2019
DOI: 10.3390/safety5040073
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The Effect of All-Terrain Vehicle Crash Location on Emergency Medical Services Time Intervals

Abstract: Over 100,000 all-terrain vehicle (ATV)-related injuries are evaluated in U.S. emergency departments each year. In this study, we analyzed the time intervals for emergency medical services (EMS) providers responding to ATV crashes in different location types. Data from the Iowa State Trauma Registry and a statewide ATV crash/injury database was matched with Iowa EMS Registry records from 2004-2014. Ground ambulance responses to 270 ATV crashes were identified, and response characteristics and time intervals wer… Show more

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Cited by 3 publications
(5 citation statements)
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References 27 publications
(51 reference statements)
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“…This increased driving time is not unusual in literature related to emergency services and has been related to distance from services in literature related specifically to paramedic transport in rural areas (Adeyemi, Paul, & Arif, 2022;Miller, James, Holmes, & Van Houtven, 2020;Smith, English, Whitman, Lewis, & Gregg, 2022). Discussion about adverse terrain indicates that such driving conditions can be dangerous, particularly in rural and remote areas with poor road conditions, lighting, and adverse weather conditions, however urban areas also have issues related the possibility of additional vehicles being involved or traffic congestion (Liu, 2022;Spencer-Goodsir et al, 2022;Wubben, Denning, & Jennissen, 2019). Similar issues have also been identified in literature related to firefighting, where response times are also considered to be important (Wan Jusoh et al, 2023).…”
Section: Discussionmentioning
confidence: 90%
“…This increased driving time is not unusual in literature related to emergency services and has been related to distance from services in literature related specifically to paramedic transport in rural areas (Adeyemi, Paul, & Arif, 2022;Miller, James, Holmes, & Van Houtven, 2020;Smith, English, Whitman, Lewis, & Gregg, 2022). Discussion about adverse terrain indicates that such driving conditions can be dangerous, particularly in rural and remote areas with poor road conditions, lighting, and adverse weather conditions, however urban areas also have issues related the possibility of additional vehicles being involved or traffic congestion (Liu, 2022;Spencer-Goodsir et al, 2022;Wubben, Denning, & Jennissen, 2019). Similar issues have also been identified in literature related to firefighting, where response times are also considered to be important (Wan Jusoh et al, 2023).…”
Section: Discussionmentioning
confidence: 90%
“…The higher fatality rates in more rural and remote areas may be partially explained by longer EMS patient access and transfer times. On average, first aid rendering times are 38% longer for crashes in remote locations versus accessible locations, disregarding the time interval from crash to EMS notification (Wubben et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…Although the desired outcomes may seem well below the accuracy delivered by state-of-theart GPS devices/tracking algorithms, very high accuracy is not critical for the success of this system. For instance, personal locator beacons (PLBs), which have an accuracy of about 100 m when interfaced with GPS, have been used successfully to aid EMS responders in locating ATV crash patients and stranded mount hikers (Maritime New Zealand, 2018;Mountain Gear and ACR Electronics, 2007;BusinessWire, 2008;The Gisborne Herald, 2017;Wubben et al, 2019). In addition, a benchmark of 100 m, 67% of the time, was previously proposed by Funke et al (2000) when evaluating the accuracy of automated collision notification systems (ACNs) for automobiles.…”
Section: Vehicle Trackingmentioning
confidence: 99%
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“…For example, fatal crash information cannot be used for analysis if invalid or unknown emergency response times recorded. [ 17 18 ] Other recent investigations consider ERT issues related to all-terrain vehicle crash location,[ 19 ] cardiac arrest,[ 20 ] ambulance crashes,[ 21 ] and machine learning analysis approaches. [ 22 ]…”
Section: Introductionmentioning
confidence: 99%