Preventing Chronic Disease | Tailoring Community-Based Wellness Initiatives With Latent Class Analysis — Massachusetts Community Transformation Grant Projects - CDC
Tailoring Community-Based Wellness Initiatives With Latent Class Analysis — Massachusetts Community Transformation Grant Projects
Mariana Arcaya, ScD, MCP; Timothy Reardon, MCP; Joshua Vogel, MPH; Bonnie K. Andrews, MPH; Wenjun Li, PhD; Thomas Land, PhD
Suggested citation for this article: Arcaya M, Reardon T, Vogel J, Andrews BK, Li W, Land T. Tailoring Community-Based Wellness Initiatives With Latent Class Analysis — Massachusetts Community Transformation Grant Projects. Prev Chronic Dis 2014;11:130215. DOI: http://dx.doi.org/10.5888/pcd11.130215
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Abstract
Introduction
Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts’ municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach.
Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts’ municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach.
Methods
Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains.
Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains.
Results
Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges.
Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges.
Conclusion
Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.
Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.
Author Information
Corresponding Author: Mariana Arcaya, ScD, MCP, Metropolitan Area Planning Council, 60 Temple Pl, Boston, MA 02111. Telephone: 617-933-0727. E-mail:marcaya@mapc.org.
Author Affiliations: Timothy Reardon, Metropolitan Area Planning Council, Boston, Massachusetts; Joshua Vogel, Bonnie K. Andrews, Thomas Land, Massachusetts Department of Public Health, Boston, Massachusetts; Wenjun Li, University of Massachusetts Medical School, Worcester, Massachusetts.
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