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1.
Philos Trans A Math Phys Eng Sci ; 380(2214): 20210121, 2022 Jan 10.
Article in English | MEDLINE | ID: covidwho-1603979

ABSTRACT

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.


Subject(s)
COVID-19 Testing , COVID-19 , Bias , False Positive Reactions , Humans , Models, Statistical , SARS-CoV-2 , Selection Bias , Sensitivity and Specificity
2.
Int J Lab Hematol ; 43 Suppl 1: 137-141, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1526369

ABSTRACT

INTRODUCTION: Eosinopenia has been observed during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. This study evaluated the role of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. METHODS: Information on 429 patients with confirmed COVID-19, admitted to Apollo Hospitals, Chennai, India between 04 June 2020 to 15 August 2020, was retrospectively collected through electronic records and analysed. RESULTS: 79.25% of the patients included in the study had eosinopenia on admission. The median eosinophil count in COVID-19-positive patients was 0.015 × 109 /L, and in negative patients, it was 0.249 × 109 /L. Eighteen per cent of the positive patients presented with 0 eosinophil count. Eosinopenia for early diagnosis of COVID-19 had a sensitivity of 80.68% and specificity of 100% with an accuracy of 85.24. Role of eosinopenia in prognostication of COVID-19 was found to be insignificant. There was no statistically significant difference between the median eosinophil counts in survivors and nonsurvivors. Eosinophil trends during the course of disease were found to be similar between survivors and nonsurvivors. CONCLUSIONS: Eosinopenia on admission is a reliable and convenient early diagnostic marker for COVID-19 infection, helping in early identification, triaging and isolation of the patients till nucleic acid test results are available. Role of eosinopenia as a prognostic indicator is insignificant.


Subject(s)
COVID-19 Testing/methods , COVID-19/blood , Eosinophils , Leukocyte Count , Leukopenia/etiology , Area Under Curve , Biomarkers , COVID-19/diagnosis , COVID-19/mortality , Eosinophilia/blood , Eosinophilia/etiology , Humans , India , Leukopenia/blood , Prognosis , ROC Curve , Retrospective Studies , Selection Bias , Sensitivity and Specificity , Survival Analysis
3.
PLoS Biol ; 19(4): e3001135, 2021 04.
Article in English | MEDLINE | ID: covidwho-1508487

ABSTRACT

Identifying the animal reservoirs from which zoonotic viruses will likely emerge is central to understanding the determinants of disease emergence. Accordingly, there has been an increase in studies attempting zoonotic "risk assessment." Herein, we demonstrate that the virological data on which these analyses are conducted are incomplete, biased, and rapidly changing with ongoing virus discovery. Together, these shortcomings suggest that attempts to assess zoonotic risk using available virological data are likely to be inaccurate and largely only identify those host taxa that have been studied most extensively. We suggest that virus surveillance at the human-animal interface may be more productive.


Subject(s)
Environmental Monitoring , Virus Diseases , Zoonoses/etiology , Zoonoses/prevention & control , Animals , Biodiversity , Disease Reservoirs/classification , Disease Reservoirs/statistics & numerical data , Environmental Monitoring/methods , Environmental Monitoring/standards , Host Specificity/genetics , Humans , Metagenomics/methods , Metagenomics/organization & administration , Metagenomics/standards , Phylogeny , Risk Assessment , Risk Factors , Selection Bias , Virus Diseases/epidemiology , Virus Diseases/etiology , Virus Diseases/prevention & control , Virus Diseases/transmission , Viruses/classification , Viruses/genetics , Viruses/isolation & purification , Viruses/pathogenicity , Zoonoses/epidemiology , Zoonoses/virology
4.
J Evid Based Integr Med ; 26: 2515690X211058417, 2021.
Article in English | MEDLINE | ID: covidwho-1511567
5.
PLoS One ; 16(8): e0256074, 2021.
Article in English | MEDLINE | ID: covidwho-1357435

ABSTRACT

BACKGROUND: Asian-Americans are one of the most understudied racial/ethnic minority populations. To increase representation of Asian subgroups, researchers have traditionally relied on data collection at community venues and events. However, the COVID-19 pandemic has created serious challenges for in-person data collection. In this case study, we describe multi-modal strategies for online recruitment of U.S. Vietnamese parents, compare response rates and participant characteristics among strategies, and discuss lessons learned. METHODS: We recruited 408 participants from community-based organizations (CBOs) (n = 68), Facebook groups (n = 97), listservs (n = 4), personal network (n = 42), and snowball sampling (n = 197). Using chi-square tests and one-way analyses of variance, we compared participants recruited through different strategies regarding sociodemographic characteristics, acculturation-related characteristics, and mobile health usage. RESULTS: The overall response rate was 71.8% (range: 51.5% for Vietnamese CBOs to 86.6% for Facebook groups). Significant differences exist for all sociodemographic and almost all acculturation-related characteristics among recruitment strategies. Notably, CBO-recruited participants were the oldest, had lived in the U.S. for the longest duration, and had the lowest Vietnamese language ability. We found some similarities between Facebook-recruited participants and those referred by Facebook-recruited participants. Mobile health usage was high and did not vary based on recruitment strategies. Challenges included encountering fraudulent responses (e.g., non-Vietnamese). Perceived benefits and trust appeared to facilitate recruitment. CONCLUSIONS: Facebook and snowball sampling may be feasible strategies to recruit U.S. Vietnamese. Findings suggest the potential for mobile-based research implementation. Perceived benefits and trust could encourage participation and may be related to cultural ties. Attention should be paid to recruitment with CBOs and handling fraudulent responses.


Subject(s)
Asian Americans/statistics & numerical data , Internet , Patient Selection , Adult , Asian Americans/psychology , Cultural Characteristics , Female , Humans , Male , Middle Aged , Selection Bias , Socioeconomic Factors
7.
Am J Epidemiol ; 190(8): 1681-1688, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1337251

ABSTRACT

We evaluated whether randomly sampling and testing a set number of individuals for coronavirus disease 2019 (COVID-19) while adjusting for misclassification error captures the true prevalence. We also quantified the impact of misclassification error bias on publicly reported case data in Maryland. Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of COVID-19. We examined the situation when the true prevalence is low (0.07%-2%), medium (2%-5%), and high (6%-10%). Bayesian models informed by published validity estimates were used to account for misclassification error when estimating COVID-19 prevalence. Adjustment for misclassification error captured the true prevalence 100% of the time, irrespective of the true prevalence level. When adjustment for misclassification error was not done, the results highly varied depending on the population's underlying true prevalence and the type of diagnostic test used. Generally, the prevalence estimates without adjustment for misclassification error worsened as the true prevalence level increased. Adjustment for misclassification error for publicly reported Maryland data led to a minimal but not significant increase in the estimated average daily cases. Random sampling and testing of COVID-19 are needed with adjustment for misclassification error to improve COVID-19 prevalence estimates.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , Decision Support Techniques , Statistics as Topic/methods , Bayes Theorem , COVID-19/classification , Humans , Maryland/epidemiology , Prevalence , SARS-CoV-2 , Selection Bias
8.
J Med Virol ; 92(11): 2263-2265, 2020 11.
Article in English | MEDLINE | ID: covidwho-1245448

ABSTRACT

"Retest Positive" for severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) from "recovered" coronavirus disease-19 (COVID-19) has been reported and raised several important questions for this novel coronavirus and COVID-19 disease. In this commentary, we discussed several questions: (a) Can SARS-CoV-2 re-infect the individuals who recovered from COVID-19? This question is also associated with other questions: whether or not SARS-CoV-2 infection induces protective reaction or neutralized antibody? Will SARS-CoV-2 vaccines work? (b) Why could some recovered patients with COVID-19 be re-tested positive for SARS-CoV-2 RNA? (c) Are some recovered pwith atients COVID-19 with re-testing positive for SARS-CoV-2 RNA infectious? and (d) How should the COVID-19 patients with retest positive for SARS-CoV-2 be managed?


Subject(s)
COVID-19 Nucleic Acid Testing , COVID-19/diagnosis , RNA, Viral/isolation & purification , Reinfection/diagnosis , Antibodies, Viral/blood , Humans , Immunoglobulin G/blood , SARS-CoV-2 , Selection Bias
11.
J Med Virol ; 93(1): 22-24, 2021 01.
Article in English | MEDLINE | ID: covidwho-1196425

ABSTRACT

Current literature and clinical guidelines do not include pregnant women as an a priori risk group for COVID-19. However, a gender vision of health begs the question: Why are pregnant women not considered a risk group for COVID-19? The answer is clear: historically, most community scientific studies have not considered female gender, or pregnancy as a state, to be a focus of special interest or effort. Unfortunately, this bias seems to be maintained in the COVID-19 epidemic: most current guidelines for diagnosing SARS-CoV-2 infection during pregnancy apply the same standard criteria as for the general population.


Subject(s)
COVID-19/pathology , Pregnancy Complications, Infectious/virology , Pregnant Women , SARS-CoV-2 , Selection Bias , Female , Humans , Infectious Disease Transmission, Vertical , Pregnancy , Risk Factors , Sexism
13.
Cochrane Database Syst Rev ; 2: CD013665, 2021 02 23.
Article in English | MEDLINE | ID: covidwho-1095222

ABSTRACT

BACKGROUND: The clinical implications of SARS-CoV-2 infection are highly variable. Some people with SARS-CoV-2 infection remain asymptomatic, whilst the infection can cause mild to moderate COVID-19 and COVID-19 pneumonia in others. This can lead to some people requiring intensive care support and, in some cases, to death, especially in older adults. Symptoms such as fever, cough, or loss of smell or taste, and signs such as oxygen saturation are the first and most readily available diagnostic information. Such information could be used to either rule out COVID-19, or select patients for further testing. This is an update of this review, the first version of which published in July 2020. OBJECTIVES: To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19. SEARCH METHODS: For this review iteration we undertook electronic searches up to 15 July 2020 in the Cochrane COVID-19 Study Register and the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. SELECTION CRITERIA: Studies were eligible if they included patients with clinically suspected COVID-19, or if they recruited known cases with COVID-19 and controls without COVID-19. Studies were eligible when they recruited patients presenting to primary care or hospital outpatient settings. Studies in hospitalised patients were only included if symptoms and signs were recorded on admission or at presentation. Studies including patients who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards. DATA COLLECTION AND ANALYSIS: Pairs of review authors independently selected all studies, at both title and abstract stage and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and resolved disagreements by discussion with a third review author. Two review authors independently assessed risk of bias using the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary studies were available, and whenever heterogeneity across studies was deemed acceptable. MAIN RESULTS: We identified 44 studies including 26,884 participants in total. Prevalence of COVID-19 varied from 3% to 71% with a median of 21%. There were three studies from primary care settings (1824 participants), nine studies from outpatient testing centres (10,717 participants), 12 studies performed in hospital outpatient wards (5061 participants), seven studies in hospitalised patients (1048 participants), 10 studies in the emergency department (3173 participants), and three studies in which the setting was not specified (5061 participants). The studies did not clearly distinguish mild from severe COVID-19, so we present the results for all disease severities together. Fifteen studies had a high risk of bias for selection of participants because inclusion in the studies depended on the applicable testing and referral protocols, which included many of the signs and symptoms under study in this review. This may have especially influenced the sensitivity of those features used in referral protocols, such as fever and cough. Five studies only included participants with pneumonia on imaging, suggesting that this is a highly selected population. In an additional 12 studies, we were unable to assess the risk for selection bias. This makes it very difficult to judge the validity of the diagnostic accuracy of the signs and symptoms from these included studies. The applicability of the results of this review update improved in comparison with the original review. A greater proportion of studies included participants who presented to outpatient settings, which is where the majority of clinical assessments for COVID-19 take place. However, still none of the studies presented any data on children separately, and only one focused specifically on older adults. We found data on 84 signs and symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. Only cough (25 studies) and fever (7 studies) had a pooled sensitivity of at least 50% but specificities were moderate to low. Cough had a sensitivity of 67.4% (95% confidence interval (CI) 59.8% to 74.1%) and specificity of 35.0% (95% CI 28.7% to 41.9%). Fever had a sensitivity of 53.8% (95% CI 35.0% to 71.7%) and a specificity of 67.4% (95% CI 53.3% to 78.9%). The pooled positive likelihood ratio of cough was only 1.04 (95% CI 0.97 to 1.11) and that of fever 1.65 (95% CI 1.41 to 1.93). Anosmia alone (11 studies), ageusia alone (6 studies), and anosmia or ageusia (6 studies) had sensitivities below 50% but specificities over 90%. Anosmia had a pooled sensitivity of 28.0% (95% CI 17.7% to 41.3%) and a specificity of 93.4% (95% CI 88.3% to 96.4%). Ageusia had a pooled sensitivity of 24.8% (95% CI 12.4% to 43.5%) and a specificity of 91.4% (95% CI 81.3% to 96.3%). Anosmia or ageusia had a pooled sensitivity of 41.0% (95% CI 27.0% to 56.6%) and a specificity of 90.5% (95% CI 81.2% to 95.4%). The pooled positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.25 (95% CI 3.17 to 5.71) and 4.31 (95% CI 3.00 to 6.18) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The pooled positive likelihood ratio of ageusia alone was only 2.88 (95% CI 2.02 to 4.09). Only two studies assessed combinations of different signs and symptoms, mostly combining fever and cough with other symptoms. These combinations had a specificity above 80%, but at the cost of very low sensitivity (< 30%). AUTHORS' CONCLUSIONS: The majority of individual signs and symptoms included in this review appear to have very poor diagnostic accuracy, although this should be interpreted in the context of selection bias and heterogeneity between studies. Based on currently available data, neither absence nor presence of signs or symptoms are accurate enough to rule in or rule out COVID-19. The presence of anosmia or ageusia may be useful as a red flag for COVID-19. The presence of fever or cough, given their high sensitivities, may also be useful to identify people for further testing. Prospective studies in an unselected population presenting to primary care or hospital outpatient settings, examining combinations of signs and symptoms to evaluate the syndromic presentation of COVID-19, are still urgently needed. Results from such studies could inform subsequent management decisions.


Subject(s)
Ambulatory Care , COVID-19/diagnosis , Primary Health Care , SARS-CoV-2 , Symptom Assessment , Ageusia/diagnosis , Ageusia/etiology , Anosmia/diagnosis , Anosmia/etiology , Arthralgia/diagnosis , Arthralgia/etiology , Bias , COVID-19/complications , COVID-19/epidemiology , Cough/diagnosis , Cough/etiology , Diarrhea/diagnosis , Diarrhea/etiology , Dyspnea/diagnosis , Dyspnea/etiology , Fatigue/diagnosis , Fatigue/etiology , Fever/diagnosis , Fever/etiology , Headache/diagnosis , Headache/etiology , Humans , Myalgia/diagnosis , Myalgia/etiology , Outpatient Clinics, Hospital/statistics & numerical data , Pandemics , Physical Examination , Selection Bias , Symptom Assessment/classification , Symptom Assessment/statistics & numerical data
14.
BMC Med Res Methodol ; 21(1): 30, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1079210

ABSTRACT

BACKGROUND: In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates. METHODS: In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19. RESULTS: We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study. CONCLUSIONS: The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.


Subject(s)
Binomial Distribution , COVID-19/transmission , Computer Simulation , Disease Transmission, Infectious/statistics & numerical data , Humans , Infectious Disease Medicine/statistics & numerical data , Likelihood Functions , Pandemics , Population Surveillance , SARS-CoV-2 , Selection Bias
15.
Epidemiol Prev ; 44(5-6 Suppl 2): 260-270, 2020.
Article in English | MEDLINE | ID: covidwho-1068147

ABSTRACT

OBJECTIVES: to identify the Italian provinces with excess mortality during the COVID-19 epidemics using the mortality data provided in April 2020 by the Italian National Institute of Statistics (Istat) that, by design, included only the municipalities with at least 20% mortality increase compared to the same period in 2015-19. Inference with the aim to identify increased mortality at provincial level was a very important task when the Istat data were released in April, but the naïve aggregation of the selected municipalities was not sensible to due to the selection criteria of the municipalities used by Istat. DESIGN: use of a permutation-based approach to identify the Italian provinces with excess mortality during the first month of the COVID-19 epidemics using the data made available from Istat and taking into account the biased inclusion criteria. SETTING AND PARTICIPANTS: the number of deaths from any cause from 1 January was available for each year of the 2015-2020 period. Data were stratified by municipality, sex and 21 age categories. The third data release (R3) included 1,686 of the 7,904 Italian municipalities with increased mortality in 2020, covering about 40% of the Italian population. Results were compared with those obtainable with the fifth data release (R5), made available in June, when the selection of the municipalities was no longer based on increased mortality and which included more than 90% of the Italian population. R5 was considered the gold standard. MAIN OUTCOME MEASURES: excess of deaths from any cause in the Italian provinces between 1 March and 4 April; relative risk (RR); permutation p-values; permutation-based adjusted relative risk; population coverage. RESULTS: the results of this study, which are based on two different test statistics, identify 17 and 33 provinces (out of 103) with increased overall mortality, respectively, controlling the family-wise error rate at 0.05 level. Most of the identified provinces are neighbouring provinces in the northern regions of Lombardy, Emilia-Romagna, Piedmont, Liguria, Marche and Tuscany, where most of the COVID-19 cases and deaths were identified. The comparison with data from R5 shows that all the identified provinces had an increase in overall mortality, mostly (31/34) above 25%. On average, the adjusted RR slightly underestimates the RR from R5, underestimating the large RR and overestimating the small RR. CONCLUSIONS: this was, to the best of the authors' knowledge, the first attempt to aggregate the Istat data at province level and obtain a reliable and generalizable statistical inference. This permutation-based approach provides a feasible approach to take into account the selection bias that was present in the data and could be used for analysing other types of data that present some type of selection bias.


Subject(s)
COVID-19/epidemiology , Mortality/trends , Pandemics , SARS-CoV-2 , Academies and Institutes , COVID-19/mortality , Databases, Factual , Geography, Medical , Humans , Italy/epidemiology , Registries , Selection Bias , Urban Population/statistics & numerical data
16.
Fertil Steril ; 114(6): 1172-1173, 2020 12.
Article in English | MEDLINE | ID: covidwho-1007933
17.
Int J Antimicrob Agents ; 57(1): 106222, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-996947

ABSTRACT

During the emerging COVID-19 (coronavirus disease 2019) pandemic, initially there were no proven treatment options. With the release of randomised controlled trial (RCT) results, we are beginning to see possible treatment options for COVID-19. The RECOVERY trial showed an absolute risk reduction in mortality by 2.8% with dexamethasone, and the ACTT-1 trial showed that treatment with remdesivir reduced the time to recovery by 4 days. Treatment with hydroxychloroquine (HCQ) and lopinavir/ritonavir did not show any mortality benefit in either the RECOVERY or World Health Organization (WHO) Solidarity trials. The National Institutes of Health (NIH) and Brazilian HCQ trials did not show any benefit for HCQ based on the seven-point ordinal scale outcomes. The randomisation methodologies utilised in these controlled trials and the quality of published data were reviewed to examine their adaptability to treat patients. We found that the randomisation methodologies of these trials were suboptimal for matching the studied groups based on disease severity among critically-ill hospitalised COVID-19 patients with high mortality rates. The published literature is very limited regarding the disease severity metrics among the compared groups and failed to show that the data are without fatal sampling errors and sampling biases. We also found that there is a definite need for the validation of data in these trials along with additional important disease severity metrics to ensure that the trials' conclusions are accurate. We also propose proper randomisation methodologies for the design of RCTs for COVID-19 as well as guidance for the publication of COVID-19 trial results.


Subject(s)
COVID-19/drug therapy , Randomized Controlled Trials as Topic/statistics & numerical data , COVID-19/mortality , Critical Illness , Hospitalization , Humans , Hydroxychloroquine/therapeutic use , Lopinavir/therapeutic use , Mortality , Randomized Controlled Trials as Topic/methods , Ritonavir/therapeutic use , Selection Bias
18.
Cochrane Database Syst Rev ; 6: CD013652, 2020 06 25.
Article in English | MEDLINE | ID: covidwho-981322

ABSTRACT

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS-CoV-2 aim to identify previous SARS-CoV-2 infection, and may help to confirm the presence of current infection. OBJECTIVES: To assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection, and the accuracy of antibody tests for use in seroprevalence surveys. SEARCH METHODS: We undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020. SELECTION CRITERIA: We included test accuracy studies of any design that evaluated antibody tests (including enzyme-linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS-CoV-2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS-CoV-2 infection. We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR) and clinical diagnostic criteria). DATA COLLECTION AND ANALYSIS: We assessed possible bias and applicability of the studies using the QUADAS-2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random-effects logistic regression where appropriate, stratifying by time since post-symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs). MAIN RESULTS: We included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS-CoV-2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in-house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi-group designs (n = 29), inclusion of only COVID-19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID-19 infection. There were no studies exclusively in asymptomatic participants. Two-thirds of the studies (n = 33) defined COVID-19 cases based on RT-PCR results alone, ignoring the potential for false-negative RT-PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post-symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post-symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False-positive results were more common where COVID-19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post-symptom onset. At a prevalence of 20%, a likely value in surveys in high-risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands. AUTHORS' CONCLUSIONS: The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease. The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.


Subject(s)
Antibodies, Viral/blood , Betacoronavirus/immunology , Coronavirus Infections/diagnosis , Coronavirus Infections/immunology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/immunology , Antibody Specificity , COVID-19 , Coronavirus Infections/epidemiology , False Negative Reactions , False Positive Reactions , Humans , Immunoglobulin A/blood , Immunoglobulin G/blood , Immunoglobulin M/blood , Pandemics , Pneumonia, Viral/epidemiology , Reference Standards , Reverse Transcriptase Polymerase Chain Reaction/standards , Reverse Transcriptase Polymerase Chain Reaction/statistics & numerical data , SARS-CoV-2 , Selection Bias , Sensitivity and Specificity , Serologic Tests/methods , Serologic Tests/standards
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