The Bayesian Modeling of Disease Risk in Relation to a Point Source

Wakefield, John C.; Morris, Sara

doi:10.1198/016214501750332992

Cited by 41 publications

(33 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For Model 4, fixing δ = 5, there is a large uncertainty about the value of the decay parameter β, with plausible large values for α. This, in turn, indicates that the results by a Bayesian approach should be very sensitive to modeling strategies; both in terms of a priori specification and of distance function used (Wakefield and Morris, 2001).…”

Section: Resultsmentioning

confidence: 99%

“…In the last years there has been an increasing interest in modeling disease risk in relation to a point source of pollution in a Bayesian framework; see, for example, Wakefield and Morris (2001), Lawson et al (2003) and Congdon (2003). The problem of inference sensitivity to prior distributions is raised by Wakefield and Morris (2001) for models based on aggregated data.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of inference sensitivity to prior distributions is raised by Wakefield and Morris (2001) for models based on aggregated data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity analysis of the relationship between disease occurrence and distance from a putative source of pollution

et al. 2008

View full text Add to dashboard Cite

Abstract.The relation between disease risk and a point source of pollution is usually investigated using distance from the source as a proxy of exposure. The analysis may be based on case-control data or on aggregated data. The definition of the function relating risk of disease and distance is critical, both in a classical and in a Bayesian framework, because the likelihood is usually very flat, even with large amounts of data. In this paper we investigate how the specification of the function relating risk of disease with distance from the source and of the prior distributions on the parameters of the function affects the results when case-control data and Bayesian methods are used. We consider different popular parametric models for the risk distance function in a Bayesian approach, comparing estimates with those derived by maximum likelihood. As an example we have analyzed the relationship between a putative source of environmental pollution (an asbestos cement plant) and the occurrence of pleural malignant mesothelioma in the area of Casale Monferrato (Italy) in 1987-1993. Risk of pleural malignant mesothelioma turns out to be strongly related to distance from the asbestos cement plant. However, as the models appeared to be sensitive to modeling choices, we suggest that any analysis of disease risk around a putative source should be integrated with a careful sensitivity analysis and possibly with prior knowledge. The choice of prior distribution is extremely important and should be based on epidemiological considerations.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of the relationship between disease occurrence and distance from a putative source of pollution

et al. 2008

View full text Add to dashboard Cite

show abstract

“…Besag, York and Mollié (1991) proposed in the fully Bayesian setting a Poisson model with intrinsic conditional autoregressive prior and gave an application to disease mapping. A modification of this model for the point-source problem with high-resolution count data can be found in Kokki et al (2001) and in Wakefield and Morris (2001). In spite of being flexible, the autoregressive hierarchical model is computationally demanding and turned out to be instable for low-count data, see the discussion in Kokki et al (2001).…”

Section: Introductionmentioning

confidence: 97%

Poisson Regression with Change‐Point Prior in the Modelling of Disease Risk around a Point Source

Kokki

Penttinen

2003

Biometrical J

View full text Add to dashboard Cite

Bayesian estimation of the risk of a disease around a known point source of exposure is considered. The minimal requirements for data are that cases and populations at risk are known for a fixed set of concentric annuli around the point source, and each annulus has a uniquely defined distance from the source. The conventional Poisson likelihood is assumed for the counts of disease cases in each annular zone with zone-specific relative risk and parameters and, conditional on the risks, the counts are considered to be independent. The prior for the relative risk parameters is assumed to be piecewise constant at the distance having a known number of components. This prior is the well-known change-point model. Monte Carlo sampling from the posterior results in zone-specific posterior summaries, which can be applied for the calculation of a smooth curve describing the variation in disease risk as a function of the distance from the putative source. In addition, the posterior can be used in the calculation of posterior probabilities for interesting hypothesis. The suggested model is suitable for use in geographical information systems (GIS) aimed for monitoring disease risks. As an application, a case study on the incidence of lung cancer around a former asbestos mine in eastern Finland is presented. Further extensions of the model are discussed.

show abstract

“…This information is crucial to prevent and control a variety of health conditions such as chronic and infectious diseases, injuries, or health-related behaviors (Thacker and A C C E P T E D M A N U S C R I P T Berkelman, 1988;Lawson and Kleinman, 2005). There is a wide range of spatial and spatio-temporal methods and software that can be applied as a surveillance tool, and these are useful for highlighting areas at high risk (Moraga et al, 2015), detecting disease clusters (Moraga and Montes, 2011), assessing spatial variations in temporal trends (Moraga and Kulldorff, 2016), early detection of epidemics (Stelling et al, 2010), assessing disease risk in relation to a putative source (Wakefield and Morris, 2001), and identifying disease risk factors (Hagan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

SpatialEpiApp : A Shiny web application for the analysis of spatial and spatio-temporal disease data

Moraga

2017

Spatial and Spatio-temporal Epidemiology

View full text Add to dashboard Cite

During last years, public health surveillance has been facilitated by the existence of several packages implementing statistical methods for the analysis of spatial and spatio-temporal disease data. However, these methods are still inaccesible for many researchers lacking the adequate programming skills to effectively use the required software. In this paper we present SpatialEpiApp, a Shiny web application that integrate two of the most common approaches in health surveillance: disease mapping and detection of clusters. SpatialEpiApp is easy to use and does not require any programming knowledge. Given information about the cases, population and optionally covariates for each of the areas and dates of study, the application allows to fit Bayesian models to obtain disease risk estimates and their uncertainty by using R-INLA, and to detect disease clusters by using SaTScan. The application allows user interaction and the creation of interactive data visualizations and reports showing the analyses performed.

show abstract

The Bayesian Modeling of Disease Risk in Relation to a Point Source

Cited by 41 publications

References 39 publications

Sensitivity analysis of the relationship between disease occurrence and distance from a putative source of pollution

Sensitivity analysis of the relationship between disease occurrence and distance from a putative source of pollution

Poisson Regression with Change‐Point Prior in the Modelling of Disease Risk around a Point Source

SpatialEpiApp : A Shiny web application for the analysis of spatial and spatio-temporal disease data

Contact Info

Product

Resources

About