The psychosocial aid response after the 22/03/2016 attacks in Belgium: a community case study.

Introduction: After the terrorist attacks, early psychosocial care is provided to people considered at risk of developing mental health issues due to the attacks. Despite the clear importance of such early intervention, there is very few data on how this is registered, who is targeted, and whether target-recipients accept such aid. Methods: Using registry data from the Centre General Wellbeingwork (CAW), a collection of centers in the regions Brussels and Flanders that provide psychosocial care, we examined the early psychosocial care response after the terrorist attacks of 22/03/2016 in Belgium. Results: In total, 327 people were listed to be contacted by the CAW, while only 205 were reached out to (62.7%). Most were contacted within a month (84.9%), and were victims of the attacks (69.8%). Overall, the majority was female (55.6%). Conclusion: Overall, target recipients were witnesses and survivors of the attacks, though a large proportion of people were not reached by the early outreach.

A day in the life: psychological impact on emergency responders during the 22 March 2016 terrorist attacks.

Introduction: Terrorist attacks can cause severe long-term mental health issues that need treatment. However, in the case of emergency responders, research is often vague on the type of stressors that emergency responders encounter. For example, in addition to the threat that they work under, studies have shown that ill-preparation adds to the stress experienced by emergency responders. However, few studies have looked into the experience of emergency responders. In this study, we looked at the experience of emergency responders during the 22 March 2016 terrorist attacks in Belgium. Methods: We used a qualitative design, in which we interviewed different types of emergency responders. Police officers, nurses, soldiers, firefighters, and Red Cross volunteers were included. Interviews were coded by two researchers and analyzed using a thematic approach. Results: Four large themes were developed: constant threat and chaos, frustrations with lack of preparedness and training, ethical decisions, and debriefings. In addition, although emergency responders encountered constant threat, they often felt that they were ill-prepared for such attacks. One specific example was their lack of training in tourniquet usage. Furthermore, in a disaster setting, the emergency responders had to make life-and-death decisions for which they were not always prepared. Finally, debriefings were conducted in the aftermath of the attacks. Whereas most were perceived as positive, the debriefings among police officers were viewed as insufficient. Conclusions: Emergency responding to terrorist attacks has many different dimensions of events that can cause stress. Our study revealed that preparation is key, not only in terms of material but also in terms of ethics and debriefings.

Post-what stress? A review of methods of research on posttraumatic stress during COVID-19.

BACKGROUND: Post-traumatic stress (PTS) was extensively investigated during the COVID-19 pandemic. However, numerous researchers have raised concerns regarding the adherence of many of these studies to the diagnostic criteria for PTSD as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). This review aimed to provide insight into the methodology of research on PTS during the COVID-19 pandemic. METHODS: Two independent reviewers examined a total of 1129 studies published between 1/01/2020 and 1/07/2023. The investigation focused on the scales employed to assess PTS, the diagnostic framework used (DSM or ICD), whether there was referral to an index-event in the PTS measurement and country where data collection took place. RESULTS: Among the 1129 studies, 70.0% did not provide any indication of an index-event to which PTS symptoms were attributed. Only 11.3% of the studies explicitly indicated an index-event associated with the PTS symptoms. Furthermore, 54.1% of the studies utilized scales based on DSM-IV criteria. Finally, the majority of PTS-studies had data collections in China, United States and Italy. CONCLUSION: A limited number of studies conducted during the COVID-19 pandemic reported use of an index-event in their PTS measurement. Furthermore, most studies used scales based on a previous iteration of the most used diagnostical system, namely the DSM. This delay in the implementation of new diagnostic criteria, may impair the clinical relevance of scientific studies.

Comment on 'Posttraumatic stress disorder and depression after the 2018 Strasbourg Christmas Market terrorist attack: a comparison of exposed and non-exposed police personnel': moving towards shared methodology in terrorism-research.

In the recent article 'Post-traumatic stress disorder and depression following the 2018 Strasbourg Christmas Market terrorist attack: a comparison of exposed and non-exposed police personnel,' important insights are provided about the association between terrorism exposure and Post-Traumatic Stress Disorder (PTSD). However, in our opinion, there are several methodological issues that limit the results of this study. In this letter, we discuss the problematic use of partial PTSD, the adjustment of the PCL-5 to refer only to a specific terrorist attack, and the significance of criterion A.

Predicting return to work after traumatic brain injury using machine learning and administrative data.

BACKGROUND: Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models. AIM: The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI. METHODS: This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC). RESULTS: The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment. DISCUSSION: While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.

Traumatic brain injury hospitalizations in Belgium: A brief overview of incidence, population characteristics, and outcomes.

Background: There is a need for complete and accurate epidemiological studies for traumatic brain injury (TBI). Secondary use of administrative data can provide country-specific population data across the full spectrum of disease. Aim: This study aims to provide a population-based overview of Belgian TBI hospital admissions as well as their health-related and employment outcomes. Methods: A combined administrative dataset with deterministic linkage at individual level was used to assess all TBI hospitalizations in Belgium during the year 2016. Discharge data were used for patient selection and description of injuries. Claims data represented the health services used by the patient and health-related follow-up beyond hospitalization. Finally, social security data gave insight in changes to employment situation. Results: A total of 17,086 patients with TBI were identified, with falls as the predominant cause of injury. Diffuse intracranial injury was the most common type of TBI and 53% had injuries to other body regions as well. In-hospital mortality was 6%. The median length of hospital stay was 2 days, with 20% being admitted to intensive care and 28% undergoing surgery. After hospitalization, 23% had inpatient rehabilitation. Among adults in the labor force pre-injury, 72% of patients with mild TBI and 59% with moderate-to-severe TBI returned to work within 1 year post-injury. Discussion: Administrative data are a valuable resource for population research. Some limitations need to be considered, however, which can in part be overcome by enrichment of administrative datasets with other data sources such as from trauma registries.

Quantifying injury severity for traumatic brain injury with routinely collected health data.

BACKGROUND: Routinely collected health data (RCHD) offers many opportunities for traumatic brain injury (TBI) research, in which injury severity is an important factor. OBJECTIVE: The use of clinical injury severity indices in a context of RCHD is explored, as are alternative measures created for this specific purpose. To identify useful scales for full body injury severity and TBI severity this study focuses on their performance in predicting these currently used indices, while accounting for age and comorbidities. DATA: This study utilized an extensive population-based RCHD dataset consisting of all patients with TBI admitted to any Belgian hospital in 2016. METHODS: Full body injury severity is scored based on the (New) Injury Severity Score ((N)ISS) and the ICD-based Injury Severity Score (ICISS). For TBI specifically, the Abbreviated Injury Scale (AIS) Head, Loss of Consciousness and the ICD-based Injury Severity Score for TBI injuries (ICISS) were used in the analysis. These scales were used to predict three outcome variables strongly related to injury severity: in-hospital death, admission to intensive care and length of hospital stay. For the prediction logistic regressions of the different injury severity scales and TBI severity indices were used, and error rates and the area under the receiver operating curve were evaluated visually. RESULTS: In general, the ICISS had the best predictive performance (error rate between 0.06 and 0.23; AUC between 0.82 [0.81;0.83] and 0.86 [0.85;0.86]). A clearly increasing error rate can be noticed with advancing age and accumulating comorbidity. CONCLUSION: Both for full body injury severity and TBI severity, the ICISS tends to outperform other scales. It is therefore the preferred scale for use in research on TBI in the context of RCHD. In their current form, the severity scales are not suitable for use in older populations.

Predictors of return to work after moderate-to-severe traumatic brain injury: a systematic review of current literature and recommendations for future research.

PURPOSE: To identify factors associated with employment between six months and five years after traumatic brain injury (TBI). METHODS: Using a predefined search algorithm, four electronic databases were searched for literature published between 2014 and the first half of April 2021 containing predictors of employment outcome. Data were selected in accordance with the PRISMA flow and the whole process was conducted by two reviewers who had to attain a consensus. The study results were discussed with an expert panel, in order to provide guidance for future research on this topic. RESULTS: This review found clear evidence for employment status at time of injury, occupation at time of injury, Glasgow Coma Scale, length of stay, disability level and primary payer to be predictors of return to work after TBI. CONCLUSIONS: More literature investigating in depth the functioning and environmental factors is required for further improvement of predictions, rehabilitation and policy.Implications for rehabilitationThis study identifies predictors of return to work in TBI patients, which can be used to identify patients with high risk early in the recovery process.Current literature shows difficulties with general functioning are a barrier for return to work, but gives no indication about effective therapeutic interventions.More knowledge about modifiable factors is desirable to improve rehabilitation and, thereby, employment outcomes after TBI.

The Incremental Hospitalization Cost of Significant Transport-Related Traumatic Brain Injury.

AIM: This study aims to determine the incremental cost of acute hospitalization for traumatic brain injury (TBI) compared with matched controls. A second purpose is to identify the factors contributing to this hospital costs. METHODOLOGY: Analyses were performed on administrative data for injured patients, hospitalized in Belgium between 2009 and 2011 following a road traffic accident. Cases were matched to a control with similar injuries but without TBI. The incremental hospitalization cost of TBI and the factors contributing to the hospital costs were determined using multivariable regression modeling with gamma distribution and log link. RESULTS: A descriptive comparison of cases and controls shows clear differences in healthcare utilization and costs. The presence of a TBI increases the cost by a factor between 1.66 (95% confidence interval: 1.52-1.82) and 2.08 (95% confidence interval: 1.72-2.51). Regarding healthcare utilization, the most important determinants of hospital costs are surgical complexity, use of magnetic resonance imaging, intensive care unit admission, and mechanical ventilation. DISCUSSION: To our knowledge, this is the first matched-control study calculating the incremental hospitalization cost of TBI. The insights provided by this study are relevant in the context of prospective payments and can be an incentive for investments in prevention policies and extramural care.

Health care utilization after hospitalization following a road traffic accident.

Purpose: In recent years, there has been an increasing interest in measuring and modeling health care utilization. However, only limited research has been performed in the field of health care utilization following road traffic accidents. This article aims to measure the incremental health care utilization after hospital discharge after a road traffic accident and explore the association between socio-demographic and injury-related variables and health care utilization.Material and methods: Generalized linear models with negative binomial distribution and log-link were executed per type of health care provider (general practitioner, medical specialists, rehabilitation services and outpatient nursing care) and per type of discharge location (discharged to home, discharged to in-hospital rehabilitation). Health care utilization of the 6 months after discharge was compared with the 6 months before the accident (baseline care).Results: Health care utilization six months after discharge is significantly higher than baseline care, except for outpatient nursing care and general practitioners in in-hospital rehabilitation. The increase in visits to medical specialists ranged on average between 1 and 2.2 visits. For general practitioner, there was an increase of 0.4 visits and 0.8 in outpatient nursing care for those who returned home after acute hospitalization. The average increase in rehabilitation services ranged between 3.6 and 20. Associated influential factors differ per health care provider and discharge destination.Conclusion: Evidence of this study suggests higher health care utilization during the first 6 months following hospitalization due to a road traffic injury, compared with baseline care. Associated variables differ per type of health care provider and discharge-destination. More in-depth research on subgroups is needed.Implications for rehabilitationHealth care utilization varies across different patient characteristics and type of injuries which should be considered in the communication with patients on their care trajectory post-discharge.General descriptions of health care utilization in traffic victims at the population level are lacking. Output similar to our study could serve as a reference for post-discharge care planning.The research output can be a starting point for future research on quality indicators of the expected quantity of care.Efforts must be made to estimate suchlike reference tables on post-discharge services in other patient groups and secondary data are a suitable data-source for those analyses.

The incremental cost of traumatic brain injury during the first year after a road traffic accident.

This study aims to determine the incremental cost of TBI during the first year after a traffic accident, compared to other patients with similar non-TBI injuries. Secondly, identification of factors associated with medical costs of TBI is pursued. Analyses were performed on administrative data for traffic victims hospitalised in Belgium between 2009 and 2011. Medical costs attributable to the accident are estimated over one year post-injury. Cases with TBI were matched to controls with similar non-TBI injuries to determine the incremental cost of TBI. Both aims of this research were assessed using regression analysis. The incremental cost of TBI is estimated to range between € 10 042 (95%CI [€8198; €11 887]) and €21 715 (95%CI [€13 5889; €29 540]). Age, problems with self-reliance, survival status, the occurrence of acute events and severity of TBI are significant predictors of medical costs. As to healthcare utilisation, MRI usage, inpatient rehabilitation facilities, nursing homes and readmissions to acute hospital stand out as having most influence on costs. This study reveals a considerable incremental cost of TBI. Policy-making bodies should be made aware of this phenomenon and a diversified policy should be considered when financing programs are discussed.