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Regression Modelling of Disease Risk in Relation to Point Sources

Author

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  • Peter Diggle
  • Sara Morris
  • Paul Elliott
  • Gavin Shaddick

Abstract

We describe a class of models for the investigation of possible raised risk of disease around putative sources of environmental pollution. An adaptation of the point process method suggested by Diggle and Rowlingson is presented, allowing the use of routinely available aggregated data and incorporating the more general distance–risk model suggested by Elliott and co‐workers. An application to data on cancers of the stomach around municipal solid waste incinerators is presented.

Suggested Citation

  • Peter Diggle & Sara Morris & Paul Elliott & Gavin Shaddick, 1997. "Regression Modelling of Disease Risk in Relation to Point Sources," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 491-505, September.
    • Handle: RePEc:bla:jorssa:v:160:y:1997:i:3:p:491-505
    DOI: 10.1111/j.1467-985X.1997.00076.x
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    Cited by:

    1. Rachel C. Nethery & Yue Yang & Anna J. Brown & Francesca Dominici, 2020. "A causal inference framework for cancer cluster investigations using publicly available data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1253-1272, June.
    2. Alexandre Rodrigues & Peter Diggle & Renato Assuncao, 2010. "Semiparametric approach to point source modelling in epidemiology and criminology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 533-542, May.
    3. D. G. T. Denison & C. C. Holmes, 2001. "Bayesian Partitioning for Estimating Disease Risk," Biometrics, The International Biometric Society, vol. 57(1), pages 143-149, March.
    4. Álvaro Briz‐Redón & Jorge Mateu & Francisco Montes, 2022. "Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 97-120, February.
    5. Barry Trevelyan & Matthew Smallman‐Raynor & Andrew D. Cliff, 2005. "The spatial structure of epidemic emergence: geographical aspects of poliomyelitis in north‐eastern USA, July–October 1916," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 701-722, November.
    6. Hossain, Md. Monir & Lawson, Andrew B., 2009. "Approximate methods in Bayesian point process spatial models," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2831-2842, June.
    7. Brian Conroy & Lance A. Waller & Ian D. Buller & Gregory M. Hacker & James R. Tucker & Mark G. Novak, 2023. "A Shared Latent Process Model to Correct for Preferential Sampling in Disease Surveillance Systems," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(3), pages 483-501, September.

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