ArticleEGEMS (Washington, DC)2019
Making Better Use of Population Health Data for Community Health Needs Assessments.
Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Geospatial analysis of area-level social and behavioral factors associated with short interpregnancy intervals in North Carolina, U.S.Preventive medicine reports · 2025Article
- Health Inequalities at the Intersection of Race and Poverty in Southern Appalachia: A Community Health Needs Assessment Panel Survey 2012-2024.North Carolina medical journal · 2025Article
- Article
- Inclusive and intersectoral: community health improvement planning opportunities to advance the social determinants of health and health equity.BMC public health · 2024Article
- A scoping review of community health needs and assets assessment: concepts, rationale, tools and uses.BMC health services research · 2023Article
- An action research partnership in an urban Texas county to explore barriers and opportunities for collaborative community health needs assessments.Frontiers in public health · 2023Article
- OPTIK: a database for understanding catchment areas to guide mobilization of cancer center assets.Database : the journal of biological databases and curation · 2020Article
- Beyond CHNAS: Performance Measurement for Community Health Improvement.EGEMS (Washington, DC) · 2019Article
- A Community-Up Approach to Improve Health and Wellbeing in a Dutch Neighbourhood. A Case Study.International journal of integrated careArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RESEARCH
objectiveNon-profit hospitals are required to work with community organizations to prepare a Community Health Needs Assessment (CHNA) and implementation strategy (IS). In concert with the health care delivery system's transformation from volume to value and efforts to enhance multi-sector collaboration, such community health improvement (CHI) processes have the potential to bridge efforts of the health care delivery sector, public health agencies, and community organizations to improve population health. Having a shared measurement system is critical to achieving collective impact, yet despite the availability of community-level data from a variety of sources, many CHI processes lack clear, measurable objectives and evaluation plans. Through an in-depth analysis of ten exemplary CHI processes, we sought to identify best practices for population health measurement with a focus on measures for needs assessments and priority setting. STUDY
designBased on a review of the scientific literature, professional publications and presentations, and nominations from a national advisory panel, we identified 10 exemplary CHI processes. Criteria of choice were whether (1) the CHIs articulate a clear definition of intended outcomes; (2) clear, focused, measurable objectives and expected outcomes, including health equity; (3) expected outcomes are realistic and addressed with specific action plans; and (4) whether the plans and their associated performance measures become fully integrated into agencies and become a way of being for the agencies. We then conducted an in-depth analysis of CHNA, IS, and related documents created by health departments and leading hospitals in each process. POPULATION STUDIED: U.S. hospitals. PRINCIPAL
findingsCensus, American Community Survey, and similar data are available for smaller areas are used to describe the populations covered, and, to a lesser extent, to identify health issues where there are disparities and inequities.Common data sources for population health profiles, including risk factors and population health outcomes, are vital statistics, survey data including BRFSS, infectious disease surveillance data, hospital & ED data, and registries. These data are typically available only at the county level, and only occasionally are broken down by race, ethnicity, age, poverty.There is more variability in format and content of ISs than CHNAs; the most developed models include population-level goals/objectives and strategies with clear accountability and metrics. Other hospital IS's are less developed.
conclusionsThe county is the unit of choice because most population health profile data are not available for sub-county areas, but when a hospital serves a population more broadly or narrowly defined, appropriate data are not available to set priorities or monitor progress.Measure definitions are taken from the original data sources, so comparisons across measures is difficult. Thus, although CHNAs cover many of the same topics, the measures used vary markedly. Using the same community health profile, e.g. County Health Rankings, would simplify benchmarking and trend analysis.Implications for Policy or Practice: It is important to develop population health data that can be disaggregated to the appropriate geographical level and to groups defined by race and ethnicity, socioeconomic status, and other factors associated with health outcomes.
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