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A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping: disaggregation of areal unit based data to high-resolution grids

A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping: disaggregation of areal unit based data to high-resolution grids
A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping: disaggregation of areal unit based data to high-resolution grids
The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.
Vaccination coverage, spatial misalignment, Bayesian inference, INLA-SPDE, Demographic and Health Surveys
0962-2802
Utazi, Chigozie
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Thorley, Julia
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Alegana, Victor
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Ferrari, Matthew
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Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Takahashi, Saki
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Metcalf, C.J.E.
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Lessler, Justin
37da6ebb-d802-4202-99eb-b183d5fbcede
Tatem, Andrew
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Utazi, Chigozie
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Thorley, Julia
d79524b8-5f1a-45cb-8b9a-89751ae461db
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Ferrari, Matthew
c6c0101b-9fb3-4244-9f73-e8481ff821ed
Nilsen, Kristine
306e0bd5-8139-47db-be97-47fe15f0c03b
Takahashi, Saki
9fdda80e-7d39-457d-9633-c61b9da6deb0
Metcalf, C.J.E.
6b7f06bd-e6b4-4c9c-a3e2-027d710aff1d
Lessler, Justin
37da6ebb-d802-4202-99eb-b183d5fbcede
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Utazi, Chigozie, Thorley, Julia, Alegana, Victor, Ferrari, Matthew, Nilsen, Kristine, Takahashi, Saki, Metcalf, C.J.E., Lessler, Justin and Tatem, Andrew (2018) A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping: disaggregation of areal unit based data to high-resolution grids. Statistical Methods in Medical Research. (doi:10.1177/0962280218797362).

Record type: Article

Abstract

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

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e-pub ahead of print date: 19 September 2018
Keywords: Vaccination coverage, spatial misalignment, Bayesian inference, INLA-SPDE, Demographic and Health Surveys

Identifiers

Local EPrints ID: 425397
URI: https://eprints.soton.ac.uk/id/eprint/425397
ISSN: 0962-2802
PURE UUID: 42ce3639-eb95-44a0-b9b3-30690abbf20c
ORCID for Victor Alegana: ORCID iD orcid.org/0000-0001-5177-9227
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 17 Oct 2018 16:30
Last modified: 10 Dec 2019 01:37

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