The Hierarchical Age-Period-Cohort model: Why does it find the results that it finds?

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

Age-period-cohort analysis for trends in body mass index in Ireland

Background: Obesity is a growing problem worldwide and can often result in a variety of negative health outcomes. In this study we aim to apply partial least squares (PLS) methodology to estimate the separate effects of age, period and cohort on the trends in obesity as measured by body mass index (BMI). Methods. Using PLS we will obtain gender specific linear effects of age, period and cohort on obesity. We also explore and model nonlinear relationships of BMI with age, period and cohort. We analysed the results from 7,796 men and 10,220 women collected through the SLAN (Surveys of Lifestyle, attitudes and Nutrition) in Ireland in the years 1998, 2002 and 2007. Results: PLS analysis revealed a positive period effect over the years. Additionally, men born later tended to have lower BMI (-0.026 kg·m-2 yr-1, 95% CI: -0.030 to -0.024) and older men had in general higher BMI (0.029 kg·m -2 yr-1, 95% CI: 0.026 to 0.033). Similarly for women, those born later had lower BMI (-0.025 kg·m-2 yr-1, 95% CI: -0.029 to -0.022) and older women in general had higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.025 to 0.033). Nonlinear analyses revealed that BMI has a substantial curvilinear relationship with age, though less so with birth cohort. Conclusion: We notice a generally positive age and period effect but a slightly negative cohort effect. Knowing this, we have a better understanding of the different risk groups which allows for effective public intervention measures to be designed and targeted for these specific population subgroups

The use of a chronic disease and risk factor surveillance system to determine the age, period and cohort effects on the prevalence of obesity and diabetes in South Australian adults--2003-2013

Age, period and cohort (APC) analyses, using representative, population-based descriptive data, provide additional understanding behind increased prevalence rates

Playing with the rules and making misleading statements : a response to Luo, Hodges, Winship, and powers

Version of RecordSelf-fundedPublishe

The uses and abuses of an age-period-cohort method : on the linear algebra and statistical properties of intrinsic and related estimators

202105 bcrcNot applicableRGCPolyU 15334616Published12 month

The Impact of Obesity on US Mortality Levels: The Importance of Age and Cohort Factors in Population Estimates

Objectives. To estimate the percentage of excess death for US Black and White men and women associated with high body mass, we examined the combined effects of age variation in the obesity–mortality relationship and cohort variation in age-specific obesity prevalence. Methods. We examined 19 National Health Interview Survey waves linked to individual National Death Index mortality records, 1986–2006, for age and cohort patterns in the population-level association between obesity and US adult mortality. Results. The estimated percentage of adult deaths between 1986 and 2006 associated with overweight and obesity was 5.0% and 15.6% for Black and White men, and 26.8% and 21.7% for Black and White women, respectively. We found a substantially stronger association than previous research between obesity and mortality risk at older ages, and an increasing percentage of mortality attributable to obesity across birth cohorts. Conclusions. Previous research has likely underestimated obesity’s impact on US mortality. Methods attentive to cohort variation in obesity prevalence and age variation in obesity’s effect on mortality risk suggest that obesity significantly shapes US mortality levels, placing it at the forefront of concern for public health action