The unrealized potential: cohort effects and age-period-cohort analysis
Jongho Heo, Sun-Young Jeon, Chang-Mo Oh, Jongnam Hwang, Juhwan Oh, Youngtae Cho
This study aims to provide a systematical introduction of age-period-cohort (APC) analysis to South Korean readers who are unfamiliar with this method (we provide an extended version of this study in Korean). As health data in South Korea has substantially accumulated, population-level studies that explore long-term trends of health status and health inequalities and identify macrosocial determinants of the trends are needed.
Analyzing long-term trends requires to discern independent effects of age, period, and cohort using APC analysis. Most existing health and aging literature have used cross-sectional or short-term available panel data to identify age or period effects ignoring cohort effects. This under-use of APC analysis may be attributed to the identification (ID) problem caused by the perfect linear dependency across age, period, and cohort. This study explores recently developed three APC models to address the ID problem and adequately estimate the effects of A-P-C: intrinsic estimator-APC models for tabular age by period data; hierarchical cross-classified random effects models for repeated cross-sectional data; and hierarchical APC-growth curve models for accelerated longitudinal panel data.
APC studies will reveal how distinct are the trajectories of Korean aging, how period and cohort effects have been changed, and what are the biological, historical, and socio-structural factors that contribute to health status and inequalities throughout the life cycle of the Korean population.
Several national-level health data in Korea are available for the APC methodologies. Data for an IE-APC analysis on incidence or mortality rates due to chronic diseases, including cancers can be obtained from the Korean Statistical Information Service (https://kosis.kr). By utilizing the APC analyses, researchers can analyze long-term trends of health status, health behaviors, health inequalities, aging, chronic diseases, and life cycle.
Furthermore, social and health policies should be specified for dimensions of not only period or age but also cohort. For example, policies may be required based on the cohort size to predict service demands of education, healthcare, and welfare areas to allocate resources. Cohort-specific policies, such as additional childbirth and rearing supports in the early life cycle during economic recessions, may be useful.
Birth cohort, Cohort effects, Identification problem, Age effects, Period effects
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5 December 2017
Epidemiol Health 2017; 39: e2017056.