A Multilevel Analysis of Factors Associated with Childhood Diarrhea in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Childhood diarrhea is the major contributor to the deaths of children under the age of 5 years in Ethiopia, but evidence at the national level to identify the contributing factors associated with diarrhea by considering the clustering effects is limited. Hence, this study aimed to identify factors associated with childhood diarrhea at the individual and community levels. METHODS: A secondary data analysis was conducted using the 2011 and 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 23 321 children with their mothers were included in this study, and multilevel logistic regression models were applied for the data analysis. RESULTS: The odds of diarrhea among female children were 13% lower (AOR = 0.87; 95% CI: 0.79-0.94) compared with male children. The odds of diarrhea among children aged between 13 and 24 months were 31% higher than (AOR = 1.31; 95% CI: 1.17-1.47) their younger counter parts. Children aged ⩾25 months (AOR = 0.50; 95% CI: 0.45-0.56), those whose mothers were unemployed (AOR = 0.79; 95% CI: 0.73-0.87), and children live in households between 2 and 3 under-5 children (AOR = 0.87; 95% CI: 0.79-0.96) were associated with lower odds of experiencing diarrhea. The odds of diarrhea among children whose mother had no formal education were 49% higher than (AOR = 1.49; 95% CI: 1.08-2.07) their counterparts. Besides, children residing in city administrations (AOR = 0.69; 95% CI: 0.58-0.82) had lower odds of experiencing diarrhea than children living in agrarian regions. CONCLUSIONS: At the individual level (sex and age of the child, mother's employment status, and educational level, and the number of under-5 children) and the community-level (contextual region) were found to be significant factors associated with childhood diarrhea in Ethiopia.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".