A study published in PLoS One shows a link between neighborhood socioeconomic (nSES) factors and time to prostate cancer (PCa) diagnosis.
In this study, researchers assessed 358 high-risk men with a family history of PCa and/or Black race. The population of interest were aged 35 to 69 years and enrolled in the Prostate Cancer Risk Assessment Program. The primary outcome was defined as time to PCa diagnosis, and factors that were evaluated for primary endpoint correlation included: age at baseline enrollment, race/ethnicity, PCa family history, and prostate-specific antigen and digital rectal exam at baseline. The researchers defined neighborhood or nSES factors by the economic, physical, and social characteristics of a census tract based on patient location. They linked 78 nSES variables (such as employment and income) via geocoding and built models to analyze the data using Cox regression and LASSO machine-learning.
According to the results, the combined modeling showed nSES variables are notably linked with time PCa diagnosis. The investigators observed that mode of transportation and low income were significant in white men with a family history of PCa, while home ownership and unemployment were notable in Black men with and without a family history of PCa. Overall, the five-year predicted probability of PCa was higher in men with a plethora of nSES variables (high neighborhood score) compared with men with a low score.
The researchers concluded, “Preliminary findings from this limited sample demonstrate a proof of concept that predicted probability of PCa may be augmented with the addition of publicly-available, readily accessible nSES factors from the U.S. census. While additional studies with larger sample sizes and additional validation and replication steps are needed, findings suggest that neighborhood variables could potentially add useful clinical information for high-risk men undergoing PCa risk assessment.”