Association of climatic factors with infectious diseases in the Arctic and subarctic region – a systematic review
Bibliographic record
Abstract
BACKGROUND: The Arctic and subarctic area are likely to be highly affected by climate change, with possible impacts on human health due to effects on food security and infectious diseases. OBJECTIVES: To investigate the evidence for an association between climatic factors and infectious diseases, and to identify the most climate-sensitive diseases and vulnerable populations in the Arctic and subarctic region. METHODS: A systematic review was conducted. A search was made in PubMed, with the last update in May 2013. Inclusion criteria included human cases of infectious disease as outcome, climate or weather factor as exposure, and Arctic or subarctic areas as study origin. Narrative reviews, case reports, and projection studies were excluded. Abstracts and selected full texts were read and evaluated by two independent readers. A data collection sheet and an adjusted version of the SIGN methodology checklist were used to assess the quality grade of each article. RESULTS: In total, 1953 abstracts were initially found, of which finally 29 articles were included. Almost half of the studies were carried out in Canada (n=14), the rest from Sweden (n=6), Finland (n=4), Norway (n=2), Russia (n=2), and Alaska, US (n=1). Articles were analyzed by disease group: food- and waterborne diseases, vector-borne diseases, airborne viral- and airborne bacterial diseases. Strong evidence was found in our review for an association between climatic factors and food- and waterborne diseases. The scientific evidence for a link between climate and specific vector- and rodent-borne diseases was weak due to that only a few diseases being addressed in more than one publication, although several articles were of very high quality. Air temperature and humidity seem to be important climatic factors to investigate further for viral- and bacterial airborne diseases, but from our results no conclusion about a causal relationship could be drawn. CONCLUSIONS: More studies of high quality are needed to investigate the adverse health impacts of weather and climatic factors in the Arctic and subarctic region. No studies from Greenland or Iceland were found, and only a few from Siberia and Alaska. Disease and syndromic surveillance should be part of climate change adaptation measures in the Arctic and subarctic regions, with monitoring of extreme weather events known to pose a risk for certain infectious diseases implemented at the community level.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| 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".