Merging electronic health record and CDPH data on COVID-19 vaccination rates to examine structural, neighborhood, and individual-level disparities in uptake

There are stark inequities in COVID-19 vaccination uptake across California and nationwide. The data for understanding the drivers of these inequities are available in several datasets that can be merged to gain new insights about disparities, such as by race/ethnicity, insurance/income, and language at both the individual and neighborhood levels. Specifically, we are poised to combine data such as complete COVID-19 data from electronic health records (EHRs) from two large healthcare systems in San Francisco, statewide CDPH data, and publicly available datasets with place-based measures (such as those aggregated and curated on our HealthAtlas.ucsf.edu website) to conduct multi-level and geospatial regression analyses to advance our understanding of COVID-19 vaccination disparities. These modeling approaches combined with the use of linked data from several unique sources can provide insight to advance equity within the delivery and evaluation of current COVID-19 vaccination interventions at CDPH, as well as future programs.