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

Nkhoma, Ed, Hunt, Harris, Harris Ann

Description

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 and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years 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 are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. Results Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative 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 accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation.

Informations

• Publié par: biomed
• Publié le: 01 janvier 2004
• Nombre de lectures: 547
• Langue: English

Extrait

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Address: 1 Department of Epidem iology, University of North Carolin a at Chapel Hill, CB#7435 , 2106-B McGavran-Greenberg Hall, Chapel Hill, NC 27599–7435, USA, 2 Department of Public and Community Health, University of Maryland College Park, 2371 HHP Building, Valley Drive, College Park, Maryland, 20742, USA, 3 Department of Health Management and Policy, Univ ersity of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Wo rth, Texas, 76107, USA and 4 Department of Environmental and Occupational Health, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, Fort Wo rth, Texas, 76107, USA Email: Ella T Nkhoma* - [e-mail]; Chiehwen Ed Hsu - [e-mail]; Victoria I Hunt - [e-mail]; Ann Marie Harris - [e-mail] Corresponding author *

Published: 27 October 2004 Received: 21 July 2004 International Journal of Health Geographics 2004, 3 :25 doi:10.1186/1476-072X-3-25  Accepted: 27 October 2004 This article is available from: http:// www.ij-healthgeographics.com/content/3/1/25 © 2004 Nkhoma et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the orig inal work is properly cited.

Research Open Access Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001 Ella T Nkhoma* 1 , Chiehwen Ed Hsu 2 , Victoria I Hunt 3 and Ann Marie Harris 4

Abstract Background: Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Ce nters for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortal ity rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accide ntal poisoning mortality among Te xas counties, and if so, whether there are variations in clustering and risk according to gender an d race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regressi on was used to evaluate risk differences. Results: Several significant (p < 0.05) accidental pois oning mortality clusters were identified in different regions of Texas. The geographic and temp oral persistence of clus ters was found to vary by racial group, gender, and race/gender combinatio ns, and most of the clusters persisted into the present decade. Poisson re gression revealed significant differen ces in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1. 25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population wa s 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 accidental poisoning mortality clusters in Texas, demonstrate the persistenc e of these clusters into the present decade, and show the spat iotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person , this study demonstrates the util ity of the spatial scan statistic combined with GIS and regression methods in iden tifying priority areas for public health planning and resource allocation.