Development and validation of PubMed and Ovid MEDLINE search filters for exposure pathways linking climate change with human health
Bibliographic record
Abstract
ABSTRACT Introduction Climate change (CC) has major public and global health impacts to which policymakers need to respond. High-quality evidence syntheses (ES) are essential for policy-making. Search filters - validated combinations of search terms - play an important role in implementing robust search methods for ES. The identification of climate-health evidence presents challenges, such as the volume and multidisciplinary nature of the evidence and the fact that relevant studies do not consistently state their link to CC. Thus, our aim was to develop search filters for two search interfaces of the MEDLINE database. Methods CC impacts human health via several exposure pathways: extreme weather events, heat stress, air quality, water quality and quantity, food supply and safety, vector distribution and ecology, and social factors. We established a gold standard by comprehensively identifying health-related ES mentioning CC in five literature databases in February 2021. After screening 8,614 search results, we identified 110 ES for inclusion, extracted their included studies, and classified them according to exposure pathways. From this gold standard we empirically derived search terms per pathway and tested their performance with an independent set of studies. Results We extracted 2,324 studies from the first 79 ES. Based on a gold standard with 1,572 relevant studies indexed in PubMed, it was possible to develop and validate search filters with a sensitivity of 95%, 97% and 99% for six of the seven major climate-health exposure pathways. Filter development was not possible for one pathway due to the lack of coverage in MEDLINE. Conclusion We designed ready-to-use PubMed and Ovid MEDLINE search filters with a graded sensitivity for most exposure pathways linking CC with human health. These can be deployed by public and global health researchers conducting ES or primary research on climate-health to ensure robust identification of relevant evidence. KEY MESSAGES What is already known on this topic : The identification of evidence linking human health with climate change in literature databases such as MEDLINE presents challenges. Empirically derived search filters, which are validated combinations of search terms that can be readily used, are lacking for this topic. What this study adds : We present search filters with a graded sensitivity for six of the seven major climate-health exposure pathways (air quality, extreme weather events, food supply and safety, heat stress, vector distribution and ecology, water quality and quantity). A search filter for the pathway ‘social factors’ was not viable, suggesting that it requires other databases and complementary search methods to be used. How this study might affect research, practice or policy : The new search filters will help health researchers to identify relevant studies with a relationship to climate change. The filters can be applied independently from specific research questions (interventions, prognosis, associations, impacts, diseases, populations, or regions) as they focus on the major exposure pathways linking health with climate change.
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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 | 0.203 | 0.522 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.076 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".