Is The Age-Period-Cohort Model Well Suited to an Epidemic Context? The Case of the French BSE Epidemic

C. Sala, B. Durand, D. Costagliola, C. Ducrot, D. Calavas
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Abstract

Abstract Although Age-Period-Cohort (APC) models are routinely used in time-trend analyses of chronic diseases, few examples of their application to epidemic diseases are available. APC analyses of the French bovine spongiform encephalopathy (BSE) epidemic revealed an unexpected period effect, which was attributed to the design of the study, in connection with low BSE prevalence and a short surveillance period. The aim of our study was to evaluate the relevance of this hypothesis, the behaviour of the APC model in an epidemic context (e.g. the BSE epidemic) and the impact of including the period effect in an APC model on the estimate of birth cohort effects. We simulated datasets mimicking the French BSE epidemic and its variable pattern, as well as duration and the surveillance time period, and analysed them with a categorical APC model. Results showed a period effect in 44% of analysed datasets, although no period effect had been introduced in the data simulation process. This type of artefactual period effect was detected when a sudden change in cohort prevalence occurred over a short period of time. Additionally, in the context of BSE, including a period effect in the model may dramatically affect the estimation of cohort prevalence, depending on epidemic pattern and, as expected, duration and the surveillance time period according to the year in which highly infected birth cohorts are detected as BSE cases.
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年龄-时期-队列模型适合于流行病背景吗?法国疯牛病流行案例
虽然年龄-时期-队列(Age-Period-Cohort, APC)模型通常用于慢性疾病的时间趋势分析,但很少有将其应用于流行病的例子。对法国牛海绵状脑病(BSE)流行的APC分析揭示了一种意想不到的时期效应,这归因于该研究的设计,与低疯牛病流行率和短监测期有关。我们研究的目的是评估这一假设的相关性,流行病背景下APC模型的行为(例如疯牛病),以及APC模型中包括时期效应对出生队列效应估计的影响。我们模拟了法国疯牛病流行及其变化模式、持续时间和监测时间段的数据集,并使用分类APC模型对其进行了分析。结果显示44%的分析数据集存在周期效应,尽管在数据模拟过程中没有引入周期效应。当队列患病率在短时间内发生突然变化时,检测到这种类型的人工周期效应。此外,在疯牛病的背景下,在模型中包括时期效应可能会极大地影响队列患病率的估计,这取决于流行模式,以及(如预期的)持续时间和根据检测到高度感染的出生队列为疯牛病病例的年份的监测时间段。
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