Association between temperature variability and hospitalizations among First Nations Australians in Central Australia
Bibliographic record
Abstract
<h2>Abstract</h2><h3>Background</h3> Little is known about the temperature and health effects of the desert climate in Central Australia, Northern Territory (NT). The objectives of this study are to assess the risks of all-cause and cause-specific hospitalizations for cardiovascular diseases (CVDs), infectious diseases (IDs), mental disorders and respiratory diseases (RDs). We also assessed the effect of temperature variability (TV) on hospitalizations amongst First Nations Australians. <h3>Methods</h3> Daily hospitalization data from 2010 to 2021 for all-cause and cause-specific conditions from Alice Springs Hospital, Central Australia, were used. TV was calculated from the standard deviation of the minimum and maximum temperatures for the exposure days. Quasi-Poisson regression model was applied to assess the association between TV and hospitalizations, and by demographic characteristics including First Nations status. <h3>Results</h3> A total of 127,755 hospitalizations were recorded. Increased risks of total all-cause, ID, and RD hospitalizations were observed following exposure to TV for a range of hot temperatures but were more consistent for presummer (spring) moderately hot temperatures. Exposure to TV for the previous 6 days (i.e., TV0-5) was associated with an estimated 1.8 % (95 % CI: 0.33 %, 3.29 %) increase in all-cause, 4.94 % (95 % CI: 0.16 %, 9.95 %) increase in ID, and a 4.48 % (95 % CI: 1.47 %, 7.59 %) increase in RD hospitalizations for moderately hot temperatures. In First Nations Australians TV0-4 was associated with an estimated 1.98 % increase (95 % CI, 0.44, 3.55 %) in total hospitalizations. Increased risks of total hospitalizations were also observed among the <18 and >45 years age groups and women for different TV exposure days. Overall, 12.7 % (i.e., 16,205 cases) of total hospitalizations were attributed to exposure to TV (TV0-5). <h3>Conclusions</h3> With projected climate change, increasing exposure to extreme temperatures in Central Australia is more likely. Therefore, concerted and context-specific adaptation approaches are essential to mitigate temperature-related health effects and address the gap in health outcomes for First Nations peoples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".