The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults

Abstract

Purpose: Although obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race and/or ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered, and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence.

Methods: Data from the Centers for Disease Control and Prevention's 2013 Behavioral Risk Factor Surveillance System were used to calculate state-specific obesity prevalence and gender inequality measures. Global and local Moran's indices were calculated to determine spatial autocorrelation.

Results: Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score = 4.89, P-value < .001). Local Moran's indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the United States.

Conclusions: Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality.

Keywords: Behavioral risk factor surveillance; Continental population groups; Environment; Ethnic groups; Female; Male; Obesity; Social environment; Spatial analysis; United States.

Figure 1. Age-adjusted US obesity prevalence, obesity gender difference and ratio, & corresponding local moran’s indices

Data source: 2013 Behavioral Risk Factor Surveillance System. Data were age-standardized to US 2000 projected population. Obesity prevalence, gender prevalence difference and gender prevalence ratio show global spatial autocorrelation (z-score = 5.10, 4.89, 4.72 respectively (p-values < 0.001)). Bottom row represents Local Moran’s Indices (LMI) (p-value ≤ 0.01) with ‘high-high’ indicating states with high values near other states with high values, ‘low-low’ indicating states with low values near other states with low values, ‘low-high’ and ‘high-low’ indicating spatial outliers, with all other states having non-significant LMI. The cluster of states with higher female obesity prevalence (high-high) includes Mississippi, Tennessee, and N. Carolina. The cluster of states with higher male obesity prevalence (low-low) includes Montana, North Dakota, South Dakota, and Minnesota. Florida and Arkansas are spatial outliers.

Figure 2. Age-adjusted obesity gender difference, by race/ethnicity

Data source: 2013 Behavioral Risk Factor Surveillance System, data pooled across 2011–13 for the four non-White racial/ethnic groups. Data were age-standardized to US 2000 projected population. The darkest color represents states with higher female prevalence and the lightest represents states with higher male prevalence. States patterned with diagonal lines were excluded from spatial analysis due to small sample sizes (n <200). Gender difference among all race/ethnicities combined (z-score = 4.89, p-value < 0.001) and among non-Hispanic Whites (z-score = 3.25, p-value < 0.001) show positive global spatial autocorrelation; GMI of prevalence differences among non-NH White races/ethnicities are not significant at the 0.05 level.. Other races include: Asian, Native American, Alaskan Native and Pacific Islander.