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dc.contributor.authorDaca, Chanvo S. L.-
dc.contributor.authorSchumann, Barbara-
dc.contributor.authorArnaldo, Carlos-
dc.contributor.authorSebastian, Miguel San-
dc.date.accessioned2024-05-16T07:07:46Z-
dc.date.available2024-05-16T07:07:46Z-
dc.date.issued2022-
dc.identifier.citationChanvo S. L. Daca, Barbara Schumann, Carlos Arnaldo & Miguel San Sebastian (2022) Wealth inequalities in reproductive and child health preventive care in Mozambique: a decomposition analysis, Global Health Action, 15:1, 2040150, DOI: 10.1080/16549716.2022.2040150en_US
dc.identifier.otherhttps://www.tandfonline.com/doi/pdf/10.1080/00036846.2023.2203457-
dc.identifier.urihttp://www.repositorio.uem.mz/handle258/940-
dc.language.isoengen_US
dc.publisherTaylor & Francis Groupen_US
dc.relation.ispartofseries15;2043-
dc.rightsopenAcessen_US
dc.subjectSocioeconomic inequalityen_US
dc.subjectDecomposition analysisen_US
dc.subjectHealth preventive careen_US
dc.subjectMozambiqueen_US
dc.titleWealth inequalities in reproductive and child health preventive care in Mozambique: a decomposition analysisen_US
dc.typearticleen_US
dc.description.resumoBackground: Assessing the gap between rich and poor is important to monitor inequalities in health. Identifying the contribution to that gap can help policymakers to develop inter ventions towards decreasing that difference. Objective: To quantify the wealth inequalities in health preventive measures (bed net use, vaccination, and contraceptive use) to determine the demographic and socioeconomic con tribution factors to that inequality using a decomposition analysis. Methods: Data from the 2015 Immunisation, Malaria and AIDs Indicators Survey were used. The total sample included 6946 women aged 15–49 years. Outcomes were use of insecticide treated nets (ITN), child vaccination, and modern contraception use. Wealth Index was the exposure variable and age, marital status, place of residence, region, education, occupation, and household wealth index were the explanatory variables. Wealth inequalities were assessed using concentration indexes (Cindex). Wagstaff-decomposition analysis was con ducted to assess the determinants of the wealth inequality. Results: The Cindex was −0.081 for non-ITN, −0.189 for lack of vaccination coverage and −0.284 for non-contraceptive use, indicating a pro-poor inequality. The results revealed that 88.41% of wealth gap for ITN was explained by socioeconomic factors, with education and wealth playing the largest roles. Lack of full vaccination, socioeconomic factors made the largest contribution, through the wealth variable, whereas geographic factors came next. Finally, the lack of contraceptive use, socioeconomic factors were the main explanatory factors, but to a lesser degree than the other two outcomes, with wealth and education contributing most to explaining the gap. Conclusion: There was a pro-poor inequality in reproductive and child preventive measures in Mozambique. The greater part of this inequality could be attributed to wealth, education, and residence in rural areas. Resources should be channeled into poor and non-educated rural communities to tackle these persistent inequities in preventive care.en_US
dc.journalGlobal Health Actionen_US
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