Association between income-level and obesity. Table 1 compares the distribution of the sample’s characteristics for the total sample and by sex. On average about one-third of the population were obese with a significant higher rate of obesity for women (P< .001). Income quintiles showed that the in comparison with men women population in our sample was in lower and middle quintiles (23.8% women vs. 20.2% men) and men was in top quintile (37.5% men vs. 33.5% women).
Overall, the sample age was 47.0 18.0 years old with a slightly older women population (47.9 (17.9) vs. 46.0 (18.0)). Women sample had higher percent of black non-Hispanic (NH) and men sample had higher percent of Mexican-American with non-significant differences between percent of white NH and other racial/ethnic groups in both samples. The majority of the women had more than a high school education (61.1%) and were covered by any kind of health insurance. Two-third of the women had never smoked (59.0%), currently drank (67.0%), were physically active (57.4), and were healthy (82.5%). In comparison 46.5% of the men had never smoked, 85.2% were currently drank, 61.5% were physically active and 84.2% were healthy. Women were live more alone than men (14.9% vs. 11.8%) and only 43.7% of them were head of household in compare with 72.3% of men. Table 1 details information on women and women.
Table 1.
The association between income-inequality and obesity in women and men is displayed in Table 2. Based on the results of the unadjusted model, rich population were less obese (PR:0.85; CI:0.79-0.91); however, in the adjusted model, the association of income-level and obesity has disappeared (PR:0.94; CI:0.87-1.01), this gives us this idea to run stratified model for women and men. Based on the full model (Model 2) results, the obese population was more women, black NH or Mexican American. They were married, had higher education, former smokers and drinkers, not physically active with poor health condition.
Table 2.
Model 2 is an adjusted model 1 with including sex (=1 if female) as a covariate, we ran an interaction term of income inequality categories and sex. The association of income-level and obesity in the women sample is displayed in Table 3. Model 3 and model 4 present the association between income-inequality and obesity in women. Based upon unadjusted model women in 4th and 5th income-quintile group less likely to be obese (PR: 0.87; CI: 0.81-0.93) and (PR: 0.69; CI: 0.64-0.75), respectively. After adjusting model, the association between income-level and obesity in 4th quintile has disappeared but remained significant in rich population (PR: 0.84; CI: 0.76-0.93). There is negative association between income-level and obesity in women.
Model 5 and model 6 present the association between income-inequality and obesity in men. Interestingly, there is a positive association between income-inequality and obesity in men in unadjusted and adjusted models for example in 4th quartile (PR: 1.20; CI: 1.08-1.32) and 5th quartile (PR: 1.24; CI: 1.13-1.37) more likely to be obese.
Table 3.
Our adjusted models for women and men showed that individuals with obesity were black NH or Mexican American NH, with High school graduates. They were former smoker or drinker, with no vigorous activities and fair-poor health condition.
As presented in the regression results there is different association between obesity and income-inequality in men and women, to understand more about these differences in men and women we have used Lorenz curve and Gini Coefficient. Figure 1 - Lorenz curves - shows the Gini coefficient for income to poverty ratio in men and women population in the US between 1999-2016, to plot these curves we used average GC with jackknife standard errors. This figure compares GC between men and women and obese and non-obese population by sex.
In the panel A, the blue solid line plots distribution of income in non-obese women and the dash-red line shows distribution in non-obese men. The blue line stays over the red line for about 40% of population and closer to the perfect equality line that means smaller income inequality within women and greater inequality within men. Interestingly, there is a different pattern in the obese population. For all income quintiles, obese women suffer more from income inequality than obese men (See panel B). For example, in obese men lower 25% of population observed only 7.1% of income and 53% of income observed by 75% of population and rest of them observed by top 25% of population. In obese women these distributions changed to 6% and 48% of first 25% and 75% of population and 52% of income observed by top 25% of population. The Gini coefficient rose from 0.295 (SE: 0.002) in men to 0.322 (SE: 0.002) in women, the jackknife standard errors for these estimators are very small and there is a significant difference between GC in men and women (P<0.001).
In panels C and D, we compared the GC between obese and non-obese men and women population. As presented obese women between the fifth and tenth percentile of the population (middle and higher income population) suffer more from income-inequality (panel C), GC moved between 0.342 (SE: 0.003) in obese women to 0.0305(0.002) in non-obese women. For men there is a higher income inequality in non-obese population (Panel D), the red-dash-line the represent obese-men stays above the solid blue line (non-obese men) meaning lower income inequities for all groups of income, the GC moved from 0.285 (SE: 0.003) in obese men to 0.298 (SE:0.002) in non-obese men.
Figure 1. The Lorenz Curves and Gini Coefficients in men and women, 1999-2016
Sensitivity analysis. As a sensitivity analysis and with using the CDC approach household income was defined as a categorical variable with ≤130% as low-income, >130% to ≤350% middle income, and >350% as higher middle income household [1]. We found similar patterns with the original models, for example negative association between income-inequality and obesity in women with a positive association in men (See Table 4).
Table 4.