Differential impact of environment on malaria due to control interventions in Uganda, 2010-2018
Bibliographic record
Abstract
Background and aim Studies have estimated the impact of environment on malaria incidence although few have explored the differential impact due to vector control interventions. We aimed to evaluate the influence of temperature, rainfall, humidity, and vegetation, in presence or absence of long-lasting insecticide treated bednets (LLIN) and indoor residual spraying (IRS). Methods This study used weekly malaria cases from 2010 to 2018 from six health facility-based malaria surveillance in Uganda. Environmental variables were extracted from remote sensing sources and include enhanced vegetation index (MODIS), cumulative rainfall (ARC2), minimum and maximum temperature (ERA5), specific humidity (ERA5), averaged over different time periods (one to four months). Non-linearity of environmental variables was investigated, and general linear models based on a negative binomial distribution was used to explore the influence of ITN and LLIN on the malaria-environment relationship. Results A total of 204,252 malaria cases were laboratory confirmed and the median (range) weekly cases was 58.0 (0-597), rainfall 18.6 mm (0-129), minimum temperature 17.6˚C (12.3-24.2), maximum temperature 26.7˚C (20.1-34.8), and humidity 0.014 kg.kg (0.006-0.018). The best fit model was with the meteorological measures averaged over 3 months. All environmental variables showed a relatively linear pattern. Both IRS and LLIN were significantly associated with risk reduction (IRR: 0.39, 95% CI: 0.36–0.42 ; IRR: 0.71, 95% CI: 0.67–0.75, respectively). Marginal effects of environmental variables showed that joint effect of IRS and LLIN reduced the weekly predicted counts of malaria by 72.5% compared to no intervention. Conclusion LLIN and IRS both reduced the influence of environmental drivers of malaria and therefore morbidity in various transmission setting in Uganda. The benefits appeared to be greatest when the two interventions are used in combination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".