School of Geographical Sciences and Centre for Multilevel Modelling, University of Bristol, University Road, Bristol BS8 1SS, UK.
Soc Sci Med. 2014 Nov;120:21-30. doi: 10.1016/j.socscimed.2014.09.008. Epub 2014 Sep 6.
There is ongoing debate regarding the shape of life-course trajectories in mental health. Many argue the relationship is U-shaped, with mental health declining with age to mid-life, then improving. However, I argue that these models are beset by the age-period-cohort (APC) identification problem, whereby age, cohort and year of measurement are exactly collinear and their effects cannot be meaningfully separated. This means an apparent life-course effect could be explained by cohorts. This paper critiques two sets of literature: the substantive literature regarding life-course trajectories in mental health, and the methodological literature that claims erroneously to have 'solved' the APC identification problem statistically (e.g. using Yang and Land's Hierarchical APC-HAPC-model). I then use a variant of the HAPC model, making strong but justified assumptions that allow the modelling of life-course trajectories in mental health (measured by the General Health Questionnaire) net of any cohort effects, using data from the British Household Panel Survey, 1991-2008. The model additionally employs a complex multilevel structure that allows the relative importance of spatial (households, local authority districts) and temporal (periods, cohorts) levels to be assessed. Mental health is found to increase throughout the life-course; this slows at mid-life before worsening again into old age, but there is no evidence of a U-shape--I argue that such findings result from confounding with cohort processes (whereby more recent cohorts have generally worse mental health). Other covariates were also evaluated; income, smoking, education, social class, urbanity, ethnicity, gender and marriage were all related to mental health, with the latter two in particular affecting life-course and cohort trajectories. The paper shows the importance of understanding APC in life-course research generally, and mental health research in particular.
关于心理健康的人生轨迹模式,目前存在争议。许多人认为这种关系呈 U 型,心理健康在中年之前随着年龄的增长而下降,然后逐渐改善。然而,我认为这些模型存在年龄-时期-队列(APC)识别问题的困扰,即年龄、队列和测量年份完全共线,无法对其进行有意义的区分。这意味着明显的人生轨迹效应可能可以通过队列来解释。本文对两组文献进行了批判:一组是关于心理健康人生轨迹的实质性文献,另一组是错误地声称在统计学上“解决”了 APC 识别问题的方法学文献(例如,使用杨和兰的层次 APC-HAPC 模型)。然后,我使用 HAPC 模型的变体,在强但合理的假设下,使用 1991-2008 年英国家庭面板调查的数据,对心理健康(通过一般健康问卷测量)的人生轨迹进行建模,而不受任何队列效应的影响。该模型还采用了复杂的多层次结构,可以评估空间(家庭、地方当局区)和时间(时期、队列)层面的相对重要性。研究发现,心理健康状况在整个生命周期中呈上升趋势;从中年开始,这种趋势会减缓,然后在老年时再次恶化,但没有 U 型的证据——我认为这些发现是由于与队列过程混淆所致(即最近的队列通常心理健康状况更差)。还评估了其他协变量;收入、吸烟、教育、社会阶层、城市化、种族、性别和婚姻都与心理健康有关,其中后两个因素尤其会影响人生轨迹和队列轨迹。本文表明,理解 APC 在人生轨迹研究中普遍存在的重要性,尤其是在心理健康研究中。