A latent process model for forecasting multiple time series in environmental public health surveillance

Morrison, Kathryn T., Shaddick, Gavin ORCID: https://orcid.org/0000-0002-4117-4264, Henderson, Sarah B. and Buckeridge, David L. 2016. A latent process model for forecasting multiple time series in environmental public health surveillance. Statistics in Medicine 35 (18) , pp. 3085-3100. 10.1002/sim.6904

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Abstract

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003–2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	?? VCO ??
Publisher:	Wiley
ISSN:	0277-6715
Date of Acceptance:	21 January 2016
Last Modified:	26 Jul 2024 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/170764

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