Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience.

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.

Prevalence, characteristics, and outcome of subclinical vasculitis in polymyalgia rheumatica: a retrospective cohort study.

OBJECTIVES: Two recent meta-analyses reported subclinical vasculitis in 22-23% of patients with polymyalgia rheumatica (PMR). We aimed to evaluate the prevalence, characteristics, and outcome of subclinical vasculitis among our PMR patients. METHODS: Consecutive patients with GCA/PMR spectrum disease with isolated PMR symptoms who underwent FDG PET imaging between 2003-2020 and who were followed for ≥6 months, were included retrospectively. Vasculitis was defined as FDG uptake ≥ grade 2 in any vessel. RESULTS: We included 337 patients, of whom 31 (9%) with subclinical vasculitis. Among those with subclinical vasculitis, 21 (58%) had isolated large vessel vasculitis, 3 (10%) had isolated cranial vasculitis and 7 (23%) had both cranial and large vessel vasculitis. The glucocorticoid (GC) starting dose and GC doses during follow-up were higher in those with subclinical vasculitis until 12 months after diagnosis (p< 0.001). There was no difference in the duration of GC treatment (25 vs 20 months, p= 0.187). Cox proportional hazard regression analyses showed no difference in the proportion of patients able to stop GC (HR 0.78 [95% CI 0.49-1.25], p= 0.303) and in the proportion of patients with relapse (HR 0.82 [95%CI 0.50-1.36], p= 0.441). CONCLUSION: Only 9% of our PMR patients had subclinical vasculitis with a predilection for large vessel vasculitis. There were no differences in relapse rate and duration of GC treatment, however those with subclinical vasculitis received higher GC doses until 12 months after diagnosis. Prospective interventional trials are needed to evaluate the outcome of PMR patients with and without subclinical vasculitis treated with similar GC protocol.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a 2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations.

BACKGROUND: The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches. METHODS: In very small populations, methodological challenges exacerbate. iSTORE's ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence. RESULTS: The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias. CONCLUSION: Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

Identity Functioning in Patients with an Eating Disorder: Developmental Trajectories throughout Treatment.

Increasing research has indicated a strong association between identity functioning and eating disorder (ED) symptomatology. However, a detailed investigation of identity throughout ED treatment is lacking. The present longitudinal study examined identity in inpatients with an ED and explored its simultaneous change with ED symptomatology throughout treatment. A total of 225 female patients completed questionnaires at admission. From these 225 patients participating at admission (Wave 1), 110 also participated in at least one additional measurement wave, with 43.64% (n = 48) participating at admission and during treatment, 16.36% (n = 18) participating at admission and at discharge, and 40% (n = 44) participating at admission, during treatment and at discharge. Questionnaires on identity synthesis, identity confusion, identity processes, and ED symptomatology were completed. Latent growth curve modeling was used to address the research questions. Throughout treatment, a decrease in identity confusion and an increase in identity synthesis and adaptive identity processes were found. Accordingly, increases in identity synthesis and identification with commitment were related to general decreases in the drive for thinness and body dissatisfaction. Similarly, such decreases in ED symptoms were related to general decreases in identity confusion and ruminative exploration. The present study points to an increase in identity functioning throughout treatment, and longitudinal associations between identity functioning and ED symptomatology were found. Helping patients to decrease their ruminative exploration and to increase their identification with previously made life commitments and treating body/weight concerns could both be helpful in ED treatment.

Gout and incidence of 12 cardiovascular diseases: a case-control study including 152 663 individuals with gout and 709 981 matched controls.

BACKGROUND: Gout, a common crystal arthropathy, is associated with increased risk of cardiovascular disease. We aimed to identify how this risk varies by individual cardiovascular disease across a broad spectrum of conditions. METHODS: In this matched case-control study, we used linked primary and secondary electronic health records from the UK Clinical Practice Research Datalink to assemble a cohort of individuals with a first-time diagnosis of gout between Jan 1, 2000 and Dec 31, 2017, who were aged 80 years or younger at diagnosis, and free of cardiovascular diseases up to 12 months after diagnosis. The control cohort comprised up to five control individuals per patient with gout, matched on age, sex, socioeconomic status, geographical region, and calendar time, randomly selected among individuals free of gout at any time before and during the study period. The cohorts were followed up until June 30, 2019. We investigated the incidence of 12 cardiovascular diseases and used Cox proportional hazards models to examine differences in people with and without gout, overall and by subgroups of sex, age, socioeconomic status, and year of study inclusion. We further adjusted models for known cardiovascular risk factors (blood pressure, BMI, smoking status, cholesterol, type 2 diabetes, chronic kidney disease, and history of hypertension). FINDINGS: We identified 152 663 individuals with gout (mean age 56·2 years [SD 13·3]; 120 324 [78·8%] men and 32 339 [21·2%] women) and 709 981 matched controls (mean age 56·5 years [13·2]; 561 002 [79·0%] men and 148 979 [21·0%] women). Of these individuals, 31 479 (20·6%) with gout and 106 520 (15·0%) without gout developed cardiovascular disease during a median follow-up of 6·5 years (IQR 3·1-10·5). Patients with gout had higher risk of cardiovascular diseases than matched controls (hazard ratio [HR] 1·58 [95% CI 1·52-1·63]). Excess risk of cardiovascular disease in gout was greater in women than men (women: HR 1·88 [1·75-2·02]; men: HR 1·49 [1·43-1·56]), and, among all age groups, was highest in younger individuals (HR in people aged <45 years: 2·22 [1·92-2·57]). Excess risk was observed across all 12 cardiovascular diseases investigated. Patients with gout had higher BMI than matched controls (mean difference 2·90 kg/m2 [95% CI 2·87-2·93]) and higher prevalence of chronic kidney disease, dyslipidaemia, history of hypertension, obesity, and type 2 diabetes. Adjusting for known cardiovascular risk factors attenuated but did not eliminate the excess risk of cardiovascular disease related to gout (adjusted HR 1·31 [1·27-1·36]). INTERPRETATION: Patients with gout had an excess risk of developing a broad range of cardiovascular diseases that extend beyond atherosclerotic diseases and include heart failure, arrhythmias, valve disease, and thromboembolic diseases. Excess risk was highest in women and younger individuals. These findings suggest that strategies to reduce cardiovascular risk in patients with gout need to evolve and be implemented in clinical practice. FUNDING: Research Foundation Flanders.

Oxytocin and state attachment responses to secure base support after stress in middle childhood.

We tried to replicate the finding that receiving care increases children's oxytocin and secure state attachment levels, and tested whether secure trait attachment moderates the oxytocin and state attachment response to care. 109 children (9-11 years old; M = 9.59; SD = 0.63; 34.9% boys) participated in a within-subject experiment. After stress induction (Trier Social Stress Test), children first remained alone and then received maternal secure base support. Salivary oxytocin was measured eight times. Secure trait and state attachment were measured with questionnaires, and Secure Base Script knowledge was assessed. Oxytocin levels increased after receiving secure base support from mother after having been alone. Secure state attachment changed less. Trait attachment and Secure Base Script knowledge did not moderate oxytocin or state attachment responses to support. This might mean that, regardless of the attachment history, in-the-moment positive attachment experiences might have a beneficial effect on trait attachment development in middle childhood.

An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation.

Within the causal association paradigm, a method is proposed to assess the validity of a continuous outcome as a surrogate for a binary true endpoint. The methodology is based on a previously introduced information-theoretic definition of surrogacy and has two main steps. In the first step, a new model is proposed to describe the joint distribution of the potential outcomes associated with the putative surrogate and the true endpoint of interest. The identifiability issues inherent to this type of models are handled via sensitivity analysis. In the second step, a metric of surrogacy new to this setting, the so-called individual causal association is presented. The methodology is studied in detail using theoretical considerations, some simulations, and data from a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. A user-friendly R package Surrogate is provided to carry out the evaluation exercise.

A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases.

Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size of N = 6 $N=6$ , whereas the matched GPC method could not control the type I error.

Longitudinal trajectory of medial gastrocnemius muscle growth in the first years of life.

AIM: To define the longitudinal trajectory of gastrocnemius muscle growth in 6- to 36-month-old children with and without spastic cerebral palsy (SCP) and to compare trajectories by levels of gross motor function (Gross Motor Function Classification System, GMFCS) and presumed brain-lesion timing. METHOD: Twenty typically developing children and 24 children with SCP (GMFCS levels I-II/III-IV = 15/9), were included (28/16 females/males; mean age at first scan 15.4 months [standard deviation 4.93, range 6.24-23.8]). Three-dimensional freehand ultrasound was used to repeatedly assess muscle volume, length, and cross-sectional area (CSA), resulting in 138 assessments (mean interval 7.9 months). Brain lesion timing was evaluated with magnetic resonance imaging classification. Linear mixed-effects models defined growth rates, adjusted for GMFCS levels and presumed brain-lesion timing. RESULTS: At age 12 months, children with SCP showed smaller morphological muscle size than typically developing children (5.8 mL vs 9.8 mL, p < 0.001), while subsequently no differences in muscle growth were found between children with and without SCP (muscle volume: 0.65 mL/month vs 0.74 mL/month). However, muscle volume and CSA growth rates were lower in children classified in GMFCS levels III and IV than typically developing children and those classified in GMFCS levels I and II, with differences ranging from -56% to -70% (p < 0.001). INTERPRETATION: Muscle growth is already hampered during infancy in SCP. Muscle size growth further reduces with decreasing functional levels, independently from the brain lesion. Early monitoring of muscle growth combined with early intervention is needed.

The value of point-of-care tests for the detection of SARS-CoV-2 RNA or antigen in bronchoalveolar lavage fluid.

BACKGROUND: Transmission of SARS-CoV-2 from donor to recipient is a clinically relevant risk for developing severe COVID-19 after lung transplantation (LTx). This risk of iatrogenic transmission can be reduced by timely detection of viral RNA or antigen in samples of bronchoalveolar lavage (BAL) fluid obtained at the time of lung procurement. We aimed to retrospectively evaluate the detection of SARS-CoV-2 RNA or antigen in BAL fluid samples using three point-of-care tests (POCTs). METHODS: BAL fluid samples came from patients hospitalized in an intensive care unit during the COVID-19 pandemic. These pandemic samples were scored as positive or negative for SARS-CoV-2 by a RT-qPCR comparator assay for orf1ab. Three commercially available POCTs were then evaluated: cobas SARS-CoV-2 & Influenza A/B assay with the cobas Liat RT-qPCR system (Roche Diagnostics), ID NOW COVID-19 and COVID-19 2.0 (Abbott), and SARS-CoV-2 Rapid Antigen Test (RAT) (Roche Diagnostics). Samples from the pre-pandemic era served as negative controls. RESULTS: We analyzed a total of 98 BAL fluid samples, each from a different patient: 58 positive pandemic samples (orf1ab Ct<38), 20 putatively negative pandemic samples (orf1ab Ct≥38), and 20 pre-pandemic samples. Univariate logistic regression shows that the probability of detection was highest for cobas Liat, followed by ID NOW, and then RAT. Of clinical relevance, cobas Liat detected SARS-CoV-2 RNA in 30 of the 31 positive pandemic samples that were collected within 10 days after RT-qPCR diagnosis of SARS-CoV-2 infection. None of the 20 pre-pandemic samples had a false-positive result for any POCT. CONCLUSIONS: POCTs enable the detection of SARS-CoV-2 RNA or antigen in BAL fluid samples and may provide additional information to decide if donor lungs are suitable for transplantation. Detection of respiratory pathogens with POCTs at the time of donor lung procurement is a potential strategy to increase safety in LTx by preventing iatrogenic transmission and severe postoperative infections.

Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive-definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials.

BACKGROUND: Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS: It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION: Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.

Assessing the impact of COVID-19 passes and mandates on disease transmission, vaccination intention, and uptake: a scoping review.

PURPOSE: Policymakers have struggled to maintain SARS-CoV-2 transmission at levels that are manageable to contain the COVID-19 disease burden while enabling a maximum of societal and economic activities. One of the tools that have been used to facilitate this is the so-called "COVID-19 pass". We aimed to document current evidence on the effectiveness of COVID-19 passes, distinguishing their indirect effects by improving vaccination intention and uptake from their direct effects on COVID-19 transmission measured by the incidence of cases, hospitalizations, and deaths. METHODS: We performed a scoping review on the scientific literature of the proposed topic covering the period January 2021 to September 2022, in accordance with the PRISMA-ScR guidelines for scoping reviews. RESULTS: Out of a yield of 4,693 publications, 45 studies from multiple countries were retained for full-text review. The results suggest that implementing COVID-19 passes tends to reduce the incidence of cases, hospitalizations, and deaths due to COVID-19. The use of COVID-19 passes was also shown to improve overall vaccination uptake and intention, but not in people who hold strong anti-COVID-19 vaccine beliefs. CONCLUSION: The evidence from the literature we reviewed tends to indicate positive direct and indirect effects from the use of COVID-19 passes. A major limitation to establishing this firmly is the entanglement of individual effects of multiple measures being implemented simultaneously.

The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

The COVID-19 pandemic has brought about valuable insights regarding models, data, and experiments. In this narrative review, we summarised the existing literature on these three themes, exploring the challenges of providing forecasts, the requirement for real-time linkage of health-related datasets, and the role of 'experimentation' in evaluating interventions. This literature review encourages us to broaden our perspective for the future, acknowledging the significance of investing in models, data, and experimentation, but also to invest in areas that are conceptually more abstract: the value of 'team science', the need for public trust in science, and in establishing processes for using science in policy. Policy-makers rely on model forecasts early in a pandemic when there is little data, and it is vital to communicate the assumptions, limitations, and uncertainties (theme 1). Linked routine data can provide critical information, for example, in establishing risk factors for adverse outcomes but are often not available quickly enough to make a real-time impact. The interoperability of data resources internationally is required to facilitate sharing across jurisdictions (theme 2). Randomised controlled trials (RCTs) provided timely evidence on the efficacy and safety of vaccinations and pharmaceuticals but were largely conducted in higher income countries, restricting generalisability to low- and middle-income countries (LMIC). Trials for non-pharmaceutical interventions (NPIs) were almost non-existent which was a missed opportunity (theme 3). Building on these themes from the narrative review, we underscore the importance of three other areas that need investment for effective evidence-driven policy-making. The COVID-19 response relied on strong multidisciplinary research infrastructures, but funders and academic institutions need to do more to incentivise team science (4). To enhance public trust in the use of scientific evidence for policy, researchers and policy-makers must work together to clearly communicate uncertainties in current evidence and any need to change policy as evidence evolves (5). Timely policy decisions require an established two-way process between scientists and policy makers to make the best use of evidence (6). For effective preparedness against future pandemics, it is essential to establish models, data, and experiments as fundamental pillars, complemented by efforts in planning and investment towards team science, public trust, and evidence-based policy-making across international communities. The paper concludes with a 'call to actions' for both policy-makers and researchers.

Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium.

Introduction: COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. Methods: We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis. Results: Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. Conclusion: It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.

Association Between Vascular 18F-Fluorodeoxyglucose Uptake at Diagnosis and Change in Aortic Dimensions in Giant Cell Arteritis : A Cohort Study.

BACKGROUND: Previous studies have shown that patients with giant cell arteritis (GCA) who have vascular 18F-fluorodeoxyglucose (FDG) uptake at diagnosis are at increased risk for thoracic aortic complications. OBJECTIVE: To measure the association between vascular FDG uptake at diagnosis and the change in aortic dimensions. DESIGN: Prospective cohort study. SETTING: University Hospitals Leuven. PATIENTS: 106 patients with GCA and FDG positron emission tomography (PET) imaging 3 days or less after initiation of glucocorticoids. MEASUREMENTS: Patients had PET and computed tomography (CT) imaging at diagnosis and CT imaging yearly for a maximum of 10 years. The PET scans were scored 0 to 3 in 7 vascular areas and summed to a total vascular score (TVS). The PET scan results were positive when FDG uptake was grade 2 or greater in any large vessel. The association between vascular FDG uptake and aortic dimensions was estimated by linear mixed-effects models with random intercept and slope. RESULTS: When compared with patients with a negative PET scan result, those with a positive scan result had a greater increase in the diameter of the ascending aorta (difference in 5-year progression, 1.58 mm [95% CI, 0.41 to 2.74 mm]), the diameter of the descending aorta (1.32 mm [CI, 0.38 to 2.26 mm]), and the volume of the thoracic aorta (20.5 cm³ [CI, 4.5 to 36.5 cm³]). These thoracic aortic dimensions were also positively associated with TVS. Patients with a positive PET scan result had a higher risk for thoracic aortic aneurysms (adjusted hazard ratio, 10.21 [CI, 1.25 to 83.3]). LIMITATION: The lengthy inclusion and follow-up period resulted in missing data and the use of different PET machines. CONCLUSION: Higher TVS was associated with greater yearly increase in thoracic aortic dimensions. Performing PET imaging at diagnosis may help to estimate the risk for aortic aneurysm formation. PRIMARY FUNDING SOURCE: None.

A dashboard for the evaluation of the effect of school closures on wellbeing of children and parents.

BACKGROUND: We present a dashboard for the evaluation of the impact of school closures on children and parents during the first wave of the COVID pandemic in 2020 on the various components of wellbeing. METHODS: Starting from an explorative literature search by a team of experts from diverse fields (e.g., epidemiology, virology, psychology, education, sociology), we developed a dashboard that allows for the quick evaluation of the general effect of school closures on various indicators of well-being in different groups and for the quality of the available research, at a time where a crisis is ongoing. RESULTS: It is concluded that there is evidence that the school closures reduced the transmission of COVID in the first wave in springtime 2020. Nevertheless, a multitude of studies show that the school closures also had a negative impact on different components of wellbeing such as academic achievement, time spent on learning and mental health. Furthermore, the school closures affected not only the children and adolescents, but also the parents that were forced to provide more childcare and help with schoolwork. Longitudinal studies on large representative samples with repeated assessments of wellbeing are necessary to understand the long-term effects of the school closures. CONCLUSIONS: The dashboard provides a first visual overview of the effects of school closures on wellbeing, and can serve as the basis for a future more systematic review and meta-analysis of the effects of school closures on wellbeing. It can be considered as a paradigm for rapid obtention of scientific evidence, during a quickly unfolding crisis, also in view of underpinning policy advice.

Reflections on the concept of optimality of single decision point treatment regimes.

In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated by the fact that the concept of optimality of decision rules in general and treatment regimes in particular has received so far relatively little attention and discussion, we will present a number of reflections on it, starting from the basics of any optimization problem. Specifically, we will analyze the search space and the to be optimized criterion function underlying the search of single decision point OTRs, along with the many choice aspects that show up in their specification. Special attention is paid to formal characteristics and properties as well as to substantive concerns and hypotheses that may guide these choices. We illustrate with a few empirical examples taken from the literature. Finally, we discuss how the presented reflections may help sharpen statistical thinking about optimality of decision rules for treatment assignment and to facilitate the dialogue between the statistical consultant and the applied researcher in search of an OTR.