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Marketing authorization holders of vaccines typically need to report brand-specific vaccine effectiveness (VE) to the regulatory authorities as part of their regulatory obligations. COVIDRIVE (now id. DRIVE) is a European public-private partnership for respiratory pathogen surveillance and studies of brand-specific VE with long-term follow-up. We report the final VE results from a two-dose primary series AZD1222 (ChAdOx1 nCoV-19) vaccine schedule in ≥18-year-old individuals not receiving boosters. Patients (N = 1,333) hospitalized with severe acute respiratory infection at 14 hospitals in Austria, Belgium, Italy, and Spain were included in the test-negative case-control study in 2021-2023. Absolute VE was calculated using generalized additive model (GAM), generalized estimating equation (GEE), and spline-based area under the curve (AUC, measuring VE up to 6 months after the last dose of AZD1222). Overall VE (against coronavirus disease 2019 [COVID-19] hospitalization) of an AZD1222 primary series was estimated as 65% using GEE (95% confidence interval [CI]: 52.9-74.5), and 69% using GAM (95% CI: 50.1-80.9) over the 22-month study period (comparator group: unvaccinated patients). The AUC of the spline-based VE estimate was 74.1% (95% CI: 60.0-88.3). VE against hospitalization in study participants who received their second AZD1222 dose 2 months or less before hospitalization was 86% using GEE (95% CI: 77.8-91.4), 93% using GAM (95% CI: 67.2-98.6). During this study period, where mainly the severe acute respiratory syndrome coronavirus 2 Omicron variant was circulating, a two-dose primary series AZD1222 vaccination conferred protection against COVID-19 hospitalization up to at least 6 months after the last dose., (© The Author(s) 2025. Published by Oxford University Press on behalf of the European Public Health Association.)

Background: Prior studies have reported lower effectiveness of XBB.1.5-adapted vaccines against hospitalization related to the Omicron JN.1 variant than the XBB variant. This study evaluated the effectiveness and durability of the BNT162b2 XBB.1.5-adapted vaccine against JN.1-related hospitalization during the 2023-2024 season in Europe., Methods: A test-negative case-control study was carried out in adults (≥18 y) hospitalized between 2 October 2023 and 2 April 2024 with severe acute respiratory infection (SARI) within the id.DRIVE partnership. This study included nine sites across Belgium, Germany, Italy, and Spain. Cases had a laboratory-confirmed JN.1 infection or a positive SARS-CoV-2 test with symptom onset during JN.1 predominance; controls had a negative SARS-CoV-2 test and symptom onset during JN.1 predominance. The primary objective was to estimate BNT162b2 XBB.1.5-adapted vaccine effectiveness (VE) against COVID-19 hospitalization. One case was matched with up to four controls, according to symptom onset date and site. Multivariable analyses were adjusted for symptom onset date, age, sex, and number of chronic conditions., Findings: Among 308 test-positive cases and 1117 test-negative controls, BNT162b2 XBB.1.5-adapted VE against hospitalization compared to no vaccination this season was 53.8% (95% CI 38.4-65.4) after a median of 63 days following vaccination. Protection was sustained through five months; VE was 52.2% (95% CI 41.3-61.1) 2 to <4 weeks after vaccination, 48.9% (95% CI 17.9-68.2) at 4 to <8 weeks, and ranged from 54.6% to 59.5% at 4-week intervals from 8 to <22 weeks., Interpretation: BNT162b2 XBB.1.5-adapted vaccine provided protection against JN.1-related hospitalization, regardless of prior vaccination history, with no evidence of waning through five months. These data support yearly vaccination against COVID-19 to prevent severe illness during the respiratory virus season., Funding: Pfizer., Competing Interests: All authors have completed the ICMJE disclosure form and declare: Pfizer Inc. funded this study. C Marques, HRV, JLN, JMML, JY, MMu, LJ, and SV are employees of Pfizer Inc. HRV, JLN, JMML, JY, MMu, LJ, and SV hold stocks or stock options in Pfizer Inc. EC-S, KB, LD, LdM, MMi, and TMPT are employees of P95 Epidemiology & Pharmacovigilance, a company providing scientific services in the field of vaccines. KB and TMPT declare stock or stock options in P95. KB has received consulting fees from Pfizer Inc. for conducting this study, royalties for the book ‘Vaccination Programmes: epidemiology, monitoring, evaluation’ by Hahné, Bollaerts, Farrington, and consulting fees from AstraZeneca, Bavarian Nordic, CureVac, Janssen, GSK, Pfizer, Novavax, Valneva, and the World Health Organization. AOS reports that Pfizer partially funded the hospital network for the collection of data via id.DRIVE. IC reports support from Pfizer for congress attendance. AM-I reports being a co-principal investigator from VAHNSI (Fisabio). Fisabio received funding from P95 via id.DRIVE to conduct the study. BG and GI declare payment from the id.DRIVE Consortium to their institutions for conducting the study. BG further declares that data collaboration between P95 and Pfizer is reported. GI has received grants or contracts, payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events, support for attending meetings and/or travel, and declares participation on a Data Safety Monitoring Board (DSMB) or Advisory Board from GSK, MSD, Sanofi, Pfizer, Seqirus, Moderna, AstraZeneca, Viatris, and Novavax. GR declares payments to his institution by the CAPNETZ foundation, of whose executive board he is the chairman. Furthermore, GR reports personal fees from Astra Zeneca, Atriva, Boehringer Ingelheim, GSK, Insmed, MSD, Sanofi, Novartis, and Pfizer for consultancy during advisory board meetings and personal fees from AstraZeneca, Berlin Chemie, BMS, Boehringer Ingelheim, Chiesi, Essex Pharma, Grifols, GSK, Insmed, MSD, Roche, Sanofi, Solvay, Takeda, Novartis, Pfizer, and Vertex for lectures. SO-R declares payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from GSK and Sanofi. AÁ, C Martin, EC-S, GLtK, LdM, LD and MMI declare no conflicts of interest., (© 2024 The Authors.)

Aims: Periprosthetic proximal femoral fractures (PFFs) are a major complication after total hip arthroplasty (THA). Health status after PFF is not specifically investigated. The aim of this study is to evaluate the health status pattern over two years after sustaining a PFF., Methods: A cohort of patients with PFF after THA was derived from the Brabant Injury Outcomes Surveillance (BIOS) study. The BIOS study, a prospective, observational, multicentre follow-up cohort study, was conducted to obtain data by questionnaires pre-injury and at one week, and one, three, six, 12, and 24 months after trauma. Primary outcome measures were the EuroQol five-dimension three-level questionnaire (EQ-5D-3L), the Health Utility Index 2 (HUI2), and the Health Utility Index 3 (HUI3). Secondary outcome measures were general measurements such as duration of hospital stay and mortality., Results: A total of 70 patients with a PFF were included. EQ-5D utility scores were significantly lower on all timepoints except at six months' follow-up compared to pre-injury. EuroQol visual analogue scale (EQ-VAS) scores at one month's follow-up were significantly lower compared to pre-injury. The percentage of reported problems at two years was higher for all dimensions except anxiety/depression when compared to pre-injury. The mean EQ-5D utility score was 0.26 higher in males compared to females (95% confidence interval (CI) 0.01 to 0.42; p = 0.003). The mean EQ-VAS score for males was 8.9 points higher when compared to females over all timepoints (95% CI 1.2 to 16.7; p = 0.027). Mortality was 10% after two years' follow-up., Conclusion: PFF patients are a frail population with substantial functional impairment at baseline. Post-injury, they have a significant and clinically relevant lower health status two years after trauma when compared to pre-injury. Health status improves the most between one and three months after injury. Two years after PFF, more patients experience problems in mobility, self-care, usual activities, and pain/discomfort than pre-injury., Competing Interests: None declared., (© 2024 Nieboer et al.)

Background: Vaccine effectiveness (VE) studies with long-term follow-up are needed to understand durability of protection against severe COVID-19 outcomes conferred by primary-series vaccination in individuals not receiving boosters. COVIDRIVE is a European public-private partnership evaluating brand-specific vaccine effectiveness (VE). We report a prespecified interim analysis of primary-series AZD1222 (ChAdOx1 nCoV-19) VE., Methods: Seven Study Contributors in Europe collected data on individuals aged ≥18 years who were hospitalised with severe acute respiratory infection (June 1st, 2021-September 5th, 2022) and eligible for COVID-19 vaccination prior to hospitalisation. In this test-negative case-control study, individuals were defined as test-positive cases or test-negative controls (SARS-CoV-2 RT-PCR) and were either fully vaccinated (two AZD1222 doses, 4-12 weeks apart, completed ≥14 days prior to symptom onset; no booster doses) or unvaccinated (no COVID-19 vaccine prior to hospitalisation). The primary objective was to estimate AZD1222 VE against COVID-19 hospitalisation. A literature review and meta-regression were conducted to contextualise findings on durability of protection., Findings: 761 individuals were included during the 15-month analysis period. Overall AZD1222 VE estimate was 72.8% (95% CI, 53.4-84.1). VE was 93.8% (48.6-99.3) in participants who received second AZD1222 doses ≤8 weeks prior to hospitalisation, with spline-based VE estimates demonstrating protection (VE ≥ 50%) 30 weeks post-second dose. Meta-regression analysis (data from seven publications) showed consistent results, with ≥80% protection against COVID-19 hospitalisation through ∼43 weeks post-second dose, with some degree of waning., Interpretation: Primary-series AZD1222 vaccination confers protection against COVID-19 hospitalisation with enduring levels of VE through ≥6 months., Funding: AstraZeneca., Competing Interests: WM, CMG, MO, and ST declare employment by AstraZeneca and ownership of AstraZeneca stocks/shares. LdM, AD, CW-T, WH-M, LD, GR, ALS, TYCN, and KB declare employment by P95. ALS and TYCN were contracted to AstraZeneca at the time of this work. P95 received consulting fees from several COVID-19 vaccine manufacturers, including AstraZeneca, for the COVIDRIVE study. AC declares funding from COVIDRIVE industry partners (AstraZeneca, Janssen, Moderna, Novavax, CureVac, Sanofi, Valneva, GlaxoSmithKline, Bavarian Nordic) for the COVIDRIVE consortium, of which FISABIO is the coordinator, and honoraria for lectures and educational events from GlaxoSmithKline and for presentations from MSD and HIPRA. GM declares no conflicts of interest related to this analysis, and honoraria from GlaxoSmithKline associated with herpes virus vaccine. AM-I declares funding from COVIDRIVE industry partners (AstraZeneca, Janssen, Moderna, Novavax, CureVac, Sanofi, Valneva, GlaxoSmithKline, Bavarian Nordic) for the COVIDRIVE consortium, of which FISABIO is the coordinator, and honoraria for educational events from MSD and for presentations from Sanofi Pasteur. GI declares no conflicts of interest. SO-R declares payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from EXCEMED and Sanofi. SB declares speaker honorarium from GlaxoSmithKline. CM declares advisory board participation for AstraZeneca in 2021., (© 2023 The Author(s).)

Purpose: The aim of this study was to determine recovery trajectories and prognostic factors for poor recovery in frail and non-frail patients after hip fracture., Methods: Patients with a hip fracture aged 65 years and older admitted to a hospital in the Netherlands from August 2015 to November 2016 were asked to complete questionnaires at one week and one, three, six, 12, and 24 months after injury. The questionnaires included the ICEpop CAPability measure for older people, Health Utility Index, and the Hospital Anxiety Depression Scale. Latent class trajectory analysis was used to determine trajectories of recovery. Patient and injury characteristics for favourable and unfavourable outcome were compared with logistic regression., Results: In total, 696 patients were included of which 367 (53%) patients were frail. Overall, recovery trajectories in frail patients were worse compared to trajectories in non-frail patients. In frail patients, poor recovery was significantly associated with dementia. Lower age was a prognostic factor for good recovery. Immobility, loneliness and weight loss were prognostic for respectively poor capability and symptoms of anxiety and depression. In non-frail patients, recovery after hip fracture was associated with loneliness and the type of hip fracture., Conclusion: Although frailty is associated with poor recovery in older patients with hip fracture, a large proportion of frail patients show good recovery. Loneliness determines poor recovery with anxiety and depressive symptoms., Trail Registration: ClinicalTrials.gov identifier: NCT02508675 (July 27, 2015)., (© 2022. The Author(s) under exclusive licence to SICOT aisbl.)

Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors., (© 2022. The Author(s).)

Purpose: Patients' expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics., Methods: The data of a cohort of adult trauma patients until the age of 75 years with a length of hospital stay of 3 days and more were derived from the Brabant Injury Outcome Surveillance (BIOS) study. The EuroQol-5D 3-level version and the Health Utilities Index were used 1 week, and 1, 3, 6, 12, and 24 months after injury. Four prediction models, for mobility, pain, self-care, and daily activity, were developed using LMMs with ordinal latent states and patient characteristics as predictors for the latent states., Results: In total, 1107 patients were included. Four models with three ordinal latent states were developed, with different covariates in each model. The prediction of the (ordinal) latent states in the LMMs yielded pseudo-R 2 values between 40 and 53% and between 21 and 41% (depending of the type R 2 used) and classification errors between 24 and 40%. Most patients seem to recover fast as only about a quarter of the patients remain with severe problems after 1 month., Conclusion: The use of LMMs to model the development of physical function post-injury is a promising way to obtain a prediction of the physical recovery. The step-by-step prediction fits well with the outpatient follow-up and it can be used to inform the patients more tailor-made to manage the expectations., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: The Patient-Reported Outcomes Information System (PROMIS®) is more and more extensively being used in medical literature in patients with an orthopedic fracture. Yet, many articles studied heterogeneous groups with chronic orthopedic disorders in which fracture patients were included as well. At this moment, there is no systematic overview of the exact use of PROMIS measures in the orthopedic fracture population. Therefore this review aimed to provide an overview of the PROMIS health domains physical health, mental health and social health in patients suffering an orthopedic fracture., Methods: This systematic review was documented according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. We searched Embase, Medline, Web of Science Core Collection, and Cochrane Central Register of controlled Trials, CINAHL and Google Scholar in December 2020 using a combination of MeSH terms and specific index terms related to orthopedic fractures and PROMIS questionnaires. Inclusion criteria were available full text articles that were describing the use of any PROMIS questionnaires in both the adult and pediatric extremity fracture population., Results: We included 51 relevant articles of which most were observational studies (n = 47, 92.2%). A single fracture population was included in 47 studies of which 9 involved ankle fractures (9/51; 17.6%), followed by humeral fractures (8/51; 15.7%), tibia fractures (6/51; 11.8%) and radial -or ulnar fractures (5/51; 9.8%). PROMIS Physical Function (n = 32, 32/51 = 62.7%) and PROMIS Pain Interference (n = 21, 21/51 = 41.2%) were most frequently used questionnaires. PROMIS measures concerning social (n = 5/51 = 9.8%) and mental health (10/51 = 19.6%) were much less often used as outcome measures in the fracture population. A gradually increasing use of PROMIS questionnaires in the orthopedic fracture population was seen since 2017., Conclusion: Many different PROMIS measures on multiple domains are available and used in previous articles with orthopedic fracture patients. With physical function and pain interference as most popular PROMIS measures, it is important to emphasize that other health-domains such as mental and social health can also be essential to fracture patients., (© 2022. The Author(s).)

Background: Through improvements in trauma care there has been a decline in injury mortality, as more people survive severe trauma. Patients who survive severe trauma are at risk of long-term disabilities which may place a high economic burden on society. The purpose of this study was to estimate the health care and productivity costs of severe trauma patients up to 24 months after sustaining the injury. Furthermore, we investigated the impact of injury severity level on health care utilization and costs and determined predictors for health care and productivity costs., Methods: This prospective cohort study included adult trauma patients with severe injury (ISS≥16). Data on in-hospital health care use, 24-month post-hospital health care use and productivity loss were obtained from hospital registry data and collected with the iMTA Medical Consumption and Productivity Cost Questionnaire. The questionnaires were completed 1 week and 1, 3, 6, 12 and 24 months after injury. Log-linked gamma generalized linear models were used to investigate the drivers of health care and productivity costs., Results: In total, 174 severe injury patients were included in this study. The median age of participants was 55 years and the majority were male (66.1%). The mean hospital stay was 14.2 (SD = 13.5) days. Patients with paid employment returned to work 21 weeks after injury. In total, the mean costs per patient were €24,760 with in-hospital costs of €11,930, post-hospital costs of €7,770 and productivity costs of €8,800. Having an ISS ≥25 and lower health status were predictors of high health care costs and male sex was associated with higher productivity costs., Conclusions: Both health care and productivity costs increased with injury severity, although large differences were observed between patients. It is important for decision-makers to consider not only in-hospital health care utilization but also the long-term consequences and associated costs related to rehabilitation and productivity loss., Competing Interests: The authors have declared that no competing interests exist.

Background and Purpose: Prediction of long-term recovery of a poststroke cognitive disorder (PSCD) is currently inaccurate. We assessed whether diffusion-weighted imaging (DWI)-based measures of brain connectivity predict cognitive recovery 1 year after stroke in patients with PSCD in addition to conventional clinical, neuropsychological, and imaging variables., Methods: This prospective monocenter cohort study included 217 consecutive patients with a clinical diagnosis of ischemic stroke, aged ≥50 years, and Montreal Cognitive Assessment score below 26 during hospitalization. Five weeks after stroke, patients underwent DWI magnetic resonance imaging. Neuropsychological assessment was performed 5 weeks and 1 year after stroke and was used to classify PSCD as absent, modest, or marked. Cognitive recovery was operationalized as a shift to a better PSCD category over time. We evaluated 4 DWI-based measures of brain connectivity: global network efficiency and mean connectivity strength, both weighted for mean diffusivity and fractional anisotropy. Conventional predictors were age, sex, level of education, clinical stroke characteristics, neuropsychological variables, and magnetic resonance imaging findings (eg, infarct size). DWI-based measures of brain connectivity were added to a multivariable model to assess additive predictive value., Results: Of 135 patients (mean age, 71 years; 95 men [70%]) with PSCD 5 weeks after ischemic stroke, 41 (30%) showed cognitive recovery. Three of 4 brain connectivity measures met the predefined threshold of P <0.1 in univariable regression analysis. There was no added value of these measures to a multivariable model that included level of education and infarct size as significant predictors of cognitive recovery., Conclusions: Current DWI-based measures of brain connectivity appear to predict recovery of PSCD but at present have no added value over conventional predictors.

Objectives: To determine prognostic factors for health status and recovery patterns during the first 2 years after injury in the clinical trauma population., Design: A prospective longitudinal cohort study., Setting: Ten participating hospitals in Brabant, the Netherlands., Participants: Injured adult patients admitted to a hospital between August 2015 and November 2016 were followed: 4883 (50%) patients participated., Main Outcome Measures: Primary outcome was health status, measured with the EuroQol-5-dimensions-3-levels (EQ-5D), including a cognition item and the EuroQol Visual Analogue Scale. Health status was collected at 1 week, 1, 3, 6, 12 and 24 months after injury. Potential prognostic factors were based on literature and clinical experience (eg, age, sex, pre-injury frailty (Groningen Frailty Index), pre-injury EQ-5D)., Results: Health status increased mainly during the first 6 months after injury with a mean EQ-5D utility score at 1 week of 0.49 and 0.79 at 24 months. The dimensions mobility, pain/discomfort and usual activities improved up to 2 years after injury. Lower pre-injury health status, frailty and longer length of stay at the hospital were important prognostic factors for poor recovery. Spine injury, lower and upper extremity injury showed to be prognostic factors for problems after injury. Traumatic brain injury was a prognostic factor for cognitive problems., Conclusion: This study contributes to the increase in knowledge of health recovery after injury. It could be a starting point to develop prediction models for specific injury classifications and implementation of personalised medicine., Trial Registration Number: NCT02508675., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: With the improved survival rates after trauma, the population of patients with disabilities increased. The knowledge about functional outcome and the relationship between health status and functional outcome is limited. The aim of the present prospective cohort study was to describe the functional outcome and health status over time, and the relationship between both., Methods: Adult severely injured patients (ISS≥16) were included if hospitalised in Noord-Brabant within 48 h after injury between August 2015 and December 2016. The functional outcome (Glasgow Outcome Scale Extended - GOSE) and health status (EQ-5D) were measured at 1, 3, 6, 12 and 24 months after injury. Logistic and linear mixed models were used to examine functional outcome and health status over time. Measurements were divided into short- (1-3 months), mid- (6-12 months) and long-term (24 months)., Results: In total 239 severely injured patients were included. Functional outcome and health status improved over time. Prognostic factors during two years were a longer hospital length of stay, female gender and Glasgow Coma Scale. Besides age was a prognostic factor for health status and education level for functional outcome. A higher ASA classification was a long-term prognostic factor for a lower functional outcome and a lower health status. The patients with a good functional recovery showed a significant higher EQ-5D utility score and patients with a poor functional recovery reported significant more problems in the EQ-5 domains., Conclusion: There is a good relationship between the functional outcome and the health status during two years after a severe injury. It appears reliable to use functional outcome in terms of physical impairments in daily clinic to determine patients at risk for both a lower functional outcome and a lower health status over time., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020. Published by Elsevier Ltd.)

Background: With the ageing population, the number of older trauma patients has increased. The aim of this study was to assess non-surgical health care costs of older trauma patients and to identify which characteristics of older trauma patients were associated with high health care costs., Methods: Trauma patients aged ≥65 years who were admitted to a hospital in Noord-Brabant, the Netherlands, were included in the Brabant Injury Outcome Surveillance (BIOS) study. Non-surgical in-hospital and up to 24- months post-hospital health care use were obtained from hospital registration data and collected with the iMTA Medical Consumption Questionnaire which patients completed 1 week and 1, 3, 6, 12 and 24 months after injury. Log-linked gamma generalized linear models were used to identify cost-driving factors., Results: A total of 1910 patients were included in the study. Mean total health care costs per patient were €12,190 ranging from €8390 for 65-69 year-olds to €15,550 for those older than 90 years. Main cost drivers were the post-hospital costs due to home care and stay at an institution. Falls (72%) and traffic injury (15%) contributed most to the total health care costs, although costs of cause of trauma varied with age and sex. In-hospital costs were especially high in patients with high injury severity, frailty and comorbidities. Age, female sex, injury severity, frailty, having comorbidities and having a hip fracture were independently associated with higher post-hospital health care costs., Conclusions: In-hospital health care costs were chiefly associated with high injury severity. Several patient and injury characteristics including age, high injury severity, frailty and comorbidity were associated with post-hospital health care costs. Both fall-related injuries and traffic-related injuries are important areas for prevention of injury in the older population.

Objective: To describe the prevalence and prognostic factors of symptoms of anxiety and depression and posttraumatic stress symptoms (PTSS) after injury in the clinical trauma population., Design: Multicenter, prospective, observational cohort study., Setting: Ten hospitals in Noord-Brabant, The Netherlands., Participants: Four thousand two hundred thirty-nine adult patients (N=4239) admitted due to injury between August 2015 and December 2016., Interventions: Patients were asked to complete a questionnaire at 1 week and at 1, 3, 6, and 12 months after injury., Main Outcome Measures: The Hospital Anxiety and Depression Scale was used to assess anxiety and depressive symptoms and the Impact of Event Scale was used to assess PTSS., Results: The prevalence of symptoms of anxiety and depression decreased from 10% and 12%, respectively, at 1 week after injury to 7% and 7% at 12 months after injury. Acute traumatic stress symptoms were present in 13% at 1 week and PTSS was prevalent in 10% of the participants at 12 months after injury. Strong prognostic factors for poor psychological outcome in multivariable logistic mixed models were preinjury frailty, psychological complaints and nonworking status preinjury, female sex, low educational level, and accident category (ie, traffic accident, work-related accident, or accidents at home compared to sport injuries)., Conclusions: Psychological distress is a common health problem during the first year after injury. Important prognostic factors for psychological distress include psychological complaints before injury and frailty. Early recognition of psychological problems after injury could facilitate discussion between caregivers and patients and improve recovery., (Copyright © 2019 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Introduction: Survival after trauma has considerably improved. This warrants research on non-fatal outcome. We aimed to identify characteristics associated with both short and long-term health status (HS) after trauma and to describe the recovery patterns of HS and psychological outcomes during 24 months of follow-up., Methods: Hospitalized patients with all types of injuries were included. Data were collected at 1 week 1, 3, 6, 12, and 24 months post-trauma. HS was assessed with the EuroQol-5D-3L (EQ-5D-3L) and the Health Utilities Index Mark 2 and 3 (HUI2/3). For the screening of symptoms of post-traumatic stress, anxiety and depression, the Impact of Event Scale (IES) and the Hospital Anxiety and Depression Scale (HADS) subscale anxiety (HADSA) and subscale depression (HADSD) were used. Recovery patterns of HS and psychological outcomes were examined with linear mixed model analyses., Results: A total of 4,883 patients participated (median age 68 (Interquartile range 53-80); 50% response rate). The mean (Standard Deviation (SD)) pre-injury EQ-5D-3L score was 0.85 (0.23). One week post-trauma, mean (SD) EQ-5D-3L, HUI2 and HUI3 scores were 0.49 (0.32), 0.61 (0.22) and 0.38 (0.31), respectively. These scores significantly improved to 0.77 (0.26), 0.77 (0.21) and 0.62 (0.35), respectively, at 24 months. Most recovery occurred up until 3 months. At long-term follow-up, patients of higher age, with comorbidities, longer hospital stay, lower extremity fracture and spine injury showed lower HS. The mean (SD) scores of the IES, HADSA and HADSD were respectively 14.80 (15.80), 4.92 (3.98) and 5.00 (4.28), respectively, at 1 week post-trauma and slightly improved over 24 months post-trauma to 10.35 (14.72), 4.31 (3.76) and 3.62 (3.87), respectively., Discussion: HS and psychological symptoms improved over time and most improvements occurred within 3 months post-trauma. The effects of severity and type of injury faded out over time. Patients frequently reported symptoms of post-traumatic stress., Trial Registration: ClinicalTrials.gov identifier: NCT02508675., Competing Interests: The authors have declared that no competing interests exist.

Purpose: While survival rates after a trauma are increasing a considerable part of the trauma population are still at risk for both short and long term disabilities. Little is known about prognostic factors over time after a severe trauma. The aim of the present prospective cohort study was to examine trauma and patient related prognostic factors for a lower health status over time after a severe trauma., Methods: A multicentre prospective observational cohort study was conducted. Adult trauma patients with severe injuries (ISS ≥ 16) were included from August 2015 until November 2016 if admitted to one of the hospitals in Noord-Brabant (the Netherlands). Outcome measure was health status, measured by the EuroQol-5D (EQ-5D utility and EQ-Visual analogue scale) and the Health Utilities Index (HUI2 and HUI3) one week and one, three, six, and twelve months after injury. Patient and trauma characteristics were analysed as prognostic factors with linear mixed models. The effect of each prognostic factor over time was analysed by adding the interaction term between the prognostic factor and time point in a multivariable linear mixed model, adjusted for confounders. Additionally, the risk factors for problems in the EQ-5 dimensions of HS and cognition were analysed., Results: In total 239 severely injured patients participated. Pre-injury health status, hospital length of stay, ISS and comorbidities were significant prognostic factors for a lower health status. A younger age and extremity injury were prognostic factors for a lower health status until one month after trauma and unemployment before trauma and comorbidities six until twelve months after trauma. In the EQ-5 dimensions 44.1% remained problems in mobility, 15.3% in self-care, 46.4% in activity, 53.3% in pain, 32.5% in anxiety and 35.7% in cognition., Conclusions: Lower pre-injury health status, longer hospital length of stay, higher ISS, and comorbidities were significant prognostic factors for a lower health status during one year after a severe injury. A younger age and an extremity injury were short-term prognostic factors and unemployment before trauma and comorbidities were long-term prognostic factors. Even after twelve months patients in our population reported more problems in all EQ-5D dimensions when compared to the Dutch reference population.

This study aimed to provide a detailed overview of the health care and productivity costs of non-fatal road traffic injuries by road user type. In a cohort study in the Netherlands, adult injury patients admitted to a hospital as a result of a traffic accident completed questionnaires 1 week and 1, 3, 6, 12 and 24 months after injury, including the iMTA Medical Consumption and Productivity Cost Questionnaire. In-hospital, post-hospital medical costs and productivity costs were calculated up to two years after traffic injury. In total, 1024 patients were included in this study. The mean health care costs per patient were € 8200. The mean productivity costs were € 5900. Being female, older age, with higher injury severity and having multiple comorbidities were associated with higher health care costs. Higher injury severity and being male were associated with higher productivity costs. Pedestrians aged ≥ 65 years had the highest mean health care costs (€ 18,800) and motorcyclists the highest mean productivity costs (€ 9000). Bicycle injuries occurred most often in our sample (n = 554, 54.1%) and accounted for the highest total health care and productivity costs. Considering the high proportion of total costs incurred by bicycle injuries, this is an important area for the prevention of traffic injuries.

Aim: The aim of this study was to determine prognostic factors for medical and productivity costs, and return to work (RTW) during the first two years after trauma in a clinical trauma population., Methods: This prospective multicentre observational study followed all adult trauma patients (≥18 years) admitted to a hospital in Noord-Brabant, the Netherlands from August 2015 through November 2016. Health care consumption, productivity loss and return to work were measured in questionnaires at 1 week, 1, 3, 6, 12 and 24 months after injury. Data was linked with hospital registries. Prognostic factors for medical costs and productivity costs were analysed with log-linked gamma generalized linear models. Prognostic factors for RTW were assessed with Cox proportional hazards model. The predictive ability of the models was assessed with McFadden R2 (explained variance) and c-statistics (discrimination)., Results: A total of 3785 trauma patients (39% of total study population) responded to at least one follow-up questionnaire. Mean medical costs per patient (€9,710) and mean productivity costs per patient (€9,000) varied widely. Prognostic factors for high medical costs were higher age, female gender, spine injury, lower extremity injury, severe head injury, high injury severity, comorbidities, and pre-injury health status. Productivity costs were highest in males, and in patients with spinal cord injury, high injury severity, longer length of stay at the hospital and patients admitted to the ICU. Prognostic factors for RTW were high educational level, male gender, low injury severity, shorter length of stay at the hospital and absence of comorbidity., Conclusions: Productivity costs and RTW should be considered when assessing the economic impact of injury in addition to medical costs. Prognostic factors may assist in identifying high cost groups with potentially modifiable factors for targeted preventive interventions, hence reducing costs and increasing RTW rates., Competing Interests: The authors have declared that no competing interests exist.

Objectives: The aims of this study were to examine the pattern of changes over time in health status (HS) and quality of life (QoL) in the first year after hip fracture and to quantify the association between frailty at the onset of hip fracture and the change in HS and QoL 1 year later. The major hypothesis was that frailty, a clinical state of increased vulnerability, is a good predictor of QoL in patients recovering from hip fracture., Design: Prospective, observational, follow-up cohort study., Setting: Secondary care. Ten participating centres in Brabant, the Netherlands., Participants: 1091 patients entered the study and 696 patients completed the study. Patients with a hip fracture aged 65 years and older or proxy respondents for patients with cognitive impairment were included in this study., Main Outcome Measures: The primary outcomes were HS (EuroQol-5 Dimensions questionnaire) and capability well-being (ICEpop CAPability measure for Older people). Prefracture frailty was defined with the Groningen Frailty Indicator (GFI), with GFI ≥4 indicating frailty. Participants were followed up at 1 month, 3 months, 6 months and 1 year after hospital admission., Results: In total, 371 patients (53.3%) were considered frail. Frailty was negatively associated with HS (β -0.333; 95% CI -0.366 to -0.299), self-rated health (β -21.9; 95% CI -24.2 to -19.6) and capability well-being (β -0.296; 95% CI -0.322 to -0.270) in elderly patients 1 year after hip fracture. After adjusting for confounders, including death, prefracture HS, age, prefracture residential status, prefracture mobility, American Society of Anesthesiologists grading and dementia, associations were weakened but remained significant., Conclusions: We revealed that frailty is negatively associated with QoL 1 year after hip fracture, even after adjusting for confounders. This finding suggests that early identification of prefracture frailty in patients with a hip fracture is important for prognostic counselling, care planning and the tailoring of treatment., Trial Registration Number: NCT02508675., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Significant differences exist between retrospectively collected pre-injury Health Status (HS) of trauma patients and the HS of the general population. Compared to the general population, the trauma population includes a larger proportion of individuals with a low level of socio-economic status. The aim was to compare retrospectively collected pre-injury HS with HS of a sample of Dutch individuals not only adjusted for age and gender, but also for educational level., Methods: Within three months post-trauma, pre-injury HS (n = 2987) was collected by using the EuroQol-five-dimension-3-level (EQ-5D-3L) questionnaire. Data were abstracted from the Brabant Injury Outcome Surveillance. The reference cohort (n = 1839) included a sample of the Dutch general population. Multiple regression was used to compare HS of both cohorts., Results: A higher recalled pre-injury EQ-5D-3L score of the injury cohort was reported compared to the HS of the reference cohort after adjustment for age (β = 0.014 [95% CI: 0.001,0.027] for males and β = 0.018 [95% CI: -0.001, 0.036] for females). After adjustment for age and educational level, the Beta showed a ≥10% increasement: males; unadjusted β = 0.006 [95% CI: -0.007, 0.019] to β = 0.014 [95% CI: 0.001, 0.027] after age adjustment to β = 0.020 [95% CI: 0.007, 0.033] after adjustment for age and educational level, females; unadjusted β = -0.018 [95% CI: -0.035, -0.001] to β = 0.018 [95% CI: -0.001, 0.036] after age adjustments to β = 0.025 [95% CI: 0.007, 0.043] after adjustments for age and educational level. After adjustment for age, gender and educational level, the injury cohort reported prior to the trauma less problems on the 'pain/discomfort' (OR = 0.522 [95% CI: 0.454, 0.602]) and the 'anxiety/depression' (OR = 0.745 [95% CI: 0.619, 0.897]) dimensions, as compared to the reference cohort. In contrast, the injury cohort reported significantly more problems on the 'self-care' dimension (OR = 1.497 [95% CI: 0.1.112, 2.016]) prior to the trauma., Conclusions: Injured patients report better recalled pre-injury HS compared to the HS of the reference cohort. After adjustment for educational level, the difference in HS between the injury cohort and the reference cohort increases, underlining that other confounders might also influence HS., (Copyright © 2019. Published by Elsevier Ltd.)

Introduction: The overestimation of survival predictions in the ageing trauma population results in negative benchmark numbers in hospitals that mainly treat elderly patients. The aim of this study was to develop and validate a modified Trauma and Injury Severity Score (TRISS) for accurate survival prediction in the ageing blunt trauma population., Methods: This retrospective study was conducted with data from two Dutch Trauma regions. Missing values were imputed. New prediction models were created in the development set, including age (continuous or categorical) and Anesthesiologists Physical Status (ASA). The models were externally validated. Subsets were created based on age (≥75 years) and the presence of hip fracture. Model performance was assessed by proportion explained variance (Nagelkerke R2), discrimination (Area Under the curve of the Receiver Operating Characteristic, AUROC) and visually with calibration plots. A final model was created based on both datasets., Results: No differences were found between the baseline characteristics of the development dataset (n = 15,530) and the validation set (n = 15,504). The inclusion of ASA in the prediction models showed significant improved discriminative abilities in the two subsets (e.g. AUROC of 0.52 [95% CI: 0.46, 0.58] vs. 0.74 [95% CI: 0.69, 0.78] for elderly patients with hip fracture) and an increase in the proportion explained variance (R2 = 0.32 to R2 = 0.35 in the total cohort). The final model showed high agreement between observed and predicted survival in the calibration plot, also in the subsets., Conclusions: Including ASA and age (continuous) in survival prediction is a simple adjustment of the TRISS methodology to improve survival predictions in the ageing blunt trauma population. A new model is presented, through which even patients with isolated hip fractures could be included in the evaluation of trauma care., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Previous research showed that there is no agreement on a practically applicable model to use in the evaluation of trauma care. A modification of the Trauma and Injury Severity Score (modified TRISS) is used to evaluate trauma care in the Netherlands. The aim of this study was to evaluate the prognostic ability of the modified TRISS and to determine where this model needs improvement for better survival predictions., Methods: Patients were included if they were registered in the Brabant Trauma Registry from 2010 through 2015. Missing values were imputed according to multiple imputation. Subsets were created based on age, length of stay, type of injury and injury severity. Probability of survival was calculated with the modified TRISS. Discrimination was assessed with the Area Under the Receiver Operating Curve (AUROC). Calibration was studied graphically., Results: The AUROC was 0.84 (95% CI: 0.83, 0.85) for the total cohort (N = 69 747) but only 0.53 (95% CI: 0.51, 0.56) for elderly patients with hip fracture. Overall, calibration of the modified TRISS was adequate for the total cohort, with an overestimation for elderly patients and an underestimation for patients without brain injury., Conclusions: Outcome comparison conducted with TRISS-based predictions should be interpreted with care. If possible, future research should develop a simple prediction model that has accurate survival prediction in the aging overall trauma population (preferable with patients with hip fracture), with readily available predictors., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Trauma databases often contain relatively high proportions of missing physiologic values. Multiple imputation (MI) could be a possible adequate solution for the missing values. This study aimed to demonstrate the influence of more simplified imputation models on standardized W statistic (Ws) (number of excess survivors per hundred patients that would be achieved if the study center treated identically the same case mix as the reference population)., Methods: Data from three trauma care networks in the Netherlands were used to investigate local differences in missing data. Five different imputation models (MI 1 to 5) were created based on literature and expert opinion. A sixth database was created using maximal single imputation and a seventh database with only complete case analysis (CCA). The Ws values were calculated for the three regions separately., Results: A total of 8,853, 24,487, and 8,599 observations were examined in region 1, region 2, and region 3, respectively. The Ws in region 1 ranged from -0.48 (95% confidence interval [CI], -1.71 to 0.80) for CCA to 0.53 (95% CI, -0.19 to 1.26) for MI 4 and a range of 0.40 (95% CI, -0.91 to 0.10) for CCA to -0.32 (-0.69, 0.04) for MI 1 and MI 4 was found in region 2. The Ws for region 3 ranged from -0.19 (-0.83 to 0.45) in all MI data sets to -0.12 (-0.76 to 0.52) in the CCA data set. Although there were no significant differences between the Ws of the imputation data sets and the CCA analysis, large differences were found in the region with the most missing values., Conclusion: Different imputation strategies did influence Ws values. Supplementary variables showed no additional value for the imputation process and a more simplified imputation model could be used to adequately impute missing data., Level of Evidence: Prognostic, level II.

Introduction: Increasing numbers of patients with hip fractures also have advanced comorbidities. A majority are treated surgically. However, a significantly increasing percentage of medically unfit patients with unacceptably high risk of perioperative death are treated nonoperatively. Important questions about patients' prefracture quality of life (QOL) and future perspectives should be asked before considering different treatment options to assess what kind of treatment is advisable in frail elderly high-risk patients with a hip fracture., Objective: The aim of this review was to provide an overview of differences in mortality, health-related QOL [(HR)QOL], functional outcome, and costs between nonoperative management (NOM) and operative management (OM) of hip fractures in patients above 65 years., Methods: A systematic literature search was performed in EMBASE, OvidSP, PubMed, Cochrane Central, and Web of Science for observational studies and trials. Observational studies and randomized controlled trials comparing NOM with OM in hip fracture patients were selected. The methodological quality of the selected studies was assessed according to the Methodological Index for Nonrandomized Studies (MINORS) or Furlan checklist., Results: Seven observational studies were included with a total of 1189 patients, of whom 242 (20.3%) were treated conservatively. The methodological quality of the studies was moderate (mean: 14.7, standard deviation [SD]: 1.5). The 30-day and 1-year mortalities were higher in the nonoperative group (odds ratio [OR]: 3.95, 95% confidence interval [CI]: 1.43-10.96; OR: 3.84, 95% CI: 1.57-9.41). None of the included studies compared QOL, functional outcome, or health-care costs between the 2 groups., Conclusion: This systematic review and meta-analysis demonstrated that only a few studies with small number of patients comparing NOM with OM were published. A significantly higher 30-day and 1-year mortality was revealed in nonoperatively treated hip fracture patients. No data were found examining (HR)QOL and costs. Further work is needed to enable shared decision-making and to initiate NOM in frail elderly patients with advanced comorbidity and limited life expectancy., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Trauma is the leading cause of death in individuals younger than 40 years. There are many different models for predicting patient outcome following trauma. To our knowledge, no comprehensive review has been performed on prognostic models for the general trauma population. Therefore, this review aimed to describe (1) existing mortality prediction models for the general trauma population, (2) the methodological quality and (3) which variables are most relevant for the model prediction of mortality in the general trauma population., Methods: An online search was conducted in June 2015 using Embase, Medline, Web of Science, Cinahl, Cochrane, Google Scholar and PubMed. Relevant English peer-reviewed articles that developed, validated or updated mortality prediction models in a general trauma population were included., Results: A total of 90 articles were included. The cohort sizes ranged from 100 to 1,115,389 patients, with overall mortality rates that ranged from 0.6% to 35%. The Trauma and Injury Severity Score (TRISS) was the most commonly used model. A total of 258 models were described in the articles, of which only 103 models (40%) were externally validated. Cases with missing values were often excluded and discrimination of the different prediction models ranged widely (AUROC between 0.59 and 0.98). The predictors were often included as dichotomized or categorical variables, while continuous variables showed better performance., Conclusion: Researchers are still searching for a better mortality prediction model in the general trauma population. Models should 1) be developed and/or validated using an adequate sample size with sufficient events per predictor variable, 2) use multiple imputation models to address missing values, 3) use the continuous variant of the predictor if available and 4) incorporate all different types of readily available predictors (i.e., physiological variables, anatomical variables, injury cause/mechanism, and demographic variables). Furthermore, while mortality rates are decreasing, it is important to develop models that predict physical, cognitive status, or quality of life to measure quality of care., (Copyright © 2016 Elsevier Ltd. All rights reserved.)