Michael E Shin1, William J McCarthy. 1. Department of Geography, UCLA Center for Health Policy Research, University of California, Los Angeles (UCLA), 1255 Bunche Hall, Los Angeles, CA 90095-1524, USA. Electronic address: shinm@geog.ucla.edu.
Abstract
OBJECTIVE: We examined whether stable, county-level, voter preferences were significantly associated with county-level obesity prevalence using data from the 2012 US Presidential election. County voting preference for the 2012 Republican Party presidential candidate was used as a proxy for voter endorsement of personal responsibility approaches to reducing population obesity risk versus approaches featuring government-sponsored, multi-sectoral efforts like those recommended by the Centers for Disease Control Centers for Disease Control (CDC, 2009). METHOD: Cartographic visualization and spatial analysis were used to evaluate the geographic clustering of obesity prevalence rates by county, and county-level support for the Republican Party candidate in the 2012 U.S. presidential election. The spatial analysis informed the spatial econometric approach employed to model the relationship between political preferences and other covariates with obesity prevalence. RESULTS: After controlling for poverty rate, percent African American and Latino populations, educational attainment, and spatial autocorrelation in the error term, we found that higher county-level obesity prevalence rates were associated with higher levels of support for the 2012 Republican Party presidential candidate. CONCLUSION: Future public health efforts to understand and reduce obesity risk may benefit from increased surveillance of this and similar linkages between political preferences and health risks.
OBJECTIVE: We examined whether stable, county-level, voter preferences were significantly associated with county-level obesity prevalence using data from the 2012 US Presidential election. County voting preference for the 2012 Republican Party presidential candidate was used as a proxy for voter endorsement of personal responsibility approaches to reducing population obesity risk versus approaches featuring government-sponsored, multi-sectoral efforts like those recommended by the Centers for Disease Control Centers for Disease Control (CDC, 2009). METHOD: Cartographic visualization and spatial analysis were used to evaluate the geographic clustering of obesity prevalence rates by county, and county-level support for the Republican Party candidate in the 2012 U.S. presidential election. The spatial analysis informed the spatial econometric approach employed to model the relationship between political preferences and other covariates with obesity prevalence. RESULTS: After controlling for poverty rate, percent African American and Latino populations, educational attainment, and spatial autocorrelation in the error term, we found that higher county-level obesity prevalence rates were associated with higher levels of support for the 2012 Republican Party presidential candidate. CONCLUSION: Future public health efforts to understand and reduce obesity risk may benefit from increased surveillance of this and similar linkages between political preferences and health risks.
Authors: Brian Houle; James Holt; Cathleen Gillespie; David S Freedman; Michele Reyes Journal: Am J Public Health Date: 2008-12-04 Impact factor: 9.308
Authors: B Crammond; C Van; S Allender; A Peeters; M Lawrence; G Sacks; H Mavoa; B A Swinburn; B Loff Journal: Obes Rev Date: 2012-11-21 Impact factor: 9.213