007 votor analysis report

VOTOR Analysis Report

School of Public Health and Preventive Medicine Monash University

Associate Professor Belinda Gabbe

Mr Cameron Gosling Ms Melissa Hart

Professor Peter Cameron

7th December, 2011

Research report #: 1211-007-R2

Accompanying documents to this research summary

Title Report number

VOTOR Analysis Report Summary of Research Findings 1211-007-R2B

Report Number # 1211-007-R2 Page 2 of 53

Executive Summary

Data collected by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR) provides the most comprehensive collection of outcomes data relating to orthopaedic trauma worldwide. Data from the registry can profile orthopaedic injuries across hospitals, regions, patients and injury sub-groups, including descriptions of the causes of injury and tracking of pain and quality of life outcomes over the course of injury recovery. The registry also allows comparison of outcomes between specific patient and injury sub-groups. This report provides a detailed overview of the VOTOR data from March 2007, coinciding with the inclusion of Geelong and Northern Hospitals to the registry. The data provided in this report focused on: providing an overview of the VOTOR population and their outcomes over time; comparing the profile of VOTOR patients by participating hospital; comparing transport and non-transport related cases; and identifying predictors of outcome at 6 and 12-months post-injury, with a particular focus on the association between TAC and WorkCover compensation and patient outcomes.

From March 2007 to June 30 2011, 20,728 cases were registered by VOTOR (55% male; mean age 54.6 years). A low fall (41%) was the most common mechanism of injury with transport-related cases accounting for 33% of all VOTOR cases. Isolated lower extremity (39%) and isolated upper extremity (21%) fractures were the most common orthopaedic injuries reported. Ten per cent of VOTOR cases require ICU admission in 10% of cases and experience a median (IQR) length of stay of 5 (2.6-9.8) days. Sixty per cent are discharged directly home.

The profile of VOTOR cases has remained relatively stable since 2007. However, there are substantial differences in the profile of cases managed at each of the participating hospitals. Cases definitively managed at The Alfred and Royal Melbourne Hospitals show a greater degree of injury severity (e.g. multiple injuries) and complexity (e.g. greater comorbidity), and a much higher proportion of transport-related cases, compared to the Geelong and Northern Hospitals. These differences reflect the structure of the Victorian State Trauma System and suggest that the pre-hospital trauma triage guidelines are being followed.

The overall in-hospital mortality rate for admitted orthopaedic trauma is low at 2 per cent relative to the approximately 10% in-hospital mortality of major trauma patients in Victoria. The key predictors of in-hospital mortality are increasing age, male gender, the presence of comorbidities and sustaining associated head or intra-thoracic injuries. Compensable status is not associated with in-hospital mortality. In contrast, TAC or WorkCover compensation, and private health insurance, are associated with discharge to inpatient rehabilitation. Other predictors of discharge to inpatient rehabilitation are the presence of injuries to multiple orthopaedic regions, associated non-orthopaedic injuries, female gender, older age and the presence of comorbidities.

VOTOR has demonstrated excellent follow-up with over 85% follow-up of patients at both 6- and 12-months and less than 2% of cases opting off the registry. The prevalence of disability is high for all outcomes. For most outcomes and patient sub-groups, there is improvement in outcomes from 6 to 12-months post-injury. The predictors of poor long-term outcomes are relatively consistent for all long term outcome measures. Of note, the risk-adjusted odds of a poor outcome is significantly higher for TAC and WorkCover compensable patients for all outcomes and time points. Privately insured patients do not show this pattern of association. Other key predictors of poor outcomes are the presence of comorbidities, lower levels of education, associated head or thoracic injuries, and injuries involving multiple orthopaedic regions. In particular, the group of patients sustaining fractures to the spine, upper and lower extremities demonstrate the poorest functional, pain and health-related quality of life outcomes, and show deterioration in outcomes from 6 to 12-months post-injury for a number of outcomes.

This report provides a detailed summary of the data available from VOTOR, highlighting the high follow-up rates, important clinical information captured, and the breadth of outcome measures collected by VOTOR. The content of the report was developed with assistance from the VOTOR-TAC Liaison Group and will inform the next major analyses undertaken by VOTOR of the VOTOR-


CRD linked dataset. This linkage will be important for identifying relevant outcomes for patients following trauma, and improved prediction of TAC clients at high risk of poor outcome and high cost claims. Combined with the clinical sub-projects aimed at evaluating treatment approaches, VOTOR can be used to inform the development of interventions aimed at improving patient care and outcomes, and evidenced-based clinical guidelines. The registry can then monitor the impact of the implementation of measures aimed at improving patient outcomes. An example is the use of VOTOR data for evaluating the change in TAC claims model as part of the TAC2015 evaluation. Overall, improved patient outcomes should reduce the overall burden and cost of injury.


Table of Contents EXECUTIVE SUMMARY ..................................................................................................... 2 ABOUT THE VICTORIAN ORTHOPAEDIC TRAUMA OUTCOMES REGISTRY (VOTOR) ............................................................................................................................................. 6 Brief history of VOTOR ................................................................................................................................... 6 How VOTOR data are collected ...................................................................................................................... 6

ABOUT THIS REPORT ....................................................................................................... 7 OVERVIEW OF VOTOR CASES ........................................................................................ 8 Number of cases .............................................................................................................................................. 8 Demographic profile of VOTOR cases .......................................................................................................... 8 Cause of VOTOR cases ................................................................................................................................. 10 Injuries sustained by VOTOR cases ............................................................................................................ 11 In-hospital outcomes of VOTOR cases ....................................................................................................... 13

COMPARISON OF TRANSPORT AND NON-TRANSPORT RELATED CASES ............. 14 Number of cases ............................................................................................................................................ 14 Demographic profile of cases by transport status .................................................................................... 15 Injury profile of cases by transport status .................................................................................................. 15 In-hospital outcomes of VOTOR cases by transport status ..................................................................... 16

PREDICTORS OF IN-HOSPITAL OUTCOMES ................................................................ 17 In-hospital mortality ...................................................................................................................................... 17 Predictors of discharge destination ............................................................................................................ 18

LONG TERM OUTCOMES ................................................................................................ 20 Follow-up rates .............................................................................................................................................. 20

OUTCOMES BY TRANSPORT STATUS .......................................................................... 21 GOS-E ............................................................................................................................................................. 21 Return to work ............................................................................................................................................... 22 Health-related quality of life (HRQoL) .......................................................................................................... 22 Pain ................................................................................................................................................................. 25

OUTCOMES BY ORTHOPAEDIC INJURY GROUP......................................................... 25 GOS-E ............................................................................................................................................................. 25 Return to work ............................................................................................................................................... 27 Health-related quality of life (HRQoL) .......................................................................................................... 28 Pain ................................................................................................................................................................. 33

PREDICTORS OF FUNCTIONAL OUTCOME .................................................................. 34 GOS-E ............................................................................................................................................................. 34 Return to work ............................................................................................................................................... 35 Moderate to severe pain ............................................................................................................................... 36


Physical health (PCS-12) .............................................................................................................................. 38 Mental health (MCS-12) ................................................................................................................................. 39

SUMMARY OF FINDINGS ................................................................................................ 41 APPENDIX A ..................................................................................................................... 42 Victorian Orthopaedic Trauma Outcomes Registry Steering Committee ................................................ 42

APPENDIX B ..................................................................................................................... 43 Publications and Presentations ................................................................................................................... 43

APPENDIX C ..................................................................................................................... 49 Current VOTOR Subprojects ........................................................................................................................ 49


About the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR) Brief history of VOTOR The introduction of the Victorian State Trauma System (VSTS) has resulted in improved outcomes for major trauma patients through enhanced integration of trauma services and processes. Development and implementation of the Victorian State Trauma Registry (VSTR) has contributed to this success. The Victorian Orthopaedic Trauma Outcomes Registry (VOTOR) was initially funded as a pilot project by the Victorian Trauma Foundation (VTF) in 2003, and has complemented the VSTR.

VOTOR first started as a collaborative project involving orthopaedic surgeons from The Alfred and Royal Melbourne Hospitals (RMH), and the Department of Epidemiology and Preventive Medicine (DEPM) at Monash University, with the key aim of establishing a comprehensive database of patient, injury event, admission, diagnosis, management and long term outcomes, with a strong focus on a high risk, high cost group of patients. Eight years of development has established VOTOR as a comprehensive and robust monitoring system for orthopaedic trauma in Victoria using a sentinel site approach. Funding has enabled expansion to include additional sites (Geelong and Northern Hospitals), high follow-up rates, and increased ability to capture procedural and implant data. Since the commencement of VOTOR over 20,000 orthopaedic trauma admissions have been registered and a substantial proportion of these admissions are TAC clients.

VOTOR collects detailed and accurate injury data, treatment techniques, specific hospital administration data, and extensive patient-reported outcomes data. The VOTOR project is further enhanced by a multi-disciplinary and independent approach to the conduct of all research, which is important for research integrity and the uptake of recommendations from the research undertaken. Through the completion of identified and future sub-projects and analyses, coordination with the actuary and economic analysis capacity of TAC, and the engagement of the clinical community, VOTOR is ideally placed to inform the development of clinical guidelines and clinical best practice, and guide changes to TAC claims management processes and policies, for common orthopaedic injuries.

How VOTOR data are collected All patients admitted to The Alfred, Royal Melbourne Hospital, Geelong Hospital and Northern Hospital with an emergency admission (>24 hours) for an orthopaedic injury (fracture and/ or soft tissue injury (if treated surgically)) are eligible for inclusion on the registry. Patients with a pathological fracture related to metastatic disease are excluded. Patients are identified by the discharge diagnosis through ICD-10 reports from the hospitals. The registry uses an opt-off consent process where all eligible patients (or their next of kin in the case of deaths) are sent a letter and a brochure to explain the purpose of the registry, why they have been included, the registry processes and how to opt-off if they choose to do so. VOTOR has ethics approval from all participating hospitals and Monash University, and the opt-off rate for VOTOR patients is approximately 1.9%, assuring almost complete capture of eligible patients for analysis.

The VOTOR dataset is fully linked with the VSTR to eliminate duplication of effort and to streamline follow-up procedures. Data relating to the hospital admissions are obtained electronically from the participating hospitals and include: (i) Patient details; (ii) Injury event details; (iii) Injury diagnoses; (iv) Injury management; (v) Key in-hospital indicators; (vi) Complications and pre-morbid conditions; and (vii) Implant information. Additionally, a standardised telephone interview is completed at 6 and 12-months after injury by trained registry staff. This process is fully integrated with the VSTR to prevent multiple calls to the same patient. The data collected by telephone interview includes: (i) Pre-morbid status; (ii) Pain; (iii) Functional level using the Glasgow Outcome Scale Extended; (iv) Work disability; (v) Health-related quality of life using the SF-12 and EQ-5D; and (vi) Living status and need for additional care. Linkage with the Victorian deaths registry is also undertaken to establish post-discharge mortality.


About this report

This report provides a detailed overview of the VOTOR data from March 2007. Data from this date were selected from this report as this represents the timeframe for the expansion of VOTOR to include the Geelong and Northern Hospitals, enabling comparisons across the participating hospitals. While not included in this report, data from August 2003 to February 2007 continue to be used for the specific sub-projects of VOTOR relating to individual injuries and management techniques, and are included in the current linkage project with the ISCRR Compensation Research Database (CRD). The data provided in this report focus on:

i. Providing an overview of the VOTOR population and their outcomes over time

ii. Comparison of the profile of VOTOR patients by participating hospital and, therefore, level of care within the Victorian State Trauma System (VSTS)

iii. Comparison of transport and non-transport related cases

iv. Predictors of outcome at 6 and 12-months following injury, with a particular focus on the association between TAC and WorkCover compensation and patient outcomes.

The findings of this report should be considered in the context of the wider array of VOTOR sub-projects undertaken previously, and those ongoing. A list of all VOTOR sub-projects, publications and presentations is provided as appendices to this report.


Overview of VOTOR cases

Number of cases Since March 2007, when Geelong Hospital joined VOTOR, 20,728 cases have been registered across the participating hospitals. Figure 1 shows the number of cases registered for each hospital, and overall, from July 2007-June 2011. There has been a small increase in the number of registered cases from RMH, Geelong and Northern over the 4-year period, with the overall number of registered patients rising from 4,652 cases in 2007-08 to 5,180 cases in 2010-11.

Figure 1: Number of registered VOTOR patients over time

Demographic profile of VOTOR cases The majority of VOTOR cases are male (n=11,394, 55%), have a mean (SD) age of 54.6 (23.4) years, and had no pre-existing conditions according to the Charlson Comorbidity Index (CCI) (n=15,106, 73%). While the proportion of male cases has decreased slightly over time, and the mean age has increased, the proportion of cases without comorbidities has increased (Table 1), indicating a healthier orthopaedic trauma population.


Table 1: Demographic profile of VOTOR cases over time Descriptor 2007-08 2008-09 2009-10 2010-11 Gender n (%)

Male Female

2619 (56) 2033 (44)

2703 (56) 2112 (44)

2829 (54) 2415 (46)

2738 (53) 2442 (47)

Age Mean (SD) years 53.8 (23.3) 53.1 (23.2) 55.7 (23.4) 56.3 (23.6) Comorbid status

n (%) None CCI=1 CCI 2

3210 (69) 1032 (22) 410 (9)

3491 (73) 928 (19) 396 (8)

3874 (74) 981 (19) 389 (7)

3942 (76) 829 (16) 409 (8)

The demographic profile of cases differs across VOTOR participating hospitals with the major trauma service (MTS) hospital (Alfred and RMH) cases predominantly male and younger compared to the regional trauma service (RTS, Geelong) and metropolitan trauma service (MeTS, Northern) (Table 2). Of note, comorbid conditions were more prevalent in the cases managed at The Alfred which could reflect a preference to transfer more complex cases to this site, or differences in ICD-10 coding procedures at The Alfred compared to the other sites.

Table 2: Demographic profile of VOTOR cases by hospital Descriptor Alfred RMH Geelong Northern Gender n (%)

Male Female

4847 (60) 3195 (40)

3623 (61) 2294 (39)

1739 (41) 2494 (59)

1185 (47) 1351 (53)

Age Mean (SD) years 52.3 (23.2) 50.9 (23.1) 62.4 (23.1) 57.6 (21.7) Comorbid status

n (%) None CCI=1 CCI 2

5458 (68) 1874 (23) 710 (9)

4379 (74) 1113 (19) 425 (7)

3280 (77) 619 (15) 334 (8)

1989 (78) 346 (14) 201 (8)


Cause of VOTOR cases The most prevalent cause of admission to the participating hospitals with an orthopaedic injury was a low fall, followed my motor vehicle and motorcycle crashes (Figure 2). The proportion of cases caused by a low fall has increased over time, while the number of pedal cyclist cases has also risen (Table 3).

Figure 2: Cause of injury of VOTOR cases

Table 3: Cause of VOTOR cases over time Cause of injury 2007-08

n (%) 2008-09 n (%)

2009-10 n (%)

2010-11 n (%)

Low fall (


Table 4: Cause of VOTOR cases by hospital Cause of injury Alfred

n (%) RMH n (%)

Geelong n (%)

Northern n (%)

Low fall (


Table 5: Injury profile of VOTOR cases over time Descriptor 2007-08 2008-09 2009-10 2010-11 Orthopaedic n (%)

Isolated LEa

Isolated UEb

Spinal only Multiple LE UE and LE Soft tissue Spine and LE Multiple UE Spine and UE Spine, UE and LE

1816 (39) 989 (21) 589 (13) 322 (7) 203 (4) 204 (4) 153 (3) 159 (3) 128 (3) 89 (2)

1820 (38) 1031 (21) 601 (12) 369 (8) 22 (5) 154 (3) 186 (4) 174 (4) 167 (3) 93 (2)

1997 (38) 1090 (21) 755 (14) 349 (7) 241 (5) 213 (4) 186 (4) 154 (3) 175 (3) 84 (2)

2073 (40) 1150 (22) 655 (13) 315 (6) 235 (5) 222 (4) 135 (3) 167 (3) 145 (3) 83 (2)

Other injuries n (%) Yes Intra-thoracic Multiple rib fractures Intracranial Intra-abdominal

499 (11) 428 (10) 304 (7) 226 (5)

551 (12) 524 (11) 359 (8) 270 (6)

576 (11) 576 (11) 354 (7) 243 (5)

422 (8) 476 (10) 277 (6) 192 (4)

a LE, lower extremity; b UE, upper extremity

Consistent with the VSTS triage guidelines, the proportion of isolated upper and lower extremity fractures was lower at the MTS hospitals when compared to the Northern and Geelong (Table 6). Similarly, the prevalence of non-orthopaedic injuries, indicating multi-trauma, was substantially lower at Geelong and Northern compared to the MTS hospitals. It is clear that multiple fractures, spinal fractures and multi-trauma patients are being managed at the MTS hospitals. Isolated extremity fractures accounted for 78% of Geelong cases and 80% of Northern cases, compared to 48% at The Alfred and 54% at RMH (Table 6).

Table 6: Injury profile of VOTOR cases by hospital Descriptor Alfred RMH Geelong Northern Orthopaedic n (%)

Isolated LEa

Isolated UEb


2518 (31) 1378 (17) 1501 (19) 603 (7) 413 (5) 287 (4) 414 (5) 293 (4) 403 (5) 232 (3)

2114 (36) 1078 (18) 911 (14) 448 (8) 333 (6) 232 (4) 260 (4) 183 (3) 224 (4) 134 (2)

2118 (50) 1179 (28) 182 (4) 226 (5) 142 (3) 224 (5) 14 (


In-hospital outcomes of VOTOR cases Of the 20,728 VOTOR cases, there were 418 in-hospital deaths, a death rate of 2%. Ten per cent (n=2071) of the VOTOR cases experienced an ICU stay, and the median (IQR) hospital length of stay was 5.0 (2.6-9.8) days. Of the survivors to hospital discharge, the most common destinations at discharge were home (n=11907, 60%), inpatient rehabilitation (n=5360, 27%), hospital for convalescence (n=1658, 8%) and a nursing home (n=427, 2%).

The in-hospital death rate, ICU admission rate, and hospital length of stay have remained consistent over the past four years. In 2010-11, the proportion discharged directly home was consistent with previous years, but the proportion of cases discharged to inpatient rehabilitation decreased and the proportion discharged to a hospital for convalescence increased (Table 7). The observed change in discharge patterns occurred only at RMH and The Northern. The reasons for this change are unclear but could represent differences in discharge practices at these sites, difficulty accessing inpatient rehabilitation beds, or changes in coding practices.

Table 7: In-hospital outcomes of VOTOR cases over time Descriptor 2007-08 2008-09 2009-10 2010-11 ICU stay n (%) yes 457 (10) 506 (11) 524 (10) 454 (9) Length of stay Median (IQR) days 5.1 (2.7-10.1) 5.0 (2.6-10.0) 4.9 (2.5-9.4) 4.9 (2.4-9.2) In-hospital death n (%) yes 98 (2.1) 104 (2.2) 101 (1.9) 98 (1.9) Discharge destination*

n (%) Home Inpatient rehabilitation Other

2684 (58) 1267 (27) 701 (15)

2855 (59) 1333 (28) 627 (13)

3062 (58) 1451 (28) 731 (14)

2859 (55) 990 (19) 1331 (26)

* Of survivors to hospital discharge

The in-hospital outcomes of VOTOR cases differed substantially across hospitals, reflecting the difference in case-mix. The proportion admitted to ICU was much higher at the MTS hospitals than the Geelong and Northern, and the length of stay at these hospitals was also longer (Table 8). The discharge destination for The Alfred cases was vastly different to the Geelong and Northern hospitals with 42% discharged to inpatient rehabilitation compared to less than 1% of Geelong and Northern cases. Despite equal designation in the VSTS, the proportion of RMH cases discharged to a hospital for convalescence was five-fold higher than The Alfred (Table 8).

Table 8: In-hospital outcomes of VOTOR cases by hospital Descriptor Alfred RMH Geelong Northern ICU stay n (%) yes 1201 (15) 784 (13) 74 (2) 12 (


Comparison of transport and non-transport related cases

For the purposes of this report, case was considered transport-related if the cause of injury was a motor vehicle, motorcycle or pedal cyclist crash, a pedestrian incident or coded as other transport (e.g. rail, truck, etc.).

Number of cases Overall, 7,081 transport-related and 13,324 non-transport related, cases were captured by VOTOR since March 2007. Transport-related cases accounted for approximately one third of VOTOR cases, contributing more than 1500 cases per year (Figure 4). The proportion of transport-related cases has decreased marginally over the past two years, which is consistent with the increase in low fall related cases noted earlier.

Figure 4: Number and percentage of transport cases by year

The pre-hospital triage guidelines are structured to ensure that potential major trauma cases are preferentially transported to the MTS hospitals. The triage criteria for identifying a potential major trauma case include a list of particular fractures (e.g. pelvis, femur and tibia), and mechanistic criteria with a focus on transport-related incidents. Forty-five per cent of VOTOR cases at the MTS hospitals were transport-related, compared to 14% at the Geelong and Northern hospitals, reflecting the triage guidelines for the VSTS (Table 9).


Table 9: Distribution of transport-related VOTOR cases by hospital Descriptor Non-transport

n (%) Transport n (%)

The Alfred 4271 (55) 3541 (45) RMH 3250 (55) 2609 (45) Geelong 3620 (86) 581 (14) Northern 2183 (86) 350 (14)

While motor vehicle crashes accounted for almost half of the transport-related cases at The Alfred (44%) and RMH (42%), motorcycle crashes were the most prevalent cause of transport-related injury at Geelong (34%) and the Northern (56%) hospitals. More than one fifth (22%) of Geelong transport-related cases were the pedal cyclists compared to 13% at The Alfred and 11% at RMH.

Demographic profile of cases by transport status The demographic profile of transport and non-transport related cases are shown in Table 10. Transport-related cases were younger, more commonly male, and were marginally healthier prior to injury (as measured by the CCI) (Table 10). Seventy-five per cent of transport-related cases were confirmed TAC compensable, compared to less than 1% of non-transport cases which were predominantly coded to falls (Table 10).

Table 10: Demographic profile of VOTOR cases over time Descriptor Non-transport Transport Gender n (%)

Male Female

6133 (46) 7191 (54)

5085 (72) 1996 (28)

Age Mean (SD) years 61.8 (22.6) 41.1 (18.5) Comorbid status n (%)

None CCI=1 CCI 2

9474 (71) 2507 (19) 1343 (10)

5368 (76) 1402 (20) 311 (4)

Fund status n (%) Medicare/non-compensable TAC Private insurance/DVA WorkCover/other compensable

9935 (75) 47 (


Table 11: Injury profile of VOTOR cases by transport status Descriptor Non-transport Transport Orthopaedic n (%)

Isolated LEa

Isolated UEb


6235 (47) 3038 (23) 1390 (10) 781 (6) 386 (3) 604 (5) 214 (2) 374 (3) 239 (2) 63 (


Predictors of in-hospital outcomes

In-hospital mortality A multivariate logistic regression model was generated to evaluate predictors of in-hospital mortality. The model was adjusted for age, gender, comorbid status (CCI), injuries sustained, fund source, and injury mechanism (transport-related or not). The results are shown in Table 13. The odds of mortality were lower for women relative to men, and those with isolated upper extremity fractures relative to cases with isolated lower extremity fracture. The odds of mortality increased with age, more severe comorbidities, sustaining an associated head or intra-thoracic injury, and for cases involving spinal fractures only, and cases with both spinal and lower extremity fractures. There was no association between compensable status and mortality.

Table 13: Predictors of in-hospital mortality of VOTOR cases (multivariable analysis) Descriptor Adjusted odds ratio

(95% CI) Age 1.05 (1.04, 1.05) Gender Male (reference)

Female - 0.64 (0.51, 0.80)

Comorbid status None (reference) CCI=1 CCI 2

- 1.22 (0.92, 1.61) 5.38 (4.20, 6.88)

Transport status Non-transport (reference) Transport

- 0.61 (0.34, 1.09)

Fund source Not compensable (reference) TAC WorkCover/other compensable Private/DVA

- 1.19 (0.65, 2.20) 0.33 (0.08, 1.38) 1.10 (0.83, 1.45)

Orthopaedic injury Isolated LE (reference) Soft tissue Isolated UEb

Multiple UE Multiple LEa

UE and LE Spinal only Spine and UE Spine and LE Spine, UE and LE

- 0.98 (0.33, 2.94) 0.56 (0.39, 0.82) 0.58 (0.26, 1.29) 1.01 (0.58, 1.29) 1.05 (0.64, 1.74) 1.41 (1.04, 1.90) 0.31 (0.14, 0.66) 1.79 (1.07, 3.00) 1.43 (0.78, 2.65)

Head injury No (reference) Yes

- 8.05 (6.02, 10.74)

Intra-thoracic injury No (reference) Yes

- 3.10 (2.05, 4.70)

Multiple rib fractures No (reference) Yes

- 0.87 (0.60, 1.27)

Intra-abdominal injury No (reference) Yes

- 1.02 (0.65, 1.58)



Predictors of discharge destination A multivariate, multinomial regression model was generated to evaluate predictors of discharge destination (home, rehabilitation or other). For this model, the in-hospital deaths were excluded and the model was adjusted for age, gender, comorbid status (CCI), injuries sustained, transport status, fund source, pre-injury disability. Compared to patients discharged directly home, cases discharged to inpatient rehabilitation were more likely to be older, female, have documented comorbidities, pre-existing disability, funded by TAC, WorkCover or private insurance, head injured, and involving fractures to multiple regions (Table 14).

Table 14: Predictors of discharge destination of VOTOR cases (multivariable analysis) Descriptor Rehabilitation vs.

discharge to home RRR (95% CI)

Other destination vs. discharge home RRR (95% CI)

Age 1.049 (1.046, 1.052) 1.049 (1.046, 1.052) Gender Male (reference)

Female - 1.56 (1.42, 1.71)

- 1.30 (1.16, 1.45)


- 1.81 (1.62, 2.01) 2.89 (2.43, 3.42)

- 1.53 (1.35, 1.74) 2.74 (2.29, 3.27)

Pre-injury disability None (reference) Mild Moderate to severe

- 1.64 (1.42, 1.89) 1.42 (1.23, 1.65)

- 1.65 (1.42, 1.91) 1.60 (1.39, 1.83)

Transport status Non-transport (reference) Transport

- 1.09 (0.92, 1.29)

- 1.10 (0.89, 1.37)


- 2.68 (2.25, 3.20) 1.62 (1.27, 2.07) 1.25 (1.09, 1.43)

- 1.09 (0.85, 1.41) 0.27 (0.15, 0.50) 1.17 (1.03, 1.33)

Orthopaedic injury Isolated LEa (reference) Soft tissue Isolated UEb

Multiple UE Multiple LE

UE and LE Spinal only Spine and UE Spine and LE Spine, UE and LE

- 0.17 (0.12, 0.24) 0.20 (0.17, 0.23) 0.40 (0.31, 0.52) 1.45 (1.23, 1.71) 3.54 (2.91, 4.31) 0.49 (0.43, 0.55) 0.65 (0.52, 0.81) 2.60 (2.10, 3.21) 6.36 (4.42, 9.17)

- 0.49 (0.36, 0.67) 0.34 (0.30, 0.39) 0.49 (0.36, 0.67) 0.87 (0.70, 1.07) 2.37 (1.83, 3.07) 0.57 (0.48, 0.69) 0.71 (0.45, 1.11) 1.75 (1.16, 2.64) 3.73 (1.90, 7.33)


- 4.31 (3.65, 5.09)

- 2.08 (1.47, 2.94)


- 1.80 (1.53, 2.11)

- 1.13 (0.76, 1.67)

Multiple rib fractures

No (reference) Yes

- 0.84 (0.72, 0.99)

- 0.58 (0.42, 0.80)

Intra-abdominal injury

No (reference) Yes

- 2.28 (1.89, 2.76)

- 1.61 (1.07, 2.44)



Patients discharged to an alternative destination were more likely to be older, female, have documented comorbidities and pre-existing disability. However, cases discharged to alternative destinations (e.g. hospital for convalescence or aged care facility) were less likely to be TAC or WorkCover compensable (Table 14).


Long term outcomes

Follow-up rates Of the 20,728 VOTOR cases registered to the 30th June 2011, 19,431 were eligible for 6-month follow-up and 16,484 were eligible for 12-month follow-up. The follow-up rate at 6-months was 87% (n=16949) and 89% (n=14625) at 12-months.

Figure 5: Follow-up rates over time

The follow-up rates consistently exceeded 85% for all years and time points except for the 6-month time point in 2007-08 (Figure 5). The reason for the low 6-month follow-up rate in 2007-08 was the delay in receiving data from the Northern hospital in time for this follow-up, with the Northern follow-up rate only 67% for 2007-08 compared to 94% at Geelong, 90% at The Alfred and 87% at RMH. The follow-up rates remain highest for Geelong and The Alfred hospitals.

Figure 6: Follow-up rates by participating hospital


Outcomes by transport status

GOS-E The Glasgow Outcome Scale Extended (GOS-E) is a measure of functional outcome following injury. The GOS-E rates the function of the patient into eight categories ranging from death (GOS-E=1) to upper good recovery (GOS-E=8). A rating of upper good recovery represents complete recovery from injury, while a score of five or above represents a return to independent living.

Valid GOS-E scores were available for 17,759 at 6-months and 15,557 at 12-months. Non-transport cases reported good recovery in 39% of cases at 6-months which increased to 43% by 12-months. Transport cases in contrast reported greater moderate disability (6-months: 54%; 12-months: 12%) and severe disability (6-months: 48%; 12-months: 9%) levels than the non-transport cases.

Table 15: Functional outcomes of VOTOR patients Descriptor Non-transport


Total n (%)

6-months Death Vegetative state Lower severe disability Upper severe disability Lower moderate disability Upper moderate disability Lower good recovery Upper good recovery

787 (7) 35 (


Return to work Just over half (n=9455, 54%) of VOTOR cases with a known pre-injury work status were working prior to injury. The return to work rate at 6 and 12-months was higher for non-transport-related cases. Of the cases returning to work, 90% of non-transport and 88% of transport cases had returned to their pre-injury workplace at 6-months. Of those returning to their pre-injury workplace, 94% of non-transport and 90% of transport cases had returned to the same role within the organisation. By 12-months post-injury, only 85% of non-transport and 80% of transport-related cases who had returned to work were working at their pre-injury place of employment. Of those who had returned to their pre-injury place of employment, 94% of non-transport and 91% of transport-related cases had returned to the same role within the organisation.

Table 16: Return to work outcomes of VOTOR patients Descriptor Non-transport


Total n (%)

6-months No Yes

740 (19) 3113 (81)

1500 (35) 2750 (65)

2240 (28) 5863 (72)

12-months No Yes

524 (16) 2857 (85)

1052 (28) 2676 (72)

1576 (22) 5533 (78)

Health-related quality of life (HRQoL) Two measures of HRQoL are collected by VOTOR: (i) the SF-12; and (ii) the EQ-5D. The EQ-5D was not added to the protocol until 2009 and, therefore, the number of cases with an EQ-5D score is much lower than for the SF-12.

SF-12

Valid SF-12 scores were recorded for 10,332 cases at 6-months (66% of survivors at 6-months), reflecting the proportion of cases with associated head injury and the high prevalence of elderly cases in the VOTOR dataset. A valid SF-12 score were only able to be collected for 18% of cases aged 85 years and over.

Table 17: SF-12 scores of VOTOR patients Descriptor Non-transport

Mean (SD) Transport Mean (SD)

Total Mean (SD)

6-months MCS-12 PCS-12

52.9 (10.4) 42.0 (11.8)

50.3 (11.6) 40.4 (12.1)

51.8 (11.0) 41.3 (11.9)

12-months MCS-12 PCS-12

52.8 (10.2) 44.2 (12.1)

49.9 (11.5) 42.5 (12.2)

51.6 (10.9) 43.4 (12.2)

Figure 7 shows the standardised mean difference (SMD) of the PCS-12 and MCS-12 scores by transport status at 6 and 12-months post-injury. The SMD provides a method of showing the degree of deviation from the population norm by standardising individual scores by age and gender. An SMD of zero suggests no difference to population norms and a score above zero suggests SF-12 summary scores above population norms. The SMD for the physical and mental health scores of all VOTOR cases were substantially below the population norms at 6 and 12-months, with the transport-related cases showing scores markedly below the non-transport cases and the population norms. Only the MCS-12 for the non-transport cases was close to the zero line, indicating scores near population standards. The physical and mental health of transport and non-transport-related cases improved at 12-months compared to 6-months (Figure 7).


Figure 7: Standardised Mean Difference SF-12 scores

EQ-5D

The EQ-5D is a generic measure of HRQoL. The instrument includes five items relating to mobility, usual activities, self-care, pain/discomfort, and anxiety/depression, each with three levels of response. The EQ-5D also includes a visual analogue scale (VAS) ranging from 0-100 with zero representing worst imaginable health state and 100 representing the best imaginable health state. The responses to each of the five items are combined with tariffs (based on UK population data) to provide a summary score with 0 representing a health state equivalent to death, 1 representing perfect health and a score less than zero representing health states worse than death.

Valid EQ-5D scores were recorded for 7,285 cases at 6-months, and 6,518 cases at 12-months, reflecting the late addition of the EQ-5D to the VOTOR follow-up protocol. A higher proportion of transport cases reported problems with usual activities, pain/discomfort and anxiety/depression at both 6-months and 12-months post-injury compared to non-transport related cases (Table 18). The EQ-5D VAS and summary scores were lower for transport cases at both time points indicating poorer recovery from their injuries. The VAS and summary scores did improve in both groups from 6- to 12-months demonstrating some recovery over time regardless of transport status (Table 18).


Table 18: EQ-5D outcome scores of VOTOR patients Descriptor Non-transport


Total n (%)

6-months Mobility None Moderate Severe

2468 (55) 1978 (44) 22 (


Pain A numerical rating scale (NRS) is used to collect information about pain at follow-up. The patient is asked to describe their pain at the time of interview on a scale from zero (no pain at all) to 10 (worst possible pain). A score of five or higher represents moderate to severe persistent pain.

Valid pain scores were recorded for 8,607 cases at 6-months and 7,199 cases at 12-months. Pain outcome scores are reported in Table 19. The mean pain score was significantly lower in non-transport related cases than transport cases (p4) was higher at both 6-months (25%) and 12-months (22%) compared to non-transport cases at both time points (19% and 18% respectively).

Table 19: Pain outcomes of VOTOR patients Descriptor Non-transport


Total n (%)

6-months Mean (SD) score None/Mild pain Moderate/Severe Pain

1.8 (2.6) 3996 (81) 942 (19)

2.4 (2.8) 2736 (75) 933 (25)

2.1 (2.8) 6732 (78) 1875 (22)

12-months Mean (SD) score None/Mild pain Moderate/Severe Pain

1.6 (2.6) 3376 (82) 724 (18)

2.1 (2.7) 2404 (78) 695 (22)

1.8 (2.7) 5780 (80) 1419 (20)

Outcomes by orthopaedic injury group

GOS-E The GOS-E scores are reported by injury groups at 6- and 12-months in Table 20. The percentage of cases reporting a full recovery at 6-months ranged from 7% in the group with injuries to the spine, upper and lower extremity, to 35% in the group with isolated upper extremity fractures. Post-discharge mortality by 6-months was most prevalent in the isolated lower extremity fracture group, reflecting the high proportion of hip fractures patients in this group.

By 12-months, the proportion of cases fully recovered ranged from 10% (spine, upper and lower extremity fractures) to 43% (soft tissue injury). Sixteen per cent of cases in the isolated lower extremity fracture group who survived to hospital discharge had died by 12-months post-injury.


Table 20: Functional outcomes of VOTOR patients by orthopaedic injury group Injury group Death

n (%) Vegetative State n (%)

Lower Severe Disability n (%)

Upper Sever Disability n (%)

Lower Moderate Disability n (%)

Upper Moderate Disability n (%)

Lower Good Recovery n (%)

Upper Good Recovery n (%)

6-months Soft tissue Isolated UEb

Multiple UE

Isolated LEa

Multiple LE UE and LE Spinal only Spine and UE Spine and LE Spine, UE and LE

15 (2) 130 (4) 15 (3) 756 (11) 44 (4) 59 (7) 195 (8) 19 (3) 33 (5) 23 (7)

0 5 (


Return to work The return to work rate at 6 and 12-months was highest for soft tissue and isolated upper extremity injury cases (Table 21). Return to work rates were lowest for cases with a combination of spinal, upper and lower extremity injuries at both 6-months (33%) and 12-months (52%). For cases returning to work, those that had returned to their pre-injury workplace ranged between 88% for isolated lower extremity, spinal only and spine, upper and lower extremity injuries to 92% for multiple upper extremity, spine and upper extremity and multiple rib fractures at 6-months. Of those returning to their pre-injury work place, return to the same role within the organisation ranged between 86% for spinal and lower extremity cases and 96% for soft tissue and isolated upper extremity cases. By 12-months, post-injury cases who had returned to work, pre-injury place of employment return rates ranged between 71% for spinal, upper and lower extremity cases and 85% of isolated and multiple upper extremity. Of those who had returned to their pre-injury place of employment, returning to the same role within the organisation ranged between 77% for spinal, upper and lower extremity cases and 96% for isolated upper extremity cases.

Table 21: Return to work outcomes of VOTOR patients by orthopaedic injury group Injury group 6-month

n (%) 12-months n (%)

Soft tissue Isolated UEa

Multiple UE Isolated LEb


441 (85) 1652 (87) 276 (79) 1785 (74) 486 (63) 240 (52) 876 (72) 243 (63) 191 (49) 67 (33)

409 (90) 1458 (87) 241 (84) 1700 (80) 488 (73) 245 (61) 802 (76) 232 (70) 223 (63) 92 (52)

a UE, upper extremity; b LE, lower extremity


Health-related quality of life (HRQoL) SF-12

The mean (SD) MCS-12 and PCS-12 scores for 6- and 12-months are reported in Table 22. The mean physical health (PCS-12) scores were lowest in cases with spinal, upper extremity and lower extremity and highest in cases with soft tissue injuries at both 6- and 12-months (Table 22). The mental health (MCS-12) scores demonstrated a similar pattern.

Table 22: SF-12 scores of VOTOR patients by orthopaedic injury group Injury group 6-months

MCS-12 Mean (SD)

6-months PCS-12 Mean (SD)

12-months MCS-12 Mean (SD)

12-months PCS-12 Mean (SD)




52.7 (10.5) 52.7 (10.3) 51.8 (10.9) 52.4 (10.7) 52.0 (11.3) 50.9 (11.5) 50.6 (11.4) 50.8 (11.9) 49.4 (11.7) 48.7 (12.4)

45.4 (11.0) 45.4 (11.1) 42.3 (11.1) 40.0 (11.9) 38.8 (11.8) 37.4 (11.6) 41.1 (12.0) 41.1 (11.7) 37.8 (11.8) 34.3 (11.5)

51.6 (10.6) 52.9 (9.9) 52.4 (10.3) 52.1 (10.7) 51.6 (10.8) 50.4 (11.5) 50.3 (11.6) 50.6 (11.0) 47.7 (12.3) 47.9 (12.1)

48.0 (11.2) 47.1 (11.3) 44.9 (11.1) 42.5 (12.1) 41.3 (12.5) 40.1 (12.1) 42.5 (12.1) 43.7 (11.5) 39.0 (12.4) 36.8 (12.3)


Figure 8 shows the standardised mean difference (SMD) of the PCS-12 and MCS-12 scores by injury group at 6 and 12-months post-injury. The SMD for the physical and mental health scores of most VOTOR cases were below the population norms at 6 and 12-months. Only the MCS-12 for the isolated upper extremity and soft tissue cases was close to the zero line, indicating scores near population standards. Improvement from 6 to 12-months was evident, particularly with respect to physical health scores (Figure 8). The PCS-12 scores were lowest where multiple injuries, and injuries of multiple regions, were sustained. Consistent with other outcomes, cases combined spinal and extremity fractures (upper and lower limb) demonstrated the poorest physical and mental health scores.


Figure 8: Standardised Mean Difference SF-12 scores by orthoapedic injury group


EQ-5D

Mobility

The proportion of cases with moderate/severe mobility problems ranged from 16-66% at 6-months, and 15-59% at 12-months. Mobility issues were highest for cases involving lower extremity fracture (Figure 9). There was improvement in mobility from 6- to 12-months for all injury groups except the group sustaining spinal, upper extremity and lower extremity fractures, and those with isolated upper fractures.

Figure 9: EQ-5D Mobility outcomes for VOTOR patients by injury group

Self-care

The proportion of cases with moderate/severe problems with self-care ranged from 7-30% at 6-months, and 6-32% at 12-months. Self-care issues were highest for cases involving multiple upper extremity fractures and cases involving injuries to multiple regions (Figure 10). Improvement for all injury groups was evident from 6 to 12-months except for the group with fractures of the spine, upper and lower extremity fractures for whom almost a third continued to report problems with self-care at 12-months.


Figure 10: EQ-5D self-care outcomes for VOTOR patients by injury group

Usual activities

The proportion of cases with moderate/severe problems with usual activities ranged from 43-78% at 6-months, and 33-71% at 12-months. Usual activities difficulties were highest for cases involving injuries to multiple regions (Figure 11). Improvement for all injury groups was evident from 6 to 12-months (Figure 11).

Figure 11: EQ-5D usual activities outcomes for VOTOR patients by injury group


Pain/discomfort

The proportion of cases with moderate/severe pain/discomfort ranged from 46-77% at 6-months, and 36-74% at 12-months. Moderate/severe pain/discomfort was most prevalent in cases involving lower extremity and spine fractures (Figure 12). Improvement for all injury groups was evident from 6 to 12-months, except for the group with upper and lower extremity fractures. Most improvement was noted in the soft tissue and upper extremity injury groups (Figure 12).

Figure 12: EQ-5D pain and discomfort outcomes for VOTOR patients by injury group

Anxiety/depression

The proportion of cases reporting problems with anxiety/depression ranged from 24-45% at 6-months, and 24-53% at 12-months. Unlike the other items of the EQ-5D, there was little decrease in the prevalence of anxiety/depression from 6 to 12-months, with 53% of the group with spine, upper and lower extremity fractures reporting problems with this item at 12-months compared to 41% at 6-months (Figure 13).

Figure 13: EQ-5D pain and discomfort outcomes for VOTOR patients by injury group


EQ-5D VAS and summary scores

Table 23 shows the EQ-5D summary and VAS scores by orthopaedic injury group at 6 and 12-months post-injury. There was improvement for most injury groups from 6 to 12-months, although the group with spine, upper and lower extremity fractures demonstrated a decrease in health from 6 to 12-months (Table 23).

Table 23: EQ-5D VAS scores and summary scores of VOTOR patients by orthopaedic injury group

Injury group 6-months EQ-5D VAS Mean (SD)

6-months Score Mean (SD)

12-months EQ-5D VAS Mean (SD)

12-months Score Mean (SD)




78.4 (16.9) 78.5 (17.2) 76.9 (17.2) 74.1 (18.5) 72.2 (18.5) 70.4 (19.8) 72.8 (18.9) 73.1 (19.1) 68.7 (20.9) 66.2 (19.5)

0.79 (0.25) 0.77 (0.26) 0.70 (0.28) 0.69 (0.27) 0.66 (0.27) 0.63 (0.31) 0.68 (0.29) 0.69 (0.29) 0.58 (0.31) 0.58 (0.32)

80.4 (15.6) 80.1 (18.2) 76.8 (19.0) 75.7 (19.1) 74.4 (19.3) 70.3 (21.1) 73.8 (21.0) 74.5 (18.8) 70.1 (22.2) 65.7 (24.3)

0.81 (0.27) 0.80 (0.26) 0.75 (0.27) 0.72 (0.28) 0.69 (0.29) 0.67 (0.29) 0.71 (0.30) 0.73 (0.27) 0.63 (0.33) 0.56 (0.35)


Pain Pain outcomes, as measured by the NRS for pain, by orthopaedic group are reported in Figure 14. Mean pain rating scores were lowest in soft tissue injury cases at both 6- and 12-months, with the highest mean pain scores reported for cases with spine, upper extremity and lower extremity injuries. The greatest proportion of cases with moderate/severe pain at 6-months was identified in the spine, upper extremity and lower extremity group (36%), while at 12-months it was for the spine and lower extremity group (32%). A reduction in pain was evident for all injury groups.

Figure 14: 6-month and 12-month pain outcomes and mean pain scores by injury group.


Predictors of functional outcome

Multivariable models were fitted for the following outcomes: (i) GOS-E at 6 months; (ii) GOS-E at 12-months; (iii) return to work at 6-months; (iv) return to work at 12-months; (v) moderate/severe pain at 6-months; (vi) moderate/severe pain at 12-months; (vii) PCS-12 at 6-months; (viii) PCS-12 at 12-months; (ix) MCS-12 at 6-months; and (x) MCS-12 at 12-months. Each model was fitted adjusting for age, gender, comorbid status, mechanism of injury, injuries sustained, fund source, pre-injury disability, level of education and working prior to injury (for the non-return to work models). Multiple imputation using chained equations was used to impute missing education, pre-injury disability and work status prior to injury responses. The GOS-E models were fitted using an ordinal logistic regression, while return to work and pain were fitted using logistic regression, and the SF-12 summary score models were fitted using linear regression.

GOS-E The result from the multivariate analysis of 6- and 12-month predictors of functional outcomes (GOS-E) is presented in Table 24. Adjusting for pre-injury disability and all other factors, increasing age, the presence of comorbidities, lower levels of education, injuries involving the upper and lower limb or upper limb, lower limb and spine, head injury and intra-thoracic injury were associated with lower odds of a better functional outcome at 6 and 12-months after injury. Relative to non-compensable cases, TAC and WorkCover cases demonstrated significantly lower odds of a better functional outcome, while privately insured cases demonstrated odds of improved functional outcome consistent with non-compensable cases. Pedal cyclists and those injured in a fall from height demonstrated significantly higher odds of a better functional outcome than cases injured in a low fall (Table 24).

Table 24: Predictors of functional outcome (GOS-E) (multivariable analysis) Descriptor 6-months

n=16,748 AOR (95% CI)

12-months n=14,617 AOR (95% CI)


Female - 0.98 (0.92, 1.04)

- 1.01 (0.94, 1.08)


- 0.54 (0.50, 0.58) 0.34 (0.30, 0.38)

- 0.52 (0.48, 0.56) 0.32 (0.28, 0.36)


- 0.41 (0.37, 0.45) 0.18 (0.17, 0.20)

- 0.47 (0.43, 0.52) 0.23 (0.21, 0.26)

Level of education University degree (reference) Advanced diploma/certificate Finished high school Year 9-11 or equivalent Year 8 or below

- 0.79 (0.71, 0.87) 0.76 (0.70, 0.85) 0.70 (0.64, 0.77) 0.60 (0.53, 0.68)

- 0.80 (0.72, 0.89) 0.73 (0.63, 0.84) 0.68 (0.61, 0.75) 0.59 (0.52, 0.68)

Working prior to injury

No (reference) Yes

- 0.63 (0.58, 0.69)

- 0.77 (0.70, 0.85)

Mechanism of injury

Low fall (reference) Motor vehicle Motorcycle Pedal cyclist Pedestrian

- 1.09 (0.95, 1.26) 1.07 (0.93, 1.23) 1.74 (1.50, 2.02) 1.12 (0.95, 1.33)

- 1.17 (1.00, 1.37) 1.14 (0.97, 1.33) 1.91 (1.62, 2.26) 1.12 (0.93, 1.35)


High fall Struck by/collision with Other

1.65 (1.49, 1.81) 1.16 (0.99, 1.36) 1.14 (1.01, 1.28)

1.66 (1.50, 1.84) 1.23 (1.03, 1.46) 1.23 (1.08, 1.39)


- 0.66 (0.58, 0.76) 0.61 (0.52, 0.72) 1.01 (0.93, 1.09)

- 0.61 (0.52, 0.71) 0.60 (0.50, 0.71) 0.98 (0.89, 1.07)

Orthopaedic injury Isolated LE (reference) Soft tissue Isolated UE Multiple UE Multiple LE UE and LE Spinal only Spine and UE Spine and LE Spine, UE and LE

- 1.54 (1.07, 2.20) 2.26 (2.10, 2.44) 1.38 (1.19, 1.61) 1.07 (0.96, 1.19) 0.84 (0.73, 0.95) 1.30 (1.19, 1.42) 1.18 (1.00, 1.38) 0.87 (0.74, 1.00) 0.64 (0.52, 0.78)

- 1.76 (1.20, 2.57) 1.99 (1.83, 2.16) 1.49 (1.26, 1.77) 1.07 (0.95, 1.21) 0.85 (0.74, 0.97) 1.13 (1.03, 1.24) 1.17 (0.98, 1.40) 0.87 (0.74, 1.02) 0.73 (0.58, 0.91)


- 0.66 (0.59, 0.74)

- 0.67 (0.59, 0.76)


- 0.83 (0.74, 0.94)

- 0.80 (0.71, 0.91)


No (reference) Yes

- 1.34 (1.19, 1.50)

- 1.39 (1.23, 1.57)


No (reference) Yes

- 0.91 (0.79, 1.03)

- 0.90 (0.78, 1.03)

Return to work The result from the multivariate analysis of 6- and 12-month predictors of return to work is presented in Table 25. Adjusting for pre-injury disability and all other factors, increasing age, the presence of comorbidity, head injured patients, TAC and WorkCover compensable, motor vehicle and pedestrian cases, and the groups with multiple regions injured involving the lower extremity, demonstrated lower odds of return to work at 6 and 12-months post-injury. Pedal cyclists and those with isolated upper extremity injuries demonstrated the highest odds of return to work.

Table 25: Predictors of return to work (multivariable analysis) Descriptor 6-months

n=8176 AOR (95% CI)

12-months n=7134 AOR (95% CI)


Female - 1.05 (0.92, 1.19)

- 0.99 (0.86, 1.15)


- 0.69 (0.60, 0.81) 0.45 (0.32, 0.64)

- 0.74 (0.63, 0.87) 0.54 (0.32, 0.65)


- 0.79 (0.63, 1.01) 0.43 (0.32, 0.58)

- 0.71 (0.55, 0.92) 0.36 (0.27, 0.50)



- 0.48 (0.41, 0.57) 0.57 (0.47, 0.70) 0.40 (0.34, 0.48) 0.27 (0.20, 0.35)

- 0.56 (0.46, 0.69) 0.66 (0.53, 0.84) 0.42 (0.34, 0.52) 0.24 (0.18, 0.33)

Mechanism of injury

Low fall (reference) Motor vehicle Motorcycle Pedal cyclist Pedestrian High fall Struck by/collision with Other

- 0.56 (0.43, 0.72) 0.81 (0.63, 1.03) 1.74 (1.27, 2.40) 0.60 (0.44, 0.82) 1.02 (0.82, 1.26) 0.74 (0.56, 0.99) 0.70 (0.56, 0.87)

- 0.56 (0.41, 0.75) 0.85 (0.63, 1.14) 1.36 (0.93, 1.99) 0.54 (0.38, 0.76) 0.76 (0.59, 0.98) 0.79 (0.56, 1.12) 0.67 (0.51, 0.85)


- 0.52 (0.40, 0.66) 0.53 (0.41, 0.69) 0.94 (0.77, 1.15)

- 0.50 (0.40, 0.63) 0.52 (0.41, 0.65) 1.09 (0.86, 1.39)


- 1.26 (0.72, 2.20) 2.06 (1.74, 2.45) 1.29 (0.96, 1.72) 0.70 (0.58, 0.84) 0.54 (0.43, 0.68) 1.24 (1.05, 1.47) 0.98 (0.76, 1.28) 0.54 (0.42, 0.69) 0.32 (0.23, 0.46)

- 1.64 (0.83, 3.27) 1.56 (1.29, 1.89) 1.35 (0.95, 1.92) 0.77 (0.62, 0.95) 0.56 (0.43, 0.71) 1.12 (0.92, 1.36) 0.96 (0.71, 1.28) 0.73 (0.55, 0.95) 0.56 (0.39, 0.80)


- 0.62 (0.51, 0.74)

- 0.67 (0.55, 0.82)


- 0.76 (0.63, 0.92)

- 0.71 (0.59, 0.88)


No (reference) Yes

- 1.24 (1.03, 1.49)

- 1.26 (1.03, 1.54)


No (reference) Yes

- 0.81 (0.66, 0.99)

- 0.85 (0.68, 1.06)

Moderate to severe pain The predictors of moderate/severe pain at 6- and 12-months are presented in Table 26. Only gender, compensable status and mechanism of injury were consistent predictors of persistent moderate/severe pain at 6 and 12-months post-injury. Women, TAC compensable and WorkCover compensable cases demonstrated significantly higher odds of reporting moderate to severe pain, while pedal cyclist patients were significantly less likely to report moderate to severe pain at follow-up.


Table 26: Predictors of persistent moderate/severe pain (multivariable analysis) Descriptor 6-months

n=8365 AOR (95% CI)

12-months n=6960 AOR (95% CI)


Female - 1.48 (1.31, 1.68)

- 1.32 (1.15, 1.52)


- 1.06 (0.92, 1.23) 1.18 (0.91, 1.53)

- 1.21 (1.03, 1.43) 1.10 ((0.82, 1.48)


- 1.60 (1.34, 1.90) 2.46 (1.05, 2.96)

- 1.47 (1.21, 1.80) 2.68 (2.18, 3.28)


- 1.42 (1.18, 1.72) 1.38 (1.12, 1.71) 1.72 (1.43, 2.06) 1.83 (1.46, 2.29)

- 1.47 (1.18, 1.83) 1.39 (1.08, 1.80) 1.57 (1.26, 1.95) 2.15 (1.65, 2.76)


No (reference) Yes

- 0.86 (0.74, 1.01)

- 0.83 (0.69, 0.99)

Mechanism of injury


- 1.54 (1.17, 2.02) 1.18 (0.89, 1.56) 0.67 (0.47, 0.95) 1.35 (0.97, 1.88) 1.19 (0.99, 1.44) 1.41 (1.03, 1.91) 1.42 (1.13, 1.78)

- 1.34 (0.99, 1.83) 0.95 (0.69, 1.30) 0.50 (0.33, 0.77) 1.50 (1.04, 2.16) 1.22 (0.99, 1.50) 1.14 (0.80, 1.63) 0.93 (0.71, 1.21)


- 1.84 (1.41, 2.40) 2.19 (1.64, 2.93) 1.06 (0.89, 1.26)

- 2.20 (1.61, 3.01) 2.30 (1.63, 3.23) 1.39 (1.12, 1.72)


- 0.82 (0.45, 1.50) 0.74 (0.64, 0.87) 0.83 (0.61, 1.14) 0.94 (0.76, 1.16) 1.16 (0.91, 1.49) 1.02 (0.86, 1.21) 1.10 (0.83, 1.47) 1.15 (0.89, 1.50) 1.43 (1.01, 2.05)

- 1.32 (0.71, 2.46) 0.85 (0.71, 1.01) 0.85 (0.59, 1.22) 1.26 (1.00, 1.58) 1.26 (0.95, 1.67) 1.35 (1.11, 1.64) 0.90 (0.63, 1.28) 1.66 (1.24, 2.23) 1.32 (0.87, 1.98)


- 0.96 (0.77, 1.19)

- 0.88 (0.69, 1.13)


- 1.19 (0.98, 1.46)

- 0.93 (0.74, 1.18)

Multiple rib No (reference) - -


fractures Yes 0.96 (0.78, 1.17) 1.19 (0.94, 1.50) Intra-abdominal injury

No (reference) Yes

- 1.10 (0.88, 1.38)

- 1.15 (0.89, 1.49)

Physical health (PCS-12) The predictors of physical health scores (PCS-12) are reported in Table 27. Where the 95% confidence intervals of the beta coefficients do not include zero, the result is statistically significant. However, a 2 to 3 point difference is considered clinically significant for population analyses. Adjusting for pre-injury disability and all other factors, higher comorbidity scores (CCI>1), TAC and WorkCover compensable, and patients with spine, upper and lower extremity injuries demonstrating statistically and clinically significantly lower physical health scores. Pedal cyclists, and isolated upper extremity fracture cases, demonstrated clinically and significantly higher physical health scores.

Table 27: Predictors of physical health scores (PCS-12) (multivariable analysis) Descriptor 6-months

n=9886 (95% CI)

12-months n=8165 (95% CI)

Age -0.07 (-0.09, -0.06) -0.08 (-0.10, -0.06) Gender Male (reference)

Female - -1.23 (-1.73, -0.73)

- -1.38 (-1.93, -0.82)


- -0.82 (-1.44, -0.20) -2.95 (-4.09, -1.80)

- -1.46 (-2.15, -0.77) -3.15 (-4.43, -1.86)


- -4.51 (-5.26, -3.77) -8.86 (-9.69, -8.04)

- -4.23 (-5.06, -3.39) -8.48 (-9.41, -7.55)


- -1.44 (-2.12, -0.77) 0.11 (-0.65, 0.86) -1.09 (-1.76, -0.42) -1.50 (-2.45, -0.55)

- -1.58 (-2.33, -0.82) -0.74 (-1.60, 0.12) -1.34 (-2.10, -0.57) -3.21 (-4.22, -2.19)


No (reference) Yes

- 0.74 (0.08, 1.39)

- 1.24 (0.51, 1.96)

Mechanism of injury


- -0.39 (-1.46, 0.68) -1.03 (-2.07, 0.01) 2.39 (1.36, 3.42) -1.95 (-3.30, -0.61) 0.46 (-0.26, 1.19) -0.09 (-1.26, 1.07) -0.22 (-1.09, 0.65)

- -0.31 (-1.50, 0.89) -0.33 (-1.48, 0.83) 3.59 (2.42, 4.75) -1.50 (-3.04, 0.04) 0.18 (-0.63, 1.00) 0.15 (-1.15, 1.46) -0.10 (-1.06, 0.86)


- -3.88 (-4.87, -2.88) -5.04 (-6.24, -3.84) 0.17 (-0.47, 0.81)

- -5.08 (-6.18, -3.98) -4.82 (-6.12, -3.52) -0.08 (-0.81, 0.66)

Orthopaedic injury Isolated LE (reference) Soft tissue

- 1.44 (-1.13, 4.02)

- 2.10 (-0.68, 4.88)


Isolated UE Multiple UE Multiple LE UE and LE Spinal only Spine and UE Spine and LE Spine, UE and LE

4.40 (3.81, 4.98) 1.08 (-0.07, 2.23) -1.60 (-2.43, -0.76) -1.92 (-2.96, -0.89) 1.14 (0.43, 1.85) 2.07 (-0.16, 2.29) -1.41 (-2.58, -0.24) -4.86 (-6.48, -3.22)

3.42 (2.77, 4.07) 1.01 (-0.28, 2.30) -1.50 (-2.42, -0.58) -1.40 (-2.56, -0.25) 0.20 (-0.59, 0.99) 1.15 (-0.23, 2.53) -2.27 (-3.57, -0.97) -3.92 (-5.72, -2.12)


- 1.11 (0.18, 2.04)

- 1.43 (0.39, 2.46)


- -0.25 (-1.12, 0.62)

- 0.35 (-0.63, 1.32)


No (reference) Yes

- -0.06 (-1.71, 0.34)

- -0.46 (-1.43, 0.52)


No (reference) Yes

- -0.68 (-1.71, 0.34)

- -1.25 (-2.38, -0.12)

Mental health (MCS-12) The predictors of mental health scores (MCS-12) are reported in Table 28. Where the 95% confidence intervals of the beta coefficients do not include zero, the result is statistically significant. However, a 2 to 3 point difference is considered clinically significant for population analyses. Adjusting for pre-injury disability and all other factors, only TAC and WorkCover compensable cases demonstrated statistically and clinically significant reductions in mental health scores at both time points (Table 28).

Table 28: Predictors of mental health scores (MCS-12) (multivariable analysis) Descriptor 6-months

n=9886 (95% CI)

12-months n=8165 (95% CI)


Female - -1.29 (-1.77, -0.80)

- -1.28 (-1.81, -0.75)


- -1.67 (-2.27, -1.06) -0.51 (-1.63, 0.60)

- -1.79 (-2.45, -1.14) 0.55 (-0.68, 1.77)


- -2.98 (-3.71, -2.26) -5.44 (-6.25, -4.64)

- -2.15 (-2.95, -1.35) -5.23 (-6.12, -4.34)


- -0.40 (-1.07, 0.26) 0.02 (-0.73, 0.76) -0.92 (-1.57, -0.26) -1.32 (-2.20, -0.44)

- -0.65 (-1.37, 0.08) -0.16 (-0.96, 0.65) -1.42 (-2.14, -0.71) -1.60 (-2.64, -0.56)


No (reference) Yes

- 1.24 (0.60, 1.88)

- 0.89 (0.20, 1.59)

Mechanism of injury

Low fall (reference) Motor vehicle

- -0.81 (-1.86, -0.25)

- -0.27 (-1.38, 0.85)


Motorcycle Pedal cyclist Pedestrian High fall Struck by/collision with Other

0.89 (-0.15, 1.92) 0.40 (-0.61, 1.41) -2.10 (-3.42, -0.77) 0.05 (-0.65, 0.75) -2.09 (-3.24, -0.94) -1.22 (-2.06, -0.37)

0.33 (-0.75, 1.42) -0.04 (-1.15, 1.06) -1.87 (-3.28, -0.46) -0.80 (-1.56, -0.04) -1.59 (-2.83, -0.36) -0.85 (-1.76, 0.06)


- -4.13 (-5.11, -3.14) -3.50 (-4.66, -2.33) -1.21 (-1.84, -0.58)

- -4.31 (-5.35, -3.26) -4.39 (-5.63, -3.16) -0.93 (-1.62, -0.23)


- -1.71 (-4.21, 0.81) 0.05 (-0.52, 0.62) -0.85 (-1.97, 0.27) 0.14 (-0.68, 0.95) -0.50 (-1.51, 0.51) -0.91 (-1.60, -0.23) -0.51 (-1.70, 0.68) -1.00 (-2.14, 0.14) -1.34 (-2.93, 0.25)

- -3.07 (-5.72, -0.43) 0.49 (-0.13, 1.11) 0.27 (-0.96, 1.50) 0.18 (-0.70, 1.05) -0.28 (-1.38, 0.82) -0.80 (-1.55, -0.05) 0.18 (-1.14, 1.49) -1.88 (-3.11, -0.64) -1.03 (-2.74, 0.69)


- -1.11 (-2.01, -0.20)

- -1.36 (-2.35, -0.37)


- 0.43 (-0.42, 1.28)

- 0.78 (-0.15, 1.71)


No (reference) Yes

- -0.49 (-1.34, 0.35)

- -1.53 (-2.46, -0.60)


No (reference) Yes

- -0.65 (-1.65, 0.35)

- -1.33 (-2.41, -0.26)


SUMMARY OF FINDINGS

Overview Since March 2007, 20,728 cases have been registered at the four participating VOTOR hospitals. The majority were male (55%) with a mean (SD) age of 54.6 (23.4) years. The prevalence of documented comorbidities was low (27%) suggesting a relatively healthy population. A low fall (41%) was the most common mechanism of injury with transport-related cases accounting for 33% of cases. Isolated lower extremity (39%) and isolated upper extremity (21%) fractures were the most common orthopaedic injuries sustained. Ten per cent of cases required an admission to ICU, the in-hospital mortality rate was 2%, and the median (IQR) length of stay was 5.0 (2.6-9.8) days. Sixty per cent of cases were discharged directly home from hospital.

Predictors of in-hospital mortality The odds of mortality were lower for women relative to men, and those with isolated upper extremity fractures relative to cases with isolated lower extremity fracture. The odds of mortality increased with age, more severe comorbidities, sustaining an associated head or intra-thoracic injury, and for cases involving spinal fractures only, and cases with both spinal and lower extremity fractures. There was no association between compensable status and mortality.

Predictors of discharge destination Compared to patients discharged directly home, cases discharged to inpatient rehabilitation were more likely to be older, female, have documented comorbidities, pre-existing disability, funded by TAC, WorkCover or private insurance, head injured, and involving fractures to multiple regions

Transport vs. Non-transport related cases Forty-five per cent of VOTOR cases at the MTS hospitals were transport-related, compared to 14% at the Geelong and Northern hospitals, reflecting the triage guidelines for the VSTS. Transport-related cases were younger, more commonly male and healthier prior to injury. The pattern of injuries sustained differed also with transport-related cases involving injuries to multiple orthopaedic regions and a higher prevalence of associated non-orthopaedic injuries. Despite the greater injury severity and complexity, including a higher ICU admission rate, the overall in-hospital mortality was comparable between transport and non-transport-related cases. The long term outcomes of transport-related cases were generally poorer. After adjustment for differences in the profile of patients and compensable status, it was evident that compensable status was a stronger predictor of poorer outcome than the mechanism of injury.

Groups at high risk of poor long term outcome There was considerable consistency in the predictors of outcome across the outcome measures at 6 and 12-months post-injury. The key predictors of poorer outcome were TAC or WorkCover compensable status, increasing age, female gender, the presence of pre-existing medical conditions, the presence of head or intra-thoracic injury, lower levels of education, and sustaining injuries to multiple orthopaedic regions. In contrast, cases with soft tissue injury only, isolated upper extremity fractures, pedal cyclists demonstrated consistently better risk-adjusted long term outcomes. The results of these models will be important for the next stage of analysis, where VOTOR and TAC claims data will be linked to develop early prognostic models for poor outcome and high cost claims.


APPENDIX A

Victorian Orthopaedic Trauma Outcomes Registry Steering Committee Professor Peter Cameron

Head of Critical Care Research Division, School of Public Health and Preventive Medicine, Monash University. Academic Director of Emergency and Trauma Care, The Alfred Hospital

Associate Professor Belinda Gabbe

Head, Emergency and Trauma Research, Department of Epidemiology and Preventive Medicine, Monash University

Mr Cameron Gosling

Research Fellow, Department of Epidemiology and Preventive Medicine, Monash University

Ms Melissa Hart

Project Coordinator, Department of Epidemiology and Preventive Medicine, Monash University

Ms Ann Sutherland

Research Nurse, Department of Epidemiology and Preventive Medicine, Monash University

Associate Professor Elton Edwards

Consultant Orthopaedic Surgeon, The Alfred

Ms Sue Liew

Head of Orthopaedic Surgery, The Alfred

Associate Professor Andrew Bucknill

Director of Orthopaedics, The Royal Melbourne Hospital

Professor Richard de Steiger

Consultant Orthopaedic Surgeon. Chairman of Musculoskeletal Clinical Institute, The Epworth Hospital

Associate Professor Martin Richardson

Consultant Orthopaedic Surgeon, Epworth Hospital

Mr David Bainbridge

Head of Orthopaedics, The Geelong Hospital

Associate Professor Richard Page

Consultant Orthopaedic Surgeon, The Geelong Hospital Director, Barwon Orthopaedic Research Unit and Fellowship Programme

Mr Raphael Hau

Head of Orthopaedics, The Northern Hospital


APPENDIX B

Publications and Presentations Accepted for publication

1. Dowrick AS, Gabbe BJ, Williamson OD, Cameron PA. Outcome instruments for the assessment of the upper extremity following trauma: a review. Injury 2005; 36: 468-476.

2. Dowrick AS, Gabbe BJ, Williamson OD. Does the presence of an upper extremity injury affect outcomes following major trauma? Journal of Trauma 2005; 58: 1175-1178.

3. Edwards ER, Graves SE, McNeil JJ, Williamson OD, Urquhart DM, Cicuttini FM. Orthopaedic trauma: Establishment of an outcomes registry to evaluate and monitor treatment effectiveness. Injury 2006; 37: 95-96.

4. Urquhart DM, Edwards ER, Graves SE, Williamson OD, McNeil JJ, Kossmann T, Richardson MD, Harrison DJ, Hart MJ, Cicuttini FM. Characterisation of orthopaedic trauma admitted to Adult Level 1 Trauma Centres. Injury 2006; 37: 120-127.

5. Dowrick AS, Gabbe BJ, Williamson OD, Cameron PA. Does the disabilities of the arm, shoulder and hand (DASH) scoring system only measure disability due to injuries to the upper limb? Journal of Bone and Joint Surgery Britain 2006; 88B: 524-527.

6. Urquhart DM, Williamson OD, Gabbe BJ, Cicuttini FM, Cameron PA, Richardson MD, Edwards ER. Outcomes of patients with orthopaedic trauma admitted to Level One Trauma Centres. Australian and New Zealand Journal of Surgery 2006; 76: 600-606.

7. Gabbe BJ, Cameron PA, Graves SE, Edwards ER. Pre-injury status: Are orthopaedic trauma patients different to the general population? Journal of Orthopaedic Trauma 2007;21(4): 223-228

8. Dowrick AS, Gabbe BJ, Williamson OD, Wolfe R, Cameron PA. A comparison of self-reported and independently observed disability in an orthopaedic trauma population. J Trauma. 2006; 61: 447-52

9. Gabbe BJ, Williamson OD, Edwards ER, Graves SE, Richardson MD, Cameron PA. The relationship between compensable status and long term outcomes following orthopaedic trauma. Medical Journal of Australia 2007; 187(1): 14-17.

10. Wall C, Richardson M, Lowe A, Brand C, Lynch C, De Steiger R. Survey of Management of Acute Traumatic Compartment Syndrome of the Leg in Australia. ANZ Journal of Surgery. 77(9):733-737, September 2007

11. Ferguson M, Brand C, Lowe A, Gabbe B, Dowrick A, Hart M, Richardson M. Outcomes of Isolated Tibial Shaft Fractures Treated at Level 1 Trauma Centres. Injury 2008; 39(2):187-195

12. Dowrick AS. Investigating the use of patient-reported outcome assessment instruments in patients following upper extremity orthopaedic trauma. Thesis submitted and fulfilled for the degree of Doctor of Philosophy, Monash University, June 2008

13. Andrew NE, Gabbe BJ, Wolfe R, Williamson OD, Richardson MD, Edwards ER, Cameron PA. 12-month outcomes of serious orthopaedic sport and active recreation related injuries admitted to Level 1 trauma centres in Melbourne, Australia. Clinical Journal of Sports Medicine 2008;18(5): 387-393

14. Urquhart DM, Hanna F, Graves SE, Wang Y, Cameron PA, Hannaford A, Cicuttini FM. In-Hospital Outcomes and Hospital Resource Utilization of Hip Replacement Procedures. Australian and New Zealand Journal of Surgery. 2008; 78:1-6


15. Williamson OD, Gabbe BJ, Cameron PA, Edwards ER, Richardson MD on behalf of VOTOR. Predicting persisting pain following orthopaedic injury: a prospective cohort study. J Orthop Trauma 2009; 23:139-144

16. Yang Z, Lowe AJ, de la Harpe DE, Richardson MD. Factors that predict poor outcomes in patients with traumatic vertebral body fractures. Injury 2010:41(2):226-230

17. Abadollahi S, de Steiger R, Gruen R, Richardson MR. Management guideline in haemodynamically unstable patients with pelvic fractures: Outcomes and challenges. Emergency Medicine Australasia. 2011;22(6):556-564.

18. Gabbe BJ, Sutherland AM, Hart MJ, Cameron PA. Population based capture of long term functional and quality of life outcomes after major trauma: The experiences of the Victorian State Trauma Registry. Journal of Trauma 2010; 69(3):532-536

19. Williamson OD, Gabbe BJ, Forbes A, Wolfe R, Sutherland AM, Cameron PA. Comparing the responsiveness of functional outcome assessment instruments for trauma registries. In press Journal of Trauma 2011; 71(1): 63-68

20. Gabbe BJ, de Steiger R, Esser M, Bucknill A, Russ M, Cameron PA. Predictors of mortality following severe pelvic ring fractures: Results of a population based study. Injury 2011; 42(10): 985-991

21. Gosling CM, Gabbe BJ, Williamson OD, Sutherland AM, Cameron PA. Validity of outcome measures used to assess on and six month outcomes in orthopaedic trauma patients. Australasian Epidemiologist. 2011;18(3):15-19.

22. Gabbe BJ, Lyons RA, Sutherland AM, Hart MJ, Cameron PA. Level of agreement between patient and proxy responses to the EQ-5D Health Questionnaire 12 months following injury. Journal of Trauma (Accepted for publication)

23. Liu DS, Sofiadellis F, Ashton M, MacGill K, Webb A. Early soft tissue coverage and negative pressure wound therapy optimizes patient outcomes in lower limb trauma. Injury (Accepted for publication).

24. Andrew NA, Wolfe R, Cameron PA, Richardson MA, Page R, Bucknill A, Gabbe BJ. Return to pre-injury health status and function 12-months after hospitalization for sport and active recreation related orthopaedic injury. Injury Prevention (Accepted for publication pending minor revisions).

25. Kimmel LA, Holland AE, Edwards ER, Cameron PA, de Steiger R, Page R, Gabbe BJ. Discharge destination following lower limb fracture: Development of a prediction model to assist with decision making. Injury (Accepted for publication).

26. Gabbe BJ, Harrison JE, Lyons RA, Jolley D. Modelling long term disability following injury: A comparison of three approaches for handling multiple injuries. PLoS One 2011;6(9):e25862.

Published abstracts 1. Urquhart D, Edwards E, Graves S. Establishment of the Victorian Orthopaedic Trauma

Outcomes Registry (VOTOR). Proceedings of the Australasian Trauma Society Meeting, Adelaide, 2003.

2. Dowrick A, Williamson O, Gabbe B, McLellan S, White S, Collins L, Finch C. Upper extremity orthopaedic injury associated with major trauma in Victoria. Proceedings of the Australasian Trauma Society Meeting, Adelaide, 2003.

3. Edwards ER, Graves SE, Urquhart DM. Establishment of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR). Proceedings of the 11th Meeting of the Combined Orthopaedic Associations, Sydney, 2004.


4. Gabbe B, Urquhart D, Dowrick A. Establishment of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR). Proceedings of the 7th Australian Injury Prevention Conference, Mackay, 2004.

5. Dowrick AS, Gabbe BJ, Williamson OD, Cameron PA. A comparison of the Short Musculoskeletal Functional Assessment (SMFA) and the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire in orthopaedic trauma. Alfred Research Week, Melbourne, 2004.

6. Dowrick AS, Gabbe BJ, Williamson OD, Cameron PA. Self-report of disability in an orthopaedic trauma population using the Short Musculoskeletal Functional Assessment (SMFA) and the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire in orthopaedic trauma. Alfred Research Week, Melbourne, 2004.

7. Dowrick AS, Gabbe BJ, Williamson OD, Cameron PA. Self-report

007 votor analysis report

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