Holly Hartman

Importance: Black men are more likely to die of prostate cancer than white men. In men with similar stages of disease, the contribution of biological vs nonbiological differences to this observed disparity is unclear. Objective: To quantify the association of black race with long-term survival outcomes after controlling for known prognostic variables and access to care among men with prostate cancer. Design, Setting, and Participants: This multiple-cohort study included updated individual patient-level data of men with clinical T1-4N0-1M0 prostate cancer from the following 3 cohorts: Surveillance, Epidemiology, and End Results (SEER [n = 296 273]); 5 equal-access regional medical centers within the Veterans Affairs health system (VA [n = 3972]); and 4 pooled National Cancer Institute-sponsored Radiation Therapy Oncology Group phase 3 randomized clinical trials (RCTs [n = 5854]). Data were collected in the 3 cohorts from January 1, 1992, through December 31, 2013, and analyzed from April 27, 2017, through April 13, 2019. Exposures: In the VA and RCT cohorts, all patients received surgery and radiotherapy, respectively, with curative intent. In SEER, radical treatment, hormone therapy, or conservative management were received. Main Outcomes and Measures: Prostate cancer-specific mortality (PCSM). Secondary measures included other-cause mortality (OCM). To adjust for demographic-, cancer-, and treatment-related baseline differences, inverse probability weighting (IPW) was performed. Results: Among the 306 100 participants included in the analysis (mean [SD] age, 64.9 [8.9] years), black men constituted 52 840 patients (17.8%) in the SEER cohort, 1513 (38.1%) in the VA cohort, and 1129 (19.3%) in the RCT cohort. Black race was associated with an increased age-adjusted PCSM hazard (subdistribution hazard ratio [sHR], 1.30; 95% CI, 1.23-1.37; P < .001) within the SEER cohort. After IPW adjustment, black race was associated with a 0.5% (95% CI, 0.2%-0.9%) increase in PCSM at 10 years after diagnosis (sHR, 1.09; 95% CI, 1.04-1.15; P < .001), with no significant difference for high-risk men (sHR, 1.04; 95% CI, 0.97-1.12; P = .29). No significant differences in PCSM were found in the VA IPW cohort (sHR, 0.85; 95% CI, 0.56-1.30; P = .46), and black men had a significantly lower hazard in the RCT IPW cohort (sHR, 0.81; 95% CI, 0.66-0.99; P = .04). Black men had a significantly increased hazard of OCM in the SEER (sHR, 1.30; 95% CI, 1.27-1.34; P < .001) and RCT (sHR, 1.17; 95% CI, 1.06-1.29; P = .002) IPW cohorts. Conclusions and Relevance: In this study, after adjustment for nonbiological differences, notably access to care and standardized treatment, black race did not appear to be associated with inferior stage-for-stage PCSM. A large disparity remained in OCM for black men with nonmetastatic prostate cancer.

PURPOSE: Comparative efficacy research performed using population registries can be subject to significant bias. There is an absence of objective data demonstrating factors that can sufficiently reduce bias and provide accurate results. METHODS: MEDLINE was searched from January 2000 to October 2016 for observational studies comparing two treatment regimens for any diagnosis of cancer, using SEER, SEER-Medicare, or the National Cancer Database. Reporting quality and statistical methods were assessed using components of the STROBE criteria. Randomized trials comparing the same treatment regimens were identified. Primary outcome was correlation between survival hazard ratio (HR) estimates provided by the observational studies and randomized trials. Secondary outcomes included agreement between matched pairs and predictors of agreement. RESULTS: Of 3,657 studies reviewed, 350 treatment comparisons met eligibility criteria and were matched to 121 randomized trials. There was no significant correlation between the HR estimates reported by observational studies and randomized trials (concordance correlation coefficient, 0.083; 95% CI, -0.068 to 0.230). Forty percent of matched studies were in agreement regarding treatment effects (κ, 0.037; 95% CI, -0.027 to 0.1), and 62% of the observational study HRs fell within the 95% CIs of the randomized trials. Cancer type, data source, reporting quality, adjustment for age, stage, or comorbidities, use of propensity weighting, instrumental variable or sensitivity analysis, and well-matched study population did not predict agreement. CONCLUSION: We were unable to identify any modifiable factor present in population-based observational studies that improved agreement with randomized trials. There was no agreement beyond what is expected by chance, regardless of reporting quality or statistical rigor of the observational study. Future work is needed to identify reliable methods for conducting population-based comparative efficacy research.

Importance: Cancer treatment delay has been reported to variably impact cancer-specific survival and coronavirus disease 2019 (COVID-19)-specific mortality during the severe acute respiratory syndrome coronavirus 2 pandemic. During the pandemic, treatment delay is being recommended in a nonquantitative, nonobjective, and nonpersonalized manner, and this approach may be associated with suboptimal outcomes. Quantitative integration of cancer mortality estimates and data on the consequences of treatment delay is needed to aid treatment decisions and improve patient outcomes. Objective: To obtain quantitative integration of cancer-specific and COVID-19-specific mortality estimates that can be used to make optimal decisions for individual patients and optimize resource allocation. Design, Setting, and Participants: In this decision analytical model, age-specific and stage-specific estimates of overall survival pre-COVID-19 were adjusted by the probability of COVID-19 (individualized by county, treatment-specific variables, hospital exposure frequency, and COVID-19 infectivity estimates), COVID-19 mortality (individualized by age-specific, comorbidity-specific, and treatment-specific variables), and delay of cancer treatment (impact and duration). These model estimates were integrated into a web application (OncCOVID) to calculate estimates of the cumulative overall survival and restricted mean survival time of patients who received immediate vs delayed cancer treatment. Using currently available information about COVID-19, a susceptible-infected-recovered model that accounted for the increased risk among patients at health care treatment centers was developed. This model integrated the data on cancer mortality and the consequences of treatment delay to aid treatment decisions. Age-specific and cancer stage-specific estimates of overall survival pre-COVID-19 were extracted from the Surveillance, Epidemiology, and End Results database for 691 854 individuals with 25 cancer types who received cancer diagnoses in 2005 to 2006. Data from 5 436 896 individuals in the National Cancer Database were used to estimate the independent impact of treatment delay by cancer type and stage. In addition, data from 275 patients in a nested case-control study were used to estimate the COVID-19 mortality rate by age group and number of comorbidities. Data were analyzed from March 17 to May 21, 2020. Exposures: COVID-19 and cancer. Main Outcomes and Measures: Estimates of restricted mean survival time after the receipt of immediate vs delayed cancer treatment. Results: At the time of the study, the OncCOVID web application allowed for the selection of up to 47 individualized variables to assess net survival for an individual patient with cancer. Substantial heterogeneity was found regarding the association between delayed cancer treatment and net survival among patients with a given cancer type and stage, and these 2 variables were insufficient to discriminate the net impact of immediate vs delayed treatment. Individualized overall survival estimates were associated with patient age, number of comorbidities, treatment received, and specific local community estimates of COVID-19 risk. Conclusions and Relevance: This decision analytical modeling study found that the OncCOVID web-based application can quantitatively aid in the resource allocation of individualized treatment for patients with cancer during the COVID-19 global pandemic.

Importance: As the field of medicine strives for equity in care, research showing the association of social determinants of health (SDOH) with poorer health care outcomes is needed to better inform quality improvement strategies. Objective: To evaluate the association of SDOH with prostate cancer-specific mortality (PCSM) and overall survival (OS) among Black and White patients with prostate cancer. Data Sources: A MEDLINE search was performed of prostate cancer comparative effectiveness research from January 1, 1960, to June 5, 2020. Study Selection: Two authors independently selected studies conducted among patients within the United States and performed comparative outcome analysis between Black and White patients. Studies were required to report time-to-event outcomes. A total of 251 studies were identified for review. Data Extraction and Synthesis: Three authors independently screened and extracted data. End point meta-analyses were performed using both fixed-effects and random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed, and 2 authors independently reviewed all steps. All conflicts were resolved by consensus. Main Outcomes and Measures: The primary outcome was PCSM, and the secondary outcome was OS. With the US Department of Health and Human Services Healthy People 2030 initiative, an SDOH scoring system was incorporated to evaluate the association of SDOH with the predefined end points. The covariables included in the scoring system were age, comorbidities, insurance status, income status, extent of disease, geography, standardized treatment, and equitable and harmonized insurance benefits. The scoring system was discretized into 3 categories: high (≥10 points), intermediate (5-9 points), and low (<5 points). Results: The 47 studies identified comprised 1 019 908 patients (176 028 Black men and 843 880 White men; median age, 66.4 years [IQR, 64.8-69.0 years]). The median follow-up was 66.0 months (IQR, 41.5-91.4 months). Pooled estimates found no statistically significant difference in PCSM for Black patients compared with White patients (hazard ratio [HR], 1.08 [95% CI, 0.99-1.19]; P = .08); results were similar for OS (HR, 1.01 [95% CI, 0.95-1.07]; P = .68). There was a significant race-SDOH interaction for both PCSM (regression coefficient, -0.041 [95% CI, -0.059 to 0.023]; P < .001) and OS (meta-regression coefficient, -0.017 [95% CI, -0.033 to -0.002]; P = .03). In studies with minimal accounting for SDOH (<5-point score), Black patients had significantly higher PCSM compared with White patients (HR, 1.29; 95% CI, 1.17-1.41; P < .001). In studies with greater accounting for SDOH variables (≥10-point score), PCSM was significantly lower among Black patients compared with White patients (HR, 0.86; 95% CI, 0.77-0.96; P = .02). Conclusions and Relevance: The findings of this meta-analysis suggest that there is a significant interaction between race and SDOH with respect to PCSM and OS among men with prostate cancer. Incorporating SDOH variables into data collection and analyses are vital to developing strategies for achieving equity.