ABSTRACT
There 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. We 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. 13 292 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% (95% CI 35.2% to 51.3%), n = 15 975, 63 studies), weakness (40.0% (95% CI 27.9% to 53.5%), n = 221, 3 studies), fatigue (37.8% (95% CI 31.6% to 44.4%), n = 21 101, 67 studies), dysgeusia (37.2% (95% CI 29.8% to 45.3%), n = 13 686, 52 studies), myalgia (25.1% (95% CI 19.8% to 31.3%), n = 66 268, 76 studies), depression (23.0% (95% CI 11.8% to 40.2%), n = 43 128, 10 studies), headache (20.7% (95% CI 16.1% to 26.1%), n = 64 613, 84 studies), anxiety (15.9% (5.6% to 37.7%), n = 42 566, 9 studies) and altered mental status (8.2% (95% CI 4.4% to 14.8%), n = 49 326, 19 studies). Heterogeneity for most clinical manifestations was high.Neurological and neuropsychiatric symptoms of COVID-19 in the pandemic's 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. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
The 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. For this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750), we searched MEDLINE, EMBASE, CINAHL and PsycINFO to 20 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 pooled prevalence using generalized linear mixed models. Heterogeneity was measured with I 2. Subgroup analyses were conducted for COVID-19 hospitalization, severity and duration of follow-up. From 2844 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 most commonly moderate. The most prevalent neuropsychiatric symptom was sleep disturbance [pooled prevalence = 27.4% (95% confidence interval 21.4-34.4%)], followed by fatigue [24.4% (17.5-32.9%)], objective cognitive impairment [20.2% (10.3-35.7%)], anxiety [19.1% (13.3-26.8%)] and post-traumatic stress [15.7% (9.9-24.1%)]. Only two studies reported symptoms in control groups, both reporting higher frequencies in COVID-19 survivors versus controls. Between-study heterogeneity was high (I 2 = 79.6-98.6%). There was little or no evidence of differential symptom prevalence based on hospitalization status, severity or follow-up duration. Neuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing but indicates a particularly high prevalence of insomnia, fatigue, cognitive impairment and anxiety disorders in the first 6 months after infection.
ABSTRACT
OBJECTIVES: A major open question, affecting the decisions of policy makers, is the estimation of the true number 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. METHODS: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. RESULTS: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture-recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. CONCLUSIONS: The proposed method answers to a fundamental open question: "How many undetected cases are going around?". CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.