Disparities in obesity rates: Analysis by ZIP code area

Abstract

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.

Economic Consequences of the Obese

“…At the state level, or as measured by congressional districts, the highest rates of obesity are found in the lowest income areas (25,26). Aggregate census data also support the finding that the proportion of families living in poverty is strongly correlated with higher neighborhood rates of obesity and type 2 diabetes (27). Both domestically and internationally, the cruel causation of poverty and obesity thus runs both ways-to be poor is often to be obese and vice versa.…”

Impact of Rurality, Broiler Operations, and Community Socioeconomic Factors on the Risk of Campylobacteriosis in Maryland

“…Louis et al (2005) found increased campylobacter rates to be correlated with air temperature with the most striking seasonal effect observed for children under 5 years old (Louis, 2005). Similar studies supporting a seasonal peak in spring and summer are summarized by Sopwith et al (2008): a European study (Nylen et al, 2002) showed that the timing of the seasonal peak varied, occurring earlier in Wales (weeks [23][24][25][26][27] (1998) measured higher numbers campylobacters shed by dairy cows in the summer, when the cattle were drinking lake water, than in the winter, when they were provided with chlorinated municipal water, providing an opportunity for heightened recontamination of surface water in summer months.…”

Distribución espacial de la malnutrición infantil en Yerba Buena, Tucumán, Argentina (2014-2015)

“…A tales fines, se han considerado las ventajas del uso de la sección censal como unidad de estudio de la variabilidad espacial de resultados en salud (Drewnowskia, Rehma y Solet, 2007;Carvalho-Alice, Costa-Maria, Ferreira-Luiz y Batista-Filho, 2000). El trabajo con áreas geográficas pequeñas posibilitó la identificación de radios con mayores porcentajes y mayor aporte a la situación de malnutrición infantil en el departamento de Yerba Buena, Tucumán, Argentina.…”

“…El trabajo con áreas geográficas pequeñas posibilitó la identificación de radios con mayores porcentajes y mayor aporte a la situación de malnutrición infantil en el departamento de Yerba Buena, Tucumán, Argentina. Abordajes similares pueden documentarse en otros trabajos internacionales, donde la elaboración de cartografía con escalas pequeñas se ha empleado para distinguir áreas con mayores prevalencias de baja talla y exceso de peso, en niños (Hernández-Vásquez, Bendezú-Quispe, Díaz-Seijas y Santero, 2016; Carvalho-Alice, Costa-Maria, Ferreira-Luiz y Batista-Filho, 2000), y obesidad, en adolescentes y adultos (Drewnowskia, Rehma y Solet, 2007;Mobley, Finkelstein, Khavjou y Will, 2004). …”