Associations of WHO/UNICEF Joint Monitoring Program (JMP) Water, Sanitation and Hygiene (WASH) Service Ladder service levels and sociodemographic factors with diarrhoeal disease among children under 5 years in Bishoftu town, Ethiopia: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To determine the associations of WHO/UNICEF Joint Monitoring Program Water, Sanitation and Hygiene (WASH) Service Ladder service levels and sociodemographic factors with diarrhoeal disease among children under 5 years in Bishoftu town, Ethiopia. DESIGN: A community-based cross-sectional study. SETTING: Bishoftu town, Ethiopia, January-February 2022. PARTICIPANTS: A total of 1807 mothers with at least one child under 5 years were included. Sociodemographic and WASH variables were collected using a structured questionnaire. 378 drinking water samples were collected. OUTCOME: The response variable was diarrhoeal disease among children under 5 years. RESULTS: The 2-week prevalence of diarrhoeal disease among children under 5 years was 14.8%. Illiteracy (adjusted OR 3.15; 95% CI 1.54 to 6.47), occupation (0.35; 0.20 to 0.62), mother's age (1.63; 1.15 to 2.31), family size (2.38; 1.68 to 3.39), wealth index (5.91; 3.01 to 11.59), residence type (1.98; 1.35 to 2.90), sex of the child (1.62; 1.17 to 2.24), child's age (3.52; 2.51 to 4.93), breastfeeding status (2.83; 1.74 to 4.59), food storage practice (3.49; 1.74 to 8.26), unimproved drinking water source (8.16; 1.69 to 39.46), limited drinking water service (4.68; 1.47 to 14.95), open defecation practice (5.17; 1.95 to 13.70), unimproved sanitation service (2.74; 1.60 to 4.67), limited sanitation service (1.71; 1.10 to 2.65), no hygiene service (3.43; 1.91 to 6.16) and limited hygiene service (2.13; 1.17 to 3.86) were significantly associated with diarrhoeal disease. CONCLUSION: In this study, diarrhoea among children is a significant health issue. Child's age, drinking water service, residence type and hygiene service were the largest contributors with respect to the prevalence of diarrhoeal disease. This investigation provides information that could help to inform interventions to reduce childhood diarrhoea. The findings suggest that state authorities should initiate robust WASH strategies to achieve the Sustainable Development Goal 3 agenda.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".