使用生物标志物和其他生理测量指标预测社会保障管理局残疾福利的领取情况：来自健康与退休研究的数据证据。
Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures: Evidence From the Health and Retirement Study.
机构信息
1 Mathematica Policy Research, Washington, DC, USA.
2 University of Michigan, Ann Arbor, USA.
出版信息
J Aging Health. 2019 Apr;31(4):555-579. doi: 10.1177/0898264317737893. Epub 2017 Oct 27.
OBJECTIVES
The objective of this study was to assess how well physiological measures, including biomarkers and genetic indicators, predict receipt of Social Security Administration (SSA) disability benefits among U.S. adults aged 51 to 65 years.
METHOD
We used data from the 2006 to 2012 waves of the Health and Retirement Study (HRS), linked to SSA administrative data. Using logistic regression, we predicted benefit receipt (either Social Security Disability Insurance or Supplemental Security Income) using 19 distinct physiological markers, adjusting for age, sex, race, and select medication use. We then calculated the propensity (i.e., predicted probability) that each HRS respondent received benefits and assessed how well propensity score-based classifications could identify beneficiaries and nonbeneficiaries.
RESULTS
Thirteen percent of respondents received benefits. Using the propensity score cut point that maximized the sum of sensitivity and specificity, the model correctly predicted 75.9% of beneficiaries and 73.5% of nonbeneficiaries.
DISCUSSION
Physiological measures have moderate power to predict SSA disability benefit receipt.
目的
本研究旨在评估包括生物标志物和遗传指标在内的生理指标在多大程度上可以预测美国 51 至 65 岁成年人获得社会保障管理局（SSA）残疾津贴的情况。
方法
我们使用了来自健康与退休研究（HRS）2006 年至 2012 年各轮的数据，并将其与 SSA 管理数据进行了关联。我们使用逻辑回归，根据年龄、性别、种族和某些药物使用情况，用 19 种不同的生理标记物来预测受益（即社会保障残疾保险或补充保障收入）。然后，我们计算了每位 HRS 受访者获得福利的倾向（即预测概率），并评估了基于倾向得分的分类方法识别受益人和非受益人的效果如何。
结果
13%的受访者获得了福利。使用最大程度地提高敏感性和特异性之和的倾向得分切点，该模型正确地预测了 75.9%的受益人，73.5%的非受益人。
讨论
生理指标对预测 SSA 残疾津贴领取情况具有中等的预测能力。
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