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Multilevel Modelling of the Geographical Distributions of Diseases

Author

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  • Ian H. Langford
  • Alistair H. Leyland
  • Jon Rasbash
  • Harvey Goldstein

Abstract

Multilevel modelling is used on problems arising from the analysis of spatially distributed health data. We use three applications to demonstrate the use of multilevel modelling in this area. The first concerns small area all‐cause mortality rates from Glasgow where spatial autocorrelation between residuals is examined. The second analysis is of prostate cancer cases in Scottish counties where we use a range of models to examine whether the incidence is higher in more rural areas. The third develops a multiple‐cause model in which deaths from cancer and cardiovascular disease in Glasgow are examined simultaneously in a spatial model. We discuss some of the issues surrounding the use of complex spatial models and the potential for future developments.

Suggested Citation

  • Ian H. Langford & Alistair H. Leyland & Jon Rasbash & Harvey Goldstein, 1999. "Multilevel Modelling of the Geographical Distributions of Diseases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(2), pages 253-268.
    • Handle: RePEc:bla:jorssc:v:48:y:1999:i:2:p:253-268
    DOI: 10.1111/1467-9876.00153
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    Cited by:

    1. Sehyeong Kim & Youngho Kim, 2019. "Spatially Filtered Multilevel Analysis on Spatial Determinants for Malaria Occurrence in Korea," IJERPH, MDPI, vol. 16(7), pages 1-11, April.
    2. Congdon, Peter, 2007. "Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3197-3212, March.
    3. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    4. Peter Congdon, 2022. "Measuring Obesogenicity and Assessing Its Impact on Child Obesity: A Cross-Sectional Ecological Study for England Neighbourhoods," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    5. Ngianga-Bakwin Kandala & Chibuzor Christopher Nnanatu & Natisha Dukhi & Ronel Sewpaul & Adlai Davids & Sasiragha Priscilla Reddy, 2021. "Mapping the Burden of Hypertension in South Africa: A Comparative Analysis of the National 2012 SANHANES and the 2016 Demographic and Health Survey," IJERPH, MDPI, vol. 18(10), pages 1-18, May.
    6. Guanpeng Dong & Richard Harris & Kelvyn Jones & Jianhui Yu, 2015. "Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-18, June.
    7. Maksim Belitski & Sameeksha Desai, 2016. "What drives ICT clustering in European cities?," The Journal of Technology Transfer, Springer, vol. 41(3), pages 430-450, June.
    8. Glory Chidumwa & Innocent Maposa & Paul Kowal & Lisa K. Micklesfield & Lisa J. Ware, 2021. "Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2," IJERPH, MDPI, vol. 18(1), pages 1-12, January.
    9. Ngianga-Bakwin Kandala & Samuel O.M. Manda & William W. Tigbe & Henry Mwambi & Saverio Stranges, 2014. "Geographic distribution of cardiovascular comorbidities in South Africa: a national cross-sectional analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1203-1216, June.
    10. Salule Masangwi & Neil Ferguson & Anthony Grimason & Tracy Morse & Lawrence Kazembe, 2015. "The Pattern of Variation between Diarrhea and Malaria Coexistence with Corresponding Risk Factors in, Chikhwawa, Malawi: A Bivariate Multilevel Analysis," IJERPH, MDPI, vol. 12(7), pages 1-16, July.
    11. Joel Karlsson & Jonas Månsson, 2014. "Getting a full-time job as a part-time unemployed: How much does spatial context matter?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 179-195, August.
    12. Harvey Goldstein & Jon Rasbash & William Browne & Geoffrey Woodhouse & Michel Poulain, 2000. "Multilevel Models in the Study of Dynamic Household Structures," European Journal of Population, Springer;European Association for Population Studies, vol. 16(4), pages 373-387, December.
    13. Congdon, Peter, 2006. "A model for non-parametric spatially varying regression effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 422-445, January.
    14. Darren J. Mayne & Geoffrey G. Morgan & Bin B. Jalaludin & Adrian E. Bauman, 2018. "Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia," IJERPH, MDPI, vol. 15(2), pages 1-24, February.
    15. Congdon, P., 2007. "Bayesian modelling strategies for spatially varying regression coefficients: A multivariate perspective for multiple outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2586-2601, February.
    16. Marco Alfò & Cecilia Vitiello, 2003. "Finite mixtures approach to ecological regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 93-108, February.
    17. Moraga, Paula & Lawson, Andrew B., 2012. "Gaussian component mixtures and CAR models in Bayesian disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1417-1433.
    18. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
    19. Fabio Divino & Viviana Egidi & Michele Antonio Salvatore, 2009. "Geographical mortality patterns in Italy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(18), pages 435-466.

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