Prevalence and predictors of adverse events following exposure to long-lasting insecticidal nets used for malaria prevention: a community based cross-sectional study in the Democratic Republic of the Congo
Bibliographic record
Abstract
BACKGROUND: Malaria morbidity and mortality increase in the Democratic Republic of the Congo (DRC) may be the consequence of the low utilization rate of long-lasting insecticidal nets (LLINs) resulting from poor compliance due to adverse events (AEs). This study aimed at determining the prevalence and predictors of AEs following the mass distribution of LLINs in the Kisantu Health Zone (KHZ), a high malaria-endemic region in the DRC. METHODS: A community-based cross-sectional study embedded was conducted within a randomized controlled trial (RCT) after the mass distribution of LLINs in 30 villages located in DRC KHZ. A three-stage sampling method was used without replacement to select 1790 children. Data was collected on adverse events (AEs) using a reporting form and information on demographics, nutritional status, and house characteristics. This was done using a structured questionnaire administered to household heads. Logistic regression models were used to identify predictors of AEs following the mass distribution of LLINs. RESULT: In a total of 1790 children enrolled, 17.8% (95% CI 16.1-19.7) experienced AEs. The most common AEs were respiratory-related (61%). Around 60% of AEs occurred within 24 h of use, and 51% were resolved without treatment. Sleeping under deltamethrin LLINs (Adjusted OR, 95% CI 5.5 [3.8-8.0]) and zinc roofing (Adjusted OR, 95% CI 1.98 [1.1-3.57]) were associated with the risk of reporting an AE following the mass distribution of LLINs. CONCLUSION: Approximately 1 out of 5 children had an AE within 24 h following LLIN use. These adverse events were often respiratory-related. LLINs and roofing types were associated with a higher risk of reporting AEs. However, further research using a robust study design is needed to confirm these findings. Future studies should design and implement interventions aiming to reduce AEs and improve compliance with LLINs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".