Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

Dianna M. Smith, Alison Heppenstall and Monique Campbell
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Dianna M. Smith: School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Alison Heppenstall: School of Geography, University of Leeds, Leeds LS2 9JT, UK
Monique Campbell: School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK

J, 2021, vol. 4, issue 2, 1-11

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.

Keywords: spatial microsimulation; public health; individual-based models; simulation models (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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