Mapping Obesity Coverage in Florida Counties Using Interactive Web-Based Mapping Tools to Support Targeted Policy and...

Obesity is among the most common global public health issues in the 21^st century and contributes significantly to cardiovascular morbidity and mortality burden. The success of well-targeted policies and intervention strategies aimed at addressing obesity depends heavily on understanding the effect of geographical location on obesity and other predictors. The study aim was to quantify county-level geographical differences in obesity across Florida counties while simultaneously identifying predictors of obesity prevalence.

This study used the 2019 data from the Florida state-based telephone surveillance systems, known as the Behavioral Risk Factor Surveillance System (BRFSS) which provides county-level data on measures of the prevalence of personal health behaviors that are risk factors for morbidity and mortality. The survey collected data on a total sample of 54,260 adults residing in 67 counties of Florida. This study applied Bayesian geospatial models and interactive web-based mapping approaches to analyze and map county-level geographical differences in the risk of obesity. The estimated coefficients were presented as log mean with their associated 95% credible intervals (Cr.Is).

The study identified sedentary lifestyle (log mean = 0.023, 95% Cr.I: 0.006, 0.039) as the only risk factor independently associated with increased burden of obesity. The results showed substantial county-level geographical differences in the predicted obesity prevalence with an overall obesity prevalence of 68.6% with a range of 59.0%-75.7%. Residing in Holmes was associated with the highest burden of obesity. Furthermore, the prevalence was relatively high in Levy, Columbia, Lafayette, Hendry, Bradford, Calhoun, Dixie, Okeechobee, and Gadsden counties.

The substantial county-level geographical difference in obesity prevalence found is of great importance for sound public health policy and intervention strategies at the local level. The geospatial modeling supported by the web-based spatial mapping tool employed in this study can help guide the design of geographical prioritization of targeted public health policies and intervention strategies to combat adult obesity and its associated mortality.