In addition to economic factors and geographic area poverty, area income inequality — the extent to which income is distributed in an uneven manner across a population — has been found to influence health outcomes and obesity. We used a spatialbased approach to describe interactions between neighboring areas with the objective of generating new insights into the relationships between countylevel income inequality, poverty, and obesity prevalence across New York State (NYS).
We used data from the 2015 American Community Survey and 2013 obesity estimates from the Centers for Disease Control and Prevention for NYS to examine correlations between countylevel economic factors and obesity. Spatial mapping and analysis were conducted with ArcMap. Ordinary least squares modeling with adjusting variables was used to examine associations between countylevel obesity percentages and countylevel income inequality (Gini index). Univariate spatial analysis was conducted between obesity and Gini index, and globally weighted regression and Hot Spot Analysis were used to view spatial clustering.
Although higher income inequality was associated with lower obesity rates, a higher percentage of poverty was associated with higher obesity rates. A higher percentage of Hispanic population was associated with lower obesity rates. When tested spatially, higher income inequality was associated with a greater decrease in obesity in southern and eastern NYS counties than in the northern and western counties, with some differences by sex present in this association.
Increased income inequality and lower poverty percentage were significantly linked to lower obesity rates across NYS counties for men. Income inequality influence differed by geographic location. These findings indicate that in areas with high income inequality, currently unknown aspects of the environment may benefit lowincome residents. Future studies should also include environmental factors possibly linked to obesity.
Economic factors have been linked to numerous health outcomes, including obesity (
In the United States, obesity is related to poverty, low individual income, and foodinsecurity (
Our study used a crosssectional design of publicly available data sources to create estimates related to NYS residents. Data from the American Community Survey (ACS) (
Countylevel income inequality was measured by the Gini coefficient, or Gini index, which represents income dispersion across an area, assigning values from 0 to 1: the higher the number, the greater an area’s income inequality. The numerator of the coefficient is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. We converted this ratio into an index by multiplying each value by 100. Gini index was the only variable not separated by sex. In the ACS data set, racial groups were recorded as counts and were converted to percentages by dividing the counts for each racial group by the total estimated number of people in each county. We used the Gini index in this study because it is the most commonly used measure of income inequality; however, we acknowledge the existence of other measures, such as Atkinson’s measures, Theil’s T, and Theil’s L, and that our results may not necessarily have held if these other measures were used instead of the Gini index (
The dependent variable, obesity prevalence, was drawn from the Centers for Disease Control and Prevention (CDC) statistical estimates (
We examined the association between countylevel independent variables and obesity prevalence with ArcMap (Esri) by using ordinary least squares (OLS). OLS is a variation of linear regression, a statistical method that examines associations between multiple independent variables and a single dependent variable; once the assumptions are satisfied, the regression output indicates the strength of the association between the dependent variable and each of the independent variables. These assumptions, include linear parameters, random sampling, no multicollinearity, no autocorrelation, a conditional mean of zero, and normally distributed error terms; all of them were satisfied, meaning that our OLS models are efficient and represent a linear unbiased estimator of variable coefficients.
Final models included countylevel Gini index, poverty percentage (defined as having an income below the Federal Poverty Level), adjusted for median age, percentage AfricanAmerican, percentage Hispanic, percentage married, and percentage with at least a high school education. Statistical significance was set at
Two spatial tests, geographically weighted regression (GWR) and GetisOrd GI* Hot Spot Analysis (Esri), were used to add a different dimension to our analysis. GWR created a separate ordinary least squares (OLS) model for every county while considering spatial factors, such as the distances and OLS models of neighboring counties. GWR measured relationships that vary across space, whereas OLS linear regression assumes these relationships apply equally over an entire geographic area (
Hot Spot Analysis was conducted on the GWR regression results; this test determines whether the different coefficients of the Gini index variable for each county that GWR returned are randomly dispersed, or whether unusually high or unusually low values are clustered together. Hot Spot Analysis tests for clusters of similar values in a set of spatial data, indicating when similar values are close to one another. The method is specific, enabling us to detect possible local spatial associations whereas other methods, such as Moran’s I, does not (
Although standard OLS regression makes one model for the entire state, giving an overall sense of a variable’s effect on obesity rates, GWR combined with Hot Spot Analysis provides information about the degree of effect a variable has in different areas. This allowed for observation of differences in the effect of income inequality on obesity prevalence across NYS.
The median age in our data set of the NYS population was 38.1 years; 48.5% were men, 15.6% were black, 18.4% were Hispanic, 44.5% were married, and 85.6% were high school graduates. During the time that these data were collected, the response rate varied by county; however, for NYS, the overall response rate of housing units was 93.3%, and the overall response rate of group quarters was 95.2%.
The OLS regression showed that among all adults, a higher countylevel Gini index (or higher inequality) (β, −0.37;
Variable  β Coefficient  Standard Error 


Intercept  16.91  21.06  .43 
Gini index  −.37  .14  .01 
Poverty  .42  .14  .004 
Median age  .09  .10  .36 
AfricanAmerican, %  .14  .10  .14 
Hispanic, %  −.22  .09  .009 
Married, %  .22  .10  .03 
High school graduate, %  .08  .16  .64 
Calculated by Gini index drawn from 5year estimates of the American Community Survey for 2015.
Based on an ordinary least squares multivariable linear regression model. Poverty percentage and sociodemographic variables were drawn from 5year estimates of the American Community Survey for 2015. The dependent variable, obesity percentage, is based on 2013 CDC County Data Indicators (
The intercept of the OLS regression model. Defined, in this case, as the expected value of obesity prevalence if all independent variables used in the equation are set to 0.
Defined as percentage of population with annual incomes below the Federal Poverty Level.
Variable  β Coefficient  Standard Error 


Intercept  35.68  15.89  .03 
Gini index  −.41  .13  .004 
Poverty  .31  .14  .03 
Median age  .04  .10  .68 
AfricanAmerican, %  .07  .09  .48 
Hispanic, %  −.26  .08  <.001 
Married, %  .21  .08  .01 
High school graduate, %  −.04  .13  .76 
Calculated by Gini index drawn from 5year estimates of the American Community Survey for 2015.
Based on an ordinary least squares multivariable linear regression model. Poverty percentage and sociodemographic variables were drawn from 5year estimates of the American Community Survey for 2015. The dependent variable, obesity percentage, is based on 2013 CDC estimates based on the BRFSS (Behavioral Risk Factor Surveillance System) survey (
The intercept of the OLS regression model. Defined, in this case, as the expected value of obesity prevalence if all independent variables used in the equation are set to 0.
Defined as percentage of population with annual incomes below the Federal Poverty Level.
Variable  β Coefficient  Standard Error 


Intercept  19.82  22.92  .39 
Gini index  −.34  .15  .03 
Poverty, %  .38  .13  .004 
Median age  .08  .10  .40 
AfricanAmerican, %  .18  .10  .07 
Hispanic, %  −.20  .09  .03 
Married, %  .15  .10  .14 
High school graduate, %  .05  .18  .80 
Calculated by Gini index drawn from 5year estimates of the American Community Survey for 2015.
Based on an ordinary least squares multivariable linear regression model. Poverty percentage and sociodemographic variables were drawn from 5year estimates of the American Community Survey for 2015. The dependent variable, obesity percentage, is based on 2013 CDC estimates based on the BRFSS (Behavioral Risk Factor Surveillance System) survey (
The intercept of the OLS regression model. Defined, in this case, as the expected value of obesity prevalence if all independent variables used in the equation are set to 0.
Defined as percentage of population with annual incomes below the Federal Poverty Level.
The GWR analysis showed that a 1% increase in income inequality was associated with a greater decrease in obesity prevalence in southern NYS than in the western state for both sexes. The effect of the Gini index on obesity prevalence was highest in southern and eastern NYS, but showed a downward trend toward the north and west. These associations were stronger among men (
Results of geographically weighted regression (GWR) tests for men, mapping the individual ordinary least squares (OLS) coefficient constructed by GWR to each county in New York State. Data are from the American Community Survey and from CDC County Data Indicators estimates (
Results of geographically weighted regression (GWR) tests for women, mapping the individual ordinary least squares coefficient constructed by GWR to each county in New York State. Data are from the American Community Survey and from CDC County Data Indicators (
Hot Spot Analysis tests confirmed GWR results: a large area exists in the southeast where the effect of the Gini index is unusually high compared with its surrounding areas, and a large area in the west where this effect is unusually low compared with neighboring areas. From the results of the GWR and Hotspot tests, we observed a connection between the differing effects of income inequality (Gini index) and its relation to geographical direction in NYS. Moving east the absolute effect of income inequality on obesity increased, whereas moving west it, decreased, which the Hot Spot test confirmed.
Our study examined associations between obesity prevalence and countylevel income inequality and poverty percentage among adults in NYS. As we hypothesized, income inequality was inversely associated with obesity prevalence, and a difference in the geographical effect on income inequality and obesity was observed. Our findings using spatial analyses can help public health officials and lawmakers to tailor health initiatives to different geographical areas, thereby improving the sustainability of these initiatives on the wellbeing of the population.
The negative correlation of income inequality with obesity is not unilateral; a study of 21 developed countries showed that income inequality was positively correlated with obesity prevalence in men and women (
Countrylevel studies examining national data suggested a detrimental effect of high income inequality to mean BMI and prevalence of obesity (
When considering poverty, our study agrees with similar studies conducted among populations of adult men and women in various countries. A study of Canadian men and women found that rich men and poor women were more likely to be obese (
A study that examined Gini index in adults at the US county and tract levels showed that the addition of potential confounders changed the degree of the association between income inequality and obesity, because area level factors such as neighborhood environment (eg, availability of parks and recreation, healthy food), and local policies may have an effect on residents’ weight status (
Countylevel poverty was positively associated with obesity in our study. A study of 1,150 children that used data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development found that poverty in very early life was associated with obesity in adolescence (
Studies looking at the relationship between poverty and obesity, have used the term “povertyobesity paradox” to indicate the positive relationship often found between poverty and obesity. Similar results were observed among the elderly by using data from the Survey of Health, Ageing, and Retirement and from the English Longitudinal Study of Ageing (
Our study has numerous strengths, including the use of OLS regression and the relatively high number of counties that NYS has compared with other states. The data used were CDC estimates derived from statistical estimates that sought to minimize error, and from ACS data, which is a conglomerate of half a decade of data collected from a high number of interviews. Another strength of our study is the use of GWR and Hot Spot Analysis to determine obesity prevalence geographically, a combined approach that has not often been tried in the literature, allowing for spatial analysis. These results are also highly generalizable. This study was conducted with large data sets, improving the generalizability of the findings. A similar approach can be conducted for the entire United States as needed.
Our study also had limitations. The study’s crosssectional design limited our ability to infer causality. Also, some of the variables in the BRFSS dataset are selfreported and may be subject to desirability or recall bias (
In conclusion, we found that income inequality was inversely associated with obesity prevalence in NYS counties, although this effect differed by sex. Also, the effect of income inequality differed geographically; income inequality was weaker in western NYS and stronger in the east. This trend did not differ by sex. Poverty percentage, however, was positively associated with obesity. Future studies can use spatialbased multiple regression models by introducing potential arealevel factors that may contribute to the differing geographical effects of income inequality on obesity. The findings can help design effective programs that will be tailored to address the unique needs of the geographic locations, thus improving the sustainability of health outcomes.
The authors received no financial support for this research. No copyrighted photos, surveys, instruments, or tools were used. This work was supported in part by National Science Foundation Advanced Cyberinfrastructure no. 1443054 and by National Science Foundation Information and Intelligent Systems 135088.
The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.