Estimating and communicating spatial certainty when childhood cancers co-cluster Grant uri icon


  •  PROJECT SUMMARY/There are several recent advances, in disease cluster evaluation that if used collectively could avoid a myriadof statistical faults, including the Texas Sharp Shooter Fallacy. These advances include models that estimatethe exceedance probability (EP); defined as the Bayesian probability that the relative risk at a specificlocation is greater than 1. When applied across continuous space, the EP provides a sensitive identificationof disease clusters with varying cluster boundaries and sizes and with explicit, spatially-varying certainty.Furthermore, extending the model to multiple disorders can objectively combine disorders with commonspatial patterns thereby enhancing the effective sample size. The problem is that childhood cancer is so rarethat the prior distributions for the spatial parameters may unduly influence the results. What we need most isan objective way to combine CC when specific CC have common spatial risk patterns. The long-term goal is toprevent diseases caused by environmental exposures. The overall objective of this application, which is thenext step in our long-term goal, is to find the most objective way to pool CC subgroups when they sharecommon spatial risk patterns. Our central hypothesis is that CC have common spatial patterns near someenvironmental hazards. The hypothesis is formulated based on our preliminary findings. The rationale thatunderlies the proposed research is that recently developed Bayesian multivariate spatial modeling is the linkthat we need to mitigate spatial uncertainty and restore public faith in cluster investigations. The centralhypothesis will be tested and the objective of this application attained by pursuing the following specific aims:1. Evaluate case-excess for single CC using univariate geostatistical modeling of EP. We postulate,based on our current studies, that geostatistical modeling of the EP will provide an improved sensitivity forcluster detection by allowing flexible cluster shapes, sizes and statistical certainty. 2, Evaluate case-excessfor multiple CC using multivariate geostatistical modeling of EP. We postulate, based on our preliminarystudies, that multiple CC share common geographic patterns near some toxic sites and multivariate modelingof the CC will enhance the sensitivity of cluster detection. With respect to expected outcomes, the workproposed in aim 1 will identify significant risk patterns of individual CC near some Texas Superfund Sites. Aim2 will identify CC with common geographic risk patterns at these locations. This contribution is significantbecause we live in an era in which the public''s vigilance is sought for all environmental risks and thepublic''s input must be encouraged and validated and, most importantly, addressed, objectively. Thecontribution is innovative because the proposed research combines recent advances to resolve issues in themodeling and reporting of spatial uncertainty in disease cluster investigation.

date/time interval

  • 2017 - 2020