Nasal swab is a good alternative sample for detecting SARS-CoV-2 with rapid antigen test: A meta-analysis.

BACKGROUND: We aim to determine if nasal samples have equivalent detection sensitivity to nasopharyngeal swabs for RAT and evaluate the diagnostic accuracy of nasal swabs with RAT. METHODS: PubMed and Web of Science were searched for eligible studies published before August 23, 2022. A bivariate random effects model was used to perform the quantitative synthesis. RESULTS: The pooled sensitivity, pooled specificity, positive likelihood ratio, negative likelihood ratio, and summary AUC on nasal swabs with RAT were 0.81 (95% CI, 0.77-0.85), 1.00 (95% CI: 0.99-1.00), 0.97 (95% CI, 0.95-0.98), 298.91 (95% CI, 144.71-617.42) and 0.19 (95% CI, 0.15-0.23), respectively. WHO required RAT kits to perform with a sensitivity of 0.80 and a specificity of 0.97, nasal swabs (0.81) achieved the required sensitivity while nasopharyngeal swabs (0.75) did not. The symptomatic population yielded higher pooled sensitivity than the asymptomatic population (0.86 versus 0.71), with a pooled sensitivity of 0.90 for five days of symptom onset. CONCLUSION: Nasal sampling had a great performance and yielded a high sensitivity in detecting SARS-CoV-2 using RAT, we believe that RAT performed with nasal swabs is a good alternative for detecting SARS-CoV-2, especially early in the onset of symptoms.

The roles of risk perception, negative emotions and perceived efficacy in the association between COVID-19 infection cues and preventive behaviors: a moderated mediation model.

Preventive behaviors during the COVID-19 pandemic are especially critical to the protection of individuals whose family members or acquaintances have been infected. However, limited research has explored the influence of infection cues on preventive behaviors. This study proposed an interaction model of environment-cognitive/affective-behavior to elucidate the mechanism by which infection cues influence preventive behaviors and the roles of risk perception, negative emotions, and perceived efficacy in that influence. To explore the relationships among these factors, we conducted a cross-sectional online survey in 34 provinces in China during the first wave of the COVID-19 pandemic. A total of 26,511 participants responded to the survey, and 20,205 valid responses (76.2%) were obtained for further analysis. The moderated mediation results show that infection cues positively predicted preventive behaviors in a manner mediated by risk perception and negative emotions. Moreover, perceived efficacy moderated the influence of infection cues not only on preventive behaviors but also on risk perception and negative emotions. The higher the perceived efficacy, the stronger these influences were. These findings validated our model, which elucidates the mechanisms underlying the promoting effect of infection cues on preventive behaviors during the initial stage of the COVID-19 pandemic. The implications of these results for the COVID-19 pandemic and beyond are discussed.

Evaluating the Value of Anti-SARS-CoV-2 Antibody-Based Tests for COVID-19 Diagnosis.

BACKGROUND: The early detection of COVID-19 patients is fundamental for containing the pandemic. A reverse-transcriptase quantitative polymerase chain reaction (RT-PCR), which detects SARS-CoV-2 RNA, is the gold standard diagnostic test, although it can contribute to false-negative results. Consequently, supplementary diagnostic tests are urgently needed. METHODS: To assess the value of anti-SARS-CoV-2 antibody-based tests for confirming COVID-19, a retrospective study was conducted on 3120 inbound overseas travelers who underwent a 14-day government quarantine in Xiamen from August 2020 to October 2020. The diagnostic accuracy of the total antibody that detected the anti-SARS-CoV-2 antibody and the RT-PCR that detected SARS-CoV-2 RNA was determined in comparison to the clinical diagnosis. RESULTS: The COVID-19 positive rate was 3.14% (98/3120). The sensitivity and specificity of the RT-PCR test on the first day of quarantine were 14.29% and 100%, respectively, and the sensitivity and specificity of the total antibody were 93.88% and 99.40%, respectively. The kappa value between an RT-PCR on the first day of quarantine and a clinical diagnosis was 0.24 (95% CI, 0.14-0.35), indicating poor consistency. The kappa value between total antibodies and a clinical diagnosis was 0.88 (95% CI, 0.83-0.93), indicating perfect consistency. There were no differences in the positive rates of an RT-PCR in symptomatic COVID-19 (7.41% (2/27)) and asymptomatic COVID-19 (16.90 (12/71) (p = 0.338). Similarly, the positive rate of the total antibody tests showed no difference in symptomatic COVID-19 (96.30% (26/27)) and asymptomatic COVID-19 (92.96% (66/71)) (p = 0.676). CONCLUSION: SARS-CoV-2 antibodies are developed by the body in response to an infection or after vaccination; this can easily lead to a missed diagnosis. In the context of low sensitivity for an RT-PCR, SARS-CoV-2 antibody detection is an effective adjunct to RT-PCR detection, which can improve the diagnostic accuracy of COVID-19 and provide an effective complement to the false-negative results of an RT-PCR.

The mechanism of governments' and individuals' influence on protective behaviours during the second wave of COVID-19: a multiple mediation model.

Background: The SARS-CoV-2 virus continues to spread and resurge globally with signs of a second wave, despite actions by governments to curb the COVID-19 pandemic. However, evidence-based strategies to combat COVID-19 recurrence are poorly documented. Objective: To reveal how governments and individuals should act to effectively cope with future waves, this study proposed a preventive model of COVID-19 resurgence. Method: A questionnaire survey was conducted among 1,137 residents of Beijing, where the epidemic reoccurred. Structural equation model was used to explore the mechanism among government intervention, perceived efficacy, positive emotions, posttraumatic growth (PTG) and protective behaviours. Results: Data analysis revealed that during COVID-19 resurgence, government intervention could directly and indirectly influence protective behaviours through individual factors (i.e. perceived efficacy, positive emotions), and PTG could mediate the indirect pathway to protective behaviours. Conclusions: These findings implied that government intervention needs to be integrated with individual factors to effectively control repeated COVID-19 outbreaks.

Antecedentes: El virus SARS-CoV-2 continúa propagándose y resurgiendo a nivel mundial con signos de una segunda ola, a pesar de las acciones de los gobiernos para frenar la pandemia de COVID-19. Sin embargo, las estrategias basadas en evidencia para combatir la recurrencia de COVID-19 están pobremente documentadas.Objetivo: Para revelar cómo deben actuar los gobiernos y las personas para hacer frente de manera efectiva a futuras olas, este estudio propuso un modelo preventivo del resurgimiento de COVID-19.Método: Se realizó una encuesta entre 1.137 residentes de Beijing, donde la epidemia volvió a ocurrir. Se utilizó un modelo de ecuación estructural para explorar el mecanismo entre la intervención del gobierno, la eficacia percibida, las emociones positivas, el crecimiento postraumático (CPT) y las conductas protectoras.Resultados: El análisis de datos reveló que durante el resurgimiento de COVID-19, la intervención del gobierno podría influir directa e indirectamente en los comportamientos de protección a través de factores individuales (es decir, eficacia percibida, emociones positivas), y CPT podría mediar en el camino indirecto hacia los comportamientos de protección.Conclusiones: Estos hallazgos implicaron que la intervención del gobierno debe integrarse con factores individuales para controlar de manera efectiva los brotes repetidos de COVID-19.

Diagnostic accuracy of rapid antigen test for SARS-CoV-2: A systematic review and meta-analysis of 166,943 suspected COVID-19 patients.

To assess the diagnostic accuracy of the rapid antigen test (RAT) compared with RT-PCR (reference standard) for SARS-CoV-2, we searched MEDLINE/PubMed and Web of Science for relevant records. The QUADAS-2 tool was used to assess study quality, and quantitative synthesis was conducted using a bivariate random-effects model. The meta-analysis included 135 studies (166,943 samples). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.76 (95%CI: 0.73-0.79), 1.00 (95%CI: 1.00-1.00), 276.1 (95% CI, 184.1-414.1), 0.24 (95% CI, 0.21-0.27), and 1171 (95% CI, 782-1755), respectively. Compared to other sample types, nasal samples had the best RAT sensitivity [0.79 (95%CI: 0.71-0.85)]. The sensitivities of the different RAT kits ranged from 0.41 (95%CI: 0.23-0.61) to 0.90 (95%CI: 0.70-0.97). Sensitivity was markedly better in samples with lower Ct, and RAT achieved excellent pooled sensitivity at 1.00 (95%CI: 0.70-1.00) among samples with Ct < 20. Testing within 10 days of symptom onset resulted in a high sensitivity. For ≤ 3, ≤ 7, and ≤ 10 days, the sensitivities were 0.91 (95%CI: 0.83-0.96), 0.89 (95%CI: 0.84-0.93), and 0.88 (95%CI: 0.83-0.92), respectively. RAT kits show high sensitivity and specificity in early infection, especially when the viral load is high. Moreover, using nasal samples for antigen testing, which are moderately sensitive and patient-friendly, is a reliable alternative to nasopharyngeal sampling. RAT might be effective for fighting the COVID-19 pandemic; however, it must be complemented by the careful handling of negative test results.

The association between intolerance of uncertainty and Internet addiction during the second wave of the coronavirus disease 2019 pandemic: A multiple mediation model considering depression and risk perception.

Repeated outbreaks of coronavirus disease 2019 (COVID-19) have forced people to shift most of their work and life activities from offline to online, leading to a growing problem of Internet dependence and even Internet addiction. However, the mechanism of the association between COVID-19-related intolerance of uncertainty (COVID-19 IU) and Internet addiction during the second wave of COVID-19 is still unclear. The current study explored the association between COVID-19 IU and Internet addiction as mediated by depression and risk perception based on the Uncertainty-Depression-Perception-Addiction model (UDPA). A total of 1,137 adult participants were recruited, and COVID-19 IU, depression, risk perception, Internet addiction, and demographic variables were analyzed. The results showed that COVID-19 IU was significantly and positively associated with Internet addiction and that this relationship was mediated in parallel by depression and risk perception. Our findings further extend the Interaction of Person-Affect-Cognition-Execution (I-PACE) model from the perspective of applicability in the unique context of COVID-19. Furthermore, the study suggests that individuals could decrease their dependence on the Internet to prevent Internet addiction during the second wave of the pandemic through effective interventions that include lowering COVID-19 IU, improving emotion regulation, and developing reasonable perceptions of risk.

Clinical features and risk factors associated with severe COVID-19 patients in China.

BACKGROUND: Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world. In this study, we aimed to identify the risk factors for severe COVID-19 to improve treatment guidelines. METHODS: A multicenter, cross-sectional study was conducted on 313 patients hospitalized with COVID-19. Patients were classified into two groups based on disease severity (nonsevere and severe) according to initial clinical presentation. Laboratory test results and epidemiological and clinical characteristics were analyzed using descriptive statistics. Univariate and multivariate logistic regression models were used to detect potential risk factors associated with severe COVID-19. RESULTS: A total of 289 patients (197 nonsevere and 92 severe cases) with a median age of 45.0 (33.0, 61.0) years were included in this study, and 53.3% (154/289) were male. Fever (192/286, 67.1%) and cough (170/289, 58.8%) were commonly observed, followed by sore throat (49/289, 17.0%). Multivariate logistic regression analysis suggested that patients who were aged ≥ 65 years (OR: 2.725, 95% confidence interval [CI]: 1.317-5.636; P = 0.007), were male (OR: 1.878, 95% CI: 1.002-3.520, P = 0.049), had comorbid diabetes (OR: 3.314, 95% CI: 1.126-9.758, P = 0.030), cough (OR: 3.427, 95% CI: 1.752-6.706, P < 0.001), and/or diarrhea (OR: 2.629, 95% CI: 1.109-6.231, P = 0.028) on admission had a higher risk of severe disease. Moreover, stratification analysis indicated that male patients with diabetes were more likely to have severe COVID-19 (71.4% vs. 28.6%, χ2 = 8.183, P = 0.004). CONCLUSIONS: The clinical characteristics of those with severe and nonsevere COVID-19 were significantly different. The elderly, male patients with COVID-19, diabetes, and presenting with cough and/or diarrhea on admission may require close monitoring to prevent deterioration.

Prevalence of comorbidities and its effects in coronavirus disease 2019 patients: A systematic review and meta-analysis

A review. An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China;the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. The aim of this meta-anal. was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients. A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through Feb. 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models. Seven studies were included in the meta-anal., including 1 576 infected patients. The results showed the most prevalent clin. symptom was fever (91.3%, 95% CI: 86-97%), followed by cough (67.7%, 95% CI: 59-76%), fatigue (51.0%, 95% CI: 34-68%) and dyspnea (30.4%, 95% CI: 21-40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.88-6.22) resp. We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.

Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China; the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. AIMS: The aim of this meta-analysis was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients. METHODS: A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through February 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models. RESULTS: Seven studies were included in the meta-analysis, including 1 576 infected patients. The results showed the most prevalent clinical symptom was fever (91.3%, 95% CI: 86-97%), followed by cough (67.7%, 95% CI: 59-76%), fatigue (51.0%, 95% CI: 34-68%) and dyspnea (30.4%, 95% CI: 21-40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.88-6.22) respectively. CONCLUSION: We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.