Regional variation in the severity of pesticide exposure outcomes: applications of geographic information systems and spatial scan statistics

• Published in: Clinical toxicology (Philadelphia, Pa.) Pa.), 2009-03, Vol.47 (3), p.248-252
• Main Authors: Sudakin, Daniel L., Power, Laura E.
• Format: Article
• Language: English
• Subjects: Analysis of Variance; Biological and medical sciences; Cluster Analysis; Data Interpretation, Statistical; Databases, Factual; Epidemiology; Geography; Humans; Insecticides; Medical sciences; Monte Carlo Method; Oregon - epidemiology; Pesticide; Pesticides - adverse effects; Pesticides - analysis; Pesticides - poisoning; Pesticides, fertilizers and other agrochemicals toxicology; Poison Control Centers; Poison Control Centers - statistics & numerical data; Poisoning - epidemiology; Poisoning - mortality; Risk; Statistics; Toxicology; Treatment Outcome
• ISSN: 1556-3650; 1556-9519
• DOI: 10.1080/15563650802646694

Introduction: Geographic information systems and spatial scan statistics have been utilized to assess regional clustering of symptomatic pesticide exposures reported to a state Poison Control Center (PCC) during a single year. In the present study, we analyzed five subsequent years of PCC data to test whether there are significant geographic differences in pesticide exposure incidents resulting in serious (moderate, major, and fatal) medical outcomes. Methods: A PCC provided the data on unintentional pesticide exposures for the time period 2001-2005. The geographic location of the caller, the location where the exposure occurred, the exposure route, and the medical outcome were abstracted. Results: There were 273 incidents resulting in moderate effects (n = 261), major effects (n = 10), or fatalities (n = 2). Spatial scan statistics identified a geographic area consisting of two adjacent counties (one urban, one rural), where statistically significant clustering of serious outcomes was observed. The relative risk of moderate, major, and fatal outcomes was 2.0 in this spatial cluster (p = 0.0005). Conclusions: PCC data, geographic information systems, and spatial scan statistics can identify clustering of serious outcomes from human exposure to pesticides. These analyses may be useful for public health officials to target preventive interventions. Further investigation is warranted to understand better the potential explanations for geographical clustering, and to assess whether preventive interventions have an impact on reducing pesticide exposure incidents resulting in serious medical outcomes.