Department of Sociology, Center for Demography of Health and Aging, and Center for Demography and Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Maryland Population Research Center, University of Maryland, College Park, MD, 20742, USA.
Demography. 2019 Dec;56(6):2323-2347. doi: 10.1007/s13524-019-00826-x.
Longitudinal methods aggregate individual health histories to produce inferences about aging populations, but to what extent do these summaries reflect the experiences of older adults? We describe the assumption of gradual change built into several influential statistical models and draw on widely used, nationally representative survey data to empirically compare the conclusions drawn from mixed-regression methods (growth curve models and latent class growth analysis) designed to capture trajectories with key descriptive statistics and methods (multistate life tables and sequence analysis) that depict discrete states and transitions. We show that individual-level data record stasis irregularly punctuated by relatively sudden change in health status or mortality. Although change is prevalent in the sample, for individuals it occurs rarely, at irregular times and intervals, and in a nonlinear and multidirectional fashion. We conclude by discussing the implications of this punctuated equilibrium pattern for understanding health changes in individuals and the dynamics of inequality in aging populations.
纵向方法汇总个体健康史,以推断人口老龄化,但这些总结在多大程度上反映了老年人的经验?我们描述了几个有影响力的统计模型中内置的逐渐变化假设,并利用广泛使用的具有代表性的全国调查数据,从混合回归方法(增长曲线模型和潜在类别增长分析)得出的结论与旨在捕捉轨迹的关键描述性统计和方法(多状态生命表和序列分析)进行实证比较,这些方法描绘了离散状态和转换。我们表明,个体层面的数据记录不规则地被健康状况或死亡率的相对突然变化所打断。尽管变化在样本中很普遍,但对于个体来说,它很少发生,而且时间和间隔不规则,并且呈非线性和多向性。最后,我们讨论了这种间断平衡模式对理解个体健康变化和老龄化人口不平等动态的影响。