Inequalities in measles immunization coverage in Ethiopia: a cross-sectional analysis of demographic and health surveys 2000–2016
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».