Brian Arndt, MD Assistant Professor Department of Family Medicine

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 MedicineUW School of Medicine & Public Health

WREN ConferenceSeptember 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-PHINEXType 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 EHRType 2 Diabetes Prevalence

2007-2009

Criteria Count Prevalence

Problem 8,975 4.7%

Encounter 9,673 5.0%

Problem or Encounter 10,605 5.5%

Problem/ Encounter and Labs/Meds 9,034 4.7%

2007-2009 Adult Type 2 Diabetes

WI BRFSS Data UW DFM Clinic Data

*NPrevalence

(95% CI) NPrevalence

(95% CI)

Overall 2,007 7.2(6.8-7.7) 9,023 6.0(5.9-6.1)

Sex

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)

Age Group

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)



*NPrevalence


(95% CI)

Race/Ethnicity

White (Non-Hispanic) 1,617 6.9 (6.4-7.4) 7,676 5.9 (5.8-6.0)

Black (Non-Hispanic) 210 11.7 (8.5- 14.9) 514 11.1 (10.2-12.0)

Other (Non-Hispanic) 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)



*NPrevalence


(95% CI)

BMI

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)



*NPrevalence


(95% CI)

Smoking

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)

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

MilwaukeeEconomic Hardship Index

Diabetes Co-MorbiditiesOdds 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




IVD- Cardiac 16.2% 7.9 7.4-8.4

IVD – Cerebral 4.4% 5.7 5.0-6.4

CHF 9.1% 9.2 8.4-10.1





MI 2.1% 6.4 5.4-7.7

PTCA 1.8% 6.9 5.8-8.4

Dementia 3.2% 3.7 3.3-4.3


Diabetes Co-morbiditiesConclusions

• 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

• 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 P-value

Age Group 0.0033

18-240.92 [0.52 - 1.60]

25-341.26 [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 P-value

Sex 0.0031

Male1.00

Female0.85 [0.76 - 0.95]

Economic Hardship Index 0.0011

EHI <201.00

EHI 20 to <301.56 [1.18 - 2.05]

EHI >301.74 [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

Collaborative Effort – Thank you!

• 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

• Larry Hanrahan-DPH

• 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

Brian Arndt, MD Assistant Professor Department of Family Medicine

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