Climate change and global health outcome indicators: A scoping review
Bibliographic record
Abstract
Background The impact of climate change on human health is not evenly distributed and is affected by regional geography and vulnerability of the local population. Official statistics that report these uneven impacts are needed to facilitate strategic planning and resource allocation. Purpose Identify globally defined indicators of the impacts of climate change on human health to inform the design of official statistics. Methods We followed recognized methods guidance for scoping reviews. Results Reviewing 4415 unique records, we extracted 73 unique and 33 repeated indicators from 20 sources. Temperature-related indicators were the most common (27%, 29/106), but many were repeated. Injury or illness indicators were more frequent than mortality indicators, with 59% (43/73) and 37% (27/73) respectively. Following breakdown of the categories into smaller, more specific outcomes, mortality from extreme weather events (n = 10) and illness due to zoonoses/vector-borne diseases (n = 9) were the most prevalent indicators. There was an absence/gap of indicators for five secondary categories. Conclusion Synthesis of climate-sensitive health indicators is crucial for establishing a cohesive official statistics framework to monitor the health impacts of climate change-related events. The abundance (and gaps) of indicators across categories of health effects aids in prioritization of developing new indicators and improving data availability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.045 | 0.054 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".