Objective: To use a joint modeling approach to examine the association between longitudinal changes in depressive symptoms and mortality. Research on the relationship of depression to mortality has yielded mixed results. Limitations of previous studies include mostly one-time assessment of depression, short follow-ups, and failure to model appropriately changes in depression.
Methods: Data were obtained from the Florida Retirement Study, a prospective cohort study of community-dwelling oldest old individuals. At baseline, 879 people (mean age = 80.6 years, 65.8% women) had a comprehensive psychosocial assessment, including the Center of Epidemiological Studies-Depression Scale (CES-D). They were then assessed annually up to 11 years. Longitudinal changes of CES-D, modeled by a joint modeling approach of repeated measures and survival data, were used to predict mortality at follow-up (15 years after baseline), at the same time adjusting for five classes of covariates.
Results: The total mortality rate was 69.9%. CES-D at baseline was not predictive of mortality at 15-year follow-up after adjusting for baseline covariates. The joint modeling revealed that an annual increase of 1 point in CES-D scores over the years was associated with a 57% higher risk of mortality (HR = 1.57, p < .001) at follow-up. Compared with those whose CES-D scores were stable over time, subjects with increasing CED-D scores over time had a 70% increase in mortality risk, p < .001, and their median survival time was 4 years shorter.
Conclusion: Although baseline CES-D was not predictive of mortality, the increase in depressive symptoms over time was associated with higher mortality. It is important to assess longitudinal changes in depression.