Fatal Agricultural Injuries in Pennsylvania, 2015-2017: A Comparative Analysis of Two Systems’ Data Collection Methods and Datasets

Abstract

tified in Pennsylvania across both systems. Differences between the two systems included coding, such as victim age and demographics, as well as inclusion criteria, such as the time between the incident and victim death. Of the 104 agricultural fatalities, 73% were identified through the PA-FF dataset, and 53% were identified through the AIN dataset. AIN included a higher proportion of female victims and roadway incidents, whereas PA-FF included a significantly higher proportion of the identified Anabaptist cases ( 2 = 22.329, df = 2, p < 0.001). Although PA-FF may have an advantage by including death certificates, this study revealed that PA-FF alone missed mortality data and certain risk factors, such as roadway fatalities related to farm equipment. When comparing two datasets, the inclusion criteria should be considered. Supplemental surveillance programs such as these would benefit from a periodic review between two or more datasets to ensure that agricultural fatalities are captured more accurately.

Redesigning a Sentinel Surveillance System for Collecting and Disseminating Near Real-Time Agricultural Injury Reports: System Usability Study

“…Perhaps most notably, media reports do not always collect enough detail, do not cover all AFF fatalities, and cover even fewer stories relating to AFF nonfatal injuries. There are also some gaps in collection efforts, and there is room for improvement in interagency collaborations, a topic of future research [56]. In addition, previous studies have identified a discrepancy in the number of drowning fatalities among youth as being poorly captured by news media [25].…”

Stakeholders’ Consumption of Agricultural Injury Reports from News Media: A Six-Year Analysis of Website Usage and Visitor Analytics

“…(1) building new emphases on improved coding systems [11]; (2) enhanced collaborative efforts with partners and government datasets [12]; and (3) exploring injuries in vulnerable, underrepresented populations, such as youth [13][14][15]. The AgInjuryNews system collects news media reports from news agencies across the United States and Canada and codes them based on multiple factors, including: injury agent, incident location, Farm and Agricultural Injury Classification (FAIC) codes, victim demographics, and more [16].…”