Background: The incidence of esophageal adenocarcinoma (EAC) has increased five-fold in the United States since 1975. The aim of our study was to estimate future U.S. EAC incidence and mortality and to shed light on the potential drivers in the disease process that are conduits for the dramatic increase inEACincidence. Methods: A consortium of three research groups calibrated independent mathematical models to clinical and epidemiologic data includingEACincidence from the Surveillance, Epidemiology, and End Results (SEER 9) registry from 1975 to 2010. We then used a comparative modeling approach to project EAC incidence and mortality to year 2030. Results: Importantly, all three models identified birth cohort trends affecting cancer progression as a major driver of the observed increases in EAC incidence and mortality. All models predict that incidence and mortality rates will continue to increase until 2030 but with a plateauing trend for recent male cohorts. The predicted ranges of incidence and mortality rates (cases per 100,000 person years) in 2030 are 8.4 to 10.1 and 5.4 to 7.4, respectively, for males, and 1.3 to 1.8 and 0.9 to 1.2 for females. Estimates of cumulative cause-specific EAC deaths between both sexes for years 2011 to 2030 range between 142,300 and 186,298, almost double the number of deaths in the past 20 years. Conclusions: Through comparative modeling, the projected increases in EAC cases and deaths represent a critical public health concern that warrants attention from cancer control planners to prepare potential interventions. Impact: Quantifying this burden of disease will aid health policy makers to plan appropriate cancer control measures.

doi.org/10.1158/1055-9965.EPI-13-1233, hdl.handle.net/1765/56889
Cancer Epidemiology, Biomarkers & Prevention
Erasmus MC: University Medical Center Rotterdam

Kong, C. Y., Kroep, J., Curtius, K., Hazelton, W., Jeon, J., Meza, R., … Luebeck, E. G. (2014). Exploring the recent trend in esophageal adenocarcinoma incidence and mortality using comparative simulation modeling. Cancer Epidemiology, Biomarkers & Prevention, 23(6), 997–1006. doi:10.1158/1055-9965.EPI-13-1233