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

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...

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Autores principales: Ferreira, Leonardo Z., Utazi, C. Edson, Huicho, Luis, Nilsen, Kristine, Hartwig, Fernando P., Tatem, Andrew J., Barros, Aluisio J. D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670533/
https://www.ncbi.nlm.nih.gov/pubmed/36397019
http://dx.doi.org/10.1186/s12889-022-14371-7
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author Ferreira, Leonardo Z.
Utazi, C. Edson
Huicho, Luis
Nilsen, Kristine
Hartwig, Fernando P.
Tatem, Andrew J.
Barros, Aluisio J. D.
author_facet Ferreira, Leonardo Z.
Utazi, C. Edson
Huicho, Luis
Nilsen, Kristine
Hartwig, Fernando P.
Tatem, Andrew J.
Barros, Aluisio J. D.
author_sort Ferreira, Leonardo Z.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14371-7.
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spelling pubmed-96705332022-11-18 Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling Ferreira, Leonardo Z. Utazi, C. Edson Huicho, Luis Nilsen, Kristine Hartwig, Fernando P. Tatem, Andrew J. Barros, Aluisio J. D. BMC Public Health Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-14371-7. BioMed Central 2022-11-17 /pmc/articles/PMC9670533/ /pubmed/36397019 http://dx.doi.org/10.1186/s12889-022-14371-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ferreira, Leonardo Z.
Utazi, C. Edson
Huicho, Luis
Nilsen, Kristine
Hartwig, Fernando P.
Tatem, Andrew J.
Barros, Aluisio J. D.
Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title_full Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title_fullStr Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title_full_unstemmed Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title_short Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
title_sort geographic inequalities in health intervention coverage – mapping the composite coverage index in peru using geospatial modelling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9670533/
https://www.ncbi.nlm.nih.gov/pubmed/36397019
http://dx.doi.org/10.1186/s12889-022-14371-7
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