Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach - Soheil Hashtarkhani; Yiwang Zhou; Fekede Asefa Kumsa; Shelley White-Means; David L Schwartz; Arash Shaban-Nejad (JMIR Cancer)
Abstract BackgroundBreast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. ObjectiveThis study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. MethodsData on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large-scale dataset of social determinants of health, comprising 13 variables for 72,337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 ResultsGeospatial analysis revealed elevated screening rates in the eastern and northern United States, while central and midwestern regions exhibited lower rates. The random forest model demonstrated superior performance, with an R2 ConclusionsThese findings underscore the significance of social determinants and the accessibility of mammography services in explaining the variability of breast cancer screening rates in the United States, emphasizing the need for targeted policy interventions in areas with relatively lower screening rates.