A Systematic Review of Households’ Fecal Sludge Management Situation to Identify Gaps and Improve Services: A Case of Kigali City, Rwanda
Bibliographic record
Abstract
Background: Kigali, Rwanda’s rapidly growing capital, faces major challenges in household-level Fecal Sludge Management (FSM), with over 89% of households using pit latrines and only 48% accessing unshared sanitation. FSM services are limited, costly, and poorly executed, leading to frequent illegal dumping. Objective: This review analyzes the literature on sanitation in Kigali to identify key gaps, synergies, and recommendations for improvement. Methods: Following PRISMA 2020 guidelines, 73 relevant publications were selected from various scientific and governmental sources. Publications were included only if they were published from 2013 to 2024 and had information on sanitation in Kigali. NOS and JBI tools were utilized to assess the quality of included publications. Results: Data were categorized into four themes, (1) access to sanitation, (2) FSM services, (3) public health, and (4) sanitation governance, and analyzed using thematic, narrative, and descriptive methods. Findings reveal a dysfunctional FSM service chain, weak policy enforcement due to overlapping responsibilities, underfunding, and limited private sector participation. These issues contribute to poor sanitation, inadequate hygiene, and prevalence of diarrheal diseases and Tropical Neglected Diseases, especially among young children. Conclusions: The review recommends strengthening governance and clarifying roles, enforcing adaptable regulations, promoting public–private partnerships, and managing the full FSM service chain more effectively. Future research should focus on developing context-specific technologies and financing strategies to support sustainable FSM solutions in Kigali.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".