Randomised pragmatic waitlist trial with process evaluation investigating the effectiveness of peer support after brain injury: protocol.

Traumatic brain injury (TBI) is an important global health problem. Formal service provision fails to address the ongoing needs of people with TBI and their family in the context of a social and relational process of learning to live with and adapt to life after TBI. Our feasibility study reported peer support after TBI is acceptable to both mentors and mentees with reported benefits indicating a high potential for effectiveness and likelihood of improving outcomes for both mentees and their mentors. To (a) test the effectiveness of a peer support intervention for improving participation, health and well-being outcomes after TBI and (b) determine key process variables relating to intervention, context and implementation to underpin an evidence-based framework for ongoing service provision. A randomised pragmatic waitlist trial with process evaluation. Mentee participants (n=46) will be included if they have moderate or severe TBI and are no more than 18 months post-injury. Mentor participants (n=18) will be people with TBI up to 6 years after injury, who were discharged from inpatient rehabilitation at least 1 year prior. The primary outcome will be mentee participation, measured using the Impact on Participation and Autonomy questionnaire after 22 weeks. Primary analysis of the continuous variables will be analysis of covariance with baseline measurement as a covariate and randomised treatment as the main explanatory predictor variable at 22 weeks. Process evaluation will include analysis of intervention-related data and qualitative data collected from mentors and service coordinators. Data synthesis will inform the development of a service framework for future implementation. Ethics approval has been obtained from the New Zealand Health and Disability Ethics Committee (19/NTB/82) and Auckland University of Technology Ethics Committee (19/345). Dissemination of findings will be via traditional academic routes including publication in internationally recognised peer-reviewed journals. ACTRN12619001002178. [Abstract copyright: © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Reducing recurrent stroke: Methodology of the motivational interviewing in stroke (MIST) randomized clinical trial

Rationale Recurrent stroke is prevalent in both developed and developing countries, contributing significantly to disability and death. Recurrent stroke rates can be reduced by adequate risk factor management. However, adherence to prescribed medications and lifestyle changes recommended by physicians at discharge after stroke is poor, leading to a large number of preventable recurrent strokes. Using behavior change methods such as Motivational Interviewing early after stroke occurrence has the potential to prevent recurrent stroke. Aims and/or hypothesis The overall aim of the study is to determine the effectiveness of motivational interviewing in improving adherence to medication and lifestyle changes recommended by treating physicians at and after hospital discharge in stroke patients 12 months poststroke to reduce risk factors for recurrent stroke. Design Recruitment of 430 first-ever stroke participants will occur in the Auckland and Waikato regions. Randomization will be to intervention or usual care groups. Participants randomized to intervention will receive four motivational interviews and five follow-up assessments over 12 months. Nonintervention participants will be assessed at the same time points. Study outcomes Primary outcome measures are changes in systolic blood pressure and low-density lipoprotein levels 12 months poststroke. Secondary outcomes include self-reported adherence and barriers to prescribed medications, new cardiovascular events (including stroke), changes in quality of life, and mood. Discussion The results of the motivational interviewing in stroke trial will add to our understanding of whether motivational interviewing may be potentially beneficial in the management of stroke and other diseases where similar lifestyle factors or medication adherence are relevant

Future and potential spending on health 2015-40 : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential.Peer reviewe

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Traumatic brain injury (TBI) and spinal cord injury (SCI) are increasingly recognised as global health priorities in view of the preventability of most injuries and the complex and expensive medical care they necessitate. We aimed to measure the incidence, prevalence, and years of life lived with disability (YLDs) for TBI and SCI from all causes of injury in every country, to describe how these measures have changed between 1990 and 2016, and to estimate the proportion of TBI and SCI cases caused by different types of injury. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016 to measure the global, regional, and national burden of TBI and SCI by age and sex. We measured the incidence and prevalence of all causes of injury requiring medical care in inpatient and outpatient records, literature studies, and survey data. By use of clinical record data, we estimated the proportion of each cause of injury that required medical care that would result in TBI or SCI being considered as the nature of injury. We used literature studies to establish standardised mortality ratios and applied differential equations to convert incidence to prevalence of long-term disability. Finally, we applied GBD disability weights to calculate YLDs. We used a Bayesian meta-regression tool for epidemiological modelling, used cause-specific mortality rates for non-fatal estimation, and adjusted our results for disability experienced with comorbid conditions. We also analysed results on the basis of the Socio-demographic Index, a compound measure of income per capita, education, and fertility. FINDINGS: In 2016, there were 27·08 million (95% uncertainty interval [UI] 24·30-30·30 million) new cases of TBI and 0·93 million (0·78-1·16 million) new cases of SCI, with age-standardised incidence rates of 369 (331-412) per 100 000 population for TBI and 13 (11-16) per 100 000 for SCI. In 2016, the number of prevalent cases of TBI was 55·50 million (53·40-57·62 million) and of SCI was 27·04 million (24·98-30·15 million). From 1990 to 2016, the age-standardised prevalence of TBI increased by 8·4% (95% UI 7·7 to 9·2), whereas that of SCI did not change significantly (-0·2% [-2·1 to 2·7]). Age-standardised incidence rates increased by 3·6% (1·8 to 5·5) for TBI, but did not change significantly for SCI (-3·6% [-7·4 to 4·0]). TBI caused 8·1 million (95% UI 6·0-10·4 million) YLDs and SCI caused 9·5 million (6·7-12·4 million) YLDs in 2016, corresponding to age-standardised rates of 111 (82-141) per 100 000 for TBI and 130 (90-170) per 100 000 for SCI. Falls and road injuries were the leading causes of new cases of TBI and SCI in most regions. INTERPRETATION: TBI and SCI constitute a considerable portion of the global injury burden and are caused primarily by falls and road injuries. The increase in incidence of TBI over time might continue in view of increases in population density, population ageing, and increasing use of motor vehicles, motorcycles, and bicycles. The number of individuals living with SCI is expected to increase in view of population growth, which is concerning because of the specialised care that people with SCI can require. Our study was limited by data sparsity in some regions, and it will be important to invest greater resources in collection of data for TBI and SCI to improve the accuracy of future assessments

Frequency of fatigue and its changes in the first 6 months after traumatic brain injury: results from the CENTER-TBI study

Background: Fatigue is one of the most commonly reported subjective symptoms following traumatic brain injury (TBI). The aims were to assess frequency of fatigue over the first 6 months after TBI, and examine whether fatigue changes could be predicted by demographic characteristics, injury severity and comorbidities. Methods: Patients with acute TBI admitted to 65 trauma centers were enrolled in the study Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI). Subj

Tracheal intubation in traumatic brain injury

Background: We aimed to study the associations between pre- and in-hospital tracheal intubation and outcomes in traumatic brain injury (TBI), and whether the association varied according to injury severity. Methods: Data from the international prospective pan-European cohort study, Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI), were used (n=4509). For prehospital intubation, we excluded self-presenters. For in-hospital intubation, patients whose tracheas were intubated on-scene were excluded. The association between intubation and outcome was analysed with ordinal regression with adjustment for the International Mission for Prognosis and Analysis of Clinical Trials in TBI variables and extracranial injury. We assessed whether the effect of intubation varied by injury severity by testing the added value of an interaction term with likelihood ratio tests. Results: In the prehospital analysis, 890/3736 (24%) patients had their tracheas intubated at scene. In the in-hospital analysis, 460/2930 (16%) patients had their tracheas intubated in the emergency department. There was no adjusted overall effect on functional outcome of prehospital intubation (odds ratio=1.01; 95% confidence interval, 0.79–1.28; P=0.96), and the adjusted overall effect of in-hospital intubation was not significant (odds ratio=0.86; 95% confidence interval, 0.65–1.13; P=0.28). However, prehospital intubation was associated with better functional outcome in patients with higher thorax and abdominal Abbreviated Injury Scale scores (P=0.009 and P=0.02, respectively), whereas in-hospital intubation was associated with better outcome in patients with lower Glasgow Coma Scale scores (P=0.01): in-hospital intubation was associated with better functional outcome in patients with Glasgow Coma Scale scores of 10 or lower. Conclusion: The benefits and harms of tracheal intubation should be carefully evaluated in patients with TBI to optimise benefit. This study suggests that extracranial injury should influence the decision in the prehospital setting, and level of consciousness in the in-hospital setting. Clinical trial registration: NCT02210221