Factors Associated With The Propagation Of Cholera In Epidemics Across The Geopolitical Zones Of Nigeria: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Cholera remains a significant public health challenge in Nigeria, with recurring outbreaks driven by environmental, socioeconomic, and healthcare-related factors. This systematic review examines the propagation of cholera across Nigeria’s six geopolitical zones, identifying key risk factors and regional disparities to inform targeted interventions. Methods The study adhered to the PRISMA guidelines, analyzing 40 peer-reviewed studies published between 2015 and 2024. Data were extracted from databases such as PubMed, Scopus, and Cochrane Library, alongside grey literature. Eligible studies included observational and interventional research focusing on cholera risk factors, WASH (Water, Sanitation, and Hygiene) infrastructure, healthcare preparedness, and population mobility. Quality assessment was performed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool. Results The review identified contaminated water sources, poor sanitation, and seasonal flooding as primary environmental drivers of cholera, particularly in the Northwest, Northeast, and South-South zones. Socioeconomic factors such as poverty, overcrowding, and inadequate healthcare access exacerbated outbreaks, especially in conflict-affected regions like the Northeast. Behavioral practices, including unsafe water storage and street food consumption, further contributed to transmission. WASH deficiencies showed a strong correlation with cholera incidence, with the Northeast having the highest case rates (180 per 100,000). Healthcare system preparedness varied, with the Southwest demonstrating faster response times (6 days) compared to the Northeast (14 days). Public health interventions reduced cholera cases by up to 50% in some regions, though challenges like vaccine hesitancy and logistical barriers persisted. Conclusion Cholera propagation in Nigeria is multifaceted, requiring region-specific strategies that address environmental, socioeconomic, and healthcare vulnerabilities. Strengthening WASH infrastructure, expanding vaccination coverage, and improving emergency response systems are critical to mitigating future outbreaks. Policymakers must prioritize sustainable interventions tailored to the unique challenges of each geopolitical zone to achieve long-term cholera control.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".