Long-term risk for depressive symptoms after a medical diagnosis

Daniel Polsky, Jalpa A. Doshi, Steven Marcus, David Oslin, Aileen Rothbard, Niku Thomas, Christy L. Thompson

Research output: Contribution to journalArticlepeer-review


Background: This study examines the risk of development of significant depressive symptoms after a new diagnosis of cancer, diabetes, hypertension, heart disease, arthritis, chronic lung disease, or stroke. Methods: The study used 5 biennial waves (1992-2000) of the Health and Retirement Study to follow a sample of 8387 adults (aged 51 to 61 years and without significant depressive symptoms in 1992) from 1994 to 2000. Time-dependent Cox regression models estimated adjusted hazard ratios (HRs) for an episode of significant depressive symptoms after a new diagnosis for each of the 7 medical conditions. Results: Within 2 years of initial diagnosis, subjects with cancer had the highest hazard of depressive symptoms (HR, 3.55; 95% confidence interval [CI], 2.79-4.52), followed by subjects with chronic lung disease (HR, 2.21; 95% CI, 1.64-2.79) and heart disease (HR, 1.45; 95% CI, 1.09-1.93). The hazard for depressive symptoms for most of these diseases decreased over time; however, subjects with heart disease continued to have a higher risk for depressive symptoms even 2 to 4 years and 4 to 8 years after diagnosis, and a significantly higher hazard for depressive symptoms developed for persons with arthritis 2 to 4 years after diagnosis (HR, 1.46; 95% CI, 1.11-1.92). Conclusion: The findings identify several high-risk patient groups who might benefit from depression screening and monitoring to improve health outcomes in this vulnerable population facing new medical illnesses.

Original languageEnglish (US)
Pages (from-to)1260-1266
Number of pages7
JournalArchives of internal medicine
Issue number11
StatePublished - Jun 13 2005
Externally publishedYes

ASJC Scopus subject areas

  • Internal Medicine


Dive into the research topics of 'Long-term risk for depressive symptoms after a medical diagnosis'. Together they form a unique fingerprint.

Cite this