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Factors associated with return‐to‐work and health outcomes among survivors of road crashes in Victoria

Factors associated with return‐to‐work and health outcomes among survivors of road crashes in... The social and economic cost of traffic‐related injury in Australia has been estimated to be $15 billion annually, with lost productivity and workplace disruption representing 23% of this economic cost, second only to vehicle repairs (27%). As an expression of social reintegration, return to work (RTW) is perceived to be a key milestone in the rehabilitation of the injured patient. There is a significant body of literature examining rates and factors associated with RTW among injured populations. Much of this literature has, however, focused on a particular type of injury such as traumatic brain injury, spinal cord injury and lower extremity injury. Among inclusive studies, RTW rates range from 60–90%, one to two years following major trauma. Younger age, higher educational level, higher pre‐injury income and positive social support have been associated with improved RTW outcomes. Indicators of injury severity (e.g. length of stay) and disability associated with traumatic brain injury (TBI), spinal cord injury (SCI) and orthopaedic trauma are associated with lower rates of RTW. Studies that have examined the nature of ‘work’ indicate that ‘blue collar’ or manual workers have slower RTW rates than their ‘white collar’ counterparts. There is a rich body of injury outcomes research conducted in Australia though most utilise mixed injury cause samples to examine recovery after head injury or the incidence and determinants of acute stress disorder and post‐traumatic stress disorder (PTSD). Few studies have specifically examined RTW outcomes for survivors of traffic crashes. In a recent study using a mixed injury cause sample presenting to adult major trauma centres in the state of Victoria, Gabbe et al. reported on disability and RTW outcomes for a sample of orthopaedic trauma patients. By 12‐months post‐injury, 84% of non‐compensable patients (60% fell; 8.6% struck by object; 6.6% motorcyclist; 6.2% cyclist; 0.8% pedestrian; 0.4% vehicle occupant; 16.7%‘other cause’) had RTW in contrast to 67% for Transport Accident Commission (TAC) compensable patients (53.5% vehicle occupant; 27.3% motorcyclist; 11.1% pedestrian; 5.8% cyclist; 2.2%‘other cause’). The compensable group were reported to have, on average, lower physical health and mental health than the non‐compensable group despite adjusting for age, head injury status, injury severity and discharge status. Despite a number of caveats, particularly surrounding comparability of the two groups with respect to mechanism, the authors concluded that “our study adds to the evidence that compensation schemes may impede recovery from injury” (p 17). The implications of this finding is critical to explore as the TAC provides no‐fault insurance for those injured in road crashes in Victoria under the provisions of the Transport Accident Act 1986 . Specifically, the likely confound between injury type, persistent disabilities and RTW must be considered. This is examined among a group of individuals injured in road crashes in Victoria, all of whom were eligible for TAC compensation. This paper expands on previously reported findings by Fitzharris et al. The study of 62 patients admitted to hospital specifically excluded those with moderate or severe head injury and spinal cord injury due to well stated rehabilitation challenges associated with these injuries. In elaborating on these findings, the aim of this paper is to explore the relationships between injury type, disability, work role and RTW rates. Methods The method of the study is described in detail elsewhere. We summarise the key points below. Participants and setting Patients aged 18 to 59 years admitted to a Victorian adult Level 1 Trauma Centre and two metropolitan teaching hospitals following involvement in a road traffic crash were eligible for the study. Recruitment was conducted in the period February 2004 and March 2005 inclusive. Eligible patients were those admitted for a period greater than 24 hours with a Glasgow Coma Scale (GCS) ≥13. Exclusion criteria were: a) the presence of an Abbreviated Injury Severity (AIS) score of 3 or higher (‘serious’) head, spinal, or vertebral column injury; b) crashes involving a fatality; c) burn injuries resulting from a vehicle fire; d) post‐traumatic amnesia (PTA) ≥24 hours; e) pre‐existing cognitive impairment; f) deliberate self‐harm; g) history of psychosis; h) illicit drug dependence; i) occupants of a stolen vehicle; and j) medically unfit to provide informed consent. Non‐English speakers and those residing outside of Victoria were excluded due to budgetary constraints. Recruitment and assessment procedures Consecutive admissions were screened for eligibility and patients were approached when medically appropriate to seek participation. A full description of the study was provided and once informed consent was gained the first of three interviews (t2, within 14 days; T2, 6–8 weeks; T3, 6–8 months) was conducted. Seventy‐four patients consented to the study and 68 interviews were conducted (91.8%), with six patients lost to interview due to discharge, withdrawal of consent while one interview was abandoned due to discomfort. Of the 68 participants who completed the in‐patient interview (t2), 64 completed T2 at approximately two months post‐crash (94% retention), and 62 completed T3 at approximately eight months post‐crash (91% retention). Those lost to follow‐up were due to failure to return the questionnaire booklet (n=3); moved residence (n=2); and one deceased in a subsequent motor vehicle crash. Non‐responders at T3 (n=6) were more likely to be male (n=5; 83.6%) than female (n=1, 16.7%), and were marginally younger than the overall sample. Assessment Measures – an extensive questionnaire and psychometric tests were used. The interview included demographic questions, factors related to and perceptions of the crash, occupational and social functioning and pre‐existing health, while the T2 and T3 interviews focused on the same health outcomes with temporally specific items replacing those focused on ‘acute’ responses. The number of weeks post‐crash that patients RTW was noted at both T2 and T3 with this information validated using pay slips, time sheets and/or personal diaries. Injuries were coded according to the Abbreviated Injury Scale (AIS), 1998 revision, and the Injury Severity Score (ISS) was calculated. The injury event, diagnosis and management were coded as per the International Statistical Classification of Diseases and related Health Problems 10th Revision – Australian modification (ICD‐10‐AM) coding system. The Glasgow Coma Scale (GCS) was obtained from paramedic and hospital medical records. Pre‐crash employment occupation was coded according to the Australian Standard Classification of Occupations (ASCO). Two health measures used are reported here. The SF‐36 is a measure of general health status and quality of life, providing eight domain scores as well as a physical (PCS) and mental component summary score (MCS). Administration of the SF‐36 at t2 focused on ‘before the crash’ while at T2 and T3 the reference time was ‘since the crash’. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess harmful alcohol consumption and was completed at t2 with reference made to ‘prior to the crash’ and at T3 with reference being ‘since the crash’. Data analysis The sample was divided into patients with lower extremity fractures (LEF) and those without (non‐LEF) given the well‐reported difference in RTW outcomes between these two goups. The lower extremity fracture group is defined by ICD‐10‐AM codes S32 (bony pelvis), S72 (femur), S82 (lower leg) and S92 (ankle and foot). Comparisons between the patient groups were made using Fishers Exact test, chi‐square tests and Repeated Measures ANOVA's where appropriate. Z‐scores were used to compare the SF‐36 scores of the sample with Australian norms (18–64 years, employed), while comparisons within and between the injury groups were made using the non‐parametric repeated measures Wilcoxon Signed Ranks Test and independent samples Mann‐Whitney U test respectively; these were used as SF‐36 data is non‐normally distributed. Univariate logistic regression was used to determine the association between RTW and SF‐36 scores, which were modelled linearly. As the time to RTW was recorded and not all patients RTW by the T3, the Kaplan‐Meier survival analysis plot was used to describe the rate of RTW with the Log Rank Test to determine differences between the two groups. A Cox proportional hazards model was used to examine the rate of RTW for the injury groups while adjusting for demographic and injury factors. Analysis was performed using STATA v.8. Statistical significance was set at p ≤ 0.05. University and Hospital Ethics Committees of the participating institutions approved the research. Results Of the 62 patients who completed the T3 interview, 60 form the basis of analysis. As we are concerned with RTW outcomes it was necessary to exclude one patient not employed at the time of the crash and one patient who failed to note their RTW date. Patient demographics Demographic, road user type and baseline injury details are presented in Table 1 . Approximately two‐thirds of the LEF group were male compared to half in the non‐LEF group while age, marital status, area of residence and GCS were similar between the two groups. There were approximately equal numbers of drivers and passengers in the two groups however all the motorcyclists in the sample sustained a LEF. The mean length of stay (LOS) was significantly longer and the mean ISS was significantly higher for the lower extremity fracture group compared to the non‐LEF group ( p ≤ 0.05). Of the 14 patients admitted to rehabilitation, all had sustained a lower extremity fracture, of which seven were drivers of motor vehicles. 1 Demographic, injury severity measures and selected psychological health outcomes of the lower extremity facture (LEF) and non lower extremity (non‐LEF) fracture groups. Characteristic Non‐LEF (n=30) LEF (n=30) % Male 50% (15) 66.7% (20) Roaduser Driver 40% (12) 36.7% (11) Passenger 10% (3) 6.7% (2) Motorcyclist Nil 43.3% (13) Bicyclist 46.7% (14) 6.7% (2) Pedestrian 3% (1) 6.7% (2) Marital status Married / living with partner 53.3% (16) 63.3% (19) Age (years) Mean (SD) 37.9 (11.2) 35.4 (13.1) Range, Median 19.9–58.8, 38.4 19.0–56.2, 31.3 Rural residence 20% (6) 16.7% (5) Glasgow Coma Scale (GCS) (%) GCS 15 83.3% (25) 70% (21) GCS 14 13.3% (4) 23.3% (7) GCS 13 3.3% (1) 6.7% (2) Length of Stay (days) Mean (SD) a 4.8 (4.7) 7.1 (3.7)* %>7 days 20% (6) 36.7% (11) Range 2–25, 3 2–17, 6.5 Injury Severity Score (ISS) Mean (SD) a 8.0 (6.9) 12.9 (9.1) a Range, Median 1–29, 5 4–41, 10 % Major Trauma: ISS>15 10% (3) 23.3% (7) Separation type (%) Home 100% (30) 53.3% (16) Rehabilitation / private Nil 46.7% (14) Maximum Abbreviated Injury Scale score for injuries in non lower extremity body regions No injury Nil 13.3% (4) MAIS 1–2 (minor, moderate) 66.7% (20) 63.3% (19) MAIS 3–5 (serious, severe critical) 33.3% (10) 23.3% (7) Notes: a) p ≤ 0.05 Injury outcomes and characteristics of the LEF and non‐LEF groups Table 2 presents the nature of injuries sustained between the two injury groups. Six participants in the LEF group sustained bilateral lower extremity injuries, while all dislocations, sprains and strains of joints of the lower leg were associated with lower extremity fractures. With respect to injuries in other regions, a higher proportion of patients in the non‐LEF group (53.5%) sustained fractures of the upper extremity compared to patients in the LEF group (26.7%)( p ≤ 0.05), while a slightly higher proportion of patients in the LEF group sustained a concussive injury ( p =0.07). There were no other differences evident. 2 Principal injuries of the sample. Injury (ICD‐10‐AM) LEF (n=30) % (n) Non‐LEF (n=30) % (n) Fracture of lower extremity, including pelvis 100% (30) N/A Fracture of lower leg 56.7% (17) N/A Fracture of femur 26.7% (8) N/A Fracture lumbar spine pelvis a 20% (6) N/A Fracture of ankle and foot 10% (3) N/A Fracture of upper extremity b 26.7% (8) 53.3% (16) Fracture of forearm 30% (9) 10% (3) Fracture of shoulder upper arm 13.3% (4) 20% (6) Fracture hand wrist 3.3% (1) 10% (3) Fracture of ribs sternum & thoracic spine 30% (9) 20% (6) Concussive injury, LOC<30 min; unspecified 23.3% (7) 6.7% (2) Traumatic injury of lung 20% (6) 10% (3) Fracture of skull and facial bones 16.7% (5) 3.3% (1) Dislocation, sprain, strain joint ligaments lower leg c 23.3% (7) Nil Injury of eye and orbit 6.7% (2) 6.7% (2) Other & unspecified injury of neck 3.3% (1) 6.7% (2) Notes: a) All pelvic fractures; b) p≤0.05 c) All associated with lower extremity‐pelvis injury; Health outcomes SF‐36 Health Status compared to Australian norms Pre‐injury and follow‐up SF‐36 MCS scores for both injury groups did not differ from normative scores for employed Australian adults ( Figure 1 ). For the non‐LEF group, the pre‐injury PCS score did not differ from norms, however was significantly lower at T2 ( p ≤0.05) and again equivalent at T3 indicating improved physical health. For the LEF group, the pre‐injury PCS score was higher than Australian norms but significantly lower at both T2 and T3. 1 MCS and PCS scores for LEF and non‐LEF injury groups, with Australian norms for employed adults aged 18–64 years. SF‐36 health status within and between the injury groups Compared to pre‐crash (56.8), the T2 mean PCS score for the LEF group was 30.2 (46.9% reduction; p ≤ 0.05) and 43.1 at T3 (24% reduction; p ≤ 0.05). For the non‐LEF group, the mean PCS score was 54.9 at baseline, 42.3 at T2 (22.9% reduction; p ≤ 0.05) and 52.1 at T3 (5% reduction; p ≤ 0.05); hence the improvement from T2 to T3 was statistically significant (23% increase; p ≤ 0.05). While the baseline PCS scores between the injury groups were not statistically different, at T2 and T3 PCS scores were lower for the LEF group compared to the non‐LEF group ( p ≤ 0.05). Disaggregation of the PCS summary score indicated a significantly lower physical functioning and the role physical sub‐domain scores at T2 and T3 for the LEF group compared to the non‐LEF group. There was no difference on general health domain scores at either follow‐up time or body pain at T2, however the bodily pain score for the LEF group was lower (worse) than for the non‐LEF group at T3 indicating a difference in recovery. There was little change in the overall MCS scores from pre‐injury scores and the pattern was similar for both injury groups. However, examination of the four sub‐domain scores that form the composite MCS score indicates a significantly lower social functioning SF‐36 MCS domain score for the LEF group compared to the non‐LEF group at T2 (44.2 vs. 70.8; p ≤ 0.05) and T3 (75.0 vs. 89.6; p ≤ 0.05), indicating on‐going and significant impact on social activities; indeed, the non‐LEF group score was similar to their pre‐injury score (88.3) and this was not the case for the LEF group (90.8). In addition, the vitality score was lower for the LEF group compared to the non‐LEF group at T3 (57.3 vs. 67.7; p ≤ 0.05). Alcohol use Alcohol consumption decreased in the non‐LEF group (Pre: 5.5, 95%CI: 3.8–7.2; Post: 3.9, 95%CI: 2.4–5.3) and increased in the LEF group following the crash (Pre: 6.4, 95%CI: 4.7–8.0; Post: 7.3, 95%CI: 4.8–9.9); this differential change was statistically significant (p ≤ 0.05), as was the difference at T3 ( p ≤ 0.05, Bonferroni correction). Occupation and Return‐to‐Work outcomes Table 3 presents the key occupation and RTW outcomes for the two injury groups. Using the ASCO system the number of patients holding managerial, administration or professional positions and clerical or sales and service occupations was similar. There were more trades‐people in the LEF group (20%) than in the non‐LEF group (10%) ( Table 3 ). Most patients were employed on a full‐time basis with a small number being part‐time employed or students supplementing their income with part‐time or casual work. By T3, approximately equal numbers had RTW (approximately 90%) while seven (11.6%) had yet to RTW by T3 (3 non‐LEF; 4 LEF). 3 Occupation and return‐to‐work outcomes. Characteristic Non‐LEF (n=30) LEF (n=30) Pre‐injury occupation Manager/Administration/Professional 66.7% (20) 60% (18) Clerical, Sales & Service 23.3% (7) 20% (6) Trades and related 10% (3) 20% (6) Pre‐injury employment profile Full time employed 73.3% (22) 76.7% (23) a Full time work; self‐employed 10% (3) 6.7% (2) Part‐time / casual 6.7% (2) 6.7% (2) Student (+ part‐time/casual work) 10% (3) 10% (3) Return‐to‐work outcome at T3 Returned to work 90% (27) 83.3% (25) Unable to work 10% (3) 13.3% (4) Returned to work / unable to study 0% (0) 3.3% (1) Time to return to work (post discharge acute hospital)* Median (weeks) 3 12 95% CI (weeks) 2–4 5.7–18.3 Role on return‐to‐work at T3 Same role 53.3% (16) 46.7% (14) Different role (part‐time, lighter duties, new role) 36.7% (11) 40% (12) No longer employed 3.3% (1) 6.7% (2) Yet to RTW 6.7% (2) 6.7% (2) Salary suffered as consequence of crash 43.3% (13) 56.7% (17) Notes: a) 1 also studying; *p≤0.05 Of the seven patients yet to RTW (4 LEF, 3 non‐LEF), four (3 LEF, 1 non‐LEF) stated they were no longer employed at T3 as a consequence of injuries sustained. Of the four LEF patients yet to RTW at T3, three were male, three were motorcyclists and one was a driver, two had an ISS >15; two were admitted to rehabilitation, and all were tradespeople or manual workers. Of the non‐LEF patients (3) yet to RTW, all were female, all were discharged home, all had an ISS<15, and there was one driver, one cyclist and one passenger and one was in a trade or manual occupation. Analysis shows that at T3 SF‐36 PCS and MCS scores were associated with whether a patient had RTW ( p ≤ 0.05) but this was not the case for the variable indicating lower extremity injury; this indicates that by eight months post‐crash persisting disability is critical in whether a patient RTW or not. Tradespeople were less likely to have RTW by T3 than professional and clerical occupational groups ( p ≤ 0.05). Despite the similar proportion of patients having RTW by T3, there was a significant difference in the rate of RTW. As shown in both Table 3 and Figure 2 the median time to RTW for non‐LEF group patients was three weeks post‐discharge (95th % CI: 2–4 weeks), and 12 weeks (95th % CI: 5.7–18.3 weeks) for patients in the LEF group ( p ≤0.05), reflecting the slower rate of RTW. Figure 2 reflects the finding that seven patients were yet to RTW by T3. 2 Kaplan‐Meier survival plot of proportion of patients RTW by time since discharge. Approximately half the patients RTW in the same role while 13 patients (43.3%) in the non‐LEF group stated their salary had suffered as a consequence of the crash in contrast to 17 patients (56.7%) in the LEF group. Modelling time to RTW The Cox proportional hazards model was used to assess occupational, injury and demographic factors associated with the RTW rate ( Table 4 ). Patients in the LEF injury group RTW at a rate 69% slower than those in the non‐LEF group controlling for factors noted in Table 4 (HR: 0.31; 95%CI: 0.16–0.58). The maximum AIS score (MAIS) of injuries sustained in other body regions was included to adjust for this potentially confounding influence. Those with MAIS 3–5 injuries in regions other than the lower extremity RTW at a rate 56% slower than those with minor (MAIS1) or no injury (HR: 0.42, CI: 0.20–0.88, p =0.02). The model was adjusted for pre‐crash health and these factors were associated with the rate of RTW. Patients holding a trade and related occupation pre‐crash had a significantly slower RTW rate than both professional workers (HR: 0.31, CI: 0.16–0.58, p =0.003) and clerical, sales and service workers (HR: 0.22, CI: 0.06–0.81, p =0.02). There was no difference in the RTW rate between the clerical and professional occupation groups. 4 Factors associated with time to return‐to‐work. Factor HR 95th% CI Lower‐Upper p Lower Extremity Fracture (LEF) 0.31 0.16–0.58 <0.001 Age 0.98 0.95–1.00 0.07 MAIS for non‐lower extremity injuries (Reference category AISO/1) MAIS 2 0.70 0.34–1.44 0.4 MAIS 3–5 0.42 0.20–0.88 0.02 Occupation (Reference category Prof, Managers & Admin) Clerical, Sales & Service 0.84 0.39–1.83 0.7 Trades and related 0.19 0.06–0.57 0.003 SF‐36 pre‐injury PCS 1.06 1.01–1.12 0.01 SF‐36 pre‐injury MCS 1.03 1.00–1.07 0.05 Time spent in rehabilitation was unable to be assessed as only patients with lower extremity fractures were admitted to another care facility post‐discharge from the treating hospital. This is clearly related to injury type and impacts upon RTW outcomes. Marital status and gender was not associated with RTW rate in this sample. Discussion In this study of healthy employed adults admitted to hospital following injuries sustained in traffic crashes – all of whom were covered by the TAC no‐fault compensation scheme – we set out to explore the relationships between injury type, disability, work role and RTW outcomes. Among this relatively small, yet representative sample of admitted patients within the context of the eligibility criteria, those with lower extremity fractures experienced a longer length of stay and higher mean ISS than those without such fractures. Of those patients requiring in‐patient rehabilitation, all had sustained a lower extremity fracture. Despite pre‐injury physical health, as measured by the SF‐36, being equal to or better than Australian norms, at 2.5 months post‐crash physical health was impaired for both injury groups though remained impaired only for the LEF group at eight‐months post‐crash. The extent of impairment in physical health was greater among the LEF group at both follow‐up times compared to the non‐LEF group. For mental health, there appeared to be little change from pre‐injury scores, however, disaggregation of the MCS indicated significantly impaired social functioning at both follow‐up times for the LEF group compared to the non‐LEF group as well as impaired vitality eight‐months post‐crash. Despite these differences in health, there was no difference in the proportion of those having RTW eight‐months post‐crash with the proportions being 86% and 90% for the LEF and non‐LEF group respectively. On‐going impairments in health were associated with the failure to RTW eight‐months post‐crash as was having a manual (trade) occupation. Of those that did RTW, 44% returned in a different role and there was little difference between the injury groups. There were however significant differences in the RTW rate, or time taken to RTW. After adjustment for baseline parameters and potential confounding variables, having sustained a lower extremity injury was associated with a significantly slower RTW rate as was the nature of one's occupation and the severity of injuries sustained in body regions other than the lower extremity. The implications of the results reported here are simple: both injury type and severity and the nature of one's occupation have an influence on the rate and pattern of return‐to‐work following injury, reflecting earlier research. Further, persisting disability has a direct influence on the likelihood of returning to work. Clearly disability and RTW are not mutually exclusive outcomes. The findings reported here highlight a number of key issues with respect to measuring outcome following injury. First, the component summary scores (i.e. PCS and MCS) are reductionist and do not in themselves necessarily reveal the extent of impairments experienced across sub‐domains – indeed they are by definition summary measures. This point can be evidenced by the disaggregation of the MCS score that showed social functioning being significantly poorer among those with lower extremity fractures eight‐months post‐crash. It is interesting to reflect that among this group alcohol intake was significantly higher – and indeed increased from pre‐crash levels – than the non‐lower extremity group whose alcohol intake actually reduced from pre‐crash levels. The second point to be made is whether the dichotomous outcome of RTW is a sufficient indicator of a successful outcome post‐injury. The findings reported here highlight the problematic nature of using a dichotomous yes/no RTW outcome as there were clear differences in the rate of RTW as well as in the pattern of RTW. Certainly in the field of occupational injury, RTW measures are common and as Krause et al. note, RTW measures serve to define the burden of occupational injury for society and individuals directly affected by occupational injury, as well as determining the efficacy and cost‐effectiveness of intervention programs and policies in assisting in the maintenance of occupational roles. The specific RTW index used is a matter of debate, with Butler et al. suggesting that first RTW is not a useful measure of RTW due to multiple patterns of RTW. Indeed, Harris et al. recognised this point when conducting a meta‐analysis of 129 studies seeking to examine the influence of workers compensation schemes (33% included litigation) on outcome post‐surgery by specifically excluding outcome scores measuring time‐to‐RTW. Harris et al. noted that RTW ‘…outcome is influenced by confounding factors such as job characteristics and social factors' (p 1645). In their review, the likelihood of an unsatisfactory outcome was reported to be three times higher among compensated patients than the uncompensated patient. In discussing these results four possible scenarios were considered: 1) psychological factors related to the injury; 2) factors associated with the at times adversarial compensation process itself; 3) the notion of secondary gain where benefit is derived from ‘assuming the sick role’, though this impact was considered to be small; and 4) tertiary gain – to the benefit of a third party. However, it may prove that RTW rates and RTW patterns might prove useful for those injured in non‐work related traffic crashes given the disconnect between the injury event (and thus eligibility for TAC coverage) and employment. This discussion of how to best measure outcome post injury is important as it reflects on the perceived success or failure of schemes designed for those injured and seeking rehabilitation, benefit or redress. In this vein fundamental differences between the eligibility and operation of fault‐based and no‐fault compensation schemes as well as differences in the mechanisms and patterns of occupational injury and traffic crash related injury must be recognised. As a counter to the negative impact of the workers compensation scheme on outcomes noted above, the transformation of the fault‐based compensation scheme in the province of Saskatchewan, Canada, to a no‐fault scheme was associated with improved outcomes among those involved in traffic crashes, with the suggestion being this result might be associated with less opportunity for financial gain and less exposure to the adversarial tort system, particularly with respect to compensation claims for pain and suffering. In this study, all patients were covered by the TAC no‐fault system. It is important to note that the TAC scheme provides coverage only for those involved in traffic crashes and acts as a no‐fault scheme that supplements, rather than replaces, the tort system. However, for a claim in the courts to be pursued, the injured person must have suffered a serious disability. The findings of this study can be interpreted in either a positive fashion or a negative fashion – that 90% had returned to work eight‐months after sustaining a serious injury is positive, however, it remains the case that a proportion remained out‐of‐work, that some RTW faster than others, and for those that did return to work, only half did so in the same role. These results highlight the complexity of defining a successful outcome and indicate that multiple measures are required to measure a successful recovery following injury. By extension, evaluating the value of compensation systems in promoting successful outcomes is equally complex. While the findings reported here have served as the basis to explore issues in defining and measuring outcome post‐injury they are in themselves important. Clearly a subset of patients experience persistent disability and problems in RTW. The study has strengths with respect to its prospective design and high retention rates. However, the scale of the study is small and the generalisability of the findings is limited. Those with serious head and spinal cord injury were deliberately excluded as were children and older adults. There are unique issues with these groups from structural anatomical injury through to re‐integration with schooling and ageing. Using administrative datasets, the target sample represents 43% of those admitted to Victorian hospitals due to road injury. The issue of secondary gain among those yet to RTW requires acknowledgement, however that injury type and occupation is associated so strongly with both the likelihood and the rate of RTW the influence of secondary gain seems small. The role of private income protection must be considered within this context in future studies. While an attempt was made to examine a range of factors associated with RTW, due to the small sample size it was not possible to include a larger range of socio‐economic factors that might influence the RTW rate. A larger and more inclusive study is required to test findings reported here, however the extensive nature of the multi‐dimensional data required represents a formidable challenge requiring multi‐centre co‐operation. A study that utilises the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) measure supplemented by a psychological test battery covering depression and anxiety disorders to compare health outcomes of those injured in the course of their occupation and in road crashes, and hence covered by different compensation schemes (fault‐based, no‐fault, litigation) would be able to appropriately examine factors associated with short and long‐term post‐injury outcomes. This type of study design would permit an assessment of the value of the various forms of compensation and modes of assistance and recourse open to the injured patient. Conclusions Despite persistent levels of disability the RTW outcome was close to 90% eight‐months post‐crash and expectedly the rate of RTW differed for manual workers and those with lower extremity injuries. This paper highlights that despite apparent high levels of patients returning to work, a substantial proportion did so in a different role while approximately one‐tenth were yet to return‐to‐work eight months post‐crash. Also expected, though still important to document, was the finding that persistent disability was associated with the inability to RTW. In elaborating on these findings, it is suggested that it is important to remain wary about reductionist measures when considering outcomes and it is crucial to distinguish between occupational injury and traffic‐related injury; this is particularly pertinent with respect to RTW outcomes when used to consider the efficacy of different compensation schemes. The assessment of outcome post‐injury is complex and requires consideration of multiple dimensions of health and social resources available to those injured. The WHO ICF offers one such framework and in combination with psychological test batteries is highly recommended. Acknowledgements The data collected here was done so as part of the first author's PhD candidature at Monash University under the supervision of Professor Brian Fildes, Dr Judith Charlton and Professor Claes Tingvall. During this time Michael Fitzharris was a Research Fellow in the Department of Trauma Surgery, The Alfred, the National Trauma Research Institute and the Department of Emergency Medicine, Southern Health. The authors gratefully acknowledge the significant contribution of each participant as well as that of the treating nursing and medical staff of the hospitals involved. The authors wish to thank Professor Thomas Kossmann, Professor Johannes Wenzel and Dr Pam Rosengarten for supporting the study. Thanks also to Louise Niggemeyer RN, Manager, Trauma Registry, The Alfred, Michelle Srage RN & Claire Sage RN, Trauma Care‐Coordinators, The Alfred, and Meg Lindsay RN, Southern Health, for vital support without which this research would not be possible. Michael Fitzharris acknowledges the financial contribution of the MUARC Foundation and Monash University for providing stipend funding and the MUARC Doctoral Students Research Fund (DSRF) for supporting this research activity. The views expressed are those of the authors and do not necessarily represent those of Monash University, the Accident Research Centre, the MUARC Foundation, Alfred Health or Southern Health. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Australian and New Zealand Journal of Public Health Wiley

Factors associated with return‐to‐work and health outcomes among survivors of road crashes in Victoria

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References (40)

Publisher
Wiley
Copyright
© 2010 The Authors. Journal Compilation © 2010 Public Health Association of Australia
ISSN
1326-0200
eISSN
1753-6405
DOI
10.1111/j.1753-6405.2010.00500.x
pmid
23331359
Publisher site
See Article on Publisher Site

Abstract

The social and economic cost of traffic‐related injury in Australia has been estimated to be $15 billion annually, with lost productivity and workplace disruption representing 23% of this economic cost, second only to vehicle repairs (27%). As an expression of social reintegration, return to work (RTW) is perceived to be a key milestone in the rehabilitation of the injured patient. There is a significant body of literature examining rates and factors associated with RTW among injured populations. Much of this literature has, however, focused on a particular type of injury such as traumatic brain injury, spinal cord injury and lower extremity injury. Among inclusive studies, RTW rates range from 60–90%, one to two years following major trauma. Younger age, higher educational level, higher pre‐injury income and positive social support have been associated with improved RTW outcomes. Indicators of injury severity (e.g. length of stay) and disability associated with traumatic brain injury (TBI), spinal cord injury (SCI) and orthopaedic trauma are associated with lower rates of RTW. Studies that have examined the nature of ‘work’ indicate that ‘blue collar’ or manual workers have slower RTW rates than their ‘white collar’ counterparts. There is a rich body of injury outcomes research conducted in Australia though most utilise mixed injury cause samples to examine recovery after head injury or the incidence and determinants of acute stress disorder and post‐traumatic stress disorder (PTSD). Few studies have specifically examined RTW outcomes for survivors of traffic crashes. In a recent study using a mixed injury cause sample presenting to adult major trauma centres in the state of Victoria, Gabbe et al. reported on disability and RTW outcomes for a sample of orthopaedic trauma patients. By 12‐months post‐injury, 84% of non‐compensable patients (60% fell; 8.6% struck by object; 6.6% motorcyclist; 6.2% cyclist; 0.8% pedestrian; 0.4% vehicle occupant; 16.7%‘other cause’) had RTW in contrast to 67% for Transport Accident Commission (TAC) compensable patients (53.5% vehicle occupant; 27.3% motorcyclist; 11.1% pedestrian; 5.8% cyclist; 2.2%‘other cause’). The compensable group were reported to have, on average, lower physical health and mental health than the non‐compensable group despite adjusting for age, head injury status, injury severity and discharge status. Despite a number of caveats, particularly surrounding comparability of the two groups with respect to mechanism, the authors concluded that “our study adds to the evidence that compensation schemes may impede recovery from injury” (p 17). The implications of this finding is critical to explore as the TAC provides no‐fault insurance for those injured in road crashes in Victoria under the provisions of the Transport Accident Act 1986 . Specifically, the likely confound between injury type, persistent disabilities and RTW must be considered. This is examined among a group of individuals injured in road crashes in Victoria, all of whom were eligible for TAC compensation. This paper expands on previously reported findings by Fitzharris et al. The study of 62 patients admitted to hospital specifically excluded those with moderate or severe head injury and spinal cord injury due to well stated rehabilitation challenges associated with these injuries. In elaborating on these findings, the aim of this paper is to explore the relationships between injury type, disability, work role and RTW rates. Methods The method of the study is described in detail elsewhere. We summarise the key points below. Participants and setting Patients aged 18 to 59 years admitted to a Victorian adult Level 1 Trauma Centre and two metropolitan teaching hospitals following involvement in a road traffic crash were eligible for the study. Recruitment was conducted in the period February 2004 and March 2005 inclusive. Eligible patients were those admitted for a period greater than 24 hours with a Glasgow Coma Scale (GCS) ≥13. Exclusion criteria were: a) the presence of an Abbreviated Injury Severity (AIS) score of 3 or higher (‘serious’) head, spinal, or vertebral column injury; b) crashes involving a fatality; c) burn injuries resulting from a vehicle fire; d) post‐traumatic amnesia (PTA) ≥24 hours; e) pre‐existing cognitive impairment; f) deliberate self‐harm; g) history of psychosis; h) illicit drug dependence; i) occupants of a stolen vehicle; and j) medically unfit to provide informed consent. Non‐English speakers and those residing outside of Victoria were excluded due to budgetary constraints. Recruitment and assessment procedures Consecutive admissions were screened for eligibility and patients were approached when medically appropriate to seek participation. A full description of the study was provided and once informed consent was gained the first of three interviews (t2, within 14 days; T2, 6–8 weeks; T3, 6–8 months) was conducted. Seventy‐four patients consented to the study and 68 interviews were conducted (91.8%), with six patients lost to interview due to discharge, withdrawal of consent while one interview was abandoned due to discomfort. Of the 68 participants who completed the in‐patient interview (t2), 64 completed T2 at approximately two months post‐crash (94% retention), and 62 completed T3 at approximately eight months post‐crash (91% retention). Those lost to follow‐up were due to failure to return the questionnaire booklet (n=3); moved residence (n=2); and one deceased in a subsequent motor vehicle crash. Non‐responders at T3 (n=6) were more likely to be male (n=5; 83.6%) than female (n=1, 16.7%), and were marginally younger than the overall sample. Assessment Measures – an extensive questionnaire and psychometric tests were used. The interview included demographic questions, factors related to and perceptions of the crash, occupational and social functioning and pre‐existing health, while the T2 and T3 interviews focused on the same health outcomes with temporally specific items replacing those focused on ‘acute’ responses. The number of weeks post‐crash that patients RTW was noted at both T2 and T3 with this information validated using pay slips, time sheets and/or personal diaries. Injuries were coded according to the Abbreviated Injury Scale (AIS), 1998 revision, and the Injury Severity Score (ISS) was calculated. The injury event, diagnosis and management were coded as per the International Statistical Classification of Diseases and related Health Problems 10th Revision – Australian modification (ICD‐10‐AM) coding system. The Glasgow Coma Scale (GCS) was obtained from paramedic and hospital medical records. Pre‐crash employment occupation was coded according to the Australian Standard Classification of Occupations (ASCO). Two health measures used are reported here. The SF‐36 is a measure of general health status and quality of life, providing eight domain scores as well as a physical (PCS) and mental component summary score (MCS). Administration of the SF‐36 at t2 focused on ‘before the crash’ while at T2 and T3 the reference time was ‘since the crash’. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess harmful alcohol consumption and was completed at t2 with reference made to ‘prior to the crash’ and at T3 with reference being ‘since the crash’. Data analysis The sample was divided into patients with lower extremity fractures (LEF) and those without (non‐LEF) given the well‐reported difference in RTW outcomes between these two goups. The lower extremity fracture group is defined by ICD‐10‐AM codes S32 (bony pelvis), S72 (femur), S82 (lower leg) and S92 (ankle and foot). Comparisons between the patient groups were made using Fishers Exact test, chi‐square tests and Repeated Measures ANOVA's where appropriate. Z‐scores were used to compare the SF‐36 scores of the sample with Australian norms (18–64 years, employed), while comparisons within and between the injury groups were made using the non‐parametric repeated measures Wilcoxon Signed Ranks Test and independent samples Mann‐Whitney U test respectively; these were used as SF‐36 data is non‐normally distributed. Univariate logistic regression was used to determine the association between RTW and SF‐36 scores, which were modelled linearly. As the time to RTW was recorded and not all patients RTW by the T3, the Kaplan‐Meier survival analysis plot was used to describe the rate of RTW with the Log Rank Test to determine differences between the two groups. A Cox proportional hazards model was used to examine the rate of RTW for the injury groups while adjusting for demographic and injury factors. Analysis was performed using STATA v.8. Statistical significance was set at p ≤ 0.05. University and Hospital Ethics Committees of the participating institutions approved the research. Results Of the 62 patients who completed the T3 interview, 60 form the basis of analysis. As we are concerned with RTW outcomes it was necessary to exclude one patient not employed at the time of the crash and one patient who failed to note their RTW date. Patient demographics Demographic, road user type and baseline injury details are presented in Table 1 . Approximately two‐thirds of the LEF group were male compared to half in the non‐LEF group while age, marital status, area of residence and GCS were similar between the two groups. There were approximately equal numbers of drivers and passengers in the two groups however all the motorcyclists in the sample sustained a LEF. The mean length of stay (LOS) was significantly longer and the mean ISS was significantly higher for the lower extremity fracture group compared to the non‐LEF group ( p ≤ 0.05). Of the 14 patients admitted to rehabilitation, all had sustained a lower extremity fracture, of which seven were drivers of motor vehicles. 1 Demographic, injury severity measures and selected psychological health outcomes of the lower extremity facture (LEF) and non lower extremity (non‐LEF) fracture groups. Characteristic Non‐LEF (n=30) LEF (n=30) % Male 50% (15) 66.7% (20) Roaduser Driver 40% (12) 36.7% (11) Passenger 10% (3) 6.7% (2) Motorcyclist Nil 43.3% (13) Bicyclist 46.7% (14) 6.7% (2) Pedestrian 3% (1) 6.7% (2) Marital status Married / living with partner 53.3% (16) 63.3% (19) Age (years) Mean (SD) 37.9 (11.2) 35.4 (13.1) Range, Median 19.9–58.8, 38.4 19.0–56.2, 31.3 Rural residence 20% (6) 16.7% (5) Glasgow Coma Scale (GCS) (%) GCS 15 83.3% (25) 70% (21) GCS 14 13.3% (4) 23.3% (7) GCS 13 3.3% (1) 6.7% (2) Length of Stay (days) Mean (SD) a 4.8 (4.7) 7.1 (3.7)* %>7 days 20% (6) 36.7% (11) Range 2–25, 3 2–17, 6.5 Injury Severity Score (ISS) Mean (SD) a 8.0 (6.9) 12.9 (9.1) a Range, Median 1–29, 5 4–41, 10 % Major Trauma: ISS>15 10% (3) 23.3% (7) Separation type (%) Home 100% (30) 53.3% (16) Rehabilitation / private Nil 46.7% (14) Maximum Abbreviated Injury Scale score for injuries in non lower extremity body regions No injury Nil 13.3% (4) MAIS 1–2 (minor, moderate) 66.7% (20) 63.3% (19) MAIS 3–5 (serious, severe critical) 33.3% (10) 23.3% (7) Notes: a) p ≤ 0.05 Injury outcomes and characteristics of the LEF and non‐LEF groups Table 2 presents the nature of injuries sustained between the two injury groups. Six participants in the LEF group sustained bilateral lower extremity injuries, while all dislocations, sprains and strains of joints of the lower leg were associated with lower extremity fractures. With respect to injuries in other regions, a higher proportion of patients in the non‐LEF group (53.5%) sustained fractures of the upper extremity compared to patients in the LEF group (26.7%)( p ≤ 0.05), while a slightly higher proportion of patients in the LEF group sustained a concussive injury ( p =0.07). There were no other differences evident. 2 Principal injuries of the sample. Injury (ICD‐10‐AM) LEF (n=30) % (n) Non‐LEF (n=30) % (n) Fracture of lower extremity, including pelvis 100% (30) N/A Fracture of lower leg 56.7% (17) N/A Fracture of femur 26.7% (8) N/A Fracture lumbar spine pelvis a 20% (6) N/A Fracture of ankle and foot 10% (3) N/A Fracture of upper extremity b 26.7% (8) 53.3% (16) Fracture of forearm 30% (9) 10% (3) Fracture of shoulder upper arm 13.3% (4) 20% (6) Fracture hand wrist 3.3% (1) 10% (3) Fracture of ribs sternum & thoracic spine 30% (9) 20% (6) Concussive injury, LOC<30 min; unspecified 23.3% (7) 6.7% (2) Traumatic injury of lung 20% (6) 10% (3) Fracture of skull and facial bones 16.7% (5) 3.3% (1) Dislocation, sprain, strain joint ligaments lower leg c 23.3% (7) Nil Injury of eye and orbit 6.7% (2) 6.7% (2) Other & unspecified injury of neck 3.3% (1) 6.7% (2) Notes: a) All pelvic fractures; b) p≤0.05 c) All associated with lower extremity‐pelvis injury; Health outcomes SF‐36 Health Status compared to Australian norms Pre‐injury and follow‐up SF‐36 MCS scores for both injury groups did not differ from normative scores for employed Australian adults ( Figure 1 ). For the non‐LEF group, the pre‐injury PCS score did not differ from norms, however was significantly lower at T2 ( p ≤0.05) and again equivalent at T3 indicating improved physical health. For the LEF group, the pre‐injury PCS score was higher than Australian norms but significantly lower at both T2 and T3. 1 MCS and PCS scores for LEF and non‐LEF injury groups, with Australian norms for employed adults aged 18–64 years. SF‐36 health status within and between the injury groups Compared to pre‐crash (56.8), the T2 mean PCS score for the LEF group was 30.2 (46.9% reduction; p ≤ 0.05) and 43.1 at T3 (24% reduction; p ≤ 0.05). For the non‐LEF group, the mean PCS score was 54.9 at baseline, 42.3 at T2 (22.9% reduction; p ≤ 0.05) and 52.1 at T3 (5% reduction; p ≤ 0.05); hence the improvement from T2 to T3 was statistically significant (23% increase; p ≤ 0.05). While the baseline PCS scores between the injury groups were not statistically different, at T2 and T3 PCS scores were lower for the LEF group compared to the non‐LEF group ( p ≤ 0.05). Disaggregation of the PCS summary score indicated a significantly lower physical functioning and the role physical sub‐domain scores at T2 and T3 for the LEF group compared to the non‐LEF group. There was no difference on general health domain scores at either follow‐up time or body pain at T2, however the bodily pain score for the LEF group was lower (worse) than for the non‐LEF group at T3 indicating a difference in recovery. There was little change in the overall MCS scores from pre‐injury scores and the pattern was similar for both injury groups. However, examination of the four sub‐domain scores that form the composite MCS score indicates a significantly lower social functioning SF‐36 MCS domain score for the LEF group compared to the non‐LEF group at T2 (44.2 vs. 70.8; p ≤ 0.05) and T3 (75.0 vs. 89.6; p ≤ 0.05), indicating on‐going and significant impact on social activities; indeed, the non‐LEF group score was similar to their pre‐injury score (88.3) and this was not the case for the LEF group (90.8). In addition, the vitality score was lower for the LEF group compared to the non‐LEF group at T3 (57.3 vs. 67.7; p ≤ 0.05). Alcohol use Alcohol consumption decreased in the non‐LEF group (Pre: 5.5, 95%CI: 3.8–7.2; Post: 3.9, 95%CI: 2.4–5.3) and increased in the LEF group following the crash (Pre: 6.4, 95%CI: 4.7–8.0; Post: 7.3, 95%CI: 4.8–9.9); this differential change was statistically significant (p ≤ 0.05), as was the difference at T3 ( p ≤ 0.05, Bonferroni correction). Occupation and Return‐to‐Work outcomes Table 3 presents the key occupation and RTW outcomes for the two injury groups. Using the ASCO system the number of patients holding managerial, administration or professional positions and clerical or sales and service occupations was similar. There were more trades‐people in the LEF group (20%) than in the non‐LEF group (10%) ( Table 3 ). Most patients were employed on a full‐time basis with a small number being part‐time employed or students supplementing their income with part‐time or casual work. By T3, approximately equal numbers had RTW (approximately 90%) while seven (11.6%) had yet to RTW by T3 (3 non‐LEF; 4 LEF). 3 Occupation and return‐to‐work outcomes. Characteristic Non‐LEF (n=30) LEF (n=30) Pre‐injury occupation Manager/Administration/Professional 66.7% (20) 60% (18) Clerical, Sales & Service 23.3% (7) 20% (6) Trades and related 10% (3) 20% (6) Pre‐injury employment profile Full time employed 73.3% (22) 76.7% (23) a Full time work; self‐employed 10% (3) 6.7% (2) Part‐time / casual 6.7% (2) 6.7% (2) Student (+ part‐time/casual work) 10% (3) 10% (3) Return‐to‐work outcome at T3 Returned to work 90% (27) 83.3% (25) Unable to work 10% (3) 13.3% (4) Returned to work / unable to study 0% (0) 3.3% (1) Time to return to work (post discharge acute hospital)* Median (weeks) 3 12 95% CI (weeks) 2–4 5.7–18.3 Role on return‐to‐work at T3 Same role 53.3% (16) 46.7% (14) Different role (part‐time, lighter duties, new role) 36.7% (11) 40% (12) No longer employed 3.3% (1) 6.7% (2) Yet to RTW 6.7% (2) 6.7% (2) Salary suffered as consequence of crash 43.3% (13) 56.7% (17) Notes: a) 1 also studying; *p≤0.05 Of the seven patients yet to RTW (4 LEF, 3 non‐LEF), four (3 LEF, 1 non‐LEF) stated they were no longer employed at T3 as a consequence of injuries sustained. Of the four LEF patients yet to RTW at T3, three were male, three were motorcyclists and one was a driver, two had an ISS >15; two were admitted to rehabilitation, and all were tradespeople or manual workers. Of the non‐LEF patients (3) yet to RTW, all were female, all were discharged home, all had an ISS<15, and there was one driver, one cyclist and one passenger and one was in a trade or manual occupation. Analysis shows that at T3 SF‐36 PCS and MCS scores were associated with whether a patient had RTW ( p ≤ 0.05) but this was not the case for the variable indicating lower extremity injury; this indicates that by eight months post‐crash persisting disability is critical in whether a patient RTW or not. Tradespeople were less likely to have RTW by T3 than professional and clerical occupational groups ( p ≤ 0.05). Despite the similar proportion of patients having RTW by T3, there was a significant difference in the rate of RTW. As shown in both Table 3 and Figure 2 the median time to RTW for non‐LEF group patients was three weeks post‐discharge (95th % CI: 2–4 weeks), and 12 weeks (95th % CI: 5.7–18.3 weeks) for patients in the LEF group ( p ≤0.05), reflecting the slower rate of RTW. Figure 2 reflects the finding that seven patients were yet to RTW by T3. 2 Kaplan‐Meier survival plot of proportion of patients RTW by time since discharge. Approximately half the patients RTW in the same role while 13 patients (43.3%) in the non‐LEF group stated their salary had suffered as a consequence of the crash in contrast to 17 patients (56.7%) in the LEF group. Modelling time to RTW The Cox proportional hazards model was used to assess occupational, injury and demographic factors associated with the RTW rate ( Table 4 ). Patients in the LEF injury group RTW at a rate 69% slower than those in the non‐LEF group controlling for factors noted in Table 4 (HR: 0.31; 95%CI: 0.16–0.58). The maximum AIS score (MAIS) of injuries sustained in other body regions was included to adjust for this potentially confounding influence. Those with MAIS 3–5 injuries in regions other than the lower extremity RTW at a rate 56% slower than those with minor (MAIS1) or no injury (HR: 0.42, CI: 0.20–0.88, p =0.02). The model was adjusted for pre‐crash health and these factors were associated with the rate of RTW. Patients holding a trade and related occupation pre‐crash had a significantly slower RTW rate than both professional workers (HR: 0.31, CI: 0.16–0.58, p =0.003) and clerical, sales and service workers (HR: 0.22, CI: 0.06–0.81, p =0.02). There was no difference in the RTW rate between the clerical and professional occupation groups. 4 Factors associated with time to return‐to‐work. Factor HR 95th% CI Lower‐Upper p Lower Extremity Fracture (LEF) 0.31 0.16–0.58 <0.001 Age 0.98 0.95–1.00 0.07 MAIS for non‐lower extremity injuries (Reference category AISO/1) MAIS 2 0.70 0.34–1.44 0.4 MAIS 3–5 0.42 0.20–0.88 0.02 Occupation (Reference category Prof, Managers & Admin) Clerical, Sales & Service 0.84 0.39–1.83 0.7 Trades and related 0.19 0.06–0.57 0.003 SF‐36 pre‐injury PCS 1.06 1.01–1.12 0.01 SF‐36 pre‐injury MCS 1.03 1.00–1.07 0.05 Time spent in rehabilitation was unable to be assessed as only patients with lower extremity fractures were admitted to another care facility post‐discharge from the treating hospital. This is clearly related to injury type and impacts upon RTW outcomes. Marital status and gender was not associated with RTW rate in this sample. Discussion In this study of healthy employed adults admitted to hospital following injuries sustained in traffic crashes – all of whom were covered by the TAC no‐fault compensation scheme – we set out to explore the relationships between injury type, disability, work role and RTW outcomes. Among this relatively small, yet representative sample of admitted patients within the context of the eligibility criteria, those with lower extremity fractures experienced a longer length of stay and higher mean ISS than those without such fractures. Of those patients requiring in‐patient rehabilitation, all had sustained a lower extremity fracture. Despite pre‐injury physical health, as measured by the SF‐36, being equal to or better than Australian norms, at 2.5 months post‐crash physical health was impaired for both injury groups though remained impaired only for the LEF group at eight‐months post‐crash. The extent of impairment in physical health was greater among the LEF group at both follow‐up times compared to the non‐LEF group. For mental health, there appeared to be little change from pre‐injury scores, however, disaggregation of the MCS indicated significantly impaired social functioning at both follow‐up times for the LEF group compared to the non‐LEF group as well as impaired vitality eight‐months post‐crash. Despite these differences in health, there was no difference in the proportion of those having RTW eight‐months post‐crash with the proportions being 86% and 90% for the LEF and non‐LEF group respectively. On‐going impairments in health were associated with the failure to RTW eight‐months post‐crash as was having a manual (trade) occupation. Of those that did RTW, 44% returned in a different role and there was little difference between the injury groups. There were however significant differences in the RTW rate, or time taken to RTW. After adjustment for baseline parameters and potential confounding variables, having sustained a lower extremity injury was associated with a significantly slower RTW rate as was the nature of one's occupation and the severity of injuries sustained in body regions other than the lower extremity. The implications of the results reported here are simple: both injury type and severity and the nature of one's occupation have an influence on the rate and pattern of return‐to‐work following injury, reflecting earlier research. Further, persisting disability has a direct influence on the likelihood of returning to work. Clearly disability and RTW are not mutually exclusive outcomes. The findings reported here highlight a number of key issues with respect to measuring outcome following injury. First, the component summary scores (i.e. PCS and MCS) are reductionist and do not in themselves necessarily reveal the extent of impairments experienced across sub‐domains – indeed they are by definition summary measures. This point can be evidenced by the disaggregation of the MCS score that showed social functioning being significantly poorer among those with lower extremity fractures eight‐months post‐crash. It is interesting to reflect that among this group alcohol intake was significantly higher – and indeed increased from pre‐crash levels – than the non‐lower extremity group whose alcohol intake actually reduced from pre‐crash levels. The second point to be made is whether the dichotomous outcome of RTW is a sufficient indicator of a successful outcome post‐injury. The findings reported here highlight the problematic nature of using a dichotomous yes/no RTW outcome as there were clear differences in the rate of RTW as well as in the pattern of RTW. Certainly in the field of occupational injury, RTW measures are common and as Krause et al. note, RTW measures serve to define the burden of occupational injury for society and individuals directly affected by occupational injury, as well as determining the efficacy and cost‐effectiveness of intervention programs and policies in assisting in the maintenance of occupational roles. The specific RTW index used is a matter of debate, with Butler et al. suggesting that first RTW is not a useful measure of RTW due to multiple patterns of RTW. Indeed, Harris et al. recognised this point when conducting a meta‐analysis of 129 studies seeking to examine the influence of workers compensation schemes (33% included litigation) on outcome post‐surgery by specifically excluding outcome scores measuring time‐to‐RTW. Harris et al. noted that RTW ‘…outcome is influenced by confounding factors such as job characteristics and social factors' (p 1645). In their review, the likelihood of an unsatisfactory outcome was reported to be three times higher among compensated patients than the uncompensated patient. In discussing these results four possible scenarios were considered: 1) psychological factors related to the injury; 2) factors associated with the at times adversarial compensation process itself; 3) the notion of secondary gain where benefit is derived from ‘assuming the sick role’, though this impact was considered to be small; and 4) tertiary gain – to the benefit of a third party. However, it may prove that RTW rates and RTW patterns might prove useful for those injured in non‐work related traffic crashes given the disconnect between the injury event (and thus eligibility for TAC coverage) and employment. This discussion of how to best measure outcome post injury is important as it reflects on the perceived success or failure of schemes designed for those injured and seeking rehabilitation, benefit or redress. In this vein fundamental differences between the eligibility and operation of fault‐based and no‐fault compensation schemes as well as differences in the mechanisms and patterns of occupational injury and traffic crash related injury must be recognised. As a counter to the negative impact of the workers compensation scheme on outcomes noted above, the transformation of the fault‐based compensation scheme in the province of Saskatchewan, Canada, to a no‐fault scheme was associated with improved outcomes among those involved in traffic crashes, with the suggestion being this result might be associated with less opportunity for financial gain and less exposure to the adversarial tort system, particularly with respect to compensation claims for pain and suffering. In this study, all patients were covered by the TAC no‐fault system. It is important to note that the TAC scheme provides coverage only for those involved in traffic crashes and acts as a no‐fault scheme that supplements, rather than replaces, the tort system. However, for a claim in the courts to be pursued, the injured person must have suffered a serious disability. The findings of this study can be interpreted in either a positive fashion or a negative fashion – that 90% had returned to work eight‐months after sustaining a serious injury is positive, however, it remains the case that a proportion remained out‐of‐work, that some RTW faster than others, and for those that did return to work, only half did so in the same role. These results highlight the complexity of defining a successful outcome and indicate that multiple measures are required to measure a successful recovery following injury. By extension, evaluating the value of compensation systems in promoting successful outcomes is equally complex. While the findings reported here have served as the basis to explore issues in defining and measuring outcome post‐injury they are in themselves important. Clearly a subset of patients experience persistent disability and problems in RTW. The study has strengths with respect to its prospective design and high retention rates. However, the scale of the study is small and the generalisability of the findings is limited. Those with serious head and spinal cord injury were deliberately excluded as were children and older adults. There are unique issues with these groups from structural anatomical injury through to re‐integration with schooling and ageing. Using administrative datasets, the target sample represents 43% of those admitted to Victorian hospitals due to road injury. The issue of secondary gain among those yet to RTW requires acknowledgement, however that injury type and occupation is associated so strongly with both the likelihood and the rate of RTW the influence of secondary gain seems small. The role of private income protection must be considered within this context in future studies. While an attempt was made to examine a range of factors associated with RTW, due to the small sample size it was not possible to include a larger range of socio‐economic factors that might influence the RTW rate. A larger and more inclusive study is required to test findings reported here, however the extensive nature of the multi‐dimensional data required represents a formidable challenge requiring multi‐centre co‐operation. A study that utilises the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF) measure supplemented by a psychological test battery covering depression and anxiety disorders to compare health outcomes of those injured in the course of their occupation and in road crashes, and hence covered by different compensation schemes (fault‐based, no‐fault, litigation) would be able to appropriately examine factors associated with short and long‐term post‐injury outcomes. This type of study design would permit an assessment of the value of the various forms of compensation and modes of assistance and recourse open to the injured patient. Conclusions Despite persistent levels of disability the RTW outcome was close to 90% eight‐months post‐crash and expectedly the rate of RTW differed for manual workers and those with lower extremity injuries. This paper highlights that despite apparent high levels of patients returning to work, a substantial proportion did so in a different role while approximately one‐tenth were yet to return‐to‐work eight months post‐crash. Also expected, though still important to document, was the finding that persistent disability was associated with the inability to RTW. In elaborating on these findings, it is suggested that it is important to remain wary about reductionist measures when considering outcomes and it is crucial to distinguish between occupational injury and traffic‐related injury; this is particularly pertinent with respect to RTW outcomes when used to consider the efficacy of different compensation schemes. The assessment of outcome post‐injury is complex and requires consideration of multiple dimensions of health and social resources available to those injured. The WHO ICF offers one such framework and in combination with psychological test batteries is highly recommended. Acknowledgements The data collected here was done so as part of the first author's PhD candidature at Monash University under the supervision of Professor Brian Fildes, Dr Judith Charlton and Professor Claes Tingvall. During this time Michael Fitzharris was a Research Fellow in the Department of Trauma Surgery, The Alfred, the National Trauma Research Institute and the Department of Emergency Medicine, Southern Health. The authors gratefully acknowledge the significant contribution of each participant as well as that of the treating nursing and medical staff of the hospitals involved. The authors wish to thank Professor Thomas Kossmann, Professor Johannes Wenzel and Dr Pam Rosengarten for supporting the study. Thanks also to Louise Niggemeyer RN, Manager, Trauma Registry, The Alfred, Michelle Srage RN & Claire Sage RN, Trauma Care‐Coordinators, The Alfred, and Meg Lindsay RN, Southern Health, for vital support without which this research would not be possible. Michael Fitzharris acknowledges the financial contribution of the MUARC Foundation and Monash University for providing stipend funding and the MUARC Doctoral Students Research Fund (DSRF) for supporting this research activity. The views expressed are those of the authors and do not necessarily represent those of Monash University, the Accident Research Centre, the MUARC Foundation, Alfred Health or Southern Health.

Journal

Australian and New Zealand Journal of Public HealthWiley

Published: Apr 1, 2010

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