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

• Published in: Annals of epidemiology Vol. 26; no. 4; pp. 293 - 298
• Main Authors: Gartner, Danielle R, Taber, Daniel R, Hirsch, Jana A, Robinson, Whitney R
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2016
• Subjects: Adult; Behavioral risk factor surveillance; Behavioral Risk Factor Surveillance System; Continental population groups; Continental Population Groups - statistics & numerical data; Environment; Ethnic groups; Ethnic Groups - statistics & numerical data; Exercise; Female; Humans; Internal Medicine; Male; Middle Aged; Minority Groups - statistics & numerical data; Obesity; Obesity - epidemiology; Obesity - ethnology; Prevalence; Sex Factors; Social Class; Social environment; Socioeconomic Factors; Spatial Analysis; United States; United States - epidemiology; Obesity; GMI; CDC; United States; Local Moran’s Index; Male; LMI; Social Environment; Behavioral Risk Factor Surveillance System; Body mass index; Behavioral Risk Factor Surveillance; Spatial Analysis; Ethnic Groups; Centers for Disease Control and Prevention; Environment; Female; Global Moran’s Index; Continental Population Groups; BRFSS; US; BMI; Social environment; Continental population groups; Ethnic groups; Spatial analysis; Behavioral risk factor surveillance
• ISSN: 1047-2797; 1873-2585
• DOI: 10.1016/j.annepidem.2016.02.010

Abstract Purpose While 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/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 (BRFSS) 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 <0.001). 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 U.S. Conclusion 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.