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Published on: July 27, 2018
A Community-Engaged Approach to Collecting Rural Health Surveillance Data
Jini E Puma1, Elaine S Belansky1, Reginaldo Garcia1
1Rocky Mountain Prevention Research Center, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado.
Community engagement in health surveillance improves data quality for rural areas. This approach helps address challenges like limited data and underrepresented populations, leading to better health decisions.
Area of Science:
- Public Health
- Community Health
- Health Surveillance
Background:
- Rural communities face unique challenges in health surveillance, including limited county-level data and underrepresented populations.
- Existing state and national systems often do not adequately address local health concerns in rural settings.
- Accurate health status data is crucial for data-driven decisions on resource allocation, services, and interventions.
Purpose of the Study:
- To employ a community-engaged approach for collecting population-based health status data in a rural area.
- To address the specific challenges faced by rural communities in health data collection.
- To improve the quality, relevance, and utility of health surveillance information.
Main Methods:
- A community-based participatory research (CBPR) approach was utilized.
- Over 1,500 residents were randomly selected across 6 rural and frontier counties using a stratified, multistage cluster design.
- Surveys were primarily administered via telephone.
Main Results:
- Response rates varied by county, ranging from 59% to 80% (overall 66%).
- While males and younger adults were underrepresented, Hispanics, low-income residents, and cell phone-only users were adequately represented.
- Prevalence rates for chronic diseases and health behaviors demonstrated variation across counties.
Conclusions:
- Engaging stakeholders enhances the quality, relevance, and utility of collected health surveillance data.
- Community-engaged efforts can successfully reach populations that are typically difficult to access.
- Improved health status data accuracy supports better data-driven decision-making for community health priorities.
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