Estimating the Prevalence of
Diabetes in Wisconsin Through
an Innovative Data Exchange
Between a Department of Family
Medicine and Public Health
Brian Arndt, MD
Assistant Professor
Department of Family Medicine
UW School of Medicine & Public Health
WREN Conference
September 15, 2011
Background
• Diabetes is a prevalent chronic disease
affecting over 475,000 adults in Wisconsin
• Wisconsin Behavioral Risk Factor
Surveillance System (WI BRFSS) data
provide annual statewide diabetes
prevalence estimates
– Data not useful for estimating prevalence
at smaller geographic areas
– Unable to track quality performance
indicators (e.g. A1c levels)
Alternative Surveillance Data
• Electronic Health Record (EHR) data from
UW Department of Family Medicine (DFM)
Clinics to identify a population with diabetes
at a census block level
– Geographic analyses and maps may lead to
the identification and surveillance of Wisconsin
patients with diabetes at the neighborhood level
• Contains parameters for quality evaluation
(A1c, BP, Cholesterol, Kidney health, etc.)
Project Goals
• Can EHR data improve diabetes prevalence
estimates over telephone survey data?
• How do diabetes prevalence estimates based
on DFM clinic data and BRFSS compare?
• Evaluate Risk, Control, & Co-morbidities
• Link EHR data to community indicators
(Median Income, Economic Hardship Index)
BRFSS Diabetes Definition
• Have you ever been told by a doctor that
you have diabetes?
– Gestational diabetes and pre-diabetes
excluded
• Does not distinguish between Type 1 and
Type 2
UW MED-PHINEX
Type 2 Diabetes Definition
• Problem list AND Encounter diagnosis
• Problem list OR Encounter Dx, AND
– Fasting glucose ≥ 126 mg/dL
– 2 hour GTT glucose ≥ 200 mg/dL
– Random glucose ≥ 200 mg/dL
– A1c ≥ 6.5%
or
– Anti-diabetic medication Rx ≥ 1
>2
UW DFM EHR
Type 2 Diabetes Prevalence
2007-2009
Criteria
Count
Prevalence
Problem
8,975
4.7%
Encounter
Problem or
Encounter
Problem/
Encounter and
Labs/Meds
9,673
5.0%
10,605
5.5%
9,034
4.7%
2007-2009 Adult Type 2 Diabetes
WI BRFSS Data
UW DFM Clinic Data
*N
Prevalence
(95% CI)
N
Prevalence
(95% CI)
2,007
7.2(6.8-7.7)
9,023
6.0(5.9-6.1)
Female
1109
7.0 (6.4-7.7)
4,329
5.2 (5.1-5.4)
Male
898
7.5 (6.8-8.2)
4,694
6.9 (6.7-7.1)
18-34
34
1.2(0.5-1.8)
366
0.7 (0.6-0.8)
35-64
959
7.0 (6.4-7.7)
5,589
6.8 (6.6-7.0)
65+
991
18.3 (16.7-19.8)
3,068
17.4 (16.8-18.0)
Overall
Sex
Age Group
2007-2009 Adult Type 2 Diabetes
WI BRFSS Data
UW DFM Clinic Data
*N
Prevalence
(95% CI)
N
Prevalence
(95% CI)
White (NonHispanic)
1,617
6.9 (6.4-7.4)
7,676
5.9 (5.8-6.0)
Black (NonHispanic)
210
11.7 (8.5- 14.9)
514
11.1 (10.2-12.0)
Other (NonHispanic)
124
10.5 (6.6-14.3)
281
6.2 (5.5-6.9)
Hispanic
31
5.5 (2.8-8.1)
352
7.0 (6.3-7.8)
Race/Ethnicity
2007-2009 Adult Type 2 Diabetes
WI BRFSS Data
UW DFM Clinic Data
*N
Prevalence
(95% CI)
N
Prevalence
(95% CI)
Normal or
Underweight
(<25.0)
249
2.7 (2.2-3.2)
513
1.6 (1.5-1.8)
Overweight
(25.0 - <30.0)
613
6.3 (5.5-7.1)
1,458
4.4 (4.2-4.7)
Obese
(30.0 - <40.0)
775
12.6 (11.4-13.9)
3,178
11.2 (10.9-11.6)
Morbidly Obese
(≥40.0)
233
26.7 (21.5-31.9)
1,440
22.3 (21.3-23.3)
BMI
2007-2009 Adult Type 2 Diabetes
WI BRFSS Data
UW DFM Clinic Data
*N
Prevalence
(95% CI)
N
Prevalence
(95% CI)
Never
865
5.9 (5.2-6.5)
3,619
5.1 (5.0-5.3)
Former
845
11.2 (10.1-12.3)
3,377
10.2 (9.8-10.5)
Current
294
5.8 (4.7-6.8)
1,326
5.2 (5.0-5.5)
Passive
NA
-
105
6.7 (5.4-7.9)
Smoking
Multivariate Logistic Regression of
Type 2 Diabetes Risk in Adults
• Good agreement with BRFSS
• Each factor is a significant predictor in
direction expected:
– Age, Gender, Race / Ethnicity, Smoking, BMI,
Median Income
• Insurance Status & Economic Hardship
also predict risk
• DFM data volume 4x greater (or more)
compared to BRFSS – provides greater
precision and resolution
Economic Hardship Index
• Census data from the Census Block
Group level
• Index from 1 to 100 (No → Very Hard)
• Variables include:
– Crowded housing
– Federal poverty level
– Unemployment
– Less than high school
– Dependency (% under 18 or over 64)
– Median income per capita
Wisconsin
Economic Hardship Index
Madison
Economic Hardship Index
Milwaukee
Economic Hardship Index
Diabetes Co-Morbidities
Odds Ratio = P(Disease | Diabetes)
P(Disease | No Diabetes)
Co-Morbidity
Prevalence
OR
95% CI
Depression
25.1%
1.7
1.7-1.8
Asthma
11.0%
1.5
1.4-1.6
COPD
8.4%
4.2
3.8-4.5
CKD
26.1%
9.6
9.1-10.2
Among 9,023 Adult Patients with Type 2 Diabetes
Diabetes Co-Morbidities
Odds Ratio = P(Disease | Diabetes)
P(Disease | No Diabetes)
Co-Morbidity
Prevalence
OR
95% CI
IVD- Cardiac
IVD – Cerebral
CHF
16.2%
4.4%
9.1%
7.9
5.7
9.2
7.4-8.4
5.0-6.4
8.4-10.1
Among 9,023 Adult Patients with Type 2 Diabetes
Diabetes Co-Morbidities
Odds Ratio = P(Disease | Diabetes)
P(Disease | No Diabetes)
Co-Morbidity Prevalence
MI
PTCA
Dementia
2.1%
1.8%
3.2%
OR
95% CI
6.4
6.9
3.7
5.4-7.7
5.8-8.4
3.3-4.3
Among 9,023 Adult Patients with Type 2 Diabetes
Diabetes Co-morbidities
Conclusions
• Each risk is significant
• Higher complexity likely leads to higher
utilization & cost
• Next Steps – data mining
– What predicts co-morbidity?
– Which co-morbidities group together?
– What predicts clusters ?
Predictors of HbA1c Control in
Patients with Type 2 Diabetes
Kristin Gallagher
University of Wisconsin
Department of Population Health Sciences
M.S. Thesis
June 2011
Methods
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Adult Type 2 Diabetes Definition
Current A1c Value / Binary at 7%
Logistic Regression
Predictors of Poor A1c Control (>7%)
– Age, Gender, Race / Ethnicity, Economic
Hardship Index, BMI, Depression
Regression Results
Poor A1c Control
Characteristic
OR
95% CI
Age Group
P-value
0.0033
18-24
0.92
[0.52 - 1.60]
25-34
1.26
[0.98 - 1.62]
35-44
1.26
[1.08 - 1.46]
45-54
1.23
[1.09 -1.39]
55-64
1.00
Race/Ethnicity
<.0001
White (Non-Hispanic)
1.00
Black (Non-Hispanic)
1.48
[1.20 - 1.83]
Other (Non-Hispanic)
1.45
[1.09 - 1.93]
Hispanic/Latino
2.08
[1.60 - 2.71]
Regression Results
Poor A1c Control
Characteristic
OR
95% CI
Sex
P-value
0.0031
Male
1.00
Female
0.85
[0.76 - 0.95]
Economic Hardship
Index
0.0011
EHI <20
1.00
EHI 20 to <30
1.56
[1.18 - 2.05]
EHI >30
1.74
[1.28 - 2.37]
BMI
<.0001
Normal or Underweight
1.00
Overweight
1.09
[0.83 - 1.44]
Obese
1.59
[1.23 - 2.06]
Morbidly Obese
1.76
[1.34 - 2.32]
Conclusions
• Socio-demographic factors:
– Middle age groups, black, Hispanic, and other
race/ethnicities, obese, and morbidly obese BMI
were all significantly associated with having
higher odds of being in poor control
– Patients living in areas with increased economic
hardship index (20-30; >30) have higher odds of
being in poor control – this was significant
• Health factors:
– Those without depression were found to have
significantly higher odds of being in poor control
Diabetes Next Steps
• Evaluate comorbidity predictors
• HEDIS performance definitions & analysis
(PCP & clinic level; P4P)
• Measures of utilization in population x status
• Data mining & modeling community factors
• Expand variables exchanged
Diabetes Next Steps –
GIS / Spatial Analysis
Diabetes Next Steps –
GIS / Spatial Analysis
Collaborative Effort –
Thank you!
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Brian Arndt-UW DFM
Amy Bittrich-DPH
Bill Buckingham-UW APL
Jenny Camponeschi-DPH
Michael Coen-UW Biostats
Tim Chang-UW Biostats
Dan Davenport-UW Health
Kristin Gallager-UW Pop
Health
Theresa Guilbert (PI)-UW
Peds
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Larry Hanrahan-DPH
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Lynn Hrabik-DPH
Angela Nimsgern-DPH
David Page-UW Biostats
Mary Beth Plane-UW DFM
David Simmons-UW DFM
Aman Tandias-SLH
Jon Temte-UW DFM
Kevin Thao-UW DFM
Carrie Tomasallo-DPH