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Injury and illness definitions and data collection procedures for use in epidemiological studies in Athletics (track and field): Consensus statement Br 

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Injury and illness definitions and data collection procedures for use in epidemiological studies in Athletics (track and field): Consensus statement Br 

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Br J Sports Med 2014;48:483-490.

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Recordable health-related incidents (injury/illness)

A recordable health-related incident in Athletics is defined as Any physical or psychological complaint or manifestation experienced by an athlete, irrespective of the need for medical attention or time loss from Athletics activities.

Recordable incidents resulting from participation in Athletics competition and training should be included in surveillance studies, and incidents that are probably not directly related to Athletics may also be recorded, but these should be analysed separately. The term 'incident' was adopted in this statement, rather than 'injury' or 'medical condition', 14-16 , 18 in order to highlight the desire to also collect syndromic (prediagnostic) data and overuse injuries. As in the Tennis consensus statement, 16 the term 'manifestation' was added to the term 'complaint', due to the fact that athletes do not always feel or experience a medically recognised condition. However, an athlete may have external signs that would alert a health professional to a condition, such as a skin lesion or osteophyte. The term 'psychological' was transferred from the Tennis consensus report, 16 as overtraining and burnout may occur also in Athletics. Also following Tennis Consensus examples, 16 sun-related skin conditions and exercise-induced asthma could be recorded as resulting from participation in Athletics. However, road traffic incidents and domestic accidents should not be taken into account.

Injury

An Athletics injury is defined as: 'A physical complaint or observable damage to body tissue produced by the transfer of energy experienced or sustained by an athlete during participation in Athletics training or competition, regardless of whether it received medical attention or its consequences with respect to impairments in connection with competition or training. ' 2 , 5 , 19

Illness

An Athletics illness is defined as: "A physical or psychological complaint or manifestation by an athlete not related to injury, regardless of whether it received medical attention or its consequences with respect to impairments in connection with competition or training." 2 , 5 , 19

Medical attention

Medical attention refers to an assessment of an athlete's medical condition by a qualified medical/healthcare practitioner, such as a physician, physiotherapist, nurse, osteopath, chiropractor or qualified athletic trainer (in the USA). A field request by an athlete for medical attention, for example, for adjustment of a knee taping, should not be recorded as an 'incident' unless it is identified as a treatable condition with evidence of aggravation of the athlete's existing knee condition. A medical-attention definition has most often been employed in studies of injuries and illnesses during major international competitions such as the Olympic Games and Athletics championships where all athletes have similar access to medical care. 1-5 , 19 , 20 However, it should be recognised that in studies where athletes' medical coverage is non-uniform, the use of a medical attention definition may lead to systematic bias in surveillance data. 21

Time loss

"A time loss injury or illness is one that leads to the athlete being unable to take full part in athletics training and/or competition the day after the incident occurred." 14 , 15 , 18 It should be noted that studies applying a time loss definition can miss a large share of overuse conditions. 22-24

Severity of incidents

In prospective studies of injury incidence, the severity of a recordable incident is defined as: The number of days that have elapsed from the day after the onset of the incident to the day of the athlete's return to full participation in Athletics training and become fully available for competition.14

The severity is thus determined by the number of days of absence from full Athletics participation. The day on which an incident first occurs does not count when determining injury severity; the assessment of severity (days lost) starts on the following day if the athlete is unable to take part in full and/or normal training and/or competition. Severity should be reported as minor (1-7 days) which could be subdivided into slight (1 day), minimal (2-3 days), mild (4-7 days), moderately serious (8-28 days), serious (>28 days-6 months) or long-term (>6 months). 14 , 25

Some athletes can continue sports without using the injured body part or by adjusting the exposure to the injured body part, although participation is not full.

When studies aim to monitor the prevalence of injury and illness problems and their consequences, the use of alternative methods of measuring the severity of incidents is relevant, such as the score proposed by Clarsen et al. 23 , 24 In particular, this approach can be used to monitor the progress of overuse injuries over time.

Full recovery

In many Athletics events, competing is seldom possible without full function, and a period of unrestricted training is usually required beforehand. However, many sudden and gradual-onset incidents develop into chronic conditions, with athletes returning to training while still experiencing adverse effects. Thus, a common notion of full recovery is important to enable the calculation of severity and the classification of recurrent incidents. 25 Ideally, recovery should be determined by expert medical opinion. However, the lack of uniform medical coverage may be a factor limiting the feasibility of this approach in athletics studies. Therefore, 'return-to-play' guidelines should be used, whereby full recovery is defined as 'the return to full athletics training and (availability for) competitions'. Consequently, the ability to train or compete ('return-to-play') in most events of Athletics assumes full recovery and normal function.

The normal function and participation in Athletics may be evaluated using performance during competitions as a reference, as well as the type/mode, volume and intensity of training that can be completed without symptoms.

Recurrent conditions

In congruence with previous consensus statements in different sports, 14-16 , 20 a recurrent condition qualifies as a recordable incident if defined as An incident of the same type and at the same site linked to an index incident and which occurs after an athlete's return to full function and participation ("full recovery") from the index recordable incident.

Following Fuller et al's 26 framework for recording recurrences, reinjures and exacerbations in injury surveillance, an index incident is defined as the first recorded incident in a series of incidents constituting a recurrent condition. Recurrent conditions should be subcategorised into reinjuries and exacerbations. A reinjury is a repeat episode of a fully recovered index injury. An exacerbation is worsening in the state of a non-recovered index injury or illness. 26

Overuse conditions involving continual or sporadic experiences of pain or other physical discomfort should be analysed separately. 27

For example, an athlete may rapidly develop pain, and miss 2 weeks of normal training before returning to normal (competition-level) training while still experiencing pain; some 6 weeks later the pain level increases to such an extent that the athlete misses a further 4 weeks of normal training. The first episode should be recorded as a sudden onset incident with a severity of 14 days, while the second episode should be recorded as an exacerbation with a severity of 28 days. 26

Subsequent injuries

Recordable incidents in Athletics are often succeeding or recurrent. In particular, injuries (of either the same or a different type) can be strongly influenced by a previous injury. Therefore, correct categorising of subsequent injuries (multiple, recurrent, exacerbation or new) before the analysis is essential (eg, using the SIC model). 27 The categorisation of subsequent injuries needs to take into account the different modes of onset of injuries and the dependency structures between injury types. Also, it is important to declare the analytical assumptions regarding the repeated injuries, whether or not they are in a distinct order and are competing events. 27

Illness-affected system (modified from references 2, 5, 19 and 20)

Illness symptoms (modified from references 2, 5, 19 and 20)

Mode of onset

A consensual decision was reached to categorise injury and illness incidents according to the onset, in contrast to the traditional diagnostic classification of acute versus chronic progress. A sudden onset incident refers to an episode where the experienced distress or disability developed during minutes, seconds or less, while a gradual onset incident refers to an episode that developed during hours, days or more. The consensus group proposed that recordable incidents should be classified as follows:

Sudden onset incident refers to a condition resulting from a specific identifiable episode resulting in a rapid onset of experienced distress or disability. Subsequently, sudden onset injuries can be categorised according to the cause of incident in:

Traumatic injuries defined as a condition caused by an identifiable single external transfer of energy. Examples of traumatic sudden onset injuries include a bone fracture caused by a fall or a ligament tear caused by contact with an obstacle.

Overuse injuries referring to a condition to which no identifiable single external transfer of energy can be associated. Multiple accumulative bouts of energy transfer could result in this kind of injury. Examples of overuse sudden onset injuries include tendon tears.

Gradual onset incident refers to a condition that manifests itself over a period of time, or when there is a gradual increase in the intensity of experienced distress or disability, without a single identifiable event being responsible for the condition. Examples of gradual onset conditions include overtraining syndromes and overuse injuries, such as tendinosis/tendinopathies.

Athletic event groups (modified from references 1, 2 and 32)

Notes: (1) This event group classification is based on the Athletics Olympic programme and also includes all official events at different official International Association of Athletics Federation (IAAF) Championships (indoors, outdoors, age groups, cross country, road races and race walking); (2) on previous Athletics studies, 1 , 2 , 3 , 32 marathon was separated from other long-distance runs as the distance is well enough defined for purposes of between-study comparisons; half-marathon and road races can be added in this group depending of the study design and studied populations; (3) hurdles may in some studies assemble small populations and can be added to sprints group; (4) ultra running, mountain running and other extreme distance road races were not included in this classification of event categories but could make up further athletic event groups and (5) cross-country race can be separated from the long-distance runs for comparison with other cross-country studies.

Study design and implementation

Epidemiological studies in Athletics should ideally follow a prospective cohort design and record injury and illness incidence, thus allowing determination of risk factors. Cross-sectional study designs using prevalence measures are valuable for describing the burden of long-term, particularly overuse-related, conditions. Study designs involving repeated cross-sectional data collection can be used to establish prevalence trends. Any study must be approved by a recognised institutional (ethics) review board. It is recommended that studies are approved and endorsed by the Athletics organisations involved with the study population. Each athlete should be informed in writing about what is expected from her/him, the duration of the study, the risks and benefits of study participation and the procedures for the reporting of results (individually or at group level).

Data collection in prospective studies should ideally be performed on a weekly basis. Motivating the reporting physicians, physiotherapists or athletes to consistently report in longitudinal studies is a key issue. Discontinued reporting from a participant should trigger an alarm generating a contact with the physician/physiotherapist/athlete to encourage participation and to determine whether she/he is able to continue in the study. 33 While it is highly recommended that injuries and illnesses in cohort studies are diagnosed and reported by qualified medical personnel, 14 , 15 prediagnostic data on injury and illness can also be collected by athlete self-reports. 6 , 12 The recording and reporting process should, if possible, be electronically managed and the system compliant with the guidelines of the Food and Drug Administration (FDA) in the USA (FDA 21 CFR Part 11). 34 If computer-based tools are used for recoding of data, the electronic forms should be individually tested, even if the corresponding paper-based forms have been validated.

Prevalence trends can be monitored using studies with a panel, or if not possible, cross-sectional design, where athletes are asked to complete injury and illness questionnaires at regular time intervals, preferably each week. The Oslo Sports Trauma Research Centre (OSTRC) Questionnaire on Health Problems can be used for this purpose, 23 , 24 which focuses on four key questions on injury and illness with regard to: (1) consequences on sports participation; (2) consequences on training volume; (3) consequences on sports performance and (4) the degree to which the athlete has experienced symptoms.

Exposure

Athletic exposure divided into training and/or in competition should be reported in all studies. Exposure can be recorded based on time (eg, in training) or on participations/start (eg, in competition).

Competition exposure is defined as: "Competition including warm-up, the interval between starts during a competition day, competition and cool-down."

Training exposure is defined as: "Bodily movements or isometric force produced by skeletal muscles requiring energy expenditure and aimed at maintaining or improving an athlete's athletic performance." This includes skills, capabilities (power, speed, stamina), strength and conditioning and cross training (eg, running, cycling and swimming). Training volume is defined as the number of trained hours per week. Intensity of training can be assessed by using different intensity zones or effort scales per training session. 35 By combining weekly hours and intensity of training per event, a training load rank index can be computed. 6 , 35 Exercise and social sports activities that are not scheduled as part of an athlete's formal training programme should not be included as training exposure. If the aim of a study is to evaluate the incidence of injury associated with specific conditions (eg, training activities and capabilities), then the exposure to these specific conditions has to be recorded.

Calculation of prevalence and incidence

Incidence (requiring that the study population is 'healthy' with regard to the condition under study at the start of the data collection) is used to report findings from traditional prospective studies and prevalence is used to report long-term conditions in cross-sectional studies. Prospective studies based on panel designs use serial and average measures of prevalence to describe the magnitude of injury and illness. 23 , 24

Prevalence is calculated by dividing the number of athletes with physical or psychological problems at/during a defined time period by the total number of athletes.

Incidence is calculated as number of new injuries or illnesses in relation to exposure (see above). Incidence should be calculated separately for illnesses, training injuries and in-competition injuries.

The overall incidence should be calculated as number of new injuries or illnesses recorded during the study period divided by a total athlete exposure hours (per 1000 h of athletic practice) or number of athletes (per 1000 athletes). The total athlete exposure hours is computed as the sum of recorded hours of training and competition during the period of the study.

Incidence of training injuries should be calculated as the number of new training injuries per 1000 h of training for Athletics. If the aim of a study is to measure the incidence of an injury associated with specific training activities, then the incidence of training injuries should be reported as a function of the athletes' exposure to the specific training activities; for example, the number of injuries during technical training (running/hurdles technique, jumps, throws, etc) per 1000 athlete-hours of technical training.

Incidence of in-competition injuries should be calculated as the number of new injuries in competition per 1000 registered athletes, per 1000 competing athletes and/or per 1000 athlete participations. 1 , 18 Registered athletes have been previously defined as all athletes officially registered for the competition by the governing body (entry list), independent of whether or not they participate in any competition. 18 These include athletes who do not start and athlete who do not come to the competition. 18 The number of competing athletes is defined as all athletes who start at least once in an event, irrespective of whether or not the athlete finishes the event. Athletes who do not finish (DNF), who are disqualified (DQ) or whose result is not measured (NM) are counted as having started. 18 The number of athlete participations (or number of starts) is calculated by adding up the number of athletes starting in all heats, rounds, qualifications and finals of a particular discipline or day, including DNF, DQ and NM. 18 If an athlete starts more than once in the same or a different discipline, each start is counted. In combined disciplines (eg, heptathlon), the starts in each subdiscipline are counted. 18

Incidence of in-competition illnesses should be calculated as the number of new illnesses per 1000 registered athletes or per 1000 competing athletes. 1 , 18 To compare different championships, the incidence of in-competition illnesses can also be reported as new illnesses per 1000 athlete days. 3 , 36-38 The number of athlete days is calculated by multiplying the number of championship days by the number of registered athletes.

Statistical analysis

Incidence data should preferably be analysed with models based on the Cox proportional hazards regression method. Intrasubject correlations might be considered. 39 Statistical models which take into account the complexity of competing events, repeated injuries, such as marginal methods, and multistate models with competing risk components are recommended. 40 Different analysis for part-time loss and full-time loss may be considered. In addition, analysis of time between incidences has a substantial scientific value.

Serial measures of prevalence should be analysed using statistical models that account for the correlation of repeated outcome measures within participants over time, for example, generalised estimating equations or mixed-models.

Reporting of results

The presentation of the results should follow the 'STrengthening the Reporting of OBservational studies in Epidemiology' (STROBE) protocol for reporting of observational epidemiological studies ( http://www.strobe-statement.org).41-43 The study population should be clearly defined with the number of participants, their age (mean and range), gender, main event and level of performance. The data should be reported in detail, including comments on the possible effects of missing data on the study results.

If the sample size is large enough, results should be reported separately for men and women, events, age categories and, if possible for ethnic group, country or geographical area (continents). For Athletics, athletes are divided into the following age categories: master (over 35years), senior (from 23 to 35 years), under 23 years, juniors (under 20 years) and youth (under 18 years). The circumstances (competition heat, round, final or training), severity (time loss), mode of onset, body location, side, type and causes should be accurately reported.

View Abstract

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Injury and illness definitions and data collection procedures for use in epidemiological studies in Athletics (track and field): Consensus statement

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Background Movement towards sport safety in Athletics through the introduction of preventive strategies requires consensus on definitions and methods for reporting epidemiological data in the various populations of athletes. Objective To define health-related incidents (injuries and illnesses) that should be recorded in epidemiological studies in Athletics, and the criteria for recording their nature, cause and severity, as well as standards for data collection and analysis procedures.

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Robert L Grant , senior lecturer in health and social care statistics

¹St George's, University of London and Kingston University, London SW17 0RE, UK

Correspondence to: R L Grant robert.grant{at}sgul.kingston.ac.uk

Odds ratios are a necessary evil in medical research; although used as a measure of effect size from logistic regressions and case-control studies, they are poorly understood. This paper provides practical advice for authors and readers on converting odds ratios to relative risks

The odds ratio is a common measure in medical research of the effect size comparing two groups (treatments or risk factors) in terms of an outcome that is either present or absent. However, the odds ratio is poorly understood. 1 2 3 The relative risk (also called the risk ratio) is more intuitive, but cannot be obtained from case-control studies or (except in rare instances) logistic regressions. Because the misunderstanding arises from the odds itself, simply describing it as a proportional change (for example, explaining an odds ratio of 0.8 as "treatment X was associated with a 20% reduction in the odds of the outcome") is not helpful for most people. This is a problem when communicating results to healthcare professionals and policy makers, discussing treatment options with patients, or seeking to conduct a meta-analysis of studies reporting effect sizes in a mixture of odds ratios and relative risks.

Unfortunately, confusion about odds is not the only problem; there is also a danger of inaccuracy when communicating odds ratios. When the outcome is rare, the odds ratio and relative risk are about the same. Medical papers sometimes rely too heavily on this approximation, discussing odds ratios in terms of risks. Not only is the odds ratio a poor approximation for outcomes which are not "rare" in the study, a statistical analysis with a single odds ratio, common to all participants, does not imply a single common relative risk. In fact, the relative risk depends also on the risk of the outcome in the baseline or control group. For brevity, I will refer to this as the baseline risk. Because of this, the same odds ratio could imply a very different relative risk for subgroups of the population with different baseline risks.

The formula for converting an odds ratio to a relative risk is straightforward 4 5 (fig, table 1):

Relative risk=odds ratio/(1−p ₀+(p ₀ ×odds ratio))

(Where p ₀ is the baseline risk.)

The odds ratio is always further away from 1 than the relative risk, but they are more similar when the baseline risk is small, as seen by the near diagonal line in the figure for 0.1 baseline risk. Although the basic conversion is well understood, more complex analyses may not have a simple "baseline risk." For example, the box describes the difference between odds and risks using the example of a clinical trial of a smoking cessation strategy. 6 Odds ratios are used to summarise the effect of the strategy on various dichotomous outcomes, and these are also adjusted for ward discharge rates. This means that the baseline risk (in the usual care group) will differ between wards, according to the definition of a confounding factor. 7 Therefore, a single odds ratio can imply a range of relative risks, depending on age.

Box: Definitions of odds and risks

Therefore:

Advice for authors of medical research

Authors of medical research should consider converting odds ratios to relative risks in this way, and should provide the observed risks if possible, because best practice in communicating risks requires absolute as well as relative measures. 8 Unfortunately, the risks cannot be obtained at all for case-control studies. In this case, if a range of plausible baseline risks can be agreed among authors, perhaps based on published evidence, this can be used to create a range of plausible relative risks.

When an odds ratio is adjusted for covariates (typically by logistic regression), there is no longer a single shared baseline risk. Instead, different values of the covariates in different participants mean that their baseline risk differs, and therefore the relative risk differs too. In such a situation, authors can use an average baseline risk, or present a range of relative risks corresponding to an observed or otherwise plausible range of baseline risks.

A single average risk can be calculated in the context of logistic regression by finding the mean observed value of each covariate and entering these values, along with the baseline or control group indicator, into the regression equation. This will yield a baseline odds that can then be converted to the baseline risk by the following equation:

Risk=odds/(1+odds)

Therefore, the odds ratio is converted to an "average relative risk."

It may be preferable to provide a range of relative risks rather than a single average, because the baseline risk is a measure of risk in the population and could still differ markedly for individual people. A relatively simple approach to this is to use the regression model to calculate the baseline odds for every study participant, rank these odds, and calculate deciles (the values dividing the study participants into ten equally sized subgroups in order of their baseline odds). The deciles for baseline odds are then converted to baseline risks, and hence the odds ratio is converted to deciles of adjusted relative risks.

However, in more complex models, such as where the odds ratio for the intervention or risk factor of interest is altered by one or more covariates (an "interaction" or "effect modification"), this simple averaging may fail to capture all the interdependencies in the data. Modern statistical software-for example, Stata 9 and the R package "effects" 10 -allows for the calculation of "marginal effects," which incorporate these complexities to estimate individuals' risks, and authors should be prepared to seek advice on the best way to obtain and communicate these results.

Advice for clinical readers of medical research

Most published research providing an odds ratio as a measure of effect size should also provide sufficient information for the baseline risk, and hence the relative risk, to be calculated. If numbers in each group are given, the crude relative risk can be calculated directly (box). Even if not all this information is present, readers can still make some useful attempts at conversion, which is particularly important for those conducting meta-analyses and systematic reviews. Readers will need to decide on a plausible range of baseline risks and thus derive a plausible range of relative risks. In the case of odds ratios adjusted for covariates-which is common in contemporary medical research-readers will not be able to carry out the approach suggested above for authors (unless they can access the original data). Therefore, a simple sensitivity analysis is likely to be helpful, using an upper and lower plausible limit. This plausible range could be derived from similar, previously published research, or could use expert opinions.

Example: cluster randomised controlled trial of hospital based smoking cessation

An illustrative analysis was conducted on Murray and colleagues' cluster randomised controlled trial, 6 comparing systematic identification and support to quit smoking with discretionary support (usual care) in a hospital setting. This study reported a primary outcome and six secondary outcomes, with baseline risk (proportions of participants with the outcome in the usual care arm) ranging from 6% to 29% for different outcomes (table 2). Here, all the outcomes are positive but it still makes sense to describe them as risks. The crude odds ratios and relative risks could be calculated from the reported data, but estimating adjusted relative risks requires a decision about a plausible range of baseline risks at different levels of the confounding factor (in this case, ward discharge rates). Because this covariate varies between care settings, the Cochrane review cited by Murray and colleagues was taken as an approximate guide for the primary outcome, quitting at four weeks. It included 23 studies with 100 or more participants in the control arm. 11 The baseline risks ranged from 6% to 53% (interquartile range 9-35), which is about half to double the baseline risk in Murray and colleagues' study. 6 Follow-up times varied, but this range appeared plausible; therefore, 9% and 35% were used for sensitivity analysis.

The plausible crude relative risks ranged from 1.78 to 2.58, which clearly contained the true known value of 2.28. The plausible adjusted relative risks ranged from 1.52 to 1.91, which cannot be verified but still represented a large increase in smokers' chances of still having quit at four weeks' follow-up.

A doctor can clearly advise their patient that "research suggests this intervention will improve your chances of quitting by somewhere between 52% and 91%, depending on how soon you leave hospital." This advice is more likely to be understood than the alternative ("will more than double your odds"), or the incorrect assumption that the odds ratio can be described as an approximate relative risk and any differences ignored ("will more than double your chances"). Considering the secondary outcomes from Murray and colleagues makes this clear: those with large baseline risks, or large odds ratios, have very large differences between odds ratios and relative risks.

Conclusion

When communicating risks to patients, it is important to be able to frame the statistics in a meaningful and easily understood metric. 8 Misinterpretation of the odds ratio can lead to serious overestimation of the benefits or risks in medical decision making. It is relatively simple to convert odds to relative risks given the baseline risk or baseline odds of the outcome. However, any statistical model which contains a single odds ratio that is constant for all covariate values does not imply a constant relative risk, so to communicate relative risks effectively it is essential to provide a range of relative risks for different covariate values. These calculations can be conducted with simple approximations or with modern statistical software, and authors of medical research should make use of these to improve communication of their findings. For readers of published research, there will remain situations where sufficient information for such a conversion is not given, and here they should interpret odds ratios with caution; sensitivity analysis over plausible relative risks is advisable. Conversion is also potentially helpful in meta-analyses, where different metrics can prevent studies from being combined.

Notes

I am grateful to Fiona Reid and Caroline Rogers for their advice on drafts of this paper.

Contributor: RLG conceived of the need for practical advice on this topic, and the general approach set out above. RLG carried out the example analysis and wrote the paper. RLG is a medical statistician with an interest in effective communication of research findings, and he acts as guarantor for this paper.

Funding: None received.

Competing interests: I have read and understood the BMJ Group policy on declaration of interests, and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: None required; this entirely methodological paper involved no contact with or data from humans.

Provenance and peer review: Not commissioned; externally peer reviewed.

Cite this as: BMJ 2014;348:f7450

Footnotes

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Converting an odds ratio to a range of plausible relative risks for better communication of research findings

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Robert L Grant , senior lecturer in health and social care statistics 1St George's, University of London and Kingston University, London SW17 0RE, UK Correspondence to: R L Grant robert.grant{at}sgul.kingston.ac.uk Odds ratios are a necessary evil in medical research; although used as a measure of effect size from logistic regressions and case-control studies, they are poorly understood.

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Simple guide to the difference between OR and RR

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Simple guide to the difference between OR and RR

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Written by Clay Smith

Idiot's Guide

That's right - I will be your guide. The good thing about having an idiot for a guide is that I have to make it simple to understand it myself, which means, hopefully, you will understand it as well.

Probability or Odds

Probability
Probability means the risk of an event happening divided by the total number of people at risk of having that event. I will use the example in a recent JAMA article. In a deck of 52 cards, there are 13 spades. So, the risk (or probability) of drawing a card randomly from the deck and getting spades is 13/52 = 0.25 = 25%. The numerator is the number of spades, and the denominator is the total number of cards.

Odds
Odds seems less intuitive. It is the ratio of the probability a thing will happen over the probability it won't. In the spades example, the probability of drawing a spade is 0.25. The probability of not drawing a spade is 1 - 0.25. So the odds is 0.25/0.75 or 1:3 (or 0.33 or 1/3 pronounced 1 to 3 odds).

Moving back and forth
To go from odds to probability, simply take the numerator/(denominator + numerator). In the spades example, the odds of 1/3 is converted by taking 1/1+3 = 0.25 - and now we are back to probability. To go from probability to odds, simply take the numerator/(denominator-numerator). In the spades example, given that the probability of drawing a spade is 1/4, take 1/(4-1) = 1:3 odds or odds = 0.33.

Statistical Significance
If an odds ratio (OR) is 1, it means there is no association between the exposure and outcome. So, if the 95% confidence interval for an OR includes 1, it means the results are not statistically significant. Example, exposure to colored vs white Christmas lights was associated with an increase in jocularity score, OR = 1.2 (95%CI 0.98-1.45). Sorry, this is not statistically significant. Let's just go with white lights...

Use
Either the OR or risk ratio (RR) could be used in many study types. However, only the OR can be used in case-control studies. Because in order to calculate the RR, one must know the risk. Risk is a probability, a proportion of those exposed with an outcome compared to the total population exposed. This is impossible in a case-control study, in which those who already have the outcome are included without knowing the total population exposed.

Risk Ratio

RR is a very intuitive concept. It is the probability (or risk) of one outcome over the probability (risk) of another. Let's use a study we covered on JF to discuss this concept. Survival was lower in pediatric patients intubated during arrest compared with those not intubated: 411/1135 (36%) vs 460/1135 (41%). So, the RR is 36.2%/40.5% = 0.89. This means survival was reduced by a factor of 0.89 for pediatric arrest patients who were intubated during arrest vs. those who were not. As an example, if survival was expected to be 40%, then intubating during arrest would reduce it to: 40% x 0.89 = 35.6%.

Let's do one more example. Supination-flexion (SF) vs hyperpronation (HP) to reduce nursemaid's elbow was more likely to fail. The risk of failure with SF was 96/351 (27%) vs. 32/350 (9%) with HP. The RR was 3. This has a very intuitive meaning: risk of failure with SF was three times more likely than HP.

Odds Ratio

The OR is a way to present the strength of association between risk factors/exposures and outcomes. If the OR is <1, odds are decreased for an outcome; OR >1 means the odds are increased for a given outcome. Let's look at the examples again and consider odds.

For pediatric arrest, the risk of survival if intubated during arrest was 411/1135 (36%) vs 460/1135 (41%) if not intubated. Let's convert to odds and calculate an OR.

Intubated: 411/1135-411 = 411/724 = 0.57 odds.

Non-intubated: 460/1135-460 = 460/675 = 0.68 odds.

Note, this is very close to the RR ( 0.89) but is a slight overestimate of the effect on the outcome. This is always the case with the OR compared to the RR - it overestimates the effect.

Take the example of supination-flexion vs hyperpronation for nursemaid's. The risk of failure for SF was 96/351 vs. 32/350 with HP. Let's convert this to odds.

Note, the OR overestimates the RR, which was 3. Although one could say the risk of failure using SF is 3 times greater than HP, one could not say, based on the OR, the risk was 3.74 times greater. The OR and RR are not the same. What could be said is that the odds of failure is 3.74 times greater.

Risk Ratio vs Odds Ratio

Whereas RR can be interpreted in a straightforward way, OR can not. A RR of 3 means the risk of an outcome is increased threefold. A RR of 0.5 means the risk is cut in half. But an OR of 3 doesn't mean the risk is threefold; rather the odds is threefold greater. Interpretation of an OR must be in terms of odds, not probability. Again, the OR will always be an overestimate compared to the RR. However, the RR and OR will be similar for rare outcomes, <10%. But the OR increasingly overestimates RR as outcomes exceed 10%. This is easier to understand with an example.

Pretend a new vape, Vapalicious, is associated with cancer.

80/100 people who use it get cancer.

20/100 who don't use it get cancer.

The risk of getting cancer is 4 times greater in Vapalicious users. RR = 0.8/0.2 = 4

Note how distorted the OR becomes in this example. OR = (80/20)/(20/80) = 16

What if Vapalicious rarely caused cancer?

5/1000 get cancer with Vapalicious vs 2.5/1000 for non-users.

RR = 2.

OR = 2 as well (actually 2.005)

With rare outcomes, the RR and OR are very similar.

Why Does This Matter?

This matters because we often equate the OR and RR. Unwary researchers, reviewers, or news media might report a 16-fold increased risk of cancer from Vapalicious. In fact, there was a 4-fold increased ris
k of cancer from Vapalicous. Not that I plan to use Vapalicious (or any other vape), but a 16-fold vs 4-fold increase is a gross overestimation of the effect.

What Does the OR Mean?

So, what does an OR mean? Here it is in plain language.

An OR of 1.2 means there is a 20% increase in the odds of an outcome with a given exposure.

An OR of 2 means there is a 100% increase in the odds of an outcome with a given exposure. Or this could be stated that there is a doubling of the odds of the outcome. Note, this is not the same as saying a doubling of the risk.

An OR of 0.2 means there is an 80% decrease in the odds of an outcome with a given exposure.

Summary

Odds Ratio is a measure of the strength of association with an exposure and an outcome.

OR > 1 means greater odds of association with the exposure and outcome.

OR = 1 means there is no association between exposure and outcome.

OR < 1 means there is a lower odds of association between the exposure and outcome.

If the 95% confidence interval for the OR includes 1, the results are not statistically significant.

OR and RR are not the same.

OR always overestimate RR, but...

OR approximates RR when the outcome is rare but markedly overestimates it as outcome exceeds 10%.

References

1. Odds ratios - current best practice and use

2. When odds ratios can mislead

3. Life in the Fast Lane ultra-concise summary

4. The odds ratio by Bland and Altman, of Bland-Altman plot fame

5. Wikipedia aka source of all statistical knowledge

6. How odds are used in gambling

7. A beginner's guide

8. Explaining odds ratios

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That's right - I will be your guide into the wonderful world of odds ratios. The good thing about having an idiot for a guide is that I have to make it simple to understand it myself, which means, hopefully, you will understand it as well.

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Risk factors for sports injuries — a methodological approach 

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Risk factors for sports injuries — a methodological approach 

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British Journal of Sports Medicine 2003;37:384-392.

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Abstract

The methodology for studies designed to investigate potential risk factors for sports injury is reviewed, using the case of hamstring strains as an example. Injuries result from a complex interaction of multiple risk factors and events. Therefore, a multivariate statistical approach should be used. In addition, the sample size of the study needs to be considered carefully. Sample size mainly depends on the expected effect of the risk factor on injury risk, and to detect moderate to strong associations 20-50 injury cases are needed, whereas small to moderate associations would need about 200 injured subjects. Studies published to date on the risk factors for hamstring strains have methodological limitations, and are too small to detect small to moderate associations.

Risk factors and injury mechanisms-the Meeuwisse model

Risk factors are traditionally divided into two main categories: internal (or intrinsic) athlete-related risk factors and external (or extrinsic) environmental risk factors.16, ²⁶ An important point is that risk factors can be divided into modifiable and non-modifiable factors. Although non-modifiable risk factors such as gender and age may be of interest, as a minimum it is important to study factors which are potentially modifiable through physical training or behavioural approaches, such as strength, balance, or flexibility. However, merely to establish the internal and external risk factors for sports injuries is not enough. To establish a complete understanding of the causes, the mechanisms by which they occur must also be identified. In other words, sports injuries result from a complex interaction of multiple risk factors and events of which only a fraction have been identified.

Therefore, studies on the aetiology of sports injuries require a dynamic model that accounts for the multifactorial nature of sports injuries, and in addition, takes the sequence of events eventually leading to an injury into account. One such dynamic model is described by Meeuwisse.²⁷ This model describes how multiple factors interact to produce injury (fig 1).

In studies on the aetiology of sports injuries, this model can be used to explore the interrelationships between risk factors and their contribution to the occurrence of injury. Meeuwisse classifies the internal risk factors as predisposing factors that act from within, and that may be necessary, but seldom sufficient, to produce injury. In his theoretical model, external risk factors act on the predisposed athlete from outside and are classified as enabling factors in that they facilitate the manifestation of injury. It is the presence of both internal and external risk factors that renders the athlete susceptible to injury, but the mere presence of these risk factors is usually not sufficient to produce injury. The sum of these risk factors and the interaction between them "prepares" the athlete for an injury to occur in a given situation. Meeuwisse describes the inciting event as the final link in the chain that causes an injury, and such events are regarded as necessary causes. He also states that such an inciting event is usually directly associated with the onset of injury.

As shown in figure 1, we would argue that it is necessary to expand the traditional approach to describing the inciting event. Firstly, the term injury mechanism is often used to describe the inciting event in biomechanical terms only. For example, an ankle sprain could be described as resulting from an inversion injury, or an ACL injury from valgus trauma to the knee. However, to be complete, the description of the injury mechanism needs to account for all of the events leading to the situation where the injury took place.²⁸ Examples of this include the playing situation (eg a two man block in volleyball), the position in the field of play (eg in the scoring box in soccer), the interaction with other players (eg being tackled from the side in American football), the skill performed by the injured player (eg a jump shot by a team handball player). Describing an ACL injury as a non-contact or contact injury does provide meaningful information, but leaves us far from having a complete understanding of the inciting event. If patterns can be established in the events leading to an injury situation, this information can potentially be more important and easier to apply to prevent injuries than an exact biomechanical description of joint motion at the point of injury. Secondly, the inciting event can-especially for overuse injuries-sometimes be distant from the outcome. For example, for a stress fracture in a long distance runner the inciting event is not usually the single training session when pain became evident, but the training and competition programme he or she has followed over the previous weeks or months.

Study designs for risk factor analyses-the case of hamstring strains

Three main study designs are available to study risk factors for sports injuries: case control studies, cohort studies and intervention studies (preferably done as a randomised controlled trial). In the following, we will illustrate the principles, strengths, and disadvantages of each of these three study designs using hamstring strains as an example (table 1).

Hamstring muscle strains (tears of the semimembranosus, the semitendinosus, or the biceps femoris muscles) occur in the myotendinous junction at any location along them, and are common in a number of sports-sprint, martial arts, water skiing, soccer, and other forms of football.²¹⁻²⁵ The hamstring muscles are two-joint muscles that extend the hip joint and flex the knee joint. Although there are no studies that have examined the injury mechanisms directly and these may differ between sports, injuries are mainly thought to occur during maximum sprinting, when resisting knee extension or at foot strike.29, ³⁰ A number of candidate risk factors have been proposed for hamstring strains (for example, poor posture, neuromeningeal tightness, decreased muscle control, poor technique, ethnicity, muscle fatigue),³¹⁻³⁷ but for the purposes of this discussion we will focus on three internal factors: previous injury, reduced ROM, and poor hamstrings strength. As shown below, the other factors have not been examined properly. In theory, limited ROM for hip flexion could mean that the muscle is vulnerable close to maximum length when muscle tension is at its maximum. Low hamstring strength would mean that the forces necessary to resist knee extension and start hip extension during maximal sprints could surpass the tolerance of the muscle-tendon unit. Hamstring strength is often expressed relative to quadriceps strength as the hamstrings: quadriceps ratio, since it is the relation between the ability of the quadriceps to generate speed and the capacity of the hamstrings to resist the resulting forces that is believed to be critical. A previous injury can cause scar tissue to form in the musculature, resulting in a less compliant area with increased risk of injury. A previous injury can also lead to reduced ROM or reduced strength, thereby indirectly affecting injury risk.³⁶

In a case control study design, the approach is to compare the frequency or level of potential risk factors between a group of injured athletes and an otherwise comparable group of injury-free athletes. Often, information on risk factors is collected retrospectively, because the approach is to identify persons with the injury of interest and then look backward in time to identify factors that may have caused it. For hamstring strains, this could mean comparing a group of patients treated at a sports medicine clinic with a group of healthy athletes from the same sports. Three important assumptions must be met to use this approach.³⁸ Firstly, the cases that are selected must be representative for all patients with the injury in question. Secondly, the controls must be representative of the population of injury-free athletes. Thirdly, the information on potential risk factors must be collected with adequate accuracy, and in the same way from cases and controls. In the first study on risk factors for hamstrings strains published by Burkett in 1970,³⁹ he compared 17 injured and 50 healthy athletes by measuring their hamstring:quadriceps ratio and ROM for hip flexion. The results showed that the injured athletes had a lower strength ratio, while there was no difference in ROM. This study illustrates another important limitation of the case control approach-that is, to distinguish between risk factors and injury sequelae. Although all the subjects were tested after they had returned to full performance after their injury, it is not possible to know what the strength of the injured athletes was prior to injury. In other words, reduced strength could be a risk factor, or simply a result of the injury. The same limitations apply to the studies of Worrell et al ⁴⁰ and Jönhagen et al.⁴¹

The second, and in most cases preferable, study design is the cohort study, where all data are collected in a standardised manner prospectively in time. The approach involves measuring potential risk factors before injuries occur, after which new cases and exposure are reported during a period of follow up. Quality control is simplified and completeness can be secured to a high degree. Prospective cohort studies can provide direct and accurate estimates of incidence and relative risk. As will be discussed later, the main disadvantage of the cohort study design is that study size is critical. It may be necessary to include and monitor a large number of athletes for an exceedingly long study period, particularly for less common injury types.

A cohort study to assess risk factors for hamstring strains would involve examining a group of currently healthy athletes at baseline to test their hamstrings strength, hip ROM, and obtaining their history for previous hamstring strains. This cohort would then be followed prospectively to record injuries during a defined period of time, typically one or two seasons. At the end of the study, injured and non-injured athletes can be compared to examine whether there were any differences in strength, ROM, or the prevalence of former hamstring strains between the groups. To date, all three factors have not been examined in the same study (see table 1), but in the study of Bennell et al ⁴² they used a similar approach to examine the effects of hamstring strength and former injury on the risk for new strains. The isokinetic strength of the hamstrings and quadriceps muscles was tested with an isokinetic dynamometer before the start of the season in a group of 102 Australian rules footballers. Twelve players sustained clinically diagnosed hamstring strains that caused them to miss one or more matches during the ensuing season. However, although they did not observe any differences in strength between the injured and the non-injured players, the power to detect even large differences may be low with only 12 injury cases. Nevertheless, players with previous injury were twice as likely to suffer a new injury compared with players with no history of a previous injury.

As emphasised in the Meeuwisse model,²⁷ the preferred approach is to use a multivariate model, to control for interactions and confounding factors. For example, such an approach can be used to distinguish between the effect of previous injury per se, and the effect previous injury may have through, for example, reduced strength. Meeuwisse⁴³ has reviewed the concepts of interaction (when two factors work together to produce a risk which is greater or lesser than expected) and confounding (when an association between two variables of interest could be due to the effects of a third variable) when assessing risk factors for athletic injury, and how to distinguish between them.

The third study design that can be used to study the effect of a particular risk factor on sports injury is the intervention study. Randomised large scale clinical trials provide the strongest evidence for both the causal nature of a modifiable risk factor, as well as the effectiveness of modifying that factor on preventing injury outcomes.³⁸ This approach involves determining if a particular intervention designed to eliminate or at least reduce a risk factor, also results in a reduced risk of injury. For example, to test the association between hamstrings strength and injury risk, one could select a cohort of athletes at risk, and randomly assign half of them to a programme of strength training for the hamstrings. The relationship between strength as a risk factor and injury risk is established if strength training can be shown to lead to fewer injuries in the intervention group. A similar approach using a stretching programme could be used to test the relationship between ROM and the risk of hamstring strains. To date, no randomised controlled study has been conducted to test the effect of potential risk factors on hamstring strains. However, Heiser et al ³⁷ compared the risk of reinjury between American football players who underwent two different rehabilitation programmes after acute hamstring strains in a retrospective study. One group, treated in the period from 1978 to 1982, followed a rehabilitation programme that included an isokinetic strength training programme. The other group, treated from 1973 to 1977, did not. They showed that the risk of reinjury was reduced significantly in the strength training group (7.7% v 1.1%) by the addition of post-injury testing and strength training. Since specific strength training appears to have modified injury risk, this study indicates that reduced strength is a risk factor for reinjury.

However, although randomised controlled trials can provide the strongest evidence to evaluate cause-effect relationships, they are limited to risk factors that can be modified (for example through special training programmes or use of protective equipment), and they are usually used to assess the effect of only one factor at the time. However, additional factors can be included by including more groups if the factors can be assumed to be additive on injury risk. For instance, a study could have one control group, one strength training group, one stretching group, and one group doing stretching and strength training to test the effects of strength and flexibility on hamstring strains, a 2×2 factorial design. Deciding when to initiate a clinical trial can therefore be controversial. They should not be undertaken until there is a substantial body of knowledge suggesting that intervention may be effective, but not so late that conducting them would be considered unethical.³⁸ Finally, it is arguably unethical to undertake a clinical trial simply to prove harm. Therefore, the first step to establish the relationship between potential risk factors and sports injuries will in most cases be to conduct a prospective cohort study.

Statistical methods in sports injury risk factor studies

Risk factors of interest in injury research can be classified in two groups, continuous variables and categorical variables. Examples of continuous variables include most anthropometric and physiological factors, such as body composition, age, strength, flexibility, aerobic power or running speed. Examples of categorical (grouping) variables include gender (male v female), previous injury (no previous injury v history of previous injury), player function (attacker v defender). Statistical analyses or risk factor data have, as mentioned above, usually been done in a simplistic manner without much use of statistical modelling tools-as a rule using univariate tests, where the effect of each risk factor is tested separately. An example of a univariate test to assess the effect on injury risk of a categorical variable is the χ ² test, which compares the injury rate between groups. As an example, the effect of previous injury status can be assessed by comparing the risk of injury between previously injured and non-injured athletes. In a recent Icelandic study by Arnason et al on hamstring strains among soccer players the players were followed for one season and each leg was categorised as healthy or previously injured based on their injury history of hamstring strains.⁴⁴ A total of 10 re-injuries occurred in the 74 previously injured thighs, while there were only nine new strains among the 442 thighs that had been classified as healthy. Based on these numbers risk can be expressed as the odds ratio (OR) or the relative risk (RR, sometimes also referred to as risk ratio).⁴⁵ The RR is the ratio of the rate of injury in previously injured thighs (10/74; 13.5%) to the rate of injury in thighs without previous injury (9/442; 2.0%). The OR is the ratio of the odds in the previously injured group (10 injured: 64 with no injury) to the odds in the healthy group (9 injured: 433 with no injury). Thus, the OR may be calculated as (10/64)/(9/433) = 7.5 (Z Wald = 4.17; p<0.0001), while the RR was (10/74)/(9/442) = 6.6 (Z Wald = 4.27; p<0.0001). In other words, the risk of injury is about seven times higher in previously injured thighs. The OR and RR are similar when the frequency of injury is low; they diverge when injuries become more frequent.

It should be noted that in this analysis of previous injury as a risk factor, the limbs of the athlete were used as the unit of analysis rather than the person. Although we typically treat the athlete as the unit of analysis (for variables such as age, gender, training background, etc), the question is whether we should treat their past history of injury differently (that is, treat the limb as the unit of analysis rather than the individual). It seems reasonable to assume that the factors that contribute towards an increased risk of reinjury are related to the injured limb (for example, reduced proprioception, increased joint laxity, scar tissue formation, reduced ROM), and not the person. On the other hand, it may be argued that some athletes are more prone to injury than others-for example, because of an aggressive playing style. With few exceptions,44, ⁴⁶ until now the person rather than the limb has been used as the unit of analysis in studies where past history of injury has been examined as a risk factor-for example, for ankle sprains⁴⁷⁻⁴⁹ and hamstring strains.50, ⁵¹ Whether to treat the limb or the person as the unit of analysis depends on the risk factor. If the risk factor can be assumed to represent a characteristic of the person, such as age or gender, the person should be used as the unit of analysis. If the risk factor in question can be assumed to describe a characteristic of the limb, such as may be argued for previous injury, the limb should be used instead.

The same study by Arnason et al ⁴⁴ also shows how the effect of a continuous variable can be evaluated. This is usually done using simple t tests or alike to compare the average between injured and non-injured groups. In this study the average age of the players with a new hamstring strain (n=18) was 27.8 years, while the age of the uninjured players (n=280) was 23.8 years (p<0.001, t test). In other words, from this it appears that older players were more susceptible for hamstring strains. However, this type of analysis does not reveal whether the injury risk increases linearly with age. It may be that only players over a certain age-for example, 30 years, have an increased risk, while the risk is low for players under 30, regardless of their age. This hypothesis would be relevant, if hamstrings strains were the result of age related processes within muscles which begin after the age of 30. The opposite hypothesis could be that the youngest players are at greater risk because they are not accustomed to playing at a high level, perhaps with an insufficient training background. In a case like this, where the relationship between a continuous risk factor and risk of injury can be assumed not to be linear, the risk factor can be dichotomised at a certain cut off point-for example, one standard deviation (1 SD) below or above the mean value for the entire cohort. In other words, a continuous variable can be transformed to a categorical grouping variable, in this example three groups: older (players older than the mean +1 SD), young (those with an age lower than the mean −1 SD), as well as an intermediate reference group (with an age within ±1 SD of the mean). This way, the risk of injury can be compared between older players and the intermediate reference group, or between younger players and the reference group, if that is thought to be relevant. Table 2 shows an example of how this was done in the same Icelandic study. The mean age for the entire cohort was 24 years, with a standard deviation of four years. Based on this, the players were separated into three age groups, <20 yrs, 20-28 yrs and >28 yrs, and ORs could be calculated for risk of injury to compare the older and younger groups with the reference group. The results show that the odds ratio for older players was 1.77 compared with the reference group (p<0.001), while there were no injuries among the youngest players.

However, a univariate statistical approach may be too simplistic, since injuries may be generated by the interplay of several factors. A multivariate approach is often necessary in sports injury risk factor studies, and one statistical model commonly used to achieve this is the linear logistic regression model. The restriction here is that observation times-the number of hours each subject participates in training or matches-must be about equal. In many sports injury studies this assumption is reasonably met by including an observation period of one or more full sport seasons for every subject. This model assumes a linear relationship between the logarithm of the odds of having an injury and the risk factors.⁴⁵ If the frequency of the injury is p (eg 10%), the linear univariate logistic model can be written Log (p/1-p) = α + β * x in the univariate case, where x is the risk factor, eg age. The most interesting factor in the model is β. It tells how much the log odds increase by increasing x with 1 unit, eg an age increase of one year. Another way to express this is as the odds ratio, which in this case is calculated as exp (β * x). In the same Icelandic study⁴⁴ a logistic regression analysis was performed for risk of injury due to hamstring strains versus age as risk factor. The estimate of β was 0.33, giving a 1 year OR = exp (0.33) = 1.40. This means that for every one year a player gets older the risk of a new hamstring strain can be estimated to increase by as much as 40% in this player population.

The advantage of the logistic regression model is that it can easily be extended to include one or more confounding risk factors by adding more risk factor terms in the model, ie log (p/1-p) = β o + β ₁X + β ₂X ₂ + β ₃X ₃ etc.⁴⁵ Such risk factors can be continuous or categorical variables. In the Icelandic study this was done by adding age (X ₁), previous hamstring strain (X ₂, 1 or 0), body weight (X ₃), and body composition (X ₄). The final two factors were not significant, and the paper reported β ₁ = 0.34 and β ₂ = 2.45. The risk of a new hamstring strain was thus estimated to be e 2.45 = 11.6 times higher among athletes with a previous hamstring strain than among those without, adjusted for differences in age, body weight, and body composition. From this model we also see that the risk increases by 40% for every year the athlete gets older (e 0.34 = 1.40), adjusted for previous injury, etc. In other words, age is a significant risk factor in itself, the increased risk of injury in older players is not just a result of the higher prevalence of previous injury among older players.

In this model, it is not necessary to know the exact exposure time of each athlete, but it must be assumed that participation has been about equal for every athlete. However, this is not always the case, exposure may be reduced because of injury and athletes may leave the team for a number of reasons other than injury (see fig 2). A more advanced model to study risk of injury is the Cox proportional hazards regression models.⁵² The time from the start of the follow up period until the event (that is, the first injury) or the end of follow up (for players that are not injured) is here the main variable. Time is measured as the number of hours of exposure for each player (such as training and match participation). The strength of this approach is that the method can adjust for the fact that playing time can vary greatly between players on a team. This may be important, since the best players play more games than the substitutes, and perhaps even train harder. It also takes censorship into account, such as abbreviated lengths of follow up for other reasons than injury (for example, sickness, moving, quitting the sport) (see example in fig 2). The method assumes that risk factors affect injury risk in a proportional manner across time. Both continuous and grouping variables can be used as risk factors in the model, as in the logistic model. In this model the logarithm of the hazard rate is linear in the risk factors-that is, log hazard rate = α + β * x, when x is a single risk factor. Relative risk (RR) for a change in x can be estimated by exp (β * x). Thus, the interpretation is similar to that of logistic regression, the difference being that RR is estimated instead of OR.

This model, as well as the logistic model, can also be used to explore potential interactive effects. If one wants to explore if age works interactively with previous hamstring strain on the risk for new hamstring strains, the model would include linear terms for age (continuous) and previous hamstring strain (categorical) with an addition term equal to the product of the two factors. Interaction is declared if the product term is statistically significant in the regression model. Note that the statistical power for a test of interaction on risk will usually be much smaller than for demonstrating an effect of each factor. p Values for interaction are often therefore chosen to be higher than 5%, (for example, 10-15%). The concept of confounding will be difficult to interpret if interaction is present, so a pre-test of interaction is recommended before a regular multivariate and additive risk factor analysis is undertaken.

When considering whether to use a logistic or a Cox regression model, the advantage of logistic regression is that it is somewhat simpler. It does not require the investigator to monitor the participation in training and matches for each individual. Whereas accurate attendance records can be obtained when following teams or athletes at the elite or professional level, this may not be possible when studying younger or less organised teams. In such a case, if all athletes are followed for a full season, the logistic model would be chosen. The limitation of the logistic model is that it assumes equal probability of injury for all included. Another limitation is if the observation period is long, resulting in a large proportion of the subjects being injured. In contrast, in a Cox model exposure time is worked into the model, which will provide unbiased estimates of relative risks. Therefore, if individual exposure time can be collected with a reasonable accuracy-and that requires a well developed recording system-the Cox model approach would be preferable in most cases.

Risk factors for sports injuries-the significance of study size

In addition to a proper study design and choice of appropriate statistical methods, another factor which needs to be considered carefully is the sample size of the study. As pointed out by Murphy et al,¹⁸ a persistent problem is the relatively small number of subjects and injuries included in the risk factor studies published so far. For example, in the studies available on risk factors for hamstring strains the number of injured athletes ranges from 6 to 31, as shown in table 1. A study by Orchard⁵¹ on Australian rules footballers is the only exception with as many as 672 injuries, but this study only included descriptive questionnaire data (for example, age, history of previous injury, height, weight). No test data were available on other, potentially modifiable risk factors (for example, strength, ROM). For the rest of the cohort studies available, the number of cases may have been too small to provide a definite answer. In addition, there is typically no mention of how sample size has been calculated, which means that it is not possible to interpret a negative result.

In technical terms, the studies may lack power. The power of a study is its ability to demonstrate that there is an association between a risk factor and injury, given that the association exists.⁴⁵ A typical value is 80%-that is, the study is designed so that the chance of detecting a true difference is 80%, and we accept that we will miss the true difference in 20% of instances. If we need to be more confident that we will not overlook a true effect, we may need a power of 90% or more, which will require many more subjects. The factors that affect the power of a study are: the strength of the true association between the risk factor and injury risk (the stronger the association, the fewer cases are needed), the injury frequency (in general, the more frequent the injury, the fewer cases are needed), and the significance level (usually set at the p=0.05 level).

Sample size calculations should be performed specific to the type of statistical test which will be used to evaluate the main effect. Below we have outlined how sample size can be calculated using a univariate Cox model without adjustments for other factors.⁵³ It is possible to perform power calculations taking additional risk factors into account, but these more complicated models are beyond the scope of this review. In addition to the power and significance level, the investigator has to consider the coefficient β in the Cox model and the injury frequency p. The injury frequency p can often be estimated with good precision from previous studies for most injury types, but the strength of the relationship between a risk factor and the frequency of that injury type (β) is usually uncertain. A practical solution is to decide on a given value of β (the RR = e β * x for a given difference x) which is thought to be clinically important, and calculate sample size so that the probability of missing such an effect is low. However, it should be acknowledged that it may be difficult to decide what is clinically important-that is, which value of β should be decided on.

When the study is finished sample size can be recalculated from the estimated value of β, or alternatively the power of the study to detect an effect can be calculated, should the study fail to find a significant relationship. An alternative is to incorporate interim analyses into the study design to examine outcome data at an early stage to adjust the sample size calculation or extend the length of the study. Often the reason for non-significance is that the strength of the relationship has been overestimated-that the effect of the risk factor is lower than anticipated. Sometimes, though, the injury frequency can be lower than anticipated, and cause loss of power as well.

Schmoor et al ⁵⁴ have among others given formulas for calculation of sample size with a Cox regression model (box 1), and table 3 shows the sample size for some chosen values of p and relative risk. From the table it is evident that only rather strong relationships in combination with a fairly prevalent injury type will defend a design with less than 300 subjects. The table also illustrates how the sample size mainly depends on the effect size, but also to some degree by the projected proportion of injured subjects. Moderate to strong associations can be detected with 30-40 injury cases, whereas small to moderate associations would need more than 200 cases. As shown in table 1, this means that the cohort studies published to date on risk factors for hamstring strains have limited statistical power, and can only be expected to have detected strong relationships. In other words, a negative finding could result from a type 2 error (overlooking a true effect), since the studies are too small to detect anything but strong relationships.

The effect of measurement errors

The ability to identify the effect of a potential risk factor also depends on how accurately the factor can be measured at baseline. As shown in fig 1, a number of internal and external factors can potentially influence injury risk. The challenge when designing a risk factor study is to decide which factors to measure, and how to measure them. Although non-modifiable risk factors may be of interest, studies should at least focus on modifiable factors which can be subject to intervention by physical training or behavioural approaches. Ideally, these factors should be easy to measure and with excellent precision. Unfortunately, this is not always the case. It is important to collect methodological information on risk factors when planning a study, since the measurement error can make it more or less difficult to detect risk factor associations with injury risk. Factors like age and sex, height, and weight are typically measured with high precision. A typical risk factor in many sport injury studies is the variable "previous injury of the same type". The measurement error here is probably low and its association to injury may therefore be easier to detect statistically. However, the ability to recall and properly classify a previous injury depends on the injury type, as discussed below. Other variables such as maximal O ₂ uptake, strength, and ROM have a variable degree of measurement errors.⁵⁵

The precision of a measurement method or test for a risk factor can be assessed by making two measurements of the variable on the same group of subjects some time apart. The precision can be expressed in several ways-for example, as the intraclass correlation coefficient (ICC). An ICC of 1.00 signifies a perfect precision-that is, the method gives the exact same result in both cases. For example, the ICC for maximal hamstring strength ranges between 0.82 and 0.97,⁵⁶ while the ICC of a passive knee extension test to measure hamstring flexibility is 0.99.⁵⁷ A study to detect associations between hamstring strength risk of hamstring strains will therefore have an extra burden to carry with respect to loss of power. If the ICC is 0.8, the regression coefficient will be inflated by this factor, so the error-free association between risk factor and injury risk will be β ₁/0.8. This means that the necessary sample size (see formulae in box 1) will increase by a factor of approximately (1/0.8)² ≈ 1.56, by more than 50%. In other words, a cohort study to detect small to moderate associations would not only need at least 100 cases, but more than 150 cases, if the method used to measure the risk factor in question has an ICC of less than 0.8.

A final point to consider is the accuracy of the recording of injured cases. A recording system, often with the help of team medical staff, has to be established to document all injuries of interest during the observation period. The key is to not miss any of the injuries of interest, and to make sure that there are no false positives by using appropriate methods and precise diagnostic criteria-for example, documenting that an alleged ACL injury really is an ACL injury through an arthroscopic exam or MRI. Not to miss any injuries is a significant challenge when using team medical staff or coaches to record injuries, especially at lower levels where the physical therapist or physician does not attend all training sessions and matches. This can probably be accomplished with more severe injuries, such as ACL injuries, where it is extremely unlikely that a player can continue to train or play after a complete rupture. In contrast, for hamstring strains minor injuries only resulting in reduced performance or a few days of missed participation may be overlooked. Also, it can be difficult to distinguish a true muscle strain injury from other conditions which can lead to pain in the posterior thigh. In the hamstring studies available, the diagnosis was made by the team physical therapist or physician based on a clinical examination only in all but one study. The exception is the study by Verrall et al,⁵⁰ where they made a point of documenting all hamstring strain injuries through the use of MRI. They showed that of the 32 clinically diagnosed hamstring strain injuries recorded, six of the patients had normal scans. A precise recording system-where no injuries are missed and all injuries are properly documented-is an essential prerequisite in risk factor studies.

Conclusions

Studies on the aetiology of sports injuries need to account for the multifactorial nature of sports injuries by including as many relevant risk factors as possible and using a multivariate statistical approach. The sample size of the study needs to be considered carefully and this depends mainly on the expected effect of the risk factor on injury risk. To detect moderate to strong associations 20-50 injury cases are needed, whereas small to moderate associations would need about 200 injured subjects. Further studies are needed on the risk factors for hamstring strains, since the studies that have been published to date suffer from methodological limitations and are too small to detect small to moderate associations.

Acknowledgements

The Oslo Sports Trauma Research Center has been established at the Norwegian University of Sport & Physical Education through generous grants from the Royal Norwegian Ministry of Culture, the Norwegian Olympic Committee & Confederation of Sport, Norsk Tipping AS, and Pfizer AS. We would like to thank our colleagues for comments to the manuscript.

View Abstract

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Risk factors for sports injuries - a methodological approach

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The methodology for studies designed to investigate potential risk factors for sports injury is reviewed, using the case of hamstring strains as an example. Injuries result from a complex interaction of multiple risk factors and events. Therefore, a multivariate statistical approach should be used.

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Development and validation of a new method for the registration of overuse injuries in sports injury epidemiology

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Development and validation of a new method for the registration of overuse injuries in sports injury epidemiology

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Br J Sports Med 2013;47:495-502.

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Development and validation of a new method for the registration of overuse injuries in sports injury epidemiology: the Oslo Sports Trauma Research Centre (OSTRC) Overuse Injury Questionnaire

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Background Current methods for injury registration in sports injury epidemiology studies may substantially underestimate the true burden of overuse injuries due to a reliance on time-loss injury definitions. Objective To develop and validate a new method for the registration of overuse injuries in sports.

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Objective To determine the incidence and characteristics of injuries in female and male gymnastics disciplines (artistic, rhythmic and trampoline) during three Olympic Games with a view to ultimately improving injury prevention.

Methods The National Olympic Committee's head physicians and the medical teams of the Local Organising Committee of the Olympic Games reported daily the occurrence (or non-occurrence) of newly sustained injuries in artistic, rhythmic and trampoline gymnastics on a standardised report form during the 2008, 2012 and 2016 Summer Olympic Games.

Results During the three Olympic Games, 81 injuries were reported in a total of 963 registered gymnasts, corresponding to an incidence of 84 injuries (95% CI 67 to 102) per 1000 registered gymnasts, with no difference in injury incidence between female and male gymnasts. Thirty-eight per cent of injuries led to time-loss from sport. The most frequent injury location and injury type were the ankle (22%) and sprain (35%), respectively. The most common diagnosis was ankle sprain (14% of all injuries and 23% of time-loss injuries). The injury incidence was highest in female (107±35) and male artistic gymnastics (83±32), followed by female rhythmic gymnastics (73±30), and lower in male (63±69) and female (43±43) trampoline gymnastics.

Conclusions Research should focus on preventing injuries in artistic gymnastics and of the condition of ankle sprain. Injury surveillance studies should be continued during major championships and throughout the entire competitive season as the Olympic Games provides only a snapshot (although an important one).

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Gymnastics injury incidence during the 2008, 2012 and 2016 Olympic Games: analysis of prospectively collected surveillance data from 963 registered gymnasts during Olympic Games

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Objective To determine the incidence and characteristics of injuries in female and male gymnastics disciplines (artistic, rhythmic and trampoline) during three Olympic Games with a view to ultimately improving injury prevention.

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Abstract

Injury prediction is one of the most challenging issues in sports and a key component for injury prevention. Sports injuries aetiology investigations have assumed a reductionist view in which a phenomenon has been simplified into units and analysed as the sum of its basic parts and causality has been seen in a linear and unidirectional way. This reductionist approach relies on correlation and regression analyses and, despite the vast effort to predict sports injuries, it has been limited in its ability to successfully identify predictive factors. The majority of human health conditions are complex. In this sense, the multifactorial complex nature of sports injuries arises not from the linear interaction between isolated and predictive factors, but from the complex interaction among a web of determinants. Thus, the aim of this conceptual paper was to propose a complex system model for sports injuries and to demonstrate how the implementation of complex system thinking may allow us to better address the complex nature of the sports injuries aetiology. According to this model, we should identify features that are hallmarks of complex systems, such as the pattern of relationships (interactions) among determinants, the regularities (profiles) that simultaneously characterise and constrain the phenomenon and the emerging pattern that arises from the complex web of determinants. In sports practice, this emerging pattern may be related to injury occurrence or adaptation. This novel view of preventive intervention relies on the identification of regularities or risk profile, moving from risk factors to risk pattern recognition.

Introduction

Injury prediction is one of the most challenging issues in sports and a key component for injury prevention, since the successful identification of injury predictors forms the basis for effective preventive measures. 1 Traditionally, scientific investigations have assumed a reductionist view in an attempt to understand sports injuries. According to this approach, the phenomenon (injury) has been reduced into units and explained by rational deduction. 2 , 3 The sports injury literature has revealed important injury predictors by means of typical statistics tools, such as logistic regression. However, for some injuries (eg, hamstring strain and patellar tendinopathy (PT)), these techniques have not yet yielded consistent identification of risk factors. 4 , 5 Unfortunately, these inconsistencies show that the majority of the human health conditions are complex. In this sense, we need a broader approach to better understand the complex relationships between risk factors/predictors and injuries. 2-4 , 6-8

To address this issue, Meeuwisse et al 9 developed a dynamic, recursive model for risk and causes of sports injuries, considering that the injury has a non-linear behavior. This model brought many advances to the understanding of sports injuries aetiology, because it assumes that there may be recurrent changes in susceptibility to injury along the participation in sports, and the primary risk factors exposure can produce adaptations and continuously change the risk. Despite the recognition of the non-linear and recursive characteristics of sports injuries, the model described by Meeuwisse et al 9 was not sufficient to address the complex interactions among several factors.

The multifactorial and complex nature of sports injuries arises not from the linear combination of isolated and predictive factors, but from the interaction among what Philippe and Mansi 8 called 'the web of determinants'. These determinants may be linked to each other in a non-linear manner, in the sense that small changes in a few determinants can lead to large and, sometimes, unexpected consequences. 6 , 10 We propose that, to fully uncover this complex nature of sports injury aetiology, a complex systems approach is necessary. This approach rests on identifying interactions and clarifying how these interactions contribute to the emergence of sports injuries. 10 , 11 In addition, it allows seeking regularities (see explanation in the characteristics of a complex system section), by means of pattern recognition techniques, 12 that enable the identification of the risk profile for an athlete or group. Thus, the aim is to introduce a complex system model for sports injuries and to demonstrate how the implementation of complex system thinking may allow us to better address the dynamic nature of the sports injuries aetiology.

This conceptual paper was developed on the basis of reflections on the difficulties related to sports injuries prediction and how different areas are trying to solve similar problems. These reflections and subsequent exhaustive literature review led to several discussions among the authors that resulted in this work. To facilitate the understanding of our proposition, this paper is organised into six sections: introduction; a paradigm shift: from reductionism to complexity describing the reductionist and complexity paradigms; characteristics of a complex system; a complex model for sports injury which assumes sports injury is an emergent phenomenon that arises from interactions within a web of determinants; methodological implications of using a complex systems approach and applying a complex model for sports injury.

A paradigm shift: from reductionism to complexity

The simplification of complex problems into basic units is the classical science method of analysis in the reductionism paradigm. 2 , 4 , 8 This approach has focused on identifying isolated factors that are frequently assumed as causes of injuries or diseases. 8 , 12 In many aspects, this has been a successful approach, such as establishing a strong causal connection between smoking and lung cancer. 13 Reductionism has thus helped identify some apparently existing linear relationships. 8 Linearity assumes that the outcome is somehow related to the sum of the system's units and that it can be predicted (although subject to random errors) by looking for these direct relationships. 7 , 8 However, this linear association characterises a singular circumstance of relationship: the closer the exposure is to the end point, the greater is its impact. 8 A typical example in sports injury is the identification of dynamic knee valgus (DKV) as a risk factor for ACL rupture. 14 It has been identified that female athletes who demonstrated knee valgus during landing, 50 ms after initial contact, are five times more prone to injuring their ACL than male athletes (relative risk (RR)=5.3, p=0.002). 15 Since the scales we use measure the triggering factors that are proximal to end point outcome, such linear relationships can be revealed. 8 The concern here is that this relationship represents only a segment of the total picture. 8

The picture is different when the initial event (exposure) is distant or when there are several factors at multiple levels (biomechanical, behavioural, physiological, etc) that can influence each other. For instance, restriction in ankle dorsiflexion range of motion (ROM) and training load are found to be risk factors (although inconsistent) for PT. 5 However, in some cases, restricted ankle dorsiflexion ROM only will be relevant in the presence of high training load. This last risk factor will influence the former because high frequency of vertical jump increases the total mechanical energy reaching the tissues, which should be dissipated by the hip, knee and ankle. In the presence of restricted ankle dorsiflexion ROM, this energy may not be properly dissipated at the ankle and could overload the patellar tendon. 16 Additionally, the amount of training load could be modulated by the excessive demands of external sponsors or coaches' training philosophy. This chain of possible related events establishes a complex web of injury determinants, which requires a complexity approach to be better understood. 8 Historically, complexity thinking was influenced by developments in several areas, such as information theory, cybernetics and systems theory, and, in turn, has been adopted by many disciplines. 6 , 10 , 17-20 Biology and medicine recognise that living organisms are understood as complex systems characterised by multiple interactions among units, self-organisation, non-linearity and emergent properties. 6 , 21 More recently, a complex systems approach has been used in epidemiology to improve the knowledge about multilevel causes of health and diseases. 12

Prediction of complex problems, such as financial crises, 20 diseases, 12 , 21 athlete high performance 22 and, potentially, sports injury should involve the identification of the complex web of determinants. In sports, the focus should be on identifying the stable (sometimes complex) relationships among injury determinants that support the emergence of injuries and not on the contribution of isolated factors. 11 We need to be aware that when we reduce a complex problem into its units, the relationships are neglected and prediction may not be possible. 11 , 23 Sports injury will be better understood if we recognise the frequent patterns of interaction among multilevel risk factors instead of focusing on identifying risk factor at the units level. 11 Accordingly, Mendiguchia et al, 4 Quatman et al 2 and Hulme and Finch 11 have proposed a paradigm shift in sports injury research. These authors emphasised that the literature is limited to consistently identifying predictive factors, because current research methods based on unidirectional and analytical approaches neglect the multifactorial and complex conditions for sports injuries emergence. 11 The lack of proper methodological approaches limits our ability to identify effective preventive strategies, as we frequently fail to properly recognize the web of injuries determinants. Consequently, some injuries rates, such as hamstring strain, have not improved over the past three decades. 24

The complexity paradigm contributes to examining sports injury in terms of relationships rather than factors. However, complexity paradigm should not completely replace reductionism. 11 , 12 It is possible to argue that complexity paradigm is an advance from reductionism. Although linear approaches may facilitate the identification of triggering factors that are close to the sports injury mechanism, they fail to recognise the inherent non-linearity of biological processes that are related to sports injuries. This interrelationship explains why the search for cause-effect relationships will always be problematic. Given the complexity of the sports injuries process, the future of aetiology research must advance towards the integrated view of the complex systems approach.

Characteristics of a complex system

The understanding about interactions is the cornerstone of the complex systems approach. von Bertalanfly 25 has defined a complex system as a whole with units (parts) that interact with each other. However, according to Rosen, 26 these interactions are complex in the sense that the participating units are modified by the occurring interactions or due to the fact that new and unpredictable units emerge during the process. When units A and B interact, the behaviour of A in interaction with B is different from the behaviour of A alone. 25 , 26 Regarding sports injuries, the athlete should be analysed as a complex system and the research focus would be on how relationships between units (ie, biomechanical, behavioural, physiological and psychological) give rise to the collective behaviour of the athlete and how the athlete interacts and forms stable relationships (regularities) with his/her environment.

Complex systems are dynamic, open systems with inherent non-linearity due to the existence of recursive loops and complex interactions among units, which spontaneously organise themselves to generate emerging properties that cannot be deduced solely from their original properties (self-organisation). 3 , 10 Such systems are posed with some level of uncertainty as the multiple levels and scales (size and time) of the interacting units are frequently unknown or not directly observable. 17 These characteristics are explained in more detail below.

A complex model for sports injury

We propose an alternative model for sports injury that might serve as a new perspective for understanding injury aetiology. This model is graphically represented in figure 1. Interacting units of a complex system result in a web of determinants, in which the units interact with each other in unpredictable and unplanned ways (frequently unknown). 7 , 8 These complex interactions arise from the system's own history and form observable regularities (risk or protective profile), which in turn emerge as a global pattern (injury or adaptation). This emerging pattern constrains the interactions among the system's units (the model's recursive loops) and will dynamically shape the existing interactions. In this case, the web of determinants may change according to the outcome they produce.

Complexity requires accepting some level of uncertainty (despite a possible determinism) in regard to the occurrence of the phenomena. 17 As in a weather forecast, prediction is improving over time, but errors are always a possibility. To face this uncertainty problem, this paper proposes that sports injury research needs to concentrate on analysing the observable regularities that arise from the existing simple and complex interactions among the elements of the web of determinants and not the units themselves (risk factors). Consequently, to identify the overall functioning of a system in relation to its principles and improve prediction, it is necessary to apply complex system methods.

Methodological implications of using a complex systems approach

Improvements in sports injuries prediction, as well as in prevention, depend on coherence among the phenomena of interest (sports injuries as an emergent event), philosophical paradigm (complexity) and methods of analysis. In this sense, non-linear and complex system approaches should be explored. At the methodological level, complexity assumes that prediction relies on probability and not on straightforward causality (even in deterministic systems, the lack of knowledge about the initial conditions of the existing elements leads to a great degree of uncertainty). To improve our prediction capability, we need to capture stable interactions among units (risk factors) and the observable regularities (profiles). Economists have considered characteristics of individuals and of global societal dynamics to show how different stock market trading strategies can emerge from simple rules. 20 Likewise, meteorologists have been using complex system computation technics to improve the weather forecast. 36

Advances in predicting complex problems, such as financial crises and sports performance, 20 , 22 , 37 have been possible by using classification methods in the statistical learning arena like neural networks (eg, Self-Organizing Feature Maps (SOFM)). 38 Pfeiffer and Hohmann 22 found that sports talent development outcome was better predicted by means of the non-linear method of neural network (SOFM) than by linear methods such as discriminant analysis (DA). Percentages of total correctly predicted cases by SOFM were 87.9% vs 69% by DA. The authors highlighted that neural networks are able to recognise global patterns of different talent development and that SOFM are worthwhile tools in the analysis of non-linear sports talent processes. 22

In medicine, the array of recursive partitioning based methods such as classification and regression trees (CART), random forests and boosting, which allow non-linear interactions among predictors, as well as depict and make use of these interactions, have been successful in identifying the subset of risk and predictive factors to explain different outcomes. 30 , 39 , 40 Bittencourt et al 30 have used CART to predict high frontal plane knee angle (DKV) during landing from a vertical jump. 30 They have shown that the contribution of the shank-forefoot varus alignment to the occurrence of high frontal knee angle depended on the presence of hip abductor weakness. Varus alignment and abductor weakness were defined in terms of cut-off points, in the sense that having values above or below the cut-offs did not increase or decrease the probability of the occurrence of DKV (non-linearity). 30 Recently, an agent-based model (ABMs) has been used in epidemiology to capture complex relationships not identified by logistic regression. 41 Although these methods are not commonly introduced in statistical courses, computer programs are becoming easily available on the internet. For example, the software NetLogo (written in java) allows modelling ABMs, by means of simple commands, and enables the construction of webs of interrelated units. 42 We proposed that these statistical models should be adopted in sports science to investigate processes involving interactions among different predictors and identify dynamic interactions, with the final purpose of prediction.

Another statistical approach that might be an appropriate candidate to reveal possible interacting predictors of sports injury is machine/statistical learning. Statistical learning has been expressed as the overlapping area of statistics, computational data mining, artificial intelligence and engineering. 38 Much of its use has been in the area of supervised learning, in which an observed outcome is investigated by looking at the patterns of other factors, in an attempt to learn which pattern leads to that specific outcome. Many of the statistical learning tools do not necessarily yield only one coefficient or rate of change for one specific predictor. For example, in the case of CART, a predictor may play a role in several splits of different branches, depending on the other predictors higher up in the tree. 30

Up to the recent development of Bayesian Additive Regression Trees, 43 the usual expected and idolised 'p value' that accompanies the regression models, or any other form of inferential confidence or probability value on point estimates, is not part of the output obtained from recursive partitioning models. Owing to these two main issues, the paradigm has to be challenged to move into the arena of prediction rather than on examining the impact of a factor and on relying on other measures of 'success' such as misclassification rates. Statistical learning tools may be more accurate in predictions than the usual linear models, and the amount of assumptions could be lessened. 38 Accordingly, we need to ask ourselves, without taking away the epidemiological importance of identifying risk factors and their potential modifiability, whether we are aiming at prediction or solely at finding relationships. In order to catch patterns and be able to prevent, we need to predict.

Apart from statistical issues, we also have to methodologically incorporate the complex system thinking. Meeuwisse et al 9 highlighted the dynamic nature of risk factors, as the pattern of change in one variable (ie, change of strength during a season) could influence injury risk more than its absolute value in one point in time. Such a concept requires examining risk factor change longitudinally over a period of time, in order to incorporate the history of the event. For this reason, study designs and statistical analyses must consider the existence of complex interactions and changing risks. In practice, assessing risk profile should occur along the season, not only in the preseason, and the analyses should focus on the changing web of determinants that lead to an emergent injury.

Applying complex model for sports injury

Sports injury is a complex emergent phenomenon 11 and needs to be seen through a 'lens of complexity'. In this case, we should seek to identify features that are present in complex systems: (1) the pattern of relationships (interactions) between units (determinants); (2) the regularities (profiles) that simultaneously characterise and constrain the phenomenon and (3) the emerging pattern that arises from the complex web of determinants. The sports injuries literature has long sought for some of these features. However, the existing information about these features is still not organised according to a complex system framework that allows us to have some insights on injury prediction (despite its limitations).

To illustrate the complexity, let us consider the example of ACL injury, where there are some observed regularities that lead to its emergence. Figure 2A depicts the web of determinants for an ACL injury in basketball. For basketball athletes, the typical unanticipated environmental events (UEE), 44 the presence of DKV 44 and hip weakness (HW) are the main elements comprising the web of determinants. These elements take part in most of the existing interactions (unidirectional or bidirectional relationships) and, as such, influence and are influenced by other elements. In this case, the interacting configuration could be pictured as follows: the presence of DKV is influenced by fatigue, 44 hip muscle strength, neuromuscular control (ability to properly recruit the required muscles), foot complex anatomical alignment and training load. These variables are themselves modulated by other factors such as age and sex (eg, influence of age on hip muscle strength). 45 In addition, training load influences the attention level and anxiety, which may increase fatigue and, by this connection, increase DKV. The manner in which these interactions play and how they together influence the occurrence of an ACL injury will create a risk profile (regularity) that is specific for the analysed context.

A single risk factor (ie, DKV) does not warrant the occurrence of the injury. On the other hand, the identification of the risk profile may inform about the probability of the injury occurrence. Since injury is a complex phenomenon characterised by uncertainties and inherent non-linearity, an ACL injury will emerge when a specific pattern of interaction happens in the presence of an inciting event of a given value. Thus, the best manner to predict an injury is by understanding the interactions among the web of determinants and not the determinants themselves.

As another example, figure 2B depicts the web of determinants of an ACL injury in ballet dancers. Owing to the type of training received and the need for movement quality, these athletes do not have, as the main elements, the presence of DKV and HW. 46 In addition, in ballet, UEE are rare when compared to collective sports. 46 In this sense, fatigue, attention level, anxiety and, possibly, other unidentified variables may be the main interacting units of the web of determinants. Fatigue, in turn, is modulated by the amount of training sessions, which will also influence other psychological factors that may be differently influenced by sex. In the presence of a specific interacting pattern, the level of fatigue may alter the quality of movement and lead to the occurrence of DKV, which may produce an ACL injury. The web of determinants for ballet dancers has a different configuration of interactions among factors, when compared to basketball players. Similar factors have different contributions (weights) and interact differently with other factors, as the context of practice also differs. However, in each context, the observance of a given interacting pattern or risk profile will be related to a given probability of ACL occurrence.

Instead of looking for the units (first level risk factors), we should look for the existing pattern of interactions among the units (regularities). Only after we learn about those regularities, the identification of the relationships between contributing units (second level risk factors) will be necessary. This approach will allow the development of effective interventions to address the identified risk profile. Therefore, injury prediction, as in a stock market and weather forecast, should rely on the probability that the presence of observable regularities (risk or protective profile) may be associated with the emergence of a given injury.

Conclusion

Recently, a complex systems approach has been used to predict complicated problems in medicine, biology, economics and social science. This conceptual paper serves as a challenge and as a support, as it indicates the weaknesses of the established model of thinking and proposes a model, which moves us from trying to find 'causes' to finding 'relations' that support the emergence of a sports injury. The limitation of our 'explanation power' of complex phenomena should not prevent us from trying to improve our 'predictive power' for injury occurrence. Adopting the complex systems approach may push us forward in terms of concepts and methods to improve sports injury prediction. In this sense, moving research from isolated risk factors to injury pattern recognition, by means of identification of the complex pattern of interactions among the web of determinants, is obligatory. Although difficult, it is feasible to identify and even understand the regularities of a web of determinants using real data and statistical modelling. This approach may be the only option if we accept the non-linearity and complexity of sports injury.

Acknowledgments

The authors would like to acknowledge Dr Roberto Bittencourt and Dr Rosana Sampaio for introducing the role of complexity in health.

View Abstract

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Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept

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Injury prediction is one of the most challenging issues in sports and a key component for injury prevention. Sports injuries aetiology investigations have assumed a reductionist view in which a phenomenon has been simplified into units and analysed as the sum of its basic parts and causality has been seen in a linear and unidirectional way.

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Matching the choice of injury/illness definitionto study setting, purpose and design:one size does not fit all!

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Matching the choice of injury/illness definitionto study setting, purpose and design:one size does not fit all!

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Br J Sports Med 2014;48:510–512

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Colorful Minimalist Design Process Infographic Poster