Distance-Based Mapping of Disease Risk

Jeffery Caroline (), Ozonoff Al (), White Laura Forsberg () and Pagano Marcello ()
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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

The International Journal of Biostatistics, 2013, vol. 9, issue 2, 265-290

Abstract: 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.

Date: 2013
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DOI: 10.1515/ijb-2012-0024

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