ArticleQuality & quantity
The hierarchical age-period-cohort model: Why does it find the results that it finds?
Article in Quality & quantity. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Life Course and Cohort Patterns in Screen Time Among School-Aged Children in the United States.Journal of pediatrics. Clinical practice · 2025Article
- Age-period-cohort effects of adult cigarette smoking in the united States, 1971-2020.Scientific reports · 2025Article
- Article
- Gender differences in the prevalence of mental distress in East and West Germany over time: a hierarchical age-period-cohort analysis, 2006-2021.Social psychiatry and psychiatric epidemiology · 2024Article
- Age, period and cohort effects in depression prevalence among Canadians 65+, 1994 to 2018: A multi-level analysis.The International journal of social psychiatry · 2023Article
- Describing and explaining age, period, and cohort trends in Americans' vocabulary knowledge.Population research and policy review · 2023Article
- Age-period-cohort analysis of depression trends: are depressive symptoms increasing across generations in Germany?European journal of ageing · 2022Article
- Trends in activities of daily living disability among Chinese older adults from 1998 to 2018: an age-period-cohort analysis.European journal of ageing · 2022Article
- Past, present, and future trends of overweight and obesity in Belgium using Bayesian age-period-cohort models.BMC public health · 2022Article
- Getting a Grip on Secular Changes: Age-Period-Cohort Modeling of Grip Strength in the English Longitudinal Study of Ageing.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Gender and tobacco epidemic in South Korea: implications from age-period-cohort analysis and the DPSEEA framework.BMJ open · 2022Article
- Cohort Variation in U.S. Violent Crime Patterns from 1960 to 2014: An Age-Period-Cohort-Interaction Approach.Journal of quantitative criminology · 2021Article
- Trends in cannabis use and attitudes toward legalization and use among Australians from 2001-2016: an age-period-cohort analysis.Addiction (Abingdon, England) · 2021Article
- Pain Trends Among American Adults, 2002-2018: Patterns, Disparities, and Correlates.Demography · 2021Article
- Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices.SSM - population health · 2020Review
- Constraints in Random Effects Age-Period-Cohort Models.Sociological methodology · 2020Article
- Long-term trends in the body mass index and obesity risk in Estonia: an age-period-cohort approach.International journal of public health · 2020Article
- Health behaviors and psychological distress: changing associations between 1997 and 2016 in the United States.Social psychiatry and psychiatric epidemiology · 2020Article
- A Hierarchical Age-Period-Cohort Analysis of Breast Cancer Mortality and Disability Adjusted Life Years (1990-2015) Attributable to Modified Risk Factors among Chinese Women.International journal of environmental research and public health · 2020Article
- Flexible age-period-cohort modelling illustrated using obesity prevalence data.BMC medical research methodology · 2020Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is claimed the hierarchical-age-period-cohort (HAPC) model solves the age-period-cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure-that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439-1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125-128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends.
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