Predicting Receipt of Social Security Administration Disability Benefits Using Biomarkers and Other Physiological Measures: Evidence from the Health and Retirement Study

Publisher: Journal of Aging and Health (online ahead of print)
Oct 27, 2017
Authors
Laura Blue, Lakhpreet Gill, Jessica Faul, Kevin Bradway, and David Stapleton

Key Finding:

  • Physiological measures have moderate power to predict SSA disability benefit receipt.

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.