Urban-rural inequalities in suicide among elderly people in China: a systematic review and meta-analysis
Bibliographic record
Abstract
China has an unusual pattern of suicides, with overall suicide rates in rural areas higher than urban areas. While suicide rates have decreased dramatically, older people increasingly contribute to the overall burden of suicide. However, it is unclear if elderly people within rural areas experience greater suicide risk than those in urban areas. We aimed to systematically review the incidence of suicide in rural and urban China among the elderly (aged over 60 years), with a view to describing the difference in rates between rural and urban areas and trends over time. Chinese and English language articles were searched for using four databases: EMBASE (Ovid), MEDLINE (Ovid), PsycINFO (EBSCOhost) and CNKI (in Chinese). Articles describing completed suicide among elderly people in both rural and urban areas in mainland China were included. The adapted Newcastle-Ottawa Scale (NOS) was used to assess risk of bias. One reviewer (ML) assessed eligibility, performed data extraction and assessed risk of bias, with areas of uncertainty discussed with the second reviewer (SVK). Random effects meta-analysis was conducted. Suicide methods in different areas were narratively summarised. Out of a total 3065 hits, 24 articles were included and seven contributed data to meta-analysis. The sample size of included studies ranged from 895 to 323.8 million. The suicide rate in the general population of China has decreased in recent decades over previous urban and rural areas. Suicide rates amongst the elderly in rural areas are higher than those in urban areas (OR = 3.35; 95% CI of 2.48 to 4.51; I 2 = 99.6%), but the latter have increased in recent years. Insecticide poisoning and hanging are the most common suicide methods in rural and urban areas respectively. Suicide rates for these two methods increase with age, being especially high in elderly people. The pattern of suicide in China has changed in recent years following urbanisation and aging. Differences in suicide rates amongst the elderly exist between rural and urban areas. Addressing the high suicide rate amongst the elderly in rural China requires a policy response, such as considering measures to restrict access to poisons.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".