Association of Climate Factors with Infectious Diseases in Arctic and Subarctic Regions a Systematic Review
Bibliographic record
Abstract
Background: Climate change is likely to affect human infectious disease incidences in the Arctic and subarctic regions. However, we need to know more about climate sensitivity of such diseases in order to develop adaptation measures. Objectives: To scrutinize the evidence for an association between weather/climate factors and infectious diseases, and to identify the most climate-sensitive diseases in the Arctic and subarctic region. Methods: A systematic review was conducted. A search was made in PubMed, last update May 2013. Only articles addressing human infectious diseases as the outcome, climate or weather factors as the exposure, and Arctic or subarctic areas as the study location were included. Narrative reviews, case reports and projection studies were excluded. Abstracts and selected full texts were read and evaluated by two independent readers, and an adjusted version of the SIGN 50 checklist was used to assess the quality grade of each article. The synthesis of results was done by disease groups. Results: In total, 1953 abstracts were found, of which 29 articles were finally included. In Canada, 14 studies were conducted; the rest came from Scandinavia, Russia and Alaska. Strong evidence was found for an association between weather or climate variability and food- and waterborne diseases. The association between climate and vector- and rodent borne diseases was less clear, since only a few diseases were addressed in more than one publication. Air temperature and humidity appeared to be important factors for viral- and bacterial airborne diseases. Conclusions: Studies on food and waterborne diseases provided the strongest evidence for climate sensitivity. Not all regions were represented in the publications, and no study about climate change impacts was included. Disease and syndromic surveillance should be part of climate change adaptation measures in the North, and more high-quality studies should address the link between climate and specific diseases.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".