Conceived and designed the experiments: BJG JEH RAL DJ. Performed the experiments: BJG DJ. Analyzed the data: BJG DJ. Wrote the paper: BJG JEH RAL DJ.
The authors have declared that no competing interests exist.
Injury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare approaches to modelling the impact of multiple injury health states on disability as measured by the Glasgow Outcome Scale – Extended (GOS-E).
12-month functional outcomes for 11,337 survivors to hospital discharge were drawn from the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry. ICD-10 diagnosis codes were mapped to the GBD 2010 injury health states. Cases with a GOS-E score >6 were defined as “recovered.” A split dataset approach was used. Cases were randomly assigned to development or test datasets. Probability of recovery for each health state was calculated using the development dataset. Three logistic regression models were evaluated: a) additive, multivariable; b) “worst injury;” and c) multiplicative. Models were adjusted for age and comorbidity and investigated for discrimination and calibration.
A single injury health state was recorded for 46% of cases (1–16 health states per case). The additive (C-statistic 0.70, 95% CI: 0.69, 0.71) and “worst injury” (C-statistic 0.70; 95% CI: 0.68, 0.71) models demonstrated higher discrimination than the multiplicative (C-statistic 0.68; 95% CI: 0.67, 0.70) model. The additive and “worst injury” models demonstrated acceptable calibration.
The majority of patients survived with persisting disability at 12-months, highlighting the importance of improving estimates of non-fatal injury burden. Additive and “worst” injury models performed similarly. GBD 2010 injury states were moderately predictive of recovery 1-year post-injury. Further evaluation using additional measures of health status and functioning and comparison with the GBD 2010 disability weights will be needed to optimise injury states for future GBD studies.
The Global Burden of Disease (GBD) Study estimated the burden of injury based on selected injury health states
Limitations to the GBD Study methodology have been identified. Firstly, the number of health states was limited to 33, and the extent to which these combine injuries with different disability outcomes into a single injury health state was not evaluated. Secondly, durations of disability were derived from expert opinion, and disability weights from panel studies, rather than empirical data questioning the validity of these key elements of the YLD calculations. Thirdly, the approach ignored the potential impact of multiple injuries on disability estimates. The global burden of disease estimates are being updated in the GBD 2010 Study
It is common for more than one injury to occur in a single injury event and for multiple injuries to be ICD-coded for an admission. Two country-specific burden of injury studies have considered the presence of multiple injuries in their burden estimates
In contrast to the injury literature, a number of studies have evaluated methods for modelling the impact of co-occurring (comorbid) health conditions on health-related quality of life (HRQL), with varying results
The aims of this study were to: (i) establish the 12-month disability associated with each of the GBD 2010 injury health states; and (ii) compare approaches to modelling the impact of multiple injury health states on disability.
The Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry have been approved by the Human Research Ethics Committee at each participating hospital and the Monash University Human Research Ethics Committee.
Data from two large clinical registries were extracted for this project. The Victorian State Trauma Registry (VSTR) is a population-based trauma registry which captures data for all major trauma patients in the state of Victoria (population 5.4 million)
The registries use an opt-off consent process where all eligible cases are included on the registry, and patients (or their next of kin) are provided with a letter and a brochure stating the aims of the registry, the data collected, and that patients will be followed-up. The brochure provides the details for how to opt-off and the opt-off rate for both registries is less than 1%. At the follow-up interview, verbal consent to complete the interview is obtained. An opt-off consent is used due to the impracticability of informed consent, and the potential for selection bias, in the registry setting
All cases aged 15 years and over, and with a date of admission from 1 October 2006 to 30 June 2009 (inclusive), were extracted for analysis to correspond with the commencement of routine 12-month follow-up of VSTR patients. In-hospital deaths were excluded, as were the less than 1% of cases where the hospital did not provide ICD-10 diagnosis codes for the admission.
For all eligible cases, demographic details, comorbid status, injury event details, in-hospital outcomes, all International Classification of Diseases 10th Revision Australian modification (ICD-10-AM) diagnosis codes and the 12-month functional outcome of patients were extracted for analysis. The Charlson Comorbidity Index (CCI) was used as a measure of comorbid status and involves the weighting of 19 conditions to provide a single index of comorbid status
All adult (≥15 years) VSTR and VOTOR survivors to hospital discharge are followed-up at 6 and 12-months after injury using a standardised telephone interview to collect measures of functional and HRQL outcomes. The methodology for follow-up is published in detail elsewhere
Descriptive statistics including mean and standard deviation, or median and interquartile range, were used to summarise continuous variables. Categorical variables were summarised using case counts and percentages. Multiple response tables were generated to define the distribution of GBD 2010 injury health states across the cases. Injury-specific probabilities of recovery (IPR) were generated for each injury health state as the proportion of cases with the injury health state who achieved a GOS-E score of 7 (lower good recovery) or 8 (upper good recovery) at 12-months following injury. For the worst injury model, the lowest IPR for each case was used in the model while the product of all IPRs for each case was used in the multiplicative model.
Three approaches to modelling the relationship between injury health state/s and disability were considered: a) an “additive” or multivariable model where it was assumed that the impact of each injury health state on disability was constant irrespective of the presence of other injury health states or other covariates; b) a “worst injury” or minimal approach model where only the lowest IPR was included in the model; and c) a “multiplicative” model where the product of the IPRs was included in the model, assuming that each injury health state contributed a constant proportional decrement to outcome.
A split dataset approach was used
All models were fitted with age, and then with and without comorbid status, as previous studies using trauma registry data have found no significant improvement in model performance from the inclusion of comorbid status over age alone using mortality as the outcome
The predictive performance of the models was assessed in terms of discrimination and calibration
The concordance, or C-statistic, was used as a measure of model discrimination. This statistic measures the capacity of the model to discriminate between participants who experience the outcome of interest and those that do not
There were 13,315 VSTR and VOTOR cases during the study period who survived to hospital discharge. Of these, 1902 (14.3%) were lost to follow-up, leaving 11,412 cases with a valid GOS-E score at 12-months. Thirty-seven of the 44 GBD 2010 health states were represented, of which 12 health states were present in less than 100 cases. For these low frequency injury health states, the case was removed if the low frequency health state was the only injury sustained by the patient (n = 75).
Overall, there were 11,337 cases in the dataset for analysis, with 5,650 randomised to the training dataset and 5,687 cases to the test dataset. The characteristics of cases in the training and test datasets were comparable (
Variable | Training dataset (n = 5650) | Test dataset (n = 5687) | |
|
Mean (SD) years | 52.8 (23.1) | 52.9 (23.6) |
|
n (%) | ||
Male | 3352 (59.3) | 3381 (59.5) | |
Female | 2298 (40.7) | 2306 (40.5) | |
|
n (%) | ||
Low fall | 2068 (36.9) | 2067 (36.6) | |
Motor vehicle | 896 (16.0) | 928 (16.4) | |
High fall | 686 (12.2) | 656 (11.6) | |
Motorcycle | 579 (10.3) | 585 (10.4) | |
Pedal cyclist | 237 (4.2) | 256 (4.6) | |
Pedestrian | 249 (4.5) | 256 (4.6) | |
Struck by/collision with person | 195 (3.5) | 183 (3.2) | |
Struck by/collision with object | 157 (2.8) | 169 (3.0) | |
Cutting/piercing object | 76 (1.4) | 87 (1.5) | |
Other | 457 (8.2) | 459 (8.1) | |
|
n (%) | ||
None | 3888 (68.8) | 3853 (67.8) | |
1 | 1281 (22.7) | 1344 (23.6) | |
2–6 | 481 (8.5) | 490 (8.6) | |
n (%) | |||
No | 4740 (83.9) | 4755 (83.7) | |
Yes | 906 (16.1) | 929 (16.3) | |
|
Median (IQR |
5.9 (3.0–11.1) | 6.0 (3.0–11.1) |
|
n (%) | ||
1 | 2627 (46.5) | 2617 (46.0) | |
2 | 1303 (23.1) | 1367 (24.0) | |
3 | 697 (12.3) | 686 (12.1) | |
4 | 385 (6.8) | 407 (7.2) | |
5 | 258 (4.6) | 255 (4.5) | |
6 | 149 (2.6) | 145 (2.6) | |
>6 | 231 (4.1) | 210 (3.6) |
Data missing for 91 cases.
ICU - Intensive Care Unit, data missing for 7 cases.
IQR - Interquartile range.
Injury health state descriptor | Training dataset | Test dataset |
(n = 5650) | (n = 5687) | |
n (%) |
n (%) |
|
Moderate/severe traumatic brain injury | 1519 (27.0) | 1532 (26.9) |
Open wound | 1345 (23.8) | 1422 (25.0) |
Patella/tibia/fibula fracture | 1155 (20.4) | 1070 (18.8) |
Vertebral column fracture | 1099 (19.5) | 1073 (18.9) |
Severe chest injury | 996 (17.6) | 1012 (17.8) |
Radius/ulna fracture | 833 (14.7) | 850 (14.9) |
Clavicle/scapula/humerus fracture | 875 (15.5) | 769 (13.5) |
Neck of femur fracture | 767 (13.6) | 764 (13.4) |
Other muscle/tendon injury | 500 (8.9) | 521 (9.2) |
Skull fracture | 466 (8.3) | 487 (8.6) |
Other and unspecified injuries | 458 (8.1) | 519 (9.1) |
Facial fracture | 446 (7.9) | 493 (8.7) |
Abdominal/pelvic organ injury | 439 (7.8) | 480 (8.4) |
Pelvic fracture | 451 (8.0) | 440 (7.7) |
Foot bone fracture | 330 (5.8) | 309 (5.4) |
Femur fracture – not involving neck | 294 (5.2) | 299 (5.3) |
Sternal/single rib fracture | 281 (5.0) | 280 (4.9) |
Hand/wrist fracture | 204 (3.6) | 215 (3.8) |
Knee soft tissue injury | 174 (3.1) | 156 (2.7) |
Shoulder soft tissue injury | 154 (2.7) | 144 (2.5) |
Eye injury | 156 (2.8) | 129 (2.3) |
Nerve injury | 124 (2.2) | 110 (1.9) |
Spinal cord injury – neck level | 84 (1.5) | 80 (1.4) |
Spinal cord injury – other | 47 (0.8) | 71 (1.3) |
Hip dislocation | 58 (1.0) | 59 (1.0) |
Burns – minor | 30 (0.5) | 25 (0.4) |
Poisoning | 14 (0.3) | 22 (0.4) |
Burns ≥20% body surface area | 12 (0.2) | 12 (0.2) |
Lower airway burns | 11 (0.2) | 14 (0.3) |
Finger amputation | 7 (0.1) | 6 (0.1) |
Other fracture | 4 (0.1) | 5 (0.1) |
Amputation of one upper limb | 4 (0.1) | 3 (<0.1) |
Burns – other serious | 4 (0.1) | 4 (0.1) |
Amputation of one lower limb | 4 (0.1) | 5 (0.1) |
Crush injury | 2 (<0.1) | 2 (<0.1) |
Thumb amputation | 2 (<0.1) | 2 (<0.1) |
Drowning/non-fatal submersion | 1 (<0.1) | 3 (<0.1) |
Total percentage >100% as cases can have more than one injury health state.
GOS-E |
Training dataset | Test dataset | |
(n = 5650) | (n = 5687) | ||
n (%) | n (%) | ||
1 | Death | 377 (6.7) | 420 (7.4) |
2 | Vegetative state | 12 (0.2) | 24 (0.4) |
3 | Lower severe disability | 691 (12.2) | 681 (12.0) |
4 | Upper severe disability | 320 (5.7) | 336 (5.9) |
5 | Lower moderate disability | 786 (13.9) | 710 (12.5) |
6 | Upper moderate disability | 1094 (19.4) | 1149 (20.2) |
7 | Lower good recovery | 901 (15.9) | 957 (16.8) |
8 | Upper good recovery | 1469 (26.0) | 1410 (24.8) |
Glasgow Outcome Scale – Extended.
The most common injury health states represented in the dataset were moderate/severe traumatic brain injury (TBI), open wounds, severe chest injuries, lower and upper limb fractures, skull fractures and organ injuries (
Injury health state | Cases | Recovered | IPR |
(n) | (n) | ||
Spinal cord injury – neck | 84 | 18 | 0.21 (0.13, 0.30) |
Neck of femur fracture | 767 | 169 | 0.22 (0.19, 0.25) |
Hip dislocation | 58 | 14 | 0.24 (0.13, 0.35) |
Femur fracture – not involving neck | 294 | 70 | 0.24 (0.19, 0.29) |
Spinal cord injury – other | 47 | 12 | 0.26 (0.13, 0.38) |
Nerve injury | 124 | 35 | 0.28 (0.20, 0.36) |
Eye injury | 156 | 47 | 0.30 (0.23, 0.37) |
Pelvic fracture | 451 | 141 | 0.31 (0.27, 0.36) |
Other and unspecified injuries | 458 | 153 | 0.33 (0.29, 0.38) |
Facial fracture | 446 | 150 | 0.34 (0.29, 0.38) |
Open wound | 1365 | 464 | 0.34 (0.32, 0.37) |
Moderate/severe traumatic brain injury | 1519 | 535 | 0.35 (0.33, 0.38) |
Vertebral column fracture | 1099 | 381 | 0.35 (0.32, 0.38) |
Skull fracture | 466 | 168 | 0.36 (0.32, 0.40) |
Severe chest injury | 996 | 357 | 0.36 (0.33, 0.39) |
Knee soft tissue injury | 174 | 62 | 0.36 (0.29, 0.43) |
Foot bone fracture | 330 | 118 | 0.36 (0.31, 0.41) |
Sternal/single rib fracture | 281 | 104 | 0.37 (0.31, 0.43) |
Hand/wrist fracture | 204 | 82 | 0.40 (0.33, 0.47) |
Shoulder soft tissue injury | 154 | 61 | 0.40 (0.32, 0.47) |
Clavicle/scapula/humerus fracture | 875 | 353 | 0.40 (0.37, 0.44) |
Abdominal/pelvic organ injury | 439 | 179 | 0.41 (0.36, 0.45) |
Patella/tibia/fibula fracture | 1155 | 521 | 0.45 (0.42, 0.48) |
Other muscle/tendon injury | 500 | 229 | 0.46 (0.41, 0.50) |
Radius/ulna fracture | 833 | 419 | 0.50 (0.47, 0.54) |
IPR; Injury probability of recovery.
Each model was fitted in the training dataset, with the results shown in
The figure is a plot the predicted versus the observed recovery in the training dataset. The 45° line represents perfect fit of the model.
Model | Area under curve | H-L |
LR |
|
(95% CI) | (p-value) | (p-value) | ||
Additive | Unadjusted |
0.67 (0.65, 0.68) | 18.63 (0.017) | |
Age | 0.70 (0.69, 0.72) | 23.92 (0.002) | 232.58 (<0.001) | |
Age and comorbidity | 0.72 (0.70, 0.73) | 16.50 (0.036) | 98.81 (<0.001) | |
Worst injury | Unadjusted | 0.66 (0.64, 0.67) | 6.91 (0.546) | |
Age | 0.69 (0.67, 0.70) | 20.80 (0.008) | 70.00 (<0.001) | |
Age and comorbidity | 0.70 (0.69, 0.72) | 16.05 (0.042) | 117.24 (<0.001) | |
Multiplicative | Unadjusted | 0.61 (0.59, 0.62) | 114.94 (<0.001) | |
Age | 0.68 (0.67, 0.69) | 36.22 (<0.001) | 338.94 (<0.001) | |
Age and comorbidity | 0.69 (0.68, 0.71) | 11.99 (0.152) | 117.15 (<0.001) |
Hosmer-Lemeshow statistic.
Likelihood ratio test.
Model fitted without age or comorbidity.
The models, using the IPRs calculated from the training dataset and adjusted for age and comorbid status, were fitted in the test dataset, with the results shown in
The figure is a plot the predicted versus the observed recovery in the test dataset. The 45° line represents perfect fit of the model.
Model | Area under curve | H-L statistica |
(95% CI) | (p-value) | |
Additive | 0.70 (0.69, 0.71) | 12.77 (0.120) |
Worst injury | 0.70 (0.68, 0.71) | 12.83 (0.118) |
Multiplicative | 0.68 (0.67, 0.70) | 25.79 (0.001) |
Hosmer-Lemeshow statistic.
The aims of this study were to explore, for the first time, the GBD 2010 Study injury health states, and the performance of different approaches to modelling the relationship between these injury health states and disability at 12-months following injury. The data presented are important for guiding the methods for estimating YLD as the study provides important information about the prevalence of disability for each injury health state and is the first to evaluate the relationship between multiple injuries and disability following injury.
Using the injury health states generated for the GBD 2010 study, the prevalence of disability at 12-months post-injury across the health states was high with more than half of the cohort still affected by injury at this time point. The “worst injury”, additive and multiplicative models were developed in a training dataset and then validated using a test dataset to explore and validate different models for combining the full spectrum of injuries sustained. The results showed concordance lower than methodologically similar studies based on mortality outcomes, and no clearly superior approach to modelling these injury health states to predict recovery at 12-months following injury, although the additive and “worst injury” models showed higher concordance and discrimination than the multiplicative approach.
Numerous studies have modelled the relationship between multiple injury diagnoses and mortality following injury
In comparison, the concordances observed in the test dataset in the current study did not exceed 0.70, which equates to a 70% chance that given two patients, one who will recover and one who will continue to have disability at 12-months, the model will assign a higher probability of recovery to the patient who recovers. Only the additive and “worst injury” models demonstrated acceptable calibration in the test dataset, suggesting problems with goodness-of-fit for the multiplicative approach.
The lower concordance and variation from perfect fit of the calibration curves could suggest that recovery after injury is more difficult to predict than mortality and/or reflect the injury health states evaluated. Cohort studies have found additional factors not included in the current models, such as level of education, marital status, socioeconomic status, compensation status and injury severity, to be important predictors of long term outcome after injury
Most studies of mortality following injury have used individual ICD diagnosis codes to represent injury conditions in models. In the current study, we modelled ICD-coded data after collapsing the more than 1200 ICD-10 injury diagnosis codes into 44 GBD 2010 injury health states. Many of the injury health states combine a number of injury diagnoses, potentially combining injuries with different probabilities of recovery and duration of disability into a single group. Evidence of this heterogeneity can be seen in
Overall, more than half of the study sample had sustained more than one injury health state, with 7% sustaining more than five, an occurrence considered “extremely rare” by the authors of the Iranian burden of disease and injury study
This is the first study investigating modelling approaches to disability after injury and limitations of the study require acknowledgement. The data were drawn from trauma registries which focus on severe and orthopaedic injury cases. Consequently, some GBD injury health states were not represented at all in the data or were represented by too few cases to generate a reliable estimate of the probability of recovery. Additionally, injury health states involving combined more than one injury type were likely to be over-represented by the more severe injury in the injury health state. For example, moderate to severe TBI would likely include a higher proportion of severe head injured patients than a more general hospital discharge dataset due to the inclusion criteria for the VSTR. The implications of the case-mix on the generalisability of the study findings are not clear as comparable disability datasets are not available. However, given that hospital discharge datasets would likely contain a wider distribution of injury severities, and greater heterogeneity in disability outcomes, the potential for reduced model fit is possible.
The follow-up rate at 12-months was 86% of all registered patients. Whether the disability outcomes of the patients lost to follow-up differed to the respondents is not known. It should be noted that follow-up commenced for nearly all patients who survived to discharge, because only about 1% of patients had opted-out of the registers. In contrast, studies based on an opt-in consent process typically can commence follow-up on only about half of the discharged patients, with much greater potential for bias
Overall, the majority of patients survived their injuries but were not fully recovered 12 months after onset. The evident potential for injury patients developing persistent disability highlights the importance of improving methods for estimating of the burden of non-fatal injury, and for applying them. This study was a first attempt to assess the relationship between the 2010 GBD injury health states and long term disability, including the investigation of modelling different methods of handling multiple injuries. The results show that the additive and “worst injury” models performed better than the multiplicative model, although concordance did not exceed 0.70 for any model. Factors likely to have contributed to the relatively poor fit were heterogeneity for the study outcome in at least some of the GBD 2010 injury health states, and use of models that did not include certain known predictors of the outcome (in order to replicate GBD methods). The next steps will be to investigate improved classification of injury health states, the handling of post-discharge and longer term mortality in burden estimates, and investigation of additional outcomes such as health-related quality of life. The burden based on GBD 2010 Disability Weights, which had not been released at the time of writing, will be compared with burden based on prospectively measured outcomes.
The investigators, project staff (Mimi Morgan, Sue McLellan, Melissa Hart, Ann Sutherland), data collectors and participating hospitals of the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry are thanked for their assistance in this project. Andrew Hannaford is sincerely thanked for his assistance in extracting the data for analysis.