Age Period Cohort Effect
Description
Age Period Cohort Effect
Age period cohort (APC) analysis plays an important role in understanding time-varying elements in epidemiology. In particular, APC analysis discerns three types of time varying phenomena: Age effects, period effects and cohort effects. (1)
Age effects are variations linked to biological and social processes of aging specific to individuals.(2) They include physiologic changes and accumulation of social experiences linked to aging, but unrelated to the time period or birth cohort to which an individual belongs. In epidemiological studies age effects are usually denoted by varying rates of diseases across age groups.
Period effects result from external factors that equally affect all age groups at a particular calendar time. It could arise from a range of environmental, social and economic factors e.g. war, famine, economic crisis. Methodological changes in outcome definitions, classifications, or method of data collection could also lead to period effects in data. (3)
Cohort effects are variations resulting from the unique experience/exposure of a group of subjects (cohort) as they move across time. The most commonly defined group in epidemiology is the birth cohort based on year of birth and it is described as difference in the risk of a health outcome based on birth year. Thus a cohort effect occurs when distributions of disease arise from an exposure affect age groups differently. In epidemiology, a cohort effect is conceptualized as an interaction or effect modification due to a period effect that is differentially experienced through age-specific exposure or susceptibility to that event or cause.(4)
In contrast to this conceptualization of cohort effect as an effect modification in epidemiology, sociological literature consider cohort effect as a structural factor representing the sum of all unique exposures experienced by the cohort from birth. In this case, age and period effect are conceived as confounders of cohort effect and APC analysis aims to disentangle the independent effect of age, period and cohort.(4) Most of the APC analysis strategies are based on the sociological model of cohort effect, conceptualize independent effect of age, period and cohort effect.
Identification problem in APC: APC analysis aims at describing and estimating the independent effect of age, period and cohort on the health outcome under study. The different strategies used aims to partition variance into the unique components attributable to age, period, and cohort effects (4). However, there is a major impediment to independently estimating age, period, and cohort effects by modeling the data which is know as the “identification problem” in APC. This is due to the exact linear dependency among age, period, and cohort: Period – Age = Cohort; that is, given the calendar year and age, one can determine the cohort (birth year) (5). The presence of perfectly collinear predictors (age, period and cohort) in a regression model will produce a singular non-identifiable design matrix, from which it is statistically impossible to estimate unique estimates for the three effects. (5)