Influence of the Scandinavian climate pattern on the UK asthma mortality: a time series and geospatial study
Bibliographic record
Abstract
OBJECTIVE: It is well known that climate variability and trends have an impact on human morbidity and mortality, especially during the winter. However, there are only a handful of studies that have undertaken quantitative investigations into this impact. We evaluate the association between the UK winter asthma mortality data to a well-established feature of the climate system, the Scandinavian (SCA) pattern. METHODS: Time series analysis of monthly asthma mortality through the period of January 2001 to December 2015 was conducted, where the data were acquired from the UK's Office for National Statistics. The correlations between indices of important modes of climate variability impacting the UK such as the North Atlantic Oscillation as well as the SCA and the asthma mortality time series were computed. A grid point correlation analysis was also conducted with the asthma data with sea level pressure, surface wind and temperature data acquired from the European Centre for Medium-Range Weather Forecasts. RESULTS: We find that sea level pressure and temperature fluctuations associated with the SCA explain ~20% (>95% CL) of variance in the UK asthma mortality through a period of 2001-2015. Furthermore, the highest winter peak in asthma mortality occurred in the year 2015, during which there were strong northwesterly winds over the UK that were the result of a sea level pressure pattern similar to that associated with the SCA. CONCLUSIONS: Our study emphasises the importance of incorporating large-scale geospatial analyses into future research of understanding diseases and its environmental impact on human health.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".