Coronavirus Disease 2019 Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Positivity in a Large Survey of First Responders and Healthcare Personnel, May-July 2020

The laboratory parameters in predicting the severity and death of COVID-19 patients: Future pandemic readiness strategies

The range of clinical manifestations associated with the infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encompasses a broad spectrum, ranging from flu-like symptoms to the occurrence of multiple organ failure and death. The severity of the coronavirus disease 2019 (COVID-19) is categorized based on clinical presentation and is divided into three distinct levels of severity identified as non-severe, severe, and critical. Although individuals of all age groups are susceptible to SARS-CoV-2 infection, middle-aged and older adults are more frequently impacted, with the latter being more likely to develop severe illness. Various laboratory characteristics observed in hospitalized COVID-19 patients have been correlated with adverse outcomes. These include elevated levels of D-dimer, liver enzymes, lactate dehydrogenase, C-reactive protein, ferritin, prothrombin time, and troponin, as well as decreased lymphocyte and platelets counts. This review investigated the relationship between baseline clinical characteristics, initial laboratory parameters upon hospital admission, and the severity of illness and mortality rates among COVID-19 patients. Although the COVID-19 pandemic has concluded, understanding the laboratory predictors of virus severity and mortality remains crucial, and examining these predictors can have long-term effects. Such insights can help healthcare systems manage resources more effectively and deliver timely and appropriate care by identifying and targeting high-risk individuals. This knowledge can also help us better prepare for future pandemics. By examining these predictors, we can take steps to protect public health and mitigate the impact of future pandemics.

Serological surveillance reveals a high exposure to SARS-CoV-2 and altered immune response among COVID-19 unvaccinated Cameroonian individuals

Surveillance of COVID-19/SARS-CoV-2 dynamics is crucial to understanding natural history and providing insights into the population's exposure risk and specific susceptibilities. This study investigated the seroprevalence of SARS-CoV-2 antibodies, its predictors, and immunological status among unvaccinated patients in Cameroon. A multicentre cross-sectional study was conducted between January and September 2022 in the town of Douala. Patients were consecutively recruited, and data of interest were collected using a questionnaire. Blood samples were collected to determine Immunoglobin titres (IgM and IgG), interferon gamma (IFN- γ) and interleukin-6 (IL-6) by ELISA, and CD4+ cells by flow cytometry. A total of 342 patients aged 41.5 ± 13.9 years were included. Most participants (75.8%) were asymptomatic. The overall crude prevalence of IgM and IgG was 49.1% and 88.9%, respectively. After adjustment, the seroprevalence values were 51% for IgM and 93% for IgM. Ageusia and anosmia have displayed the highest positive predictive values (90.9% and 82.4%) and specificity (98.9% and 98.3%). The predictors of IgM seropositivity were being diabetic (aOR = 0.23, p = 0.01), frequently seeking healthcare (aOR = 1.97, p = 0.03), and diagnosed with ageusia (aOR = 20.63, p = 0.005), whereas those of IgG seropositivity included health facility (aOR = 0.15, p = 0.01), age of 40-50 years (aOR = 8.78, p = 0.01), married (aOR = 0.21, p = 0.02), fever (aOR = 0.08, p = 0.01), and ageusia (aOR = 0.08, p = 0.01). CD4+, IFN-γ, and IL-6 were impaired in seropositive individuals, with a confounding role of socio-demographic factors or comorbidities. Although the WHO declared the end of COVID-19 as a public health emergency, the findings of this study indicate the need for continuous surveillance to adequately control the disease in Cameroon.

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Consistent comparison of symptom-based methods for COVID-19 infection detection

BACKGROUND

During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.

PURPOSE

This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.

METHODS

Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.

RESULTS

Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% - 71.11%), logistic regression techniques (F1-score: 39.91% - 71.13%), and tree-based machine learning models (F1-score: 45.07% - 73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.

CONCLUSIONS

Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

Taste loss as a distinct symptom of COVID-19: a systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19 taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 235 meeting all inclusion criteria. Drawing on previous studies and guided by early meta-analyses, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct measures of taste are at least as sensitive as those obtained by self-report and that the preponderance of evidence confirms taste loss is a symptom of COVID-19. The meta-analysis showed that, among 138,015 COVID-19-positive patients, 36.62% reported taste dysfunction (95% confidence interval: 33.02%-40.39%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 15) versus self-report (n = 220) methodologies (Q = 1.73, df = 1, P = 0.1889). Generally, males reported lower rates of taste loss than did females, and taste loss was highest among middle-aged adults. Thus, taste loss is likely a bona fide symptom of COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.

In-hospital outcomes of SARS-CoV-2-infected health care workers in the COVID-19 pandemic first wave, Quebec, Canada

Background

Health care workers (HCW), particularly immigrants and ethnic minorities are at increased risk for SARS-CoV-2 infection. Outcomes during a COVID-19 associated hospitalization are not well described among HCW. We aimed to describe the characteristics of HCW admitted with COVID-19 including immigrant status and ethnicity and the associated risk factors for Intensive Care unit (ICU) admission and death.

Methods

Adults with laboratory-confirmed community-acquired COVID-19 hospitalized from March 1 to June 30, 2020, at four tertiary-care hospitals in Montréal, Canada were included. Demographics, comorbidities, occupation, immigration status, country of birth, ethnicity, workplace exposures, and hospital outcomes (ICU admission and death) were obtained through a chart review and phone survey. A Fine and Gray competing risk proportional hazards model was used to estimate the risk of ICU admission among HCW stratified by immigrant status and region of birth.

Results

Among 1104 included persons, 150 (14%) were HCW, with a phone survey participation rate of 68%. HCWs were younger (50 vs 64 years; p<0.001), more likely to be female (61% vs 41%; p<0.001), migrants (68% vs 55%; p<0.01), non-White (65% vs 41%; p<0.001) and healthier (mean Charlson Comorbidity Index of 0.3 vs 1.2; p<0.001) compared to non-HCW. They were as likely to be admitted to the ICU (28% vs 31%; p = 0.40) but were less likely to die (4% vs. 17%; p<0.001). Immigrant HCW accounted for 68% of all HCW cases and, compared to Canadian HCW, were more likely to be personal support workers (PSW) (54% vs. 33%, p<0.01), to be Black (58% vs 4%) and to work in a Residential Care Facility (RCF) (59% vs 33%; p = 0.05). Most HCW believed that they were exposed at work, 55% did not always have access to personal protective equipment (PPE) and 40% did not receive COVID-19-specific Infection Control (IPAC) training.

Conclusion

Immigrant HCW were particularly exposed to COVID-19 infection in the first wave of the pandemic in Quebec. Despite being young and healthy, one third of all HCW required ICU admission, highlighting the importance of preventing workplace transmission through strong infection prevention and control measures, including high COVID-19 vaccination coverage.

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19

BACKGROUND

COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 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

We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions.

SELECTION CRITERIA

Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people 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, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable.

MAIN RESULTS

We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%.

AUTHORS' CONCLUSIONS

Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

SARS-CoV-2 seroprevalence among healthcare personnel at a large health system in Atlanta

Background

Estimates of the prevalence of SARS-CoV-2 antibodies and factors associated with infection among healthcare personnel (HCP) vary widely. We conducted a serosurvey of HCP at a large public healthcare system in the Atlanta area.

Materials and methods

All employees of Grady Health System were invited to participate in mid-2020; a volunteer sample of those completing testing was included. Asymptomatic HCP were offered testing for IgG antibody and for SARS-CoV-2 RNA using polymerase chain reaction (PCR). Symptomatic HCP were offered PCR testing. Antibody index values for IgG and cycle threshold values for PCR were evaluated for those with a positive result. An online survey was distributed at the time of testing.

Results

624 of 1677 distributed surveys (37.2%) were completed by 608 unique HCP. The majority were female (76.4%) and provided clinical care (70.9%). The most common occupations were clinician (24.8%) and nurse (23.5%). 37 of 608 (6.1%) HCP had detectable IgG. Exposure to a confirmed case of COVID-19 outside of the hospital was associated with detectable IgG (12.8% vs 4.4%, p = 0.02), but exposure to a patient with COVID-19 was not.

Conclusions

Among HCP in a large healthcare system, 6.1% had detectable SARS-CoV-2 IgG. Seropositivity was associated with exposures outside of the healthcare setting.

Taste loss as a distinct symptom of COVID-19: a systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19 taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 241 meeting all inclusion criteria. Drawing on previous studies and guided by early meta-analyses, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct measures of taste are at least as sensitive as those obtained by self-report and that the preponderance of evidence confirms taste loss is a symptom of COVID-19. The meta-analysis showed that, among 138,897 COVID-19-positive patients, 39.2% reported taste dysfunction (95% confidence interval: 35.34%-43.12%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 18) versus self-report (n = 223) methodologies (Q = 0.57, df = 1, P = 0.45). Generally, males reported lower rates of taste loss than did females, and taste loss was highest among middle-aged adults. Thus, taste loss is likely a bona fide symptom of COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.

Exploring the Clinical Utility of Gustatory Dysfunction (GD) as a Triage Symptom Prior to Reverse Transcription Polymerase Chain Reaction (RT-PCR) in the Diagnosis of COVID-19: A Meta-Analysis and Systematic Review

BACKGROUND

The diagnosis of COVID-19 is made using reverse transcription polymerase chain reaction (RT-PCR) but its sensitivity varies from 20 to 100%. The presence of gustatory dysfunction (GD) in a patient with upper respiratory tract symptoms might increase the clinical suspicion of COVID-19.

AIMS

To perform a systematic review and meta-analysis to determine the pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-) and diagnostic odds ratio (DOR) of using GD as a triage symptom prior to RT-PCR.

METHODS

PubMed and Embase were searched up to 20 June 2021. Studies published in English were included if they compared the frequency of GD in COVID-19 adult patients (proven by RT-PCR) to COVID-19 negative controls in case control or cross-sectional studies. The Newcastle-Ottawa scale was used to assess the methodological quality of the included studies.

RESULTS

21,272 COVID-19 patients and 52,298 COVID-19 negative patients were included across 44 studies from 21 countries. All studies were of moderate to high risk of bias. Patients with GD were more likely to test positive for COVID-19: DOR 6.39 (4.86-8.40), LR+ 3.84 (3.04-4.84), LR- 0.67 (0.64-0.70), pooled sensitivity 0.37 (0.29-0.47) and pooled specificity 0.92 (0.89-0.94). While history/questionnaire-based assessments were predictive of RT-PCR positivity (DOR 6.62 (4.95-8.85)), gustatory testing was not (DOR 3.53 (0.98-12.7)). There was significant heterogeneity among the 44 studies (I2 = 92%, p < 0.01).

CONCLUSIONS

GD is useful as a symptom to determine if a patient should undergo further testing, especially in resource-poor regions where COVID-19 testing is scarce. Patients with GD may be advised to quarantine while repeated testing is performed if the initial RT-PCR is negative.

FUNDING

None.

Taste loss as a distinct symptom of COVID-19: A systematic review and meta-analysis

Chemosensory scientists have been skeptical that reports of COVID-19 taste loss are genuine, in part because before COVID-19, taste loss was rare and often confused with smell loss. Therefore, to establish the predicted prevalence rate of taste loss in COVID-19 patients, we conducted a systematic review and meta-analysis of 376 papers published in 2020-2021, with 241 meeting all inclusion criteria. Additionally, we explored how methodological differences (direct vs. self-report measures) may affect these estimates. We hypothesized that direct prevalence measures of taste loss would be the most valid because they avoid the taste/smell confusion of self-report. The meta-analysis showed that, among 138,897 COVID-19-positive patients, 39.2% reported taste dysfunction (95% CI: 35.34-43.12%), and the prevalence estimates were slightly but not significantly higher from studies using direct (n = 18) versus self-report (n = 223) methodologies (Q = 0.57, df = 1, p = 0.45). Generally, males reported lower rates of taste loss than did females and taste loss was highest in middle-aged groups. Thus, taste loss is a bona fide symptom COVID-19, meriting further research into the most appropriate direct methods to measure it and its underlying mechanisms.