Influence of weather and climate on disease in the Australian Imperial Force during the First World War
Bibliographic record
Abstract
The death rate in the Australian Imperial Force (AIF) during the First World War (WW1) was 18.5%, higher than rates for the UK and Canada. Around 9% of reported AIF deaths resulted from diseases and were predominantly climate sensitive. AIF hospital admissions for non-battle conditions exceeded the total number of AIF enlistments. To our knowledge, the climatic influences on these high morbidity and mortality rates have not previously been quantified. Analysing these influences provides a case study that highlights the importance in accounting for climate in determining the future health, capacity and ultimate efficacy of armed forces, particularly in a time of increasing climatic extremes. To analyse the climate-health outcomes relationship, we re-examined data available in Australia’s WW1 official war histories (OWH) by C. E. W. Bean and A. G. Butler, the Australian War Memorial Roll of Honour (ROH) and the National Archives WW1 personnel files (NAA). We then reviewed meteorological data and identified that the European 1917 winter was the coldest winter for 26 yr. We have calculated the AIF UK official morbidity figure of 77743 could be under-reported by up to 2.7-fold. European winter disease deaths exceeded summer disease deaths by a factor of 3. Over 61% of AIF disease deaths in Europe occurred during the extreme 1917 winter and the Spanish flu outbreak during the 1919 winter, whereby 69% were respiratory infections. Climate-related diseases also severely affected the AIF at Gallipoli (Turkey) and the Light Horse regiments in the Jordan Valley between September and December 1918.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".