Current Epidemiology Reports

, Volume 5, Issue 4, pp 418–431 | Cite as

Use of Age-Period-Cohort Analysis in Cancer Epidemiology Research

  • Caitlin C. Murphy
  • Yang Claire YangEmail author
Social Epidemiology (A Aiello, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Social Epidemiology


Purpose of Review

Age-period-cohort (APC) models simultaneously estimate the effects of age—biological process of aging; time period—secular trends that occur in all ages simultaneously; and birth cohort—variation among those born around the same year or from one generation to the next. APC models inform understanding of cancer etiology, natural history, and disparities. We reviewed findings from recent studies (published 2008–2018) examining age, period, and cohort effects and summarized trends in age-standardized rates and age-specific rates by birth cohort. We also described prevalence of cancer risk factors by time period and birth cohort, including obesity, current smoking, human papilloma virus (HPV), and hepatitis C virus (HCV).

Recent Findings

Studies (n = 29) used a variety of descriptive analyses and statistical models to document age, period, and cohort trends in cancer-related outcomes. Cohort effects predominated, particularly in breast, bladder, and colorectal cancers, whereas period effects were more variable. No effect of time period was observed in studies of breast, bladder, and oral cavity cancers. Age-specific prevalence of obesity, current smoking, HPV, and HCV also varied by birth cohort, which generally paralleled cancer incidence and mortality rates.


We observed strong cohort effects across multiple cancer types and less consistent evidence supporting the effect of time period. Birth cohort effects point to exposures early in life—or accumulated across the life course—that increase risk of cancer. Birth cohort effects also illustrate the importance of reconsidering the timing and duration of well-established risk factors to identify periods of exposure conferring the greatest risk.


Incidence Time factors SEER program Risk factors Age factors 


Grant Support

This work was supported by the National Cancer Institute (P30CA142543) and National Center for Advancing Translational Sciences (KL2TR001103 to Dr. Murphy) at the National Institutes of Health.

Compliance with Ethical Standards

Conflict of Interest

Caitlin C. Murphy and Yang Claire Yang each declare no potential conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Division of Epidemiology, Department of Clinical SciencesUniversity of Texas Southwestern Medical CenterDallasUSA
  2. 2.Harold C. Simmons Comprehensive Cancer CenterDallasUSA
  3. 3.Department of Sociology, Lineberger Cancer Center, and Carolina Population CenterUniversity of North Carolina at Chapel HillChapel HillUSA

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