ObjectiveWe aimed to describe the associations of age and sex with the risk of COVID-19 in different severity stages ranging from infection to death.DesignSystematic review and meta-analysis.Data sourcesPubMed and Embase through 4 May 2020.Study selectionWe considered cohort and case–control studies that evaluated differences in age and sex on the risk of COVID-19 infection, disease severity, intensive care unit (ICU) admission and death.Data extraction and synthesisWe screened and included studies using standardised electronic data extraction forms and we pooled data from published studies and data acquired by contacting authors using random effects meta-analysis. We assessed the risk of bias using the Newcastle-Ottawa Scale.ResultsWe screened 11.550 titles and included 59 studies comprising 36.470 patients in the analyses. The methodological quality of the included papers was high (8.2 out of 9). Men had a higher risk for infection with COVID-19 than women (relative risk (RR) 1.08, 95% CI 1.03 to 1.12). When infected, they also had a higher risk for severe COVID-19 disease (RR 1.18, 95% CI 1.10 to 1.27), a higher need for intensive care (RR 1.38, 95% CI 1.09 to 1.74) and a higher risk of death (RR 1.50, 95% CI 1.18 to 1.91). The analyses also showed that patients aged 70 years and above have a higher infection risk (RR 1.65, 95% CI 1.50 to 1.81), a higher risk for severe COVID-19 disease (RR 2.05, 95% CI 1.27 to 3.32), a higher need for intensive care (RR 2.70, 95% CI 1.59 to 4.60) and a higher risk of death once infected (RR 3.61, 95% CI 2.70 to 4.84) compared with patients younger than 70 years.ConclusionsMeta-analyses on 59 studies comprising 36.470 patients showed that men and patients aged 70 and above have a higher risk for COVID-19 infection, severe disease, ICU admission and death.PROSPERO registration numberCRD42020180085.

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing.The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. Simulations are reproducible. The comparisons show why these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables.This study shows that valid inferences can only be obtained if the dataset gathers a large number of clusters. In addition, it highlights that heteroscedastic MI methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, the method of Quartagno and Carpenter (2016a) appears generally accurate for binary variables, the method of Resche-Rigon and White (2016) with large clusters, and the approach of Jolani et al. (2015) with small clusters.

Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a consequence, it is no longer possible to evaluate between-study heterogeneity and to estimate study-specific predictor effects, or to include all individual studies, which severely hampers the development and validation of prediction models. Here, we describe a novel approach for imputing systematically missing data and adopt a generalized linear mixed model to allow for between-study heterogeneity. This approach can be viewed as an extension of Resche-Rigon's method (Stat Med 2013), relaxing their assumptions regarding variance components and allowing imputation of linear and nonlinear predictors. We illustrate our approach using a case study with IPD-MA of 13 studies to develop and validate a diagnostic prediction model for the presence of deep venous thrombosis. We compare the results after applying four methods for dealing with systematically missing predictors in one or more individual studies: complete case analysis where studies with systematically missing predictors are removed, traditional multiple imputation ignoring heterogeneity across studies, stratified multiple imputation accounting for heterogeneity in predictor prevalence, and multilevel multiple imputation (MLMI) fully accounting for between-study heterogeneity. We conclude that MLMI may substantially improve the estimation of between-study heterogeneity parameters and allow for imputation of systematically missing predictors in IPD-MA aimed at the development and validation of prediction models.

IntroductionEarly literature on the COVID-19 pandemic indicated striking ethnic inequalities in SARS-CoV-2-related outcomes. This systematic review and meta-analysis aimed to describe the presence and magnitude of associations between ethnic groups and COVID-19-related outcomes.MethodsPubMed and Embase were searched from December 2019 through September 2020. Studies reporting extractable data (ie, crude numbers, and unadjusted or adjusted risk/ORs) by ethnic group on any of the five studied outcomes: confirmed COVID-19 infection in the general population, hospitalisation among infected patients, and disease severity, intensive care unit (ICU) admission and mortality among hospitalised patients with SARS-CoV-2 infection, were included using standardised electronic data extraction forms. We pooled data from published studies using random-effects meta-analysis.Results58 studies were included from seven countries in four continents, mostly retrospective cohort studies, covering a total of almost 10 million individuals from the first wave until the summer of 2020. The risk of diagnosed SARS-CoV-2 infection was higher in most ethnic minority groups than their White counterparts in North American and Europe with the differences remaining in the US ethnic minorities after adjustment for confounders and explanatory factors. Among people with confirmed infection, African-Americans and Hispanic-Americans were also more likely than White-Americans to be hospitalised with SARS-CoV-2 infection. No increased risk of COVID-19 outcomes (ie, severe disease, ICU admission and death) was found among ethnic minority patients once hospitalised, except for a higher risk of death among ethnic minorities in Brazil.ConclusionThe risk of SARS-CoV-2 diagnosis was higher in most ethnic minorities, but once hospitalised, no clear inequalities exist in COVID-19 outcomes except for the high risk of death in ethnic minorities in Brazil. The findings highlight the necessity to tackle disparities in social determinants of health, preventative opportunities and delay in healthcare use. Ethnic minorities should specifically be considered in policies mitigating negative impacts of the pandemic.PROSPERO registration numberCRD42020180085.

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In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain valid inferences in the presence of missing data. However, MI of clustered data such as multicenter studies and individual participant data meta-analysis requires advanced imputation routines that preserve the hierarchical structure of data. In clustered data, a specific challenge is the presence of systematically missing data, when a variable is completely missing in some clusters, and sporadically missing data, when it is partly missing in some clusters. Unfortunately, little is known about how to perform MI when both types of missing data occur simultaneously. We develop a new class of hierarchical imputation approach based on chained equations methodology that simultaneously imputes systematically and sporadically missing data while allowing for arbitrary patterns of missingness among them. Here, we use a random effect imputation model and adopt a simplification over fully Bayesian techniques such as Gibbs sampler to directly obtain draws of parameters within each step of the chained equations. We justify through theoretical arguments and extensive simulation studies that the proposed imputation methodology has good statistical properties in terms of bias and coverage rates of parameter estimates. An illustration is given in a case study with eight individual participant datasets.

In two studies, the psychometric properties of an online self-reliant verbal working memory task (the Monkey game) for primary school children (6–12 years of age) were examined. In Study 1, children (n = 5,203) from 31 primary schools participated. The participants completed computerized verbal and visual–spatial working memory tasks (i.e., the Monkey game and the Lion game) and a paper-and-pencil version of Raven’s Standard Progressive Matrices. Reading comprehension and math achievement test scores were obtained from the schools. First, the internal consistency of the Monkey game was examined. Second, multilevel modeling was used to examine the effects of classroom membership. Multilevel multivariate regression analysis was used to examine the Monkey game’s concurrent relationship with the Lion game and its predictive relationships with reading comprehension and math achievement. Also, age-related differences in performance were examined. In Study 2, the concurrent relationships between the Monkey game and two tester-led computerized working memory tasks were further examined (n = 140). Also, the 1- and 2-year stability of the Monkey game was investigated. The Monkey game showed excellent internal consistency, good concurrent relationships with the other working memory measures, and significant age differences in performance. Performance on the Monkey game was also predictive of subsequent reading comprehension and mathematics performance, even after controlling for individual differences in intelligence. Performance on the Monkey game was influenced by classroom membership. The Monkey game is a reliable and suitable instrument for the online computerized and self-reliant assessment of verbal working memory in primary school children.

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