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Distance-Based Mapping of Disease Risk

Author

Listed:
  • Jeffery Caroline

    (Liverpool School of Tropical Medicine, Department of International Public Health, Monitoring and Evaluation Technical assistance and Research group, Liverpool L3 5QA, United Kingdom)

  • Ozonoff Al

    (Boston Children’s Hospital, Clinical Research Center, Boston MA 02115, United States)

  • White Laura Forsberg

    (Boston University School of Public Health, Department of Biostatistics, Boston MA 02118, United States)

  • Pagano Marcello

    (Harvard School of Public Health, Department of Biostatistics, Boston MA 02115, United States)

Abstract

In this article, we consider the problem of comparing the distribution of observations in a planar region to a pre-specified null distribution. Our motivation is a surveillance setting where we map locations of incident disease, aiming to monitor these data over time, to locate potential areas of high/low incidence so as to direct public health actions.We propose a non-parametric approach to distance-based disease risk mapping inspired by tomographic imaging. We consider several one-dimensional projections via the observed distribution of distances to a chosen fixed point; we then compare this distribution to that expected under the null and average these comparisons across projections to compute a relative-risk-like score at each point in the region. The null distribution can be established from historical data. Scores are displayed on the map using a color scale.In addition, we give a detailed description of the method along with some desirable theoretical properties. To further assess the performance of this method, we compare it to the widely used log ratio of kernel density estimates. As a performance metric, we evaluate the accuracy to locate simulated spatial clusters superimposed on a uniform distribution in the unit disk. Results suggest that both methods can adequately locate this increased risk but each relies on an appropriate choice of parameters. Our proposed method, distance-based mapping (DBM), can also generalize to arbitrary metric spaces and/or high-dimensional data.

Suggested Citation

  • Jeffery Caroline & Ozonoff Al & White Laura Forsberg & Pagano Marcello, 2013. "Distance-Based Mapping of Disease Risk," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 265-290, May.
  • Handle: RePEc:bpj:ijbist:v:9:y:2013:i:2:p:265-290:n:3
    DOI: 10.1515/ijb-2012-0024
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    References listed on IDEAS

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    1. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21, February.
    2. Vidal Rodeiro, Carmen L. & Lawson, Andrew B., 2005. "An evaluation of the edge effects in disease map modelling," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 45-62, April.
    3. Sabel, Clive E. & Gatrell, Anthony C. & Löytönen, Markku & Maasilta, Paula & Jokelainen, Matti, 2000. "Modelling exposure opportunities: estimating relative risk for motor neurone disease in Finland," Social Science & Medicine, Elsevier, vol. 50(7-8), pages 1121-1137, April.
    4. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    5. Olson, K.L. & Grannis, S.J. & Mandl, K.D., 2006. "Privacy protection versus cluster detection in spatial epidemiology," American Journal of Public Health, American Public Health Association, vol. 96(11), pages 2002-2008.
    6. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18, January.
    7. Kowalski J. & Pagano M. & DeGruttola V., 2002. "A Nonparametric Test of Gene Region Heterogeneity Associated With Phenotype," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 398-408, June.
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