These tables are crosstabs analysis for A chi-square test of the assoc.pdf
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These tables are crosstabs analysis for A chi-square test of the association between the predictor variable and the outcome variable (mortality) Predictor variable : Socio-Demographic Characteristics and chronic disease presence among COVID-19 patients and the outcome = mortality, Interpret the results?? REFERENCE Age: in Years Race: white, black, Other races Sex: Female / Male Marital status: single / Married Tobacco use: never smoke, Ex-smoker, Missing Alcohol use: Drinking, Not Drinking, Missing Comorbidity: 0 = no chronic disease, 1 = patient have one chronic disease, 2 = patient have two chronic diseases, 3= patient has three or more Diabetes: Yes/No Hypertension: Yes/No Chronic Lung Disease: Yes/No Chronic Heart Disease: Yes/No \begin{tabular}{lcc} \hline variables ( n = 276 ) & Mortality (fireq) & P-Value \\ \hline Comorbidity & 1 & .193 \\ 0 & 10 & \\ 1 & 15 & \\ 2 & 26 & \\ 3 & & 137 \\ Diabetes Mellitus & 67 & \\ Yes & 44 & \\ No & & \\ Hypertension & 101 & \\ Yes & 13 & \\ No & & \\ Chronic Lung Disease(y/n) & 24 & \\ Yes & 89 & \\ No & 921 \\ Chronic Lung Disease type & 9 & \\ Asthma & 4 & \\ COPD & 88 & \\ Lung cancer & 40 & \\ Missing & 73 & \end{tabular}.
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© 2014 Laureate Education, Inc. Page 1 of 2 Module 2 A.docx
© 2014 Laureate Education, Inc. Page 1 of 2
Module 2 Application Data Set
This data set on the following page does not contain actual patient information.
However, relationships have been built into the measures that may reflect actual
associations found in health.
The invented background for this data is that of a sample of blood pressures and BMIs
measured for clinic patients who were then followed for up to a 20-year period. These
patients were followed until their deaths, and their final age was recorded. A data
dictionary describing the measures follows the data.
Data
ID
Num
Age
BP
sex
Systolic
BP
Diastolic
BP
BMI
Death
age
diabetic
Place
1 68 0 139 81 25 84 0 1
2 57 1 170 110 41 64 1 3
3 41 1 151 109 36 59 1 3
4 79 0 140 85 30 81 0 2
5 49 0 165 100 39 69 1 2
6 68 0 141 82 29 86 0 1
7 69 0 138 81 25 89 0 1
8 70 1 149 85 32 78 0 2
9 39 1 189 100 48 47 1 3
10 42 1 152 88 35 60 1 3
11 51 1 150 90 38 70 1 3
12 66 0 153 89 36 73 0 2
13 70 1 145 85 30 81 0 2
14 75 0 139 79 23 92 0 1
15 72 0 144 86 29 91 0 1
16 42 1 171 91 44 61 1 3
17 71 1 150 90 35 74 0 2
18 58 0 141 87 30 75 0 1
19 40 0 175 93 44 60 1 2
20 25 1 180 90 45 40 1 3
21 48 0 146 85 29 67 0 1
22 29 1 160 97 39 49 1 3
23 73 0 137 81 25 92 0 1
24 66 1 167 95 40 68 1 3
25 55 0 190 99 50 65 1 2
© 2014 Laureate Education, Inc. Page 2 of 2
Data Dictionary
Name Type Label Values Missing Measure
ID_Num
Numeric
A unique and encrypted ID
number for a patient
None
Nominal
Age_BP
Numeric
Initial visit age in years when
a patient’s blood pressure
was recorded
None
Scale
sex
Numeric
Sex of patient
0 = female,
1 = male
None
Nominal
Systolic_BP
Numeric
Systolic blood pressure at
initial visit
None
Scale
Diastolic_BP
Numeric
Diastolic blood pressure at
initial visit
None
Scale
BMI Numeric Body Mass Index
None Scale
Death_age
Numeric
Follow-up age at death in
whole years
None
Scale
diabetic
Numeric
Diabetic status of patient
0 =
nondiabetic,
1 = diabetes
None
Nominal
Place
Numeric
Census designation for
patient’s residence
1 = rural, 2
= suburban,
3 = urban
None
Ordinal
W
A
L
D
E
N
U
N
I
V
E
R
S
I
T
Y
P
U
B
H
6
0
3
2
/
8
0
3
2
S
P
S
S
R
E
V
E
A
L
E
D
P
U
B
H
6
0
3
3
/
8
0
3
3
I
N
T
E
R
P
R
E
T
A
T
I
O
N
&
A
P
P
O
F
D
A
T
A
T
yp
es of V
ariables &
L
evels of M
easu
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t
T
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Cardiovascular disease is a major cause of disability and premature death throughout the world and contributes substantially to the escalating costs of health care.
The underlying pathology is atherosclerosis, which develops over many years and is usually advanced by the time symptoms occur, generally in middle age.
Acute coronary and cerebrovascular events frequently occur suddenly and are often fatal before medical care can be given.
Modification of risk factors has been shown to reduce mortality and morbidity in people with diagnosed or undiagnosed cardiovascular disease.
The role of genetic factors in Hypertension among Iraqi citizens
This document summarizes a study examining the role of genetic factors in hypertension among Iraqi citizens. The study included 140 patients divided into a case group of 120 hypertensive patients and a control group of 30 normotensive patients. Data on demographics, family history, blood pressure, and other medical variables were collected and analyzed. Statistical analysis found a significant relationship between genetic factors and hypertension, with a p-value of 0.001. Patients with a positive family history of hypertension in a first-degree relative had 3.98 times higher odds of having hypertension themselves. The study concluded that genetics play an important role in hypertension risk among Iraqi citizens.
© 2014 Laureate Education, Inc. Page 1 of 2 Module 2 A.docx
© 2014 Laureate Education, Inc. Page 1 of 2
Module 2 Application Data Set
This data set on the following page does not contain actual patient information.
However, relationships have been built into the measures that may reflect actual
associations found in health.
The invented background for this data is that of a sample of blood pressures and BMIs
measured for clinic patients who were then followed for up to a 20-year period. These
patients were followed until their deaths, and their final age was recorded. A data
dictionary describing the measures follows the data.
Data
ID
Num
Age
BP
sex
Systolic
BP
Diastolic
BP
BMI
Death
age
diabetic
Place
1 68 0 139 81 25 84 0 1
2 57 1 170 110 41 64 1 3
3 41 1 151 109 36 59 1 3
4 79 0 140 85 30 81 0 2
5 49 0 165 100 39 69 1 2
6 68 0 141 82 29 86 0 1
7 69 0 138 81 25 89 0 1
8 70 1 149 85 32 78 0 2
9 39 1 189 100 48 47 1 3
10 42 1 152 88 35 60 1 3
11 51 1 150 90 38 70 1 3
12 66 0 153 89 36 73 0 2
13 70 1 145 85 30 81 0 2
14 75 0 139 79 23 92 0 1
15 72 0 144 86 29 91 0 1
16 42 1 171 91 44 61 1 3
17 71 1 150 90 35 74 0 2
18 58 0 141 87 30 75 0 1
19 40 0 175 93 44 60 1 2
20 25 1 180 90 45 40 1 3
21 48 0 146 85 29 67 0 1
22 29 1 160 97 39 49 1 3
23 73 0 137 81 25 92 0 1
24 66 1 167 95 40 68 1 3
25 55 0 190 99 50 65 1 2
© 2014 Laureate Education, Inc. Page 2 of 2
Data Dictionary
Name Type Label Values Missing Measure
ID_Num
Numeric
A unique and encrypted ID
number for a patient
None
Nominal
Age_BP
Numeric
Initial visit age in years when
a patient’s blood pressure
was recorded
None
Scale
sex
Numeric
Sex of patient
0 = female,
1 = male
None
Nominal
Systolic_BP
Numeric
Systolic blood pressure at
initial visit
None
Scale
Diastolic_BP
Numeric
Diastolic blood pressure at
initial visit
None
Scale
BMI Numeric Body Mass Index
None Scale
Death_age
Numeric
Follow-up age at death in
whole years
None
Scale
diabetic
Numeric
Diabetic status of patient
0 =
nondiabetic,
1 = diabetes
None
Nominal
Place
Numeric
Census designation for
patient’s residence
1 = rural, 2
= suburban,
3 = urban
None
Ordinal
W
A
L
D
E
N
U
N
I
V
E
R
S
I
T
Y
P
U
B
H
6
0
3
2
/
8
0
3
2
S
P
S
S
R
E
V
E
A
L
E
D
P
U
B
H
6
0
3
3
/
8
0
3
3
I
N
T
E
R
P
R
E
T
A
T
I
O
N
&
A
P
P
O
F
D
A
T
A
T
yp
es of V
ariables &
L
evels of M
easu
rem
en
t
T
yp
es of V
ariables
y
A
variable is an
y factor, trait, con
d
ition
, or d
isease
th
.
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Sarnak2019
Cardiovascular disease is very common in patients with chronic kidney disease.
- CVD is the leading cause of death in patients with CKD, even in early stages of kidney disease and those with low levels of albuminuria. Reduced kidney function and increased albuminuria are associated with higher risk of CVD events and mortality.
- The prevalence of CVD is extremely high in patients on dialysis, with over 70% of dialysis patients having CVD. CVD is responsible for about 40% of all deaths in dialysis patients.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...
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These tables are crosstabs analysis for A chi-square test of the assoc.pdf
- 1. These tables are crosstabs analysis for A chi-square test of the association between the predictor variable and the outcome variable (mortality) Predictor variable : Socio-Demographic Characteristics and chronic disease presence among COVID-19 patients and the outcome = mortality, Interpret the results?? REFERENCE Age: in Years Race: white, black, Other races Sex: Female / Male Marital status: single / Married Tobacco use: never smoke, Ex-smoker, Missing Alcohol use: Drinking, Not Drinking, Missing Comorbidity: 0 = no chronic disease, 1 = patient have one chronic disease, 2 = patient have two chronic diseases, 3= patient has three or more Diabetes: Yes/No Hypertension: Yes/No Chronic Lung Disease: Yes/No Chronic Heart Disease: Yes/No begin{tabular}{lcc} hline variables ( n = 276 ) & Mortality (fireq) & P-Value hline Comorbidity & 1 & .193 0 & 10 & 1 & 15 & 2 & 26 & 3 & & 137 Diabetes Mellitus & 67 & Yes & 44 & No & & Hypertension & 101 & Yes & 13 & No & & Chronic Lung Disease(y/n) & 24 & Yes & 89 & No & 921 Chronic Lung Disease type & 9 & Asthma & 4 & COPD & 88 & Lung cancer & 40 & Missing & 73 & end{tabular}