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A latent process model for forecasting multiple time series in environmental public health surveillance
- Source :
- Statistics in Medicine. 35:3085-3100
- Publication Year :
- 2016
- Publisher :
- Wiley, 2016.
-
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. Copyright © 2016 John Wiley & Sons, Ltd.
- Subjects :
- Statistics and Probability
Series (stratigraphy)
Epidemiology
Computer science
Process (engineering)
Estimation theory
Univariate
Air pollution
Environmental exposure
Bivariate analysis
Population health
010501 environmental sciences
medicine.disease_cause
01 natural sciences
010104 statistics & probability
Environmental health
Statistics
Econometrics
medicine
0101 mathematics
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 02776715
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- Statistics in Medicine
- Accession number :
- edsair.doi...........49d6b667c786e1aa189c788d3533bb7c