Does inclusion of neighborhood variables improve clinical risk prediction for advanced prostate cancer in Black and White men?

Abstract

Introduction: Black men are diagnosed with high-grade prostate cancer (PCa; Gleason sum ≥7) at greater rates than White men. This persistent disparity has led to mortality rates among Black men that are twice the rate of White men. Risk prediction tools can aid clinical decision making for PCa screening, biopsy, and treatment. However, research has not integrated neighborhood-level risk factors that are associated with rates of high-grade PCa. This study sought to determine whether inclusion of neighborhood-level variables can improve prediction of high-grade PCa over the existing Prostate Cancer Prevention Trial (PCPT) calculator.

Methods: Existing PCa cases from 2005 to 2017 were ascertained from urology, radiation, and medical oncology clinics at Fox Chase Cancer Center/Temple University Health System (FCCC/TUHS). Existing databases from patient medical records, biosamples, pathology, and neighborhood data from the U.S. census were linked via geocodes. Informed by prior studies that selected social environmental variables, a series of logistic regression models were completed to predict the probability of high-grade PCa on prespecified sets of variables from the PCPT.

Results: Our best fitting, multilevel model included PCPT variables (i.e., PSA, digital rectal exam, age, race, prior biopsy, family history) as well as insurance status, neighborhood-level poverty, residence in a high risk PCa cluster, and % of Employed Men in Protective Service Occupations. However, the AUC for this model (0.673) was only marginally improved from the initial model of only PCPT variables (0.671). Further, in separate analyses by race (White, Black) the % of Employed Men in Protective Service Occupations was only significant among White men.

Discussion: Study findings demonstrate the potential for neighborhood variables to enhance current risk prediction models and identify interaction effects revealing differences across subgroups, such as race. The lack of significant associations between neighborhood variables and Black men highlight the complexity of systemic racism and neighborhood-level variables on PCa outcomes.