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. 2020 Dec 5;131(4):865–878. doi: 10.1002/lary.29286

Prevalence of Olfactory Dysfunction in Coronavirus Disease 2019 (COVID‐19): A Meta‐analysis of 27,492 Patients

Jeyasakthy Saniasiaya 1,✉, Md Asiful Islam 2, Baharudin Abdullah 3
PMCID: PMC7753439  PMID: 33219539

Abstract

Objectives/Hypothesis

Olfactory dysfunction has been observed as one of the clinical manifestations in COVID‐19 patients. We aimed to conduct a systematic review and meta‐analysis to estimate the overall pooled prevalence of olfactory dysfunction in COVID‐19 patients.

Study Design

Systematic review and meta‐analyses.

Methods

PubMed, Scopus, Web of Science, Embase, and Google Scholar databases were searched to identify studies published between 1 December 2019 and 23 July 2020. We used random‐effects model to estimate the pooled prevalence with 95% confidence intervals (CIs). Heterogeneity was assessed using the I 2 statistic and Cochran's Q test. Robustness of the pooled estimates was checked by different subgroup and sensitivity analyses This study is registered with PROSPERO (CRD42020183768).

Results

We identified 1162 studies, of which 83 studies (n = 27492, 61.4% female) were included in the meta‐analysis. Overall, the pooled prevalence of olfactory dysfunction in COVID‐19 patients was 47.85% [95% CI: 41.20–54.50]. We observed olfactory dysfunction in 54.40% European, 51.11% North American, 31.39% Asian, and 10.71% Australian COVID‐19 patients. Anosmia, hyposmia, and dysosmia were observed in 35.39%, 36.15%, and 2.53% of the patients, respectively. There were discrepancies in the results of studies with objective (higher prevalence) versus subjective (lower prevalence) evaluations. The discrepancy might be due to false‐negative reporting observed in self‐reported health measures.

Conclusions

The prevalence of olfactory dysfunction in COVID‐19 patients was found to be 47.85% based on high‐quality evidence. Due to the subjective measures of most studies pooled in the analysis, further studies with objective measures are advocated to confirm the finding.

Level of Evidence

2 Laryngoscope, 131:865–878, 2021

Keywords: Coronavirus, COVID‐19, olfactory, smell, meta‐analysis

INTRODUCTION

The world has recently been afflicted by severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2), which causes coronavirus disease 2019 (COVID‐19). China witnessed the first case of pneumonia of unknown origin reported on 8th December 2019 from Wuhan City, Hubei province, 1 and within a very short period, it started to spread globally. World Health Organization (WHO) declared COVID‐19 a public health emergency of international concern on 30th January 2020 and a global pandemic disease on 11th March 2020. As of 23rd October 2020, it has become a global pandemic with over 1.1 million deaths and 41.5 million confirmed cases worldwide. 2 As its nature and characteristics are unknown, understanding its presenting symptoms may help in earlier diagnosis. Current accumulated data indicate fever, cough, dyspnea, myalgia, arthralgia, and diarrhea to be the most predominant symptoms of SARS‐CoV‐2 infection. 1 , 3

Initially, a handful of studies reported the observation of olfactory dysfunction in COVID‐19 patients. 4 , 5 , 6 Following that the Ear, Nose, and Throat Society of UK and British Rhinological Society came up with an anecdotal report on the association between SARS‐CoV‐2 infection and olfactory dysfunction, in addition to urging new‐onset anosmia to be investigated for SARS‐CoV‐2 infection while taking precautionary isolation. 7 Similarly, the American Academy of Otolaryngology on 22 March 2020 advocated anosmia, hyposmia, and dysgeusia to be added as symptoms upon screening for COVID‐19 with measure such as precautionary isolation advised. 8 With the mounting evidence of olfactory dysfunction as a plausible symptom of COVID‐19, the Centers for Disease Control and Prevention has added olfactory dysfunction as part of COVID‐19's list of presenting symptoms. 9

With more cases being reported, 10 it is becoming apparent that the prevalence of olfactory dysfunction in COVID‐19 patients varies widely across the range. An earlier meta‐analysis by Tong et al. 11 revealed the prevalence of olfactory dysfunction in COVID‐19 patients was 52.73% based on 10 studies with 1627 patients available at that time. Remarkably, the authors stated that this figure is an underestimation due to the different type of assessment tools, which may be compounded by the smaller number of studies. Hence, another meta‐analysis evaluating newer available studies and a larger pool of patients is required to present a more representative figure of the global prevalence of olfactory dysfunction among COVID‐19 patients.

MATERIALS AND METHODS

We conducted a systematic review and meta‐analysis of the literature in accordance with the PRISMA guideline to identify studies that presented the prevalence of olfactory dysfunction in patients with COVID‐19, worldwide. 12 This study is registered with PROSPERO, number CRD42020183768.

Data Sources and Searches

PubMed, Scopus, Web of Science, Embase, and Google Scholar databases were searched to identify studies published between 1 December 2019 and 23 July 2020 without language restrictions. The following key terms were searched: coronavirus, COVID‐19, COVID19, nCoV, SARS‐CoV‐2, SARS‐CoV2, olfaction, olfactory, smell, anosmia, hyposmia, dysosmia, cacosmia, and parosmia. Complete details of the search strategy are in the Supporting Table 1. In addition to the published studies, preprints were also considered if data of interest were reported. Review articles, case reports, opinions, and perspectives were excluded. Data reported by news reports and press releases or data collected from websites or databases were not considered. To ensure a robust search procedure, references of the included studies were also reviewed. Duplicate studies were excluded by using EndNote X8 software.

Study Selection

To identify eligible studies, articles of interest were screened based on the title and abstract, followed by full text by two authors (J.S. and M.A.I.) independently. Disagreements about inclusion were discussed and resolved by consensus.

Data Extraction and Quality Assessment

Data extraction was done independently by two authors (J.S. and M.A.I.). From each eligible study, we extracted the following information into a predefined Excel spreadsheet: first author's last name; study design; country of the participants; data collection period; total number of COVID‐19 patients; number of female COVID‐19 patients; age; COVID‐19 confirmation procedure; confirmatory procedure of olfactory dysfunction; olfactory symptoms after the onset of illness; and number of recovered patients from olfactory dysfunction.

Random‐effects model was used to obtain the pooled prevalence and 95% confidence intervals (CIs) of olfactory dysfunction in patients with COVID‐19. The quality of included studies was assessed independently by two authors (J.S. and M.A.I.) using the Joanna Briggs Institute (JBI) critical appraisal tools. 13 The studies were classified as low‐quality (high‐risk of bias) if the overall score was ≤50%. 14 To assess publication bias, a funnel plot presenting prevalence estimate against the standard error was constructed and the asymmetry of the funnel plot was confirmed with Egger's test.

Data Synthesis and Analysis

Heterogeneity between studies was assessed using the I 2 statistic (I 2 > 75% indicating substantial heterogeneity) in addition to using the Cochran's Q test to identify the significance of heterogeneity. As subgroups, the prevalence of olfactory dysfunction in COVID‐19 patients from different geographical regions and in different types, including anosmia, hyposmia, and dysosmia were analyzed. To identify the source of heterogeneity and to check the robustness of the results, sensitivity analyses were performed through the following strategies: i) excluding small studies (n < 100); ii) excluding the low‐quality studies (high‐risk of bias); iii) excluding studies not reporting COVID‐19 confirmation assay; iv) considering only cross‐sectional studies, and v) excluding outlier studies. In addition, to identify the outlier studies and the sources of heterogeneity, a Galbraith plot was constructed. All the analyses and plots were generated by using metaprop codes in meta (version 4.11–0) and metafor (version 2.4–0) packages of R (version 3.6.3) in RStudio (version 1.2.5033). 15

RESULTS

Study Selection

Our search initially identified 1162 studies. After removing 738 studies [duplicate studies (n = 631), review articles (n = 69), case reports (n = 19), and non‐human studies (n = 19)]; titles and abstracts of 424 studies were screened for eligibility, of which 341 studies were excluded as those did not comply with the objective of this study. Therefore, 83 studies were included in the systematic review and meta‐analysis (Fig. 1).

Fig 1.

Fig 1

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PRISMA flow diagram of study selection.

Study Characteristics

Detailed characteristics and references of the included studies are presented in Table I. Overall, this meta‐analysis reports data from 27492 COVID‐19 patients (61.4% female). Ages of the COVID‐19 patients included in this meta‐analysis ranged from 28.0 ± 16.4 to 70.2 ± 13.9 years. Studies were from 27 countries, including Spain, Germany, Italy, France, Ireland, Belgium, Romania, Switzerland, UK, Netherlands, Poland, Israel, China, Saudi Arabia, Turkey, Iraq, Iran, Pakistan, Singapore, Korea, Uruguay, Argentina, Bolivia, Venezuela, Australia, Canada, and USA. Among the included studies, 97.5% confirmed COVID‐19 patients by using the RT‐PCR method, whereas the method was not reported in two of the studies.

TABLE I.

Major Characteristics of the Included Studies.

No. Study IDReference Study Design Country Data Collection Period Total Number of COVID‐19 Patients (Female) Age (years) (Mean ± SD/Median (IQR)/Range COVID‐19 Confirmation Procedure Type of Assessment for Olfactory Dysfunction (Subjective/Objective) Method of Assessment for Olfactory Dysfunction
 1 Abalo‐Lojo 2020 16 Cross‐sectional Spain NR 131 (75) 50.4 ± NR RT‐PCR Subjective Self‐reported
 2 Agarwal 2020 17 Cross‐sectional USA 1 March–4 April 2020 16 (4) 67.0 (38.0–95.0) RT‐PCR NR NR
 3 Alshami 2020 18 Cross‐sectional Saudi Arabia 16 March–18 April 2020 128 (69) 39.6 ± 15.5 RT‐PCR Subjective Telephone questionnaire survey
 4 Altin 2020 19 Case–control Turkey 25 March–20 April 2020 81 (40) 54.1 ± 16.9 RT‐PCR Objective Sniffin' Sticks test
 5 Beltrán‐Corbellini 2020 20 Case–control Spain 23–25 March 2020 79 (31) 61.6 ± 17.4 RT‐PCR Subjective Self‐reported questionnaire survey
 6 Biadsee 2020 21 Cross‐sectional Israel 25 March–15 April 2020 128 (70) 36.2 ± NR RT‐PCR Subjective Telephone questionnaire survey
 7 Brandsetter 2020 22 Cross‐sectional Germany NR 31 (26) 18.0–65.0 RT‐PCR Subjective Self‐reported
 8 Carignan 2020 23 Case–control Canada 10–23 March 2020 134 (81) 57.2 (42.6–64.4) RT‐PCR Subjective Telephone interview
 9 Cervilla 2020 24 Cross‐sectional Spain March–May 2020 51 (44) 43.8 ± 10.7 RT‐PCR Subjective Telephone questionnaire survey
10 Chary 2020 25 Cross‐sectional France 25 March–18 April 2020 115 (81) 47.0 (20.0–83.0) RT‐PCR Subjective Telephone interview
11 Chiesa‐Estomba 2020 26 Cross‐sectional Spain, Uruguay, Argentina, and Venezuela NR 542 (324) 34.0 ± 11.0 RT‐PCR Subjective Online questionnaire survey
12 Chiesa‐Estomba 2020a 27 Cross‐sectional Spain, Belgium, France, Canada, and UK NR 751 (477) 41.0 ± 13.0 RT‐PCR Subjective Online questionnaire survey
13 Chua 2020 28 Cross‐sectional Singapore 23 March–4 April 2020 31 (NR) NR RT‐PCR Subjective Self‐reported
14 D'Ascanio 2020 29 Cross‐sectional Italy 1 February–24 April 43 (14) 58.1 ± 15.7 RT‐PCR Subjective Self‐reported questionnaire survey
15 Dawson 2020 30 Cross‐sectional USA March–April 2020 42 (NR) NR RT‐PCR Subjective Self‐reported questionnaire survey
16 De Maria 2020 31 Cross‐sectional Italy NR 95 (NR) NR RT‐PCR Subjective Self‐reported questionnaire survey
17 Dell'Era 2020 32 Cross‐sectional Italy 10–30 March 2020 355 (163) 50.0 (40.0–59.5) RT‐PCR Subjective In person interview or telephone questionnaire survey
18 Durrani 2020 33 Cross‐sectional Pakistan 20 March–10 April 2020 30 (6) 44.0 (7.0–81.0) RT‐PCR Subjective Self‐reported
19 Freni 2020 34 Cross‐sectional Italy NR 50 (20) 37.7 ± 17.9 RT‐PCR Subjective Online questionnaire survey
20 Gelardi 2020 35 Cross‐sectional Italy NR 72 (33) 49.7 (19.0–70.0) RT‐PCR Subjective Self‐reported
21 Giacomelli 2020 4 Cross‐sectional Italy 19 March 2020 59 (19) 60.0 (50.0–74.0) NR Subjective Self‐reported questionnaire survey
22 Gorzkowski 2020 36 Cross‐sectional France 1 March–31 March 2020 229 (147) 39.7 ± 13.7 RT‐PCR Subjective Telephone questionnaire survey
23 Güner 2020 37 Cross‐sectional Turkey 10 March–10 April 2020 222 (90) 50.6 ± 16.5 RT‐PCR Subjective Self‐reported
24 Haehner 2020 38 Cross‐sectional Germany NR 34 (16) 43.2 ± 11.6 RT‐PCR Subjective Self‐reported questionnaire survey
25 Hintschih 2020 39 Cross‐sectional Germany NR 41 (28) 37 (NR) RT‐PCR Subjective Online questionnaire survey
26 Hornuss 2020 40 Cross‐sectional Germany April 2020 45 (20) 56.0 ± 16.9 RT‐PCR Objective Sniffin' Sticks test
27 Jalessi 2020 41 Cross‐sectional Iran February–March 2020 92 (30) 52.9 ± 13.2 RT‐PCR Subjective Self‐reported
28 Karadaş 2020 42 Cross‐sectional Turkey April–May 2020 239 (106) 46.4 ± 15.4 RT‐PCR Subjective Self‐reported
29 Kerr 2020 43 Cross‐sectional Ireland 24 March 2020 46 (27) 36.5 (27.0–48.0) RT‐PCR Subjective Self‐reported
30 Kim 2020 44 Cross‐sectional Korea 12–16 March 2020 172 (106) 26.0 (22.0–47.0) RT‐PCR Subjective Self‐reported questionnaire survey
31 Klopfenstein 2020 45 Cross‐sectional France 1–17 March 2020 114 (36) 47.0 ± 16.0) RT‐PCR NR NR
32 Lapostolle 2020 46 Cross‐sectional France 24 March–6 April 2020 1487 (752) 44.0 (32.0–57.0) RT‐PCR Subjective Telephone interview
33 Lazar 2020 47 Cross‐sectional Romania 28 March 2020 100 (49) 41.0 (NR) RT‐PCR Subjective Medical record review
34 Lechien 2020 48 Cross‐sectional France, Italy, Spain, Belgium, and Switzerland 22 March–10 April 2020 1420 (962) 39.0 ± 12.0 RT‐PCR Subjective Self‐reported questionnaire survey
35 Lechien 2020a 49 Cross‐sectional Belgium NR 86 (56) 41.7 ± 11.8 RT‐PCR Subjective Self‐reported questionnaire survey
36 Lechien 2020b 50 Cross‐sectional European countries 22 March–3 June 2020 2581 (1624) 44.5 ± 16.4 RT‐PCR Subjective Self‐reported
37 Lechien 2020c 51 Cross‐sectional Belgium, Italy, France, and Spain NR 417 (263) 36.9 ± 11.4 RT‐PCR Subjective Self‐reported questionnaire survey
38 Lee 2020 52 Cross‐sectional Canada 16 March–15 April 2020 56 (33) 38.0 (31.8–47.2) RT‐PCR Subjective Telephone questionnaire survey
39 Levinson 2020 53 Cross‐sectional Israel 10–23 March 2020 42 (19) 34.0 (15.0–82.0) RT‐PCR Subjective Telephone questionnaire survey
40 Liang 2020 54 Cross‐sectional China 16 March–12 April 2020 86 (42) 25.5 (6.0–57.0) RT‐PCR Subjective Self‐reported questionnaire survey
41 Lombardi 2020 55 Cross‐sectional Italy 24 February–31 March 2020 139 (82) NR RT‐PCR Subjective Self‐reported
42 Luers 2020 56 Cross‐sectional Germany 22–28 March 2020 72 (31) 38.0 ± 13.0 RT‐PCR Subjective Self‐reported questionnaire survey
43 Luigetti 2020 57 Cross‐sectional Italy 14 March–20 April 2020 213 (76) 70.2 ± 13.9 RT‐PCR Subjective Self‐reported
44 Magnavita 2020 58 Cross‐sectional Italy 27 March–30 April 2020 82 (56) NR RT‐PCR Subjective Self‐reported questionnaire
45 Mao 2020 59 Cross‐sectional China 16 January–19 February 2020 214 (127) 52.7 ± 15.5 RT‐PCR NR NR
46 Martin‐Sanz 2020 60 Case control Spain 15 March–7 April 2020 215 (171) 42.9 ± 0.6 RT‐PCR Objective VAS
47 Meini 2020 61 Cross‐sectional Italy April 2020 100 (40) 65.0 ± 15.0 RT‐PCR Subjective Telephone interview
48 Menni 2020 5 Cross‐sectional UK 24–29 March 2020 579 (400) 40.79 ± 11.84 RT‐PCR Subjective Smartphone‐based App survey
49 Menni 2020a 62 Cross‐sectional UK 24 March–21 April 2020 6452 (4638) 41.2 ± 12.1 RT‐PCR Subjective Smartphone‐based App survey
USA 726 (567) 44.6 ± 14.3
50 Mercante 2020 63 Cross‐sectional Italy 5–23 March 2020 204 (94) 52.6 ± 14.4 RT‐PCR Subjective Telephone questionnaire survey
51 Merza 2020 64 Cross‐sectional Iraq 18 March–7 April 2020 15 (6) 28.0 ± 16.4 RT‐PCR NR NR
52 Moein 2020 65 Case–control Iran 21–23 March 2020 60 (20) 46.5 ± 12.1 RT‐PCR Objective UPSIT
53 Noh 2020 66 Cross‐sectional Korea NR 199 (130) 38.0 ± 13.1 RT‐PCR Subjective In person interview
54 Otte 2020 67 Cross‐sectional Germany NR 50 (NR) 43.2 (23.0–69.0) RT‐PCR Objective Sniffin' sticks test
55 Paderno 2020 68 Cross‐sectional Italy 27 March–1 April 2020 508 (223) 55.0 ± 15.0 RT‐PCR Subjective Self‐reported questionnaire survey
56 Parente‐Arias 2020 69 Cross‐sectional Spain 3–24 March 2020 151 (98) 55.2 (18.0–88.0) RT‐PCR Subjective Self‐reported questionnaire survey
57 Patel 2020 70 Cross‐sectional UK 1 March–1 April 2020 141 (58) 45.6 (20.0–93.0) RT‐PCR Subjective Telephone interview
58 Petrocelli 2020 71 Cross‐sectional Italy 16 April–2 May 2020 300 (225) 43.6 ± 12.2 RT‐PCR Objective Olfactory threshold and identification test
59 Peyrony 2020 72 Cross‐sectional France 9 March–4 April 2020 225 (150) 62.0 (48.0–71.0) RT‐PCR Subjective Self‐reported
60 Qiu 2020 73 Cross‐sectional China, France and Germany 15 March–5 April 2020 394 (NR) NR RT‐PCR Subjective Self‐reported questionnaire survey
61 Romero‐Sánchez 2020 74 Cross‐sectional Spain 1 March–1 April 2020 841 (368) 66.4 ± 14.9 RT‐PCR Subjective Medical record review
62 Sakalli 2020 75 Cross‐sectional Turkey NR 172 (88) 37.8 ± 12.5 RT‐PCR Subjective Self‐reported questionnaire survey
63 Sayin 2020 76 Case–control Turkey NR 64 (39) 37.7 ± 11.3 RT‐PCR Subjective Self‐reported questionnaire survey
64 Seo 2020 77 Cross‐sectional Korea 28 April 2020 62 (NR) NR RT‐PCR Objective CC‐SIT
65 Sierpiński 2020 78 Cross‐sectional Poland 17–18 April 2020 1942 (1169) 50.0 (NR) RT‐PCR Subjective Telephone interview
66 Song 2020 79 Cross‐sectional China 27 January–10 March 2020 1172 (595) 61.0 (48.0–68.0) RT‐PCR Subjective Telephone interview
67 Speth 2020 80 Cross‐sectional Switzerland 3 March–17 April 2020 103 (53) 46.8 ± 15.9 RT‐PCR Subjective Telephone interview
68 Tomlins 2020 81 Cross‐sectional UK 10–30 March 2020 95 (35) 75.0 (59.0–82.0) RT‐PCR NR NR
69 Tostmann 2020 82 Cross‐sectional Netherlands 10–30 March 2020 79 (NR) NR NR Subjective Self‐reported questionnaire survey
70 Trubiano 2020 83 Cross‐sectional Australia 1–22 April 2020 28 (14) 55.0 (46.0–63.5) RT‐PCR Subjective Medical record review
71 Tudrej 2020 84 Cross‐sectional Switzerland 24 March–14 April 2020 198 (NR) NR RT‐PCR Subjective Self‐reported questionnaire survey
72 Vacchiano 2020 85 Cross‐sectional Italy NR 108 (46) 59.0 (18.0–83.0) RT‐PCR Subjective Telephone questionnaire survey
73 Vaira 2020 86 Cross‐sectional Italy 31 March–6 April 2020 72 (45) 49.2 ± 13.7 RT‐PCR Objective CCCRC
74 Vaira 2020a 87 Cross‐sectional Italy 9–10 April 2020 33 (22) 47.2 ± 10 RT‐PCR Objective CCCRC
75 Vaira 2020b 88 Cross‐sectional Italy NR 345 (199) 48.5 ± 12.8 (23–88) RT‐PCR Objective CCCCRC
76 Wee 2020 89 Cross‐sectional Singapore 26 March–10 April 2020 154 (NR) NR RT‐PCR Subjective Self‐reported questionnaire survey
77 Wi 2020 90 Cross‐sectional Korea 15 April 2020 111 (57) 41.3 ± 19.0 RT‐PCR Subjective Medical record review
78 Yan 2020 91 Cross‐sectional USA 3 March–8 April 2020 128 (67) 53.5 (40.0–65.0) RT‐PCR Subjective Self‐reported
79 Yan 2020a 92 Cross‐sectional Germany, USA, Bolivia and Venezuela NR 59 (29) 18.0–79.0 RT‐PCR Subjective Online questionnaire survey
80 Yan 2020b 93 Cross‐sectional USA 9 March–29 April 2020 46 (NR) NR RT‐PCR Subjective Medical record review
81 Zayet 2020 94 Cross‐sectional France 26 February–14 March 2020 70 (41) 56.7 ± 19.3 RT‐PCR Subjective Self‐reported questionnaire
82 Zayet 2020a 95 Case–control France 30 March–3 April 2020 95 (79) 39.8 ± 12.2 RT‐PCR Subjective Medical record review
83 Zou 2020 96 Cross‐sectional China 1 February–3 March 2020 81 (43) 58.0 (50.0–68.5) RT‐PCR Subjective Medical record review
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AAO‐HNS = American academy of otolaryngology–head and neck surgery; CC‐SIT = cross‐cultural smell identification test; CCCRC = Connecticut chemosensory clinical research center orthonasal olfaction test; IQR = interquartile range; NR = not reported; RT‐PCR = reverse transcription polymerase chain reaction; SD = standard deviation; UPSIT = University of Pennsylvania smell identification test; VAS = visual analog scale..

Outcomes

Overall, the pooled prevalence of olfactory dysfunction in COVID‐19 patients was 47.85% [95% CI: 41.20–54.50] (Fig. 2). From the subgroup analyses, we observed olfactory dysfunction in 54.40% European, 51.11% North American, 31.39% Asian, and 10.71% Australian COVID‐19 patients (Table II, Supporting Figure 1). In addition, anosmia, hyposmia, and dysosmia were observed in 35.39%, 36.15%, and 2.53% of the COVID‐19 patients, respectively (Table II, Supporting Figure 2). Interestingly, the prevalence of olfactory dysfunction was observed higher in COVID‐19 patients on objective rather than subjective evaluations (72.10% vs. 44.53%) (Table II, Supporting Figure 3). Based on the clinical severity, olfactory dysfunction was higher in non‐severe patients compared to severe patients with COVID‐19 (47.48% vs. 9.02%) (Table II, Supporting Figure 4).

Fig 2.

Fig 2

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Prevalence of olfactory dysfunction in COVID‐19 patients. [Color figure can be viewed in the online issue, which is available at www.laryngoscope.com.]

TABLE II.

Pooled Prevalence of Olfactory Dysfunction in Different Subgroups of COVID‐19 Patients.

Subgroups of COVID‐19 Patients Olfactory Dysfunction Prevalence [95% CIs] (%) Number of Studies Analyzed Total Number of COVID‐19 Patients Heterogeneity Publication Bias, Egger's Test (P Value)
I 2 (%) P Value
Olfactory dysfunction in different regions
Europe 54.40 [46.19–62.61] 49 20,738 99 <.0001 .19
North America 51.11 [41.10–61.13] 7 1,148 87 <.0001 NA
Asia 31.39 [18.26–44.51] 22 3,477 99 <.0001 .66
Australia 10.71 [0.00–22.17] 1 28 NA NA NA
Different types of olfactory dysfunction
Anosmia 35.39 [27.73–43.04] 43 10,979 99 <.0001 .11
Hyposmia 36.15 [27.65–44.64] 24 5,200 98 <.0001 .003
Dysosmia 2.53 [0.0–6.0] 1 79 NA NA NA
Evaluation types of olfactory dysfunction
Subjective 44.53 [37.59–51.47] 73 26,229 99 <.0001 .60
Objective 72.10 [59.41–84.79] 10 1,263 97 <.0001 .33
Olfactory dysfunction based on clinical severity
Severe 9.02 [2.67–15.38] 4 687 85 .001 NA
Non‐severe 47.48 [21.34–73.62] 8 5,135 100 <.0001 NA
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CIs = confidence intervals; NA = not applicable.

Detailed quality assessment of the included studies is shown in the Supporting information (Supporting Table 2, Supporting Table 3). Briefly, 95.1% of the included studies were of high‐quality (low‐risk of bias). Overall, very high levels of heterogeneity (ranging from 87% to 99%) were observed during the estimation of olfactory dysfunctions in the main analysis as well as in different subgroup analyses. Visual inspection of the funnel plot and Egger's test results showed that there was no significant publication bias (P = .84) (Fig. 3).

Fig 3.

Fig 3

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Funnel plot on the prevalence of olfactory dysfunction in COVID‐19 patients. [Color figure can be viewed in the online issue, which is available at www.laryngoscope.com.]

Sensitivity analyses on assessing olfactory dysfunction in COVID‐19 patients excluding small studies, low‐quality studies, studies where COVID‐19 confirmation test was not reported, considering only cross‐sectional studies, and excluding outlier studies showed very marginal differences in overall pooled prevalence (Table III, Supporting Figure 5). Overall, our sensitivity analyses indicated that the results of olfactory dysfunction prevalence in COVID‐19 patients are robust and reliable. As the source of heterogeneity, from the Galbraith plot, three studies were identified as the source of heterogeneity (Supporting Figure 6).

TABLE III.

Sensitivity Analyses.

Strategies of Sensitivity Analyses Olfactory Dysfunction Prevalence [95% Cis] (%) Difference of Pooled Prevalence Compared to the Main Result Number of Studies Analyzed Total Number of COVID‐19 Patients Heterogeneity
I 2 (%) P Value
Excluding small studies 46.03 [37.08–54.97] 3.8% lower 43 25,162 100 <.0001
Excluding low‐quality studies 49.03 [42.21–55.85] 2.5% higher 79 27,146 99 <.0001
Excluding studies where COVID‐19 confirmation test was not reported 48.40 [41.67–55.12] 1.1% higher 81 27,354 99 <.0001
Considering only cross‐sectional studies 46.66 [39.87–53.44] 2.5% lower 77 26,979 99 <.0001
Excluding outlier studies 47.28 [40.61–53.95] 1.2% lower 80 27,297 99 <.0001
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CIs = confidence intervals.

DISCUSSION

The route of entry of SARS‐CoV‐2 to the olfactory neuron is via the olfactory epithelium found at the nasal roof. 97 This region is exposed the most to inspired air during inspiration after it passes the nasal valve and moves upwards. The sensory neurons found at the olfactory epithelium are accountable for detecting as well as transmitting information of odors to the brain. It is noteworthy that the unique property of olfactory epithelium is its basal cell, which can regenerate throughout life. 98 , 99

The novel SARS‐CoV‐2 infection was discovered and delineated by Zhou et al. 100 on 3rd February 2020. They described that SARS‐CoV‐2 enters the cell through angiotensin‐converting enzyme 2 (ACE2). It is postulated that SARS‐CoV infiltrates cells via the interplay between its spike (S) protein and the ACE2 protein on the target cells. 101 , 102 Interestingly, the number of ACE2 cells is similar both in nasal and oral tissues, as well as lung and colon tissues, 103 although it is postulated that nasal and oral tissues may be the first site of entry by SARS‐CoV‐2. The two genes accountable for anosmia following SARS‐CoV‐2 infection are ACE2 and TMPRSS2. 104 SARS‐CoV‐2 has been shown to enter the brain via olfactory bulb on transgenic mice causing transneuronal spread and was discovered abundantly in the olfactory bulb following infection. 105 In addition, autopsy samples taken from patients with SARS showed SARS‐CoV‐2 in the brain samples. The mode of entry into the brain is postulated to be via olfactory bulb. 106 , 107 Previous experience had led to a revelation that coronaviruses have shown to share a similar structure as well as an infective pathway. 108 Hence, structural changes in the olfactory bulb ought to be assessed. 109 It is noteworthy that, reduction in the volume of olfactory bulb has been reported to result from a prior infection‐related olfactory dysfunction. 110 There are several possible mechanisms for olfactory dysfunction following SARS‐CoV‐2 infection. Among the countless existing theories, the most notable ones include olfactory cleft syndrome and postviral anosmia syndrome. 111 The former theory advocates on mucosal obstruction at the olfactory cleft results in conduction impairment of smell, 112 while the latter proposes on a neural loss mechanism whereby direct injury to the olfactory sensory neurons preceding viral infection. 113

It is noteworthy that postviral olfactory loss (PVOL) is not a novel phenomenon. Numerous virus has been advocated to enable olfactory dysfunction, including influenza virus, adenovirus, parainfluenza virus, respiratory syncytial virus, coxsackievirus, adenovirus, poliovirus, enterovirus, and herpesvirus. 114 , 115 , 116 , 117 Suguira et al. 115 in an earlier study supported parainfluenza virus (PIV) type 3 to be the primary virus responsible for PVOL. Subsequent research revealed a similar finding, whereby PIV‐3 was the leading culprit behind PVOL. 116 Tian et al. 117 studied the Sendai virus (SeV), the murine counterpart of the PIV on olfactory function and regenerative ability of the olfactory epithelium. In addition, they found that SeV impairs olfaction and persists in the olfactory epithelium and olfactory body, thus hindering the regenerative ability as well as the normal physiologic function of olfactory sensory neurons.

Suzuki et al. 114 found rhinovirus to be the predominant cause of PVOL followed by PIV‐2, Epstein–Barr virus, and coronavirus, which was identified in one patient. PIV‐3 was not, however, studied in their sample. Coronavirus was not considered in many studies as the involvement of coronavirus in PVOL was not extensively reported, and it is challenging to isolate coronavirus. 115 In addition, the challenge faced by many researchers in identifying the virus responsible for PVOL is following the delay of patients with the olfactory loss to visiting the clinic, believing the notion that PVOL will resolve spontaneously. A noteworthy study by Potter et al. 118 shed more light on the interaction between virus and host in PVOL related condition. Potter et al. suggested that a seasonal pattern emerged among influenza and non‐influenza related PVOL indicating not only variations of potency and virulence of virus but also on host susceptibility as a factor in determining the progression and manifestation of the infection. Olfactory disorders related to non‐influenza virus peaked in warmer months compared to colder months.

In our meta‐analysis, all 83 studies revealed a strong association between olfactory dysfunction and SARS‐COV‐2 infection. Overall nasal symptoms among COVID‐19 positive patients have been scarcely reported. 3 , 119 Chen et al. 3 in their series, reported only 4% of their patients had rhinorrhea; while Guan et al. 119 reported 5% of their patients demonstrated nasal obstruction. Scanty reported data on olfactory dysfunction had been attributed by either overlooked nasal symptoms by physicians, 51 or the possibility of different virus sequences leading to the various presentations. 120 The latter theory was supported based on a study by Benvenuto et al. 120 who compared genomes of 15 virus sequences from patients in various regions in China with other coronaviruses. The possibility that olfactory, as well as gustatory dysfunction, prevails among the European community has emerged. 51 in addition, lack of awareness among Asian patients in addition to unnoticed olfactory loss could have contributed to the low number of reported cases among Asian patients. Recent epiphany on olfactory dysfunction among Asian patients accruing the surge in cases has enabled olfactory dysfunction to be included in suspect case criteria for SARS‐CoV‐2 infection, allowing test to be carried out in these patients, while isolation is implemented concomitantly. 28

Female predominance was revealed among our patients (61.4%). Similarly, previous studies have shown olfactory loss postviral prevails among female patients. 121 , 122 This notion is attributed to gender‐related variation in the inflammatory process. 123 Increase in numbers of female patients can be attributed by greater tendency of females to volunteer for studies. In addition, female patients are found to be more sensitive in detecting chemosensory alteration.

Most studies involved online questionnaire either through an online application, online survey, smartphone‐based App filled up by patients or clinicians, whereas objective assessment of olfactory assessment was utilized in four studies whereby Sniffin test, University of Pennsylvania smell identification test (UPSIT), and Connecticut chemosensory clinical research center orthonasal olfaction test (CCCRC) were performed. It is noteworthy that, in our meta‐analysis, we found prevalence of olfactory dysfunction among objectively evaluated studies to be higher (72.10%) as compared to the subjectively evaluated studies (44.53%). This could be attributed by the fact that most COVID‐19 patients are unaware of their olfactory dysfunction leading to possibility of underestimation. Moein et al. 65 reported 98% of their patients were found to have olfactory dysfunction post UPSIT, of which only 35% were initially aware of their symptoms. Generally, loss of smell is only perceived upon significant loss of smell such as anosmia. Thus, it is worth noting that the prevalence of olfactory dysfunction may be higher if tested objectively. Quantitative testing of olfactory disturbance may provide rapid and cheap modality to screen COVID‐19 in a large population. Interestingly, Moein et al. 124 reported that time of testing is the most important factor in explaining the prevalence variations among studies apart from variations in question and types of olfactory testing. They found that 61% of the earlier 96% of patients who demonstrated olfactory disturbance, when retested during the late acute phase showed an improvement.

Although the jarring increase in the number of cases daily, which led to a surge in research as well as publications, we obtained only 83 studies on olfactory dysfunction in SARS‐CoV‐2 infection. This may be attributed by the fact that the substantial available peer‐reviewed studies report on hospitalized patients, which means that the self‐limiting, 125 as well as the mild group of patients, are omitted from the various studies. The notion that olfactory manifestation predominately affects the milder form of SARS‐CoV‐2 infection is inevitable. Yan et al. 92 found that most patients with olfactory disturbance with positive SARS‐CoV‐2 infection were treated as out‐patient or ambulatory and not requiring hospitalization. Yet, it is imperative to keep in mind that the nature of this virus is yet to be explored, and owing to the varying genome in virus sequencing, all SARS‐CoV‐2 infection positive patients with olfactory disturbance should not be taken lightly. Villalba et al. 126 reported on two patients who presented with anosmia as the initial symptom of SARS‐CoV‐2 infection had to be hospitalized, and unfortunately, one patient succumbed. Varying reports are available on the outcome following the PVOL. Yan et al. 92 and Klopfenstein et al. 45 demonstrated 74% and 98% resolution of olfactory symptoms and linked this short‐lived manifestation to the unique ability of olfactory epithelium to regenerate and repair following viral clearance.

In our meta‐analysis, none of the authors mentioned on specific treatment directed to smell impairment. The role of intranasal steroids is debatable in this situation accruing the possibility of triggering upper respiratory tract infection. Oral steroids used traditionally to treat idiopathic anosmia ought to be averted by all means to avoid further risk of immunosuppression in SARS‐CoV‐2 infection patients. 112 The outcome of olfactory loss revealed persistence of symptoms mentioned in some of the studies. Duration of olfactory dysfunction remains a conundrum as the nature of this novel pandemic is still a mystery. Heretofore, PVOL habitually has been shown to have a good prognosis. Despite still premature, several anecdotal reports have revealed on total or partial recuperation of olfactory loss over a few months. 127 This is owing to the fact that a longer time for regeneration following damage to olfactory neurons is required. Albeit considered innocuous, olfactory disturbance has been related to a number of detrimental effects notably on quality of life, impacts social interaction, and depression. Astonishingly, several high‐profile studies have related olfactory disturbance to a 5‐year mortality rate. 128 , 129 , 130 , 131 The unique neuroplasticity potential found in olfactory system opens to novel possibility of olfactory recovery via numerous modalities such as olfactory training. 132

Implications for Clinical Practice

The characteristics of an ideal screening tool are high probability of detecting disease (highly sensitive) and high probability of excluding disease when it is negative (highly specific). Besides being reliable, it must be cost‐effective, simple to perform, and widely available. 133 , 134 Moreover, an effective screening requires engagement of both target populations and health care providers. As olfactory dysfunction can be simply detected by using questionnaire, 135 it fulfills all these criteria and can be a useful screening tool besides temperature surveillance. Applying a specific questionnaire to detect olfactory dysfunction, especially in those with suspicious flu‐like symptoms, travel history from affected countries, and contact with COVID‐19 patients may enhance the pick‐up rate of infected patients. Furthermore, questionnaire‐based screening tool may easily be assimilated in the global health care system and more so in developing countries where cost is a factor.

Implications for Research

As there is no standardized questionnaire available to screen for olfactory dysfunction, a consensus is required to determine the most suitable questionnaire for a reliable detection. Perhaps a more refined questionnaire based on the available questionnaires can be developed by selecting the relevant questions and compare by comparing them with an objective smell test to choose the most consistent questions. Researches need to be conducted employing the more objective smell test, which will provide us information on specific odor affected by this infection. By identifying the specific associated odor link to the infection, a simple smell test can be developed particularly to screen for COVID‐19. Olfactory dysfunction may serve as prognosticators to triage and stratify patients according to different categories of severity, which can help to detect those who need immediate and urgent hospitalization. Research into this may help in preventing death among COVID‐19 patients.

Strengths

Our study has several strengths. This meta‐analysis was conducted with significant number of studies and hence including a considerable number of participants, resulting in more robust estimates. Majority of the included studies confirmed COVID‐19 subjects by using the RT‐PCR technique, which strengthens our findings. None of the analyses represented significant publication bias demonstrating that we were unlikely to have missed studies that could have altered the findings. All the conducted sensitivity analyses generated similar results to the main findings indicating the robustness of the meta‐analysis results. Based on the quality assessments, 95.1% of the studies were of high methodological quality (low‐risk of bias), which ensured a reliable result.

Limitations

Nevertheless, there are several notable limitations. Based on the search strategy and considered time period, this meta‐analysis could include participants from 27 countries from four continents; therefore, the prevalence may not represent at a global scale and generalization of the findings should be done with care. One of the major limitations in this meta‐analysis is the presence of substantial degrees of heterogeneity. Even though we examined the sources of heterogeneity by subgroup, sensitivity analyses and Galbraith plot, source of heterogeneity could not be fully explained by the factors included in the analyses. Although we comprehensively investigated the prevalence of olfactory dysfunction from the first eight‐month data of the COVID‐19 outbreak, we have somewhat characterized olfactory dysfunctions in severe versus non‐severe COVID‐19 patients due to the limited number of studies.

Another major limitation is majority of the studies used self‐reported data. When self‐reported health measures are used, both underestimation due to false negative reporting and overestimation due to false positive reporting may possibly transpire, and the results should be interpreted with caution. A meta‐analysis involving studies with large number of patients may minimize the potential bias but an amplification of the compromised methodology cannot entirely be excluded.

CONCLUSION

This meta‐analysis found the prevalence of olfactory dysfunction was 47.85% of the COVID‐19 patients based on the high quality of evidence, which suggests it as a significant initial symptom of SARS‐CoV‐2 infection. Due to the subjective measures of most studies pooled in the analysis, further studies with objective evaluations are recommended to confirm the finding.

Supporting information

Supporting Figure 1 Subgroup analyses: Prevalence of olfactory dysfunction in COVID‐19 patients from (A) Europe, (B) North America, (C) Asia, and (D) Australia.

Click here for additional data file. (636.4KB, docx)

Supporting Figure 2 Subgroup analyses: Prevalence of (A) anosmia, (B) hyposmia, and (C) dysosmia in patients with COVID‐19.

Click here for additional data file. (517.3KB, docx)

Supporting Figure 3 Subgroup analyses: Prevalence of olfactory dysfunction in COVID‐19 patients with (A) subjective and (B) objective olfactory evaluations.

Click here for additional data file. (609.8KB, docx)

Supporting Figure 4 Subgroup analyses: Prevalence of olfactory dysfunction in (A) severe and (B) non‐severe COVID‐19 patients.

Click here for additional data file. (155KB, docx)

Supporting Figure 5 Sensitivity analyses: Prevalence of olfactory dysfunction in COVID‐19 patients (A) excluding small studies (n < 100), (B) excluding low‐quality studies, (C) excluding studies without COVID‐19 confirmation method being reported, (D) considering only cross‐sectional studies, and (E) excluding outlier studies.

Click here for additional data file. (2.5MB, docx)

Supporting Figure 6 Galbraith plot identified three studies as the potential sources of heterogeneity.

Click here for additional data file. (37.4KB, docx)

Supporting Table 1 Search strategy.

Click here for additional data file. (13.7KB, docx)

Supporting Table 2 Quality assessment of the included cross‐sectional studies.

Click here for additional data file. (34.2KB, docx)

Supporting Table 3 Quality assessment of the included case–control studies.

Click here for additional data file. (17.9KB, docx)

Editor's Note: This Manuscript was accepted for publication on November 18, 2020.

Jeyasakthy Saniasiaya and Md Asiful Islam should be considered joint first author. Author order was determined alphabetically.

The authors have no funding, financial relationships, or conflicts of interest to disclose.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Figure 1 Subgroup analyses: Prevalence of olfactory dysfunction in COVID‐19 patients from (A) Europe, (B) North America, (C) Asia, and (D) Australia.

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Supporting Figure 2 Subgroup analyses: Prevalence of (A) anosmia, (B) hyposmia, and (C) dysosmia in patients with COVID‐19.

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Supporting Figure 3 Subgroup analyses: Prevalence of olfactory dysfunction in COVID‐19 patients with (A) subjective and (B) objective olfactory evaluations.

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Supporting Figure 4 Subgroup analyses: Prevalence of olfactory dysfunction in (A) severe and (B) non‐severe COVID‐19 patients.

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Supporting Figure 5 Sensitivity analyses: Prevalence of olfactory dysfunction in COVID‐19 patients (A) excluding small studies (n < 100), (B) excluding low‐quality studies, (C) excluding studies without COVID‐19 confirmation method being reported, (D) considering only cross‐sectional studies, and (E) excluding outlier studies.

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Supporting Figure 6 Galbraith plot identified three studies as the potential sources of heterogeneity.

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Supporting Table 1 Search strategy.

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Supporting Table 2 Quality assessment of the included cross‐sectional studies.

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Supporting Table 3 Quality assessment of the included case–control studies.

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