Ari Samaranayaka

Prospective Outcomes of Injury Study: recruitment, and participant characteristics, health and disability status

The Prospective Outcomes of Injury Study aims to identify predictors of disability following injury. Participants were selected from the entitlement claims register of New Zealands no-fault compensation insurer, the Accident Compensation Corporation, and followed up by interview for 2 years. This report describes changes to intended Prospective Outcomes of Injury Study methods and key characteristics of the cohort, with an emphasis on general health and disability before injury and soon afterwards. There were 2856 injured participants in the first interview, which occurred 3.2 months (median) after injury. The recruitment period was extended to enable inclusion of sufficient Māori participants. At the first interview, most participants were experiencing worse health status and increased disability compared to before injury, despite less than one-third reporting admission to hospital because of their injury. Analysis of outcome predictors related to post-injury function, disability and return-to-work soon after injury and 1 year later is now under way.

Prevalence and predictors of sub-acute phase disability after injury among hospitalised and non-hospitalised groups: a longitudinal cohort study

Introduction To reduce the burden on injury survivors and their supporters, factors associated with poor outcomes need to be identified so that timely post-injury interventions can be implemented. To date, few studies have investigated outcomes for both those who were hospitalised and those who were not. Aim To describe the prevalence and to identify pre-injury and injury-related predictors of disability among hospitalised and non-hospitalised people, three months after injury. Methods Participants in the Prospective Outcomes of Injury Study were aged 18–64 years and on an injury entitlement claims register with New Zealands no-fault injury compensation insurer, following referral by healthcare professionals. A wide range of pre-injury demographic, health and injury-related characteristics were collected at interview. Participants were categorised as ‘hospitalised’ if they were placed on New Zealands National Minimum Data Set within seven days of the injury event. Injury severity scores (NISS) and 12 injury categories were derived from ICD-10 codes. WHODAS assessed disability. Multivariable analyses examined relationships between explanatory variables and disability. Results Of 2856 participants, 2752 (96%) had WHODAS scores available for multivariable analysis; 673 were hospitalised; 2079 were not. Disability was highly prevalent among hospitalised (53.6%) and non-hospitalised (39.4%) participants, three-months after injury. In both groups, pre-injury disability, obesity and higher injury severity were associated with increased odds of post-injury disability. A range of other factors were associated with disability in only one group: e.g. female, ≥2 chronic conditions and leg fracture among hospitalised; aged 35–54 years, trouble accessing healthcare, spine or lower extremity sprains/dislocations and assault among non-hospitalised. Significance Disability was highly prevalent among both groups yet, with a few exceptions, factors associated with disability were not common to both groups. Where possible, including a range of injured people in studies, hospitalised and not, will increase understanding of the burden of disability in the sub-acute phase.

Risk factors for injury in rugby union football in New Zealand: a cohort study

Objective To identify risk factors for injury in amateur club rugby. Design Prospective cohort design; with follow-up over the 2004 season. Setting Amateur club rugby in New Zealand. Participants Seven hundred and four male rugby players, aged 13 years and over. Assessment of risk factors The study investigated the independent effect on injury incidence of age, ethnicity, rugby experience, height, weight, body mass index, physical activity, cigarette smoking, previous injury, playing while injured, grade, position, training, time of season, warm-up, foul play, weather conditions, ground conditions and protective equipment. Generalised Poisson regression was used to estimate the effect of each factor after adjusting for all other factors. Main outcome measures Game injury, defined as ‘any event that resulted in an injury requiring medical attention or causing a player to miss at least one scheduled game or team practice’. Results A total of 704 players, representing 6263 player-games, contributed information on injury and exposure. Evidence was obtained of the effect on injury incidence of increasing age, Pacific Island versus Maori ethnicity (injury rate ratio (IRR)=1.48, 1.03–2.13), ≥40 h strenuous physical activity per week (IRR=1.54, 1.11–2.15), playing while injured (IRR=1.46, 1.20–1.79), very hard ground condition (IRR=1.50, 1.13–2.00), foul-play (IRR=1.87, 1.54–2.27) and use of headgear (IRR=1.23, 1.00–1.50). Conclusions Opportunities for injury prevention might include promoting injury-prevention measures more vigorously among players of Pacific Island ethnicity, ensuring injured players are fully rehabilitated before returning to play, reducing the effects of ground hardness through ground preparation and stricter enforcement of the laws relating to foul play.

Validity of using linked hospital and police traffic crash records to analyse motorcycle injury crash characteristics

Linking hospital discharge and police traffic crash records has been used to provide information on causes and outcomes for hospitalised traffic crash cases. Motorcyclists are particularly vulnerable to injury in a traffic crash, but no published linkage studies have reported in detail on this road user group. The present study examined motorcycle traffic crash injury cases in New Zealand in 2000-2004 by probabilistically linking national hospital discharge records with police traffic crash reports. Injury cases had to have spent at least one night in hospital before being discharged and were defined as serious or moderate based on their International Classification of Disease Injury Severity Scores (ICISS). Despite a robust linkage process, only 46% of cases could be linked to a police record; 60% of the serious injuries and 41% of the moderate. The low linkage was most likely due to under-reporting of crashes to or by the police. While moderate injury cases were expected to be under-reported, the level of under-reporting of cases with serious threat-to-life injuries is concerning. To assess whether the linked dataset could provide valid information on the crash circumstances and injury outcomes of hospitalised motorcycle crash cases, the characteristics of the linked and unlinked hospital discharge cases were compared using chi-square tests and multivariate logistic regression. Serious injury cases were less likely to be linked if only one vehicle was involved, or the injured riders and passengers were younger than 20 years or spent less than one week in hospital. For moderate injury cases, there were also differences in linkage by injured body region and crash month. While these discrepancies need to taken into consideration when interpreting results, the linked hospital-police dataset has the potential to provide insights into motorcycle crash circumstances and outcomes not otherwise obtainable.

Prevalence and predictors of disability 24-months after injury for hospitalised and non-hospitalised participants: results from a longitudinal cohort study in New Zealand.

Introduction Most studies investigating disability outcomes following injury have examined hospitalised patients. It is not known whether variables associated with disability outcomes are similar for injured people who are not hospitalised. Aims This paper compares the prevalence of disability 24 months after injury for participants in the Prospective Outcomes of Injury Study who were hospitalised and those non-hospitalised, and also seeks to identify pre-injury and injury-related predictors of disability among hospitalised and non-hospitalised participants. Methods Participants, aged 18–64 years, were recruited from an injury claims register managed by New Zealand’s no-fault injury compensation insurer after referral by health care professionals. A wide range of pre-injury socio-demographic, health and psychosocial characteristics were collected, as well as injury-related characteristics; outcome is assessed using the WHODAS. Multivariable models estimating relative risks of disability for hospitalised and non-hospitalised participants were developed using Poisson regression methods. Results Of 2856 participants, analyses were restricted to 2184 (76%) participants for whom both pre-injury and 24 month WHODAS data were available. Of these, 25% were hospitalised. In both hospitalised and non-hospitalised groups, 13% experience disability (WHODAS≥10) 24 months after injury; higher than pre-injury (5%). Of 28 predictor variables, seven independently placed injured participants in the hospitalised group at increased risk of disability 24 months after injury; eight in the non-hospitalised. Only four predictors (pre-injury disability, two or more pre-injury chronic conditions, pre-injury BMI≥30 and trouble accessing healthcare services) were common to both the hospitalised and non-hospitalised groups. There is some evidence to suggest that among the hospitalised group, Māori have higher risk of disability relative to non-Māori. Conclusions At 24 months considerable disability is borne, equally, by hospitalised and non-hospitalised groups. However, predictors of disability are not necessarily consistent between the hospitalised and non-hospitalised groups, suggesting caution in generalising results from one group to the other.

Estimating person-based injury incidence: accuracy of an algorithm to identify readmissions from hospital discharge data

Background Effective use of routinely collected hospital discharge data (HDD) to estimate injury incidence requires a separate identification of new injuries from readmissions for a previous injury. The aim was to determine the accuracy of a computerised algorithm to identify injury readmissions in HDD. Methods A random sample of 2000 events (‘key events’) were selected from the 2006 injury subset of New Zealands HDD. Discharge histories from 1989 to 2007 were extracted for individuals and manually reviewed by at least two people to determine the ‘gold standard’ readmission status of each key event. The algorithm relies on four variables: unique national person identifier, dates of injury, admission and discharge. Reviewers were provided with these variables as well as additional discharge information (eg, discharge type and external cause code narrative) recorded in the HDD. Results of the manual review were compared to those obtained from the algorithm. Results The algorithm assigned 1811 (90.6%) as incident admissions compared to 1800 (90.0%) classified by the gold standard. Agreement was 97.9%, and accuracy measures (sensitivity, specificity, negative predictive value and positive predictive value) ranged from 87% to 99%. No statistically significant differences between readmission assignation by the algorithm and the gold standard were observed by age, nature of injury, external cause of injury or body region. Conclusions Any country with electronic HDD could readily identify readmissions and, thus, accurately estimate injury incidence from HDD, providing that a unique person identifier and the date of injury were included in addition to the obligatory dates of admission and discharge.

Prevalence of poor outcomes soon after injury and their association with the severity of the injury.

Little is known of injury outcomes among non-hospitalised injured populations. This study examined the occurrence of poor outcomes 3 months after injury among the 2856 participants in the Prospective Outcomes of Injury Study, most of whom had sustained injuries considered of minor severity (by injury severity scoring) and had not received treatment at a hospital facility. The prevalence of poor outcomes was high across all health, physical functioning and social functioning domains and expectation characteristics examined, including for those participants with the ‘least severe’ injuries. Approximately half of the cohort reported experiencing moderate to high pain or psychological distress and reduced social participation, and three-quarters did not consider themselves recovered. Our findings demonstrate that, to obtain an accurate understanding of injury burden, injury outcome research should not focus only on those injured persons who are hospitalised.

The characteristics of young pre-licensed drivers: Evidence from the New Zealand Drivers Study

AIM The aim of this study was to identify demographic and behavioural factors associated with pre-licensed driving. METHOD A cohort comprising 3526 newly licensed drivers aged 15-24 years old from throughout New Zealand completed a questionnaire which sought information on pre-licensed driving behaviour and factors thought to be related to this. RESULTS Almost half of the participants had driven on-road prior to passing their learner license theory test; 14% had driven more than 20 times; and 7.5% had driven more than 200 km. Multivariate logistic regression showed the results differed depending on the outcome examined. In general pre-licensed driving was significantly higher among males, among Māori, those living in a rural area, and those living in an area of high deprivation. Furthermore, those who drove pre-licensed were more likely to engage in other risky behaviours such as hazardous drinking and cannabis use, and have medium to high scores for sensation seeking and aggression/hostility. CONCLUSION The young people who were pre-licensed drivers displayed a range of demographic and behavioural characteristics that indicate they may be at higher crash risk than their peers who did not drive before licensing. Identifying those who drive before licensing and targeting road safety interventions towards this group may help reduce the high crash risk among novice drivers.

Factors predictive of subsequent injury in a longitudinal cohort study

Objectives The aims are to: (1) Determine the cumulative incidence of self-reported subsequent injury (of any anatomical site or injury type) occurring between 3 months and 12 months after a sentinel injury among participants in the Prospective Outcomes of Injury Study in New Zealand and (2) Examine the preinjury and injury-related predictors of subsequent injury. Methods Prospective Outcomes of Injury Study participants (n=2282) were interviewed 3 months, 5 months and 12 months after a sentinel injury event. Data were collected about a range of preinjury and injury-related factors at the 3-month interview and about subsequent injury at the 5-month and 12-month interviews. Poisson regression modelling was used to determine the preinjury and injury-related predictors of subsequent injury. Results Between the 3-month and 12 month interviews 28% of the participants reported at least one subsequent injury. Subsequent injury was 34% more likely among participants with a prior injury affecting them at the time of the sentinel injury compared with participants without a prior injury affecting them, and more likely among non-workers (31% more likely) and trade/manual workers (32% more likely) compared with professionals. Participants whose sentinel injury was due to assault were 43% more likely to report a subsequent injury compared with those whose sentinel injury was accidental. A subsequent injury was 23% less likely if the sentinel injury was a lower extremity fracture compared with other injuries, and 21% less likely if the sentinel injury event involved hospitalisation. Conclusions Among general injury populations it may be possible to identify people at increased risk for subsequent injury.

Factors associated with non-participation in one or two follow-up phases in a cohort study of injured adults

Objective To identify factors associated with non-participation at the 12-month and 24-month follow-up phases of a prospective cohort study of injury outcomes. Methods Associations between non-participation at follow-up phases and a range of sociodemographic, injury, health, outcome and administrative factors were examined. Results An individuals non-participation at 12 months did not necessarily mean non-participation at 24 months. Sociodemographic factors were the most salient for non-participation, regardless of the number of follow-up phases or specific phase considered. Conclusions Retention rates in prospective cohort studies of injury outcome may be improved by follow-up of everyone irrespective of previous non-participation, focusing resources to retain men, young adults, indigenous people and those living with people other than family members, and by ensuring that multiple alternative participant contacts are obtained. There is sufficient evidence to be concerned about potential bias given that several of the factors we, and others, have identified as associated with non-participation are also associated with various functional and disability outcomes following injury. This suggests detailed investigations are warranted into the effect non-participation may be having on the estimates for various outcomes.