Agrochemical Exposure and Chronic Disease Risk among U.S. Farmers Using Spatial Modeling: A Review of GIS-Based Epidemiological Approaches

Agrochemical use is an integral component of modern agricultural production in the United States, yet long-term occupational exposure among farmers has been associated with a range of chronic health outcomes. Accurately characterizing these exposures remains challenging due to spatial heterogeneity in land use, application practices, and environmental dispersion. Over the past three decades, geographic information systems (GIS) have emerged as important tools for addressing these challenges by enabling spatially explicit modeling of agrochemical exposure and its potential health impacts. This review synthesizes and critically evaluates peer-reviewed epidemiological studies published between 1995 and 2024 that apply GIS-based methods to examine associations between agrochemical exposure and chronic disease risk among U.S. farmers. The review focuses on studies employing spatial modeling techniques such as buffer and proximity analyses, land-use regression models, interpolation methods, and hybrid frameworks integrating pesticide application records, land-use data, environmental monitoring datasets, and remote sensing products. These approaches have been applied most frequently to cancer and neurodegenerative disease outcomes, with comparatively fewer studies addressing respiratory and other chronic conditions. By systematically comparing GIS-based exposure modeling strategies, this review uniquely highlights methodological strengths, recurring limitations, and sources of uncertainty across the literature, including exposure misclassification, ecological inference, temporal misalignment, and spatial scale variability. Overall, GIS-based approaches have strengthened population-level exposure assessment by improving spatial resolution and identifying geographic disparities in disease risk. The review underscores the need for greater methodological standardization, improved temporal modeling, and integration of individual-level data to advance spatial epidemiology and support occupational health surveillance, risk assessment, and policy development.