The Health Effects of Climate Change: An Overview of Systematic Reviews
Bibliographic record
Abstract
Abstract Background Although many studies have explored the health impacts of climate change, a broader overview of research is needed to guide future research and action to mitigate and adapt to the health impacts of climate change. Methods We conducted an overview of systematic reviews of health impacts of climate change. We systematically searched the literature using a predefined search strategy, inclusion, and exclusion criteria. We included systematic reviews that explored at least one health impact of climate change. We organized systematic reviews according to their key characteristics, including geographical regions, year of publication and authors’ affiliations. We mapped the climate effects and health outcomes being studied and synthesized major findings. Findings We included ninety-four systematic reviews. Most were published after 2015 and approximately one fifth contained meta-analyses. Reviews synthesized evidence about five categories of climate impacts; the two most common were meteorological and extreme weather events. Reviews covered ten health outcome categories; the three most common were 1) infectious diseases, 2) mortality, and 3) respiratory, cardiovascular, cardiopulmonary or neurological outcomes. Most reviews suggested a deleterious impact of climate change on multiple adverse health outcomes, although the majority also called for more research. Interpretation Overall, most systematic reviews suggest that climate change is associated with worse human health. Future research could explore the potential explanations between these associations to propose adaptation and mitigation strategies and could include psychological and broader social health impacts of climate change. Funding Canadian Institutes of Health Research FDN-148426
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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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".