12 Year Trajectories of Depressive Symptoms in Community-Dwelling Older Adults and the Subsequent Risk of Death Over 13 Years
Bibliographic record
Abstract
Background: Populations of depressed persons are typically comprised of individuals with different courses of depression and thus might carry different risks of death. This study aimed to identify different trajectories of depressive symptoms in community-dwelling older adults and study the risk of death across these trajectories. Methods: In the population-based Rotterdam Study, depressive symptoms (Center for Epidemiological Studies-Depression scale) at three examination rounds (1993-2004) from 3,325 dementia-free participants (mean age 64.6 ± 6.1 years) were used to identify depression trajectories by latent-class trajectory modeling. Mortality rates by trajectory were calculated over a subsequent 13 year period (2002-2015), that is using 23 years of follow-up data. Results: Five trajectories of depressive symptoms characterized by low (73.4%), decreasing (11.1%), remitting (5.1%), increasing (7.7%), and high (2.7%) depressive symptoms were identified. Compared with persons in the low symptoms trajectory, persons with a trajectory of increasing depressive symptoms (hazard ratio [HR]: 1.21 [95% CI = 1.02, 1.44]) had a higher risk of death, but not those with remitting depressive symptoms, HR: 1.06 (95% CI = 0.85, 1.32). The estimates for the high symptoms trajectory were also suggestive of a higher risk of mortality, HR: 1.20 (95% CI = 0.91, 1.58). Conclusions: Repeated measures of depression can help predict long-term health outcomes in persons with depressive symptoms. Participants with increasing symptoms over time had a higher risk of death than those with low or no depressive symptoms. Transient high depressive symptoms that remitted were not associated with a higher risk compared with those with no symptoms. Our results open avenues for etiological and prognostic research to focus upon risk factors' key to a particular trajectory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".