Murphy Caitlin C, Yang Yang Claire
Division of Epidemiology, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, Dallas, TX.
Department of Sociology, Lineberger Cancer Center, and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Curr Epidemiol Rep. 2018 Dec;5(4):418-431. doi: 10.1007/s40471-018-0174-8. Epub 2018 Oct 3.
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).
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.
年龄-时期-队列（APC）模型可同时估计年龄（衰老的生物学过程）、时期（在所有年龄段同时出现的长期趋势）和出生队列（同一年出生或不同代之间的差异）的影响。APC模型有助于理解癌症病因、自然史和差异。我们回顾了近期研究（发表于2008年至2018年）中关于年龄、时期和队列效应的研究结果，并总结了按出生队列划分的年龄标准化率和年龄别率的趋势。我们还描述了不同时期和出生队列中癌症危险因素的流行情况，包括肥胖、当前吸烟、人乳头瘤病毒（HPV）和丙型肝炎病毒（HCV）。
29项研究使用了多种描述性分析和统计模型来记录癌症相关结局中的年龄、时期和队列趋势。队列效应占主导，特别是在乳腺癌、膀胱癌和结直肠癌中，而时期效应则更具变异性。在乳腺癌、膀胱癌和口腔癌的研究中未观察到时期效应。肥胖、当前吸烟、HPV和HCV的年龄别流行率也因出生队列而异，这通常与癌症发病率和死亡率平行。
我们观察到多种癌症类型中存在强烈的队列效应，而支持时期效应的证据则不太一致。出生队列效应指向生命早期的暴露——或一生中积累的暴露——这些暴露会增加患癌风险。出生队列效应还说明了重新考虑既定危险因素的时间和持续时间以确定风险最大的暴露时期的重要性。
