Chin Hur

Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program

Abstract Background We are developing 10 de novo population-level mathematical models in 4 malignancies (multiple myeloma and bladder, gastric, and uterine cancers). Each of these sites has documented disparities in outcome that are believed to be downstream effects of systemic racism. Methods Ten models are being independently developed as part of the Cancer Intervention and Surveillance Modeling Network incubator program. These models simulate trends in cancer incidence, early diagnosis, treatment, and mortality for the general population and are stratified by racial subgroup. Model inputs are based on large population datasets, clinical trials, and observational studies. Some core parameters are shared, and other parameters are model specific. All models are microsimulation models that use self-reported race to stratify model inputs. They can simulate the distribution of relevant risk factors (eg, smoking, obesity) and insurance status (for multiple myeloma and uterine cancer) in US birth cohorts and population. Discussion The models aim to refine approaches in prevention, detection, and management of 4 cancers given uncertainties and constraints. They will help explore whether the observed racial disparities are explainable by inequities, assess the effects of existing and potential cancer prevention and control policies on health equity and disparities, and identify policies that balance efficiency and fairness in decreasing cancer mortality.

Projected Trends in the Incidence and Mortality of Uterine Cancer in the United States

Abstract Background: To develop a natural history model for uterine cancer calibrated to population-based incidence and mortality data to project future trends in the disease through 2050. Methods: We developed a state-transition microsimulation model of uterine cancer. The model begins at 18 years of age and simulates Black and White patients, includes transition states for precursor lesions, and separately models endometrioid and nonendometrioid tumors. The model was calibrated to population-based incidence and mortality data using parameter extrapolation. Results: The model closely fit population-based incidence and mortality data of uterine cancer. From 2020 to 2050, the incidence of uterine cancer is projected to increase in White women to 74.2 cases per 100,000 (compared with 57.7 cases per 100,000 in 2018) and increase to 86.9 per 100,000 (compared with 56.8 cases per 100,000 in 2018) in Black women. Among White women, incidence-based mortality will increase from 6.1 per 100,000 in 2018 to 11.2 per 100,000 in 2050, whereas incidence-based mortality in Black women will increase from 14.1 per 100,000 to 27.9 per 100,000. Endometrioid tumors are expected to increase considerably in both White and Black women; White women will experience only a slight increase in nonendometrioid tumors, whereas the incidence of these tumors will increase substantially in Black women. Conclusions: The incidence and mortality of uterine cancer are projected to increase substantially over the next three decades. Black women will experience a disproportionate increase in the disease. Impact: Projecting the incidence and mortality of uterine cancer can facilitate future cancer control efforts.