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Submission Type

Case Study

Keywords

methods, surveys

Abstract

Introduction: To address the electronic health data fragmentation that is a methodological limitation of comparative effectiveness research (CER), the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research (WICER) project is creating a patient-centered research data warehouse (RDW) by linking electronic clinical data (ECD) from New York Presbyterian Hospital’s clinical data warehouse with ECD from ambulatory care, long-term care, and home health settings and the WICER community health survey (CHS). The purposes of the research were to identify areas of overlap between the WICER CHS and two other surveys that include health behavior data (the Behavioral Risk Factor Surveillance System (BRFSS) Survey and the New York City Community Health Survey (NYC CHS)) and to identify gaps in the current WICER RDW that have the potential to affect patient-centered CER.

Methods: We compared items across the three surveys at the item and conceptual levels. We also compared WICER RDW (ECD and WICER CHS), BRFSS, and NYC CHS to the County Health Ranking framework.

Results: We found that 22 percent of WICER items were exact matches with BRFSS and that there were no exact matches between WICER CHS and NYC CHS items not also contained in BRFSS.

Conclusions: The results suggest that BRFSS and, to a lesser extent, NYC CHS have the potential to serve as population comparisons for WICER CHS for some health behavior– related data and thus may be particularly useful for considering the generalizability of CER study findings. Except for one measure related to health behavior (motor vehicle crash deaths), the WICER RDW’s comprehensive coverage supports the mortality, morbidity, and clinical care measures specified in the County Health Ranking framework but is deficient in terms of some socioeconomic factors and descriptions of the physical environment as captured in BRFSS. Linkage of these data in the WICER RDW through geocoding can potentially facilitate patient-centered CER that integrates important socioeconomic and physical environment influences on health outcomes. The research methods and findings may be relevant to others interested in either integrating health behavior data into RDWs to support patient-centered CER or conducting population-level comparisons.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

DOI

10.13063/2327-9214.1021

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