Epidemiologic trends in medically-attended tree stand fall injuries among Wisconsin deer hunters

Abstract

Background Tree stand falls are the most common injury to hunters in the USA, but there is limited research on the topic. This study examined the 5-year trends in incident tree stand fall injuries in rural north-central Wisconsin and described patient demographics and injury features. Methods A retrospective analysis was conducted on five prior hunting seasons, 2009–2013. Cases were ascertained from electronic health records via natural language processing that identified patients from the Marshfield Epidemiologic Study Area who received medical attention for a tree stand fall. Annual incidence rates were calculated using the number of hunting license holders in the target population, per administrative data from the Wisconsin Department of Natural Resources. Results There were 16,556–16,902 deer hunters in any given year, with 39 (92% male) confirmed medically-attended tree stand fall injuries in 2009–2013. Injuries mainly occurred in the lower extremities (n=23), and included two fatalities and one paralysis case. The risk of tree stand fall injuries went from 6.0 (95% CI: 3.2, 11.1) per 10,000 hunters in 2009 to 3.6 (95% CI: 1.6, 7.9) per 10,000 hunters in 2013, which was not a significant change over five years (p=0.79). Most falls occurred among archery hunters, in the evening and when descending from a tree stand. Cases were demographically similar to the general population of Wisconsin deer hunters. Conclusions The current seasonal incidence rate of tree stand fall injuries is relatively low in rural Wisconsin, but with limited signs of improvement. Continued efforts are needed to promote the long-term safety of the hunting public.

Common Injuries Associated with Falls from Tree Stands in Iowa

“…Long bone fractures and closed head injuries were less common in our study than in previous studies. 12,13 Length of hospital stay in this study was similar to that described previously 12 , 13 and correlated with the ISS. In our series, however, there was no correlation between fall height and ISS.…”

Behavior and risk probabilities of deer stand falls among Wisconsin hunters

“…More hunters may fall than get hurt, and some percentage of those who fall from a deer stand escape with either no or minor injuries (Responsive Management ), which could account for why self‐reports produce greater risk calculations. Alternatively, our calculations may underrepresent the annual risk of tree‐stand falls where gaps in medical records do not fully capture all cases seen by physicians each year (Van Wormer et al ). Patients seen by their primary care physician, rather than trauma centers or emergency rooms, may not always be recorded as a deer stand injury.…”

“…We created a life table showing the difference in the additive effects of annual tree stand use among 2 kinds of deer hunters across a range of 50 years. The selection of this area highlights a collaborative project between Wisconsin DNR and Marshfield Clinic researchers to determine the 5‐year trend in medically attended tree‐stand accidents (see Van Wormer et al ). The area makes a suitable region for this analysis because the Marshfield Clinic treats almost all of the trauma cases that occur in that part of Wisconsin and most residents in this area deer hunt near their county of residence (R. Holsman, Wisconsin Department of Natural Resources, unpublished data).…”

“…Alcohol has been linked to 7–10% of cases involving serious injuries (Fayssoux et al , Smith et al ). Van Wormer et al () found that fall victims were more likely to be overweight than the average, similarly aged patients seen at a trauma clinic. To date, there have been no published reports of the type of tree stands involved in hunter falls.…”

Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing

“…More generally, text classification is used to identify patient records for the purpose of retrospective or prospective studies aiming at improving care pathways for a category of patients, or at characterizing patient cohorts. A wide range of topics were addressed in 2016: to predict the protocol and priority of MRI brain protocols [38], to identify heart failure patients with ineffective self-management status [39], to predict suicidal ideation and heightened psychiatric symptoms [40], to detect long-bone fractures [41], abdominal aortic aneurysms [42], liver cirrhosis [43], and the region containing an abnormality [44] in radiology reports, to predict the diagnosis of breast cancer in mammography reports [45], to identify pediatric traumatic brain injury in CT reports [46], hepatocellular cancer in pathology and radiology reports [47], cerebral aneurysms [48], first episode psychosis [49], non-alcoholic liver fatty disease [50], celiac disease [51], tree stand falls [52], as well as acute coronary syndrome from admission records in Chinese [53].…”