Marked reduction in antibiotic usage following intensive malaria control in a cohort of Ugandan children
Bibliographic record
Abstract
BACKGROUND: Intensive malaria control may have additional benefits beyond reducing the incidence of symptomatic malaria. We compared antibiotic treatment of children before and after the implementation of highly effective malaria control interventions in Tororo, a historically high transmission area of Uganda. METHODS: Two successive cohorts of children, aged 0.5 to 10 years, were followed from September 2011 to October 2019 in a dedicated study clinic. Universal distribution of long-lasting insecticidal nets was conducted in 2013 and 2017. Sustained indoor residual spraying of insecticide (IRS) was initiated in December 2014. Generalized linear mixed-effects models were used to compare the incidence of antimalarial and antibiotic treatments before and after vector control measures were implemented. RESULTS: Comparing the period prior to the implementation of IRS to the period after IRS had been sustained for 4-5 years, the adjusted incidence of malaria treatments decreased from 2.68 to 0.05 per person-year (incidence rate ratio [IRR] = 0.02, 95% CI 0.01-0.03, p < 0.001), and the adjusted incidence of antibiotic treatments decreased from 4.14 to 1.26 per person-year (IRR = 0.30, 95% CI 0.27-0.34, p < 0.001). The reduction in antibiotic usage was primarily associated with fewer episodes of symptomatic malaria and fewer episodes of fever with sub-microscopic parasitemia, both of which were frequently treated with antibiotics. CONCLUSIONS: In a historically high transmission setting, the implementation of highly effective vector control interventions was followed by a marked reduction in antibiotic treatment of children. This added benefit of malaria control could have important implications for antibiotic prescribing practices, efforts to curtail antimicrobial resistance, and health system costs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".