Climate change, climate-related disasters and mental disorder in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Climate change and climate-related disasters adversely affect mental health. Low- and middle-income countries (LMICs) are particularly vulnerable to the impacts of climate change and climate-related disasters and often lack adequate mental healthcare infrastructure. We used the scoping review methodology to determine how exposure to climate change and climate-related disasters influences the presence of mental disorders among those living in LMICs. We also aimed to recognise existing gaps in this area of literature. METHODS: This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. To identify relevant studies, we searched five electronic databases (MEDLINE, EMBASE, Global Health, APA PsycInfo and Sociological Abstracts) from 1 January 2007 to 31 December 2019. We also searched the grey literature. Included studies had an adult-focused LMIC population, a climate change or climate-related disaster exposure and a mental disorder outcome. Relevant study information was extracted and synthesised. RESULTS: Fifty-eight studies were identified, most of which (n=48) employed a cross-sectional design. The most commonly studied exposure-outcome combinations were flood-related post-traumatic stress disorder (PTSD) (n=28), flood-related depression (n=15) and storm-related PTSD (n=13). The majority of studies identified a positive exposure-outcome association. However, few studies included a baseline or comparator (ie, unexposed) group, thereby limiting our understanding of the magnitude or nature of this association. There was also great heterogeneity in this literature, making studies difficult to pool or compare. Several research gaps were identified including the lack of longitudinal studies and non-uniformity of geographic coverage. CONCLUSION: To our knowledge, this was the first scoping review to investigate the relationship between climate change and climate-related disaster exposures and mental disorder outcomes in LMICs. Our findings support the need for further research, but also highlight that mental health should be a priority within LMIC climate change policy considerations.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".