Geography, Race/Ethnicity, and Obesity Among Men in the United States (original) (raw)
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Explaining Racial Disparities in Obesity Among Men: Does Place Matter?
American Journal of Men's Health, 2014
National data indicate that Black men have higher rates of obesity than White men. Black men also experience earlier onset of many chronic conditions and premature mortality linked to obesity. Explanations for these disparities have been underexplored, and existing national-level studies may be limited in their ability to explicate these long-standing patterns. National data generally do not account for race differences in risk exposures resulting from racial segregation or the confounding between race and socioeconomic status. Therefore, these differences in obesity may be a function of social environment rather than race. This study examined disparities in obesity among Black and White men living in the same social and environmental conditions, who have similar education levels and incomes using data from the Exploring Health Disparities in Integrated Communities-SWB (EHDIC-SWB) study. The findings were compared with the 2003 National Health Interview Survey (NHIS). Logistic regression was used to examine the association between race and obesity adjusting for demographics, socioeconomic status, and health conditions. In the NHIS, Black men had a higher odds of obesity (odds ratio = 1.29, 95% confidence interval = 1.12-1.49) than White men. However in the EHDIC-SWB, which accounts for social and environmental conditions of where these men live, Black men had similar odds of obesity (odds ratio = 1.06, 95% confidence interval = 0.70-1.62) compared with White men. These data highlight the importance of the role that setting plays in understanding race disparities in obesity among men. Social environment may be a key determinant of health when seeking to understand race disparities in obesity among Black and White men.
Regional disparities in obesity prevalence in the United States: A spatial regime analysis
Obesity, 2014
Objective-Significant clusters of high and low obesity counties have been demonstrated across the United States (U.S.). This study examined regional disparities in obesity prevalence and differences in the related structural characteristics across regions of the U.S. Design and Methods-Drawing on model-based estimates from the Centers for Disease Control and Prevention, regional differences in county-level adult obesity prevalence (percent of the adult population [≥ 20 years] that was obese [BMI≥30kg/m 2 ] within a county, 2009) were assessed with a LISA (Local Indicators of Spatial Association) analysis to identify geographic concentrations of high and low obesity levels. We utilized regional regime analysis to identify factors that were differentially associated with obesity prevalence between regions of the U.S. Results-High and low obesity county clusters and the effect of a number of county-level characteristics on obesity prevalence differed significantly by region. These included the positive effect of African American populations in the South, the negative effect of Hispanic populations in the Northeast, and the positive effect of unemployed workers in the Midwest and West. Conclusions-Our findings suggest the need for public health policies and interventions that account for different regional characteristics underlying obesity prevalence variation across the U.S.
Obesity, 2014
Objective: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects. Methods: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m 2 ). Results: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent 65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures. Conclusions: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic.
Obesity
Objective-State-level estimates of obesity based on self-reported height and weight suggest a geographic pattern of greater obesity in the Southeastern US; however, the reliability of the ranking among these estimates assumes errors in self-reporting of height and weight are unrelated to geographic region. Design and Methods-We estimated regional and state-level prevalence of obesity (body mass index ≥ 30 kg/m 2) for non-Hispanic black and white participants aged 45 and over were made from multiple sources: 1) self-reported from the Behavioral Risk Factor Surveillance System (BRFSS 2003-2006) (n = 677,425), 2) self-reported and direct measures from the National Health and Nutrition Examination Study (NHANES 2003-2008) (n = 6,615 and 6,138 respectively), and 3) direct measures from the REasons for Geographic and Racial Differences in Stroke (REGARDS 2003-2007) study (n = 30,239). Results-Data from BRFSS suggest that the highest prevalence of obesity is in the East South Central Census division; however, direct measures suggest higher prevalence in the West North Central and East North Central Census divisions. The regions relative ranking of obesity prevalence differs substantially between self-reported and directly measured height and weight. Conclusions-Geographic patterns in the prevalence of obesity based on self-reported height and weight may be misleading, and have implications for current policy proposals.
Differences in Obesity Among Men of Diverse Racial and Ethnic Background
American journal of men's health, 2015
Racial/ethnic disparities exist in obesity prevalence among men, with Hispanic men exhibiting the highest prevalence compared with non-Hispanic White and non-Hispanic Black men. Most studies do not parse out Hispanic groups; therefore, it is unclear whether the increases in obesity rates among Hispanic men applies to all groups or if there are particular groups of Hispanic men that are driving the increase. The goal of this study is to examine the variations in obesity among men of diverse racial/ethnic backgrounds and determine if obesity is affected by nativity. The data used in this study were from 11 years (2002-2012) of the National Health Interview Survey. Logistic regression was used to examine the relationship between race/ethnicity, obesity, and nativity. After adjusting for covariates, there are differences in obesity prevalence, with the largest prevalence among Puerto Rican men and Mexican American men. Consistent with previous literature, it has been suggested that men ...
Disparities in obesity rates: Analysis by ZIP code area
Social Science & Medicine, 2007
Obesity in the USA has been linked to individual income and education. Less is known about its geographic distribution. The goal of this study was to determine whether obesity rates in King County, Seattle, Washington state, at the ZIP code scale were associated with area-based measures of socioeconomic status and wealth. Data from the Behavioral Risk Factor Surveillance System were analyzed. At the ZIP code scale, crude obesity rates varied six-fold. In a model adjusting for covariates and spatial dependence, property values were the strongest predictor of the area-based smoothed obesity prevalence. Geocoding of health data provides new insights into the nature of social determinants of health. Disparities in obesity rates by ZIP code area were greater than disparities associated with individual income or race/ethnicity.
Race, SES, and Obesity Among Men
2011
Over the last decade, obesity has increased significantly among men but few national studies have empirically examined racial and socioeconomic differences in obesity among men. In this paper, we utilized logistic regression to evaluate the potential associations that race and socioeconomic status may have with obesity among men in the National Survey of American Life: an in-person household survey of non-institutionalized U.S. blacks and whites who lived in communities where at least 10% of the community residents were black Americans. A greater proportion of black men were likely to be obese than white men, but no interaction among race, SES, and obesity was detected when potential confounding variables were included. There was not a relationship between SES and obesity for white men, but there was an apparent positive relationship between SES and obesity for black men that did not remain significant in adjusted models. No relationship was found between age and obesity among black men, though white men who were 55 and older were more likely than those 18-34 to be obese in confounder adjusted models. Among white men, no relationships were found between obesity and education, household income, or marital status. Black men in the lowest income category were less likely to be obese than those in the highest income category, in bivariate but not adjusted models. These findings suggest that the way racial, economic, stress and behavioral factors combine to affect obesity in black and white men may be different.
Economics & Human Biology, 2007
A growing body of literature has documented a link between neighborhood context and health outcomes. However, little is known about the relationship between neighborhood context and body mass index (BMI) or whether the association between neighborhood context and BMI differs by ethnicity. This paper investigates several neighborhood characteristics as potential explanatory factors for the variation of BMI across the United States; further, this paper explores to what extent segregation and the concentration of disadvantage across neighborhoods help explain ethnic disparities in BMI. Using data geo-coded at the census tract-level and linked with individual-level data from the Third National Health and Examination Survey in the United States (U.S.), we find significant variation in BMI across U.S. neighborhoods. In addition, neighborhood characteristics have a significant association with body mass and partially explain ethnic disparities in BMI, net of individual-level adjustments. These data also reveal evidence that ethnic enclaves are not in fact advantageous for the body mass index of Hispanics-a relationship counter to what has been documented for other health outcomes.