Abstract
BackgroundUse of generalized linear models with continuous, non-linear functions for age, period and cohort makes it possible to estimate these effects so they are interpretable, reliable and easily displayed graphically. To demonstrate the methods we use data on the prevalence of obesity among Australian women from two independent data sources obtained using different study designs.MethodsWe used data from two long-running nationally representative studies: seven cross-sectional Australian National Health Surveys conducted between 1995 and 2017–18, each involving 6000–8000 women; and the Australian Longitudinal Study on Women’s Health which started in 1996 and involves more than 57,000 women in four age cohorts who are re-surveyed at three-yearly intervals or annually. Age-period-cohort analysis was conducted using generalized linear models with splines to describe non-linear continuous effects.ResultsWhen analysed in the same way both data sets showed similar patterns. Prevalence of obesity increased with age until late middle age and then declined; increased only slightly across surveys; but increased steadily with birth year until the 1960s and then accelerated.ConclusionsThe methods illustrated here make the estimation and visualisation of age, period and cohort effects accessible and interpretable. Regardless of how the data are collected (from repeated cross-sectional surveys or longitudinal cohort studies), it is clear that younger generations of Australian women are becoming heavier at younger ages. Analyses of trends in obesity should include cohort, in addition to age and period, effects in order to focus preventive strategies appropriately.
Highlights
Use of generalized linear models with continuous, non-linear functions for age, period and cohort makes it possible to estimate these effects so they are interpretable, reliable and displayed graphically
The Australian Longitudinal Study on Women’s Health (ALSWH) began in 1996 with the recruitment of more than 47,000 women in three age groups: women aged 18–23 years, 45–50 years and 70–75 years. These women were randomly sampled from the database of the Australian universal health insurance scheme, called Medicare Australia, which includes all residents of Australia
Prevalence of obesity from the National Health Surveys (NHS) was extracted from age-group and sex specific data in various Australian Bureau of Statistics (ABS) publications and summary tables [16, 17]
Summary
Use of generalized linear models with continuous, non-linear functions for age, period and cohort makes it possible to estimate these effects so they are interpretable, reliable and displayed graphically. In many public health contexts, it is important to be able to distinguish between age, period, and cohort (APC) effects as drivers of temporal changes. The introduction of new laws or taxes to reduce tobacco smoking would be expected to affect smokers in different age groups and different generations at the same time, that is, to produce period effects. Adoption of wearable devices to monitor physical activity might occur first in younger people, that is, a cohort or generational effect. The distinctions between APC effects in overweight and obesity are important for two reasons. From the perspective of prevention, measures that may affect energy intake across a population (such as changes to the food supply, through industry regulation or taxation, or interventions such as dietary guidelines and food labelling) might be expected to produce period effects.
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