Carrie Tomasallo, PhD, MPH Wisconsin Department of Health Services Division of Public Health

1. Analysis of Asthma Prevalence Using Clinical Electronic Health Records and Air Pollution Data Carrie Tomasallo, PhD, MPH Wisconsin Department of Health Services Division of Public Health Bureau of Environmental and Occupational Health carrie.tomasallo@wisconsin.gov

2. Why study chronic disease risk factors present in the environment & community?

3. Rationale • A multilevel approach that includes an ecological viewpoint may help to explain heterogeneities in chronic disease expression across socioeconomic, behavioral, and geographic boundaries that remain largely unexplained • Improved knowledge for interventions

4. Background • Asthma affects 500,000 children and adults in Wisconsin • Wisconsin Behavioral Risk Factor Surveillance System (BRFSS) data provide annual statewide asthma prevalence estimates • Alternative Surveillance Data: Health information exchange between UW Health and WI Division of Public Health (details on next slide)

5. Electronic Health Record (EHR) data from UW Department of Family Medicine, Pediatrics, and Internal Medicine Clinics to identify a patient population with asthma at a census block level HIPAA limited data set 462,000 patients (45,000 asthmatics) • UW eHealth– PHINEXUniversity of Wisconsin Electronic Health Record – Public Health Information Exchange

6. UW ehealth– PHINEX (Clinic EHR Data, 2007-2011)

7. Geographic Distribution of Patient Population, 2007-2011

8. Specific Aims Examine asthma prevalence and risk factors in clinical EHR and air pollution data • Frequency tables of asthma prevalence • Multivariate regression modeling

9. Specific Aims cont. Determine areas and populations of asthma disparity • GIS and spatial analyses of population trends

10. Current Asthma Definition Asthma diagnosis (ICD-9 code 493) in either encounter diagnosis or problem diagnosis field of clinical EHR

11. Air Pollution Data EPA Criteria air pollutant data (CO, NO2 PM2.5, PM10, SO2) modeled from stack emissions and aggregated to the census block group (CBG)

12. Air Pollution Data cont. EPA Airmod program used to construct a distance decay model from each point source Pollutant values aggregated to the CBG A five-mile radius applied for each stack Values of zero apply in areas where no data points were located

13. Results Asthma Prevalence

14. Results Asthma Prevalence cont.

15. Results Asthma Prevalence cont.

16. Results Predictive Model Prevalent Asthma = Sex + Age + Race + BMI + Smoking Status + Household Income + Insurance Status + Air Pollutants (CO, NO2, PM2.5, PM10, SO2)

17. Results Multivariate Regression for Asthma *Asthma Yes = 32,133; Asthma No = 222,964

18. Results Multivariate Regression for Asthma cont.

19. Results Multivariate Regression for Asthma cont.

20. Results Multivariate Regression for Asthma cont. *Other criteria air pollutants (CO, NO2, SO2 and PM10) were not significant at the p=0.05 level

21. Clinic Patients with Asthma by Census Block Group, Dane County, WI

22. Average Concentration of PM2.5 by Census Block Group, Dane County, WI

23. Conclusions Between 2007-2011, EHR clinic data identified 45,000 asthmatics Prevalence ranged from 6.4% (adults 65+ years) to 13.5% (children 5-11 years) Asthma prevalence highest among Black non-Hispanics (17.9%)

24. Conclusions cont. Factors associated with asthma prevalence: Younger age, female gender Black non-Hispanic race/ethnicity Former smoking status Elevated BMI Reporting government insurance Average PM2.5value Identified asthma patients at the neighborhood level in Madison, WI

25. Future Directions Refining air pollution model; alternate sources of data? Change definitions of asthma and asthma control Follow clinical outcomes for an intervention group

26. PHINEX Asthma Group Collaborative Effort - Thank You!

27. UW eHealth PHINEX Theory and MethodsWisconsin Medical Journal, June 2012 http://www.wisconsinmedicalsociety.org/_WMS/publications/wmj/pdf/111/3/124.pdf