Community benefits of mass distribution of three types of dual-active-ingredient long-lasting insecticidal nets against malaria prevalence in Tanzania: evidence from a 3-year cluster-randomized controlled trial
Bibliographic record
Abstract
Abstract Background Long-lasting insecticidal nets (LLINs) were once fully effective for the prevention of malaria; however, mosquitoes have developed resistance to pyrethroids, the main class of insecticides used on nets. Dual active ingredient LLINs (dual-AI LLINs) have been rolled out as an alternative to pyrethroid (PY)-only LLINs to counteract this. Understanding the minimum community usage at which these novel nets generate an effect that also benefits non-net users against malaria infection is vital for planning net distribution strategies and mobilization campaigns. Methods We conducted a secondary analysis of a 3-year randomized controlled trial (RCT) in 84 clusters in North-western Tanzania to evaluate the effectiveness of three dual-AI LLINs: pyriproxyfen and alpha(α)-cypermethrin (pyriproxyfen-PY), chlorfenapyr and α-cypermethrin (chlorfenapyr-PY), and the synergist piperonyl-butoxide and permethrin (PBO-PY) compared to α-cypermethrin only nets (PY-only). We measured malaria infection prevalence using 5 cross-sectional surveys between 2020 and 2022. We assessed net usage at the cluster level and malaria infection in up to two children aged between 6 months and 14 years in 45 households per cluster and compared infection prevalence between net users and non-users with the different net types and usage levels. Findings A total of 22,479 children from 12,654 households were tested for malaria using rapid diagnostic tests in January 2020, 2021, & 2022 and July 2020 & 2021. In all surveys combined, 23% (5,062/22,479) of children reported not using a net the night before the surveys. The proportion of non-net users was highest in the later surveys. Across all study arms and at each time point, users of nets had significantly lower malaria infection than non-users. Overall, malaria prevalence was 52% (2649/5062) among non-net users and 32% (5572/11845) among users (of any net). Among non-net users, community-level usage of >40% of dual-AI LLIN was significantly associated with protection against malaria infection: chlorfenapyr-PY (OR: 0.44 (95% CI: 0.27-0.71), p=0.0009), PBO-PY (OR: 0.55 (95% CI: 0.33-0.94), p=0.0277) and pyriproxyfen-PY (OR: 0.61 (95% CI: 0.37-0.99), p=0.0470) compared with non-users in clusters with >40% usage of PY-only LLINs. There was weak evidence of protection against malaria infection to non-net users in the chlorfenapyr-PY arm when community-level usage was ≤40% (OR: 0.65 (95% CI: 0.42-1.01), p=0.0528) compared to those living in clusters with >40% usage of pyrethroid-only LLINs. The study was limited to non-users which were defined as participants who did not sleep under any net the night before. This might not capture occasional net usage during the week. Conclusion Our study demonstrated that at a community usage of 40% or more of dual-AI LLINs, non-net users benefited from the presence of these nets. Noticeably, even when usage was ≤40% in the chlorfenapyr-PY arm, non-users were better protected than non-users in the higher coverage PY-only arm. The greater difference in malaria risk observed between users and non-users across all study arms indicates that nets play a crucial role in providing personal protection against malaria infection for the people using the net and that net usage needs to be maximized to realize the full potential of all nets. Funding Department for International Development, UK Medical Research Council, Wellcome Trust, and Department of Health and Social Care (#MR/R006040/1). The Bill and Melinda Gates Foundation via the Innovative Vector Control Consortium (IVCC).
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 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".