State-Level Prevalence of Obesity/overweight and Pancreatic Cancer Incidence in the United States: An Ecological Analysis Using BRFSS, CDC WONDER and ACS for the Year 2021.

Abstract

Purpose: While obesity is established as an individual-level risk factor for pancreatic cancer (PC), population-level associations remain unclear. This study examines whether state-level overweight/obesity (O/OV) prevalence corresponds with age-adjusted PC incidence across the United States.

Methods: We conducted an ecological, cross-sectional analysis using 2021 data from the Behavioral Risk Factor Surveillance System (BRFSS) for state-level O/OV prevalence (BMI ≥ 25 kg/m²) and CDC WONDER for age-adjusted incidence rates (AAIR) of PC per 100,000 population. Demographic covariates such as percentage of non-Hispanic Black (NHB) population and population ≥ 65 years old were extracted from American Community Survey (ACS) data. Prevalence of current smokers and diabetes were also extracted from BRFSS. As a descriptive exploratory approach, incidence rate ratios (IRR) were calculated relative to West Virginia (WV) which had the highest O/OV prevalence, 73.6%. For inferential analysis, a univariable ordinary least square (OLS) linear regression was used to determine the association between O/OV prevalence and PC incidence. Multivariable linear regression model was fitted to adjust for prevalence of age ≥ 65, NHB race/ethnicity, current smokers and diabetes prevalence.

Results: Across 50 US jurisdictions, including DC, the mean O/OV prevalence was 67.72%, ranging from 55.4% in DC to 73.6% in WV. Florida was excluded from the complete-case analysis and OLS due to missing BRFSS values for O/OV. Twenty-nine states with O/OV prevalence lower than WV also had lower PC incidence (concordance) whereas 20 states including DC exhibited lower O/OV prevalence yet higher PC incidence (discordance). Univariable OLS model showed no statistically significant association between O/OV and AAIR (slope: -0.035, p = 0.56; R²=0.007). Multivariable OLS well fitted model showed no significant association between O/OV and AAIR. Only Black population proportion was significantly associated with PC incidence across the states.

Conclusions: In this study, state-level O/OV prevalence failed to demonstrate a statistically significant association with PC incidence. Proportion of Black population was significantly associated with increased PC incidence. These findings suggest that population-level PC risk is likely influenced by multiple interacting metabolic, behavioral, sociodemographic, environmental, and healthcare-related factors beyond obesity alone. Further longitudinal and multivariable studies are needed to better delineate the complex relationship between obesity and PC at the population level.