District‐level estimation of vaccination coverage: discrete vs continuous spatial models
District‐level estimation of vaccination coverage: discrete vs continuous spatial models
Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low‐ and middle‐income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model‐based approaches for producing subnational estimates of HDIs using survey data, particularly cluster‐level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district‐level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district‐level data with continuous Gaussian process (GP) models that utilize geolocated cluster‐level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015‐16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between‐cluster variation in the continuous GP models did not have any real effect on the district‐level estimates. Our results provide guidance to practitioners on the reliability of these model‐based approaches for producing estimates of vaccination coverage and other HDIs.
INLA-SPDE, continuous spatial models, discrete spatial models, district-level estimation, household surveys, vaccination coverage
2197-2211
Utazi, C. Edson
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Nilsen, Kristine
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Pannell, Oliver
370b302f-0b96-4fa5-b96b-5330cfef2263
Dotse-Gborgbortsi, Winfred
02d3e356-268e-4650-9fb9-9638ccdb6eff
Tatem, Andrew J.
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1 April 2021
Utazi, C. Edson
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Pannell, Oliver
370b302f-0b96-4fa5-b96b-5330cfef2263
Dotse-Gborgbortsi, Winfred
02d3e356-268e-4650-9fb9-9638ccdb6eff
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Utazi, C. Edson, Nilsen, Kristine, Pannell, Oliver, Dotse-Gborgbortsi, Winfred and Tatem, Andrew J.
(2021)
District‐level estimation of vaccination coverage: discrete vs continuous spatial models.
Statistics in Medicine, 40 (9), .
(doi:10.1002/sim.8897).
Abstract
Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low‐ and middle‐income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model‐based approaches for producing subnational estimates of HDIs using survey data, particularly cluster‐level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district‐level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district‐level data with continuous Gaussian process (GP) models that utilize geolocated cluster‐level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015‐16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between‐cluster variation in the continuous GP models did not have any real effect on the district‐level estimates. Our results provide guidance to practitioners on the reliability of these model‐based approaches for producing estimates of vaccination coverage and other HDIs.
Text
Statistics in Medicine - 2021 - Utazi - District‐level estimation of vaccination coverage Discrete vs continuous spatial
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Accepted/In Press date: 15 January 2021
e-pub ahead of print date: 4 February 2021
Published date: 1 April 2021
Keywords:
INLA-SPDE, continuous spatial models, discrete spatial models, district-level estimation, household surveys, vaccination coverage
Identifiers
Local EPrints ID: 447509
URI: http://eprints.soton.ac.uk/id/eprint/447509
ISSN: 0277-6715
PURE UUID: 80137747-a45f-4572-8a0a-056fae0c2582
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Date deposited: 12 Mar 2021 17:36
Last modified: 10 Apr 2024 02:12
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Author:
Oliver Pannell
Author:
Winfred Dotse-Gborgbortsi
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