A Spatial Analysis of Body Mass Index and Neighborhood Factors in Community-Dwelling Older Men and Women

Abstract

The spatial distribution of obesity among the older population can yield insights into the influence of contextual factors associated with this public health problem. We tested the relationship between neighborhood-level characteristics and body mass index (BMI) using global and local spatial statistics of geographic clustering, using data derived from a random-digit-dial sample of 5,319 community-dwelling adults aged 50 to 74 residing in 1,313 census tracts in New Jersey. Geographically weighted regression modeled associations between BMI clusters and neighborhood characteristics, including metrics of structure, safety, demographics, and amenities. Across the sample panel, average BMI was 28.62 kg/m 2 for women and 28.25 kg/m 2 for men. There was significant spatial clustering of obesity by census tract, varying by gender across the state. Neighborhood characteristics were more strongly related to BMI for women than men. This research illuminates the role of neighborhood contextual

Gender Difference and Spatial Heterogeneity in Local Obesity

“…Furthermore, Moran’s I value of average local BMI based on the rook criterion is higher for women (0.17) than for men (0.10), which corresponds to the higher Moran’s I values of local obesity rates for women than for men. In other words, the greater spatial dependence on local obesity rates for women support the proposed hypothesis that local obesity is more spatially dependent for women than men, which is also consistent with findings in previous studies [ 10 , 25 ].…”

“…Physical environment characteristics include access to healthy foods, parks, recreational facilities, and doctors, mixed land use, and denser environment which directly and indirectly affect public health and increase physical activity and walkability [ 8 , 9 , 10 , 11 ]. Regarding socio-economic environment, affluent people have more resources to consume healthier foods and engage in further physical activity and educated people are more likely to incorporate healthy behaviors and habits in their lifestyle [ 8 , 10 , 11 , 12 ]. Chang et al [ 13 ] also finds that underprivileged areas are more likely to be disordered, for example, with high crime rates, which may discourage physical activity such as walking, thereby leading to obesity.…”

“…According to Graziano et al [ 24 ], women are more inclined to be affected by others’ opinions and behaviors compared to men, which suggests that obesity for women is more likely to be spatially contagious. Indeed, studies show that there are more spatial clusters on local obesity for women than for men [ 10 , 25 ].…”

“…There are studies which examine spatial variability of environmental factors on local obesity (e.g., [ 36 , 37 , 38 , 39 ]), however, most studies do not examine spatial variability in relation to gender difference. As one of the few studies examining gender difference in spatial variability on local obesity, Christman et al [ 10 ] demonstrate that poverty rate has a positive effect on local obesity in northern New Jersey while it has a negative effect on local obesity in southern New Jersey for women. The same variable has a positive effect on local obesity in northwestern New Jersey but has both positive and negative effects on local obesity in western New Jersey for men.…”

Geographically Weighted Regression in the Analysis of Unemployment in Poland

“…This is particularly true in analyses related to healthcare (e.g., incidence of diseases and access to medical services e.g., [40][41][42][43]), environmental protection (e.g., [44][45][46]), the real estate market (e.g., [47,48]), poverty (e.g., [49,50]) and migration (e.g., [51][52][53][54]). The GWR tool is also used in labour market analyses (e.g., [55][56][57][58]).…”