Predictors of immunization coverage among 12–23 month old children in Ethiopia: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Immunization is one of modern medicine’s greatest achievements in the last three decades. Annually it can prevent nearly 2 to 3 million deaths. Understanding the determinants of effective immunization coverage is a critical undertaking. Accordingly, we set out to check the best available evidence of outstanding predictors of immunization coverage among children aged 12–23 months in Ethiopia. Method Electronic databases including PubMed, Google Scholar, HINARI, and SCOPUS, Web of Science, African Journals Online, Ethiopian Medical Journals were searched. The search process, study selection, critical appraisal, and data extraction were done independently by two reviewers using Joanna Briggs Institute Meta-analysis for Review Instrument (JBI-MAStARI). The difference between reviewers was resolved with a third person. The risk of bias was assessed by the Newcastle Ottawa Tool for observational studies. Data were extracted using the Microsoft Excel checklist and exported to STATA 13. Heterogeneity was assessed using I2, Funnel plot and Egger’s test was used to check for publication bias. Results We identified 26 studies with 15,042 children with mothers/caretakers to assess factors associated with immunization coverage and significant factors were: maternal formal education, (OR = 2.45; 95% CI: 1.62–3.72), paternal formal education, (OR = 1.01; 95% CI: 0.27–3.77), residence, (OR = 2.11; 95% CI: 1.00–4.45), birth at health facility (OR = 1.86; 95% CI: 0.99–3.49), family size less than four, (OR = 1.81; 95% CI: 1.16–2.84), knowledge on age of immunization to be completed (OR = 6.18;95% CI: 3.07–12.43), knowledge on immunization schedule (OR = 2.49; 95% CI: 1.35–4.59), time to travel to health faculties, (OR = 1.74; 95% CI: 0.62–4.89), antennal care, (OR = 3.11; 95% CI: 1.64–5.88), and tetanus toxoid vaccination, (OR = 4.82; 95% CI: 2.99–7.75). Conclusion Our findings showed that literacy, residence, awareness, family size, maternal health services use, and proximity of the health facilities were factors associated with full immunization. This implies that there is a need for primary health service expansion and health education to “hard to reach areas” to improve immunization coverage for children aged 12–23 months.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".