A geospatial analysis of neighborhood hot spots and socioeconomic disparities associated with high lung cancer incidence for targeted intervention in south Florida.

Abstract

e22525 Background: This study aims to identify the locations as well as specific socioeconomic disparities associated with high lung cancer incidences in South Florida (SF). The information is used to develop strategies for increasing smoking cessation and lung cancer screening rates in underserved communities via community outreach and engagement (COE). Methods: Sylvester Comprehensive Cancer Center’s catchment area in SF was studied and consists of three urbanized counties (Miami-Dade, Broward, and Palm Beach) as well as one rural county (Monroe), with a total population of 6 million. Age-adjusted incidence of lung cancer was calculated (2010-2019 Florida cancer registry data) for every census tract in the study area. Geospatial hot spot analysis was then applied to identify statistically significant clusters of census tracts with high age-adjusted lung cancer incidences. Socioeconomic data (American Community Survey) and smoking rates (CDC Places data) associated with the hot spot census tracts were then analyzed for risk factor identification and potential barriers to screening. Results: Hot spots census tracts for lung cancer in SF are generally associated with lower socioeconomic status, including lower income, higher percent of people living in poverty, more households with no vehicles, lower education levels, and higher percent of active smokers. Such socioeconomic disparities are most pronounced in the two highly populated counties of Miami-Dade and Broward. Palm Beach County’s hot spots include retirement communities, while those in the rural county of Monroe have the highest smoking rates. Conclusions: Using this analysis, our COE team began targeting hot spot communities for intervention with a specific focus on those where smoking rates are high, and no lung cancer screening facilities exist. The results demonstrate the utilities of geospatial data analysis for identifying locations and population segments for which increased cancer prevention efforts are needed. Targeting neighborhoods with the identified disparities for increased intervention in smoking cessation and lung cancer screening can help a COE team reach at-risk populations with limited resources more effectively. [Table: see text]