Attrition and health in ageing studies: evidence from ELSA and HRS
Attrition and health in ageing studies: evidence from ELSA and HRS
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DOI：
10.14301/llcs.v2i2.115
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发表时间：
2011-05-01
影响因子：
0.9
通讯作者：
Smith, James P.
中科院分区：
文献类型：
--
作者：
Banks, James;Muriel, Alastair;Smith, James P.
本文调查了英国老龄化纵向研究 (ELSA) 和健康与退休研究 (HRS) 中与自然损耗相关的特征，特别关注自然损耗是否与健康结果和社会经济地位系统相关。我们的重点是活着的受访者的流失，而不是死亡造成的流失，因此死亡的受访者被排除在我们的分析之外。我们得到了三个主要结果。首先，ELSA 的原始流失率远高于 HRS，但这主要是由于两项调查的管理差异，还是英格兰和美国之间其他未观察到的差异，从现有证据来看，尚不清楚。其次，这些不同的流失率并没有改变关于两国健康和社会经济地位比较的核心结论。最后，在这两项研究中，在七十多岁的受访者中，很少有可观察到的特征可以预测人员流失。在 55-64 岁的受访者中，财富似乎预示着美国（而非英国）的人员流失，而低教育程度则预示着英国（而非美国）的人员流失。由于 ELSA 存在更严重的员工流失问题，因此我们对该调查中的员工流失进行了额外的分析。我们发现受访者的算术水平可以强烈预测人员流失，但这并不能解释 ELSA 中人员流失的教育梯度。
This paper investigates the characteristics associated with attrition in the English Longitudinal Study of Ageing (ELSA) and the Health and Retirement Study (HRS), with a particular focus on whether attrition is systematically related to health outcomes and socio-economic status. Our focus is on the attrition of living respondents, not attrition through death, and respondents who died are therefore excluded from our analysis. We have three main results. Firstly, raw attrition is substantially higher in ELSA than in HRS, but whether this is primarily due to differences in the administration of the two surveys, or to other unobserved differences between England and the U.S. is not clear from the available evidence. Second, these differential attrition rates do not change the core conclusions regarding comparisons between the two countries of health and socioeconomic status. Finally, very few observable characteristics predict attrition in either study among respondents in their seventies. Among respondents aged 55-64, wealth appears to predict attrition in the U.S. (but not in England), and low education predicts attrition in England (but not the U.S.). Since the more serious attrition problem exists in ELSA, we conduct additional analysis of attrition in that survey. We find that respondents' level of numeracy strongly predicts attrition, but this does not account for the education gradient in attrition in ELSA.