1. more information © 2014 Denver Public Health Colorado’s Health Observation Regional Data Service Arthur Davidson, MD, MSPH Denver Public Health Monday, July 27, 2015, 10:30-12:30pm PopMedNet User Conference Simmons College, Boston, MA 1

2. more information © 2014 Denver Public Health Agenda Discuss CHORDS experiences using PopMedNet: – specific use cases – benefits – drawbacks, and – areas for improvement for the tool. 2

3. Use Case: BMI Surveillance Obesity is associated with major morbidity, mortality and healthcare costs and contributes to substantial health disparities Obesity-related conditions include heart disease, stroke, type 2 diabetes and certain cancers, many causes of preventable death Interventions should include clinical- and community-based efforts (e.g., policies, systems and environmental changes) Interventions should be tailored to individuals or communities based on local obesity prevalence estimates A PH monitoring system should efficiently track obesity trends, and outcomes of various interventions.

4. US: Obesity Prevalence Rates by Race/Ethnicity (BRFSS) 34.9% (78.6 million) of U.S. adults are obese. (CDC) Percent Obese

5. Colorado: Self-Reported, Adult Obesity Prevalence Estimates, (BRFSS) GroupSource YearEstimateCI Adults – all201321.320.4-22.2 White2011-1318.818.2-19.4 Black (NH)2011-1330.526.6-34.7 Hispanic2011-1328.026.2-29.8 http://www.cdc.gov/obesity/data/adult.html

6. Denver: 2011 – 2012, What is your age? (BRFSS) Colo rado Beh avio ral Risk Fact or Surv eilla nce Syst em Stati stics Regi on: DE NVE R Year s: 2011 - 2012 Que stion : Wha t is your age? CA UTI ON: BRF SS PR OT OC OL STA TES THA T EST IMA TES BAS ED ON FE WE R THA N 50 OBS ERV ATI ON S ARE STA TIS TIC ALL Y UN REL IABL E. RaceAge groupN% PopStdErrLower 95% CLUpper 95% CL Black18-24 years 56.12.80.611.6 25-34 years125.41.62.28.5 35-44 years2512.12.57.217.0 45-54 years3413.72.58.818.5 55-64 years4514.32.59.519.1 65+ years4715.42.310.919.9 Hispanic18-24 years5050.12.940.359.8 25-34 years8132.33.325,838.9 35-44 years9537.43.330.943.8 45-54 years6629.23.622.236.2 55-64 years6824.53.018.630.3 65+ years4613.02.38.417.5 Other18-24 years94.61.61.57.7 25-34 years236.81.73.510.2 35-44 years174.91.42.17.7 45-54 years204.31.12.16.5 55-64 years174.31.71.07.7 65+ years122.50.90.74.3 White18-24 years6639.34.630.348.2 25-34 years18155.53.448.862.1 35-44 years17745.63.239.351.9 45-54 years21552.83.546.059.7 55-64 years32256.93.250.763.2 65+ years42969.12.963.574.7 CAUTION:BRFSS PROTOCOL STATES THAT ESTIMATES BASED ON FEWER THAN 50 OBSERVATIONS ARE STATISTICALLY UNRELIABLE.

7. BMI Monitoring System Outputs: – measure overweight/obesity rates across groups – represent those rates geographically – combine geographic analysis with social economic measures System qualities: – ease of access to longitudinal data – completeness – timeliness – representativeness – extensibility

8. Methods Denominator: Number of individuals using health care facilities normalized data - virtual data warehouse (VDW) geo-locate home residence for all patients Numerator: Number of adults with overweight/obese BMI leverage height and weight measures from meaningful use (MU) incentive payments remove biologically implausible values

9. Public Health Surveillance (Colorado): BMI Transparency (distributed data network) – Modeled on successful federal models (FDA/PCORI) Governance – Voluntary participation; unlike mandated reporting, data use agreements established/required Privacy – Minimal data necessary to achieve stated goal (de-identified to start) Technical (local instance – University of Colorado) – Infrastructure: 1) common data model, 2) emphasize data quality assessment, and 3) federated query tool

10. more information © 2014 Denver Public Health CHORDS Opportunity Colorado Health Observation Regional Data Service provide a "laboratory" to develop and evaluate scientific methods to support public health surveillance and research affords an opportunity to use existing EHR data systems for public health surveillance learn about barriers and challenges to building an accurate system to monitor public health events (e.g., conditions, behaviors and outcomes) build an event agnostic infrastructure for public health surveillance, quality assessment, and research 10

12. Results: Adult BMI Registry Summary by County Geography Census 2009-2013 BMI Registry 2011-12 BRFSS (County)Population Valid BMI % Coverage % Obese Difference Adams316,90876,88924.336.324.811.5% Arapahoe427,71998,52223.030.321.48.9% Boulder234,70037,20115.920.815.85.0% Broomfield41,57910,31324.827.717.99.8% Denver474,106158,03633.330.720.110.6% Douglas200,37335,88317.925.216.19.1% Jefferson417,448108,49226.029.119.79.4% Prowers9,1476,16967.438.632.46.2% TOTAL2,112,833525,33624.929

13. Results: Comparison BMI Registry to BRFSS Valid EHR BMI, 2009-2013BRFSS, 2011/2012 Denver N %Percent Obese Percent Overweight NPercent Obese SE Overall 161,580 197720.11.2 Male68,66495.128.037.788720.01.7 Female92,91695.932.227.6109020.11.6 Race/Ethnicity White97,73296.829.431.8132413.51.1 Black21,08095.537.829.816033.94.7 Asian/Pacific Isl.4,49594.911.627.1--- American Indian1,21896.241.829.8--- Other /multiple7,43392.333.2 9017.24.4 Unknown29,62492.430.034.3--- Hispanic15,45994.138.134.137027.62.8 Non-Hispanic59,50896.824.431.5--- Unknown Hispanic origin 86,6159533.231.8--- Age 18-24 yrs21,80590.920.522.611811.63.3 25-34 yrs32,18694.423.829.627713.12.5 35-44 yrs27,39195.533.735.030024.73.1 45-54 yrs26,62496.838.934.132428.53.2 55-64 yrs25,91297.537.234.144325.12.6 65+ yrs27,66497.928.134.851518.52.2

14. Results: Coverage Assessment of registry coverage geo-location processes established quality assurance routine for all data compared with 2010 US Census data and recent American Community Survey EHR representation of Denver County: – 30% for adults – 50% for children – 95% of patient records accurately matched for geocoding.

15. Denver: Adult Obesity Prevalence – Registry Insufficient data: <50 adults with valid BMI or <10% of 21 years of census population represented

16. more information © 2014 Denver Public Health Denver: % Children Overweight + % Families in Poverty 16

17. Limitations Selection biases – obese people have more co-morbid conditions and visit healthcare providers more frequently – omission of persons not seeking care – omission of persons with access to care barriers Misclassification – patients may be represented more than once  skew EHR results to higher obesity prevalence

18. Discussion EHR BMI data were more comprehensive than BRFSS Compared with BRFSS, EHR data showed higher obesity rates in general, by gender, and by race Objective BMI measures are presumably more accurate than self-assessment BRFSS may underestimate obesity prevalence BRFSS has limited small area analyses capacity Rate discrepancies among complementary data sources need to be resolved for a consistent message.

19. Conclusions Federated query of EHR data is feasible for monitoring population-level health indicators in a local environment. Federated queries permit more granular approaches to health assessment and are highly complementary to traditional surveys Self-reported data may typically underestimate the percent of population with overweight or obese BMI.

20. Next Steps Capture community feedback about the maps Develop method to un-duplicate individuals with health information exchange Expand data contributing partners Expand PH jurisdictions able to access the data Expand use cases: tobacco use/2 nd hand smoke exposure, cardiovascular disease risk, mental health, hepatitis C, and HIV

21. more information © 2014 Denver Public Health PopMedNet: Benefits Data Mart Client configuration - straight forward/works well. Portal web interface eliminates researcher platform con cerns. Data Mart Client connects to multiple PMN Portal Instances - useful for testing new portal versions. Results provided in multiple file formats. Request and response spread across multiple files is handy. General system flexibility (e.g., add and remove Data Marts from requests, capacity for multiple governance models). Loose coupling of Data Mart Client to the Portal. Portal doesn’t demand a specific Data Mart Client state. 21

22. more information © 2014 Denver Public Health PopMedNet: Drawbacks System configuration (e.g., projects, organizations, and users) is extremely flexible, but in practice difficult to determine which permissions takes priority. Large response datasets take longer to render when the user views the results on the portal. Portal installation/upgrading was difficult (e.g., portal database required 100 script files to run for V3 to V4 conversion). Move of web application problem, sometimes a screen doesn’t accept button clicks and the user has to refresh the page. Installation of the DataMart Client at large organizations where IT department tightly controls computer configuration has been difficult. Mostly a time issue and dependent on organizational IT process of that organization. 22

23. more information © 2014 Denver Public Health PopMedNet : Improvements Ability to control how long request results are stored by portal A portal interface that works well on tablets or smart phones – review request status through the smart phone or tablet – researcher oriented, does not need all the administration functionality. 23

24. Acknowledgements Colleagues: David Tabano (Kaiser Permanente of Colorado) Kirk Bol (Colorado Department of Public Health and Environment Alyson Shupe (Tri-county Health Department) Emily McCormick (Denver Public Health) Michael Kahn (Children’s Hospital of Colorado) Matthew Daley (Kaiser Permanente of Colorado) Funding: Kaiser Community Benefit and The Colorado Health Foundation

25. Questions/Discussion adavidson@dhha.org