ABSTRACT
BackgroundThe nature and extent of persistent neuropsychiatric symptoms after COVID-19 are not established. To help inform mental health service planning in the pandemic recovery phase, we systematically determined the prevalence of neuropsychiatric symptoms in survivors of COVID-19. MethodsFor this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750) we searched PubMed, EMBASE, CINAHL and PsycINFO to 20th February 2021, plus our own curated database. We included peer-reviewed studies reporting neuropsychiatric symptoms at post-acute or later time-points after COVID-19 infection, and in control groups where available. For each study a minimum of two authors extracted summary data. For each symptom we calculated a primary pooled prevalence using generalised linear mixed models. Heterogeneity was measured with I2. Subgroup analyses were conducted for COVID-19 hospitalisation, severity, and duration of follow-up. FindingsFrom 2,844 unique titles we included 51 studies (n=18,917 patients). The mean duration of follow-up after COVID-19 was 77 days (range 14-182 days). Study quality was generally moderate. The most frequent neuropsychiatric symptom was sleep disturbance (pooled prevalence=27{middle dot}4% [95%CI 21{middle dot}4- 34{middle dot}4%]), followed by fatigue (24{middle dot}4% [17{middle dot}5-32{middle dot}9%]), objective cognitive impairment (20{middle dot}2% [10{middle dot}3-35{middle dot}7%]), anxiety (19{middle dot}1%[13{middle dot}3-26{middle dot}8%]), and post-traumatic stress (15{middle dot}7% [9{middle dot}9-24{middle dot}1%]). Only two studies reported symptoms in control groups, both reporting higher frequencies in Covid-19 survivors versus controls. Between-study heterogeneity was high (I2=79{middle dot}6%-98{middle dot}6%). There was little or no evidence of differential symptom prevalence based on hospitalisation status, severity, or follow-up duration. InterpretationNeuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing, but indicates a particularly high frequency of insomnia, fatigue, cognitive impairment, and anxiety disorders in the first six months after infection. FundingJPR is supported by the Wellcome Trust (102186/B/13/Z). IK is funded through the NIHR (Oxford Health Biomedical Research Facility, Development and Skills Enhancement Award) and the Medical Research Council (Dementias Platform UK and Deep and Frequent Phenotyping study project grants). HH is funded by the German Research Foundation (DFG, Grant: HO 1286/16-1). The funders played no role in the design, analysis or decision to publish. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSNeuropsychiatric symptoms like cognitive impairment, fatigue, insomnia, depression and anxiety can be highly disabling. Recently there has been increasing awareness of persistent neuropsychiatric symptoms after COVID-19 infection, but a systematic synthesis of these symptoms is not available. In this review we searched five databases up to 20th February 2021, to establish the pooled prevalence of individual neuropsychiatric symptoms up to six months after COVID-19. Added value of this studyThis study establishes which of a range of neuropsychiatric symptoms are the most common after COVID-19. We found high rates in general, with little convincing evidence that these symptoms lessen in frequency during the follow-up periods studied. ImplicationsPersistent neuropsychiatric symptoms are common and appear to be limited neither to the post-acute phase, nor to recovery only from severe COVID-19. Our results imply that health services should plan for high rates of requirement for multidisciplinary services (including neurological, neuropsychiatric and psychological management) as populations recover from the COVID-19 pandemic.
Subject(s)
COVID-19ABSTRACT
ObjectivesThere is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations. MethodsWe searched MEDLINE, Embase, PsycInfo and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence. Results13,292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% [35.2--51.3], n=15,975, 63 studies), weakness (40.0% [27.9--53.5], n=221, 3 studies), fatigue (37.8% [31.6--44.4], n=21,101, 67 studies), dysgeusia (37.2% [30.0--45.3], n=13,686, 52 studies), myalgia (25.1% [19.8--31.3], n=66.268, 76 studies), depression (23.0 % [11.8--40.2], n=43,128, 10 studies), headache (20.7% [95% CI 16.1--26.1], n=64,613, 84 studies), anxiety (15.9% [5.6--37.7], n=42,566, 9 studies) and altered mental status (8.2% [4.4--14.8], n=49,326, 19 studies). Heterogeneity for most clinical manifestations was high. ConclusionsNeurological and neuropsychiatric symptoms of COVID-19 in the pandemics early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.
Subject(s)
COVID-19ABSTRACT
BackgroundWhile the number of detected SARS-CoV-2 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of SARS-CoV-2 (detected and undetected) infections in several European Countries. The question being asked is: How many cases have actually occurred? MethodsWe propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods. ResultsWe focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the Country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow. ConclusionsMany parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European Countries, where the epidemic spreads differently.
Subject(s)
COVID-19ABSTRACT
A major open question, affecting the policy makers decisions, is the estimation of the true size of COVID-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. A "modified" version of the Chao estimator is proposed, based on the cumulative time-series distribution of cases and deaths. Heterogeneity has been accounted for by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance formula has been properly derived to compute reliable confidence intervals at 95%. An application to Austrian situation is provided and results from other European Countries are mentioned in the discussion.