ArticlePublic health reports (Washington, D.C. : 1974)
Determining County-Level Counterfactuals for Evaluation of Population Health Interventions: A Novel Application of
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- A community-based study on quality of diet impacting cardio-metabolic risk; hierarchical prediction and cluster analysis from a diet survey among adults.Journal of family medicine and primary care · 2025Article
- Unsupervised learning for county-level typological classification for COVID-19 research.Intelligence-based medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesEvaluating population health initiatives at the community level necessitates valid counterfactual communities, which includes having similar population composition, health care access, and health determinants. Estimating appropriate county counterfactuals is challenging in states with large intercounty variation. We describe an application of
methodsWe described counties by using indicators from the American Community Survey, Area Health Resources Files, University of Wisconsin Population Health Institute County Health Rankings, and vital records for Michigan Medicaid-insured births for 2009, the year the intervention began (or the closest available year). We ran analyses of 1000 iterations with random starting cluster values for each of a range of number of clusters from 3 to 10 with commonly used variability and reliability measures to identify the optimal number of clusters.
resultsThe use of unstandardized features resulted in the grouping of 1 county with the intervention county in all solutions for all iterations and the frequent grouping of 2 additional counties with the intervention county. Standardized features led to no solution, and other distance measures gave mixed results. However, no county was ideal for all subpopulation analyses. PRACTICE IMPLICATIONS: Although the
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