Predicting health programme participation: A gravity‐based, hierarchical modelling approach

White, Nicole & Mengersen, Kerrie (2016) Predicting health programme participation: A gravity‐based, hierarchical modelling approach. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(1), pp. 145-166.

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Abstract

Statistical analyses of health program participation seek to address a number of objectives compatible with the evaluation of demand for current resources. In this spirit, a spatial hierarchical model is developed for disentangling patterns in participation at the small area level, as a function of population-based demand and additional variation. For the former, a constrained gravity model is proposed to quantify factors associated with spatial choice and account for competition effects, for programs delivered by multiple clinics. The implications of gravity model misspecification within a mixed effects framework are also explored. The proposed model is applied to participation data from a no-fee mammography program in Brisbane, Australia. Attention is paid to the interpretation of various model outputs and their relevance for public health policy.

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ID Code: 90762
Item Type: Journal Article
Refereed: Yes
Keywords: Spatial modelling, Health services research, Bayesian statistics
DOI: 10.1111/rssc.12111
ISSN: 1467-9876
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > Mathematical Sciences
Funding:
  • CRCSI/4.42
Copyright Owner: Copyright 2015 Royal Statistical Society
Deposited On: 26 Nov 2015 02:40
Last Modified: 01 Feb 2017 14:00

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