A Community-Driven Probability Survey of Disadvantaged Populations in Chicago:


Journal

Progress in community health partnerships : research, education, and action
ISSN: 1557-055X
Titre abrégé: Prog Community Health Partnersh
Pays: United States
ID NLM: 101273946

Informations de publication

Date de publication:
2020
Historique:
entrez: 8 1 2021
pubmed: 9 1 2021
medline: 21 10 2021
Statut: ppublish

Résumé

Community-level health data are needed to identify and prioritize the most pressing health issues at the local level. To conduct a community-driven probability health survey of disadvantaged Chicago communities in 2015-2016. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 33416610
pii: S1557055X20300097
doi: 10.1353/cpr.2020.0040
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

347-357

Auteurs

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