Death in community Australian football : A ten year national insurance claims report
- Fortington, Lauren, Finch, Caroline
- Authors: Fortington, Lauren , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Plos One Vol. 11, no. 7 (2016), p. 1-8
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
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- Description: While deaths are thought to be rare in community Australian sport, there is no systematic reporting so the frequency and leading causes of death is unknown. The aim of this study was to describe the frequency and cause of deaths associated with community-level Australian Football (AF), based on insurance-claims records. Retrospective review of prospectively collected insurance-claims for death in relation to community-level AF activities Australia-wide from 2004 to 2013. Eligible participants were aged 15+ years, involved in an Australian football club as players, coaches, umpires or supporting roles. Details were extracted for: year of death, level of play, age, sex, anatomical location of injury, and a descriptive narrative of the event. Descriptive data are presented for frequency of cases by subgroups. From 26,749 insurance-claims relating to AF, 31 cases were in relation to a death. All fatalities were in males. The initial event occurred during on-field activities of players (football matches or training) in 16 cases. The remainder occurred to people outside of on-field football activity (n = 8), or non-players (n = 7). Road trauma (n = 8) and cardiac conditions (n = 7) were the leading identifiable causes, with unconfirmed and other causes (including collapsed or not yet determined) comprising 16 cases. Although rare, fatalities do occur in community AF to both players and people in supporting roles, averaging 3 per year in this setting alone. A systematic, comprehensive approach to data collection is urgently required to better understand the risk and causes of death in participants of AF and other sports.
- Authors: Fortington, Lauren , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Plos One Vol. 11, no. 7 (2016), p. 1-8
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: While deaths are thought to be rare in community Australian sport, there is no systematic reporting so the frequency and leading causes of death is unknown. The aim of this study was to describe the frequency and cause of deaths associated with community-level Australian Football (AF), based on insurance-claims records. Retrospective review of prospectively collected insurance-claims for death in relation to community-level AF activities Australia-wide from 2004 to 2013. Eligible participants were aged 15+ years, involved in an Australian football club as players, coaches, umpires or supporting roles. Details were extracted for: year of death, level of play, age, sex, anatomical location of injury, and a descriptive narrative of the event. Descriptive data are presented for frequency of cases by subgroups. From 26,749 insurance-claims relating to AF, 31 cases were in relation to a death. All fatalities were in males. The initial event occurred during on-field activities of players (football matches or training) in 16 cases. The remainder occurred to people outside of on-field football activity (n = 8), or non-players (n = 7). Road trauma (n = 8) and cardiac conditions (n = 7) were the leading identifiable causes, with unconfirmed and other causes (including collapsed or not yet determined) comprising 16 cases. Although rare, fatalities do occur in community AF to both players and people in supporting roles, averaging 3 per year in this setting alone. A systematic, comprehensive approach to data collection is urgently required to better understand the risk and causes of death in participants of AF and other sports.
- Docking, Sean, Rio, Ebonie, Cook, Jill, Carey, David, Fortington, Lauren
- Authors: Docking, Sean , Rio, Ebonie , Cook, Jill , Carey, David , Fortington, Lauren
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 22, no. 2 (2019), p. 145-150
- Full Text: false
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- Description: Background: Tendon pathology on imaging has been associated with an increased risk of developing symptoms. This evidence is based on classifying the tendon as normal or pathological. It is unclear whether the extent of tendon pathology is associated with the development or severity of symptoms. Objectives: To investigate whether the presence and extent of tendon pathology on ultrasound tissue characterisation (UTC), or a previous history of symptoms, were associated with the development of symptoms over a football season. Methods: 179 male Australian football players underwent UTC imaging of their Achilles and/or patellar tendon at the start of the pre-season. Players completed monthly OSTRC overuse questionnaires to quantify the presence and severity of Achilles and/or patellar tendon symptoms. Risk factor analysis was performed to identify associations between imaging and the development of symptoms. Results: A pathological Achilles tendon increased the risk of developing symptoms (RR = 3.2, 95%CI 1.7–5.9). Conversely, a pathological patellar tendon was not significantly associated with the development of symptoms (RR = 1.8, 95%CI 0.9–3.7). Quantification of tendon structure using UTC did not enhance the ability to identify athletes who developed symptoms. Previous history of symptoms was the strongest predictor for the development of symptoms (Achilles RR = 3.0 95%CI 1.8–4.8; patellar RR = 3.7 95%CI 2.2–6.1). Conclusion: Tendon pathology was associated with the development of self-reported symptoms; however previous history of symptoms was a stronger risk factor. The extent of disorganisation quantified by UTC should not be used as a marker for the presence or severity of current and future symptoms.
Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
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- Description: Background: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. Aim: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. Content: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. Conclusion: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data. © 2019 Author(s).
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Full Text:
- Reviewed:
- Description: Background: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. Aim: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. Content: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. Conclusion: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data. © 2019 Author(s).
Time-to-event analysis for sports injury research part 2 : Time-varying outcomes
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
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