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Geographic inequalities in health intervention coverage - mapping the composite coverage index in Peru using geospatial modelling

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dc.contributor.author Ferreira, L. Z.
dc.contributor.author Utazi, C. E.
dc.contributor.author Huicho Oriundo, Luis
dc.contributor.author Nilsen, K.
dc.contributor.author Hartwig, F. P.
dc.contributor.author Tatem, A. J.
dc.contributor.author Barros, A.J.D.
dc.date.accessioned 2022-12-14T14:25:33Z
dc.date.available 2022-12-14T14:25:33Z
dc.date.issued 2022
dc.identifier.uri https://hdl.handle.net/20.500.12866/12870
dc.description.abstract Background: The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru. Methods: We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level. Results: CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach. Conclusions: Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness. en_US
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries BMC Public Health
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Geospatial modelling en_US
dc.subject Child health en_US
dc.subject Woman’s health en_US
dc.subject Composite coverage index en_US
dc.subject Peru en_US
dc.title Geographic inequalities in health intervention coverage - mapping the composite coverage index in Peru using geospatial modelling en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1186/s12889-022-14371-7
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.03.05
dc.relation.issn 1471-2458


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