Prevalence of Major Depressive Disorder and Correlates of Thoughts of Death, Suicidal Behaviour, and Death by Suicide in the Geriatric Population—A General Review of Literature
Bibliographic record
Abstract
Background: There has been an increase in deaths by suicide in old age in the last decade. Depression and suicide in the elderly, 60 years and above, is a major global public health concern. Determining the prevalence of depression, and correlates of death by suicide in the geriatric population, is an important first step toward addressing this public health concern. This literature review aims to determine the prevalence of major depressive disorders and the correlates of death by suicide in the geriatric population. Methods: This general review of the literature was performed using relevant search terms to determine both the prevalence of depression and the correlates of death by suicide among the geriatric population. Databases such as MEDLINE, PsycINFO, CINAHL, and PubMed were searched. Relevant and current articles were extracted, reviewed, and analyzed. The elderly population was defined as individuals 60 years and above. Only full texts articles in English were reviewed. Findings: The prevalence estimates of major depressive disorder in the elderly ranged from 5.37 to 56%. Adults aged 60 years and older have a high risk of depression that exposes them to suicide. Moreover, elderly women are more likely to experience depression than elderly men, but successful suicide is more common in men. Depression and other mental health conditions (schizophrenia, anxiety disorders) and perceived stress were found to be predictors of suicide in the elderly. Other predictors included physical illnesses such as malignancies, financial constraints, cuckoldry, and sexual dysfunction, and also social factors like living alone triggers depressive symptoms and increases suicidal risk in the elderly. Hanging was found to be the most common method of death by suicide for both sexes. While elderly women preferred poisoning, elderly men in Western countries preferred firearms. Differences in gender, the aging process and social issues were also contributing factors to methods used for suicide. Conclusions: Depression and debilitating physical illnesses were identified as significant contributors to suicide risk in the elderly population, and emphasis should be placed on identifying these factors early and treating them. Recognizing and addressing factors that predict suicide in the elderly will help to improve the mental wellbeing of the elderly.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".