Introduction
Acute traumatic brain injury (TBI) is a major cause of death, and disability. 1 The annual cost of acute TBI has been estimated at $60 billion in the USA. 2 For critically ill adult patients with acute TBI, access to specialist facilities, for example neuroscience centres, may differ across regions, but these variations are not informed by evidence of relative effectiveness or cost-effectiveness. 3 Some evidence indicates that management of acute TBI in specialist neuroscience centres rather than nonneuroscience centres, may be associated with improved clinical outcomes. 4, 5 Several case series have suggested that dedicated clinical algorithms and protocols can improve mortality and functional outcomes following acute TBI, 6, 7 but in the absence of RCTs, confounding is a key concern, and previous studies have not undertaken adequate risk-adjustment when comparing outcomes across settings, 8 or considered relative costs.
In the UK NHS, the majority of acute TBI patients admitted to neuroscience centres are managed in neurocritical care units rather than combined neuro/general critical care units. However, the recent expansion of neurocritical care facilities is based on weak evidence of benefit from managing severe head injury in specialist centres. 5 It is also unknown whether adult TBI patients without an acute 'neurosurgical' lesion benefit from 'early' transfer to a neuroscience centre; there may be a substantial risk of death from 'early' transfer and aggressive protocols of care in a neuroscience centre. 9, 10 A competing concern, is that if the initial decision is not to transfer the patient to a neuroscience centre, critical lesions may develop subsequently; the patient may require a 'late' transfer at increased risk of an unfavourable outcome. 11 While 'early' transfer to neuroscience centres has been recommended following acute TBI, the lack of evidence underpinning this guidance has been recognised by the National Institute for Health and Care Excellence (NICE), who listed this topic as a key area for further research.
12
The Risk-Adjustment in Neurocritical Care (RAIN) Study aimed first, to validate risk prediction models following acute TBI in adult critical care units in the UK, and second to compare risk-adjusted outcomes and costs of alternative care locations following acute TBI. 13 The objectives of this paper are to use the RAIN Study data to compare the effectiveness, costs, and cost-effectiveness of:
1. Management in a dedicated neurocritical care unit versus a combined neuro/general critical care unit;
2. 'Early' transfer to a neuroscience centre versus 'no or late' transfer, for patients who initially present at a non-neuroscience centre and do not require urgent neurosurgery.
Methods

Overview
This study compared the risk-adjusted outcomes, costs and lifetime cost-effectiveness of alternative care locations following acute TBI. Firstly, for patients admitted to neuroscience centres, we compared care within a dedicated neurocritical care unit ( Figure 1a , panel A) versus within a combined neuro/general critical care unit ( Figure 1a , panel B). Secondly, for patients who originally presented at a non-neuroscience centre, an 'early' transfer group was defined as those patients who transferred to a neuroscience centre, either directly from the emergency department, or from a critical care unit within 18 hours of initial hospital presentation ( Figure 1b , panel A). The 'no or late' transfer group were defined as patients who received all their critical care within a nonneuroscience centre, and those who transferred to a neuroscience centre more than 24 hours after initial hospital presentation ( Figure 1b, All adult patients admitted to participating critical care units with a confirmed TBI and a last presedation Glasgow Coma Scale (GCS) of 3-14 were included. Detailed information was collected at the time of injury and at initial hospital presentation on age, sex, cause of injury, major extracranial injury, pupil reactivity, GCS, computed tomography (CT) characteristics (Marshall CT classification   14 ).
These baseline variables were used to predict the risks of death at six months according to the IMPACT Lab 14 and CRASH CT 15 risk prediction models. There two models were chosen following a review of published risk prediction models for critically ill adult patients with acute TBI, 8, 16 and were those found to perform best in external validation undertaken in the RAIN Study.
13

Six month outcomes
For the index admission, information on vital status and the date of death were recorded on the RAIN Study Case Report Forms (CRFs). RAIN Study data were linked with death registration from the NHS information centre to give survival status and date of death for patients discharged from the initial hospital episode. At the six month follow-up, a postal questionnaire was administered to those patients known to be alive and eligible for follow-up. 13 Patients (or their carers) were asked to complete the Glasgow Outcome Scale (Extended) (GOSE), and a generic health-related quality of life (QOL) questionnaire, the EuroQol 5-dimension, 3-level version (EQ-5D-3L). Unfavourable outcome at six months was measured as death or severe disability according to the GOSE. 17 The EQ-5D-3L
profiles were combined with health state preference values from the UK general population, 18 to
give EQ-5D-3L utility index scores on a scale anchored at 0 (death) and 1 (perfect health). QualityAdjusted Life Years (QALYs) were then reported by combining data on vital status and utility score at six months. Decedents were assigned zero QALYs.
The proportion of patients who died before six months was similar following care in a dedicated neurocritical care unit versus those in a combined neuro/general critical care unit (Table 2a ). The dedicated neurocritical care unit group had a higher mean EQ-5D-3L utility index score for survivors, a lower proportion of patients with unfavourable outcomes and higher mean QALYs, but none of these differences was statistically significant, after case-mix adjustment. A lower proportion of patients died before six months in the 'early' versus the 'no or late' transfer group (adjusted odds ratio 0.52, 95% CI 0.34 to 0.80; Table 2b ). The 'early' transfer group reported higher mean EQ-5D-3L utility index score for survivors, and higher average QALYs.
The subgroup of patients aged over 70 (n=86) had a higher odds of death in the 'early' versus the 'no or late' transfer group, but the CIs around the odds ratio were wide and included 1 (Web supplement Table S5 ). For the other subgroups, a higher proportion of patients died in the 'no or late' versus the 'early' transfer group.
Resource use and costs
The cost analysis took a health and personal social services perspective. The RAIN CRFs recorded length of stay (LOS) in critical care units and general medical wards from index admission and readmission. The CRFs also recorded whether or not the patient had intracranial neurosurgery for evacuation of a mass lesion. Each critical care bed-day was assigned to the appropriate Healthcare
Resource Group (HRG) with daily organ support data recorded for the Critical Care Minimum Dataset (CCMDS). 19 Data on other health and personal social service use (between discharge from the index hospital admission and six months follow-up) was collected from a service use questionnaire administered by post at the six month follow-up. 13 Unit costs for resource use were taken from the 'Payment by Results' database 20 and the literature (see Web supplement, Table S1 ). All unit costs were reported in 2010-11 prices.
Analysis of six month costs and consequences
Regression analysis used the IMPACT Lab model 14 which performed best in external validation in the RAIN Study. For mortality and unfavourable outcome at six months after the acute TBI, odds ratios were reported with logistic regression. For the comparisons of QOL, QALYs and cost at six months, we reported mean differences after adjustment with linear regression. For the first comparison we allowed for any clustering of patients within critical care units with multilevel models. For the second comparison, patients within each critical care unit could be in either comparison arm, we used single-level regression models for case-mix adjustment and to report pre-specified subgroup analyses according to age (70 years or less, or over 70 years), major extracranial injury (yes or no) and severity of TBI (GCS of 9-14, or 3-8) . 21 Missing data were addressed with multiple imputation assuming that data were 'missing at random', 22 that is conditional on baseline covariates, resource use, and observed endpoints. We applied the analytical models to each imputed dataset (n=25), and combined the resultant estimates with Rubin's rules.
22

Lifetime cost-effectiveness analysis
We predicted patient-level lifetime costs and QALYs which required assumptions to be made about long-term survival, QOL and cost (see Web supplement for details, including Figures S1 and S2, and Tables S2-S4 ). We reported lifetime incremental costs, QALYs, and incremental cost-effectiveness ratios (ICERs). Incremental net monetary benefits (INBs) were estimated by valuing incremental QALYs at £20,000 per QALY, and subtracting from this the incremental costs. Cost-effectiveness acceptability curves (CEACs) were calculated by reporting the probability that each alternative was the most cost-effective (i.e. had a positive INB) at different ceiling ratios of willingness to pay for a QALY gain. Lifetime costs and QALYs were ethan28@example.com%. We undertook sensitivity analyses to assess whether the cost-effectiveness results were robust to alternative assumptions. The base case assumptions and sensitivity analyses undertaken were as follows:
(a) In the base case analysis, for those surviving six months after hospital presentation, subsequent death rates were assumed to be the the same as for the age-gender matched general population. 13, 23 In this sensitivity analysis, lifetime mortality was estimated by applying the parametric function judged the most plausible (Gompertz) to the observed data.
24
(b) The base case assumed that QOL at 10 years post acute TBI was 5% lower than for the agegender matched general population. 25 In the sensitivity analysis, we instead assumed that the decrement in QOL reported in the RAIN Study at six months versus the general population, was maintained over the patients' lifetime.
(c) The costs considered from six months after the acute TBI were those from readmissions to general medical wards, and from use of outpatient and community services. In the base case analysis these costs were applied for three years, after which no further costs were assumed to be attributable to the acute TBI. The sensitivity analyses considered (i) readmission costs to general medical wards for five years; and (ii) outpatient and community costs for upto 10 years.
(d) In the base case analysis, the unit costs of critical care were allowed to differ across clinical centres, according to the variations observed in the Payment by Results database. In the sensitivity analysis, the same unit costs were assumed across care locations.
(e) The base case analysis assumed QOL data from the RAIN Study applied to the six month timepoint, irrespective of the time at which the QOL questionnaire was completed. In the sensitivity analysis, we assumed that patients whose follow-up was before 150 or after 220 days, had missing QOL data, and used multiple imputation to impute their six month QOL data.
(f) We assumed that costs followed a Normal distribution in the base case analysis, whereas the sensitivity aanalysis assumed they followed a Gamma distribution.
(g) Risk equations from the IMPACT LAB model were applied in the base case analysis, and from the CRASH CT model 15 in the sensitivity analysis.
(h) 'Early' transfer to a neuroscience centre was defined as within 18 hours of hospital presentation in the base case and within eight hours of hospital presentation in the sensitivity analysis.
(i) In the base case, the second comparison included all patients irrespective of their pupil reactivity. In the sensitivity analysis, patients with neither pupil reactive were excluded.
Results

Baseline characteristics
For the patients with acute TBI admitted directly to neuroscience centres, baseline characteristics were similar between those admitted to dedicated neurocritical care units, versus combined neuro/general critical care units (Table 1) . For patients admitted to non-neuroscience centres the 'early' transfer group were on average younger and less severely ill than the 'no or late' transfer group. The 'no or late' transfer group had a higher proportion of patients with a GCS of 3-8, unreactive pupils, and a Marshall CT classification indicating non-evacuated mass lesion. The median predicted risk of death at six months was higher for the 'no or late' transfer group compared with the 'early' transfer group.
Resource use and cost up to six months
The dedicated neurocritical care unit group had a higher average LOS in critical care than the combined neuro/general critical care unit group, (Table 3a) , which led to positive incremental costs at six months of approximately £3,700. For the 'early' transfer group the average LOS in critical care, on general wards and in total, was approximately double that of the 'no or late' transfer group (Table 3b) . A higher proportion of patients in the 'early' transfer group had an intracranial procedure for evacuation of a mass lesion, at least 24 hours after initial presentation. The 'early'
transfer group, had a positive incremental cost overall (£15,000), and for each subgroup (Web supplement, Table S5 ).
Lifetime CEA
Critically ill adult patients with acute TBI admitted to neuroscience centres and managed in dedicated neurocritical care units rather than combined neuro/general critical care units, had higher average lifetime QALYs, at a small additional mean cost; the ICER was around £14,000 per QALY (Table 4a) . At a ceiling ratio of £20,000 per QALY, the INB was positive (£1,300). The probability that dedicated versus combined neurocritical care units are cost-effective, is around 60% at realistic threshold willingness to pay for a QALY gain (Web supplement Figure S3a ).
For the patients admitted to non-neuroscience centres following acute TBI, the average lifetime QALYs and costs were both higher for the 'early' transfer group than the 'no or late' transfer group.
The ICER was approximately £11,000 per QALY (Table 4b ). The corresponding probability that 'early transfer' to a neuroscience centre is cost-effective is almost 100% (Web supplement Figure S3b ).
The sensitivity analyses find that the base case findings are robust to most alternative approaches taken (Figure 2a and b) . The exception is that if the second comparison excludes patients with neither pupil reactive, the effect of early transfer on six month mortality is reduced (adjusted odds ratio 0.856, 95% CI 0.518 to 1.416), and the corresponding INB for 'early' transfer is negative with a 95% CI that includes zero (Figure 2b ).
The subgroup analysis reported that 'early' transfer was highly cost-effective for patients aged 70 or less (n=761), but was not cost-effective for those aged over 70 (n=86), (Web supplement Figure S4a ).
For all other subgroups, 'early' transfer was more cost effective than 'no or late' transfer (Web supplement Figures S4b and S4c) .
Discussion
This paper finds that for critically ill patients with acute TBI who present at a neuroscience centre, average QOL after six months is higher following care in dedicated neurocritical care units than combined neuro/general units. However, this difference is not statistically significant and dedicated critical care units have higher critical care costs. For critically ill adult patients who present at a nonneuroscience centre and do not require immediate neurosurgery following acute TBI, 'early' transfer to a neuroscience centre is associated with lower mortality after six months, and survivors have higher average QOL. The 'early' transfer group also has higher average total costs which reflect the increased survival, but the lifetime QALY gains are sufficient to justify the additional costs, and so 'early' transfer is cost-effective. This finding applies to all subgroups apart from for older patients (aged over 70). While the sensitivity analysis reports the results are generally robust to alternative assumptions, it does report that the 'early transfer' policy is no longer more effective or costeffective once patients with neither pupil reactive are excluded. The 'late or no' transfer group had a higher proportion of patients with neither pupil reactive, and may therefore include a higher proportion of patients with unmeasured characteristics associated with increased mortality. Hence, any recommendation that critically ill patients with acute TBI should be transferred 'early' to neuroscience centres, has to recognise the potential for unobserved confounding.
This paper extends the previous literature on the relative effectiveness of alternative locations of neurocritical care. [26] [27] [28] This paper is the first to evaluate the cost-effectiveness of alternative care locations following acute TBI, and uses established risk prediction models, subject to a rigorous validation study in critically-ill adults with acute TBI. The finding that average risk-adjusted QOL is higher if acute TBI patients are managed in dedicated neurocritical care units, rather than a combined neuro/general critical care unit has several possible explanations. First, a dedicated multidisciplinary team may provide more effective, immediate acute rehabilitation, and improve access to specialised neuro rehabilitation after discharge from the critical care unit. 29 Second, dedicated units may offer more aggressive monitoring and management, which can reduce morbidity, 5 and the RAIN Study finds this did not increase six-month mortality. Third, the dedicated neurocritical care units may have less severe case-mix according to unmeasured variables. Finally, the difference in the mean QOL between the settings may simply reflect chance variation as the differences was not statistically significant.
The finding that 'early' transfer to a neuroscience centre following acute TBI patients is costeffective is driven by the gains in survival and QOL observed at six months after hospital presentation. For non-surgical patients, previous research suggested that early transfer and more aggressive management may lead to increased risks that outweigh any gains, 30 whereas other studies report that delayed transfer may lead to worse outcomes. 11 In the RAIN Study, only a small proportion (6%) of the 'no or late' transfer group had a delayed transfer, and so the main contrast is between a decision to transfer the TBI patient to a neuroscience centre 'early', versus continued management at a non-neuroscience centre. Previous evidence suggests benefits from early transfer for patients presenting with a space occupying haematoma with worsening mass effect, but the RAIN Study excludes patients who required neurosurgery within 24 hours, and therefore extends the evidence on the effectiveness of early transfer, to other critically ill patients with TBI. Here, while around one third of patients in both comparison groups presented with a mass lesion, only one patient in the 'no or late' transfer group had neurosurgery after 24 hours, compared to 48(8%) of patients in the 'early' transfer group.
The definition of an 'early' transfer is arbitrary, and may differ across contexts. A potential concern is that the effectiveness of an 'early' transfer policy could be highly sensitive to the cut-off point, and previous evidence from trauma networks encourages a four hour time window for neurosurgical interventions. 3 Our sensitivity analysis suggests that applying an eight rather than an 18 hour time window from hospital presentation does not have large impact on the effectiveness or the costeffectiveness of the 'early' transfer policy.
This study has some limitations. First, the RAIN Study only measured costs and outcomes for up to six months. The lifetime CEA made assumptions when extrapolating from these data, but the sensitivity analyses suggested the results were robust to alternative extrapolation methods. Second, some follow-up data were missing for the six month endpoints, which we addressed with multiple imputation approaches. Third, in any non-randomised comparison the major concern is residual confounding. We allowed for baseline differences between the comparison arms with the published risk prediction model that performed best in external validation, 14 and the sensitivity analysis suggested the results were similar when an alternative risk adjustment method was used. However, the results could be explained by differences between the comparison groups in prognostic factors that were not in the published risk models, nor measured in the RAIN Study. For example, in the RAIN Study the definition of major extracranial injury was broad and did not recognise the injury's severity. Further research would ideally record and adjust for potential confounders beyond those measured in the RAIN Study.
Conclusions
This paper finds that for critically ill adult patients with acute TBI who present at, or are transferred to, a neuroscience centre, management in a dedicated neurocritical care unit rather than a combined neuro/general critical care unit is associated with small gains in average QOL, at low additional costs, although these differences were not statistically significant. For adult patients with acute TBI who present at a non-neuroscience centre and do not require neurosurgery within 24 hours, 'early' transfer is more cost-effective than 'no or late' transfer to a neuroscience centre, after risk adjustment.
Author's contribution
Dr Richard Grieve (Professor of Health Economics Methodology) designed the economic evaluation, contributed to the analysis and interpretation of the data, drafted and critically reviewed the manuscript.
Dr Zia Sadique (Lecturer, Health Economics) contributed to the analysis and interpretation of the data and drafted the manuscript.
Dr Manuel Gomes (Research Fellow, Health Economics) contributed to the analysis and interpretation of the data, and drafted the manuscript.
Professor Martin Smith (Consultant and Honorary Professor in Neuroanaesthesia and Neurocritical
Care) conceived and designed the study, contributed to the acquisition and interpretation of the data and critically reviewed the manuscript.
Professor Fiona Lecky (Clinical Professor in Emergency Medicine and Research Director of TARN)
contributed to the design of the study, interpretation of the data, and critically reviewed the Death (IMPACT Lab model) 22.8 (11.5, 39.6) 22.5 (11.3, 36.9) 24.6 (9.6, 48.1) 18.3 (9.4, 32. 13,153 (14,563) 28,525 (27,100) 15,001 [11,123 to 18,880 

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