Water, sanitation and hygiene and neglected tropical diseases in the Democratic Republic of Congo: a narrative review and policy perspective
Bibliographic record
Abstract
Neglected tropical diseases (NTDs) remain a significant public health challenge in the Democratic Republic of Congo (DR Congo) due to its direct connection with the scarcity of clean water, poor sanitation facilities, and insufficient hygiene practices. Therefore, investigating comprehensive strategies to decrease NTDs by addressing Water, Sanitation, and Hygiene (WASH) issues is pertinent. This paper presents a narrative review of available literatures and policy briefs, and explains the aspect of WASH in NTD control with special reference to DR Congo. We review the country-specific barriers to the incorporation of WASH in NTD programs, such as conflict and governance issues, and limitations in infrastructure. In our synthesis, although WASH interventions have been established to effectively mitigate schistosomiasis, trachoma and soil-transmitted helminths at the global level, they are complicated by factors including insecurity in the eastern provinces of the DRC, poor health governance and inadequately funded water infrastructure. We also determine community-based innovations, and policy integration opportunities. Emphasizing WASH strategies in combating NTDs can lead to better health outcomes, less disease impact, and greater quality of life for affected communities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".