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DALLAS-FORT WORTH HOSPITAL COUNCIL FOUNDATION
Diabetes in Dallas County
Provider Report
Pam Doughty, Ph.D. and Jaylene Jones, B.S.
Sponsored by Sanofi
SEPTEMBER
2011
250 DECKER DR. IRVING TX 75062
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Table of Contents
Introduction
Diabetes Key Facts
Methodology
Results
- Table 1: Diabetes Frequency w/in Top Conditions for Inpatients/Outpatients
- Table 2: Comparisons Between Hospital Product Lines
- Table 3: Top Five Diagnoses w/ Diabetes as Underlying Condition
- Table 4: Zip Code Demographics
Discussion
Conclusion
Page Number
3
3
4
5
6
7
8
10
11
14
References
15
Appendices
- Appendix 1: Top Diagnoses and Percentage of Those with Diabetes by Product Line
- Appendix 2: Map 1 - Dallas County Population by Zip Code and Diabetes Incidence
- Appendix 2: Map 2 - Diabetes Frequency by Zip Code
- Appendix 3: Map 1 - Available Food for Resources
- Appendix 3: Map 2 - Diabetic Patient Resources
- Appendix 3: Map 3 - Fast Food Availability
- Appendix 4: Map 1 - Medical Resources
- Appendix 5: Map 1 - Median Income by Zip Code
- Appendix 5: Map 2 – Unemployment Rate by Zip Code
- Appendix 6: Map 1 – Local Parks and Recreation Centers
- Appendix 7: Map 1 - Average Length of Stay by Zip Code
- Appendix 8: Map 1 – Patient Mortality Rate by Zip Code
- Appendix 8: Map 2 – Percent of Patients Discharged to Other Facilities
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17
20
21
22
23
24
25
26
27
28
29
30
31
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INTRODUCTION
Type two diabetes diagnoses in the United States
Diabetes is the seventh
leading cause of death in the
US.
have continued to increase, especially among states with
Direct Medical Costs = $116 Billion
high incidences of obesity. Examples of states with high
Indirect Medical Costs = $58 Billion
Carolina and Tennessee with a rate of 31%. Mississippi has
Dallas County Texas’ rate of
diabetes is 11.4% while the
rate in Texas is 9.6%
the highest rate of obesity with 34% of their population
Kidney Failure
prevalence of obesity are Texas, Kentucky, Louisiana, South
categorized as obese or morbidly obese. Other state
populations are beginning to show increased rates of obesity
with 30.9% in Michigan and 30.4% in Oklahoma and
Missouri 1. Diabetes has become the seventh leading
cause of death and affects 25.8 million people in the United
States 2. Patients with diabetes experience a reduction in
More common in patients with
diabetes – 45% of all kidney
failure patients have diabetes
Chest Pain - 46% of ER
patients who complain of chest
pain are diabetics
Patients with Heart Failure
45%-59% of patients with
CHF have diabetes
disease, nervous system disease (neuropathy), and
Comorbidity of Diabetes –
35% of the top five inpatient
diagnosis have diabetes as an
underlying condition
amputation2. In 2010, the United States’ fiscal cost of
Average Length of Stay
diabetes was $116 billion for direct medical costs and $58
Diabetes as a primary
diagnosis = 2.81 days
quality of life and suffer complications, which include heart
disease, stroke, high blood pressure, blindness, kidney
billion for indirect costs, such as disability, work loss and
premature mortality2.
Diabetes affects 11.4% of the population in Dallas
County, which is above both the state average of 10% and
the national average of 8% 3. Dallas County is urban with a
Diabetes as a secondary =
6.02 days
All other diagnoses without
diabetes as a comorbidity =
4.48 days
Dallas County Zip Code
with the most negative
environmental factors for
diabetic patients is 75227
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population of 2.4 million people in about 880 square miles (approximately 2,692 people per
square mile). The median income is $46,044. The population is 53.5% white, 22.3% black with
38.3% of Hispanic Origin. The total number of people in Dallas County diagnosed with diabetes
is 273,600 and of those 25,992 are of Hispanic origin. The question remains why this county has
such a high frequency of diabetes and what factors possibly contribute to this elevated
prevalence. Contributing factors to diabetes prevalence are obesity, lack of physical activity,
family history and environmental resources, such as the availability of fresh fruits and
vegetables, healthcare access and neighborhood parks/recreation center availability. This study
focuses on the environmental factors that could influence the control of diabetes in Dallas
County.
METHODOLOGY
The DFWHC Foundation (“Foundation”) has a claims data warehouse, managed by the
Information, Quality and Safety Center (IQSC), that receives claims data from 77 North Texas
hospitals. The claim records are available from 2001 for inpatients and 2006 for outpatients.
Fields within the claim record include a patient’s demographic data; payor type; up to 25
diagnosis codes; 25 procedure codes; severity of disease; total charges and charges for individual
services and is risk adjusted. The Foundation developed the regional enterprise master patient
index (REMPI), which allows the tracking of any patient over time, hospital, and payor. The
REMPI has an algorithm that accurately matches 99% of all patient encounters. To date, there
are 23 million patient encounters and over 7.3 million uniquely identified patients in the
warehouse. Currently, 80% of new patient encounters are already patients in the claims data
warehouse. In order to better understand the demographic and environmental influences on
diabetic patients, the diabetes project applied the data received from the Foundation warehouse
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and geographically mapped these results. Blinded patient data from the IQSC was mapped using
the residential zip codes for each patient in Dallas County 4. Using Census data, and other data
sources, 2,5,6 maps were developed to depict the environmental influences of those diabetic
patients.
Permission to utilize warehouse information was obtained from the hospitals through the
North Texas Healthcare Quality and Information Center (NTHIQC). No patient level data was
used for this research, other than aggregate zip code data. Using a business intelligence tool,
aggregated data was pulled for any person receiving a primary diagnosis of diabetes and
inpatients/outpatients that had any comorbidity of diabetes and all other encounters within
calendar years 2008-2009. ArcGIS, a geographical mapping system, was then used to spatially
join those primary diabetes diagnoses frequencies with their corresponding zip codes. These
maps were analyzed to determine the location of medical, nutritional, recreational resources and
patients by zip code.
RESULTS
Dallas County’s top five primary diagnoses in 2010 were pneumonia, septicemia, other
rehabilitation, urinary tract infection, and acute kidney failure. Of those top five primary
diagnoses, those patients with an underlying condition of diabetes were 29% for pneumonia,
39% for septicemia, 31% for other rehabilitation, 34% of urinary tract infection and 45% of
acute kidney failure (See Table 1). Those with diabetes had a higher mortality percentage than
those without in four of the five top inpatient diagnoses revealing that a co-morbidity of diabetes
increases your risk for mortality. Dallas County’s top seven diagnoses for emergency room
department (ER) patients were acute URI unspecified, Otitis media, abdominal pain, chest pain
unspecified, urinary tract infection, headache and other chest pain. Within those top seven
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diagnoses, 20%-45% had an underlying condition of diabetes. Specifically, of all patients who
came to the ER with chest pain as a diagnosis, 21%-25% had a comorbidity of diabetes. Of
patients presenting with abdominal pain, urinary tract infections and headache, 10% also had
diabetes (See Table 1).
Table 1: Diabetes Frequency within the Top Conditions for Inpatients and Outpatients for the
Dallas County Area
Top Five Diagnosis INPATIENTS
2009-2010 Dallas County
Pneumonia
Septicemia
Other Rehabilitation
Urinary Tract Infection
Acute Kidney Failure Unspecified
Top Seven Diagnosis
ER VISITS 2009-2010 Dallas
Acute URI Unspecified
Otitis Media
Abdominal Pain
Unspecified Chest Pain
Urinary Tract Infection
Headache
Other Chest Pain
Number of
Patients
4,359
3,142
2,816
2,447
2,355
Number of
Patients
23,979
18,576
14,677
14,511
14,302
13,531
13,217
Number of
Patients with
Diabetes
1,279
1,217
872
822
1,068
Number of
Patients with
Diabetes
392
84
1,516
3,010
1,254
1,228
2,980
% with
Diabetes
29%
39%
31%
34%
45%
% with
Diabetes
2%
0%
10%
21%
9%
9%
25%
Mortality %
3.1%
21.4%
0.1%
0.5%
3.2%
Mortality %
0%
0%
0%
0%
0%
0%
0%
Mortality %
with
Diabetes
3.5%
23.0%
0.1%
0.6%
3.5%
Mortality %
with
Diabetes
0%
0%
0%
0%
0%
0%
0%
Data was pulled to review the percentage of other diagnoses that had a comorbidity of diabetes.
The results are reported for the number of patients with other diagnoses that had a minimum of
10% of that population with diabetes. In Appendix 1, the results are reported by hospital service
line, number of patients with a specific diagnoses and the number of patients reporting a
comorbidity of diabetes. Each color in Appendix 1 under Product Line represents a hospital
service line and colors are continued in Table 2. Some of the diagnoses with a high percentage
of diabetes as comorbidity are heart failure at 59%, acute/chronic respiratory failure at 51%,
and chronic obstructive asthma and E. coli septicemia at 41%.
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The statistics in Appendix 1 were summarized by product line for Table 2. The product
lines with the most admissions in 2008-2009 for Dallas County are cardiology, pulmonary and
medicine. Of those in the top three, cardiology and neurology have the largest percentages of
diabetic patients at 42% and 41% respectively. Cardiology, Pulmonary and Medicine product
lines compose the highest percentage of patients in Dallas County in 2008-2009.
Table 2: Comparisons between Hospital Product Lines
Product Line
Behavioral
General Surgery
Oncology
Orthopedics
Gastroenterology
Neurology
Medicine
Pulmonary
Cardiology
Diabetes
Totals
Number of
Patients
2032
764
1458
3259
3488
4461
12406
12978
14033
1977
56856
Percentage of
Total
4%
1%
3%
6%
6%
8%
22%
23%
25%
3%
100%
Patients with
Diabetes
259
259
254
871
831
1627
4123
4278
6000
1946
20448
% with Diabetes
12%
34%
17%
27%
25%
41%
32%
34%
42%
98%
36%
In 2008-2009, 35% of the top 5 inpatient diagnoses in Dallas County had diabetes as an
underlying condition; the top is pneumonia (see Table 3). The data was analyzed to determine
the top four zip codes in Dallas County with the highest percentage of the top five diagnoses.
These zip codes were 75227, 75217, 75150, and 75149. Table 3 describes the comparison
between those zip codes and Dallas County as a whole. Zip code 75227 had the highest
incidence of pneumonia (33%) compared with Dallas County (29.3%); septicemia was (47.9%)
compared with Dallas County (38.7%); acute kidney failure (57.4%) compared with Dallas
County (45.4%). Other rehabilitation diagnosis was highest in 75217 (50.8%) compared with
Dallas County (30.9%) and of urinary tract infection (42.6%) compared with Dallas County
(33.6%).
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Table 3: Top Five Diagnoses with Diabetes as an Underlying Condition
Diagnosis
1
2
3
4
5
Pneumonia
Septicemia
OTH
Rehabilitation
Urinary Tract
Infection
Acute Kidney
Failure
Dallas
County
29.3%
38.7%
75227
75217
75150
75149
33.0%
33.3%
24.0%
47.9%
22.5%
34.4%
31.2%
25.0%
30.9%
43.1%
50.8%
32.4%
39.3%
33.6%
40.9%
42.6%
41.9%
33.9%
45.4%
57.4%
55.9%
42.1%
38.5%
The length of stay for all patients was calculated by subtracting the discharge date from
the admit date. Those without either a discharge date or an admit date were removed; such
patients were identified as outpatients. Comparing those with diabetes and those without
diabetes, patients with a comorbidity of diabetes (6.02) stayed an extra one and one half days
longer than those without diabetes (4.48), which is a 26% increase. Those patients with a
primary diagnosis of diabetes stayed an average of 2.81 days. Appendix 7, Map 1 is a map of
the length of stay by zip code for all patients (those with and without diabetes). The zip codes
with the highest length of stay were 75208, 75245, 75210 and 75247 with an average of 5.5-6.3
days. Within the four identified zip codes, all had stays longer (4.8-5.0 days) than the average of
Dallas County of 4.4 days. Mortality was analyzed by using the discharge status in the claims
data. Only deaths that occurred during a hospital stay were included in the figures reported. The
“death master” index mortalities were not included. The percentage of inpatient mortalities by
patient zip code was mapped to show the zip codes with the highest percentage for mortality
(Appendix 8, Map 1). The zip codes with the highest percentage of mortality were zip codes
75208, 75104 and 75247 with patient deaths at 5%-16.8% of the patient population. Within the
four zip codes with the highest number of diabetic patients, zip code 75149 had the highest
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percentage of patients who had died (3%-4.9%). The average mortality rate for Dallas County
was .1%-1.8%.
Overall, 89%-92% of all hospital patients are discharged to home either with or without
home healthcare. Those patients that did not get discharged to home were transferred to other
costly care centers because they were not well enough to go home. Those who did not go home
or were not transferred to another facility died, left without medical advice or were transferred to
a psychiatric unit. Appendix 8, Map 2 and Map 3 show the percentage of patients that were
transferred to another facility. In the zip codes in which diabetes is most prevalent, a high
percentage of patients are sent to other hospital facilities, i.e. nursing homes and short term care
facilities. The zip codes with the highest percentage of patients in Dallas County that were
transferred to another facility were 75230 and 75225 with 16.2% - 23% of all inpatients. Within
the four zip codes with the highest percentage of diabetic patients, zip code 75150 had the
highest percentage with 12.3%-16.1% of the patient population transferred to another hospital
facility.
The demographics of those four zip codes were pulled from Census data to determine if
the zip codes identified had any demographics that might explain some of the frequency of
diagnoses with diabetes as an underlying factor. The overall median age, gender, race and
ethnicity are shown in Table 4 as well as the percentage with diabetes in those four demographic
categories. The median age was late twenties to early thirties; however, the median age of
diabetics ranged from 58-63 years. A higher percentage of Hispanics live in zip codes 75217 and
75227 than in the remainder of Dallas County. Nationally, 24.4% of African Americans or
Hispanics have diabetes, while in 75217 and 75227, 74% or greater of the diabetic population are
African American or Hispanic2. Zip codes 75149 and 75150 had a majority of white residents
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with 71.2% and 76.9% respectively, in which 60% have diabetes. All four zip codes had a very
close split of 50/50 of males and females. However, females overall had a higher percentage of
diabetes than the male population in each highlighted zip code.
Table 4: Zip Code Demographics
75217
75227
75149
75150
Median Age
27 Years
28 Years
31 Years
33 Years
Median Age w/ Diabetes
58 Years
58 Years
60 Years
63 Years
Male
50.4%
48.5%
48.1%
48.2%
Males w/ Diabetes
36.6%
43.7%
43.0%
43.1%
Female
49.6%
51.5%
51.9%
51.8%
Female w/Diabetes
63.4%
56.3%
57.0%
59.9%
Caucasian
34.6%
35.6%
71.2%
76.9%
Caucasian w/ Diabetes
21.8%
22.5%
60.3%
60.7%
African American
34.8%
37.1%
15.2%
10.4%
African American w/Diabetes
39.9%
48.0%
17.4%
14.0%
Hispanic
46.4%
43.1%
16.8%
16.1%
Hispanic w/ Diabetes
35.1%
26.2%
16.3%
18.6%
Asian
0.3%
1.4%
3.0%
4.0%
Asian w/ Diabetes
0.2%
0.9%
3.2%
2.1%
Other
30.3%
25.8%
10.6%
8.7%
Other w/ Diabetes
2.7%
2.1%
2.6%
4.3%
Age
Gender
Race/Ethnicity
The four zip codes that were identified as having the highest frequency of diabetic
patients were mapped using ArcGIS software in order to overlay environmental factors that
could influence the health of those patients. The results revealed that the incidence of diabetes
was not correlated with a higher population (see Map 1 and Map 2 in Appendix 2). The zip code
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with the highest number of diabetic patients was 75227. In the Appendix 3 maps, zip code
75227 clearly shows that supermarkets and food banks are not within a mile walking distance or
a five minute driving distance of inhabitants, and fast food restaurants are prolific. Convenience
stores were the most prevalent (See Appendix 3.Map 1). Hospitals are not found within walking
distance or a five minute driving distance of the zip codes with the most prevalent incidence of
diabetes, and clinics are in clusters and not evenly spread throughout the four zip codes (See
Appendix 4). The zip codes with the highest prevalence of diabetes had high unemployment and
low income (See Maps 1 and 2 in the Appendix 5). Recreational locations and parks were
mapped for the Dallas County zip codes (See Appendix 6.Map 1). The map revealed that there
were a small number of parks within a mile walking distance and only one recreation center near
zip code 75227.
Overall, the zip codes with the highest prevalence of diabetes had a very low income
(< $35,000), an unemployment rate between 6.3% and 9.8%, few supermarkets, few food banks,
few hospitals and clustered medical clinics. However, there were many convenience stores and
fast food restaurants. Patients with diabetes had a greater likelihood of dying or be transferred to
another facility because they were too ill to go home.
DISCUSSION
Diabetes is often a comorbidity of other chronic illnesses and their symptoms. According
to the CDC, diabetes is the main cause of kidney failure 2. Within the top five inpatient diagnoses
in Dallas County, the fifth is acute kidney failure with more than 46% of patients having a
comorbidity of diabetes with an average of 49% among the four highlighted zip codes.
Pneumonia is the top diagnosis in Dallas County, and diabetes is a major co-morbidity of that
disease. Although the outpatient data revealed a lower percentile of diabetes, the diagnosis of
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chest pain and abdominal pain relate to chronic conditions (like CHF) in which diabetes is also a
high co-morbidity. The data brings to light that even when diabetes is not a primary diagnosis, it
is a prominent condition related to several of the top inpatient diagnoses in Dallas County.
Cardiology and Neurology hospital product lines have the highest percentage of diabetes
as comorbidity. These product lines include heart attack and stroke, which are known risk
factors for those with diabetes. A high percentage of patients with respiratory conditions also
had diabetes as comorbidity. Increasing the medical, nutritional and recreational resources for
those with a comorbidity of diabetes could reduce the incidence of heart disease and stroke by
improving their control of diabetes.
Patients with a primary diagnosis of diabetes stayed an average of only 2.81 days.
Patients are admitted to the hospital due to uncontrolled glucose levels. The average length of
stay may represent the average number of days it takes to bring the patients glucose levels low
enough to be discharged from the hospital. Those with diabetes as a comorbidity experienced
significantly longer stays, an average of 25% more than those without diabetes. The
complications presented with diabetes as an underlying factor may cause patients to become
more ill and need more time to recover from disease or injury.
Patients with a comorbidity of diabetes in Dallas County in specific zip codes were more
likely to be transferred to another health facility, more likely to die and stay longer in the first
hospital than other patients without diabetes. These results may be due to the higher
concentration of patients with diabetes than other zip codes. Diabetes affects the patient’s
overall health, especially as comorbidity, and is more likely to cause a patient to stay
hospitalized 25% longer, die or be transferred to another health facility instead of being
discharged home.
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There is quite a divergence in population characteristics of the four zip codes researched
as discussed previously. Such a significant difference in demographics suggests that community
resources may be a factor for diabetes prevalence. Using the Arc GIS mapping software allowed
the data to be spatially analyzed providing a picture of the resources available to the residents of
these zip codes. The limited availability of these resources can greatly influence the health
behaviors of those living in the community.
Medically, only two hospital systems are in the four zip codes with high diabetes
prevalence and one is a mental health hospital, demonstrating a paucity of 24 hour health care
access for the residents of these zip codes. Appendix 3, Maps 3 and 4, illustrate the food deserts
of these zip codes. Zip code 75227 does not have a supermarket available to its population, only
convenience stores. Brown, Vargas, Ang and Pebley suggest that the socioeconomic
environment and the traveling distance to supermarkets are associated with higher rates of
obesity in that area7. Since nutritious foods and a healthy diet are key behaviors for the control
of diabetes, living in a food desert with many fast food restaurants is detrimental for diabetic
patients’ health. Even with a chain grocery store, there is no guarantee that fresh, healthy options
are offered. In lower income areas, markets often have a smaller, more limited selection of
healthy fruits, vegetables and milk products 7. Physical activity is also important in the behavior
of diabetes. Although there are some recreation centers and local parks in the area, most have
limited hours of availability making it difficult for residents to fully utilize these facilities. The
use of parks requires good weather conditions, and Dallas is known for the numerous days over
100° in the summer and fall. The remaining days with good weather are limited in Texas.
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CONCLUSIONS
Diabetes is a complicated disease that has risk factors for cardiac and neurologic
comorbidity, especially with uncontrolled diabetes. In order improve the control of glucose
levels in diabetic patients, a regular exercise regimen and a diet including fresh fruits and
vegetables should be a part of their daily routine. Many diabetic patients in Dallas County do not
have access to fresh fruits and vegetables due to the lack of supermarkets within either a one
mile walking distance or five mile driving distance. Food banks are few, if nonexistent; in the
top four zip codes with high numbers of diabetic patients. Recreational facilities are very rare in
low-income areas with the highest prevalence of diabetes. The lack of clinics throughout the
county is also a problem as they are clustered together with large distances between these
clusters.
It may assist diabetic patients in Dallas County to place more supermarkets and food
banks with fresh foods in those four zip codes, add low cost recreational sites and more clinics.
Community groups may be able to assist by working together to improve the environmental
factors for diabetic patients of Dallas County.
Longer lengths of stay, higher mortality rates and transfers to other hospital facilities may
be the result of poor control of diabetes. Poor control can lead to slower healing time,
complications of diabetes itself and increased risk of mortality. Addressing the environmental
factors of diabetes could result in longer life, less complications, shorter lengths of stay and
better overall health that could help patients combat other illnesses and injuries.
The data used in this study was only claims data. Actual observations of the zip code
neighborhoods may discover some discrepancies or more evidence of the conclusions in this
P a g e | 15
study. Mortality rate could change with the addition of data from the mortality index for the
state of Texas.
References
1.
2.
3.
4.
5.
6.
7.
U.S. Obesity Trends. Centers for Disease Control and Prevention; 2011.
http://www.cdc.gov/obesity/data/trends.html Accessed August 26, 2011.
National diabetes fact sheet: 2011. Centers for Disease Control and Prevention; 2011.
http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. Accessed July 22, 2011.
Diabetes data: surveillance and evaluation. Texas Department of State Health Services; 2011.
http://www.dshs.state.tx.us/diabetes/tdcdata.shtm Accessed July 29,2011.
Texas zip codes, map and detailed profile. 2011. http://zipatlas.com/us/texas.htm Accessed July
23, 2011.
National diabetes information clearinghouse. U.S. Department of Health Services; 2011.
http://diabetes.niddk.nih.gov/dm/pubs/statistics/#fast. Accessed August 1, 2011.
State & county quickfacts. 2011. http://quickfacts.census.gov/qfd/states/48/48113.html
Accessed August 1, 2011.
Brown A, Vargas R, Ang A, Pebley A. The neighborhood food resource environment and the
health of residents with chronic conditions: the food resource environment and the health of
residents. Journal of General Internal Medicine. 2008;23(8):1137-1144
P a g e | 16
APPENDICES
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Appendix 1: Top Diagnoses and Percentage of those with Diabetes in Dallas County by
Product Line
Product Line
Diabetes
Diabetes
Diabetes
Cardiology
Medicine
Cardiology
Pulmonary
Cardiology
Cardiology
Cardiology
Cardiology
Pulmonary
Diagnosis 2010 Dallas
DIABETES W/MANIFEST OTH
DIABETES KETOACID TYPE II
UNCONT
DIABETES KETOACID TYPE I
UNCONT
ACUTE/CH DIASTOLIC HEART
FAILUR
RENAL HYPERT
UNSPEC/FAILURE
ACUTE/CH SYS/DIAST HEART
FAILURE
ACUTE/CHRONIC RESP
FAILURE
ATHEROSCLER NATIVE COR
ART
CONGESTIVE HEART FAILURE
UNSPEC
ACUTE/CH SYSTOLIC HEART
FAILURE
SUBENDO INFRC INIT
EPISODE
CHRONIC OBSTRUCT ASTHMA
W/EXAC
Number
of
Patients
Patients with
Diabetes
% with Diabetes
754
745
99%
449
442
98%
774
759
98%
1004
589
59%
1004
589
57%
461
234
51%
606
307
51%
1849
931
50%
1578
751
48%
1520
728
48%
1843
840
46%
963
408
42%
Medicine
E.COLI SEPTICEMIA
560
227
41%
Neurology
CEREBRAL ARTERY OCC
W/INFARCT
1900
788
41%
Cardiology
OTH CHEST PAIN
1581
636
40%
Pulmonary
RESPIRATORY FAILURE
1400
556
40%
Medicine
CELLULITIS/ABSCESS TRUNK
436
176
40%
Cardiology
UNSPEC CHEST PAIN
589
230
39%
518
196
38%
2100
748
36%
567
200
35%
Neurology
Pulmonary
Cardiology
CEREBRAL EMBOLISM
W/INFARCT
OBSTRUCT CHRONIC
BRONCHITIS W/EXAC
CAROTID ARTERY OCCLUS NO
INFARCT
P a g e | 18
Product Line
Medicine
Diagnosis 2010 Dallas
Patients with
Diabetes
% with Diabetes
1566
543
35%
452
159
35%
503
173
34%
2447
822
34%
587
197
34%
587
197
34%
523
178
34%
MORBID OBESITY
764
259
34%
Medicine
OTH REHABILITATION
2816
872
31%
Medicine
C DIFFICILE ENTERITIS
491
152
31%
Cardiology
SYNCOPE & COLLAPSE
784
237
30%
Pulmonary
PNEUMONIA ORGANISM
UNSPEC
4359
1279
29%
Pulmonary
FOOD/VOMIT PNEUMONITIS
775
225
29%
Gastroenterology
NONINFECT GASTROENTERIT
OTH
696
201
29%
Cardiology
HYPERTENSION UNSPEC
474
133
28%
Cardiology
ATRIAL FIBRILLATION
1783
491
28%
Medicine
OTH POSTOP INFECTION
854
243
28%
1321
366
28%
936
252
27%
HYPOSMOLALITY
510
137
27%
Pulmonary
AC VNUS EMB&THRMB DP
VES PRX LW EXT
449
118
26%
Orthopedics
LUMBAR DISC DISPLACEMENT
493
129
26%
Orthopedics
INTERTROCHANTERIC FX
CLOSED
597
156
26%
Neurology
Pulmonary
Medicine
Neurology
Neurology
Gastroenterology
General Surgery
Orthopedics
Pulmonary
Medicine
CELLULITIS/ABSCESS LEG
Number
of
Patients
INTRACEREBRAL
HEMORRHAGE
CHRONIC BRONCH W/ACUTE
BRONCHITIS
URIN TRACT INFECTION
UNSPEC
TRANS CEREB ISCHEMIA
UNSPEC
TRANS CEREB ISCHEMIA
UNSPEC
GASTROINTEST
HEMORRHAGE UNSPEC
LOCALIZED OSTEOARTH
UNSPEC LEG
ASTHMA UNSPEC
W/EXACERBAT
P a g e | 19
Product Line
Diagnosis 2010 Dallas
Number
of
Patients
Patients with
Diabetes
% with Diabetes
LOCALIZED PRIMARY
OSTEOARTH LEG
848
220
26%
Medicine
CELLULITIS/ABSCESS ARM
424
104
25%
Pulmonary
PUL EMBOLI/INFARCT OTH
887
212
24%
Neurology
OTH CONVULSIONS
417
90
22%
692
154
22%
515
105
20%
DEHYDRATION
783
153
20%
Gastroenterology
INTESTINAL ADHES W/OBSTR
463
94
20%
Gastroenterology
DIVERTICULITIS COLON
1114
204
0.18
Oncology
ENCOUNTER
ANTINEOPLASTIC CHEMO
1458
254
17%
Behavioral
SCHIZOAFFECTIVE UNSPEC
1016
141
14%
Behavioral
PARANOID SCHIZOPHRENIA
UNSPEC
441
53
12%
Behavioral
PSYCHOSIS UNSPEC
575
65
11%
Orthopedics
Gastroenterology
Medicine
Medicine
INTESTINAL OBSTRUCT
UNSPEC
EPILEPSY UNSPEC OTH
INTRACT
P a g e | 20
Appendix 2.Map 1: Dallas County Population by Code and Diabetes Incidence
P a g e | 21
Appendix 2.Map 2: Diabetes Frequency by Zip Code
P a g e | 22
Appendix 3.Map 1: Available Food for Purchase
P a g e | 23
Appendix 3.Map 2: Diabetic Patient Resources
P a g e | 24
Appendix 3.Map 3: Fast Food Availability
P a g e | 25
Appendix 4.Map 1: Medical Resources
P a g e | 26
Appendix 5.Map 1: Median Income
P a g e | 27
Appendix 5.Map 2: Unemployment
P a g e | 28
Appendix 6.Map 1: Recreation Availability
P a g e | 29
Appendix 7.Map 1: Average Length of Stay
P a g e | 30
Appendix 8.Map 1: Mortality Percentage
P a g e | 31
Appendix 8.Map 2: Discharge Status