Do socio-demographic factors modify the effect of weather on malaria in Kanungu District, Uganda?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda). Methods Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity. Results Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6–12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0–5 years old (IRR = 2.07 (1.40, 3.07)). Discussion Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models. Conclusion The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.054 | 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 it