Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data Carrie Tomasallo, PhD, MPH Wisconsin Division of Public Health Wisconsin Asthma Program Background Asthma is a prevalent chronic disease, affecting over 500,000 children and adults in Wisconsin Asthma is a prevalent chronic disease, affecting over 500,000 children and adults in Wisconsin Wisconsin Behavioral Risk Factor Surveillance System (WI BRFSS) data provide annual statewide asthma prevalence estimates Wisconsin Behavioral Risk Factor Surveillance System (WI BRFSS) data provide annual statewide asthma prevalence estimates data not useful for estimating prevalence at smaller geographic areas Alternative Surveillance Data UW Electronic Health (EHR) data from UW Department of Family Medicine (DFM) Clinics to identify a patient population with asthma at a census block level UW Electronic Health (EHR) data from UW Department of Family Medicine (DFM) Clinics to identify a patient population with asthma at a census block level Geographic analyses and maps may lead to the identification and surveillance of Wisconsin asthmatic patients at neighborhood level Geographic analyses and maps may lead to the identification and surveillance of Wisconsin asthmatic patients at neighborhood level Project Goals Can EHR data improve our estimate of asthma prevalence over telephone survey data? Can EHR data improve our estimate of asthma prevalence over telephone survey data? How do asthma prevalence estimates based on DFM clinic data and BRFSS compare? Identify areas and populations of asthma disparity in Wisconsin using DFM clinic data Identify areas and populations of asthma disparity in Wisconsin using DFM clinic data Rationale Current surveillance systems cannot provide local level data within Wisconsin, where many policies and interventions ultimately are designed and implemented Current surveillance systems cannot provide local level data within Wisconsin, where many policies and interventions ultimately are designed and implemented Use of EHR and socio-demographic data may improve on this method by accurately highlighting neighborhoods with high asthma prevalence in Wisconsin Use of EHR and socio-demographic data may improve on this method by accurately highlighting neighborhoods with high asthma prevalence in Wisconsin These data may allow targeted intervention These data may allow targeted intervention Limitations of WI BRFSS Asthma Prevalence Estimates Designed for prevalence estimates at the national and state level but not local levels in Wisconsin Designed for prevalence estimates at the national and state level but not local levels in Wisconsin Small samples at county-level Small samples at county-level Even smaller samples for child estimates Even smaller samples for child estimates Data obtained by self-report Data obtained by self-report Low response rates (~50%) may indicate response bias Low response rates (~50%) may indicate response bias BRFSS Asthma Prevalence by Wisconsin County Clinical and Public Health Data Exchange IRB approved limited data set of over 195,000 patients (18,000 asthmatics) seen in UW Department of Family Medicine clinics in IRB approved limited data set of over 195,000 patients (18,000 asthmatics) seen in UW Department of Family Medicine clinics in Community partnership among clinicians (pulmonologist, primary care), population health scientists (Applied Population Laboratory), and the WI Division of Public Health (Epidemiology & Public Health Informatics) Community partnership among clinicians (pulmonologist, primary care), population health scientists (Applied Population Laboratory), and the WI Division of Public Health (Epidemiology & Public Health Informatics) UW Department of Family Medicine Patient Population Location Geographic Density of 195,000 Patients Current Asthma Definition BRFSS Have you ever been diagnosed with asthma? Do you still have asthma? BRFSS Have you ever been diagnosed with asthma? Do you still have asthma? Clinical Data asthma diagnosis (ICD-9 code 493) in encounter diagnosis or problem diagnosis fields Clinical Data asthma diagnosis (ICD-9 code 493) in encounter diagnosis or problem diagnosis fields Child Asthma Prevalence *Relative Standard Error > 30% (unreliable estimate) Child Asthma Adjusted Odds Ratios BRFSS model adjusted for sex, age, race/ethnicity and household income (BMI, personal smoking status or ETS exposure not available for children in BRFSS) Clinic model adjusted for sex, age, race/ethnicity, smoking status, BMI, insurance status and census block median household income Adult Asthma Prevalence *Relative Standard Error > 30% (unreliable estimate) Adult Asthma Adjusted Odds Ratios BRFSS model adjusted for sex, age, race/ethnicity, BMI, smoking status and household income Clinic model adjusted for sex, age, race/ethnicity, BMI, smoking status, insurance status and census block median household income Clinic Patients with Asthma by Census Block Group Conclusions Between , EHR clinic data identified 18,000 asthmatics, compared to 1,850 asthmatics from WI BRFSS Between , EHR clinic data identified 18,000 asthmatics, compared to 1,850 asthmatics from WI BRFSS BRFSS and clinic prevalence estimates and OR adj were comparable BRFSS and clinic prevalence estimates and OR adj were comparable Clinic data had greater statistical power to detect associations, especially in pediatric population Clinic data had greater statistical power to detect associations, especially in pediatric population GIS analyses of clinic data identified asthma patients at the census block group GIS analyses of clinic data identified asthma patients at the census block group Future Directions Understanding where asthma prevalence is highest and what characteristics predict high prevalence Understanding where asthma prevalence is highest and what characteristics predict high prevalence Method can be applied to any chronic disease and other EHR data sets in Wisconsin or U.S. Method can be applied to any chronic disease and other EHR data sets in Wisconsin or U.S. Potential to address disparities by identifying high risk communities to target innovative interventions Potential to address disparities by identifying high risk communities to target innovative interventions Collaborative Effort Brian Arndt-UW DFM Brian Arndt-UW DFM Bill Buckingham-UW APL Bill Buckingham-UW APL Tim Chang-UW Biostats Tim Chang-UW Biostats Dan Davenport-UW Health Dan Davenport-UW Health Kristin Gallager-UW Pop Health Kristin Gallager-UW Pop Health Theresa Guilbert (PI)-UW Peds Theresa Guilbert (PI)-UW Peds Larry Hanrahan-DPH Larry Hanrahan-DPH David Page-UW Biostats David Page-UW Biostats Mary Beth Plane-UW DFM Mary Beth Plane-UW DFM David Simmons-UW DFM David Simmons-UW DFM Aman Tandias-DPH Aman Tandias-DPH Jon Temte-UW DFM Jon Temte-UW DFM Kevin Thao-UW DFM Kevin Thao-UW DFM Carrie Tomasallo-DPH Carrie Tomasallo-DPH