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Estimating health over space and time: a review of spatial microsimulation applied to public health

Estimating health over space and time: a review of spatial microsimulation applied to public health
Estimating health over space and time: a review of spatial microsimulation applied to public health
There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.
182-192
Smith, Dianna
e859097c-f9f5-4fd0-8b07-59218648e726
Heppenstall, Alison
ef76e474-a595-4f61-915c-56a15e1c64c0
Campbell, Monique, Alayne
8e43da40-9608-4105-a629-98ce994e8832
Smith, Dianna
e859097c-f9f5-4fd0-8b07-59218648e726
Heppenstall, Alison
ef76e474-a595-4f61-915c-56a15e1c64c0
Campbell, Monique, Alayne
8e43da40-9608-4105-a629-98ce994e8832

Smith, Dianna, Heppenstall, Alison and Campbell, Monique, Alayne (2021) Estimating health over space and time: a review of spatial microsimulation applied to public health. J, 4 (2), 182-192. (doi:10.3390/j4020015).

Record type: Article

Abstract

There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.

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Accepted/In Press date: 4 June 2021
Published date: 9 June 2021

Identifiers

Local EPrints ID: 449972
URI: http://eprints.soton.ac.uk/id/eprint/449972
PURE UUID: c7a10a2b-a17b-4442-a612-a3b6d302be76
ORCID for Dianna Smith: ORCID iD orcid.org/0000-0002-0650-6606

Catalogue record

Date deposited: 01 Jul 2021 16:30
Last modified: 02 Jul 2021 01:48

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Contributors

Author: Dianna Smith ORCID iD
Author: Alison Heppenstall

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