A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

The ER Stress Sensor PERK Coordinates ER-Plasma Membrane Contact Site Formation through Interaction with Filamin-A and F-Actin Remodeling.

Loss of ER Ca2+ homeostasis triggers endoplasmic reticulum (ER) stress and drives ER-PM contact sites formation in order to refill ER-luminal Ca2+. Recent studies suggest that the ER stress sensor and mediator of the unfolded protein response (UPR) PERK regulates intracellular Ca2+ fluxes, but the mechanisms remain elusive. Here, using proximity-dependent biotin identification (BioID), we identified the actin-binding protein Filamin A (FLNA) as a key PERK interactor. Cells lacking PERK accumulate F-actin at the cell edges and display reduced ER-PM contacts. Following ER-Ca2+ store depletion, the PERK-FLNA interaction drives the expansion of ER-PM juxtapositions by regulating F-actin-assisted relocation of the ER-associated tethering proteins Stromal Interaction Molecule 1 (STIM1) and Extended Synaptotagmin-1 (E-Syt1) to the PM. Cytosolic Ca2+ elevation elicits rapid and UPR-independent PERK dimerization, which enforces PERK-FLNA-mediated ER-PM juxtapositions. Collectively, our data unravel an unprecedented role of PERK in the regulation of ER-PM appositions through the modulation of the actin cytoskeleton.

Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK.

BACKGROUND: A rise in the incidence of some autoimmune disorders has been described. However, contemporary estimates of the overall incidence of autoimmune diseases and trends over time are scarce and inconsistent. We aimed to investigate the incidence and prevalence of 19 of the most common autoimmune diseases in the UK, assess trends over time, and by sex, age, socioeconomic status, season, and region, and we examine rates of co-occurrence among autoimmune diseases. METHODS: In this UK population-based study, we used linked primary and secondary electronic health records from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex and ethnicity. Eligible participants were men and women (no age restriction) with acceptable records, approved for Hospital Episodes Statistics and Office of National Statistics linkage, and registered with their general practice for at least 12 months during the study period. We calculated age and sex standardised incidence and prevalence of 19 autoimmune disorders from 2000 to 2019 and used negative binomial regression models to investigate temporal trends and variation by age, sex, socioeconomic status, season of onset, and geographical region in England. To characterise co-occurrence of autoimmune diseases, we calculated incidence rate ratios (IRRs), comparing incidence rates of comorbid autoimmune disease among individuals with a first (index) autoimmune disease with incidence rates in the general population, using negative binomial regression models, adjusted for age and sex. FINDINGS: Among the 22 009 375 individuals included in the study, 978 872 had a new diagnosis of at least one autoimmune disease between Jan 1, 2000, and June 30, 2019 (mean age 54·0 years [SD 21·4]). 625 879 (63·9%) of these diagnosed individuals were female and 352 993 (36·1%) were male. Over the study period, age and sex standardised incidence rates of any autoimmune diseases increased (IRR 2017-19 vs 2000-02 1·04 [95% CI 1·00-1·09]). The largest increases were seen in coeliac disease (2·19 [2·05-2·35]), Sjogren's syndrome (2·09 [1·84-2·37]), and Graves' disease (2·07 [1·92-2·22]); pernicious anaemia (0·79 [0·72-0·86]) and Hashimoto's thyroiditis (0·81 [0·75-0·86]) significantly decreased in incidence. Together, the 19 autoimmune disorders examined affected 10·2% of the population over the study period (1 912 200 [13·1%] women and 668 264 [7·4%] men). A socioeconomic gradient was evident across several diseases, including pernicious anaemia (most vs least deprived area IRR 1·72 [1·64-1·81]), rheumatoid arthritis (1·52 [1·45-1·59]), Graves' disease (1·36 [1·30-1·43]), and systemic lupus erythematosus (1·35 [1·25-1·46]). Seasonal variations were observed for childhood-onset type 1 diabetes (more commonly diagnosed in winter) and vitiligo (more commonly diagnosed in summer), and regional variations were observed for a range of conditions. Autoimmune disorders were commonly associated with each other, particularly Sjögren's syndrome, systemic lupus erythematosus, and systemic sclerosis. Individuals with childhood-onset type 1 diabetes also had significantly higher rates of Addison's disease (IRR 26·5 [95% CI 17·3-40·7]), coeliac disease (28·4 [25·2-32·0]), and thyroid disease (Hashimoto's thyroiditis 13·3 [11·8-14·9] and Graves' disease 6·7 [5·1-8·5]), and multiple sclerosis had a particularly low rate of co-occurrence with other autoimmune diseases. INTERPRETATION: Autoimmune diseases affect approximately one in ten individuals, and their burden continues to increase over time at varying rates across individual diseases. The socioeconomic, seasonal, and regional disparities observed among several autoimmune disorders in our study suggest environmental factors in disease pathogenesis. The inter-relations between autoimmune diseases are commensurate with shared pathogenetic mechanisms or predisposing factors, particularly among connective tissue diseases and among endocrine diseases. FUNDING: Research Foundation Flanders.

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.

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.

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.

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.

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.

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.

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.

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.

Autoimmune diseases and cardiovascular risk: a population-based study on 19 autoimmune diseases and 12 cardiovascular diseases in 22 million individuals in the UK.

BACKGROUND: Some autoimmune diseases are associated with an increased risk of cardiovascular disease. We aimed to determine whether or not this is true, and to what extent, for a broad range of autoimmune conditions. METHODS: In this population-based study, we used linked primary and secondary care records from the Clinical Practice Research Datalink (CPRD), GOLD and Aurum datasets, to assemble a cohort of individuals across the UK who were newly diagnosed with any of 19 autoimmune diseases between Jan 1, 2000, and Dec 31, 2017, younger than 80 years at diagnosis, and free of cardiovascular diseases up to 12 months after diagnosis. We also assembled a matched cohort with up to five individuals matched on age, sex, socioeconomic status, region, and calendar year, who were free of autoimmune disease and free of cardiovascular diseases up to 12 months after study entry. Both cohorts were followed up until June 30, 2019. We investigated the incidence of 12 cardiovascular outcomes and used Cox proportional hazards models to examine differences in patients with and without autoimmune diseases. FINDINGS: Of 22 009 375 individuals identified from the CPRD databases, we identified 446 449 eligible individuals with autoimmune diseases and 2 102 830 matched controls. In the autoimmune cohort, mean age at diagnosis was 46·2 years (SD 19·8), and 271 410 (60·8%) were women and 175 039 (39·2%) were men. 68 413 (15·3%) people with and 231 410 (11·0%) without autoimmune diseases developed incident cardiovascular disease during a median of 6·2 years (IQR 2·7-10·8) of follow-up. The incidence rate of cardiovascular disease was 23·3 events per 1000 patient-years among patients with autoimmune disease and 15·0 events per 1000 patient-years among those without an autoimmune disease (hazard ratio [HR] 1·56 [95% CI 1·52-1·59]). An increased risk of cardiovascular disease with autoimmune disease was seen for every individual cardiovascular disease and increased progressively with the number of autoimmune diseases present (one disease: HR 1·41 [95% CI 1·37-1·45]; two diseases: 2·63 [2·49-2·78]); three or more diseases: 3·79 [3·36-4·27]), and in younger age groups (age <45 years: 2·33 [2·16-2·51]; 55-64 years: 1·76 [1·67-1·85]; ≥75 years: 1·30 [1·24-1·36]). Among autoimmune diseases, systemic sclerosis (3·59 [2·81-4·59]), Addison's disease (2·83 [1·96-4·09]), systemic lupus erythematosus (2·82 [2·38-3·33]), and type 1 diabetes (2·36 [2·21-2·52]) had the highest overall cardiovascular risk. INTERPRETATION: These findings warrant targeted cardiovascular prevention measures, in particular in younger patients with autoimmune diseases, and further research into pathophysiological mechanisms underlying these complications. FUNDING: Horizon 2020 Marie Sklodowska-Curie Actions and European Society of Cardiology.

SARS-CoV-2 vaccine antibody response and breakthrough infections in transplant recipients.

Rates and modulators of SARS-CoV-2 vaccine nonresponse and breakthrough infections remain unclear in serially vaccinated transplant recipients. In a prospective, mono-centric, observational study, 1878 adult solid organ and hematopoietic cell transplant recipients, with prior SARS-CoV-2 vaccination, were included between March 2021 and February 2022. SARS-CoV-2 anti-spike IgG antibodies were measured at inclusion and details on SARS-CoV-2 vaccine doses and infection were collected. No life-threatening adverse events were reported after a total of 4039 vaccine doses. In transplant recipients without prior SARS-CoV-2 infection (n = 1636), antibody response rates ranged widely, from 47% in lung transplant to 90% in liver transplant and 91% in hematopoietic cell transplant recipients after third vaccine dose. Antibody positivity rate and levels increased after each vaccine dose in all types of transplant recipients. In multivariable analysis, older age, chronic kidney disease and daily dose of mycophenolate and corticosteroids were negatively associated with antibody response rate. Overall rate of breakthrough infections was 25.2% and mainly (90.2%) occurred after third and fourth vaccine dose. Lung transplant recipients had the highest rates of severe breakthrough infection (10.5%) and death (2.5%). In multivariable analysis, older age, daily dose of mycophenolate and corticosteroids were associated with severe breakthrough infection. Transplant recipients with infection before first vaccine dose (n = 160) had higher antibody response rates and levels after each vaccine dose, and a significantly lower overall rate of breakthrough infections compared to those without prior infection. Antibody response after SARS-CoV-2 vaccination and rate of severe breakthrough infections vary largely between different transplant types and are modulated by specific risk factors. The observed heterogeneity supports a tailored approach against COVID-19 in transplant recipients.

Comparing COVID-19 incidences longitudinally per economic sector against the background of preventive measures and vaccination.

In the COVID-19 pandemic, workplace transmission plays an important role. For this type of transmission, the longitudinal 14-day incidence curve of SARS-CoV-2 infections per economic sector is a proxy. In Belgium, a census of confirmed 14-day incidences per NACE-BEL sector level three is available from September 2020 until June 2021, encompassing two waves of infections. However, these high-dimensional data, with a relatively small number of NACE-BEL sectors, are challenging to analyze. We propose a nonlinear Gaussian-Gaussian model that combines parametric and semi-parametric elements to describe the incidence curves with a small set of meaningful parameters. These parameters are further analyzed with conventional statistical methods, such as CCA and linear models, to provide insight into predictive characteristics of the first wave for the second wave. Those nonlinear models classify economic sectors into three groups: sectors with two regular waves of infections, sectors with only a first wave and sectors with a more irregular profile, which may indicate a clear effect of COVID-19 vaccination. The Gaussian-Gaussian model thus allows for analyzing and comparing incidence curves and to bring out key characteristics of such curves. Finally, we consider in which other settings the proposed approach could be applied, together with possible pitfalls.

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

How to analyze continuous and discrete repeated measures in small-sample cross-over trials?

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.

A longitudinal transition imputation model for categorical data applied to a large registry dataset.

Imputation of longitudinal categorical covariates with several waves and many predictors is cumbersome in terms of implausible transitions, colinearity, and overfitting. We designed a simulation study with data obtained from a general practitioners' morbidity registry in Belgium for three waves, with smoking as the longitudinal covariate of interest. We set varying proportions of data on smoking to missing completely at random and missing not at random with proportions of missingness equal to 10%, 30%, 50%, and 70%. This study proposed a 3-stage approach that allows flexibility when imputing time-dependent categorical covariates. First, multiple imputation using fully conditional specification or multiple imputation for the predictor variables was deployed using the wide format such that previous and future information of the same patient was utilized. Second, a joint Markov transition model for initial, forward, backward, and intermittent probabilities was developed for each imputed dataset. Finally, this transition model was used for imputation. We compared the performance of this methodology with an analyses of the complete data and with listwise deletion in terms of bias and root mean square error. Next, we applied this methodology in a clinical case for years 2017 to 2021, where we estimated the effect of several covariates on the pneumococcal vaccination. This methodological framework ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. Finally, a companion R package was developed to enable the replication and easy application of this methodology.

The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021.

BACKGROUND: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios. METHODS: We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures. RESULTS: The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas. CONCLUSIONS: Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement.

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.

Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences. METHODS: A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. RESULTS: Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula: see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. CONCLUSIONS: CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.