Have there been efforts to integrate malaria and schistosomiasis prevention and control programs? A scoping review of the literature
Bibliographic record
Abstract
Malaria and schistosomiasis are two important parasitic diseases that are a particular threat to young children and pregnant women in sub-Saharan Africa. Malaria and schistosomiasis prevention and control strategies primarily focus on the distribution of long-lasting insecticidal nets and the delivery of praziquantel tablets to at-risk populations in high burden settings through mass drug administration, respectively. The objective of this scoping review was to identify previous efforts to integrate malaria and schistosomiasis prevention and control programs in the literature and to summarize the strategies and approaches used in these programs following the PRISMA-ScR guidelines. We reviewed published and grey literature using a combination of keywords and search terms following themes surrounding "malaria", "Plasmodium falciparum", "Anopheles", "schistosomiasis", "Schistosoma haematobium", "Schistosoma mansoni", and "snails". Neither a date limit nor relevant terms for prevention and control were used. Out of 6374, eight articles were included in the scoping review-three articles investigated the integration of mass drug administration for schistosomiasis with the administration of antimalarials, four articles investigated the effect of administering antimalarials on malaria, schistosomiasis, and their co-infection, and one article assessed the impact of an educational intervention on malaria and schistosomiasis knowledge and preventative behaviors. Our findings suggest that there is an opportunity to link disease control programs to increase access and coverage of interventions to improve outcomes for malaria, schistosomiasis, and their co-infection. Further research is needed on the potential benefits, feasibility, and cost-effectiveness of integrating malaria and schistosomiasis prevention and control programs.
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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 | 0.042 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".