Risk Factors of Cholera Transmission in Al Hudaydah, Yemen: Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Yemen has recently faced the largest cholera outbreak in the world, which started at the end of 2016. By the end of 2017, the cumulative reported cases from all governorates reached 777,229 with 2134 deaths. Al Hudaydah was one of the most strongly affected areas, with 88,741 (18%) cases and 244 (12%) deaths reported. OBJECTIVE: The aim of this study was to determine the risk factors associated with cholera transmission in Al Hudaydah city, Yemen. METHODS: From December 1, 2017 to January 10, 2018, a total of 104 patients with cholera (57 women and 47 men) who presented at cholera treatment centers in Al Hudaydah city with three or more watery stools in a 24-hour period and with moderate or severe dehydration were identified for inclusion in this study. Each case was matched by age and gender with two controls who were living in the neighboring house. A semistructured questionnaire was used to collect data on behavioral and environmental risk factors such as drinking water from public wells, storing water in containers, consumption of unwashed vegetables or fruits, and sharing a toilet. RESULTS: The median age of the cases and controls was 20 years (range 5-80) and 23 years (range 5-85), respectively. Only 6% of cases and 4% of controls were employed. Multivariate analysis showed that eating unwashed vegetables or fruits (odds ratio [OR] 7.0, 95% CI 1.6-30.6, P=.01), storing water in containers (OR 3.0, 95% CI 1.3-7.3, P=.01), drinking water from a public well (OR 2.5, 95% CI 1.1-5.7, P=.02), and using a public toilet (OR 5.2, 95% CI 1.1-24.4, P=.04) were significantly associated with cholera infection risk. CONCLUSIONS: The cholera transmission risk factors in Al Hudaydah city were related to water and sanitation hygiene. Therefore, increasing awareness of the population on the importance of water chlorination, and washing fruits and vegetables through a health education campaign is strongly recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".