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Background: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data., Methods: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio., Results: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage., Conclusions: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results., Highlights: EHR data are an abundant resource for studying neighborhood diversity and health.When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches.Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.

Using a multi-institutional EMR registry, we extracted housing status and evaluated the presence of several important comorbidities in order to describe the demographics and comorbidity burden of persons experiencing homelessness in northeast Ohio and compare this to non-homeless individuals of varying socioeconomic position. Of 1,974,766 patients in the EMR registry, we identified 15,920 (0.8%) as homeless, 351,279 (17.8%) as non-homeless and in the top quintile of area deprivation index (ADI), and 1,607,567 (81.4%) as non-homeless and in the lower four quintiles of area deprivation. The comorbidity burden was highest in the homeless population with depression (48.1%), anxiety (45.8%), hypertension (44.2%), cardiovascular disease (18.4%), and hepatitis (18.1%) among the most prevalent conditions. We conclude that it is possible to identify homeless individuals and document their comorbidity burden using a multi-institutional EMR registry, in order to guide future interventions to address the health of the homeless at the health-system and community level.