Disparities in treatment patterns and mortality in prostate cancer: Interaction between Black race and end-stage kidney disease.

BACKGROUND: Black men and men with end-stage kidney disease have lower rates of treatment and higher mortality for prostate cancer. We studied the interaction of end-stage kidney disease (ESKD) with Black race for treatment rates and mortality for men with prostate cancer. METHODS AND RESULTS: We included 516 Black and 551 White men with ESKD before prostate cancer 22,299 Black men, and 141,821 White men without ESKD who were 40 years or older from the Surveillance, Epidemiology, and End-Results-Medicare data (2004-2016). All Black men with or without ESKD and White men with ESKD had higher prostate-specific antigen levels at diagnosis than White men without ESKD. Black men with ESKD had the lowest rates for treatment in both local and advanced stages of prostate cancer (age-adjusted risk ratio: 0.76, 95% Confidence Interval (CI): 0.71-0.82 for local stage and age-adjusted risk ratio: 0.82, 95% CI: 0.76-0.9 for advanced stages) compared to White men without ESKD. Compared to White men without ESKD, prostate cancer-specific mortality was higher in White men with ESKD for both local and advanced stages (age-adjusted hazard ratio: 1.8, 95% CI: 1.2-2.8 and HR: 1.6, 95% CI: 1.2-2.2) and it was higher for ESKD Black men only in advanced stage prostate cancer (age-adjusted hazard ratio: 2.4, 95% CI: 1.5-3.6). CONCLUSION: Our findings suggest that having a comorbidity such as ESKD makes Black men more vulnerable to racial disparities in prostate cancer treatment and mortality.

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

Treatment Patterns and Survival Outcomes in Patients With Breast Cancer on Medicaid, Pre- and Post-Expansion.

BACKGROUND: The objective of this study was to evaluate the impact of Medicaid expansion on breast cancer treatment and survival among Medicaid-insured women in Ohio, accounting for the timing of enrollment in Medicaid relative to their cancer diagnosis and post-expansion heterogeneous Medicaid eligibility criteria, thus addressing important limitations in previous studies. METHODS: Using 2011-2017 Ohio Cancer Incidence Surveillance System data linked with Medicaid claims data, we identified women aged 18 to 64 years diagnosed with local-stage or regional-stage breast cancer (n=876 and n=1,957 pre-expansion and post-expansion, respectively). We accounted for women's timing of enrollment in Medicaid relative to their cancer diagnosis, and flagged women post-expansion as Affordable Care Act (ACA) versus non-ACA, based on their income eligibility threshold. Study outcomes included standard treatment based on cancer stage and receipt of lumpectomy, mastectomy, chemotherapy, radiation, hormonal treatment, and/or treatment for HER2-positive tumors; time to treatment initiation (TTI); and overall survival. We conducted multivariable robust Poisson and Cox proportional hazards regression analysis to evaluate the independent associations between Medicaid expansion and our outcomes of interest, adjusting for patient-level and area-level characteristics. RESULTS: Receipt of standard treatment increased from 52.6% pre-expansion to 61.0% post-expansion (63.0% and 59.9% post-expansion in the ACA and non-ACA groups, respectively). Adjusting for potential confounders, including timing of enrollment in Medicaid, being diagnosed in the post-expansion period was associated with a higher probability of receiving standard treatment (adjusted risk ratio, 1.14 [95% CI, 1.06-1.22]) and shorter TTI (adjusted hazard ratio, 1.14 [95% CI, 1.04-1.24]), but not with survival benefits (adjusted hazard ratio, 1.00 [0.80-1.26]). CONCLUSIONS: Medicaid expansion in Ohio was associated with improvements in receipt of standard treatment of breast cancer and shorter TTI but not with improved survival outcomes. Future studies should elucidate the mechanisms at play.

County-level socio-environmental factors and obesity prevalence in the United States.

AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.