Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

Background: Accidental poisoning is one of the leading causes of injury in the United States,second only to motor vehicle accidents. According to the Centers for Disease Control andPrevention, the rates of accidental poisoning mortality have been increasing in the past fourteenyears nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends,increasing linearly from 1981 to 2001. The purpose of this study was to determine if there arespatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whetherthere are variations in clustering and risk according to gender and race/ethnicity. The Spatial ScanStatistic in combination with GIS software was used to identify potential clusters between 1980 and2001 among Texas counties, and Poisson regression was used to evaluate risk differences.Results: Several significant (p < 0.05) accidental poisoning mortality clusters were identified indifferent regions of Texas. The geographic and temporal persistence of clusters was found to varyby racial group, gender, and race/gender combinations, and most of the clusters persisted into thepresent decade. Poisson regression revealed significant differences in risk according to race andgender. The Black population was found to be at greatest risk of accidental poisoning mortalityrelative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24– 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50)when the female population was used as a reference.Conclusion: The findings of the present study provide evidence for the existence of accidentalpoisoning mortality clusters in Texas, demonstrate the persistence of these clusters into thepresent decade, and show the spatiotemporal variations in risk and clustering of accidentalpoisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoningmortality by place, time and person, this study demonstrates the utility of the spatial scan statisticcombined with GIS and regression methods in identifying priority areas for public health planningand resource allocation.