Geospatial analysis of cholera patterns in Nigeria: findings from a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Persistence of cholera outbreaks in developing countries calls for concern and more targeted intervention measures for long-term control. This research undertook spatial analysis of cholera incidence in Nigeria over a seventeen-year period to determine the existence of regional hotspots and predictors. METHODS: A cross-sectional study design was used for the research. Cholera data for each of the thirty-six states and the federal capital territory (FCT) were obtained from the Nigeria Centre for Disease Control (NCDC) of the Federal Ministry of Health, Nigeria. Socioeconomic data including proportion of households using solid waste disposal (unapproved dumpsite, refuse burying, refuse burning, public dumpsite, and refuse collectors), water sources (pipe borne water, well, borehole, rain water, surface waters and water vendors), sewage disposal (water closet, pit latrines, bucket/pan, public toilet and nearby bush/stream), living in a single room and earning less than minimum wage (18,000 naira) were obtained from National Population Commission. On the other hand, proportion of illiterate adults (15 years and above) and poor people; and population density were obtained from National Bureau of Statistics. Each socioeconomic data was obtained at state level. Cholera patterns were analysed at state level using Global Moran's I while specific locations of cholera clusters were determined using Local Moran's I. Stepwise multiple regression was used to determine socioeconomic predictors of cholera incidence. RESULTS: Local Moran's I revealed significant cluster patterns in 1999, 2001, 2002, 2009 and 2010 in Adamawa, Gombe, Katsina, Bauchi, Borno, Yobe, and Kano states. Households using surface water was the significant predictor (23%) of the observed spatial variations in cholera incidence. CONCLUSIONS: Persistence of cholera outbreaks in some north east and north western states calls for more targeted, long-term and effective intervention measures especially on provision of safe sources of water supply by government and other stakeholders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".