Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling
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 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.
Child health, Composite coverage index, Geospatial modelling, Peru, Woman’s health
Ferreira, Leonardo Z.
3176827f-f29b-4d50-b2d1-d06c7f0ef23d
Utazi, Chigozie
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Huicho, Luis
108e7d9f-487a-4804-a845-e7cbd956335e
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Hartwig, Fernando P.
573a8bed-958e-4aab-b8f9-28953516db2f
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Barros, Aluisio J.D.
0ed91e55-093f-44b6-8367-940a30431f21
17 November 2022
Ferreira, Leonardo Z.
3176827f-f29b-4d50-b2d1-d06c7f0ef23d
Utazi, Chigozie
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Huicho, Luis
108e7d9f-487a-4804-a845-e7cbd956335e
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Hartwig, Fernando P.
573a8bed-958e-4aab-b8f9-28953516db2f
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Barros, Aluisio J.D.
0ed91e55-093f-44b6-8367-940a30431f21
Ferreira, Leonardo Z., Utazi, Chigozie, Huicho, Luis, Nilsen, Kristine, Hartwig, Fernando P., Tatem, Andrew and Barros, Aluisio J.D.
(2022)
Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling.
BMC Public Health, 22 (1), [2104].
(doi:10.1186/s12889-022-14371-7).
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.
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s12889-022-14371-7
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More information
Accepted/In Press date: 14 October 2022
Published date: 17 November 2022
Additional Information:
Funding Information:
This work was supported by the Bill and Melinda Gates Foundation [through the Countdown to 2030 initiative, OPP1148933]; by Wellcome [Grant Number: 101815/Z/13/Z]; and by the Associação Brasileira de Saúde Coletiva (ABRASCO).
Keywords:
Child health, Composite coverage index, Geospatial modelling, Peru, Woman’s health
Identifiers
Local EPrints ID: 474492
URI: http://eprints.soton.ac.uk/id/eprint/474492
ISSN: 1471-2458
PURE UUID: cbd00217-33dd-4d34-98c0-bcdc1559b805
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Date deposited: 23 Feb 2023 00:39
Last modified: 17 Mar 2024 03:35
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Contributors
Author:
Leonardo Z. Ferreira
Author:
Luis Huicho
Author:
Fernando P. Hartwig
Author:
Aluisio J.D. Barros
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