Online Program

Decomposing geographic disparities in health and health behaviors to inform community interventions

Monday, November 4, 2013 : 12:50 p.m. - 1:10 p.m.

Wenjun Li, PhD, Health Statistics and Geography Lab, Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA
Mariana Arcaya, ScD, MCP, Society Human Development and Health, Harvard School of Public Health, Boston, MA
Bonnie Andrews, MPH, CPH, Massachusetts Department of Public Health, Boston, MA
Thomas Land, PhD, Bureau of Community Health and Prevention, Massachusetts Department of Public Health, Boston, MA
INTRODUCTION: Good understanding of the sources of community-level disparities in health and health behaviors is critical to properly formulate community-based interventions. We developed a method to quantify relative contributions of demographic, socioeconomic and contextual factors to community-level disparities. METHOD: Using a mixed effects model, the new method decomposes the effects of significant risk factors on health outcomes into demographic, socioeconomic and contextual disparity indices. The indices are defined as the sum of the products of community average levels minus the expected state-level value of the relevant risk factors multiplied by the magnitude of their respective regression coefficients. The method is illustrated using town/city-level disparities in hypertension prevalence in Massachusetts.

RESULT: Using state average prevalence rate of 26% as a benchmark, the ratio of community-specific prevalence rate to state average rate ranged from 0.4 to 1.34. In logarithmic scale, demographic factors (sex, age, race/ethnicity), socioeconomic (education, income, employment) and contextual (e.g., per capita income, density of housing units) factors contribute 56% (range: 34%-70%), 15% (range: 8%-24%) and 25% (range: 15% to 36%), respectively, to the variation in community-level prevalence estimates. For a given community, domain specific disparity indices were frequently in opposite directions. CONCLUSION: The effects of a particular domain for town-level disparities can easily be overlooked when only overall disparities are calculated. Use of domain-specific disparity indices will help epidemiologists understand the relative importance of demographic, socioeconomic and environmental factors to the observed community-level health disparities. Such information can be used to formulate and prioritize community-based interventions.

Learning Areas:

Program planning
Public health or related research

Learning Objectives:
Analyze community-level disparities in health and health behaviors in relation to community demographic composition, socioeconomic status and environmental factors. Evaluate domain-specific disparities and their relative contributions to the overall disparities. Discuss how to use the domain-specific disparity indices to help community-based programs (e.g., CTG) identify effective intervention strategies.

Keyword(s): Community Health Planning, Health Disparities

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am Associate Professor of Medicine (Biostatistics) actively working on large public health databases. I have over 10 years of analyzing public health data after I obtained my PhD on Biostatistics and Epidemiology.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.