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A Review of Methods for the Statistical Analysis of Spatial Patterns of Disease

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  • Roger J. Marshall

Abstract

A review of methods for the analysis of the geographical distribution of disease is presented. The topic is of increasing interest to statisticians, though much groundwork has been done by epidemiologists and medical geographers. Methods for the detection and testing for apparent clusters of disease, including those near a possible environmental hazard, are reviewed. Estimating regional mortality rates, possibly to construct disease maps, and methods for investigating the association between disease rates and social, demographic and environmental factors are discussed. In addition, statistical analysis of the spatial spread of epidemics is mentioned.

Suggested Citation

  • Roger J. Marshall, 1991. "A Review of Methods for the Statistical Analysis of Spatial Patterns of Disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(3), pages 421-441, May.
    • Handle: RePEc:bla:jorssa:v:154:y:1991:i:3:p:421-441
    DOI: 10.2307/2983152
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    Cited by:

    1. Peter Congdon & Alan Smith & Christine Dean, 1998. "Assessing Psychiatric Morbidity from Community Registers: Methods for Bayesian Adjustment," Urban Studies, Urban Studies Journal Limited, vol. 35(12), pages 2323-2352, December.
    2. repec:asg:wpaper:1052 is not listed on IDEAS
    3. Govind, Rahul & Chatterjee, Rabikar & Mittal, Vikas, 2008. "Timely access to health care: Customer-focused resource allocation in a hospital network," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 294-300.
    4. Splan, Eric D. & Magerman, Adam B. & Forbes, Chad E., 2021. "Associations of regional racial attitudes with chronic illness in the United States," Social Science & Medicine, Elsevier, vol. 281(C).
    5. Papa Ousmane Cissé & Dominique Guegan & Abdou Kâ Diongue, 2018. "On the parameters estimation of the Seasonal FISSAR Model," Post-Print halshs-01832115, HAL.
    6. Papa Ousmane Cissé & Abdou Kâ Diongue & Dominique Guegan, 2016. "Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model," Documents de travail du Centre d'Economie de la Sorbonne 16013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Kulldorff, Martin & Tango, Toshiro & Park, Peter J., 2003. "Power comparisons for disease clustering tests," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 665-684, April.
    8. Bansal, Prateek & Krueger, Rico & Graham, Daniel J., 2021. "Fast Bayesian estimation of spatial count data models," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    9. Carlos Díaz-Venegas, 2014. "Identifying the Confounders of Marginalization and Mortality in Mexico, 2003–2007," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 851-875, September.
    10. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    11. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    12. Francesco Bartolucci & Alessio Farcomeni, 2022. "A hidden Markov space–time model for mapping the dynamics of global access to food," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 246-266, January.
    13. Peter Congdon, 1997. "Multilevel and Clustering Analysis of Health Outcomes in Small Areas," European Journal of Population, Springer;European Association for Population Studies, vol. 13(4), pages 305-338, December.
    14. Geoffrey M. Jacquez & Aleksander Essex & Andrew Curtis & Betsy Kohler & Recinda Sherman & Khaled El Emam & Chen Shi & Andy Kaufmann & Linda Beale & Thomas Cusick & Daniel Goldberg & Pierre Goovaerts, 2017. "Geospatial cryptography: enabling researchers to access private, spatially referenced, human subjects data for cancer control and prevention," Journal of Geographical Systems, Springer, vol. 19(3), pages 197-220, July.

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