Incidence and determinants of venous thromboembolism over 90 days in hospitalized and nonhospitalized patients with COVID-19.

INTRODUCTION: COVID-19 is associated with an increased risk of venous thromboembolism (VTE), but there is great variation among reported incidence rates. Most previous studies have focused on hospitalized patients with COVID-19, and only a few reports are from population-based registries. METHODS: We studied the 90-day incidence of VTE, associated risk factors and all-cause mortality in hospitalized and nonhospitalized patients with COVID-19 in a nationwide cohort. Data on hospitalizations and outpatient visits were extracted from two national registries with mandatory reporting linked by a unique national identification number carried by all Norwegian residents. We performed Cox proportional hazards regression to determine risk factors for VTE after infection with SARS-CoV-2. RESULTS: Our study included 30,495 patients with positive SARS-CoV-2 polymerase chain reaction with a mean (SD) age of 41.9 (17.3) years, and 53% were males. Only 2081 (6.8%) were hospitalized. The 90-day incidence of VTE was 0.3% (95% CI: 0.21-0.33) overall and 2.9% (95% CI: 2.3-3.7) in hospitalized patients. Age (hazard ratio [HR] 1.28 per decade, 95% CI: 1.11-1.48, p < 0.05), history of previous VTE (HR 4.69, 95% CI: 2.34-9.40, p < 0.05), and hospitalization for COVID-19 (HR 23.83, 95% CI: 13.48-42.13, p < 0.05) were associated with risk of VTE. CONCLUSIONS: The 90-day incidence of VTE in hospitalized and nonhospitalized patients with COVID-19 was in the lower end compared with previous reports, with considerably higher rates in hospitalized than nonhospitalized patients. Risk factors for VTE were consistent with previously reported studies.

Flexible modelling of spatial variation in agricultural field trials with the R package INLA.

KEY MESSAGE: Established spatial models improve the analysis of agricultural field trials with or without genomic data and can be fitted with the open-source R package INLA. The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ([Formula: see text]) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the [Formula: see text] and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA.

Access to a tailored mobile application enhances medication adherence among young users of antidepressants.

Introduction: Patients' adherence to antidepressants is generally reported to be poor. This study examined whether users of selective serotonin reuptake inhibitors (SSRIs) and serotonin and norepinephrine reuptake inhibitors (SNRIs) enhance medication adherence following access to a mobile application (app) tailored for this patient group. The study addresses the implementation phase of medication adherence. Methods: The study was a single group pre-post intervention design. Data were collected using the validated OsloMet Adherence-to-medication Survey tool (OMAS-37) before and after app access. Pre-app access survey (Survey 1) was conducted via social media and online newspapers, encompassing 445 SSRI/SNRI users aged 18 years and above. Post-app access survey (Survey 2) was sent to 103 SSRI/SNRI users from Survey 1. Wilcoxon Signed Rank Test compared pre- and post-intervention adherence measurements. Pearson's chi-square tests and Fisher's exact tests compared study population categories. Results: Forty-two SSRI/SNRI users, median age 26 (IQR 17), 93% identifying as female, used the app while using the same antidepressant during the 2-month period between gaining access to the app and Survey 2. There was a statistically significant reduction in non-adherence score post-app access (z = 3.57, n = 42, p < 0.001) with medium effect size (r = 0.39), indicating enhanced adherence. Total non-adherence score decreased by 39% from pre-to post-access, and there was a 12% decrease in users scoring equivalent with poor adherence (score <2) post-access. Twenty-nine of 37 non-adherence causes improved, with three showing statistical significance. Of 42 responders, 50% (n = 21) indicated using the app one to two times, while 50% (n = 21) more than three times. Approximately 69% (n = 28) found it useful, and 43% (n = 18) felt safer in their use of antidepressants after access to the app. No significant preference was observed for the app over alternative sources of information. Discussion: Enhanced medication adherence was observed among antidepressant users following access to the tailored app. Further studies are warranted to evaluate the app applicability to a broader range of antidepressants users or other patient groups, encompassing those in the initiation phase of medication adherence. The app is intended as an easily accessible supplement to the information and advice provided by prescribing physicians and dispensing pharmacists.

Implementation of guidelines on Family Involvement for persons with Psychotic disorders: a pragmatic cluster randomized trial. Effect on relatives' outcomes and family interventions received.

Background: Family interventions (FI) are recommended as part of the treatment for psychotic disorders, but the implementation in mental health services is generally poor. Recently, The Implementation of guidelines on Family Involvement for persons with Psychotic disorders (IFIP) trial, demonstrated significant improvements in implementation outcomes at cluster-level. This sub-study aims to examine the effectiveness of the IFIP intervention on relatives' outcomes and received FI. Methods: A cluster randomized controlled trial, was conducted in 15 Norwegian Community Mental Health Center (CMHC) units that were randomized to either the IFIP intervention, including implementation interventions and clinical interventions, or treatment as usual (TAU). The clinical interventions consisted of FI: basic family involvement and support (BFIS) to all patients and family psychoeducation (FPE) to as many as possible. Patients with psychotic disorders and their closest relative were invited to fill in questionnaires at inclusion and 6 months and 12 months follow-up. Received FI was reported by both relatives and clinicians. The relatives' primary outcome was satisfaction with health service support, measured by the Carer well-being and support questionnaire part B (CWS-B). The relatives' secondary outcomes were caregiver experiences, expressed emotions and quality of life. Patients' outcomes will be reported elsewhere. Results: In total 231 patient/relative pairs from the CMHC units were included (135 intervention; 96 control).The relatives in the intervention arm received an increased level of BFIS (p=.007) and FPE (p < 0.05) compared to the relatives in the control arm, including involvement in crisis planning. The primary outcome for relatives' satisfaction with health service support, showed a non-significant improvement (Cohen's d = 0.22, p = 0.08). Relatives experienced a significant reduced level of patient dependency (Cohen's d = -0.23, p = 0.03). Conclusion: The increased support from clinicians throughout FI reduced the relatives' perceived level of patient dependency, and may have relieved the experience of responsibility and caregiver burden. The COVID-19 pandemic and the complex and pioneering study design have weakened the effectiveness of the IFIP intervention, underscoring possible potentials for further improvement in relatives' outcomes. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT03869177.

Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.

We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.

Hierarchical Modelling of Haplotype Effects on a Phylogeny.

We introduce a hierarchical model to estimate haplotype effects based on phylogenetic relationships between haplotypes and their association with observed phenotypes. In a population there are many, but not all possible, distinct haplotypes and few observations per haplotype. Further, haplotype frequencies tend to vary substantially. Such data structure challenge estimation of haplotype effects. However, haplotypes often differ only due to few mutations, and leveraging similarities can improve the estimation of effects. We build on extensive literature and develop an autoregressive model of order one that models haplotype effects by leveraging phylogenetic relationships described with a directed acyclic graph. The phylogenetic relationships can be either in a form of a tree or a network, and we refer to the model as the haplotype network model. The model can be included as a component in a phenotype model to estimate associations between haplotypes and phenotypes. Our key contribution is that we obtain a sparse model, and by using hierarchical autoregression, the flow of information between similar haplotypes is estimated from the data. A simulation study shows that the hierarchical model can improve estimates of haplotype effects compared to an independent haplotype model, especially with few observations for a specific haplotype. We also compared it to a mutation model and observed comparable performance, though the haplotype model has the potential to capture background specific effects. We demonstrate the model with a study of mitochondrial haplotype effects on milk yield in cattle. We provide R code to fit the model with the INLA package.