Background: Community-level health data are needed to identify and prioritize the most pressing health issues at the local level.

Objectives: To conduct a community-driven probability health survey of disadvantaged Chicago communities in 2015–2016.

Methods: A safety-net hospital completed questionnaire development and dissemination in close partnership with a Community Advisory Committee (CAC), so the data captured accurately reflected community priorities.

Lessons Learned: The final survey sample included 1,543 adult interviews and proxy reports for 394 children, well below our original recruitment goal. Although ideal for area probability sampling, face-to-face surveys are challenging given declining response rates. Nevertheless, these data provide representative community-level data that is otherwise unavailable.

Conclusions: Hyper-local data are especially critical for diverse and segregated cities such as Chicago. Lessons learned can be applied to future community surveys done by hospital systems, health departments, and community advocates to maximize the usefulness of findings.


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pp. 347-357
Launched on MUSE
Open Access
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