A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

Anderson, Craig; Ryan, Louise

doi:10.3390/ijerph14020146

Cited by 30 publications

(34 citation statements)

References 38 publications

(49 reference statements)

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“…For examples, a separate Besag-York-Mollie model can be fit at each time point (Knorr-Held & Besag, 1998;Waller et al, 1997), or a CAR model can be combined with a random walk in time (Knorr-Held & Besag, 1998), a spline-based temporal structure (MacNab & Dean, 2001) or a local autoregressive model in time (Congdon & Southall, 2005). For a thorough review and comparison of existing spatio-temporal models, see the work of Anderson and Ryan (2017). A county-by-county exploratory analysis of our Lyme disease data suggests that a first-order autoregressive (AR(1)) model is sufficient for handling temporal dependence.…”

Section: Modeling Methodsmentioning

confidence: 99%

A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends

et al. 2018

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This paper develops a large‐scale Bayesian spatio‐temporal binomial regression model to investigate regional trends in antibody prevalence to Borrelia burgdorferi, the causative agent of Lyme disease. Our model uses Gaussian predictive processes to estimate the spatially varying trends and a conditional autoregressive scheme to account for spatio‐temporal dependence. A novel framework, easily scalable to large spatio‐temporal data, is developed. The proposed model is used to analyze about 16 million B. burgdorferi antibody Lyme tests performed on canine samples in the conterminous United States over the sixty‐month period from January 2012 to December 2016. This analysis identifies areas of increasing canine Lyme disease risk; prevalence of infection is getting worse in endemic regions and increases are also seen in non‐endemic regions. Because Lyme disease is zoonotic, affecting both humans and dogs, the analysis also serves to pinpoint areas of increasing human risk.

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Section: Modeling Methodsmentioning

confidence: 99%

A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends

et al. 2018

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“…, T ), the latent process has a general form which can be expressed as: X t,i = a i + b t + c t,i , where the terms are indexed according to the components they represent. For example, the specification in Waller et al (1997) is a direct extension of the BYM model to the spatiotemporal case where spatial dependence is modelled separately for each time point so that a i = b t = 0 and c t,i is a sum of spatial (CAR) and independent random effects at time t. In many of these spatiotemporal models (see Anderson & Ryan, 2017, for a review), separate latent variables/random effects are used to capture spatial and temporal dependence.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In this work, we consider two model choice criteria that appear to be popular in the literature for areal data models for determining the best model/parameterization from the set of models developed here for any given set of data. Also, as a diagnostic tool to assess the residuals of the best-fitting model, we employ the spatiotemporal version of the Moran's I statistic (see Anderson & Ryan, 2017). These are discussed as follows.…”

Section: Model Choice and Evaluationmentioning

confidence: 99%

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A Bayesian latent process spatiotemporal regression model for areal count data

Utazi

Afuecheta

Nnanatu

2018

Spatial and Spatio-temporal Epidemiology

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Model-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting. Spatiotemporal dependence in the autoregressive model for the latent process is modelled through its transition matrix, with a structured covariance matrix specified for its error term. The proposed model and its parameterizations are fitted in a Bayesian framework implemented via MCMC techniques. Our findings based on real-life examples show that the proposed approach is at least as effective as CAR-based models.

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“…To further investigate the quality of Model 2, we examined the spatio-temporal autocorrelation in the model residuals. A spatio-temporal model with close fit to the data should have low spatio-temporal autocorrelation in its residuals, which is indicative of a good model [49]. For Model 2, we obtained a Moran STI value of 0.009, which implies that there is minimal space-time autocorrelation in the residuals.…”

Section: Model Fitting and Prior Sensitivity Analysismentioning

confidence: 77%

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

Persad¹

2019

Medical Sciences

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Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

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A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

Cited by 30 publications

References 38 publications

A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends

A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends

A Bayesian latent process spatiotemporal regression model for areal count data

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

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