|
Abstract
|
Agricultural injury and illness surveillance has always been a difficult pursuit due to the distinct business and regulatory environment in which farming operates. Traditional occupational health surveillance efforts often fall short in adequately incorporating accurate estimates of agricultural injury or illness for a variety of reasons. Given that half the world's population is employed in the agrifood system, and it consistently ranks as one of the most hazardous jobs, more attention and innovation is needed for agricultural injury and illness surveillance.
The Journal of Agromedicine special surveillance issue highlights new advancements in surveillance science, with a wide-ranging topical and international scope. The authors present the myriad of unique ways in which they are filling the surveillance gaps. The call for papers solicited research that responded to a variety of factors. Manuscripts included in this special issue were responsive to nearly all of the topics sought.
While all manuscripts highlighted the rationale for surveillance research, a few in particular focused on this as a major theme, such as Peachey et al. Australia, McNamara et al.-Ireland, and Johnson et al. Roll-Over Protective Structures (ROPS). How surveillance research is conducted has continued to evolve over time, with emphasis on the role of artificial intelligence and machine learning.
The use of multiple datasets can often provide a more comprehensive count of agricultural injuries. For example, Becklinger used a capture-recapture methodology to study the completeness of cases identified in two publically available datasets. The benefits of employing multiple datasets can also be seen in Gilblom et al.'s research leveraging health data and geographic information systems (GIS) technology, and in Michigan, four datasets are combined to reveal the burden of non-fatal injury in agriculture.
Language: en |