There are 111 members in a dataset on pediatric MRSA skin and soft tissue infections. Ninety-five unique zip codes are represented, with 94 in the US and 1 in Canada. Two questions were asked about changes in MRSA infections over different time periods, with answer choices weighted and categorized to create maps showing response trends by zip code.

1. Maps for Peds MRSA SSTI
There are 111 members in Peds MRSA SSTI dataset. After frequency analysis, there are 95
unique zipcodes for these 111 members. One is located in Canada (EIN_ID: 518; Zipcode: M5G
1X8). Finally, there are 94 unique zipcodes in USA (please see appendix for the detailed
frequency table).

2. Peds MRSALASTYR Data Analysis
Q1: Are you seeing more MRSA SSTI in the past 12 months compared with the previous
year?
In question 1, there are five multiple answer choices. In this dataset, four answer choices
selected by these hospital members are: Somewhat fewer, About the same, Somewhat
more, and Many more. Different weighting indicators have been adopted to these four
choices because some zipcodes have more than one hospital member. It shows as
following:
Answer Weight
Somewhat fewer 1
About the same 2
Somewhat more 3
Many more 4
An equation has also been adopted to calculate final weight for each zipcode.
1
Final Weight=
n
i i
i
X W
n
=
×∑
iX : The hospital i at corresponding zipcode
iW : The weight of hospital i at corresponding zipcode
n: The number of hospitals at corresponding zipcode
Final Weight Categories
1 1
2-2.5 2
2.6-3.5 3
3.6-4 4
The final map looks like following:

3. Q2: Are you seeing more MRSA SSTI in the past 12 months compared with 2003?
In question 1, there are five multiple answer choices. In this dataset, four answer choices
selected by these hospital members are: Somewhat fewer, About the same, Somewhat
more, and Many more. Different weighting indicators have been adopted to these four
choices because some zipcodes have more than one hospital member. It shows as
following:
Answer Weight
Somewhat fewer 1
About the same 2
Somewhat more 3
Many more 4
An equation has also been adopted to calculate final weight for each zipcode.

4. 1
Final Weight=
n
i i
i
X W
n
=
×∑
iX : The hospital i at corresponding zipcode
iW : The weight of hospital i at corresponding zipcode
n: The number of hospitals at corresponding zipcode
Final Weight Categories
1 1
2-2.5 2
2.6-3.5 3
3.6-4 4
The final map looks like following:

5. zip1
zip1 Frequency
02139 1
02144 1
02903 1
03060 1
03756 1
03766 1
05405 1
06904 1
07046 1
07601 2
07740 1
08628 1
08903 1
10032 2
10304 1
10451 1
10457 1
11021 1
11201 1
14850 1
16502 1
19104 2
19341 1
20010 1
21120 1

6. zip1
zip1 Frequency
22908 1
26506 1
27599 1
27704 1
27710 2
27834 1
28232 1
28269 1
29615 1
30322 1
30342 1
30912 2
32207 1
32209 1
33176 1
33606 1
35233 3
36604 1
37232 2
37901 1
38018 1
38103 1
38105 1
39216 1
40202 1
43205 1
43614 1
44195 1
45229 1
45404 1
46202 1

7. zip1
zip1 Frequency
48108 1
53792 2
54449 2
55102 1
60612 1
60657 1
63104 1
70805 1
72202 1
75240 1
76104 1
76508 1
77555 1
78503 1
79430 1
80207 1
80218 1
84132 1
85724 2
87131 1
89102 1
90034 1
90048 1
90095 1
90740 1
90801 1
92123 1
92134 1
92620 1
92706 1
92868 2

8. zip1
zip1 Frequency
94143 3
94401 1
95817 1
97201 1
97227 2
97239 1
98105 1
M5G 1X8 1