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Predicting health programme participation: a gravity-based, hierarchical modelling approach

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  • Nicole White
  • Kerrie Mengersen

Abstract

type="main" xml:id="rssc12111-abs-0001"> Statistical analyses of health programme participation seek to address a number of objectives that are 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 to account for competition effects, for programmes delivered by multiple clinics. The implications of gravity model misspecification within a mixed effects framework are also explored. The model proposed is applied to participation data from a no-fee mammography programme in Brisbane, Australia. Attention is paid to the interpretation of various model outputs and their relevance for public health policy.

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  • Nicole White & Kerrie Mengersen, 2016. "Predicting health programme participation: a gravity-based, hierarchical modelling approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(1), pages 145-166, January.
    • Handle: RePEc:bla:jorssc:v:65:y:2016:i:1:p:145-166
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    File URL: http://hdl.handle.net/10.1111/rssc.2016.65.issue-1
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    Cited by:

    1. Nicholas J Tierney & Antonietta Mira & H Jost Reinhold & Giuseppe Arbia & Samuel Clifford & Angelo Auricchio & Tiziano Moccetti & Stefano Peluso & Kerrie L Mengersen, 2019. "Evaluating health facility access using Bayesian spatial models and location analysis methods," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.

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