Implementation of insecticide-treated malaria bed nets in Tanzania: a systematic review
Bibliographic record
Abstract
Background Malaria is a significant cause of morbidity, mortality, and economic burden among the Tanzanian population. An effective form of personal protection against malaria is the insecticide-treated bed net (ITN). Although Tanzania has made great efforts to implement ITNs in the general population, gaps in use, access, coverage, and ownership remain. We conducted a systematic review of the available data on the barriers and facilitators to the implementation of ITNs in Tanzania. Methods A comprehensive search was conducted in four databases: OVID Medline, OVID Embase, EBSCO CINAHL, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to present the review and analysis. Eligible studies were appraised to determine the quality of evidence. Various content data were extracted, including study locations, years of publication, study objectives, and barriers and facilitators to ITNs. The Consolidated Framework for Implementation Research (CFIR) facilitated a thematic analysis of the barriers and facilitators. Results Seven mixed-methods and three qualitative studies met this review’s inclusion criteria. Seven regions and ten districts within Tanzania were represented in this review, most notably the Morogoro region and its respective districts, Kilombero and Ulanga. Study dates ranged from 1995 to 2020. Facilitators of ITN implementation included cost, voucher schemes, involving locals, planning for distribution, and social marketing and communication campaigns. Similarly, barriers to ITN implementation included cost, knowledge and beliefs, a poorly developed private sector, and inadequate distribution methods. Conclusions A systematic review of studies on the implementation of ITNs in Tanzania highlights vital areas in the development of successful implementation that include: (i) the cost of ITNs, (ii) knowledge and beliefs about ITNs among potential users, and (iii) planning for the execution of ITN distribution programs. ITN implementation can be enhanced if national stakeholders invest further in processes that promote ITN procurement, such as voucher schemes, providing education sessions, integrating distribution methods that cater to locals’ preferences, and initiating the promotion of ITN months in advance of their distribution. Registration PROSPERO (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=222128)
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".