Pesticides are essential tools to protect agricultural productivity and public health. Despite their usefulness, pesticides could pose a potential risk of human illness or injury through improper use or overexposure. Predicting which pesticides or use patterns have higher potential for causing adverse effects enables efficient allocation of resources for interventions such as in-depth risk assessment and outreach. We explore several options for predicting potential illness rates, using data from California’s system of mandatory reporting of agricultural pesticide use (PUR) and the Pesticide- Illnesses Surveillance Program (PISP). Based on 15 years of data, we determined if the quantity of active ingredients applied or number of pesticide applications events alone was correlated with subsequent illness rates. We assessed the role of geoinformatics – the use of Geographical Information Systems (GIS) to visualize and analyze data -- to further improve correlations with illness rates. We also looked at the potential promise of geoinformatics for prioritizing future risk assessment and outreach. In addition, we discuss the associated challenges, including additional workload, the need to protect patients’ privacy, and potential misinterpretation of correlations by the general public.

Predicting illness rates from pesticide use data: The promise and challenges of geoinformatics

254th National Meeting (2017)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.