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To quantify the risk of deep vein thrombosis, pulmonary embolism, and bleeding after covid-19. Self-controlled case series and matched cohort study. National registries in Sweden. 1 057 174 people who tested positive for SARS-CoV-2 between 1 February 2020 and 25 May 2021 in Sweden, matched on age, sex, and county of residence to 4 076 342 control participants. Self-controlled case series and conditional Poisson regression were used to determine the incidence rate ratio and risk ratio with corresponding 95% confidence intervals for a first deep vein thrombosis, pulmonary embolism, or bleeding event. In the self-controlled case series, the incidence rate ratios for first time outcomes after covid-19 were determined using set time intervals and the spline model. The risk ratios for first time and all events were determined during days 1-30 after covid-19 or index date using the matched cohort study, and adjusting for potential confounders (comorbidities, cancer, surgery, long term anticoagulation treatment, previous venous thromboembolism, or previous bleeding event). Compared with the control period, incidence rate ratios were significantly increased 70 days after covid-19 for deep vein thrombosis, 110 days for pulmonary embolism, and 60 days for bleeding. In particular, incidence rate ratios for a first pulmonary embolism were 36.17 (95% confidence interval 31.55 to 41.47) during the first week after covid-19 and 46.40 (40.61 to 53.02) during the second week. Incidence rate ratios during days 1-30 after covid-19 were 5.90 (5.12 to 6.80) for deep vein thrombosis, 31.59 (27.99 to 35.63) for pulmonary embolism, and 2.48 (2.30 to 2.68) for bleeding. Similarly, the risk ratios during days 1-30 after covid-19 were 4.98 (4.96 to 5.01) for deep vein thrombosis, 33.05 (32.8 to 33.3) for pulmonary embolism, and 1.88 (1.71 to 2.07) for bleeding, after adjusting for the effect of potential confounders. The rate ratios were highest in patients with critical covid-19 and highest during the first pandemic wave in Sweden compared with the second and third waves. In the same period, the absolute risk among patients with covid-19 was 0.039% (401 events) for deep vein thrombosis, 0.17% (1761 events) for pulmonary embolism, and 0.101% (1002 events) for bleeding. The findings of this study suggest that covid-19 is a risk factor for deep vein thrombosis, pulmonary embolism, and bleeding. These results could impact recommendations on diagnostic and prophylactic strategies against venous thromboembolism after covid-19.

COVID-19 is a complex disease targeting many organs. Previous studies highlight COVID-19 as a probable risk factor for acute cardiovascular complications. We aimed to quantify the risk of acute myocardial infarction and ischaemic stroke associated with COVID-19 by analysing all COVID-19 cases in Sweden. This self-controlled case series (SCCS) and matched cohort study was done in Sweden. The personal identification numbers of all patients with COVID-19 in Sweden from Feb 1 to Sept 14, 2020, were identified and cross-linked with national inpatient, outpatient, cancer, and cause of death registers. The controls were matched on age, sex, and county of residence in Sweden. International Classification of Diseases codes for acute myocardial infarction or ischaemic stroke were identified in causes of hospital admission for all patients with COVID-19 in the SCCS and all patients with COVID-19 and the matched control individuals in the matched cohort study. The SCCS method was used to calculate the incidence rate ratio (IRR) for first acute myocardial infarction or ischaemic stroke following COVID-19 compared with a control period. The matched cohort study was used to determine the increased risk that COVID-19 confers compared with the background population of increased acute myocardial infarction or ischaemic stroke in the first 2 weeks following COVID-19. 86 742 patients with COVID-19 were included in the SCCS study, and 348 481 matched control individuals were also included in the matched cohort study. When day of exposure was excluded from the risk period in the SCCS, the IRR for acute myocardial infarction was 2·89 (95% CI 1·51–5·55) for the first week, 2·53 (1·29–4·94) for the second week, and 1·60 (0·84–3·04) in weeks 3 and 4 following COVID-19. When day of exposure was included in the risk period, IRR was 8·44 (5·45–13·08) for the first week, 2·56 (1·31–5·01) for the second week, and 1·62 (0·85–3·09) for weeks 3 and 4 following COVID-19. The corresponding IRRs for ischaemic stroke when day of exposure was excluded from the risk period were 2·97 (1·71–5·15) in the first week, 2·80 (1·60–4·88) in the second week, and 2·10 (1·33–3·32) in weeks 3 and 4 following COVID-19; when day of exposure was included in the risk period, the IRRs were 6·18 (4·06–9·42) for the first week, 2·85 (1·64–4·97) for the second week, and 2·14 (1·36–3·38) for weeks 3 and 4 following COVID-19. In the matched cohort analysis excluding day 0, the odds ratio (OR) for acute myocardial infarction was 3·41 (1·58–7·36) and for stroke was 3·63 (1·69–7·80) in the 2 weeks following COVID-19. When day 0 was included in the matched cohort study, the OR for acute myocardial infarction was 6·61 (3·56–12·20) and for ischaemic stroke was 6·74 (3·71–12·20) in the 2 weeks following COVID-19. Our findings suggest that COVID-19 is a risk factor for acute myocardial infarction and ischaemic stroke. This indicates that acute myocardial infarction and ischaemic stroke represent a part of the clinical picture of COVID-19, and highlights the need for vaccination against COVID-19. Central ALF-funding and Base Unit ALF-Funding, Region Västerbotten, Sweden; Strategic funding during 2020 from the Department of Clinical Microbiology, Umeå University, Sweden; Stroke Research in Northern Sweden; The Laboratory for Molecular Infection Medicine Sweden.

Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks. This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods. The paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods.

The self-controlled case series method is one of two approaches used to estimate the association between covid-19 and venous thromboembolism or bleeding. This article briefly describes the method, its assumptions, and how it was implemented in the linked study, and offers some pointers to guide the interpretation of the results.

In this large study based on data from the United Kingdom General Practice Research Database, rates of myocardial infarction and stroke increased sharply during the first three days after the diagnosis of an acute respiratory syndrome. The rates increased to a lesser degree after acute urinary tract infections. By contrast, there was no increase in risk after influenza, tetanus, or pneumococcal vaccination. This study supports the concept that inflammation is an important factor in atherosclerotic disease and that vaccination does not increase the risk of vascular events. Systemic inflammation and infections accelerate atherogenesis in animals, 1 and circulating markers of inflammation, such as C-reactive protein, predict the risk of vascular events in humans. 2 , 3 However, systemic inflammation is not a constant but varies in response to infections or to other proinflammatory stimuli. Such intermittent changes may be linked to an increase in the risk of vascular events. Indeed, inflammatory markers predict the outcome in acute vascular events 4 , 5 ; an increased leukocyte count may herald a short period of increased risk of stroke 6 ; and several small studies have suggested that there may be a transient increase in . . .

The relative frailty variance among survivors provides a readily interpretable measure of how the heterogeneity of a population, as represented by a frailty model, evolves over time. We discuss the properties of the relative frailty variance, show that it characterizes frailty distributions and that, suitably rescaled, it may be used to compare patterns of dependence across models and data sets. In shared frailty models, the relative frailty variance is closely related to the cross-ratio function, which is estimable from bivariate survival data. We investigate the possible shapes of the relative frailty variance function for the purpose of model selection, and we review available frailty distribution families in this context. We introduce several new families with contrasting properties, including simple but flexible time varying frailty models. The benefits of the approach that we propose are illustrated with two applications to bivariate current status data obtained from serological surveys.

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace.

The self-controlled case series method may be used to study the association between a time-varying exposure and a health event. It is based only on cases, and it controls for fixed confounders. Exposure and event histories are collected for each case over a predefined observation period. The method requires that observation periods should be independent of event times. This requirement is violated when events increase the mortality rate, since censoring of the observation periods is then event dependent. In this article, the case series method for rare nonrecurrent events is extended to remove this independence assumption, thus introducing an additional term in the likelihood that depends on the censoring process. In order to remain within the case series framework in which only cases are sampled, the model is reparameterized so that this additional term becomes estimable from the distribution of intervals from event to end of observation. The exposure effect of primary interest may be estimated unbiasedly. The age effect, however, takes on a new interpretation, incorporating the effect of censoring. The model may be fitted in standard loglinear modeling software; this yields conservative standard errors. We describe a detailed application to the study of antipsychotics and stroke. The estimates obtained from the standard case series model are shown to be biased when eventdependent observation periods are ignored. When they are allowed for, antipsychotic use remains strongly positively associated with stroke in patients with dementia, but not in patients without dementia. Two detailed simulation studies are included as Supplemental Material.

Despite demonstrated effectiveness in real-world settings, concerns persist regarding the safety of the quadrivalent human papillomavirus (HPV4) vaccine. We sought to assess the risk of autoimmune disorders following HPV4 vaccination among grade 8 girls eligible for Ontario’s school-based HPV vaccination program. We undertook a population-based retrospective cohort study using Ontario’s administrative health and vaccination databases from 2007 to 2013. The self-controlled case series method was used to compare the rate of a composite end point of autoimmune disorders diagnosed during days 7–60 post-vaccination (“exposed” follow-up) to that at any other time (“unexposed”). The analysis was repeated to assess the effect of a history of immune-mediated diseases and time since vaccination. We also conducted an exploratory analysis of individual autoimmune disorders. Rate ratios and 95% confidence intervals (CIs) were estimated using conditional Poisson regression, adjusted for age, seasonality, concomitant vaccinations and infections. The study cohort consisted of 290 939 girls aged 12–17 years who were eligible for vaccination between 2007 and 2013. There was no significant risk for developing an autoimmune disorder following HPV4 vaccination (n = 681; rate ratio 1.12, 95% CI 0.85–1.47), and the association was unchanged by a history of immune-mediated disorders and time since vaccination. Exploratory analyses of individual autoimmune disorders found no significant risks, including for Bell palsy (n = 65; rate ratio 1.73, 95% CI 0.77–3.89), optic neuritis (n = 67; rate ratio 1.57, 95% CI 0.74–3.33) and Graves disease (n = 47; rate ratio 1.55, 95% CI 0.92–2.63). We did not observe an increased risk of autoimmune disorders following HPV4 vaccination among teenaged girls. These findings should reassure parents and health care providers.

To the Editor: Patel et al. report an increased risk of intussusception after rotavirus vaccination. 1 We conducted a case-series analysis 2 of 151 spontaneous reports of intussusception worldwide after administration of the rotavirus vaccine RV1 (Rotarix, GlaxoSmithKline Biologicals). On the basis of a report citing the incidence of intussusception after administration of the rotavirus vaccine RotaShield (Wyeth Laboratories), 3 we tested for an increased risk of intussusception 3 to 7 days after administration of the first dose. Incidence ratios were calculated for the periods 0 through 2 days (with 0 indicating the day of vaccination), 3 through 7 days, and 8 through . . .