Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance.

OBJECTIVE: Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys. METHODS: Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness. RESULTS: The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys. CONCLUSION: The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.

Using Spatial Analysis to Inform Community Immunization Strategies.

INTRODUCTION: Recent pertussis outbreaks in the United States suggest our response to local disease outbreaks (eg, vaccine-preventable Bordetella pertussis) may benefit from understanding and applying spatial analytical methods that use data from immunization information systems at a subcounty level. METHODS: A 2012 study on Denver, CO, residents less than 19 years of age confirmed pertussis cases and immunization information system records were geocoded and aggregated to the census tract (CT) level. An algorithm assessed whether individuals were up-to-date (UTD) for pertussis vaccines. Pearson, Spearman, and Kendall correlations assessed relations between disease incidence and pertussis vaccine coverage. Using spatial analysis software, disease incidence and UTD rates were spatially weighted, and smoothed. Global and local autocorrelations based on univariate Moran's I spatial autocorrelation statistics evaluated whether a CT's rate belong to a cluster based on incidence or UTD measures. RESULTS: Overall disease incidence rate was 116.8/100 000. Assessment of pertussis vaccination coverage was available for 90% of the population. Among 134 672 Denver residents less than 19 years old, 103 496 (77%) were UTD for pertussis vaccines. Raw correlation coefficients showed weak relationships between incidence and immunization rates due to the presence of outliers. With geospatial and clustering analysis, estimates and correlation coefficients were improved with statistically significant Moran's I values for global and local autocorrelations rejecting the null hypothesis that incidence or UTD rates were randomly distributed. With evidence indicating the presence of clusters, smoothed and weighted disease incidence and UTD rates in 144 CTs identified 21 CTs (15%) for potential public health intervention. CONCLUSIONS: Correlation of raw disease incidence and vaccine UTD rates in subcounty regions showed limited association, providing limited information for decision making. By assessing for clusters using spatial analysis methods, we identified CTs with higher incidence and lower immunization coverage for targeted public health interventions.