A hierarchical Bayesian model for space–time variation of disease risk

Lagazio, Corrado; Dreassi, Emanuela; Biggeri, Annibale

doi:10.1191/147108201128069

Cited by 30 publications

(41 citation statements)

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“…For the fixed time effect model, δ 1 is set to be zero and δ 2 to δ 5 is assigned highly non-informative normal priors with zero mean and 100,000 variance. For the random time effect model, δ t is assumed to be temporally correlated and assigned a RW (1) prior, which can be modelled using the CAR distribution in WinBUGS (Lagazio et al, 2001;Thomas et al, 2004). Similar to the specification of the prior distribution of u i , the weight between neighbouring time periods (e. …”

Section: Spatial Models Using Full Bayesian Hierarchical Approachmentioning

confidence: 99%

A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK

Wang

Quddus

Ison

2013

Transportmetrica A: Transport Science

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Section: Spatial Models Using Full Bayesian Hierarchical Approachmentioning

confidence: 99%

A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK

Wang

Quddus

Ison

2013

Transportmetrica A: Transport Science

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“…The models so far proposed use mainly two time scales, generally age and calendar period (or, less frequently, birth cohort). Bernardinelli et al (1995) modelled period effects with a linear spatially structured term; Waller et al (1997) used crude or age-standardized rates by area and calendar periods with structured and unstructured space parameters indexed also over periods; Assunçao et al (2001) considered period effects that are modelled with a second degree polynomial with spatially structured priors; Sun et al (2000) considered an age-period-area table, specifying age, area and age-area-specific linear time effects; Knorr-Held and Besag (1998) improved the model proposed by Waller et al (1997) by considering two different time scales-age and calendar period; Knorr-Held (2000) gave a general classification of the different specifications of interaction terms between space and time effects; Lagazio et al (2001) proposed a model where birth cohort is the main time dimension.…”

Section: Introductionmentioning

confidence: 99%

Age–period–cohort models and disease mapping

2003

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SUMMARYJoint modelling of space and time variation of the risk of disease is an important topic in descriptive epidemiology. Most of the proposals in this field deal with at most two time scales (age-period or age-cohort). We propose a hierarchical Bayesian model that can be used as a general framework to jointly study the evolution in time and the spatial pattern of the risk of disease. The rates are modelled as a function of purely spatial terms (local effects of risk factors that do not vary in time), time effects (on the three time axes: age, calendar period and birth cohort) and space-time interactions that describe area specific time patterns.

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“…For space-time area-based data, there is a large literature in disease mapping, adopting Bayesian modelling to produce smoothed estimates of the area-year-specific disease rates (e.g., Waller, Carlin, Xia & Gelfand 1997;Knorr-Held & Besag 1998;Lagazio, Dreassi & Biggeri 2001;Assunção, Reis & Oliveira 2001;MacNab & Dean 2002;Knorr-Held & Richardson 2003). Gangnon & Clayton (2002, 2004 proposed methods that simultaneously address both the cluster detection problem and the cluster modelling approach.…”

Section: Inferential Approaches For Space-time Clustersmentioning

confidence: 99%

Space‐time cluster identification in point processes

et al. 2007

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Abstract:The authors propose a new type of scan statistic to test for the presence of space-time clusters in point processes data, when the goal is to identify and evaluate the statistical significance of localized clusters. Their method is based only on point patterns for cases; it does not require any specific knowledge of the underlying population. The authors propose to scan the three-dimensional space with a score test statistic under the null hypothesis that the underlying point process is an inhomogeneous Poisson point process with space and time separable intensity. The alternative is that there are one or more localized space-time clusters. Their method has been implemented in a computationally efficient way so that it can be applied routinely. They illustrate their method with space-time crime data from Belo Horizonte, a Brazilian city, in addition to presenting a Monte Carlo study to analyze the power of their new test. Identification d'agrégation spatio-temporelle dans des processus ponctuelsRésumé : Les auteurs proposent une statistique permettant de détecter la présence d'agrégation spatiotemporelle dans des données issues de processus ponctuels, l'objectifétant d'identifier des regroupements et d'évaluer s'ils sont statistiquement significatifs. Leur méthode ne dépend que de la disposition des observations ; elle ne nécessite aucune connaissance particulière de la population sous-jacente. Une statistique de type score mesurée sur l'ensemble de l'espaceà trois dimensions leur permet de tester l'hypothèse nullè a l'effet que le processus ponctuel sous-jacent est un processus de Poisson hétérogène dont les composantes spatiale et temporelle de l'intensité sont séparables. La contre-hypothèse està l'effet qu'il existe un ou des regroupements spatio-temporels localisés. L'implantation informatique de leur méthode est efficace au plan calcul et facile d'emploi. Les auteurs l'illustrentà l'aide de données sur la répartition spatio-temporelle de la criminalité dans la ville brésilienne de Belo Horizonte, en plus de présenter uneétude de Monte-Carlo investigant la puissance de leur test.

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A hierarchical Bayesian model for space–time variation of disease risk

Cited by 30 publications

References 0 publications

A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK

A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK

Age–period–cohort models and disease mapping

Space‐time cluster identification in point processes

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