Utilization of integrated community-based case management of childhood illness and associated factors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Despite significant progress being made in reducing under-five mortality, three-fourths of under-five deaths are still caused by preventable conditions such as pneumonia, diarrhea, malaria, and newborn issues. Integrated community case management of childhood illnesses (ICCM) could serve as a means to reduce preventable child mortality in Low- and Middle-Income countries. Our aim was to assess the overall level of ICCM utilization and its associated factors in Ethiopia. Methods Candidate studies for inclusion in this review were identified through searches across various databases, including PubMed, EMBASE, Google Scholar, and university repositories online databases, spanning from February 1, 2024, to March 18, 2024. The quality assessment of the studies included in this systematic review and meta-analysis was conducted using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data extraction and analysis were carried out using Microsoft Excel and Stata 17 software, respectively. Heterogeneity among the studies was assessed using Cochran’s Q test and I 2 statistics, while the presence of publication bias was evaluated through funnel plots and Egger’s regression asymmetry test. Subgroup analysis was performed based on sample size and study site. Results In this study, the pooled level of ICCM utilization was found to be 42.73 (95%, CI 27.65%, 57.80%) based on the evidence obtained from ten primary studies. In this review, parents’ awareness about illness (OR = 2.77, 95%, CI 2.06, 3.74), awareness about ICCM service (OR = 3.64, 95%, CI 2.16, 6.14), perceived severity of the disease (OR = 3.14, 95%, CI 2.33, 4.23), secondary/above level of education (OR = 2.57, 95%, CI 1.39, 4.77), and live within 30 min distance to the health post (OR = 3.93, 95%, CI 2.30, 6.74) were variables significantly associated with utilization of ICCM in Ethiopia. Conclusion The utilization of ICCM was found to be low in Ethiopia. Factors such as parents’ awareness about the illness, knowledge of ICCM services, perceived severity of the disease, attending a secondary or more level of education, and living within 30 min distance to the health post were significantly associated with the utilization of ICCM. Therefore, it is crucial to focus on creating awareness and improving access to high-quality ICCM services to reduce child morbidity and mortality from preventable causes.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".