Inequalities in measles immunization coverage in Ethiopia: a cross-sectional analysis of demographic and health surveys 2000–2016
Bibliographic record
Abstract
BACKGROUND: Ethiopia has low measles immunization coverage and little is known about the disparities surrounding what coverage is provided. This study assessed disparities in measles immunization and its change over time using the four Ethiopia Demographic and Health Surveys conducted between 2000 and 2016. METHODS: This is a cross-sectional analysis of data using Ethiopia Demographic and Health Surveys (EDHS) conducted between 2000 and 2016. We used the World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT) to present the inequalities. Four measures of inequality were calculated: Difference (D), Ratio (R), Population Attributable Fraction (PAF) and Population Attributable Risk (PAR). The results were disaggregated by wealth, education, residence, sex and sub-national regions and 95% Uncertainty Intervals (UIs) were computed for each point estimate to boost confidence of the findings. RESULTS: Measles immunization coverage was higher among the richest and secondary and above schools' subgroup by nearly 30 to 31 percentage points based on point estimates (D = 31%; 95% CI; 19.48, 42.66) and 29.8 percentage points (D = 29.8%; 95% CI; 16.57, 43.06) as compared to the poorest and no education subgroup respectively in the 2016 survey. Still, in the 2016 survey, substantial economic status (PAF = 36.73; 95%CI: 29.78, 43.68), (R = 1.71; 95%CI: 1.35, 2.08), education status (PAF = 45.07; 95% CI: 41.95, 48.18), (R = 1.60; 95% CI: 1.30, 1.90), place of residence (PAF = 39.84, 95% CI: 38.40, 41.27), (R = 1.47, 95% CI: 1.20, 1. 74) and regional (PAF = 71.35, 95% CI: 31.76, 110.95), (R = 3.09, 95%CI: 2.01, 4.17) inequality were observed with both simple and complex measures. There was no statistically significant difference in the prevalence of measles immunization between male and female children in all the studied years, as indicated, for instance, by measures of PAF in 2000 (PAF = 0; 95%CI: - 6.79, 6.79), 2005 (PAF = 0; 95%CI: - 6.04, 6.04), 2011(PAF = 0; 95%CI: - 3.79, 3.79) and 2016 (PAF = 2.66; - 1.67; 6.99). Overall, the inequality of measles immunization narrowed significantly by at least some of the measures between the first and the last survey periods across all the studied subgroups. CONCLUSIONS: National, regional and district levels of government should make a pledge to reduce inequalities in coverage of measles immunization. Equity-sensitive strategies, sufficient human and financial resources as well as continued research and monitoring of immunization coverage inequalities are necessary to achieve related sustainable development goals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".