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. 2023 Feb 9;52:102548. doi: 10.1016/j.tmaid.2023.102548

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

Jia-Wen Xie a,b, Ya-Wen Zheng a,b, Mao Wang a,b, Yong Lin a,b, Yun He a,b, Li-Rong Lin a,b,∗
PMCID: PMC9909360  PMID: 36758806

Abstract

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.

Keywords: COVID-19, SARS-CoV-2, Nasal swabs, Rapid antigen test, Self-test

1. Introduction

From the onset of the Coronavirus disease 2019 (COVID-19) pandemic until now, nasopharyngeal swabbing for reverse transcription polymerase chain reaction (RT-PCR) has always been regarded as the gold standard for determining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1]. Rapid testing is necessary in a pandemic situation to immediately identify individuals who may transmit SARS-CoV-2 to prevent the spread of the infection. The drawbacks of PCR, such as delays in obtaining results, and the necessity for pricey laboratory equipment as well as trained healthcare personnel, make it unsuitable for application in large screening programs [2]. Therefore, the rapid antigen test (RAT), with its short turnaround time and low cost, offers a rapid and easy-to-perform alternative to SARS-CoV-2 detection [3,4].

Currently, nasopharyngeal swabs and oropharyngeal swabs are the recommended standard sampling techniques in RAT for SARS-CoV-2 detection, yet these have some limitations such as the complexity of collection. Nasal swabs, less invasive than nasopharyngeal swabs, represent a more comfortable approach to sampling [5]. They do not require skilled professionals, providing the basis for their use as a self-sampling technique [3]. Widespread use of self-testing for SARS-CoV-2 infection, which could help reduce the workload of healthcare workers, can be a useful supplement to point-of-care tests by enabling more wide-ranging testing. Self-testing, if reliable, enables individuals to get prompt results, facilitating the early identification and isolation of COVID-19 cases [2,4,6].

The sensitivity of nasal swabs for RT-PCR has recently been demonstrated to be equivalent to that of nasopharyngeal swabs [7,8], but it is unknown whether nasal samples for RAT have similar potential. Independent validations of rapid antigen tests using nasal swabs have increased recently, however, no research has been conducted to evaluate these separate validations systematically. Therefore, the first objective of this meta-analysis was to determine if nasal samples have comparable detection sensitivity to nasopharyngeal swabs for RAT. The second objective was to fully evaluate the diagnostic accuracy of nasal swabs with RAT.

2. Materials and methods

This meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol of this study was registered in the PROSPERO database (CRD42022352020).

2.1. Literature search

We searched the MEDLINE/PubMed and Web of Science databases for relevant studies. The final literature search was performed on August 23, 2022. To identify related studies, we employed a combination of free text and MeSH terms. The main search terms were: “SARS-CoV-2”, “COVID-19”, “antigen test”, “nasal”, “nasopharyngeal”, and “specimen”. Table S1 provided detailed search strategies.

2.2. Inclusion and exclusion criteria

A study can only be included in our meta-analysis if it meets the criteria listed below: (i) using RAT as an index test, (ii) RT-PCR serves as a reference standard for assessing the performance of RAT, (iii) collecting nasal or nasopharyngeal swabs from symptomatic or asymptomatic individuals, and (iv) the sensitivity and specificity of the RAT can be found in the full text or the supplementary material. The criteria for exclusion were as follows: (i) duplicate original investigation, case reports, protocols, reviews, editorials, letters, comments, and meta-analysis articles, and (ii) data that is required for a meta-analysis is not available (through article review or calculation). Two independent reviewers assessed the full text of the papers to identify studies that matched the inclusion criteria.

2.3. Data extraction and quality assessment

Two reviewers independently extracted the following raw data from all qualified articles to recalculate pooled sensitivity and pooled specificity: true positive (TP), false positive (FP), false negative (FN), and true negative (TN) values. The risk of bias of each included publication was evaluated using The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, which assessed the following domains: patient selection, the performance of the index test, the performance of the reference test, and flow and timing [9]. The assessment procedure was carried out separately by two researchers, and disagreements between the researchers were handled through joint discussions.

2.4. Statistical analysis

The bivariate random effects model was used to perform the quantitative synthesis. We calculated each parameter of individual studies by the following formulas to derive the pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR):

Sensitivity = TP/(TP + FN).

Specificity = TN/(TN + FP).

Positive likelihood ratio = Sensitivity/(1–Specificity).

Negative likelihood ratio=(1–Sensitivity)/Specificity.

The corresponding forest plots were developed to show the overall effects. The summary receiver operating characteristic (SROC) curve generated the area under the curve (AUC) with 95% confidence intervals (CI). To evaluate heterogeneity among included studies, the chi-squared-based Q test and the I 2 statistic were applied. Significant heterogeneity was indicated by P < 0.05 or I 2 ≥ 50%. The sources of heterogeneity were investigated using meta-regression. The sensitivity analysis assessed the stability of the meta-analysis. Publication bias was evaluated by Deeks’ funnel plot. Fagan plot and Probability modifying plot reveal the relationship between the pre-test probability and the post-test probability. All analyses were performed with STATA software (Stata Corporation, College Station, TX, USA) with a P value < 0.05 deemed statistically significant.

3. Results

3.1. Characteristics of included studies

We identified 3563 publications for screening, and 49 articles utilizing nasal swabs were included in our meta-analysis (Table 1 ). To determine if nasal samples have an equivalent detection ability to nasopharyngeal samples with RAT, we also included 115 papers that employ nasopharyngeal swabs (Table S2). Among the 49 studies on nasal swabs containing 79,073 samples [[2], [3], [4],6, [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29],[30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54]], all of them were published between 2021 and 2022, and twenty-one studies were conducted in the USA [12,14,15,18,21,22,[25], [26], [27],[32], [33], [34], [35],39,44,45,[47], [48], [49], [50], [51]]. Thirty-four studies described the patients' symptom status [2,4,6,11,12,14,16,[18], [19], [20], [21], [22],[26], [27], [28], [29], [30], [31], [32],[34], [35], [36], [37], [38], [39], [40],[42], [43], [44],[46], [47], [48],50,51]. Fig. 1 depicted the inclusion and exclusion procedures.

Table 1.

Characterization of included studies using nasal swabs.

Number DOI Author Year Country TP TN FP FN SS
1 10.3389/fpubh.2021.728969 Alqahtani 2021 Bahrain 602 3420 30 131 4183
2 10.3390/diagnostics11122217 Tonen-Wolyec 2021 France 20 84 0 2 106
3 10.1002/jmv.27,812 Prazuck 2022 France 40 75 0 3 118
4 10.1002/jmv.27,249 Cassuto 2021 France 31 202 0 1 234
5 10.1002/jcla.24,203 Begum 2022 Bangladesh 80 111 3 20 214
6 10.3390/ijerph19073826 Cattelan 2022 Italy 174 51 3 54 282
7 10.1128/Spectrum.00342-21 Chiu 2021 USA 158 23,462 42 30 23,692
8 10.1007/s11845-021-02776-z Denina 2021 Italy 16 160 14 1 191
9 10.1093/ajcp/aqab173 Drain 2022 USA 23 194 0 5 222
10 10.1093/jpids/piab081 Ford 2022 USA 267 1774 2 67 2110
11 10.1016/j.jiac.2022.07.023 Fujiya 2022 Japan 39 15 0 6 60
12 10.1515/cclm-2022-0360 Hörber 2022 Germany 207 212 1 27 447
13 10.1017/ice.2021.20 James 2021 USA 86 2184 3 66 2339
14 10.1007/s15010-021-01681-y Krüger 2022 Germany 120 611 4 26 761
15 10.3390/diagnostics12051279 Medoro 2022 Italy 111 455 9 9 584
16 10.1186/s12879-021-06716-1 Mitchell 2021 USA 38 245 0 9 292
17 10.1001/jamapediatrics.2022.0080 Nelson 2022 USA 24 572 1 6 603
18 10.3390/jcm10102099 Osmanodja 2021 Germany 62 308 1 8 379
19 10.3390/diagnostics12030650 Polvere 2022 Italy 113 383 3 2 501
20 10.15585/mmwr.mm7003e3 Guerra 2021 USA 157 3116 4 142 3419
21 10.3390/v14030468 Salcedo 2022 USA 51 113 1 8 173
22 10.1136/bmjopen-2022-060,832 Sania 2022 USA 198 911 21 93 1223
23 10.3390/diagnostics11122300 Sazed 2021 Bangladesh 121 245 2 12 380
24 10.1002/jcla.24,410 Shin 2022 Korea 75 217 0 4 296
25 10.1371/journal.pone.0266,375 Sicilia 2022 Argentina 40 194 0 9 243
26 10.1093/jalm/jfac004 Stokes 2022 Canada 74 8 7 10 99
27 10.1128/spectrum.00236-22 Sun 2022 USA 51 979 20 4 1054
28 10.3201/eid2711.211,449 Surasi 2021 USA 55 642 0 72 769
29 10.3201/eid2710.210,080 Tinker 2021 USA 8 1540 0 32 1580
30 10.1128/JCM.01742-21 Almendares 2022 USA 157 3116 4 142 3419
31 10.1016/j.jiac.2022.02.016 Akashi 2022 Japan 80 690 0 30 800
32 10.1016/j.jcv.2021.104838 Bianco 2021 Italy 269 561 48 29 907
33 10.1016/j.lanwpc.2022.100486 Bond 2022 Australi 206 1489 0 67 1762
34 10.1093/jpids/piac035 Freeman 2022 USA 45 326 3 13 387
35 10.1128/spectrum.01250-22 Galliez 2022 Brazil 113 65 0 14 192
36 10.1128/spectrum.00217-22 Goodall 2022 Canada 40 763 0 22 825
37 10.1111/apm.13,189 Jakobsen 2021 Denmark 32 7008 0 34 7074
38 10.1016/j.ijid.2021.07.043 Leli 2021 Italy 114 596 30 52 792
39 10.1016/j.jcvp.2022.100080 Liu 2022 USA 89 1039 3 13 1144
40 10.1016/j.jcv.2021.105023 Okoye 2022 USA 45 3759 2 4 3810
41 10.1128/spectrum.02455-21 Patriquin 2022 Canada 154 21 1 21 197
42 10.1002/emp2.12,605 Peacock 2022 USA 133 554 8 40 735
43 10.1128/JCM.00083-21 Pollock 2021 USA 226 2004 12 66 2308
44 10.1371/journal.pone.0260,862 Pollreis 2021 USA 25 177 0 12 214
45 10.1128/Spectrum.01008-21 Siddiqui 2021 USA 179 5826 13 43 6061
46 10.1371/journal.pone.0249,710 Sood 2021 USA 127 539 9 99 774
47 10.1093/jalm/jfac023 Sukumaran 2022 India 67 83 0 19 169
48 10.1038/s41598-021-90026-8 Takeuchi 2021 Japan 37 811 0 14 862
49 10.1128/spectrum.02029-21 Wölfl-Duchek 2022 Austria 29 41 0 17 87
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Fig. 1.

Fig. 1

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PRISMA flow diagram.

3.2. Quality assessment

Table 2 presented the quality of research on nasal swabs. Regarding patient selection, 77.5% (38/49) of the studies were considered to have a low risk of bias, as they avoided improper exclusion criteria and case-control designs, and all patients were consecutively or randomly included. In the index test and reference standard domains, it was determined that all studies had a low risk of bias. Since all chosen patients were included in the analysis and received the same reference standard, 73.4% (36/49) of the studies were deemed to have a low risk of bias in flow and timing domains. Considering applicability concerns, all domains were thought to meet the objectives of this meta-analysis. We also assessed the quality of articles utilizing nasopharyngeal samples, and it was determined that all domains satisfied the goals of this meta-analysis. (Table S3).

Table 2.

Quality of studies using nasal swabs.

number study Risk of bias
 Applicability concerns
Patient selection Index test Reference standard Flow and timing Patient selection Index test Reference standard
1 Alqahtani L L L L L L L
2 Tonen-Wolyec L L L L L L L
3 Prazuck L L L U L L L
4 Cassuto L L L L L L L
5 Begum L L L L L L L
6 Cattelan L L L L L L L
7 Chiu L L L U L L L
8 Denina L L L L L L L
9 Drain L L L U U L L
10 Ford L L L U L L L
11 Fujiya L L L L L L L
12 Hörber L L L L L L L
13 James L L L L L L L
14 Krüger L L L H L L L
15 Medoro L L L L L L L
16 Mitchell L L L L L L L
17 Nelson U L L L U L L
18 Osmanodja L L L U L L L
19 Polvere L L L L L L L
20 Guerra L L L U L L L
21 Salcedo U L L L L L L
22 Sania L L L L L L L
23 Sazed L L L L L L L
24 Shin L L L L L L L
25 Sicilia U L L L L L L
26 Stokes U L L L L L L
27 Sun U L L U L L L
28 Surasi L L L L L L L
29 Tinker L L L L U L L
30 Almendares L L L L L L L
31 Akashi L L L L L L L
32 Bianco L L L L L L L
33 Bond L L L U L L L
34 Freeman U L L L L L L
35 Galliez L L L L L L L
36 Goodall U L L L U L L
37 Jakobsen L L L L U L L
38 Leli L L L L L L L
39 Liu H L L L U L L
40 Okoye L L L L L L L
41 Patriquin U L L U U L L
42 Peacock U L L L L L L
43 Pollock L L L U L L L
44 Pollreis L L L L L L L
45 Siddiqui L L L U L L L
46 Sood L L L U L L L
47 Sukumaran L L L L L L L
48 Takeuchi L L L L L L L
49 Wölfl-Duchek H L L L U L L
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H = high risk of bias; L = low risk of bias; U = unclear risk of bias.

3.3. Diagnostic performance

The article on the use of nasal swabs yielded a pooled sensitivity of 0.81 (95% CI, 0.77–0.85) (Fig. 2 A), a polled specificity of 1.00 (95% CI: 0.99–1.00) (Fig. 2B), and a summary AUC of 0.97 (95% CI, 0.95–0.98) (Fig. 2C). The pooled sensitivity (Fig. 2D), pooled specificity (Fig. 2E), and summary AUC (Fig. 2F) derived from the articles using nasopharyngeal swabs were 0.75 (95% CI, 0.71–0.78), 1.00 (95% CI: 0.99–1.00), and 0.97 (95% CI, 0.95–0.98), respectively.

Fig. 2.

Fig. 2

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Pooled sensitivity, pooled specificity, and Summary ROC curve of RAT with nasal swabs and nasopharyngeal swabs

(A) Forest plots of pooled sensitivity with nasal swabs. (B) Forest plots of pooled specificity with nasal swabs. (C) Summary ROC curve and its area under the curve with nasal swabs. (D) Forest plots of pooled sensitivity with nasopharyngeal swabs. (E) Forest plots of pooled specificity with nasopharyngeal swabs. (F) Summary ROC curve and its area under the curve with nasopharyngeal swabs.

Fig. 3 plotted the relationship between pre-test and post-test probability based on positive likelihood ratios and negative likelihood ratios. For articles on nasal samples, the pooled positive and negative likelihood ratios were 298.91 (95% CI, 144.71–617.42) and 0.19 (95% CI, 0.15–0.23), respectively (Fig. 3A). The articles using nasopharyngeal samples generated a pooled positive likelihood ratio of 233.02 (95% CI: 148.29–366.16) and a pooled negative likelihood ratio of 0.25 (95% CI: 0.22–0.29) (Fig. 3C). Based on the data analysis, we hypothesized that the pre-test probability would be 9.00%, resulting in a positive post-test probability of 97.00% and a negative post-test probability of 2.00% for nasal swabs (Fig. 3B), and a positive post-test probability of 96.00% and a negative post-test probability of 2.00% of nasopharyngeal swabs (Fig. 3D).

Fig. 3.

Fig. 3

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Probability Modifying Plot and Fagan plot for evaluating the diagnostic value

(A) Probability Modifying Plot of nasal swabs. (B) Fagan plot of nasal swabs.

(C)Probability Modifying Plot of nasopharyngeal swabs. (D) Fagan plot of nasopharyngeal swabs.

3.4. Subgroup analysis

The symptom status was analyzed by subgroups, due to the limitation of the number of included studies (Table 3 ). We found that RAT with nasal swabs in an asymptomatic population yielded a pooled sensitivity of 0.71 (95% CI, 0.63–0.79) and a pooled specificity of 1.00 (95% CI: 0.99–1.00). Despite the specificity slightly decreasing to 0.99 (95% CI: 0.99–1.00), the sensitivity increased to 0.86 (95% CI, 0.81–0.89) in the symptomatic group. The pooled sensitivity and specificity during the first five days of symptom onset were 0.90 (95% CI, 0.82–0.95) and 1.00 (95% CI: 0.64–1.00), respectively. The pooled sensitivity of RAT with nasopharyngeal swabs was 0.60 (95% CI, 0.51–0.69), 0.80 (95% CI, 0.75–0.85), and 0.76 (95% CI, 0.67–0.84) for the asymptomatic people, the symptomatic people, and people within five days of symptom onset, respectively.

Table 3.

Pooled sensitivity and specificity among subgroups of studies.

Subgroups No. of study Total Sample Size Polled Sensitivity (95% CI) Polled Specificity (95% CI)
Asymptomatic
nasal swabs 26 50,575 0.71 (0.63–0.79) 1.00 (0.99–1.00)
nasopharyngeal swabs 22 20,487 0.60 (0.51–0.69) 1.00 (1.00–1.00)
Symptomatic
nasal swabs 29 14,654 0.86 (0.81–0.89) 0.99 (0.99–1.00)
nasopharyngeal swabs 35 20,532 0.80 (0.75–0.85) 1.00 (0.99–1.00)
Within 5 days of symptom onset
nasal swabs 4 1145 0.90 (0.82–0.95) 1.00 (0.64–1.00)
nasopharyngeal swabs 8 5171 0.76 (0.67–0.84) 1.00 (0.99–1.00)
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3.5. Heterogeneity analysis

Among the 49 studies that employed nasal swabs included in this analysis, we detected significant heterogeneity in both the sensitivity and specificity data (I 2 = 93.85% and I 2 = 98.04%, respectively). This heterogeneity was independent of the threshold effect, given that there was no shoulder-arm-shaped distribution in the SROC curve (Fig. 2C), and the proportion of heterogeneity likely to be attributable to such an effect was small (0.16).

We performed a meta-regression to explore the source of heterogeneity by using the following covariate: geographical background (the USA, other), sample size (≥1000, <1000), publication year (2022, 2021), quality of the article (high, medium or low), study design (prospective study, retrospective study), and the specimen type of the reference standard (nasopharyngeal, non-nasopharyngeal). As shown in Fig. 4 , all six subgroups contributed to heterogeneity in pooled sensitivity, with geographic background and sample size being the most significant (P-value <0.001). However, in pooled specificity, no statistical differences were found for geographic background and sample size, while the other four subgroups all contributed to the heterogeneity, with P -values <0.001 for article quality.

Fig. 4.

Fig. 4

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Meta-regression for detecting heterogeneity with nasal swabs.

Meanwhile, we also analyzed the heterogeneity of the 115 articles using nasopharyngeal swabs in five aspects: sample size, publication year, quality of the article, study design, and specimen type. The results showed that all five aspects contribute to the heterogeneity of pooled sensitivity and pooled specificity (Fig. S1).

3.6. Sensitivities analysis

Goodness-of-fit and bivariate normality analyses (Fig. 5 A and B) showed that the data are basically concentrated on the diagonal， suggesting that our findings were relatively robust. Influence analysis (Fig. 5C) detected no influential observations. Outlier detection (Fig. 5D) depicted two outlier studies. The overall results were only minimally changed after the two studies were removed [24,34]. (Sensitivity: 0.81 versus 0.81; Specificity: 1.00 versus 1.00; AUC: 0.97 versus 0.96), indicating that our results were not driven by these outlying points.

Fig. 5.

Fig. 5

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Graphs for sensitivity analyses

(A) Goodness-Of-Fit. (B) Bivariate Normality. (C) Influence Analysis. (D) Outlier Detection.

The overall effect was slightly improved when we limited our analysis to studies that employed nasopharyngeal swabs for the reference method (RT-PCR). (Sensitivity: 0.84 versus 0.81; Specificity: 1.00 versus 1.00; AUC: 0.98 versus 0.97).

3.7. Publication bias

Visual inspection of Deeks' funnel plot (Fig. S2) revealed no obvious asymmetry, and a P-value of 0.66 suggested that there was no publication bias in the present meta-analysis.

4. Discussion

To the best of our knowledge, this is the first meta-analysis to evaluate the diagnostic accuracy of nasal swabs with RAT. The pooled sensitivity, pooled specificity, and summary AUC of nasal swabs with RAT were 0.81 (95% CI, 0.77–0.85), 1.00 (95% CI: 0.99–1.00), and 0.97 (95% CI, 0.95–0.98), respectively. This satisfies the performance requirements for RAT kits recommended by WHO [55], which called for a sensitivity of 0.80 and a specificity of 0.97. Additionally, our results demonstrated a pooled positive likelihood ratio of 298.91 (95% CI, 144.71–617.42) and a pooled negative likelihood ratio of 0.19 (95% CI, 0.15–0.23) (Fig. 3A). It can be seen visually from the probability modifying plot that the positive post-test probability is very high, no matter what the pre-test probability is, and we obtain a 97% post-test probability by assuming a 9% pre-test probability. This implies that antigen testing with nasal samples helps confirm the presence of SARS-CoV-2 when the result is positive, which is of great diagnostic value.

The articles using nasopharyngeal swabs generated a pooled sensitivity of 0.75 (95% CI, 0.71–0.78), a pooled specificity of 1.00 (95% CI: 0.99–1.00), and a summary AUC of 0.97 (95% CI, 0.95–0.98), which did not meet the WHO requirement of 0.80 sensitivity [55]. Compared to nasopharyngeal sampling, nasal sampling is linked to reducing sneezing or coughing during collection, which results in less droplet exposure and then lowers the risk of transmission among healthcare workers [53]. In addition to eliminating the drawbacks of nasopharyngeal swabs in terms of causing discomfort and possibly even damage to the patient [56], nasal swabs offered the option of home testing. Questionnaires were conducted in several studies, resulting that nearly all patients reported no pain in the anterior nasal collection, and it was easy to understand how to perform self-testing and interpret the test results [3,4,6,47,53,57]. Previous experience with HIV self-testing demonstrated that self-testers could perform HIV rapid diagnostic tests with reliability and accuracy [58]. Self-testing with nasal swabs instead of having professionals perform sample collection can save a lot of human resources and money, and it could increase COVID-19 testing coverage globally to better control the COVID-19 pandemic.

Not surprisingly, we found that the pooled sensitivity in the symptomatic population was significantly higher than that in the asymptomatic group. The WHO advises rapid antigen test in symptomatic individuals within the first five to seven days after the appearance of symptoms [55], and in our analysis, testing within five days of symptom onset reached a very high sensitivity of 0.90. This suggests that RAT employing samples collected from the nasal cavity early in symptom onset is a suitable choice.

We detected a high degree of interstudy heterogeneity. Through heterogeneity analysis, we found that the heterogeneity originated from the following six areas: geographical background, sample size, publication year, quality of the article, study design, and the specimen type of the reference standard. However, the bivariate random effects model we utilized produced a relatively robust statistical result, while the sensitivity analysis and publication bias test further demonstrated that our findings were reliable. Either removing the outlier studies or analyzing only the articles performing RT-PCR (reference standard) with nasopharyngeal swabs yielded results that differed minimally from our overall findings.

Although the findings of this study revealed that the rapid antigen test utilizing nasal swabs exhibited high sensitivity and specificity in detecting SARS-CoV-2, there are some limitations. The Ct values of individuals with SARS-CoV-2 infection provided in the included studies were limited, no other types of samples (e.g., saliva, oropharyngeal swabs) were investigated for comparison with nasal samples. In addition, for the two sampling techniques, our findings were derived from different patients rather than paired samples and therefore cannot be directly compared [[59], [60], [61]], it is hoped that future studies will allow a direct comparison of the sensitivity and specificity of nasal swabs and nasopharyngeal swabs with RAT using paired samples.

5. Conclusion

Nasal sampling yielded a high sensitivity in detecting SARS-CoV-2 using RAT. Given this finding, coupled with the advantages of being less invasive and allowing for self-testing, we believe that RAT performed with nasal swabs is a good alternative for detecting SARS-CoV-2, especially early in the onset of symptoms.

CRediT authorship contribution statement

Jia-Wen Xie: Methodology, Writing – original draft. Ya-Wen Zheng: Formal analysis, Investigation. Mao Wang: Validation, Data curation. Yong Lin: Visualization, Supervision. Yun He: Software, Investigation. Li-Rong Lin: Conceptualization, Writing – review & editing.

Declaration of competing interest

We declare that we have no competing interests.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 82172331, 81972028, 81672094], and the Key Projects for Province Science and Technology Program of Fujian Province, China [grant number 2020D017]. The funders played no role in the study design, data collection, analyses, the decision to publish, or manuscript preparation.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.tmaid.2023.102548.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

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Multimedia component 3
mmc3.docx (37.6KB, docx)

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