Multilevel Predictive Study of Breast Cancer Screening Patterns in Pennsylvania, 2021-2022

The purpose of this study was to identify personal, systemic, and specifically, geosocial risk factors of breast cancer screening nonadherence and to assess how machine learning techniques can improve cancer screening rates.

The study included 21 543 women aged 50 to 74 with a primary care provider at 15 family medicine clinics in southcentral Pennsylvania between January 1, 2021, and December 31, 2022. Demographics and healthcare utilization data were geocoded to Census blocks for neighborhood socioeconomic measures and analyzed using multiple logistic regression to assess association with adherence to breast cancer screening guidelines. Area deprivation index (ADI) was extracted using machine learning to integrate factors such as poverty, employment, education, and housing quality as a measure.

The study identified women in minority groups (aOR: 0.659, P < .01) have decreased odds of being screened for breast cancer, with the exception of Hispanic individuals (aOR: 1.456, P < .01). Uninsured individuals (aOR: 0.334, P < .01) and those on Medicaid (aOR: 0.793, P < .01) are at a greater risk of non-adherence to screening. Lastly, ADI and screening rates are inversely proportional.

Social determinants of health influence a person's likelihood of being screened for breast cancer. Identifying barriers to screening in these individuals, and ways in which they compound, is a crucial step in improving screening adherence.