Determining County-Level Counterfactuals for Evaluation of Population Health Interventions: A Novel Application of K -Means Cluster Analysis.

Autor:	Strutz KL; 12268 Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University College of Human Medicine, East Lansing and Grand Rapids, MI, USA., Luo Z; Department of Epidemiology and Biostatistics, Michigan State University College of Human Medicine, East Lansing, MI, USA., Raffo JE; 12268 Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University College of Human Medicine, East Lansing and Grand Rapids, MI, USA., Meghea CI; 12268 Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University College of Human Medicine, East Lansing and Grand Rapids, MI, USA., Vander Meulen P; Strong Beginnings Federal Healthy Start Program, Grand Rapids, MI, USA.; 3591 Healthier Communities, Spectrum Health, Grand Rapids, MI, USA., Roman LA; 12268 Department of Obstetrics, Gynecology and Reproductive Biology, Michigan State University College of Human Medicine, East Lansing and Grand Rapids, MI, USA.
Jazyk:	angličtina
Zdroj:	Public health reports (Washington, D.C. : 1974) [Public Health Rep] 2022 Sep-Oct; Vol. 137 (5), pp. 849-859. Date of Electronic Publication: 2021 Jul 29.
DOI:	10.1177/00333549211030507
Abstrakt:	Objectives: Evaluating 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 K -means cluster analysis for determining county-level counterfactuals in an evaluation of an intervention, a county perinatal system of care for Medicaid-insured pregnant women. Methods: We 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. Results: The 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 K -means method was successful at identifying comparison counties, differences between the intervention county and comparison counties remained. This limitation may be specific to the intervention county and the constraints of a within-state study. This method could be more useful when applied to other counties in and outside Michigan.
Databáze:	MEDLINE
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