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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 Medicine UW School of Medicine & Public Health WREN Conference September 15, 2011.

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Brian Arndt, MD Assistant Professor Department of Family Medicine

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  1. 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

  2. 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)

  3. 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.)

  4. 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)

  5. 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

  6. UW MED-PHINEXType 2 Diabetes Definition > 2 • 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

  7. UW DFM EHRType 2 Diabetes Prevalence2007-2009

  8. 2007-2009 Adult Type 2 Diabetes

  9. 2007-2009 Adult Type 2 Diabetes

  10. 2007-2009 Adult Type 2 Diabetes

  11. 2007-2009 Adult Type 2 Diabetes

  12. 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

  13. 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

  14. Wisconsin Economic Hardship Index

  15. Madison Economic Hardship Index

  16. MilwaukeeEconomic Hardship Index

  17. Diabetes Co-MorbiditiesOdds Ratio = P(Disease | Diabetes) P(Disease | No Diabetes) Among 9,023 Adult Patients with Type 2 Diabetes

  18. Diabetes Co-MorbiditiesOdds Ratio = P(Disease | Diabetes) P(Disease | No Diabetes) Among 9,023 Adult Patients with Type 2 Diabetes

  19. Diabetes Co-MorbiditiesOdds Ratio = P(Disease | Diabetes) P(Disease | No Diabetes) Among 9,023 Adult Patients with Type 2 Diabetes

  20. 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 ? Diabetes Co-morbiditiesConclusions

  21. 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

  22. 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

  23. Regression Results Poor A1c Control

  24. Regression Results Poor A1c Control

  25. 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

  26. 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

  27. Diabetes Next Steps – GIS / Spatial Analysis

  28. Diabetes Next Steps – GIS / Spatial Analysis

  29. Collaborative Effort – Thank you! • 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-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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