚ 2.31 ‚ 2.03 ‚ 0.51 ‚ 0.00 ‚ 4.85
‚ 47.58 ‚ 41.83 ‚ 10.60 ‚ 0.00 ‚
‚ 4.72 ‚ 4.94 ‚ 5.07 ‚ 1.02 ‚
(786) Symptoms i ‚ 94734 ‚ 76502 ‚ 19431 ‚ 3 ‚ 190670
nvolving respira ‚ 2.17 ‚ 1.75 ‚ 0.45 ‚ 0.00 ‚ 4.37
tory ... ‚ 49.68 ‚ 40.12 ‚ 10.19 ‚ 0.00 ‚
‚ 4.45 ‚ 4.27 ‚ 4.39 ‚ 3.06 ‚
(873) Other open ‚ 30529 ‚ 62446 ‚ 13427 ‚ 6 ‚ 106408
wound of head ‚ 0.70 ‚ 1.43 ‚ 0.31 ‚ 0.00 ‚ 2.44
‚ 28.69 ‚ 58.69 ‚ 12.62 ‚ 0.01 ‚
‚ 1.43 ‚ 3.49 ‚ 3.03 ‚ 6.12 ‚
(682) Other cell ‚ 43151 ‚ 50131 ‚ 7485 ‚ 2 ‚ 100769
ulitis and absce ‚ 0.99 ‚ 1.15 ‚ 0.17 ‚ 0.00 ‚ 2.31
ss ‚ 42.82 ‚ 49.75 ‚ 7.43 ‚ 0.00 ‚
‚ 2.03 ‚ 2.80 ‚ 1.69 ‚ 2.04 ‚
(784) Symptoms i ‚ 57975 ‚ 32752 ‚ 8803 ‚ 2 ‚ 99532
nvolving head an ‚ 1.33 ‚ 0.75 ‚ 0.20 ‚ 0.00 ‚ 2.28
d neck ‚ 58.25 ‚ 32.91 ‚ 8.84 ‚ 0.00 ‚
‚ 2.72 ‚ 1.83 ‚ 1.99 ‚ 2.04 ‚
(787) Symptoms i ‚ 48489 ‚ 36641 ‚ 8368 ‚ 4 ‚ 93502
nvolving digesti ‚ 1.11 ‚ 0.84 ‚ 0.19 ‚ 0.00 ‚ 2.14
ve sy... ‚ 51.86 ‚ 39.19 ‚ 8.95 ‚ 0.00 ‚
‚ 2.28 ‚ 2.05 ‚ 1.89 ‚ 4.08 ‚
(724) Other and ‚ 48012 ‚ 35686 ‚ 8590 ‚ 2 ‚ 92290
unspecified diso ‚ 1.10 ‚ 0.82 ‚ 0.20 ‚ 0.00 ‚ 2.11
rders... ‚ 52.02 ‚ 38.67 ‚ 9.31 ‚ 0.00 ‚
‚ 2.25 ‚ 1.99 ‚ 1.94 ‚ 2.04 ‚
(465) Acute uppe ‚ 40474 ‚ 39119 ‚ 6074 ‚ 0 ‚ 85667
r respiratory in ‚ 0.93 ‚ 0.90 ‚ 0.14 ‚ 0.00 ‚ 1.96
fecti... ‚ 47.25 ‚ 45.66 ‚ 7.09 ‚ 0.00 ‚
‚ 1.90 ‚ 2.18 ‚ 1.37 ‚ 0.00 ‚
(599) Other diso ‚ 60969 ‚ 16134 ‚ 8441 ‚ 0 ‚ 85544
rders of urethra ‚ 1.40 ‚ 0.37 ‚ 0.19 ‚ 0.00 ‚ 1.96
and ... ‚ 71.27 ‚ 18.86 ‚ 9.87 ‚ 0.00 ‚
‚ 2.86 ‚ 0.90 ‚ 1.91 ‚ 0.00 ‚



(847) Sprains an ‚ 41512 ‚ 30093 ‚ 11728 ‚ 1 ‚ 83334
d strains of oth ‚ 0.95 ‚ 0.69 ‚ 0.27 ‚ 0.00 ‚ 1.91
er an... ‚ 49.81 ‚ 36.11 ‚ 14.07 ‚ 0.00 ‚
‚ 1.95 ‚ 1.68 ‚ 2.65 ‚ 1.02 ‚
(959) Injury, ot ‚ 26923 ‚ 32218 ‚ 8585 ‚ 1 ‚ 67727
her and unspecif ‚ 0.62 ‚ 0.74 ‚ 0.20 ‚ 0.00 ‚ 1.55
ied ‚ 39.75 ‚ 47.57 ‚ 12.68 ‚ 0.00 ‚
‚ 1.26 ‚ 1.80 ‚ 1.94 ‚ 1.02 ‚
(493) Asthma ‚ 31909 ‚ 28945 ‚ 5559 ‚ 4 ‚ 66417
‚ 0.73 ‚ 0.66 ‚ 0.13 ‚ 0.00 ‚ 1.52
‚ 48.04 ‚ 43.58 ‚ 8.37 ‚ 0.01 ‚
‚ 1.50 ‚ 1.62 ‚ 1.26 ‚ 4.08 ‚
(382) Suppurativ ‚ 28101 ‚ 31329 ‚ 4974 ‚ 0 ‚ 64404
e and unspecifie ‚ 0.64 ‚ 0.72 ‚ 0.11 ‚ 0.00 ‚ 1.48
d oti... ‚ 43.63 ‚ 48.64 ‚ 7.72 ‚ 0.00 ‚
‚ 1.32 ‚ 1.75 ‚ 1.12 ‚ 0.00 ‚
(462) Acute phar ‚ 28880 ‚ 22200 ‚ 5015 ‚ 0 ‚ 56095
yngitis ‚ 0.66 ‚ 0.51 ‚ 0.11 ‚ 0.00 ‚ 1.29
‚ 51.48 ‚ 39.58 ‚ 8.94 ‚ 0.00 ‚
‚ 1.36 ‚ 1.24 ‚ 1.13 ‚ 0.00 ‚
(558) Other noni ‚ 28304 ‚ 22571 ‚ 5002 ‚ 1 ‚ 55878
nfective gastroe ‚ 0.65 ‚ 0.52 ‚ 0.11 ‚ 0.00 ‚ 1.28
nteri... ‚ 50.65 ‚ 40.39 ‚ 8.95 ‚ 0.00 ‚
‚ 1.33 ‚ 1.26 ‚ 1.13 ‚ 1.02 ‚
(V58) Other and ‚ 19777 ‚ 32117 ‚ 1537 ‚ 4 ‚ 53435
unspecified afte ‚ 0.45 ‚ 0.74 ‚ 0.04 ‚ 0.00 ‚ 1.22
rcare ‚ 37.01 ‚ 60.10 ‚ 2.88 ‚ 0.01 ‚
‚ 0.93 ‚ 1.79 ‚ 0.35 ‚ 4.08 ‚
(883) Open wound ‚ 16917 ‚ 27740 ‚ 6625 ‚ 0 ‚ 51282
of finger(s) ‚ 0.39 ‚ 0.64 ‚ 0.15 ‚ 0.00 ‚ 1.17
‚ 32.99 ‚ 54.09 ‚ 12.92 ‚ 0.00 ‚
‚ 0.79 ‚ 1.55 ‚ 1.50 ‚ 0.00 ‚
Total 2130477 1791507 442466 98 4364548
48.81 41.05 10.14 0.00 100.00

Below is SAS code for exercise 5.1-2
tables dx_prin3*eth
;



patco $countyf. serv_q $serv_q. dispn $dispnf. payer $payerf.
dx_prin3 $diag3df.
;
title 'California EDs Principal Diagnoses by Ethnicity';
run;

Below is the partial PROC FREQ output for exercise 5.1-2
California EDs Principal Diagnoses by Ethnicity

Table of dx_prin3 by eth

dx_prin3 eth(Ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚non_Hisp‚Hispanic‚masked e‚ukn_ethn‚ Total
‚anic ‚ ‚thnic ‚ic ‚
(789) Other symp ‚ 105592 ‚ 64591 ‚ 39413 ‚ 8680 ‚ 218276
toms involving a ‚ 2.42 ‚ 1.48 ‚ 0.90 ‚ 0.20 ‚ 5.00
bdome... ‚ 48.38 ‚ 29.59 ‚ 18.06 ‚ 3.98 ‚
‚ 4.89 ‚ 5.42 ‚ 4.66 ‚ 5.17 ‚
(780) General sy ‚ 100912 ‚ 57826 ‚ 43908 ‚ 8834 ‚ 211480
mptoms ‚ 2.31 ‚ 1.32 ‚ 1.01 ‚ 0.20 ‚ 4.85
‚ 47.72 ‚ 27.34 ‚ 20.76 ‚ 4.18 ‚
‚ 4.67 ‚ 4.86 ‚ 5.19 ‚ 5.26 ‚
(786) Symptoms i ‚ 102049 ‚ 40836 ‚ 40297 ‚ 7488 ‚ 190670
nvolving respira ‚ 2.34 ‚ 0.94 ‚ 0.92 ‚ 0.17 ‚ 4.37
tory ... ‚ 53.52 ‚ 21.42 ‚ 21.13 ‚ 3.93 ‚
‚ 4.73 ‚ 3.43 ‚ 4.76 ‚ 4.46 ‚
(873) Other open ‚ 49506 ‚ 29160 ‚ 23129 ‚ 4613 ‚ 106408
wound of head ‚ 1.13 ‚ 0.67 ‚ 0.53 ‚ 0.11 ‚ 2.44
‚ 46.52 ‚ 27.40 ‚ 21.74 ‚ 4.34 ‚
‚ 2.29 ‚ 2.45 ‚ 2.73 ‚ 2.75 ‚
(682) Other cell ‚ 57808 ‚ 25348 ‚ 13922 ‚ 3691 ‚ 100769
ulitis and absce ‚ 1.32 ‚ 0.58 ‚ 0.32 ‚ 0.08 ‚ 2.31
ss ‚ 57.37 ‚ 25.15 ‚ 13.82 ‚ 3.66 ‚
‚ 2.68 ‚ 2.13 ‚ 1.65 ‚ 2.20 ‚
(784) Symptoms i ‚ 50765 ‚ 26973 ‚ 17854 ‚ 3940 ‚ 99532
nvolving head an ‚ 1.16 ‚ 0.62 ‚ 0.41 ‚ 0.09 ‚ 2.28
d neck ‚ 51.00 ‚ 27.10 ‚ 17.94 ‚ 3.96 ‚
‚ 2.35 ‚ 2.26 ‚ 2.11 ‚ 2.34 ‚
(787) Symptoms i ‚ 43247 ‚ 30148 ‚ 16638 ‚ 3469 ‚ 93502
nvolving digesti ‚ 0.99 ‚ 0.69 ‚ 0.38 ‚ 0.08 ‚ 2.14
ve sy... ‚ 46.25 ‚ 32.24 ‚ 17.79 ‚ 3.71 ‚
‚ 2.00 ‚ 2.53 ‚ 1.97 ‚ 2.06 ‚
(724) Other and ‚ 53749 ‚ 19096 ‚ 16014 ‚ 3431 ‚ 92290



unspecified diso ‚ 1.23 ‚ 0.44 ‚ 0.37 ‚ 0.08 ‚ 2.11
rders... ‚ 58.24 ‚ 20.69 ‚ 17.35 ‚ 3.72 ‚
‚ 2.49 ‚ 1.60 ‚ 1.89 ‚ 2.04 ‚
(465) Acute uppe ‚ 30241 ‚ 39662 ‚ 12804 ‚ 2960 ‚ 85667
r respiratory in ‚ 0.69 ‚ 0.91 ‚ 0.29 ‚ 0.07 ‚ 1.96
fecti... ‚ 35.30 ‚ 46.30 ‚ 14.95 ‚ 3.46 ‚
‚ 1.40 ‚ 3.33 ‚ 1.51 ‚ 1.76 ‚
(599) Other diso ‚ 41656 ‚ 24426 ‚ 16165 ‚ 3297 ‚ 85544
rders of urethra ‚ 0.95 ‚ 0.56 ‚ 0.37 ‚ 0.08 ‚ 1.96
and ... ‚ 48.70 ‚ 28.55 ‚ 18.90 ‚ 3.85 ‚
‚ 1.93 ‚ 2.05 ‚ 1.91 ‚ 1.96 ‚
(847) Sprains an ‚ 42258 ‚ 17390 ‚ 20764 ‚ 2922 ‚ 83334
d strains of oth ‚ 0.97 ‚ 0.40 ‚ 0.48 ‚ 0.07 ‚ 1.91
er an... ‚ 50.71 ‚ 20.87 ‚ 24.92 ‚ 3.51 ‚
‚ 1.96 ‚ 1.46 ‚ 2.45 ‚ 1.74 ‚
(959) Injury, ot ‚ 33009 ‚ 15450 ‚ 15873 ‚ 3395 ‚ 67727
her and unspecif ‚ 0.76 ‚ 0.35 ‚ 0.36 ‚ 0.08 ‚ 1.55
ied ‚ 48.74 ‚ 22.81 ‚ 23.44 ‚ 5.01 ‚
‚ 1.53 ‚ 1.30 ‚ 1.88 ‚ 2.02 ‚
(493) Asthma ‚ 33202 ‚ 19715 ‚ 11300 ‚ 2200 ‚ 66417
‚ 0.76 ‚ 0.45 ‚ 0.26 ‚ 0.05 ‚ 1.52
‚ 49.99 ‚ 29.68 ‚ 17.01 ‚ 3.31 ‚
‚ 1.54 ‚ 1.66 ‚ 1.34 ‚ 1.31 ‚
(382) Suppurativ ‚ 21739 ‚ 30355 ‚ 9725 ‚ 2585 ‚ 64404
e and unspecifie ‚ 0.50 ‚ 0.70 ‚ 0.22 ‚ 0.06 ‚ 1.48
d oti... ‚ 33.75 ‚ 47.13 ‚ 15.10 ‚ 4.01 ‚
‚ 1.01 ‚ 2.55 ‚ 1.15 ‚ 1.54 ‚
Total 2159376 1190871 846250 168051 4364548

3. Produce a PROC FREQ in rank order of the emergency department principal procedures by
/*3. Produce a PROC FREQ in rank order of California EDs principal
procedures by ethnicity in the last half of 2007.*/

tables pr_prin2*eth
;
patco $countyf. serv_q $serv_q. pr_prin2 $proc2df. payer $payerf.
dx_prin3 $diag3df.
;
title 'California EDs Principal Procedures by Ethnicity';
run;



California EDs Principal Procedures by Ethnicity

The FREQ Procedure
Table of pr_prin2 by eth

pr_prin2 eth(Ethnicity)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚non_Hisp‚Hispanic‚masked e‚ukn_ethn‚ Total
‚anic ‚ ‚thnic ‚ic ‚
* ‚ 969256 ‚ 518622 ‚ 381057 ‚ 112715 ‚1981650
‚ 22.21 ‚ 11.88 ‚ 8.73 ‚ 2.58 ‚ 45.40
‚ 48.91 ‚ 26.17 ‚ 19.23 ‚ 5.69 ‚
‚ 44.89 ‚ 43.55 ‚ 45.03 ‚ 67.07 ‚
99:Other nonoper ‚ 575324 ‚ 351481 ‚ 231247 ‚ 25840 ‚1183892
ative procedures ‚ 13.18 ‚ 8.05 ‚ 5.30 ‚ 0.59 ‚ 27.13
‚ 48.60 ‚ 29.69 ‚ 19.53 ‚ 2.18 ‚
‚ 26.64 ‚ 29.51 ‚ 27.33 ‚ 15.38 ‚
90:Microscopic e ‚ 96949 ‚ 47912 ‚ 34675 ‚ 6000 ‚ 185536
xamination I ‚ 2.22 ‚ 1.10 ‚ 0.79 ‚ 0.14 ‚ 4.25
‚ 52.25 ‚ 25.82 ‚ 18.69 ‚ 3.23 ‚
‚ 4.49 ‚ 4.02 ‚ 4.10 ‚ 3.57 ‚
12:Operations on ‚ 70153 ‚ 37377 ‚ 32054 ‚ 4622 ‚ 144206
iris, ciliary b ‚ 1.61 ‚ 0.86 ‚ 0.73 ‚ 0.11 ‚ 3.30
ody, ... ‚ 48.65 ‚ 25.92 ‚ 22.23 ‚ 3.21 ‚
‚ 3.25 ‚ 3.14 ‚ 3.79 ‚ 2.75 ‚
36:Operations on ‚ 78384 ‚ 30773 ‚ 25689 ‚ 821 ‚ 135667
vessels of hear ‚ 1.80 ‚ 0.71 ‚ 0.59 ‚ 0.02 ‚ 3.11
t ‚ 57.78 ‚ 22.68 ‚ 18.94 ‚ 0.61 ‚
‚ 3.63 ‚ 2.58 ‚ 3.04 ‚ 0.49 ‚
29:Operations on ‚ 44832 ‚ 21534 ‚ 20636 ‚ 2067 ‚ 89069
pharynx ‚ 1.03 ‚ 0.49 ‚ 0.47 ‚ 0.05 ‚ 2.04
‚ 50.33 ‚ 24.18 ‚ 23.17 ‚ 2.32 ‚
‚ 2.08 ‚ 1.81 ‚ 2.44 ‚ 1.23 ‚
73:Other procedu ‚ 38855 ‚ 20048 ‚ 15955 ‚ 2786 ‚ 77644
res inducing or ‚ 0.89 ‚ 0.46 ‚ 0.37 ‚ 0.06 ‚ 1.78
assis... ‚ 50.04 ‚ 25.82 ‚ 20.55 ‚ 3.59 ‚
‚ 1.80 ‚ 1.68 ‚ 1.89 ‚ 1.66 ‚
71:Operations on ‚ 30687 ‚ 18477 ‚ 12017 ‚ 3656 ‚ 64837
vulva and perin ‚ 0.70 ‚ 0.42 ‚ 0.28 ‚ 0.08 ‚ 1.49
eum ‚ 47.33 ‚ 28.50 ‚ 18.53 ‚ 5.64 ‚
‚ 1.42 ‚ 1.55 ‚ 1.42 ‚ 2.18 ‚
80:Incision and ‚ 35877 ‚ 18560 ‚ 9577 ‚ 175 ‚ 64189
excision of join ‚ 0.82 ‚ 0.43 ‚ 0.22 ‚ 0.00 ‚ 1.47



t str... ‚ 55.89 ‚ 28.91 ‚ 14.92 ‚ 0.27 ‚
‚ 1.66 ‚ 1.56 ‚ 1.13 ‚ 0.10 ‚
85:Operations on ‚ 20445 ‚ 11068 ‚ 7389 ‚ 131 ‚ 39033
the breast ‚ 0.47 ‚ 0.25 ‚ 0.17 ‚ 0.00 ‚ 0.89
‚ 52.38 ‚ 28.36 ‚ 18.93 ‚ 0.34 ‚
‚ 0.95 ‚ 0.93 ‚ 0.87 ‚ 0.08 ‚
93:Physical ther ‚ 22674 ‚ 7313 ‚ 8578 ‚ 441 ‚ 39006
apy/respiratory ‚ 0.52 ‚ 0.17 ‚ 0.20 ‚ 0.01 ‚ 0.89
thera... ‚ 58.13 ‚ 18.75 ‚ 21.99 ‚ 1.13 ‚
‚ 1.05 ‚ 0.61 ‚ 1.01 ‚ 0.26 ‚
81:Repair and pl ‚ 17178 ‚ 14415 ‚ 6150 ‚ 151 ‚ 37894
astic operations ‚ 0.39 ‚ 0.33 ‚ 0.14 ‚ 0.00 ‚ 0.87
on j... ‚ 45.33 ‚ 38.04 ‚ 16.23 ‚ 0.40 ‚
‚ 0.80 ‚ 1.21 ‚ 0.73 ‚ 0.09 ‚
10:Operations on ‚ 19775 ‚ 9072 ‚ 4782 ‚ 1090 ‚ 34719
conjunctiva ‚ 0.45 ‚ 0.21 ‚ 0.11 ‚ 0.02 ‚ 0.80
‚ 56.96 ‚ 26.13 ‚ 13.77 ‚ 3.14 ‚
‚ 0.92 ‚ 0.76 ‚ 0.57 ‚ 0.65 ‚
70:Operations on ‚ 16354 ‚ 8521 ‚ 7042 ‚ 1119 ‚ 33036
vagina and cul- ‚ 0.37 ‚ 0.20 ‚ 0.16 ‚ 0.03 ‚ 0.76
de-sac ‚ 49.50 ‚ 25.79 ‚ 21.32 ‚ 3.39 ‚
‚ 0.76 ‚ 0.72 ‚ 0.83 ‚ 0.67 ‚

94:Procedures re ‚ 12613 ‚ 14386 ‚ 4928 ‚ 405 ‚ 32332
lated to the psy ‚ 0.29 ‚ 0.33 ‚ 0.11 ‚ 0.01 ‚ 0.74
che ‚ 39.01 ‚ 44.49 ‚ 15.24 ‚ 1.25 ‚
‚ 0.58 ‚ 1.21 ‚ 0.58 ‚ 0.24 ‚
51:Operations on ‚ 16513 ‚ 7031 ‚ 5076 ‚ 1030 ‚ 29650
gallbladder and ‚ 0.38 ‚ 0.16 ‚ 0.12 ‚ 0.02 ‚ 0.68
bili... ‚ 55.69 ‚ 23.71 ‚ 17.12 ‚ 3.47 ‚
‚ 0.76 ‚ 0.59 ‚ 0.60 ‚ 0.61 ‚
72:Forceps, vacu ‚ 13760 ‚ 6330 ‚ 5959 ‚ 1094 ‚ 27143
um, and breech d ‚ 0.32 ‚ 0.15 ‚ 0.14 ‚ 0.03 ‚ 0.62
elivery ‚ 50.69 ‚ 23.32 ‚ 21.95 ‚ 4.03 ‚
‚ 0.64 ‚ 0.53 ‚ 0.70 ‚ 0.65 ‚
87:Diagnostic ra ‚ 9060 ‚ 6079 ‚ 2556 ‚ 94 ‚ 17789
diology ‚ 0.21 ‚ 0.14 ‚ 0.06 ‚ 0.00 ‚ 0.41
‚ 50.93 ‚ 34.17 ‚ 14.37 ‚ 0.53 ‚
‚ 0.42 ‚ 0.51 ‚ 0.30 ‚ 0.06 ‚
82:Operations on ‚ 7632 ‚ 4190 ‚ 3411 ‚ 45 ‚ 15278
muscle, tendon, ‚ 0.17 ‚ 0.10 ‚ 0.08 ‚ 0.00 ‚ 0.35
and ... ‚ 49.95 ‚ 27.43 ‚ 22.33 ‚ 0.29 ‚
‚ 0.35 ‚ 0.35 ‚ 0.40 ‚ 0.03 ‚
76:Operations on ‚ 5374 ‚ 6280 ‚ 2461 ‚ 793 ‚ 14908
facial bones an ‚ 0.12 ‚ 0.14 ‚ 0.06 ‚ 0.02 ‚ 0.34
d joints ‚ 36.05 ‚ 42.13 ‚ 16.51 ‚ 5.32 ‚



‚ 0.25 ‚ 0.53 ‚ 0.29 ‚ 0.47 ‚
74:Cesarean sect ‚ 7408 ‚ 4012 ‚ 2672 ‚ 303 ‚ 14395
ion and removal ‚ 0.17 ‚ 0.09 ‚ 0.06 ‚ 0.01 ‚ 0.33
of fetus ‚ 51.46 ‚ 27.87 ‚ 18.56 ‚ 2.10 ‚
‚ 0.34 ‚ 0.34 ‚ 0.32 ‚ 0.18 ‚
84:Other procedu ‚ 2898 ‚ 3296 ‚ 1086 ‚ 9 ‚ 7289
res on musculosk ‚ 0.07 ‚ 0.08 ‚ 0.02 ‚ 0.00 ‚ 0.17
eleta... ‚ 39.76 ‚ 45.22 ‚ 14.90 ‚ 0.12 ‚
‚ 0.13 ‚ 0.28 ‚ 0.13 ‚ 0.01 ‚
62:Operations on ‚ 3513 ‚ 2025 ‚ 1260 ‚ 129 ‚ 6927
testes ‚ 0.08 ‚ 0.05 ‚ 0.03 ‚ 0.00 ‚ 0.16
‚ 50.71 ‚ 29.23 ‚ 18.19 ‚ 1.86 ‚
‚ 0.16 ‚ 0.17 ‚ 0.15 ‚ 0.08 ‚
11:Operations on ‚ 2869 ‚ 2337 ‚ 1435 ‚ 226 ‚ 6867
cornea ‚ 0.07 ‚ 0.05 ‚ 0.03 ‚ 0.01 ‚ 0.16
‚ 41.78 ‚ 34.03 ‚ 20.90 ‚ 3.29 ‚
‚ 0.13 ‚ 0.20 ‚ 0.17 ‚ 0.13 ‚

Total 2159376 1190871 846250 168051 4364548
49.48 27.29 19.39 3.85 100.00

4. Produce a PROC FREQ in rank order of the emergency department principal injuries by

tables ec_prin*sex
;
dx_prin3 $diag3df. ec_prin $ecodef.

;
title 'California EDs Principal Injuries by Gender';
run;

Below is Partial PROC FREQ output for exercise 5.1-4.
California EDs Principal Injuries by Gender

The FREQ Procedure

Table of ec_prin by sex



ec_prin(Principal E-code) sex(Sex)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚female ‚male ‚masked s‚unknown ‚ Total
‚ ‚ ‚ex ‚sex ‚
E8859 FALL FROM ‚ 52345 ‚ 32982 ‚ 12630 ‚ 0 ‚ 97957
SLIPPING NEC ‚ 4.68 ‚ 2.95 ‚ 1.13 ‚ 0.00 ‚ 8.76
‚ 53.44 ‚ 33.67 ‚ 12.89 ‚ 0.00 ‚
‚ 11.68 ‚ 6.28 ‚ 8.70 ‚ 0.00 ‚
E927 ACCID FROM ‚ 39039 ‚ 40638 ‚ 11591 ‚ 4 ‚ 91272
OVEREXERTION ‚ 3.49 ‚ 3.63 ‚ 1.04 ‚ 0.00 ‚ 8.16
‚ 42.77 ‚ 44.52 ‚ 12.70 ‚ 0.00 ‚
‚ 8.71 ‚ 7.74 ‚ 7.98 ‚ 11.43 ‚
E8889 FALL NOS ‚ 32796 ‚ 27514 ‚ 7752 ‚ 1 ‚ 68063
‚ 2.93 ‚ 2.46 ‚ 0.69 ‚ 0.00 ‚ 6.08
‚ 48.18 ‚ 40.42 ‚ 11.39 ‚ 0.00 ‚
‚ 7.32 ‚ 5.24 ‚ 5.34 ‚ 2.86 ‚
E9179 OBJ W-W/O ‚ 21557 ‚ 33171 ‚ 7846 ‚ 1 ‚ 62575
SUB FALL NEC ‚ 1.93 ‚ 2.96 ‚ 0.70 ‚ 0.00 ‚ 5.59
‚ 34.45 ‚ 53.01 ‚ 12.54 ‚ 0.00 ‚
‚ 4.81 ‚ 6.31 ‚ 5.40 ‚ 2.86 ‚
E9289 ACCIDENT N ‚ 25772 ‚ 26093 ‚ 6099 ‚ 2 ‚ 57966
OS ‚ 2.30 ‚ 2.33 ‚ 0.55 ‚ 0.00 ‚ 5.18
‚ 44.46 ‚ 45.01 ‚ 10.52 ‚ 0.00 ‚
‚ 5.75 ‚ 4.97 ‚ 4.20 ‚ 5.71 ‚
E9208 ACC-CUTTIN ‚ 16070 ‚ 27168 ‚ 6184 ‚ 0 ‚ 49422
G INSTRUM NEC ‚ 1.44 ‚ 2.43 ‚ 0.55 ‚ 0.00 ‚ 4.42
‚ 32.52 ‚ 54.97 ‚ 12.51 ‚ 0.00 ‚
‚ 3.59 ‚ 5.17 ‚ 4.26 ‚ 0.00 ‚
E8120 MV COLLISI ‚ 22556 ‚ 15478 ‚ 7652 ‚ 1 ‚ 45687
ON NOS-DRIVER ‚ 2.02 ‚ 1.38 ‚ 0.68 ‚ 0.00 ‚ 4.08
‚ 49.37 ‚ 33.88 ‚ 16.75 ‚ 0.00 ‚
‚ 5.03 ‚ 2.95 ‚ 5.27 ‚ 2.86 ‚
E9170 SPORTS ACC ‚ 5162 ‚ 21434 ‚ 4367 ‚ 0 ‚ 30963
W/O SUB FALL ‚ 0.46 ‚ 1.92 ‚ 0.39 ‚ 0.00 ‚ 2.77
‚ 16.67 ‚ 69.22 ‚ 14.10 ‚ 0.00 ‚
‚ 1.15 ‚ 4.08 ‚ 3.01 ‚ 0.00 ‚
E9600 UNARMED FI ‚ 6777 ‚ 15652 ‚ 2664 ‚ 3 ‚ 25096
GHT OR BRAWL ‚ 0.61 ‚ 1.40 ‚ 0.24 ‚ 0.00 ‚ 2.24
‚ 27.00 ‚ 62.37 ‚ 10.62 ‚ 0.01 ‚
‚ 1.51 ‚ 2.98 ‚ 1.83 ‚ 8.57 ‚
E8121 MV COLLISI ‚ 11501 ‚ 6512 ‚ 4565 ‚ 2 ‚ 22580
ON NOS-PASNGR ‚ 1.03 ‚ 0.58 ‚ 0.41 ‚ 0.00 ‚ 2.02
‚ 50.93 ‚ 28.84 ‚ 20.22 ‚ 0.01 ‚



‚ 2.57 ‚ 1.24 ‚ 3.14 ‚ 5.71 ‚
E8881 FALL STRIK ‚ 8173 ‚ 9917 ‚ 2343 ‚ 0 ‚ 20433
ING OBJECT NEC ‚ 0.73 ‚ 0.89 ‚ 0.21 ‚ 0.00 ‚ 1.83
‚ 40.00 ‚ 48.53 ‚ 11.47 ‚ 0.00 ‚
‚ 1.82 ‚ 1.89 ‚ 1.61 ‚ 0.00 ‚
E9288 ACCIDENT N ‚ 7658 ‚ 9596 ‚ 2609 ‚ 0 ‚ 19863
EC ‚ 0.68 ‚ 0.86 ‚ 0.23 ‚ 0.00 ‚ 1.78
‚ 38.55 ‚ 48.31 ‚ 13.13 ‚ 0.00 ‚
‚ 1.71 ‚ 1.83 ‚ 1.80 ‚ 0.00 ‚
E8888 FALL NEC ‚ 9193 ‚ 8209 ‚ 2292 ‚ 0 ‚ 19694
‚ 0.82 ‚ 0.73 ‚ 0.20 ‚ 0.00 ‚ 1.76
‚ 46.68 ‚ 41.68 ‚ 11.64 ‚ 0.00 ‚
‚ 2.05 ‚ 1.56 ‚ 1.58 ‚ 0.00 ‚
E9203 KNIFE/SWOR ‚ 6470 ‚ 9472 ‚ 2162 ‚ 0 ‚ 18104
D/DAGGER ACC ‚ 0.58 ‚ 0.85 ‚ 0.19 ‚ 0.00 ‚ 1.62
‚ 35.74 ‚ 52.32 ‚ 11.94 ‚ 0.00 ‚
‚ 1.44 ‚ 1.80 ‚ 1.49 ‚ 0.00 ‚

E8261 PED CYCL A ‚ 3308 ‚ 12031 ‚ 2341 ‚ 0 ‚ 17680
CC-PED CYCLIST ‚ 0.30 ‚ 1.08 ‚ 0.21 ‚ 0.00 ‚ 1.58
‚ 18.71 ‚ 68.05 ‚ 13.24 ‚ 0.00 ‚
‚ 0.74 ‚ 2.29 ‚ 1.61 ‚ 0.00 ‚
E918 CAUGHT BET ‚ 6451 ‚ 8052 ‚ 2289 ‚ 0 ‚ 16792
WEEN OBJECTS ‚ 0.58 ‚ 0.72 ‚ 0.20 ‚ 0.00 ‚ 1.50
‚ 38.42 ‚ 47.95 ‚ 13.63 ‚ 0.00 ‚
‚ 1.44 ‚ 1.53 ‚ 1.58 ‚ 0.00 ‚
E8809 FALL ON ST ‚ 9116 ‚ 5208 ‚ 2371 ‚ 0 ‚ 16695
AIR/STEP NEC ‚ 0.81 ‚ 0.47 ‚ 0.21 ‚ 0.00 ‚ 1.49
‚ 54.60 ‚ 31.19 ‚ 14.20 ‚ 0.00 ‚
‚ 2.03 ‚ 0.99 ‚ 1.63 ‚ 0.00 ‚
E8849 FALL-1 LEV ‚ 5215 ‚ 8559 ‚ 2313 ‚ 0 ‚ 16087
EL TO OTH NEC ‚ 0.47 ‚ 0.77 ‚ 0.21 ‚ 0.00 ‚ 1.44
‚ 32.42 ‚ 53.20 ‚ 14.38 ‚ 0.00 ‚
‚ 1.16 ‚ 1.63 ‚ 1.59 ‚ 0.00 ‚
E915 FB ENTERIN ‚ 7144 ‚ 7222 ‚ 1518 ‚ 1 ‚ 15885
G OTH ORIFICE ‚ 0.64 ‚ 0.65 ‚ 0.14 ‚ 0.00 ‚ 1.42
‚ 44.97 ‚ 45.46 ‚ 9.56 ‚ 0.01 ‚
‚ 1.59 ‚ 1.37 ‚ 1.05 ‚ 2.86 ‚
E9174 STAT OB W/ ‚ 5156 ‚ 8028 ‚ 1719 ‚ 2 ‚ 14905
O SUB FALL NEC ‚ 0.46 ‚ 0.72 ‚ 0.15 ‚ 0.00 ‚ 1.33
‚ 34.59 ‚ 53.86 ‚ 11.53 ‚ 0.01 ‚
‚ 1.15 ‚ 1.53 ‚ 1.18 ‚ 5.71 ‚
E9060 DOG BITE ‚ 6126 ‚ 6902 ‚ 1769 ‚ 0 ‚ 14797
‚ 0.55 ‚ 0.62 ‚ 0.16 ‚ 0.00 ‚ 1.32
‚ 41.40 ‚ 46.64 ‚ 11.96 ‚ 0.00 ‚
‚ 1.37 ‚ 1.31 ‚ 1.22 ‚ 0.00 ‚



Total 448249 525310 145189 35 1118783
40.07 46.95 12.98 0.00 100.00

injuries by age groupings in the last half of 2007.*/

tables ec_prin*agecat5
;

;
title 'California EDs Principal Injuries by Age Categories';
run;

Below is partial PROC FREQ output for exercise 5.1-5,
California EDs Principal Injuries by Age Categories

The FREQ Procedure

Table of ec_prin by agecat5

ec_prin(Principal E-code) agecat5(Age Categories 5)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚35-64 ye‚18-34 ye‚1-17 ye‚65 years‚masked a‚Under 1 ‚0 ‚ Total
‚ars ‚ars ‚ars ‚ & over ‚ge group‚year ‚ ‚
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
E8859 FALL FROM ‚ 29780 ‚ 12871 ‚ 21413 ‚ 27052 ‚ 6442 ‚ 398 ‚ 1 ‚ 97957
SLIPPING NEC ‚ 2.66 ‚ 1.15 ‚ 1.91 ‚ 2.42 ‚ 0.58 ‚ 0.04 ‚ 0.00 ‚ 8.76
‚ 30.40 ‚ 13.14 ‚ 21.86 ‚ 27.62 ‚ 6.58 ‚ 0.41 ‚ 0.00 ‚
‚ 8.96 ‚ 4.52 ‚ 7.09 ‚ 23.42 ‚ 9.02 ‚ 3.09 ‚ 2.04 ‚
E927 ACCID FROM ‚ 33453 ‚ 27736 ‚ 19284 ‚ 4800 ‚ 5707 ‚ 291 ‚ 1 ‚ 91272
OVEREXERTION ‚ 2.99 ‚ 2.48 ‚ 1.72 ‚ 0.43 ‚ 0.51 ‚ 0.03 ‚ 0.00 ‚ 8.16
‚ 36.65 ‚ 30.39 ‚ 21.13 ‚ 5.26 ‚ 6.25 ‚ 0.32 ‚ 0.00 ‚
‚ 10.07 ‚ 9.75 ‚ 6.38 ‚ 4.16 ‚ 7.99 ‚ 2.26 ‚ 2.04 ‚
E8889 FALL NOS ‚ 17845 ‚ 8694 ‚ 19291 ‚ 17817 ‚ 3768 ‚ 647 ‚ 1 ‚ 68063
‚ 1.60 ‚ 0.78 ‚ 1.72 ‚ 1.59 ‚ 0.34 ‚ 0.06 ‚ 0.00 ‚ 6.08
‚ 26.22 ‚ 12.77 ‚ 28.34 ‚ 26.18 ‚ 5.54 ‚ 0.95 ‚ 0.00 ‚
‚ 5.37 ‚ 3.06 ‚ 6.38 ‚ 15.42 ‚ 5.28 ‚ 5.03 ‚ 2.04 ‚
E9179 OBJ W-W/O ‚ 15662 ‚ 15031 ‚ 23864 ‚ 3351 ‚ 4031 ‚ 634 ‚ 2 ‚ 62575
SUB FALL NEC ‚ 1.40 ‚ 1.34 ‚ 2.13 ‚ 0.30 ‚ 0.36 ‚ 0.06 ‚ 0.00 ‚ 5.59
‚ 25.03 ‚ 24.02 ‚ 38.14 ‚ 5.36 ‚ 6.44 ‚ 1.01 ‚ 0.00 ‚



‚ 4.71 ‚ 5.28 ‚ 7.90 ‚ 2.90 ‚ 5.64 ‚ 4.93 ‚ 4.08 ‚
E9289 ACCIDENT N ‚ 21696 ‚ 14878 ‚ 11499 ‚ 6351 ‚ 2857 ‚ 681 ‚ 4 ‚ 57966
OS ‚ 1.94 ‚ 1.33 ‚ 1.03 ‚ 0.57 ‚ 0.26 ‚ 0.06 ‚ 0.00 ‚ 5.18
‚ 37.43 ‚ 25.67 ‚ 19.84 ‚ 10.96 ‚ 4.93 ‚ 1.17 ‚ 0.01 ‚
‚ 6.53 ‚ 5.23 ‚ 3.81 ‚ 5.50 ‚ 4.00 ‚ 5.29 ‚ 8.16 ‚
E9208 ACC-CUTTIN ‚ 14622 ‚ 16107 ‚ 12856 ‚ 2501 ‚ 3101 ‚ 234 ‚ 1 ‚ 49422
G INSTRUM NEC ‚ 1.31 ‚ 1.44 ‚ 1.15 ‚ 0.22 ‚ 0.28 ‚ 0.02 ‚ 0.00 ‚ 4.42
‚ 29.59 ‚ 32.59 ‚ 26.01 ‚ 5.06 ‚ 6.27 ‚ 0.47 ‚ 0.00 ‚
‚ 4.40 ‚ 5.66 ‚ 4.25 ‚ 2.17 ‚ 4.34 ‚ 1.82 ‚ 2.04 ‚
E8120 MV COLLISI ‚ 19149 ‚ 19301 ‚ 1269 ‚ 2801 ‚ 3161 ‚ 5 ‚ 1 ‚ 45687
ON NOS-DRIVER ‚ 1.71 ‚ 1.73 ‚ 0.11 ‚ 0.25 ‚ 0.28 ‚ 0.00 ‚ 0.00 ‚ 4.08
‚ 41.91 ‚ 42.25 ‚ 2.78 ‚ 6.13 ‚ 6.92 ‚ 0.01 ‚ 0.00 ‚
‚ 5.76 ‚ 6.78 ‚ 0.42 ‚ 2.42 ‚ 4.43 ‚ 0.04 ‚ 2.04 ‚
E9170 SPORTS ACC ‚ 2958 ‚ 7207 ‚ 18480 ‚ 113 ‚ 2197 ‚ 8 ‚ 0 ‚ 30963
W/O SUB FALL ‚ 0.26 ‚ 0.64 ‚ 1.65 ‚ 0.01 ‚ 0.20 ‚ 0.00 ‚ 0.00 ‚ 2.77
‚ 9.55 ‚ 23.28 ‚ 59.68 ‚ 0.36 ‚ 7.10 ‚ 0.03 ‚ 0.00 ‚
‚ 0.89 ‚ 2.53 ‚ 6.12 ‚ 0.10 ‚ 3.08 ‚ 0.06 ‚ 0.00 ‚
E9600 UNARMED FI ‚ 7573 ‚ 11614 ‚ 4411 ‚ 282 ‚ 1205 ‚ 4 ‚ 7 ‚ 25096
GHT OR BRAWL ‚ 0.68 ‚ 1.04 ‚ 0.39 ‚ 0.03 ‚ 0.11 ‚ 0.00 ‚ 0.00 ‚ 2.24
‚ 30.18 ‚ 46.28 ‚ 17.58 ‚ 1.12 ‚ 4.80 ‚ 0.02 ‚ 0.03 ‚
‚ 2.28 ‚ 4.08 ‚ 1.46 ‚ 0.24 ‚ 1.69 ‚ 0.03 ‚ 14.29 ‚
E8121 MV COLLISI ‚ 5004 ‚ 6953 ‚ 7034 ‚ 1217 ‚ 2022 ‚ 349 ‚ 1 ‚ 22580
ON NOS-PASNGR ‚ 0.45 ‚ 0.62 ‚ 0.63 ‚ 0.11 ‚ 0.18 ‚ 0.03 ‚ 0.00 ‚ 2.02
‚ 22.16 ‚ 30.79 ‚ 31.15 ‚ 5.39 ‚ 8.95 ‚ 1.55 ‚ 0.00 ‚
‚ 1.51 ‚ 2.44 ‚ 2.33 ‚ 1.05 ‚ 2.83 ‚ 2.71 ‚ 2.04 ‚
E8881 FALL STRIK ‚ 3633 ‚ 2007 ‚ 9789 ‚ 3346 ‚ 1251 ‚ 407 ‚ 0 ‚ 20433
ING OBJECT NEC ‚ 0.32 ‚ 0.18 ‚ 0.87 ‚ 0.30 ‚ 0.11 ‚ 0.04 ‚ 0.00 ‚ 1.83
‚ 17.78 ‚ 9.82 ‚ 47.91 ‚ 16.38 ‚ 6.12 ‚ 1.99 ‚ 0.00 ‚
‚ 1.09 ‚ 0.71 ‚ 3.24 ‚ 2.90 ‚ 1.75 ‚ 3.16 ‚ 0.00 ‚
E9288 ACCIDENT N ‚ 6365 ‚ 5291 ‚ 5128 ‚ 1547 ‚ 1285 ‚ 246 ‚ 1 ‚ 19863
EC ‚ 0.57 ‚ 0.47 ‚ 0.46 ‚ 0.14 ‚ 0.11 ‚ 0.02 ‚ 0.00 ‚ 1.78
‚ 32.04 ‚ 26.64 ‚ 25.82 ‚ 7.79 ‚ 6.47 ‚ 1.24 ‚ 0.01 ‚
‚ 1.92 ‚ 1.86 ‚ 1.70 ‚ 1.34 ‚ 1.80 ‚ 1.91 ‚ 2.04 ‚
E8888 FALL NEC ‚ 5186 ‚ 2749 ‚ 5534 ‚ 4859 ‚ 1094 ‚ 271 ‚ 1 ‚ 19694
‚ 0.46 ‚ 0.25 ‚ 0.49 ‚ 0.43 ‚ 0.10 ‚ 0.02 ‚ 0.00 ‚ 1.76
‚ 26.33 ‚ 13.96 ‚ 28.10 ‚ 24.67 ‚ 5.55 ‚ 1.38 ‚ 0.01 ‚
‚ 1.56 ‚ 0.97 ‚ 1.83 ‚ 4.21 ‚ 1.53 ‚ 2.11 ‚ 2.04 ‚
E9203 KNIFE/SWOR ‚ 6911 ‚ 6953 ‚ 2248 ‚ 985 ‚ 995 ‚ 12 ‚ 0 ‚ 18104
D/DAGGER ACC ‚ 0.62 ‚ 0.62 ‚ 0.20 ‚ 0.09 ‚ 0.09 ‚ 0.00 ‚ 0.00 ‚ 1.62
‚ 38.17 ‚ 38.41 ‚ 12.42 ‚ 5.44 ‚ 5.50 ‚ 0.07 ‚ 0.00 ‚
‚ 2.08 ‚ 2.44 ‚ 0.74 ‚ 0.85 ‚ 1.39 ‚ 0.09 ‚ 0.00 ‚
Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚35-64 ye‚18-34 ye‚1-17 ye‚65 years‚masked a‚Under 1 ‚0 ‚ Total
‚ars ‚ars ‚ars ‚ & over ‚ge group‚year ‚ ‚



E8261 PED CYCL A ‚ 5011 ‚ 3962 ‚ 7120 ‚ 386 ‚ 1197 ‚ 4 ‚ 0 ‚ 17680
CC-PED CYCLIST ‚ 0.45 ‚ 0.35 ‚ 0.64 ‚ 0.03 ‚ 0.11 ‚ 0.00 ‚ 0.00 ‚ 1.58
‚ 28.34 ‚ 22.41 ‚ 40.27 ‚ 2.18 ‚ 6.77 ‚ 0.02 ‚ 0.00 ‚
‚ 1.51 ‚ 1.39 ‚ 2.36 ‚ 0.33 ‚ 1.68 ‚ 0.03 ‚ 0.00 ‚
E918 CAUGHT BET ‚ 4442 ‚ 3929 ‚ 6220 ‚ 848 ‚ 1177 ‚ 176 ‚ 0 ‚ 16792
WEEN OBJECTS ‚ 0.40 ‚ 0.35 ‚ 0.56 ‚ 0.08 ‚ 0.11 ‚ 0.02 ‚ 0.00 ‚ 1.50
‚ 26.45 ‚ 23.40 ‚ 37.04 ‚ 5.05 ‚ 7.01 ‚ 1.05 ‚ 0.00 ‚
‚ 1.34 ‚ 1.38 ‚ 2.06 ‚ 0.73 ‚ 1.65 ‚ 1.37 ‚ 0.00 ‚
E8809 FALL ON ST ‚ 6049 ‚ 3711 ‚ 3166 ‚ 2274 ‚ 1280 ‚ 215 ‚ 0 ‚ 16695
AIR/STEP NEC ‚ 0.54 ‚ 0.33 ‚ 0.28 ‚ 0.20 ‚ 0.11 ‚ 0.02 ‚ 0.00 ‚ 1.49
‚ 36.23 ‚ 22.23 ‚ 18.96 ‚ 13.62 ‚ 7.67 ‚ 1.29 ‚ 0.00 ‚
‚ 1.82 ‚ 1.30 ‚ 1.05 ‚ 1.97 ‚ 1.79 ‚ 1.67 ‚ 0.00 ‚
E8849 FALL-1 LEV ‚ 3317 ‚ 2649 ‚ 6878 ‚ 798 ‚ 1265 ‚ 1180 ‚ 0 ‚ 16087
EL TO OTH NEC ‚ 0.30 ‚ 0.24 ‚ 0.61 ‚ 0.07 ‚ 0.11 ‚ 0.11 ‚ 0.00 ‚ 1.44
‚ 20.62 ‚ 16.47 ‚ 42.76 ‚ 4.96 ‚ 7.86 ‚ 7.34 ‚ 0.00 ‚
‚ 1.00 ‚ 0.93 ‚ 2.28 ‚ 0.69 ‚ 1.77 ‚ 9.17 ‚ 0.00 ‚
E915 FB ENTERIN ‚ 2800 ‚ 2185 ‚ 8379 ‚ 1038 ‚ 774 ‚ 709 ‚ 0 ‚ 15885
G OTH ORIFICE ‚ 0.25 ‚ 0.20 ‚ 0.75 ‚ 0.09 ‚ 0.07 ‚ 0.06 ‚ 0.00 ‚ 1.42
‚ 17.63 ‚ 13.76 ‚ 52.75 ‚ 6.53 ‚ 4.87 ‚ 4.46 ‚ 0.00 ‚
‚ 0.84 ‚ 0.77 ‚ 2.77 ‚ 0.90 ‚ 1.08 ‚ 5.51 ‚ 0.00 ‚
E9174 STAT OB W/ ‚ 3413 ‚ 3835 ‚ 5856 ‚ 808 ‚ 883 ‚ 110 ‚ 0 ‚ 14905
O SUB FALL NEC ‚ 0.31 ‚ 0.34 ‚ 0.52 ‚ 0.07 ‚ 0.08 ‚ 0.01 ‚ 0.00 ‚ 1.33
‚ 22.90 ‚ 25.73 ‚ 39.29 ‚ 5.42 ‚ 5.92 ‚ 0.74 ‚ 0.00 ‚
‚ 1.03 ‚ 1.35 ‚ 1.94 ‚ 0.70 ‚ 1.24 ‚ 0.86 ‚ 0.00 ‚
E9060 DOG BITE ‚ 4545 ‚ 2996 ‚ 5270 ‚ 1011 ‚ 928 ‚ 47 ‚ 0 ‚ 14797
‚ 0.41 ‚ 0.27 ‚ 0.47 ‚ 0.09 ‚ 0.08 ‚ 0.00 ‚ 0.00 ‚ 1.32
‚ 30.72 ‚ 20.25 ‚ 35.62 ‚ 6.83 ‚ 6.27 ‚ 0.32 ‚ 0.00 ‚
‚ 1.37 ‚ 1.05 ‚ 1.74 ‚ 0.88 ‚ 1.30 ‚ 0.37 ‚ 0.00 ‚
Total 332199 284573 302155 115511 71431 12865 49 1118783
29.69 25.44 27.01 10.32 6.38 1.15 0.00 100.00

injuries by death and gender in the last half of 2007.*/
where dispn='20';
tables ec_prin*sex
;



;
title 'California EDs Principal Injury Death by Sex';
run;

Below is partial PROC FREQ output for exercise 5.1-6.
California EDs Principal Injury Death by Sex

The FREQ Procedure

Table of ec_prin by sex

ec_prin(Principal E-code) sex(Sex)

Frequency ‚
Percent ‚
Row Pct ‚
Col Pct ‚male ‚female ‚masked s‚unknown ‚ Total
‚ ‚ ‚ex ‚sex ‚
E9654 ASSAULT-FI ‚ 86 ‚ 7 ‚ 10 ‚ 1 ‚ 104
REARM NEC ‚ 8.37 ‚ 0.68 ‚ 0.97 ‚ 0.10 ‚ 10.13
‚ 82.69 ‚ 6.73 ‚ 9.62 ‚ 0.96 ‚
‚ 13.74 ‚ 3.29 ‚ 5.43 ‚ 25.00 ‚
E8147 MV COLL W ‚ 39 ‚ 23 ‚ 11 ‚ 0 ‚ 73
PEDEST-PEDEST ‚ 3.80 ‚ 2.24 ‚ 1.07 ‚ 0.00 ‚ 7.11
‚ 53.42 ‚ 31.51 ‚ 15.07 ‚ 0.00 ‚
‚ 6.23 ‚ 10.80 ‚ 5.98 ‚ 0.00 ‚
E8120 MV COLLISI ‚ 24 ‚ 8 ‚ 14 ‚ 0 ‚ 46
ON NOS-DRIVER ‚ 2.34 ‚ 0.78 ‚ 1.36 ‚ 0.00 ‚ 4.48
‚ 52.17 ‚ 17.39 ‚ 30.43 ‚ 0.00 ‚
‚ 3.83 ‚ 3.76 ‚ 7.61 ‚ 0.00 ‚
E9229 FIREARM AC ‚ 37 ‚ 3 ‚ 5 ‚ 1 ‚ 46
CIDENT NOS ‚ 3.60 ‚ 0.29 ‚ 0.49 ‚ 0.10 ‚ 4.48
‚ 80.43 ‚ 6.52 ‚ 10.87 ‚ 2.17 ‚
‚ 5.91 ‚ 1.41 ‚ 2.72 ‚ 25.00 ‚
E966 ASSAULT-CU ‚ 33 ‚ 6 ‚ 5 ‚ 0 ‚ 44
TTING INSTR ‚ 3.21 ‚ 0.58 ‚ 0.49 ‚ 0.00 ‚ 4.28
‚ 75.00 ‚ 13.64 ‚ 11.36 ‚ 0.00 ‚
‚ 5.27 ‚ 2.82 ‚ 2.72 ‚ 0.00 ‚
E9650 ASSAULT-HA ‚ 25 ‚ 4 ‚ 3 ‚ 0 ‚ 32
NDGUN ‚ 2.43 ‚ 0.39 ‚ 0.29 ‚ 0.00 ‚ 3.12
‚ 78.13 ‚ 12.50 ‚ 9.38 ‚ 0.00 ‚
‚ 3.99 ‚ 1.88 ‚ 1.63 ‚ 0.00 ‚
E8121 MV COLLISI ‚ 10 ‚ 12 ‚ 9 ‚ 0 ‚ 31
ON NOS-PASNGR ‚ 0.97 ‚ 1.17 ‚ 0.88 ‚ 0.00 ‚ 3.02
‚ 32.26 ‚ 38.71 ‚ 29.03 ‚ 0.00 ‚
‚ 1.60 ‚ 5.63 ‚ 4.89 ‚ 0.00 ‚



E9289 ACCIDENT N ‚ 10 ‚ 13 ‚ 7 ‚ 0 ‚ 30
OS ‚ 0.97 ‚ 1.27 ‚ 0.68 ‚ 0.00 ‚ 2.92
‚ 33.33 ‚ 43.33 ‚ 23.33 ‚ 0.00 ‚
‚ 1.60 ‚ 6.10 ‚ 3.80 ‚ 0.00 ‚
E8122 MV COLLIS ‚ 20 ‚ 1 ‚ 8 ‚ 0 ‚ 29
NOS-MOTORCYCL ‚ 1.95 ‚ 0.10 ‚ 0.78 ‚ 0.00 ‚ 2.82
‚ 68.97 ‚ 3.45 ‚ 27.59 ‚ 0.00 ‚
‚ 3.19 ‚ 0.47 ‚ 4.35 ‚ 0.00 ‚
E8889 FALL NOS ‚ 13 ‚ 8 ‚ 7 ‚ 0 ‚ 28
‚ 1.27 ‚ 0.78 ‚ 0.68 ‚ 0.00 ‚ 2.73
‚ 46.43 ‚ 28.57 ‚ 25.00 ‚ 0.00 ‚
‚ 2.08 ‚ 3.76 ‚ 3.80 ‚ 0.00 ‚
E9530 INJURY-HAN ‚ 20 ‚ 7 ‚ 0 ‚ 0 ‚ 27
GING ‚ 1.95 ‚ 0.68 ‚ 0.00 ‚ 0.00 ‚ 2.63
‚ 74.07 ‚ 25.93 ‚ 0.00 ‚ 0.00 ‚
‚ 3.19 ‚ 3.29 ‚ 0.00 ‚ 0.00 ‚
E9854 UNDETER CI ‚ 20 ‚ 2 ‚ 3 ‚ 0 ‚ 25
RC-FIREARM NEC ‚ 1.95 ‚ 0.19 ‚ 0.29 ‚ 0.00 ‚ 2.43
‚ 80.00 ‚ 8.00 ‚ 12.00 ‚ 0.00 ‚
‚ 3.19 ‚ 0.94 ‚ 1.63 ‚ 0.00 ‚
E9550 INJURY-HAN ‚ 11 ‚ 5 ‚ 7 ‚ 1 ‚ 24
DGUN ‚ 1.07 ‚ 0.49 ‚ 0.68 ‚ 0.10 ‚ 2.34
‚ 45.83 ‚ 20.83 ‚ 29.17 ‚ 4.17 ‚
‚ 1.76 ‚ 2.35 ‚ 3.80 ‚ 25.00 ‚
E9651 ASSAULT-SH ‚ 21 ‚ 0 ‚ 2 ‚ 0 ‚ 23
OTGUN ‚ 2.04 ‚ 0.00 ‚ 0.19 ‚ 0.00 ‚ 2.24
‚ 91.30 ‚ 0.00 ‚ 8.70 ‚ 0.00 ‚
‚ 3.35 ‚ 0.00 ‚ 1.09 ‚ 0.00 ‚
Total 626 213 184 4 1027
60.95 20.74 17.92 0.39 100.00



2. SAS Code for OSPHD Indicator and Truth Logic Variables
with PROC MEANS and PROC TABULATE
osphd02d.sas
The program below contains SAS code that creates indicator and truth logic variables for
analysis of the OSPHD emergency department data. The PROC Means calculates the means,
sums, minimum and maximum value for each variable. The PROC Tabulate produces multiple
analyses such as hospital by payer and mean age.
/****osph02d.sas**********/

data ed2007;
set osphd.caled2007 (keep=fac_id age_yrs agecat5 sex eth race
patzip patco serv_q dispn payer ec_prin
dx_prin odx1 odx2 pr_prin opr1);

/****gender indicator variables ***/

male =(sex='M');
female =(sex='F');
othgen =(sex='U');
unkgen =(sex='*');

/** Create Gender Categories Using Truth logic**/

gendercat=1*(sex='M')+ 2*(sex='F') + 3*(sex='U') + 4*(sex='*');

/****ethnic indicator variabbles ***/

hispanic =(eth='E1');
non_hispanic =(eth='E2');
hispanic_unk =(eth='99');
hispanic_blnk =(eth='*');

/** Create Race Ethnic Categories Using Truth logic**/

ethnicat=1*(eth='E1')+ 2*(eth='E2') + 3*(eth='99') + 4*(eth='*');

/****race indicator variables ***/

native_american =(race='R1');
asian =(race='R2');
black =(race='R3');
hawaiian =(race='R4');
white =(race='R5');
othrace =(race='R9');



unkrace =(race='99');
race_blk =(race='*');

/** Create Race Categories Using Truth logic**/

racecat=1*(race='R1') + 2*(race='R2') + 3*(race='R3') + 4*(race='R4')+
5*(race='R5') + 6*(race='R9') + 7*(race='99') + 8*(race='*');

/****payer indicator variables ***/

selfpay =(payer='09');
othnonfed =(payer='11');
ppo =(payer='12');
pos =(payer='13');
epo =(payer='14');
carehmo =(payer='16');
automed =(payer='AM');
bluecross =(payer='BL');
tricare =(payer='CH');
commercial =(payer='CI');
disability =(payer='DS');
othhmo =(payer='HM');
careparta =(payer='MA');
carepartb =(payer='MB');
medical =(payer='MC');
othfed =(payer='OF');
titleV =(payer='TV');
veterans =(payer='VA');
workcomp =(payer='WC');
payoth =(payer='00');
payblank =(payer='99');

/** Create Payer Categories Using Truth logic**/

paycat=1*(payer='09') + 2*(payer='11') + 3*(payer='12') +
4*(payer='13') + 5*(payer='14') + 6*(payer='16') +
7*(payer='AM') + 8*(payer='BL') +
9*(payer='CH') + 10*(payer='CI') + 11*(payer='DS') +
12*(payer='HM') + 13*(payer='MA') + 14*(payer='MB') +
15*(payer='MC') +16*(payer='OF') +
17*(payer='TV') + 18*(payer='VA') + 19*(payer='WC') +
20*(payer='00') + 21*(payer='99');

/*******Disposition Indicator Variables*****************/

home = (dispn='01');
inpatinpatient_care = (dispn='02');
snf = (dispn='03');
intermediate_care = (dispn='04');



other_type_inst = (dispn='05');
home_health = (dispn='06');
lma = (dispn='07');
died = (dispn='20');
federal_health = (dispn='43');
home_hospice_care = (dispn='50');
hospital_hospice = (dispn='51');
hospital_swing_bed = (dispn='61');
inpatient_rehab = (dispn='62');
ltc_hospital = (dispn='63');
snf_no_cert = (dispn='64');
psych_hospital = (dispn='65');
critical_hospital = (dispn='66');
dispo_other = (dispn='00');
dispo_invalid = (dispn='99');

/*******Disposition Truth Logic Variables*****************/

dispocat= 1*(dispn='01') + 2*(dispn='02') +3*(dispn='03') +
4*(dispn='04') + 5*(dispn='05') +6*(dispn='06') +
19*(dispn='99');

/*******age category indicator variables********/

agelt1 =(agecat5='1');
age1to17 =(agecat5='2');
agege65 =(agecat5='5');
uknagecat =(agecat5='*');

/** Create Age Categories Using Truth logic**/

agegroup= 1*(agecat5='*') + 2*(agecat5='0') + 3*(agecat5='1') +
4*(agecat5='2') + 5*(agecat5='3') + 6*(agecat5='4') +
7*(agecat5='5');

/* Substrings funtions to select the*/
/* first N characters of a variable */
/* Must have length statement */

length dx_prin3 $3;
length pr_prin2 $2;

dx_prin3=substr(dx_prin,1,3);



pr_prin2=substr(pr_prin,1,2);

proc contents data=ed2007;
run;

proc means n mean sum min max data=ed2007;
var
age_yrs agelt1 age1to17 age18to34 age35to64
agege65 uknagecat
male female unkgen hispanic non_hispanic hispanic_unk
hispanic_blnk
white black native_american asian hawaiian
othrace unkrace race_blk
selfpay othnonfed ppo pos epo carehmo automed
bluecross tricare commercial disability othhmo
careparta carepartb medical othfed titleV
veterans workcomp payoth payblank
age_yrs home inpatinpatient_care snf intermediate_care
other_type_inst home_health lma died federal_health
home_hospice_care hospital_hospice hospital_swing_bed
inpatient_rehab ltc_hospital snf_no_cert psych_hospital
critical_hospital dispo_other dispo_invalid gendercat ethnicat
racecat paycat agegroup dispocat;
title 'Means of Demographic Variable in the Last Half 2007 California
EDs';
run;

Below is the Proc Means Output
Means of Demographic Variable in the Last Half 2007 California EDs
The MEANS Procedure
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
age_yrs 2887049 33.5966369 96995137.00 0 85.0000000
agelt1 4364548 0.0369594 161311.00 0 1.0000000
age1to17 4364548 0.2080758 908157.00 0 1.0000000
age18to34 4364548 0.2547050 1111672.00 0 1.0000000
age35to64 4364548 0.3356643 1465023.00 0 1.0000000
agege65 4364548 0.1169143 510278.00 0 1.0000000
uknagecat 4364548 0.0476359 207909.00 0 1.0000000
male 4364548 0.4104679 1791507.00 0 1.0000000
female 4364548 0.4881323 2130477.00 0 1.0000000
unkgen 4364548 0.1013773 442466.00 0 1.0000000
hispanic 4364548 0.2728509 1190871.00 0 1.0000000
non_hispanic 4364548 0.4947536 2159376.00 0 1.0000000
hispanic_unk 4364548 0.0385036 168051.00 0 1.0000000
hispanic_blnk 4364548 0.1938918 846250.00 0 1.0000000
white 4364548 0.5294952 2311007.00 0 1.0000000



black 4364548 0.0870402 379891.00 0 1.0000000
native_american 4364548 0.0025205 11001.00 0 1.0000000
asian 4364548 0.0264909 115621.00 0 1.0000000
hawaiian 4364548 0.0033914 14802.00 0 1.0000000
othrace 4364548 0.1592621 695107.00 0 1.0000000
unkrace 4364548 0.0290603 126835.00 0 1.0000000
race_blk 4364548 0.1627394 710284.00 0 1.0000000
selfpay 4364548 0.1748628 763197.00 0 1.0000000
othnonfed 4364548 0.0234281 102253.00 0 1.0000000
ppo 4364548 0.0607151 264994.00 0 1.0000000
pos 4364548 0.0037701 16455.00 0 1.0000000
epo 4364548 0.0029565 12904.00 0 1.0000000
carehmo 4364548 0.0416137 181625.00 0 1.0000000
automed 4364548 0.000609456 2660.00 0 1.0000000
bluecross 4364548 0.0552094 240964.00 0 1.0000000
tricare 4364548 0.0072383 31592.00 0 1.0000000
commercial 4364548 0.0355480 155151.00 0 1.0000000
disability 4364548 2.5203068E-6 11.0000000 0 1.0000000
othhmo 4364548 0.2032210 886968.00 0 1.0000000
careparta 4364548 0.0664190 289889.00 0 1.0000000
carepartb 4364548 0.0366072 159774.00 0 1.0000000
medical 4364548 0.2439499 1064731.00 0 1.0000000
othfed 4364548 0.0088073 38440.00 0 1.0000000
titleV 4364548 0.000693543 3027.00 0 1.0000000
veterans 4364548 0.000630993 2754.00 0 1.0000000
workcomp 4364548 0.0185682 81042.00 0 1.0000000
payoth 4364548 0.0147926 64563.00 0 1.0000000
payblank 4364548 0.000356051 1554.00 0 1.0000000
home 4364548 0.9411373 4107639.00 0 1.0000000
inpatinpatient_care 4364548 0.0129619 56573.00 0 1.0000000
snf 4364548 0.0028351 12374.00 0 1.0000000
intermediate_care 4364548 0.000655738 2862.00 0 1.0000000
other_type_inst 4364548 0.0040181 17537.00 0 1.0000000
home_health 4364548 0.000453197 1978.00 0 1.0000000
lma 4364548 0.0209676 91514.00 0 1.0000000
died 4364548 0.0018313 7993.00 0 1.0000000
federal_health 4364548 0.000080421 351.0000000 0 1.0000000
home_hospice_care 4364548 0.000109061 476.0000000 0 1.0000000
hospital_hospice 4364548 0.000037575 164.0000000 0 1.0000000
hospital_swing_bed 4364548 5.72797E-6 25.0000000 0 1.0000000
inpatient_rehab 4364548 0.000158092 690.0000000 0 1.0000000
ltc_hospital 4364548 0.000371402 1621.00 0 1.0000000
snf_no_cert 4364548 0.000060946 266.0000000 0 1.0000000
psych_hospital 4364548 0.0059113 25800.00 0 1.0000000
critical_hospital 4364548 0.000145949 637.0000000 0 1.0000000
dispo_other 4364548 0.0081087 35391.00 0 1.0000000
dispo_invalid 4364548 0.000150531 657.0000000 0 1.0000000
gendercat 4364548 1.7923091 7822619.00 1.0000000 4.0000000
ethnicat 4364548 2.1534363 9398776.00 1.0000000 4.0000000
racecat 4364548 5.4385742 23736918.00 1.0000000 8.0000000
paycat 4364548 9.8440441 42964803.00 1.0000000 21.0000000
agegroup 4364548 5.0968187 22245310.00 1.0000000 7.0000000
dispocat 4364548 1.4187671 6192277.00 1.0000000 19.0000000

The PROC Means validates your indicator variables and consists of a minimum value of zero
and a maximum value of one. If this does not exist, check your code. For truth logic variables,



the minimum is usually 1 and the maximum equals the total number of values assigned to the
variable. Again, if this does not occur, check your logic code. For example, pay category (paycat)
has a minimum of 0 and a maximum of 21, while the white indicator variable has a minimum of
0 and a maximum of 1.
The n=4,364,548 are the total emergency department visits in the last half of 2007. The mean
value is the percentage of each variable within a category. For example, females were 48.8
percent of the emergency department visits.

Below is the PROC Tabulate produces hospital by selfpay and mean age.
proc tabulate data=ed2007 order=freq;
class selfpay fac_id;
var age_yrs;
tables fac_id all,
(selfpay all)*(age_yrs*(n*f=6.0 mean*f=3.2)) /rts=30;
format fac_id $hospitalf.;
run;
title 'Distribution in Rank order of the Hospital Selfpay Patients';

Distribution in Rank order of the Hospital Selfpay Patients
‚ ‚ 0 ‚ 1 ‚ All ‚
‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‚fac_id ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚ARROWHEAD REGIONAL MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER ‚ 21346‚ 32‚ 15367‚ 32‚ 36713‚ 32‚
‚LOS ANGELES CO USC MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER ‚ 3309‚ 38‚ 18794‚ 29‚ 22103‚ 30‚
‚KAISER FND HOSP - SAN DIEGO ‚ 22478‚ 42‚ 1420‚ 29‚ 23898‚ 41‚
‚190034 ‚ 26546‚ 28‚ 5579‚ 29‚ 32125‚ 28‚
‚KAISER FND HOSP - BELLFLOWER‚ 17844‚ 35‚ 1835‚ 27‚ 19679‚ 34‚
‚RIVERSIDE COUNTY REGIONAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚MEDICAL CENTER ‚ 14138‚ 30‚ 10423‚ 32‚ 24561‚ 31‚
‚COMMUNITY REGIONAL MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER-FRESNO ‚ 20229‚ 33‚ 5379‚ 33‚ 25608‚ 33‚
‚KAISER FND HOSP - FONTANA ‚ 12731‚ 36‚ 3369‚ 24‚ 16100‚ 34‚



‚GROSSMONT HOSPITAL ‚ 17462‚ 41‚ 4847‚ 31‚ 22309‚ 39‚
‚SOUTHWEST HEALTHCARE SYSTEM-‚ ‚ ‚ ‚ ‚ ‚ ‚
‚MURRIETA ‚ 22553‚ 33‚ 2850‚ 29‚ 25403‚ 33‚
‚POMONA VALLEY HOSPITAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚MEDICAL CENTER ‚ 16841‚ 29‚ 3674‚ 29‚ 20515‚ 29‚
‚LONG BEACH MEMORIAL MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER ‚ 15298‚ 24‚ 2683‚ 27‚ 17981‚ 24‚
‚DOCTORS MEDICAL CENTER ‚ 18219‚ 30‚ 4887‚ 31‚ 23106‚ 30‚
‚KAWEAH DELTA DISTRICT ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚HOSPITAL ‚ 23577‚ 30‚ .‚ .‚ 23577‚ 30‚
‚KAISER FND HOSP - SOUTH ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚SACRAMENTO ‚ 16382‚ 36‚ 2707‚ 28‚ 19089‚ 35‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒŒ
‚TRI-CITY MEDICAL CENTER ‚ 18232‚ 39‚ 3587‚ 30‚ 21819‚ 37‚
‚CEDARS SINAI MEDICAL CENTER ‚ 12416‚ 40‚ 3053‚ 33‚ 15469‚ 38‚
‚KAISER FND HOSP - BALDWIN ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚PARK ‚ 11466‚ 37‚ 1421‚ 27‚ 12887‚ 36‚
‚MEMORIAL HOSPITAL MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER - MODESTO ‚ 18352‚ 36‚ 2997‚ 28‚ 21349‚ 35‚
‚SANTA CLARA VALLEY MEDICAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚CENTER ‚ 14123‚ 39‚ 3199‚ 34‚ 17322‚ 38‚
‚HOAG MEMORIAL HOSPITAL ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚PRESBYTERIAN ‚ 17674‚ 42‚ .‚ .‚ 17674‚ 42‚
‚RIVERSIDE COMMUNITY HOSPITAL‚ 15998‚ 29‚ 3214‚ 29‚ 19212‚ 29‚
‚KAISER FND HOSP - WEST LA ‚ 10814‚ 49‚ 1814‚ 34‚ 12628‚ 47‚
‚CHILDRENS HOSPITAL OF LOS ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚ANGELES ‚ 20732‚4.2‚ .‚ .‚ 20732‚4.2‚
‚KAISER FND HOSP - SUNSET ‚ 8656‚ 43‚ 1228‚ 34‚ 9884‚ 42‚
‚All ‚ 2.4E6‚ 34‚488201‚ 31‚2.89E6‚ 34‚



Exercises 5.2

1. Using osphd02d.sas, write the indicator and truth logic code for California counties.
2. Using osphd02d.sas, produce a PROC Tabulate and identify the California Counties with the
most ED deaths.
3. From a Proc Means, using the class statement for selfpay output, prepare a descriptive
statistics narrative of the findings.
4. Using a PROC Tabulate, identify the diseases presented to the California EDs when
patzip=99999 is a proxy for the homeless. Describe your findings,

Below is SAS code for exercise 5.2-1 to 4.
/*1. Using osphd02d, write the indicator and truth logic code for
California */
/*Counties. Insert them into a PROC Means and see if your code works*/

data ed2007;
set osphd.caled2007 (keep= patco);

Alameda = (patco='01');
Amador = (patco='03');
Butte = (patco='04');
Calaveras = (patco='05');
Colusa = (patco='06');
Contra_Costa = (patco='07');
El_Dorado = (patco='09');
Fresno = (patco='10');
Glenn = (patco='11');
Humboldt = (patco='12');
Imperial = (patco='13');
Kern = (patco='15');
Kings = (patco='16');
Lake = (patco='17');
Lassen = (patco='18');
LosAngeles = (patco='19');
Madera = (patco='20');
Marin = (patco='21');
Mendocino = (patco='23');
Merced = (patco='24');
Monterey = (patco='27');



Napa = (patco='28');
Nevada = (patco='29');
Orange = (patco='30');
Placer = (patco='31');
Riverside = (patco='33');
Sacramento = (patco='34');
San_Benito = (patco='35');
San_Bernardino = (patco='36');
San_Diego = (patco='37');
San_Francisco = (patco='38');
San_Joaquin = (patco='39');
SanLuisObispo = (patco='40');
San_Mateo = (patco='41');
Santa_Barbara = (patco='42');
Santa_Clara = (patco='43');
Santa_Cruz = (patco='44');
Shasta = (patco='45');
Siskiyou = (patco='47');
Solano = (patco='48');
Sonoma = (patco='49');
Stanislaus = (patco='50');
Sutter = (patco='51');
Tehama = (patco='52');
Trinity = (patco='53');
Tulare = (patco='54');
Tuolumne = (patco='55');
Ventura = (patco='56');
Yolo = (patco='57');
Yuba = (patco='58');
CountyUnknown = (patco='00');
Alpine_Inyo_Mariposa =(patco='CE');
Del_Norte_Modoc =(patco='NE');
Colusa_Glenn_Trinity =(patco='NW');
missing_county =(patco='*');

patcocat = 1*(patco='01') + 2*(patco='03') + 3*(patco='04') +
4*(patco='05') + 5*(patco='06') + 6*(patco='07') +



52*(patco='CE') + 53*(patco='NE') + 54*(patco='NW') +
55*(patco='*');

proc means n mean sum min max data=ed2007;
var Alameda Amador Butte Calaveras Colusa
Contra_Costa El_Dorado Fresno Glenn
Humboldt Imperial Kern Kings Lake Lassen
LosAngeles Madera Mendocino Marin Merced
Monterey Napa Nevada Orange Placer Riverside
Sacramento San_Benito San_Bernardino San_Diego
San_Francisco San_Joaquin SanLuisObispo
San_Mateo Santa_Barbara Santa_Clara Santa_Cruz
Shasta Siskiyou Solano Sonoma Stanislaus
Sutter Tehama Trinity Tulare Tuolumne Ventura
Yolo Yuba CountyUnknown Alpine_Inyo_Mariposa
Del_Norte_Modoc Colusa_Glenn_Trinity
patcocat
;
title 'Means of County Variable in the Last Half 2007 California EDs';
run;

Below is the output for exercise 5.2-1.
Means of County Variable in the Last Half 2007 California EDs

The MEANS Procedure

ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
Alameda 4364548 0.0426729 186248.00 0 1.0000000
Amador 4364548 0.0013669 5966.00 0 1.0000000
Butte 4364548 0.0067989 29674.00 0 1.0000000
Calaveras 4364548 0.0014618 6380.00 0 1.0000000
Colusa 4364548 0 0 0 0
Contra_Costa 4364548 0.0316010 137924.00 0 1.0000000
El_Dorado 4364548 0.0050356 21978.00 0 1.0000000
Fresno 4364548 0.0263608 115053.00 0 1.0000000
Glenn 4364548 0 0 0 0
Humboldt 4364548 0.0054936 23977.00 0 1.0000000
Imperial 4364548 0.0078324 34185.00 0 1.0000000
Kern 4364548 0.0229907 100344.00 0 1.0000000
Kings 4364548 0.0049483 21597.00 0 1.0000000
Lake 4364548 0.0034189 14922.00 0 1.0000000
Lassen 4364548 0.0011444 4995.00 0 1.0000000
LosAngeles 4364548 0.2409322 1051560.00 0 1.0000000
Madera 4364548 0.0052276 22816.00 0 1.0000000
Mendocino 4364548 0.0043157 18836.00 0 1.0000000
Marin 4364548 0.0067952 29658.00 0 1.0000000
Merced 4364548 0.0082151 35855.00 0 1.0000000
Monterey 4364548 0.0126151 55059.00 0 1.0000000
Napa 4364548 0.0037436 16339.00 0 1.0000000



Nevada 4364548 0.0028049 12242.00 0 1.0000000
Orange 4364548 0.0645817 281870.00 0 1.0000000
Placer 4364548 0.0075332 32879.00 0 1.0000000
Riverside 4364548 0.0565204 246686.00 0 1.0000000
Sacramento 4364548 0.0376119 164159.00 0 1.0000000
San_Benito 4364548 0.0018387 8025.00 0 1.0000000
San_Bernardino 4364548 0.0614015 267990.00 0 1.0000000
San_Diego 4364548 0.0691982 302019.00 0 1.0000000
San_Francisco 4364548 0.0189089 82529.00 0 1.0000000
San_Joaquin 4364548 0.0203494 88816.00 0 1.0000000
SanLuisObispo 4364548 0.0084944 37074.00 0 1.0000000
San_Mateo 4364548 0.0170856 74571.00 0 1.0000000
Santa_Barbara 4364548 0.0099568 43457.00 0 1.0000000
Santa_Clara 4364548 0.0360028 157136.00 0 1.0000000
Santa_Cruz 4364548 0.0066527 29036.00 0 1.0000000
Shasta 4364548 0.0081092 35393.00 0 1.0000000
Siskiyou 4364548 0.0019010 8297.00 0 1.0000000
Solano 4364548 0.0118056 51526.00 0 1.0000000
Sonoma 4364548 0.0125777 54896.00 0 1.0000000
Stanislaus 4364548 0.0195193 85193.00 0 1.0000000
Sutter 4364548 0.0029091 12697.00 0 1.0000000
Tehama 4364548 0.0030929 13499.00 0 1.0000000
Trinity 4364548 0 0 0 0
Tulare 4364548 0.0153677 67073.00 0 1.0000000
Tuolumne 4364548 0.0021122 9219.00 0 1.0000000
Ventura 4364548 0.0197477 86190.00 0 1.0000000
Yolo 4364548 0.0046717 20390.00 0 1.0000000
Yuba 4364548 0.0031098 13573.00 0 1.0000000
CountyUnknown 4364548 0.0265095 115702.00 0 1.0000000
Alpine_Inyo_Mariposa 4364548 0.0016483 7194.00 0 1.0000000
Del_Norte_Modoc 4364548 0.0030019 13102.00 0 1.0000000
Colusa_Glenn_Trinity 4364548 0.0019170 8367.00 0 1.0000000
patcocat 4364548 24.1934608 105593521 1.0000000 55.0000000
ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

2. Using osphd02d, produce a PROC Tabulate of the California counties in rank order of ED
deaths. Add the above code into osphd02 and the PROC Tabulate below.
/*2. Using osphd02d, produce a PROC Tabulate to identify the
California Counties with the most ED deaths*/
/*osphd02d.sas*/
class died patco;
var age_yrs;
tables patco all,
(died all)*(age_yrs*(n*f=6.0 mean*f=3.2)) /rts=30;
format patco $countyf. dispn;
title 'Distribution in Rank order of the County ED Deaths';
run;
options nolabel nodate nonumber



Below is Partial Proc Tabulate for exercise 5.2-2.

Distribution in Rank order of the County ED Deaths

‚ ‚ died ‚ ‚
‚ ‚ 0 ‚ 1 ‚ All ‚
‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‚patco ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚Los Angeles ‚632660‚ 31‚ 904‚ 62‚633564‚ 31‚
‚San Diego ‚199414‚ 36‚ 246‚ 64‚199660‚ 36‚
‚Orange ‚174899‚ 34‚ 252‚ 66‚175151‚ 34‚
‚San Bernardino ‚186261‚ 29‚ 261‚ 59‚186522‚ 29‚
‚Riverside ‚177735‚ 32‚ 252‚ 62‚177987‚ 32‚
‚Alameda ‚121411‚ 35‚ 184‚ 62‚121595‚ 35‚
‚Sacramento ‚ 98329‚ 37‚ 242‚ 60‚ 98571‚ 37‚
‚Santa Clara ‚ 91068‚ 34‚ 126‚ 66‚ 91194‚ 34‚
‚Contra Costa ‚ 94378‚ 37‚ 106‚ 63‚ 94484‚ 37‚
‚County Unknown ‚ 36316‚ 40‚ 76‚ 43‚ 36392‚ 40‚
‚Fresno ‚ 81984‚ 29‚ 128‚ 53‚ 82112‚ 29‚
‚Kern ‚ 74225‚ 30‚ 126‚ 55‚ 74351‚ 30‚
‚San Joaquin ‚ 61285‚ 34‚ 162‚ 59‚ 61447‚ 34‚
‚Ventura ‚ 65308‚ 36‚ 121‚ 65‚ 65429‚ 36‚
‚Stanislaus ‚ 66387‚ 31‚ 106‚ 56‚ 66493‚ 31‚
‚San Francisco ‚ 47693‚ 42‚ 29‚ 71‚ 47722‚ 42‚
‚San Mateo ‚ 45273‚ 39‚ 58‚ 70‚ 45331‚ 39‚
‚Tulare ‚ 54784‚ 28‚ 91‚ 59‚ 54875‚ 28‚
‚Monterey ‚ 43138‚ 29‚ 51‚ 59‚ 43189‚ 29‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒ‹ƒ ƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒ
‚All ‚2.88E6‚ 34‚ 4368‚ 61‚2.89E6‚ 34‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒŒ



3. Using the osphd02d insert the below Proc Means below and a class statement, and identify the
differences in demographic characteristics of the selfpay and non-self pay population.
var options nolabel nodate nonumber;
proc means n mean sum data=ed2007;
class selfpay;
var
age_yrs agelt1 age1to17 age18to34 age35to64
agege65 uknagecat
male female unkgen hispanic non_hispanic hispanic_unk
hispanic_blnk
white black native_american asian hawaiian
othrace unkrace race_blk
selfpay othnonfed ppo pos epo carehmo automed
bluecross tricare commercial disability othhmo
careparta carepartb medical othfed titleV
veterans workcomp payoth payblank
age_yrs home inpatinpatient_care snf intermediate_care
other_type_inst home_health lma died federal_health
home_hospice_care hospital_hospice hospital_swing_bed
inpatient_rehab ltc_hospital snf_no_cert psych_hospital
critical_hospital dispo_other dispo_invalid gendercat ethnicat
racecat paycat agegroup dispocat
Alameda Amador Butte Calaveras Colusa
Contra_Costa El_Dorado Fresno Glenn
Humboldt Imperial Kern Kings Lake Lassen
LosAngeles Madera Mendocino Marin Merced
Monterey Napa Nevada Orange Placer Riverside
Sacramento San_Benito San_Bernardino San_Diego
San_Francisco San_Joaquin SanLuisObispo
San_Mateo Santa_Barbara Santa_Clara Santa_Cruz
Shasta Siskiyou Solano Sonoma Stanislaus
Sutter Tehama Trinity Tulare Tuolumne Ventura
Yolo Yuba CountyUnknown Alpine_Inyo_Mariposa
Del_Norte_Modoc Colusa_Glenn_Trinity
patcocat
;
title 'Means of Selfpay vs Non-selfpay Patients in the Last Half 2007
California EDs';
run;

The MEANS Procedure

Means of Selfpay Variable in the Last Half 2007 California EDs

The MEANS Procedure

selfpay N Obs Variable N Mean Sum
0 3601351 age_yrs 2398848 34.2003266 82041385.00
agelt1 3601351 0.0401921 144746.00
age1to17 3601351 0.2246407 809010.00



age18to34 3601351 0.2238168 806043.00
age35to64 3601351 0.3284206 1182758.00
agege65 3601351 0.1377691 496155.00
uknagecat 3601351 0.0451267 162517.00
male 3601351 0.3976305 1432007.00
female 3601351 0.5043360 1816291.00
unkgen 3601351 0.0980174 352995.00
hispanic 3601351 0.2633628 948462.00
non_hispanic 3601351 0.5069595 1825739.00
hispanic_unk 3601351 0.0398048 143351.00
hispanic_blnk 3601351 0.1898729 683799.00
white 3601351 0.5357620 1929467.00
black 3601351 0.0840843 302817.00
native_american 3601351 0.0024996 9002.00
asian 3601351 0.0288431 103874.00
hawaiian 3601351 0.0034612 12465.00
othrace 3601351 0.1559134 561499.00
unkrace 3601351 0.0302081 108790.00
race_blk 3601351 0.1592283 573437.00
selfpay 3601351 0 0
othnonfed 3601351 0.0283930 102253.00
ppo 3601351 0.0735818 264994.00
pos 3601351 0.0045691 16455.00
epo 3601351 0.0035831 12904.00
carehmo 3601351 0.0504325 181625.00
automed 3601351 0.000738612 2660.00
bluecross 3601351 0.0669093 240964.00
tricare 3601351 0.0087723 31592.00
commercial 3601351 0.0430813 155151.00
disability 3601351 3.0544093E-6 11.0000000
othhmo 3601351 0.2462876 886968.00
careparta 3601351 0.0804945 289889.00
carepartb 3601351 0.0443650 159774.00
medical 3601351 0.2956477 1064731.00
othfed 3601351 0.0106738 38440.00
titleV 3601351 0.000840518 3027.00
veterans 3601351 0.000764713 2754.00
workcomp 3601351 0.0225032 81042.00
payoth 3601351 0.0179274 64563.00
payblank 3601351 0.000431505 1554.00
home 3601351 0.9435242 3397962.00
inpatinpatient_care 3601351 0.0136310 49090.00
snf 3601351 0.0033696 12135.00
intermediate_care 3601351 0.000761103 2741.00
other_type_inst 3601351 0.0037028 13335.00
0 3601351 home_health 3601351 0.000490927 1768.00
lma 3601351 0.0172785 62226.00
died 3601351 0.0017710 6378.00
federal_health 3601351 0.000081081 292.0000000
home_hospice_care 3601351 0.000119955 432.0000000
hospital_hospice 3601351 0.000042206 152.0000000
hospital_swing_bed 3601351 5.831145E-6 21.0000000
inpatient_rehab 3601351 0.000159385 574.0000000
ltc_hospital 3601351 0.000433171 1560.00
snf_no_cert 3601351 0.000071917 259.0000000
psych_hospital 3601351 0.0057248 20617.00
critical_hospital 3601351 0.000154109 555.0000000



dispo_other 3601351 0.0085329 30730.00
dispo_invalid 3601351 0.000145501 524.0000000
gendercat 3601351 1.7984204 6476743.00
ethnicat 3601351 2.1561878 7765189.00
racecat 3601351 5.4258571 19540416.00
paycat 3601351 11.7182707 42201606.00
agegroup 3601351 5.1183256 18432887.00
dispocat 3601351 1.4027686 5051862.00
Alameda 3601351 0.0443561 159742.00
Amador 3601351 0.0014217 5120.00
Butte 3601351 0.0072145 25982.00
Calaveras 3601351 0.0014875 5357.00
Colusa 3601351 0 0
Contra_Costa 3601351 0.0325239 117130.00
El_Dorado 3601351 0.0056257 20260.00
Fresno 3601351 0.0273695 98567.00
Glenn 3601351 0 0
Humboldt 3601351 0.0055213 19884.00
Imperial 3601351 0.0085257 30704.00
Kern 3601351 0.0226929 81725.00
Kings 3601351 0.0053013 19092.00
Lake 3601351 0.0035464 12772.00
Lassen 3601351 0.0012781 4603.00
LosAngeles 3601351 0.2346556 845077.00
Madera 3601351 0.0053746 19356.00
Mendocino 3601351 0.0045902 16531.00
Marin 3601351 0.0073050 26308.00
Merced 3601351 0.0082869 29844.00
Monterey 3601351 0.0128102 46134.00
Napa 3601351 0.0039952 14388.00
Nevada 3601351 0.0029336 10565.00
Orange 3601351 0.0687403 247558.00
Placer 3601351 0.0080248 28900.00
Riverside 3601351 0.0542274 195292.00
Sacramento 3601351 0.0386938 139350.00
San_Benito 3601351 0.0019470 7012.00
0 3601351 San_Bernardino 3601351 0.0573476 206529.00
San_Diego 3601351 0.0702398 252958.00
San_Francisco 3601351 0.0196557 70787.00
San_Joaquin 3601351 0.0197834 71247.00
SanLuisObispo 3601351 0.0088842 31995.00
San_Mateo 3601351 0.0172952 62286.00
Santa_Barbara 3601351 0.0067539 24323.00
Santa_Clara 3601351 0.0381199 137283.00
Santa_Cruz 3601351 0.0058384 21026.00
Shasta 3601351 0.0081172 29233.00
Siskiyou 3601351 0.0020576 7410.00
Solano 3601351 0.0123473 44467.00
Sonoma 3601351 0.0131720 47437.00
Stanislaus 3601351 0.0197001 70947.00
Sutter 3601351 0.0034420 12396.00
Tehama 3601351 0.0030561 11006.00
Trinity 3601351 0 0
Tulare 3601351 0.0169142 60914.00
Tuolumne 3601351 0.0023252 8374.00
Ventura 3601351 0.0200100 72063.00
Yolo 3601351 0.0049273 17745.00



Yuba 3601351 0.0036997 13324.00
CountyUnknown 3601351 0.0207850 74854.00
Alpine_Inyo_Mariposa 3601351 0.0017374 6257.00
Del_Norte_Modoc 3601351 0.0032249 11614.00
Colusa_Glenn_Trinity 3601351 0.0020309 7314.00
patcocat 3601351 24.0626093 86657902.00

1 763197 age_yrs 488201 30.6303183 14953752.00
agelt1 763197 0.0217047 16565.00
age1to17 763197 0.1299101 99147.00
age18to34 763197 0.4004589 305629.00
age35to64 763197 0.3698455 282265.00
agege65 763197 0.0185051 14123.00
uknagecat 763197 0.0594761 45392.00
male 763197 0.4710448 359500.00
female 763197 0.4116709 314186.00
unkgen 763197 0.1172319 89471.00
hispanic 763197 0.3176231 242409.00
non_hispanic 763197 0.4371571 333637.00
hispanic_unk 763197 0.0323639 24700.00
hispanic_blnk 763197 0.2128559 162451.00
white 763197 0.4999233 381540.00
black 763197 0.1009883 77074.00
native_american 763197 0.0026192 1999.00
asian 763197 0.0153918 11747.00
hawaiian 763197 0.0030621 2337.00
othrace 763197 0.1750636 133608.00
1 763197 unkrace 763197 0.0236440 18045.00
race_blk 763197 0.1793076 136847.00
selfpay 763197 1.0000000 763197.00
othnonfed 763197 0 0
ppo 763197 0 0
pos 763197 0 0
epo 763197 0 0
carehmo 763197 0 0
automed 763197 0 0
bluecross 763197 0 0
tricare 763197 0 0
commercial 763197 0 0
disability 763197 0 0
othhmo 763197 0 0
careparta 763197 0 0
carepartb 763197 0 0
medical 763197 0 0
othfed 763197 0 0
titleV 763197 0 0
veterans 763197 0 0
workcomp 763197 0 0
payoth 763197 0 0
payblank 763197 0 0
home 763197 0.9298739 709677.00
inpatinpatient_care 763197 0.0098048 7483.00
snf 763197 0.000313156 239.0000000
intermediate_care 763197 0.000158544 121.0000000
other_type_inst 763197 0.0055058 4202.00
home_health 763197 0.000275158 210.0000000
lma 763197 0.0383754 29288.00



died 763197 0.0021161 1615.00
federal_health 763197 0.000077306 59.0000000
home_hospice_care 763197 0.000057652 44.0000000
hospital_hospice 763197 0.000015723 12.0000000
hospital_swing_bed 763197 5.2411107E-6 4.0000000
inpatient_rehab 763197 0.000151992 116.0000000
ltc_hospital 763197 0.000079927 61.0000000
snf_no_cert 763197 9.1719438E-6 7.0000000
psych_hospital 763197 0.0067912 5183.00
critical_hospital 763197 0.000107443 82.0000000
dispo_other 763197 0.0061072 4661.00
dispo_invalid 763197 0.000174267 133.0000000
gendercat 763197 1.7634713 1345876.00
ethnicat 763197 2.1404526 1633587.00
racecat 763197 5.4985829 4196502.00
paycat 763197 1.0000000 763197.00
agegroup 763197 4.9953328 3812423.00
dispocat 763197 1.4942603 1140415.00
1 763197 Alameda 763197 0.0347302 26506.00
Amador 763197 0.0011085 846.0000000
Butte 763197 0.0048375 3692.00
Calaveras 763197 0.0013404 1023.00
Colusa 763197 0 0
Contra_Costa 763197 0.0272459 20794.00
El_Dorado 763197 0.0022511 1718.00
Fresno 763197 0.0216012 16486.00
Glenn 763197 0 0
Humboldt 763197 0.0053630 4093.00
Imperial 763197 0.0045611 3481.00
Kern 763197 0.0243961 18619.00
Kings 763197 0.0032822 2505.00
Lake 763197 0.0028171 2150.00
Lassen 763197 0.000513629 392.0000000
LosAngeles 763197 0.2705501 206483.00
Madera 763197 0.0045336 3460.00
Mendocino 763197 0.0030202 2305.00
Marin 763197 0.0043894 3350.00
Merced 763197 0.0078761 6011.00
Monterey 763197 0.0116942 8925.00
Napa 763197 0.0025564 1951.00
Nevada 763197 0.0021973 1677.00
Orange 763197 0.0449582 34312.00
Placer 763197 0.0052136 3979.00
Riverside 763197 0.0673404 51394.00
Sacramento 763197 0.0325067 24809.00
San_Benito 763197 0.0013273 1013.00
San_Bernardino 763197 0.0805310 61461.00
San_Diego 763197 0.0642835 49061.00
San_Francisco 763197 0.0153853 11742.00
San_Joaquin 763197 0.0230203 17569.00
SanLuisObispo 763197 0.0066549 5079.00
San_Mateo 763197 0.0160968 12285.00
Santa_Barbara 763197 0.0250709 19134.00
Santa_Clara 763197 0.0260129 19853.00
Santa_Cruz 763197 0.0104953 8010.00
Shasta 763197 0.0080713 6160.00
Siskiyou 763197 0.0011622 887.0000000



Solano 763197 0.0092493 7059.00
Sonoma 763197 0.0097734 7459.00
Stanislaus 763197 0.0186662 14246.00
Sutter 763197 0.000394394 301.0000000
Tehama 763197 0.0032665 2493.00
Trinity 763197 0 0
Tulare 763197 0.0080700 6159.00
Tuolumne 763197 0.0011072 845.0000000
Ventura 763197 0.0185103 14127.00
1 763197 Yolo 763197 0.0034657 2645.00
Yuba 763197 0.000326259 249.0000000
CountyUnknown 763197 0.0535222 40848.00
Alpine_Inyo_Mariposa 763197 0.0012277 937.0000000
Del_Norte_Modoc 763197 0.0019497 1488.00
Colusa_Glenn_Trinity 763197 0.0013797 1053.00
patcocat 763197 24.8109191 18935619.00

Exercise 5.2-5: Using a PROC Tabulate, identify the diseases presented to the California EDs
when patzip=99999 is a proxy for the homeless. Describe your findings.
/*5. Using a PROC Tabulate identify the diseases presented to the
California*/
/*EDs when patzip=99999 is a proxy for the homeless. Describe your
findings. */

where patzip='99999';
class dx_prin3 payer;
var age_yrs;
tables dx_prin3 all,
(payer all)*(age_yrs*(n*f=6.0 mean*f=3.2)) /rts=30;
;
title 'Distribution in Rank order of the Homeless Diagnoses by Payer';
run;

Exercise 5.2-4: Partial Output of PROC Tabulate to identify the diseases presented to the
California EDs when patzip=99999 is a proxy for the homeless. Describe your findings
Distribution in Rank order of the Homeless Diagnoses by Payer

„ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ†
‚ ‚ payer ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ…ƒƒƒƒƒƒƒƒƒƒ‰
‚ ‚ ‚ Medical- ‚other non-‚ ‚ ‚ Medicare ‚
‚ ‚ selfpay ‚ Cal ‚ federal ‚other HMO ‚commercial‚ Part A ‚
‚ ‡ƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒ‰
‚ ‚ age_yrs ‚ age_yrs ‚ age_yrs ‚ age_yrs ‚ age_yrs ‚ age_yrs ‚
‚ ‡ƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒˆƒƒƒƒƒƒ…ƒƒƒ‰



‚ ‚ ‚Me-‚ ‚Me-‚ ‚Me-‚ ‚Me-‚ ‚Me-‚ ‚Me-‚
‚ ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚ N ‚an ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒˆƒƒƒƒƒƒˆƒƒƒ‰
‚dx_prin3 ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚(305) Nondependent abuse of ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚drugs ‚ 1045‚ 43‚ 212‚ 47‚ 238‚ 42‚ 25‚ 37‚ 46‚ 40‚ 65‚ 50‚
‚(780) General symptoms ‚ 528‚ 37‚ 237‚ 37‚ 170‚ 40‚ 26‚ 23‚ 39‚ 29‚ 66‚ 48‚
‚(786) Symptoms involving ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚respiratory ... ‚ 317‚ 40‚ 172‚ 45‚ 128‚ 44‚ 28‚ 32‚ 19‚ 43‚ 40‚ 57‚
‚(789) Other symptoms ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚involving abdome... ‚ 277‚ 37‚ 129‚ 38‚ 134‚ 41‚ 32‚ 34‚ 22‚ 32‚ 26‚ 54‚
‚(873) Other open wound of ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚head ‚ 322‚ 34‚ 65‚ 32‚ 140‚ 38‚ 19‚ 22‚ 40‚ 40‚ 12‚ 45‚
‚(682) Other cellulitis and ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚abscess ‚ 344‚ 41‚ 110‚ 43‚ 156‚ 42‚ 17‚ 33‚ 27‚ 38‚ 28‚ 50‚
‚(298) Other nonorganic ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚psychoses ‚ 392‚ 38‚ 108‚ 40‚ 86‚ 37‚ 1‚ 21‚ 6‚ 43‚ 41‚ 44‚
‚(959) Injury, other and ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚unspecified ‚ 222‚ 33‚ 50‚ 29‚ 62‚ 35‚ 21‚ 20‚ 6‚ 22‚ 9‚ 55‚
‚(724) Other and unspecified ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚disorders... ‚ 225‚ 41‚ 84‚ 46‚ 71‚ 44‚ 16‚ 33‚ 7‚ 49‚ 21‚ 50‚
‚head and neck ‚ 145‚ 37‚ 62‚ 38‚ 72‚ 40‚ 14‚ 36‚ 10‚ 54‚ 9‚ 54‚
‚(V70) General medical ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚examination ‚ 101‚ 39‚ 35‚ 41‚ 74‚ 38‚ 14‚ 36‚ 57‚ 43‚ 5‚ 61‚
‚(V68) Encounters for ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚administrative p... ‚ 204‚ 39‚ 72‚ 48‚ 61‚ 44‚ 3‚ 16‚ 3‚ 37‚ 19‚ 61‚
‚(729) Other disorders of ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚soft tissues ‚ 180‚ 44‚ 89‚ 49‚ 59‚ 40‚ 3‚ 25‚ 9‚ 43‚ 42‚ 53‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒŒ
‡ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‰ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚(303) Alcohol dependence ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚syndrome ‚ 247‚ 46‚ 62‚ 49‚ 69‚ 40‚ 5‚ 49‚ 15‚ 47‚ 19‚ 50‚
‚(296) Affective psychoses ‚ 245‚ 39‚ 110‚ 39‚ 48‚ 40‚ 3‚ 37‚ 7‚ 34‚ 37‚ 45‚
‚(847) Sprains and strains of‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚other an... ‚ 108‚ 34‚ 28‚ 35‚ 29‚ 42‚ 13‚ 31‚ 11‚ 35‚ 10‚ 40‚
‚(719) Other and unspecified ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚disorder ... ‚ 129‚ 44‚ 74‚ 50‚ 67‚ 41‚ 9‚ 30‚ 5‚ 43‚ 26‚ 49‚
‚digestive sy... ‚ 121‚ 31‚ 56‚ 23‚ 29‚ 37‚ 18‚ 25‚ 8‚ 35‚ 8‚ 48‚



‚(295) Schizophrenic ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚psychoses ‚ 127‚ 39‚ 163‚ 41‚ 23‚ 40‚ 1‚ 26‚ 4‚ 38‚ 48‚ 44‚
‚(311) Depressive disorder, ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚not elsewh... ‚ 144‚ 40‚ 93‚ 43‚ 70‚ 39‚ 7‚ 37‚ 7‚ 36‚ 27‚ 48‚
‚(300) Neurotic disorders ‚ 118‚ 36‚ 58‚ 44‚ 25‚ 40‚ 10‚ 38‚ 10‚ 36‚ 25‚ 46‚
‚(599) Other disorders of ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚urethra and ... ‚ 88‚ 34‚ 30‚ 32‚ 13‚ 41‚ 11‚ 32‚ 14‚ 36‚ 10‚ 63‚
‚(920) Contusion of face, ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚scalp, and n... ‚ 122‚ 34‚ 32‚ 27‚ 33‚ 39‚ 5‚8.8‚ 10‚ 28‚ 5‚ 55‚
‚(493) Asthma ‚ 108‚ 41‚ 65‚ 40‚ 34‚ 42‚ 11‚ 23‚ 8‚ 21‚ 7‚ 52‚
‚(465) Acute upper ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚respiratory infecti... ‚ 70‚ 29‚ 79‚ 17‚ 17‚ 42‚ 13‚ 14‚ 5‚ 24‚ 5‚ 53‚
‚(V58) Other and unspecified ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚ ‚
‚aftercare ‚ 180‚ 38‚ 37‚ 32‚ 24‚ 44‚ 9‚ 23‚ 26‚ 47‚ 16‚ 49‚
Šƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒ‹ƒƒƒƒƒƒ‹ƒƒƒŒ

3. From a Proc Means, using the class statement for selfpay output, prepare a descriptive
statistics narrative of the findings.
4. Using a PROC Tabulate, identify the diseases presented to the California EDs when
patzip=99999 is a proxy for the homeless and describe your findings.

In the last half of 2007, there were 763,197 (17.5%) emergency department (ED) uninsured (self-
pay) visits to California hospitals and 3.6 million ED visits (82.5%) with insurance. The
uninsured were younger, 30.6 years old compared to the insured of 34.2 years. The uninsured
were more males than females (47.1% versus 41.1%) and the insured were more females than
males (50.4% versus 39.7%). The racial mix of the uninsured was white (49.9%), black (10.9%),
and all other (39.7%). The racial mix of the insured was white (53.5%), black (8.4 %), and all
other (37.9%). The payer mix of the insured was 29.6% Medical, 6.6% Bluecross, 4.3%
commercial, 5.0% Medicare HMO, 24.6% other HMO, 12.4 % Medicare, and 20.5% other.
92.9 % of the uninsured went home, 0.98% went to a skilled nursing facility, 3.8% left against
medical advice and 2.3 %had other dispositions. 94.3 % of the insured went home, 1.3% went to
a skilled nursing facility and the remaing 4.4% had other dispositions. Los Angeles EDs had
27.0% (206,483) uninsured, followed by 8% in San Bernardino (61,461), 6.7 % in Riverside
(51,394) and 6.4% in San Diego County (49,061). Los Angeles had 23.4% (845,077), followed
by 7.0% in San Diego (252,958), 6.8 % in Orange (247,558) and 5.7% in San Bernardino County
(206,529).
In summary, 17.5% of emergency department visits were uninsured compared to 82.5 percent
insured. The uninsured compared to the insured were younger, male, less white, and more black.
Fewer went home, more left against medical advice and less went to other facilities. The most
were in Los Angeles, followed by San Bernardino.


Of the 46,368 homeless visits, 23,643 had principal diagnoses treated in the last half of 2007 in
California emergency departments. Their mean age was 39 years old. The top ten diagnoses are:
1. Nondependent abuse of drugs (1,773) with a mean age of 43;
2. General symptoms (1,211) with a mean age of 38;
3. Respiratory symptoms (801) with a mean age of 44;
4. Abdominal symptoms (715) with a mean age of 39;
5. Open wound of the head (693) with a mean age of 35;
6. Cellulites and abscess (769) with a mean age of 41;
7. Other non organic psychosis (684) with a mean age of 39;
8. Injury and other unspecified (417) with mean age of 33;
9. Other unspecified disorders (478) with a mean age of 44 and
10. Symptoms involving the head and neck (349) with a mean age of 40.
In summary, these ten diagnoses contain one-third of the homeless ED visits and reflect exposure
and neglect of this young population, all of which are mostly uninsured.



3. Multiple Linear Regression Model of California ED visits
in with the response variable of Patient Age (age_years)
osphd03d.sas

Below is the linear regression model (Proc Reg) of the response variable, patient age in years and
the effects of gender, race, ethnicity payer disposition, and county, using 1 million observations.
/*osphd03d.sas*/
proc reg data=ed2007 (obs=1000000);
model age_yrs=male white hispanic selfpay snf LosAngeles;
title 'Linear regression for age of ED patients in California in
2007';
run;

The REG Procedure
Model: MODEL1


Sum of Mean

Model 6 44696134 7449356 15642.8 <.0001
Error 694621 330789063 476.21518

Coeff Var 66.13977

Parameter Estimates
Parameter Standard

Intercept 1 37.28367 0.05568 669.55 <.0001
male 1 -3.60033 0.05284 -68.14 <.0001
white 1 4.94754 0.05427 91.17 <.0001
hispanic 1 -12.38907 0.05810 -213.23 <.0001
selfpay 1 -1.49705 0.07303 -20.50 <.0001
snf 1 35.75377 0.51267 69.74 <.0001
LosAngeles 1 -3.55061 0.06132 -57.91 <.0001



As seen above in the PROC REG models, output of visit age (age_yrs) for California emergency
department visits in the last half of 2007 all of the effects are significant at p<.0001. Controlling
for visit gender, race, ethnicity, payer, disposition and county, the findings are as follows:
1. All else being equal, male visits are 3.6 years younger than females. p<.0001
2. All else being equal, white visits are 4.9 years older than non-white. p<.0001
3. All else being equal, hispanic visits are 12 years younger than non-Hispanics. p<.0001
4. All else being equal, self-pay visits are 1.5 years younger than non self-pay. p<.0001
5. All else being equal, skilled nursing facility (snf) disposition visits are 35.7 years older.
p<.0001
6. All else being equal, visits in Los Angeles are 3.5 years younger than all other counties.
p<.0001



Exercise 5.3

Using osphd03d.sas, perform the below and interpret the findings:
1. Run the model with 2 million observations and compare the intercept and beta coefficients to
the model with 1 million observations.
2. Run the model with all 4.3 million observations and compare the intercept and beta
coefficients to the two previous models.
Run the model with 2 million observations and compare the intercept and beta coefficients to the
model with 1 million observations.
/*Answer to Exercise 5.3-1 */
/*osphd03d.sas*/
proc reg data=ed2007 (obs=2000000);
2007';
run;


The REG Procedure
Model: MODEL1



Sum of Mean

Model 6 88097868 14682978 30334.7 <.0001
Error 1.3E6 630852936 484.03200

Coeff Var 67.18661

Parameter Estimates



Parameter Standard

Intercept 1 37.29365 0.04431 841.75 <.0001
male 1 -3.93503 0.03886 -101.27 <.0001
white 1 4.70930 0.04001 117.71 <.0001
hispanic 1 -13.98514 0.04093 -341.73 <.0001
selfpay 1 -0.89066 0.05157 -17.27 <.0001
snf 1 35.99326 0.39001 92.29 <.0001
LosAngeles 1 0.32646 0.03939 8.29 <.0001

1 million observations

Parameter Standard

Intercept 1 37.28367 0.05568 669.55 <.0001
male 1 -3.60033 0.05284 -68.14 <.0001
white 1 4.94754 0.05427 91.17 <.0001
hispanic 1 -12.38907 0.05810 -213.23 <.0001
selfpay 1 -1.49705 0.07303 -20.50 <.0001
snf 1 35.75377 0.51267 69.74 <.0001
LosAngeles 1 -3.55061 0.06132 -57.91 <.0001

The intercepts are almost equal as well as all of the effects with the exception of self-pay and Los
Angeles. Increasing the observations to 2 million, the self-pay visits compared to all non-selfpay visits is
0.89 years younger rather than 1.49 years in the one million-observation model. In addition, patients
from Los Angeles are 0.32 years older rather than 3.5 years younger. When it is not a random sample,
model size equal to the first million observations compared to the second million have slight but
important differences.

Run the model with all 4.36 million observations and compare the coefficients to the two
previous models
/*osphd03d.sas*/
proc reg data=ed2007;
2007';
run;
quit;
options label;
title;


The REG Procedure
Model: MODEL1






Sum of Mean

Model 6 182211742 30368624 61709.6 <.0001
Error 2.89E6 1420775878 492.12165

Coeff Var 66.02987

All Observations
Parameter Estimates

Parameter Standard

Intercept 1 36.80474 0.02970 1239.05 <.0001
male 1 -3.76998 0.02631 -143.28 <.0001
white 1 5.00609 0.02814 177.87 <.0001
hispanic 1 -13.43844 0.02848 -471.90 <.0001
selfpay 1 -1.99993 0.03499 -57.16 <.0001
snf 1 37.18328 0.24254 153.31 <.0001
LosAngeles 1 0.52051 0.03214 16.19 <.0001

Parameter Estimates

Parameter Standard

Intercept 1 37.29365 0.04431 841.75 <.0001
male 1 -3.93503 0.03886 -101.27 <.0001
white 1 4.70930 0.04001 117.71 <.0001
hispanic 1 -13.98514 0.04093 -341.73 <.0001
selfpay 1 -0.89066 0.05157 -17.27 <.0001
snf 1 35.99326 0.39001 92.29 <.0001
LosAngeles 1 0.32646 0.03939 8.29 <.0001


Parameter Standard

Intercept 1 37.28367 0.05568 669.55 <.0001
male 1 -3.60033 0.05284 -68.14 <.0001
white 1 4.94754 0.05427 91.17 <.0001
hispanic 1 -12.38907 0.05810 -213.23 <.0001
selfpay 1 -1.49705 0.07303 -20.50 <.0001
snf 1 35.75377 0.51267 69.74 <.0001



LosAngeles 1 -3.55061 0.06132 -57.91 <.0001

All three intercepts are almost equal, as well as all of the effects with the exception of self-pay. Increasing
the observations to 4.6 million, the self-pay ED visit compared to all non-selfpay ED visits are almost 2
years younger rather than 0.89, and 1.49 in the two other models. Overall, the differences between using
the full sample do have small but important differences in a few effects.



4. Logistic Regression Model of California Self Pay
Emergency Department Visits

Below is the logistic regression model of California emergency department visits and the
response variable of self-pay and the effects of age, sex, race and ethnicity.
/*osphd04.sas*/
proc logistic data=ed2007 des;
class race (param=ref ref='R3') sex (param=ref ref='F')
eth (param=ref ref='E2') dispn (param=ref ref='01')
/**home**/
patco (param=ref ref='19') /**Los Angeles**/
;

model selfpay=age_yrs sex race eth patco ;
;

;
units age_yrs=10;
title 'Logistic Regression for in Selfpay Visits in California in
2007';
run;
quit;
options label;

It should be noted regarding the log below that on my computer this regression took over 14
minutes to run. Therefore we will run in our exercises less than 4.3 million observations and
determine any differences as we had previously done with the PROC Reg model.
NOTE: PROC LOGISTIC is modeling the probability that selfpay=1.
NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: There were 4364548 observations read from the data set WORK.ED2007.
NOTE: PROCEDURE LOGISTIC used (Total process time):
real time 14:30.68
cpu time 4:13.45

Logistic Regression for in Selfpay Visits in California in 2007


Model Information

Model binary logit




Response Profile

Ordered Total

1 1 763197
2 0 3601351




race * 1 0 0 0 0 0 0
99 0 1 0 0 0 0 0
R1 0 0 1 0 0 0 0
R2 0 0 0 1 0 0 0
R3 0 0 0 0 0 0 0
R4 0 0 0 0 1 0 0
R5 0 0 0 0 0 1 0
R9 0 0 0 0 0 0 1

sex * 1 0 0
F 0 0 0
M 0 1 0
U 0 0 1

eth * 1 0 0
99 0 1 0
E1 0 0 1
E2 0 0 0




agecat5 * 1 0 0 0 0 0
0 0 1 0 0 0 0
1 0 0 0 0 0 0
2 0 0 1 0 0 0
3 0 0 0 1 0 0
4 0 0 0 0 1 0
5 0 0 0 0 0 1






Intercept
Intercept and

AIC 4046056.7 3776115.2
SC 4046070.0 3776380.9
-2 Log L 4046054.7 3776075.2



Score 237839.819 19 <.0001
Wald 199688.328 19 <.0001


Wald

agecat5 6 166688.946 <.0001
sex 3 27391.3169 <.0001
race 7 6407.0501 <.0001


Wald

eth 3 13035.6968 <.0001


Standard Wald

Intercept 1 -2.4268 0.00939 66832.9306 <.0001
agecat5 * 1 0.9237 0.0113 6716.7404 <.0001
agecat5 0 1 1.7952 0.1508 141.7844 <.0001
agecat5 2 1 0.1137 0.00891 162.8886 <.0001
agecat5 3 1 1.3528 0.00856 24957.3595 <.0001
agecat5 4 1 0.8998 0.00858 10992.7961 <.0001
agecat5 5 1 -1.1664 0.0120 9512.5575 <.0001
sex * 1 0.2887 0.00769 1408.7201 <.0001
sex M 1 0.4606 0.00279 27334.8644 <.0001
sex U 1 1.4484 0.2203 43.2430 <.0001
race * 1 -0.3685 0.00992 1379.3116 <.0001
race 99 1 -0.5190 0.0113 2113.8857 <.0001



race R1 1 -0.2394 0.0257 86.7720 <.0001
race R2 1 -0.6786 0.0108 3973.4762 <.0001
race R4 1 -0.2207 0.0236 87.7924 <.0001
race R5 1 -0.2808 0.00466 3632.0910 <.0001
race R9 1 -0.3163 0.00602 2757.8259 <.0001
eth * 1 0.3074 0.00754 1663.5127 <.0001
eth 99 1 0.0737 0.00919 64.3370 <.0001
eth E1 1 0.4137 0.00369 12548.8385 <.0001


Point 95% Wald

agecat5 * vs 1 2.519 2.464 2.575
agecat5 0 vs 1 6.021 4.481 8.091
agecat5 2 vs 1 1.120 1.101 1.140
agecat5 3 vs 1 3.868 3.804 3.934
agecat5 4 vs 1 2.459 2.418 2.501
agecat5 5 vs 1 0.311 0.304 0.319


Point 95% Wald

sex * vs F 1.335 1.315 1.355
sex M vs F 1.585 1.576 1.594
sex U vs F 4.256 2.764 6.554
race * vs R3 0.692 0.678 0.705
race 99 vs R3 0.595 0.582 0.608
race R1 vs R3 0.787 0.748 0.828
race R2 vs R3 0.507 0.497 0.518
race R4 vs R3 0.802 0.766 0.840
race R5 vs R3 0.755 0.748 0.762
race R9 vs R3 0.729 0.720 0.738
eth * vs E2 1.360 1.340 1.380
eth 99 vs E2 1.077 1.057 1.096
eth E1 vs E2 1.512 1.502 1.523


Pairs 2.7485403E12 c 0.678

As seen above in the proc logistic output of the response variable of selfpay, considering the
effects of visit age group, gender, race, and ethnicity, the findings are as follows:



1. All else being equal, those who visit California ED between 18 and 34 years old compared to
those less than 1 year old are 3.9 times more likely to be self pay (uninsured). p<.0001[CI
3.804, 3.934]
2. All else being equal, males compared to females who visit California EDs are1.5 times more
likely to be uninsured. p<.0001[CI 1.576, 1.594]
3. All else being equal, whites compared to blacks who visit California EDs are 21.3 percent
less likely to be uninsured. p<.0001[CI 0.748, 0.828]
4. All else being equal, Hispanics compared to non-Hispanics who visit California EDs are1.5
times more likely to be uninsured. p<.0001[CI 1.502 , 1.523]
The partial SAS code from osphd02d.sas is available to interpret findings.

asian =(race='R2');
black =(race='R3');
white =(race='R5');




Exercises 5.4

1. Run the selfpay logistic model with 1,000,000 observations and determine if the findings
differ significantly. As shown below, the run time was a little over 1 minute.
2. Change the logistic model of exercise 5.4-1 to consider homeless rather than selfpay.

proc logistic data=ed2007 (obs=1000000) des;
class race (param=ref ref='R3') sex (param=ref ref='F')
eth (param=ref ref='E2') agecat5 (param=ref ref='1')

;

model selfpay=agecat5 sex race eth;
;

title 'Logistic Regression for in Selfpay Visits in California in
2007';
run;
quit;
options label;

NOTE: PROC LOGISTIC is modeling the probability that selfpay=1.
NOTE: Convergence criterion (GCONV=1E-8) satisfied.
real time 1:03.67
cpu time 27.24 seconds



Model Information

Model binary logit




Response Profile

Ordered Total

1 1 159030
2 0 840970




race * 1 0 0 0 0 0 0
99 0 1 0 0 0 0 0
R1 0 0 1 0 0 0 0
R2 0 0 0 1 0 0 0
R3 0 0 0 0 0 0 0
R4 0 0 0 0 1 0 0
R5 0 0 0 0 0 1 0
R9 0 0 0 0 0 0 1

sex * 1 0 0
F 0 0 0
M 0 1 0
U 0 0 1

eth * 1 0 0
99 0 1 0
E1 0 0 1
E2 0 0 0

agecat5 * 1 0 0 0 0 0
0 0 1 0 0 0 0
1 0 0 0 0 0 0
2 0 0 1 0 0 0
3 0 0 0 1 0 0
4 0 0 0 0 1 0
5 0 0 0 0 0 1




Intercept
Intercept and




AIC 876117.78 808937.15
SC 876129.60 809173.46
-2 Log L 876115.78 808897.15



Score 59387.4263 19 <.0001
Wald 48879.8943 19 <.0001


Wald

agecat5 6 42061.6983 <.0001
sex 3 8010.3725 <.0001
race 7 1436.8306 <.0001


Wald

eth 3 1079.0166 <.0001


Standard Wald

Intercept 1 -2.7678 0.0211 17160.9522 <.0001
agecat5 * 1 1.1355 0.0258 1933.6508 <.0001
agecat5 0 1 0.6094 0.7562 0.6494 0.4203
agecat5 2 1 0.1507 0.0210 51.5961 <.0001
agecat5 3 1 1.6198 0.0201 6501.4024 <.0001
agecat5 4 1 1.0921 0.0202 2936.5548 <.0001
agecat5 5 1 -1.1878 0.0289 1688.6914 <.0001
sex * 1 0.3818 0.0174 484.0206 <.0001
sex M 1 0.5417 0.00606 7986.3940 <.0001
sex U 1 0.8830 0.5519 2.5603 0.1096
race * 1 -0.2982 0.0232 165.5796 <.0001
race 99 1 -0.4575 0.0254 323.3297 <.0001
race R1 1 -0.1989 0.0435 20.9436 <.0001
race R2 1 -0.6510 0.0242 723.6139 <.0001
race R4 1 -0.1666 0.0672 6.1559 0.0131
race R5 1 -0.2507 0.00873 824.5226 <.0001
race R9 1 -0.3394 0.0119 807.5155 <.0001
eth * 1 0.2573 0.0186 190.5329 <.0001



eth 99 1 0.2770 0.0212 171.1656 <.0001
eth E1 1 0.2605 0.00835 974.0387 <.0001


Point 95% Wald

agecat5 * vs 1 3.113 2.959 3.274
agecat5 0 vs 1 1.839 0.418 8.098
agecat5 2 vs 1 1.163 1.116 1.211
agecat5 3 vs 1 5.052 4.857 5.255
agecat5 4 vs 1 2.980 2.865 3.100
agecat5 5 vs 1 0.305 0.288 0.323
sex * vs F 1.465 1.416 1.516
sex M vs F 1.719 1.699 1.739
sex U vs F 2.418 0.820 7.132
race * vs R3 0.742 0.709 0.777
race 99 vs R3 0.633 0.602 0.665
race R1 vs R3 0.820 0.753 0.893
race R2 vs R3 0.522 0.497 0.547
race R4 vs R3 0.847 0.742 0.966
race R5 vs R3 0.778 0.765 0.792
race R9 vs R3 0.712 0.696 0.729
eth * vs E2 1.293 1.247 1.342
eth 99 vs E2 1.319 1.266 1.375
eth E1 vs E2 1.298 1.276 1.319


Pairs 133739459100 c 0.695

As seen above in the proc logistic with 1 million visits, the output of the response variable of
selfpay, considering the effects of visit age group, gender, race, and ethnicity, the findings are as
follows:
those less than 1 year old are 5.0 times more likely to be self pay (uninsured). p<.0001[CI
4.857, 5.255]
2. All else being equal, males compared to females who visit California EDs are1.7 times more
likely to be uninsured. p<.0001[CI 1.699 , 1.739]
3. All else being equal, whites compared to blacks who visit California EDs are 28 percent
less likely to be uninsured. p<.0001[CI 0.753, 0.893]
times more likely to be uninsured. p<.0001[CI 1.276, 1.319]



There are differences in the values of the effects of logistic models with 4.3 million observations
versus the model with 1-million observations.. However, they do not change the findings in any
significant way concerning the direction of the effects. Those effects that are more likely
continue to be more likely and those less likely remain less likely with only differences in the
odds ratios.
2. Change the logistic model of exercise 5.4-1 to consider homeless rather than selfpay. Again
the 4.6 million visits took 23 minutes with questionable findings.
.
:
PROC LOGISTIC is modeling the probability that homeless=1.
WARNING: Ridging has failed to improve the loglikelihood. You may want to use a different
ridging technique (RIDGING= option), or switch to using linesearch to reduce the step
size (RIDGING=NONE), or specify a new set of initial estimates (INEST= option).
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based
on the last maximum likelihood iteration. Validity of the model fit is questionable.
real time 23:19.18
cpu time 5:40.71



Model Information

Model binary logit


Response Profile

Ordered Total
Value homeless Frequency

1 1 46368
2 0 4318180

Probability modeled is homeless=1.





race * 1 0 0 0 0 0 0
99 0 1 0 0 0 0 0
R1 0 0 1 0 0 0 0
R2 0 0 0 1 0 0 0
R3 0 0 0 0 0 0 0
R4 0 0 0 0 1 0 0
R5 0 0 0 0 0 1 0
R9 0 0 0 0 0 0 1

sex * 1 0 0
F 0 0 0
M 0 1 0
U 0 0 1

eth * 1 0 0
99 0 1 0
E1 0 0 1
E2 0 0 0

agecat5 * 1 0 0 0 0 0
0 0 1 0 0 0 0
1 0 0 0 0 0 0
2 0 0 1 0 0 0
3 0 0 0 1 0 0
4 0 0 0 0 1 0
5 0 0 0 0 0 1


Ridging has failed to improve the likelihood function.




Intercept
Intercept and

AIC 513697.26 498937.36
SC 513710.55 499203.14
-2 Log L 513695.26 498897.36



Score 39505.8285 19 <.0001



Wald 243467366 19 <.0001


Wald

agecat5 6 101425783 <.0001
sex 3 63393608.4 <.0001
race 7 32233931.4 <.0001
eth 3 46386687.8 <.0001


Standard Wald

Intercept 1 -5.2457 0.000097 2925199665 <.0001
agecat5 * 1 -1.2673 0.000714 3149519.81 <.0001
agecat5 0 1 59.1960 1.3203E9 0.0000 1.0000
agecat5 2 1 0.0727 0.000291 62445.0935 <.0001
agecat5 3 1 0.8342 0.000187 19798648.4 <.0001
agecat5 4 1 1.1877 0.000134 78301886.1 <.0001
agecat5 5 1 0.1294 0.000384 113313.412 <.0001
sex * 1 0.9567 0.000235 16634980.2 <.0001
sex M 1 0.8979 0.000131 46758637.3 <.0001
sex U 1 20.4739 6.8995 8.8059 0.0030
race * 1 -0.3412 0.000182 3504440.60 <.0001
race 99 1 -1.0485 0.000437 5768647.87 <.0001
race R1 1 -0.0646 0.00155 1741.0541 <.0001
race R2 1 -1.0437 0.000844 1528065.56 <.0001
race R4 1 -1.0865 0.00253 184309.705 <.0001
race R5 1 -0.5595 0.000148 14306875.6 <.0001
race R9 1 -0.8255 0.000313 6939869.22 <.0001
eth * 1 0.7271 0.000170 18353359.9 <.0001
eth 99 1 1.6103 0.000304 28029136.3 <.0001
eth E1 1 0.0155 0.000240 4199.1113 <.0001


Point 95% Wald

agecat5 * vs 1 0.282 0.281 0.282
agecat5 0 vs 1 >999.999 <0.001 >999.999



Point 95% Wald



agecat5 2 vs 1 1.075 1.075 1.076
agecat5 3 vs 1 2.303 2.302 2.304
agecat5 4 vs 1 3.280 3.279 3.280
agecat5 5 vs 1 1.138 1.137 1.139
sex * vs F 2.603 2.602 2.604
sex M vs F 2.455 2.454 2.455
sex U vs F >999.999 >999.999 >999.999
race * vs R3 0.711 0.711 0.711
race 99 vs R3 0.350 0.350 0.351
race R1 vs R3 0.937 0.935 0.940
race R2 vs R3 0.352 0.352 0.353
race R4 vs R3 0.337 0.336 0.339
race R5 vs R3 0.571 0.571 0.572
race R9 vs R3 0.438 0.438 0.438
eth * vs E2 2.069 2.068 2.070
eth 99 vs E2 5.004 5.001 5.007
eth E1 vs E2 1.016 1.015 1.016


Pairs 200225370240 c 0.725

As seen above in the proc logistic with all visits, for the response variable of homeless
considering the effects of visit age group, gender, race, and ethnicity, the findings are as follows:
those less than 1 years old are 2.3 times more likely to be homeless. p<.0001[CI 2.302,
2.304]
2. All else being equal, males compared to females who visit California EDs are 2.4 times more
likely to be homeless. p<.0001[CI 2.454, 2.455]
less likely to be homeless. p<.0001[CI 0.935, 0.940]
percent more likely to be homeless. p<.0001[CI 1.015, 1.016]






asian =(race='R2');
black =(race='R3');
white =(race='R5');

Change the logistic model of exercise 5.4-1 to consider homeless rather than selfpay. Use one
million visits and see if the differences are signicant. As shown below the time to complete the
analysis was 1 ½ minutes.
NOTE: PROC LOGISTIC is modeling the probability that homeless=1.
real time 1:40.92
cpu time 28.81 se



Model Information

Model binary logit


Response Profile



Ordered Total
Value homeless Frequency

1 1 8821
2 0 991179

Probability modeled is homeless=1.



race * 1 0 0 0 0 0 0
99 0 1 0 0 0 0 0
R1 0 0 1 0 0 0 0
R2 0 0 0 1 0 0 0
R3 0 0 0 0 0 0 0
R4 0 0 0 0 1 0 0
R5 0 0 0 0 0 1 0
R9 0 0 0 0 0 0 1

sex * 1 0 0
F 0 0 0
M 0 1 0
U 0 0 1

eth * 1 0 0
99 0 1 0
E1 0 0 1
E2 0 0 0

agecat5 * 1 0 0 0 0 0
0 0 1 0 0 0 0
1 0 0 0 0 0 0
2 0 0 1 0 0 0
3 0 0 0 1 0 0
4 0 0 0 0 1 0
5 0 0 0 0 0 1


Ridging has failed to improve the likelihood function.




Intercept
Intercept and




AIC 101023.56 96987.890
SC 101035.37 97224.200
-2 Log L 101021.56 96947.890



Score 11113.0234 19 <.0001
Wald 71511547.1 19 <.0001


Wald

agecat5 6 29518495.1 <.0001
sex 3 15281632.4 <.0001
race 7 16124948.4 <.0001
eth 3 10587559.6 <.0001


Standard Wald

Intercept 1 -5.2779 0.000211 626201293 <.0001
agecat5 * 1 -1.2510 0.00154 655713.269 <.0001
agecat5 0 1 93.1823 8.867E16 0.0000 1.0000
agecat5 2 1 0.1193 0.000661 32571.4824 <.0001
agecat5 3 1 0.9928 0.000409 5899903.85 <.0001
agecat5 4 1 1.3755 0.000288 22757152.5 <.0001
agecat5 5 1 0.3497 0.000840 173163.456 <.0001
sex * 1 0.9343 0.000475 3871059.31 <.0001
sex M 1 0.9747 0.000289 11410577.7 <.0001
sex U 1 96.0488 2.689E17 0.0000 1.0000
race * 1 -0.6463 0.000366 3114234.89 <.0001
race 99 1 -0.3799 0.00129 87409.7501 <.0001
race R1 1 -0.9221 0.00369 62575.8348 <.0001
race R2 1 -1.5110 0.00224 453179.722 <.0001
race R4 1 -1.2860 0.00680 35799.9462 <.0001
race R5 1 -1.1222 0.000387 8388355.83 <.0001
race R9 1 -1.3047 0.000654 3983400.30 <.0001
eth * 1 1.1034 0.000346 10196015.4 <.0001
eth 99 1 0.5126 0.00131 152577.845 <.0001
eth E1 1 0.2519 0.000515 238967.375 <.0001


Point 95% Wald




agecat5 * vs 1 0.286 0.285 0.287
agecat5 0 vs 1 >999.999 <0.001 >999.999



Point 95% Wald

agecat5 2 vs 1 1.127 1.125 1.128
agecat5 3 vs 1 2.699 2.697 2.701
agecat5 4 vs 1 3.957 3.955 3.959
agecat5 5 vs 1 1.419 1.416 1.421
sex * vs F 2.545 2.543 2.548
sex M vs F 2.650 2.649 2.652
sex U vs F >999.999 <0.001 >999.999
race * vs R3 0.524 0.524 0.524
race 99 vs R3 0.684 0.682 0.686
race R1 vs R3 0.398 0.395 0.401
race R2 vs R3 0.221 0.220 0.222
race R4 vs R3 0.276 0.273 0.280
race R5 vs R3 0.326 0.325 0.326
race R9 vs R3 0.271 0.271 0.272
eth * vs E2 3.014 3.012 3.016
eth 99 vs E2 1.670 1.665 1.674
eth E1 vs E2 1.286 1.285 1.288


Pairs 8743189959 c 0.755

As seen above in the proc logistic with 1 million visits, for the output response variable of
homeless considering the effects of visit age group, gender, race, and ethnicity, the findings are
as follows:
those less than 1 years old are 2.7 times more likely to be homeless. p<.0001[CI 2.697,
2.701]
2. All else being equal, males compared to females who visit California EDs are 2.6 times more
likely to be homeless. p<.0001[CI 2.649, 2.652]
less likely to be homeless. p<.0001[CI 0.395, 0.401]
times more likely to be homeless. p<.0001[CI 1.285, 1.288]



In summary, for logistic regression of California ED visits, the differences between 4.3 million
and 1 million visits does not significantly effect the findings of the models of uninsured and
homeless.


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