eISSN 2233-6052  l  pISSN 2210-9099

Sung and Etemadifar: Multilevel Analysis of Socio-Demographic Disparities in Adulthood Obesity Across the United States Geographic Regions

Abstract

ObjectivesThe objective of this study was to examine the socio-demographic disparities in obesity among US adults across 130 metropolitan and micropolitan statistical areas.

MethodsThis study used data from the 2015 Behavioral Risk Factor Surveillance System and Selected Metropolitan/Micropolitan Area Risk Trend of 159,827 US adults aged 18 years and older. Data were analyzed using the multilevel linear regression models.

ResultsAccording to individual level analyses, socio-demographic disparities in obesity exist in the United States. Individuals with low socioeconomic status were associated with a higher body mass index. The participants from the Midwest United States tend to have higher body mass index than those who from the South. According to metropolitan and micropolitan statistical area level analyses, secondly, there were significant differences in obesity status between different areas and the relation of obesity with 5 socio-demographic factors varied across different areas. According to geospatial mapping analyses, even though obesity status by metropolitan and micropolitan statistical area level has improved overtime, differences in body mass index between United States regions are increasing from 2007 to 2015.

ConclusionSocio-demographic and regional disparities in obesity status persist among US adults. Hence, these findings underscore the need to take socio-environmental factors into account when planning obesity prevention on vulnerable populations and areas.

Keywords: body mass index; environment; obesity

2. Participants from the South MMSAs areas have higher BMI compared to those who reside in other MMSAs areas.

3. Obesity and socio-demographic status are influenced by geographic areas (130 MMSAs).

Materials and MethodsThis study used data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) and Selected Metropolitan/Micropolitan Area Risk Trends (SMART). The BRFSS is the nation’s premier system of health-related telephone surveys that collected state data on US residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 in 15 states, BRFSS now collects data in 50 states as well as the District of Columbia and 3 United States territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. BRFSS data are generally used to provide state-level estimates. BRFSS and SMART data are used to provide small area-level estimates for MMSAs which were determined by the Office of Management and Budgets. Hence, in order to create localized health information that can help public health practitioners identify local emerging health problems, plan and assess local responses, the Centers for Disease Control and Prevention analyzes BRFSS and SMART data. This specific data selected for this study was from 2015 and was city and countywide including 159,827 US adults aged 18 years and older [35]. This study did not require approval from the institutional review board because the BRFSS data was secondary data that did not include personal information.

1. Why MMSAs selected rather than another type of local administrative unit?MMSAs represent geographic areas that satisfy standard definitions determined by the United States Office of Management and Budget (OMB), which are used by the Census Bureau and other federal, state, and local governmental entities. MMSAs consist of counties and the BRFSS collects data about county of residence. This county information allows the reporting of information by MMSAs. Some cities and counties were excluded from SMART and BRFSS. In order for an MMSA to be included in SMART BRFSS there must be at least 500 respondents within the MMSA and the weighting criteria must be applicable. In order for a county to be included, the county must be within a selected MMSA and the weighting criteria must be applicable at the county level. The State’s BRFSS Coordinator handles these cases [35].

2. Measures

2.1. Dependent variable: obesity

BMI was used as a measure of obesity and it was computed by dividing an individual’s weight by their height squared. BMI is closely linked with percentage body fat and total body fat [36]. Individuals with a BMI of 25 kg/m2 to 29 kg/m2 were regarded as overweight, and individuals with a BMI of 30 kg/m2 or more were considered obese [37].

2.2. Independent variables: socio-demographic variables

Gender was categorized into males and females. Age was categorized into 18–44 years and ≥ 45 years. The education level was categorized into higher education (≥ college diploma) and lower education (< college diploma). Race was categorized into Non-White or Hispanic and Non-Hispanic White. The household income level was categorized into ≥ $50,000 and <$50,000. Regions were categorized into 5 groups (South, Northeast, Midwest, West, Puerto Rico).

2.3. Control variables

Physical activity was categorized into yes and no. Fruit consumption was categorized into ≤ 1 time per month and ≥ 1 time per week.

3. Statistical AnalysisDescriptive statistics with using chi-square (Table 1) and line graph analysis are presented in Figure 1. All descriptive analyses were carried out using STATA (version 15.0, StataCorp LLC., College Station, TX).A mainland United States map of average adult BMI (≥ 18 years) by MMSAs levels in 2007, 2011, and 2015 was created using Arc GIS 10.6 with R (Figure 2).To examine the socio-demographic and regional disparities in adulthood obesity among US adults across 130 MMSAs, 3 multilevel linear regression models of BMI were conducted using STATA. Firstly, the null hypothesis model was implemented (Model I) to determine whether there was a difference in obesity status and these statistical areas. Secondly, the random-intercepts model (Model II) was implemented which considers individual-level predictors in the fixed part to examine how the 6 socio-demographic variables affect obesity status after adjusting for obesity-related health behaviors such as physical activity and fruit consumption. Finally, the random-slope model (Model III) was implemented to examine whether or not obesity status and with the 5 socio-demographic variables varied across the 130 MMSAs.

ResultsTable 1 shows the percentages of obesity rate among US adults ≥ 18 years (N = 159,827). The average percentage of obese people (BMI ≥ 30) in the 130 MMSAs was 29.06% (SD = 4.44). Men were slightly more likely to be obese than women (29.41% versus 29.15%). Participants who were ≥ 45 years were more likely to be obese than those aged 18–44 years (30.60% versus 25.87%). Participants with a higher education (≥ college diploma) were less likely to be obese than those who had a lower education (< college diploma) (24.05% versus 33.45%). Non-White or Hispanic participants were more likely to be obese than Non-Hispanic White participants (33.38% versus 28.03%). Participants with a higher income (≥ $50,000) were less likely to be obese than those with lower income (<$50,000) (26.05% versus 33.05%). Participants from the South and the Midwest were more likely to be obese than those from the Northeast and the West (31.08% and 31.65% versus 29.96% and 25.72%).Table 2 shows the results of multilevel linear regression of socio-demographic status and BMI among US adults (≥ 18 years) in 130 MMSAs (N = 159,827). The average coefficient of Model I was 28.147. The metropolitan and micropolitan statistical area level residual variance at Model I was significant at the 0.001 level, which means that there were significant differences in obesity status between the 130 MMSAs. Model II, III show the unstandardized coefficients from the multilevel linear regression model of the association between socio-demographic variables and obesity status among US adults. According to full model (Model III), firstly, men were associated with higher BMI than women (B = 0.592, p < 0.001). Secondly, participants who were ≥ 45 years were associated with higher BMI than those aged 18–44 years (B = 0.709, p < 0.001). Thirdly, participants with higher education (≥ college diploma) were associated with lower BMI than those who with lower education (< college diploma) (B = −0.743, p < 0.001). Fourthly, Non-White or Hispanic participants were associated with higher BMI than Non-Hispanic white (B = 1.008, p < 0.001). Fifthly, participants with higher income (≥ $50,000) were associated with lower BMI than those who with lower income (<$50,000) (B = −0.292, p < 0.001). Finally, participants from the Midwest MMSAs areas were associated with higher BMI than those who from the South MMSAs areas (B = 0.504, p < 0.01). On the other hand, participants from the West MMSAs areas were associated with lower BMI than those who from South MMSAs areas (B = −0.576, p < 0.01).Random slope model (Model III) analysis shows that the metropolitan and MMSA-level residuals were all significant at the 0.001 level. It means that the obesity status relationship with the 6 socio-demographic variables varies across the 130 MMSAs.Figure 1 displays the association between prevalence of obesity according to the areas (130 MMSAs) and socioeconomic status such as education levels and household income levels. As shown in Figure 1, areas with a higher prevalence of obesity tended to have a higher proportion of people with a lower level of education and a lower household income.Figure 2 displays a mainland United States map of the average adult BMI (≥18 years) by MMSAs levels in 2007, 2011, and 2015. As shown in maps on the left, the mean BMI of all MMSAs has decreased overtime. As shown in both maps, there were no significant differences in BMI according to regions in 2007. However, over time, differences in BMI between the regions widened.

DiscussionThis study is one of the first studies in the United States to examine socio-demographic and regional disparities in adulthood obesity by MMSAs. Multilevel analysis was used to examine the socio-demographic disparities in obesity and examine how this relationship is affected by the geographic areas (130, MMSAs).

< $50,000 73,496 (45.98) 33.05 Region South 44,932 (28.11) 31.08 Northeast 32,401 (20.27) 29.96 Midwest 46,575 (29.14) 31.65 West 33,372 (20.88) 25.72 Puerto Rico 2,547 (1.59) 29.88 MMSA level (n =130) % of obese people in the MMSA where participants live (mean, SD) 29.06 4.44 BRFSS = behavioral risk factor surveillance system; MMSA = metropolitan and micropolitan statistical area; SMART = selected metropolitan/micropolitan area risk trends. Table 2 Multilevel linear regression of socio-demographic status and BMI among US adults (≥ 18 years) (N = 159,827). Model I Model II Model III Coef (SE) Coef (SE) Coef (SE) Fixed effect (individual level) Intercept 28.147*** (0.071) 27.105*** (0.103) 27.058*** (0.118) Gender Male 0.600*** (0.033) 0.592*** (0.040) Female (Ref) Age (y) ≥ 45 0.721*** (0.037) 0.709*** (0.052) 18–44 (Ref) Education level ≥ College −0.790*** (0.036) −0.743*** (0.049) < College (Ref) Race Non-White or Hispanic 1.067*** (0.043) 1.008*** (0.079) Non-Hispanic White (Ref) Income level ≥$50,000 −0.295*** (0.037) −0.292*** (0.039)
< \$50,000 (Ref)

Region Northeast −0.359* (0.166) −0.293 (0.195)
Midwest 0.432** (0.142) 0.504** (0.165)
West −0.657*** (0.160) −0.576** (0.187)
Puerto Rico −1.468* (0.618) −1.227 (0.927)
South (Ref)

Physical activity No 1.800*** (0.040) 1.800*** (0.040)
Yes (Ref)

Fruit consumption ≤ 1 per mo 0.427*** (0.036) 0.422*** (0.036)
≥ 1 per wk (Ref)

Random effect (Between MMSAs)

Intercept 0.774*** (0.053) 0.596*** (0.043) 0.653*** (0.053)

Slopes for gender 0.192*** (0.052)

Slopes for age 0.350*** (0.046)

Slopes for education level 0.324*** (0.052)

Slopes for race 0.654*** (0.073)

Slopes for income level 0.106*** (0.078)
* p < 0.05,
** p < 0.01
*** p < 0.001
BMI = body mass index; MMSA = metropolitan and micropolitan statistical area.