Dataset Information

Depression and retirement in late middle-aged U.S. workers.

ABSTRACT:

Objective

To determine whether late middle-aged U.S. workers with depression are at an increased risk for retirement.

Data source

Six biennial waves (1992-2002) of the Health and Retirement Study, a nationally representative panel survey of noninstitutionalized 51-61-year-olds and their spouses started in 1992.

Study design

Workers aged 53-58 years in 1994 were followed every 2 years thereafter, through 2002. Depression was coded as lagged time-dependent variables measuring active depression and severity of depression. The main outcome variable was a transition to retirement which was measured using two distinct definitions to capture different stages in the retirement process: (1) Retirement was defined as a transition out of the labor force in the sample of all labor force participants (N=2,853); (2) In addition a transition out of full time work was used as the retirement definition in the subset of labor force participants who were full time workers (N=2,288).

Principal findings

In the sample of all labor force participants, the presence of active depression significantly increased the hazard of retirement in both late middle-aged men (adjusted OR: 1.37 [95 percent CI 1.05, 1.80]) and women (adjusted OR: 1.40 [95 percent CI 1.10, 1.78]). For women, subthreshold depression was also a significant predictor of retirement. In the sample of full time workers, the relationship between depression and retirement was considerably weaker for women yet remained strong for men.

Conclusions

Depression and depressive symptoms were significantly associated with retirement in late middle-aged U.S. workers. Policymakers must consider the potentially adverse impact of these labor market outcomes when estimating the cost of untreated depression and evaluating the value of interventions to improve the diagnosis and treatment of depression.

SUBMITTER: Doshi JA

PROVIDER: S-EPMC2442377 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Depression and retirement in late middle-aged U.S. workers.

Doshi Jalpa A JA Cen Liyi L Polsky Daniel D

Health services research 20080401 2

<h4>Objective</h4>To determine whether late middle-aged U.S. workers with depression are at an increased risk for retirement.<h4>Data source</h4>Six biennial waves (1992-2002) of the Health and Retirement Study, a nationally representative panel survey of noninstitutionalized 51-61-year-olds and their spouses started in 1992.<h4>Study design</h4>Workers aged 53-58 years in 1994 were followed every 2 years thereafter, through 2002. Depression was coded as lagged time-dependent variables measuring ...[more]

PMID: 18370974

Similar Datasets

Project description:BackgroundA significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.MethodsThis retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM).ResultsOur predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM.ConclusionsOur study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

Dataset Information

Depression and retirement in late middle-aged U.S. workers.

Objective

Data source

Study design

Principal findings

Conclusions

Publications

Depression and retirement in late middle-aged U.S. workers.

Similar Datasets

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