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District‐level estimation of vaccination coverage: Discrete vs continuous spatial models

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
0277-6715
2197-2211
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
11fe21e7-431a-442b-a8c7-6a7cb05176d9
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
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
11fe21e7-431a-442b-a8c7-6a7cb05176d9
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Utazi, C. Edson, Nilsen, Kristine, Pannell, Oliver, Dotse-Gborgbortsi, Winfred and Tatem, Andrew (2021) District‐level estimation of vaccination coverage: Discrete vs continuous spatial models. Statistics in Medicine, 40 (9), 2197-2211. (doi:10.1002/sim.8897).

Record type: Article

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.

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More information

Accepted/In Press date: 15 January 2021
e-pub ahead of print date: 4 February 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
ORCID for Oliver Pannell: ORCID iD orcid.org/0000-0003-2559-2818
ORCID for Winfred Dotse-Gborgbortsi: ORCID iD orcid.org/0000-0001-7627-1809
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 12 Mar 2021 17:36
Last modified: 16 Sep 2021 11:12

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Contributors

Author: C. Edson Utazi
Author: Kristine Nilsen
Author: Oliver Pannell ORCID iD
Author: Andrew Tatem ORCID iD

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