Source: Masters Abstracts International, Volume: 54-01.

Adviser: Meredith H. Stowe.

Background: In the United States, approximately 45,000 snakebites occur annually and affect many people including veterinarians and farmers. Snakebites can cause significant pain and morbidity such as severe bleeding and skin necrosis. Better predictive information on snakebite risk could help prevent snakebites by identifying high risk time periods, weather patterns, and locations.

Methods: This was a retrospective analysis of the New Mexico State Poison Control Center data between January 1998 and December 2012. Monthly average meteorological data were obtained from the Parameter-elevation Regressions on Independent Slopes Model climate group. Locations of the bites were categorized as urban or rural based on the National Center for Health Statistics urban-rural classification scheme for counties. Poison regression modeling was used to evaluate the influence of urban-rural locations and meteorological factors on snakebite incidence.

Results: A total of 928 snakebites was reported and the number per year appeared to be trending up in recent years. The groups most affected by snakebites were males and those aged 45 to 49 years. Most snakebite cases were of moderate medical severity and were reported in the late afternoon or evening. A trend of increasing incidence was noted. A Poisson regression model was constructed, which found a positive association between snakebite incidence and population density in rural areas as well as higher monthly average minimum temperature. Being in an urban location was negatively associated with snakebite incidences.

Discussion: In this study, the majority of the reported snakebites resulted in significant morbidity and males were disproportionately affected by snakebites. Snakebites were more common in rural areas, and during times of higher minimum temperature. These findings suggest that predictive models for snakebite risk may be helpful in shaping preventive strategies.