Uterine cancer is the fourth most common cancer among females and the sixth most common cause of cancer-associated death in women the United States, with an estimated 66,200 new cases and 13,030 deaths in 2023 [1]. The incidence of uterine cancer is increasing and expected to surpass colorectal cancer as the third leading cause of cancer in females, and fourth leading cause of cancer death by 2040 [[2], [3], [4]]. Specifically, since 2001, uterine cancer has increased across racial and ethnic groups from 40.8 per 100,000 in 2001 to 42.9 per 100,000 in 2016 (Annual Percent Change (APC) = 0.5, p < 0.001), with the largest increases observed in non-Hispanic Black, Hispanic, and Asian women [5].

There are three main histologic types of uterine cancer: endometrioid, non-endometrioid, and sarcoma. Endometrioid cancers make up approximately 75% of all uterine cancers and have the best prognosis. Non-endometrioid cancers, which include serous and clear cell carcinomas, account for approximately 15–20% of all uterine cancers, are more aggressive, and are associated with worse outcomes. Lastly, uterine sarcomas, which arise in the myometrium, are the least common group and the least well-studied [2,3]. Non-Hispanic Black women have a two- to four-fold increased risk of developing non-endometrioid uterine cancers compared with non-Hispanic White women, which may contribute to the higher mortality observed in these women [5].

Age-period-cohort (APC) analysis plays an important role in understanding temporal elements in epidemiology by classifying three types of time-varying phenomena: age effects, period effects, and cohort effects, birth cohort effects specifically in this analysis. Age effects are variations associated with the biological changes and social experiences of aging; period effects are variations across time that affect all age groups equally; and cohort effects are variations due to common exposures or experiences of a group of people (birth cohort) as they move through time [6,7]. APC analysis can help elucidate factors that may be contributing to observed uterine cancer incidence trends. For example, changes in the prevalence of established risk factors for uterine cancer, including obesity and diabetes, which increase risk, and parity and hormonal contraceptive use, which decrease risk [8], may vary across age, time period, and birth cohort. Further, this information can be used to predict future uterine cancer incidence and prioritize interventions and populations for reducing uterine cancer risk.

There is limited research on age-period-cohort analyses of uterine cancer incidence in the U.S. Further, given that age at diagnosis, year of diagnosis, and patient birth year are linearly dependent, traditional regression models cannot effectively distinguish their relative contributions to changes in incidence without additional assumptions or constraints [9]. By utilizing a novel data visualization method in conjunction with a quantitative information theory-based analytic approach, we are better able to capture and describe these competing temporal effects which can inform model designs and targeted prevention efforts. This analysis aims to estimate and compare the effects of age, period, and cohort on uterine cancer incidence in the U.S.