The association between, depression, anxiety, and mortality in older people across eight low‐ and middle‐income countries: Results from the 10/66 cohort study
Bibliographic record
Abstract
Objectives Depression and anxiety are common mental disorders in later life. Few population‐based studies have investigated their potential impacts on mortality in low‐ and middle‐income countries (LMICs). The aim of this study is to examine the associations between depression, anxiety, their comorbidity, and mortality in later life using a population‐based cohort study across eight LMICs. Methods This analysis was based on the 10/66 cohort study including 15 991 people aged 65 years or above in Cuba, Dominican Republic, Venezuela, Mexico, Peru, Puerto Rico, China, and India, with an average follow‐up time of 3.9 years. Subthreshold and clinical levels of depression were determined using EURO‐D and ICD‐10 criteria, and anxiety was based on Geriatric Mental State (GMS)–Automated Geriatric Examination for Computer Assisted Taxonomy (AGECAT). Cox proportional hazard modelling was used to estimate how having depression, anxiety, or both was associated with mortality adjusting for sociodemographic and health factors. Results Participants with clinical depression (hazard ratio [HR]: 1.45; 95% CI, 1.24‐1.70) and subthreshold anxiety (HR: 1.26; 95% CI, 1.15‐1.38) had higher risk of mortality than those without the conditions after adjusting for sociodemographic factors and health conditions. Comorbidity of depression and anxiety was associated with a 30% increased risk of mortality but the effect sizes varied across countries (Higgins I2 = 58.8%), with the strongest association in India (HR: 1.99; 95% CI, 1.21‐3.27). Conclusions Depression and anxiety appear to be associated with mortality in older people living in LMICs. Variation in effect sizes may indicate different barriers to health service access across countries. Future studies may investigate underlying mechanisms and identify potential interventions to reduce the impact of common mental disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".