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Additional file 1. Population demographics, MCMC output (parameter estimates, trace plots and posterior distributions) for all models and log likelihood profile for infectiveness.

Additional file 1. Full search strategy tables.

Additional file 1: Additional results and sensitivity analyses that supplement the core paper and are referred to in text.

Additional file 2. PRISMA-P 2015 Checklist.

BACKGROUND: Mortality rates in hospitalised patients with COVID-19 in the UK appeared to decline during the first wave of the pandemic. We aimed to quantify potential drivers of this change and identify groups of patients who remain at high risk of dying in hospital. METHODS: In this multicentre prospective observational cohort study, the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK recruited a prospective cohort of patients with COVID-19 admitted to 247 acute hospitals in England, Scotland, and Wales during the first wave of the pandemic (between March 9 and Aug 2, 2020). We included all patients aged 18 years and older with clinical signs and symptoms of COVID-19 or confirmed COVID-19 (by RT-PCR test) from assumed community-acquired infection. We did a three-way decomposition mediation analysis using natural effects models to explore associations between week of admission and in-hospital mortality, adjusting for confounders (demographics, comorbidities, and severity of illness) and quantifying potential mediators (level of respiratory support and steroid treatment). The primary outcome was weekly in-hospital mortality at 28 days, defined as the proportion of patients who had died within 28 days of admission of all patients admitted in the observed week, and it was assessed in all patients with an outcome. This study is registered with the ISRCTN Registry, ISRCTN66726260. FINDINGS: Between March 9, and Aug 2, 2020, we recruited 80 713 patients, of whom 63 972 were eligible and included in the study. Unadjusted weekly in-hospital mortality declined from 32·3% (95% CI 31·8-32·7) in March 9 to April 26, 2020, to 16·4% (15·0-17·8) in June 15 to Aug 2, 2020. Reductions in mortality were observed in all age groups, in all ethnic groups, for both sexes, and in patients with and without comorbidities. After adjustment, there was a 32% reduction in the risk of mortality per 7-week period (odds ratio [OR] 0·68 [95% CI 0·65-0·71]). The higher proportions of patients with severe disease and comorbidities earlier in the first wave (March and April) than in June and July accounted for 10·2% of this reduction. The use of respiratory support changed during the first wave, with gradually increased use of non-invasive ventilation over the first wave. Changes in respiratory support and use of steroids accounted for 22·2%, OR 0·95 (0·94-0·95) of the reduction in in-hospital mortality. INTERPRETATION: The reduction in in-hospital mortality in patients with COVID-19 during the first wave in the UK was partly accounted for by changes in the case-mix and illness severity. A significant reduction in in-hospital mortality was associated with differences in respiratory support and critical care use, which could partly reflect accrual of clinical knowledge. The remaining improvement in in-hospital mortality is not explained by these factors, and could be associated with changes in community behaviour, inoculum dose, and hospital capacity strain. FUNDING: National Institute for Health Research and the Medical Research Council.

Background Delay to diagnosis in axial SpA (axSpA) is longer than in many other rheumatic diseases. Prolonged delay is associate with poorer outcomes, including functional impairment and quality of life. Our aims were to describe global variation in delay to diagnosis, factors associated with delay, and delay compared with PsA. Methods We searched MEDLINE, PubMed, Embase and Web of Science using a predefined protocol. Diagnostic delay was defined as years between the age at symptom onset and at diagnosis. We pooled the mean delay using random effects inverse variance meta-analysis. We examined variations in pooled estimates using prespecified subgroup analyses and sources of heterogeneity using meta-regression. Results A total of 64 studies reported the mean diagnostic delay in axSpA patients. The pooled mean delay was 6.7 years (95% CI 6.2, 7.2) with high levels of heterogeneity. Delay to diagnosis did not improve over time when stratifying results by year of publication. Studies from high-income countries (defined by the World Bank) reported longer delays than those from middle-income countries. Factors consistently reported to be associated with longer delays were lower education levels, younger age at symptom onset and absence of extra-articular manifestations (EAMs). The pooled estimate for diagnostic delay from 8 PsA studies was significantly shorter, at 2.6 years (95% CI 1.6, 3.6). Conclusion For axSpA patients, delay to diagnosis remains unacceptably prolonged in many parts of the world. Patient factors (e.g. education) and disease presentation (onset age and EAMs) should inform campaigns to improve delay.

Objective We aim to identify people with epilepsy who are unlikely to reachieve a 12‐month remission within 2 years after experiencing a breakthrough seizure following an initial 12‐month remission. Methods We apply a novel longitudinal discriminant approach to data from the Standard and New Antiepileptic Drugs study to dynamically predict the risk of a patient not achieving a second remission after a breakthrough seizure by combining both baseline covariates (collected at the time of breakthrough seizure) and follow‐up data. Results The model classifies 83% of patients. Of these, 73% of patients (95% confidence interval [CI ] = 58%‐88%) who did not achieve a second remission were correctly identified (sensitivity), and 84% of patients (95% CI = 69%‐96%) who achieved a second remission were correctly identified (specificity). The area under the curve from our model was 87% (95% CI = 80%‐94%). Patients who did not achieve a second remission were correctly identified on average after 10 months of observation postbreakthrough. Occurrence of seizures after breakthrough and the number of seizures experienced were the most informative longitudinal variables. These longitudinal profiles were influenced by the following baseline covariates: age at breakthrough seizure, presence of neurological insult, and number of antiepileptic drugs required to achieve first remission. Significance Using longitudinal data gathered during patient follow‐up allows more accurate predictions than using baseline covariates in a standard Cox model. The model developed in this paper is a useful first step in developing a tool for identifying patients who develop drug resistance after an initial remission.

Observational data facilitate examination of treatment-effect heterogeneity, but the risk of bias is substantial. The present study was undertaken to highlight methodologic considerations through an analysis of whether smoking affects response to tumor necrosis factor inhibitors (TNFi) in axial spondyloarthritis (SpA).We used longitudinal data from the British Society for Rheumatology Biologics Register for Ankylosing Spondylitis. Participants fulfilling the Assessment of SpondyloArthritis international Society criteria for axial SpA who started their first TNFi were eligible for analysis. In comparing the impact of smoking status, weighted generalized estimating equations were used to examine changes in several continuous outcome measures, including the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Ankylosing Spondylitis Disease Activity Score (ASDAS). Inverse probability weights were used to account for differences in baseline covariates and excluded participants. We separately assessed response in the first 3 months to account for nonrandom dropout.For 840 participants who started on TNFi, 1,641 assessments from 627 individuals were analyzed (69% male, mean age 46 years). A total of 33% were current smokers and 30% ex-smokers. Ex-smokers and current smokers had worse disease than never smokers at baseline. Accounting for these differences, response did not differ according to smoking status. Compared to never smokers, ex-smokers (β = -0.6, 95% confidence interval [95% CI] -1.4, 0.3) and current smokers (β = -0.4, 95% CI -1.1, 0.4) had a similar response according to the BASDAI and ASDAS (ex-smokers β = -0.1, 95% CI -0.5, 0.3; current smokers β = -0.01, 95% CI -0.4, 0.4) at 3 months.TNFi response did not differ according to baseline smoking status in this UK cohort. Conflicting results from previous studies were likely due to methodologic differences. This analysis highlights potential sources of bias that should be addressed in future studies.