Mani, Kyle

Abstract Background: Sexual and gender minority (SGM) representation is growing in the United States. This study estimated the future SGM cancer burden amidst shifting cancer patterns. Methods: Age-specific incidence data for breast, prostate, colorectal, lung, and melanoma were obtained from the Surveillance, Epidemiology, and End Results Program’s 12-registry database. Nordpred age-period-cohort models projected incidence through 2040 to 2044. Rates were applied to US Census population estimates and generation- and sex-specific SGM identification proportions from Gallup’s 2023 to 2024 surveys. Prophet was used for sensitivity analysis. Results: At ages 55 to 59, breast cancer cases among Millennial SGMs were 3,672% higher than among Silent Generation SGMs (34,850 vs. 924 cases), melanoma cases were 1,061% higher (5,526 vs. 476 cases), and colorectal cancer cases were 2,206% higher (23,594 vs. 1,023 cases). Compared with Generation X, Millennial prostate cancer cases were 142% higher (8,100 vs. 3,344 cases), and lung cancer cases were 169% higher (5,451 vs. 2,029 cases). By 2040 to 2044, Millennials were projected to account for 68.4% of breast cancer cases and 59.7% of colorectal cancer cases among SGMs. Conclusions: At ages 55 to 59, the 5-year cancer burden was projected to be 15 times higher among Millennials than Silent Generation SGMs (77,521 vs. 5,134 cases), driven primarily by the larger SGM population in younger generations. Oncology systems will need to prepare for the increase in cancer care demand. Impact: These findings identify a substantial generational shift in the SGM cancer burden and may inform oncology workforce capacity, culturally responsive cancer care, and healthcare system preparation for this growing population.

Objectives: Early identification of patients living with cancer at higher risk of death from an infection is critical in infection mortality prevention. We characterize patients living with cancer at the highest risk of dying from an infection. Methods: 7,529,481 US cancer survivors (1992 to 2015) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Standardized mortality ratios (SMRs) and 95% CIs were calculated. Fine-gray survival analysis was performed to calculate hazard ratios by adjusting for the effects of competing risks (eg, deaths due to causes other than infection). Results: Among 7,529,481 patients living with cancer (1992 to 2015), 101,167 (1.3%) died of infection. The rate of infection-specific mortality was 27.19/10,000 person-years, with an SMR of 3.29 (95% CI: 3.26-3.32, P <0.001). Patients who were older, male, black, and unmarried were at a greater risk of fatal infection. Overall, the risk of infection-specific mortality for patients living with cancer is greatest 1 year after diagnosis compared with the general population (SMR: 8.68, 95% CI: 8.53-8.84; P< 0.0001), and this risk decreases with follow-up time (SMR at >10 y after diagnosis: 2.93, 95% CI: 2.87-3.00; P< 0.0001). Among patients with Hodgkin Lymphoma, Non-Hodgkin Lymphoma, and Kaposi Sarcoma, 9.2%, 11.5%, and 82.2% of all deaths within the first year after cancer diagnosis occurred due to acute infectious disease. In contrast, for patients with liver cancer, the relative percentage of infection-specific mortality increases with follow-up time from 3.5% at <1 year after cancer diagnosis and 10.4% at 10+ years of follow-up. Conclusion: The results of this study characterize infection mortality in patients living with cancer, which can guide more targeted research and interventions in this population.

Objectives: Deaths from an unknown cause are difficult to adjudicate and oncologic studies of comparative effectiveness often demonstrate inconsistencies in incorporating these deaths and competing events (eg, heart disease and stroke) in their analyses. In this study, we identify cancer patients most at risk for death of an unknown cause. Methods: This retrospective, population-based study used cancer registry data from the Surveillance, Epidemiology, and End Results database (1992-2015). The absolute rate of unknown causes of death (COD) cases stratified by sex, marital status, race, treatment, and cancer site were calculated and a multivariable logistic regression model was applied to obtain adjusted odds ratios with 95% CIs. Results: Out of 7,154,779 cancer patients across 22 cancer subtypes extracted from Surveillance, Epidemiology, and End Results, 3,448,927 died during follow-up and 276,068 (7.4%) of these deaths were from unknown causes. Patients with an unknown COD had a shorter mean survival time compared with patients with known COD (36.3 vs 65.7 mo, P < 0.001). The contribution of unknown COD to total mortality was highest in patients with more indolent cancers (eg, prostate [12.7%], thyroid [12.3%], breast [10.7%]) and longer follow-up (eg, >5 to 10 y). One, 3, and 5-year cancer-specific survival (CSS) calculations including unknown COD were significantly decreased compared with CSS estimates excluding cancer patients with unknown COD. Conclusion: Of the patients, 7.4% died of unknown causes during follow-up and the proportion of death was higher with longer follow-up and among more indolent cancers. The attribution of high percentages of unknown COD to cancer or non-cancer causes could impact population-based cancer registry studies or clinical trial outcomes with respect to measures involving CSS and mortality.