Decomposing the gap in missed opportunities for vaccination between poor and non-poor in sub-Saharan Africa: A Multicountry Analyses

被引:30
|
作者
Ndwandwe, Duduzile [1 ]
Uthman, Olalekan A. [2 ,3 ]
Adamu, Abdu A. [1 ,3 ]
Sambala, Evanson Z. [1 ]
Wiyeh, Alison B. [1 ]
Olukade, Tawa [4 ]
Bishwajit, Ghose [5 ]
Yaya, Sanni [5 ]
Okwo-Bele, Jean-Marie
Wiysonge, Charles S. [1 ,3 ,6 ]
机构
[1] South African Med Res Council, Cochrane South Africa, Cape Town, South Africa
[2] Univ Warwick, Med Sch, Div Hlth Sci, WCAHRD, Coventry, W Midlands, England
[3] Stellenbosch Univ, Fac Med & Hlth Sci, Ctr Evidence Based Hlth Care, Div Epidemiol & Biostat,Dept Global Hlth, Cape Town, South Africa
[4] Ctr Evidence Based Global Hlth, Dept Res & Dev, Minna, Nigeria
[5] Univ Ottawa, Sch Int Dev & Global Studies, Ottawa, ON, Canada
[6] Univ Cape Town, Sch Publ Hlth & Family Med, Div Epidemiol & Biostat, Cape Town, South Africa
基金
新加坡国家研究基金会;
关键词
Missed opportunities for vaccination; inequality in missed opportunities for vaccination; immunisation coverage; DHS survey data; Decomposition analysis; sub-Saharan Africa; FUNDAMENTAL CAUSE; IMMUNIZATION; INEQUALITIES;
D O I
10.1080/21645515.2018.1467685
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Understanding the gaps in missed opportunities for vaccination (MOV) in sub-Saharan Africa would inform interventions for improving immunisation coverage to achieving universal childhood immunisation. We aimed to conduct a multicountry analyses to decompose the gap in MOV between poor and non-poor in SSA. We used cross-sectional data from 35 Demographic and Health Surveys in SSA conducted between 2007 and 2016. Descriptive statistics used to understand the gap in MOV between the urban poor and non-poor, and across the selected covariates. Out of the 35 countries included in this analysis, 19 countries showed pro-poor inequality, 5 showed pro-non-poor inequality and remaining 11 countries showed no statistically significant inequality. Among the countries with statistically significant pro-illiterate inequality, the risk difference ranged from 4.2% in DR Congo to 20.1% in Kenya. Important factors responsible for the inequality varied across countries. In Madagascar, the largest contributors to inequality in MOV were media access, number of under-five children, and maternal education. However, in Liberia media access narrowed inequality in MOV between poor and non-poor households. The findings indicate that in most SSA countries, children belonging to poor households are most likely to have MOV and that socio-economic inequality in is determined not only by health system functions, but also by factors beyond the scope of health authorities and care delivery system. The findings suggest the need for addressing social determinants of health.
引用
收藏
页码:2358 / 2364
页数:7
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