Abstract
Background
The presence of olfactory dysfunction or “loss of smell” has been reported as an atypical symptom in patients with coronavirus disease 2019 (COVID-19). We performed a systematic review and meta-analysis of the available literature to evaluate the prevalence of “loss of smell” in COVID-19 as well as its utility for prognosticating the disease severity.
Methods
An exhaustive search of the PubMed/Medline, Embase, Web of Science, Cochrane Library, LitCovid NIH, and WHO COVID-19 database was conducted through August 6th, 2020. All studies reporting the prevalence of “loss of smell” (anosmia and/or hyposmia/microsmia) in laboratory-confirmed COVID-19 patients were included. Pooled prevalence for cases (positive COVID-19 through reverse transcriptase (RT-PCR) and/or serology IgG/IgM) and controls (negative RT-PCR and/or serology) was compared, and the odds ratio (OR), 95% confidence interval (CI) and the p-value were calculated. A p-value of <0.05 was considered statistically significant.
Results
A total of 51 studies with 11074 confirmed COVID-19 patients were included. Of these, 21 studies used a control group with 3425 patients. The symptom of “loss of smell” (OR: 14.7, CI: 8.9–24.3) was significantly higher in the COVID-19 group when compared to the control group. Seven studies comparing severe COVID-19 patients with- and without “loss of smell” demonstrated favorable prognosis for patients with “loss of smell” (OR: 0.36, CI 0.27–0.48).
Conclusions
Olfactory dysfunction or “loss of smell” is a prevalent symptom in COVID-19 patients. Moreover, COVID-19 patients with “loss of smell” appear to have a milder course of the disease.
Keywords: COVID-19, Olfactory dysfunction, Loss of smell, SARS-CoV-2, Coronavirus
Introduction
The pandemic coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).1 The pandemic has resulted in significant economic and healthcare burden. Along with the pulmonary symptoms, the disease is also associated with neurological manifestations such as headache, impaired consciousness, altered gait/ataxia, seizures, diarrhea, nausea/vomiting, loss of smell, and altered taste/dysgeusia.2, 3, 4 The disease severity is associated with laboratory abnormalities such as low albumin, elevated interleukin 6, increased alanine/aspartate aminotransferase, increased total bilirubin, increased procalcitonin, increased C-reactive protein (CRP), etc.4, 5, 6, 7, 8
The “loss of smell” is an atypical symptom of COVID-19 and has been reported with varying prevalence in literature. Further, it has been observed that loss of smell is usually associated with milder form of disease compared to severe disease.2 We performed a systematic review and meta-analysis of available studies to evaluate the prevalence of “loss of smell” in COVID-19 and its utility as a prognostic indicator.
Methods
Search Strategy
A systematic search of the PubMed/Medline, Embase, Web of Science, Cochrane Library, LitCovid NIH, and WHO COVID-19 databases through August 6th, 2020, was conducted. The author (W.L.S.) created the initial search strategy using the vocabulary for “COVID-19” and “smell,” which was cross-checked by another reviewer (M.A.). We highlight an example search strategy using EMBASE in Supplementary table 1. Two independent reviewers (M.A. and H.H.) performed the initial screening and data extraction from the articles. Any discrepancy in article screening or data extraction was resolved through mutual discussion.
Inclusion and exclusion criteria
Only articles reporting the laboratory confirmed COVID-19 patients and “loss of smell” were included. Articles were excluded if they had <10 cases of interest. Articles with suspected cases of COVID-19 without a definitive laboratory diagnosis were also excluded. An adherence to “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines was observed.
Study Definition
Severe disease is defined as the presence of either respiratory distress (i.e., rate >30/min, PaO2/FiO2 <300, and/or SpO2 <93%), need for hospitalization, and/or death. Given the heterogeneity in defining the “loss of smell” across studies, we included the concepts of “anosmia (complete loss of smell)” and “hyposmia/microsmia (diminished or partial loss of smell)” collectively as “loss of smell”. Positive COVID-19 cases are defined as patients with laboratory confirmed COVID-19 (through reverse transcriptase polymerase chain reaction (RT-PCR) and/or serological evidence of COVID-19 through IgG/IgM). Controls are defined as patients with negative RT-PCR and/or serological testing.
Statistical measures and synthesis of results
The pooled prevalence of cases (COVID-19) and controls (non-COVID-19) were compared using the DerSimonian-Laird/Random-effect meta-analysis, and outcomes were reported using forest plots, proportions with 95% confidence interval (CI), odds ratio (OR) with 95% CI, p-value (<0.05 was considered statistically significant) and I2 heterogeneity (>50% considered substantial heterogeneity).9, 10, 11 Meta-analysis was conducted using comprehensive meta-analysis (BioStat, Englewood, New Jersey, USA) and Open Meta Analyst (CEBM, University of Oxford, Oxford, United Kingdom).
Risk of bias
Publication bias was assessed using a funnel plot and Egger's regression analysis. If significant publication bias was suspected, we utilized the “trim-and-fill” method and Fail-Safe N test. The presence of bias in the individual study was assessed using the Quality in Prognostic Studies (QUIPS) tool.12
Results
A total of 51 studies were included based on the search strategy mentioned previously (Fig. 1 ). Publication bias based on prevalence for “loss of smell” was noted based on visual assessment of the funnel plot and Egger's regression analysis (p = 0.01). We then used the “trim-and-fill” method to create adjusted funnel plot that did not significantly differ from the original funnel plot (Supplementary Fig. 1). The Fail-Safe N test was 504 with an alpha of 0.05. This signifies that 504 studies with effect size zero will be needed to nullify the effect noted for the current analysis. Using the QUIPS tool, only seven studies were considered low risk. The other remaining studies either did not account for confounders in their statistical analyses or outcome/prognostic factors were not adequately assessed (Table 1 ).
Table 1.
Study, year | Participation (The study sample represents population of interest on key characteristics?) | Attrition (The proportion of study sample providing outcome data is adequate?) | Prognostic factor measurement (Prognostic factor is adequately measured in study subjects?) | Outcome measurement (The outcome of interest is adequately measured in study subjects?) | Study confounders (Potential confounders are accounted for?) | Statistical analysis? (Statistical analysis appropriately designed for the study?) |
---|---|---|---|---|---|---|
Abalo-Lojo | Yes | Yes | No | Partly | No | No |
Aggrawal | Yes | Partly | No | Partly | No | Yes |
Altin | Yes | Yes | Yes | Yes | No | Yes |
Beltran-Corbellini | Yes | Yes | Yes | Yes | Yes | Yes |
Brandstetter | Yes | Yes | Yes | Yes | No | Yes |
Carigan | Yes | Yes | Yes | Yes | Yes | Yes |
Chiesa-Estomba | Yes | Yes | No | Yes | No | No |
Chiesa-Estomba 2 | Yes | No | No | Yes | No | Yes |
D'Ascanio | Yes | Yes | Yes | Partly | Partly | Yes |
Dawson | Yes | Yes | Yes | Yes | No | Yes |
Dell'Era | Yes | Yes | No | Yes | Yes | Yes |
Giacomelli | Yes | Yes | No | Yes | No | No |
Gorzkowski | Yes | Yes | No | Partly | No | Yes |
Guner | Yes | Yes | No | Partly | No | Yes |
Haehner | Yes | Partly | Yes | Yes | No | No |
Hintschich | Yes | Partly | Yes | Yes | No | Yes |
Hornus | Yes | Yes | Yes | Partly | No | No |
Izquierdo-Domínguez | Yes | Yes | Yes | Yes | Yes | Yes |
Jalessi | Yes | Yes | No | Yes | Yes | Yes |
Kai Chua | Yes | Yes | Yes | Yes | No | No |
Kempker | Yes | Partly | Yes | Yes | No | No |
Kim | Yes | Partly | No | Partly | No | No |
Klopfenstein | Yes | Yes | No | Partly | No | Partly |
Lechien (1) | Yes | Yes | No | Partly | Yes | Partly |
Lechien (2) | Yes | Yes | Partly | Partly | Yes | Yes |
Lechien (3) | Yes | Yes | No | Partly | Yes | Yes |
Lechien (4) | Yes | Yes | No | Partly | Partly | Partly |
Lee | Yes | No | Yes | Yes | Yes | Yes |
Liang | Yes | Yes | No | Partly | No | Yes |
Magnavita | Yes | Yes | Yes | Yes | No | Yes |
Mao | Yes | Yes | No | Partly | Partly | Partly |
Martin-Sanz | Yes | Yes | Yes | Yes | No | Yes |
Mishra | Yes | Yes | No | Partly | No | No |
Moein | Yes | Yes | Yes | Yes | Yes | Partly |
Noh | Yes | Yes | No | Partly | No | Yes |
Paderno (1) | Yes | No | No | Partly | Yes | Yes |
Paderno (2) | Yes | Yes | No | Partly | Yes | Yes |
Parente-Arias | Yes | Yes | No | Partly | No | Yes |
Patel | Yes | No | No | Partly | Partly | No |
Petrocelli | Yes | No | No | Partly | No | Yes |
Qiu | Yes | Yes | No | Partly | Partly | Yes |
Romero-Sanchez | Yes | Yes | No | Partly | Yes | No |
Sakalli | Yes | Yes | No | Partly | Yes | Yes |
Sayin | Yes | Partly | Yes | Yes | Yes | Yes |
Tostmann | Yes | Yes | Yes | Yes | Partly | Partly |
Tsivgoulis | Yes | Partly | Yes | Yes | Yes | No |
Vaira (1) | Yes | Yes | No | Partly | No | Partly |
Vaira (2) | Yes | Yes | No | Partly | No | Yes |
Yan (1) | Yes | Yes | Yes | Yes | Yes | Yes |
Yan (2) | Yes | Yes | No | Partly | Yes | Yes |
Zayet | Yes | Yes | Yes | Yes | Yes | Yes |
A total of 11074 COVID-19 patients (mean age 46.7 ± 10.4 years and males 46.9%) were included in the final analysis (Table 2).2 , 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, 55, 56, 57, 58, 59, 60, 61, 62 The overall prevalence of “loss of smell” in COVID-19 patients was 52.0% (CI: 42.5%-61.6%, I2 = 99.4%) (Fig. 2 ). A total of 21 studies compared these symptoms in COVID-19 patients (n = 2196) and controls (n = 3425). 13 , 14 , 18 , 19 , 21 , 25 , 27 , 28 , 34, 35, 36, 37 , 39 , 40 , 45 , 47, 48, 49 , 59 , 60 , 62 “Loss of smell” was associated significantly more in the COVID-19 group compared to non-COVID-19 group (OR: 14.7, CI: 8.9–24.3, p < 0.001, I2 = 83.2%) (Fig. 3 ). Among COVID-19 patients, the odds of patients with severe disease and “loss of smell” were significantly lower when compared to patients with severe disease and without “loss of smell” (OR: 0.36, CI 0.27–0.48, p < 0.01, I2 = 27.4% (Fig. 4 ).2 , 21 , 32 , 37 , 52 , 54 , 57
Table 2.
Study, year | Country | Center (single, dual, multi) | Study Period | Type of study | Total Patients, non COVID group, N | Total Patients, COVID group, N | Mean age, COVID group (years) | Male gender, COVID group (%) | “Loss of smell” in COVID group, N (%) | “Loss of smell” in non COVID group, N (%) |
---|---|---|---|---|---|---|---|---|---|---|
Abalo-Lojo, 2020 | Spain | Single | – | Cohort | – | 131 | 50.4 | 56 (42.6%) | 77 (58.8%) | – |
Aggrawal, 2020 | USA | Single | Mar 1-Apr 4 | Cohort | – | 16 | 65.5 | 12 (75.0%) | 3 (18.8%) | – |
Altin, 2020 | Turkey | Dual | Mar 25-Apr 20 | Cohort | 40 | 81 | 54.2 | – | 50 (61.7%) | 0 (0%) |
Beltran-Corbellini, 2020 | Spain | Dual | Mar 23-Mar 25 | Case-control | 40 | 79 | – | 48 (60.8%) | 25 (31.6%) | 4 (10.0%) |
Brandstetter, 2020 | Germany | Single | – | Cohort | 170 | 31 | – | 30 (14.9%) | 16 (51.6%) | 4 (2.4%) |
Carigan, 2020 | Canada | Single | Mar 10- Mar 23 | Case-control | 134 | 134 | 57.1 | – | 69 (51.5%) | 6 (4.5%) |
Chiesa-Estomba (1), 2020 | South America (multiple countries) | Multi | – | Cross-sectional | – | 542 | 34 | 218 (40.2%) | 444 (819%) | – |
Chiesa-Estomba (2), 2020 | Europe (multiple countries) | Multi | – | Cohort | – | 1231 | 41 | – | 970 (78.8%) | – |
D'Ascanio, 2020 | Italy | Single | Febr 1-Apr 24 | Case-control | 25 | 43 | 58.1 | – | 26 (60.5%) | – |
Dawson, 2020 | USA | Single | Mar-Apr | Cohort | 48 | 42 | – | 48 (53.3%) | 18 (42.9%) | 1 (2.1%) |
Dell'Era, 2020 | Italy | Single | Mar 10- Mar 30 | Cross-sectional | – | 355 | 50 | 192 (54.1%) | 237 (66.8%) | – |
Giacomelli, 2020 | Italy | Single | – | Cross-sectional | – | 59 | 60 | 40 (67.8%) | 14 (23.7%) | – |
Gorzkowski, 2020 | France | Single | Mar 1- Mar 31 | Cross-sectional | – | 229 | 39.7 | 82 (35.8%) | 140 (61.1%) | – |
Guner, 2020 | Turkey | Single | Mar 10-Apr 10 | Cohort | – | 222 | 50.6 | 132 (59.5%) | 19 (8.6%) | – |
Haehner, 2020 | Germany | Single | – | Cross-sectional | 466 | 34 | – | 15 (44.1%) | 21 (61.7%) | 47 (10.1%) |
Hintschich, 2020 | Germany | Single | – | Cohort | 30 | 41 | 37 | 13 (31.7%) | 22 (53.7%) | 8 (26.7%) |
Hornus, 2020 | Germany | Single | – | Cross-sectional | 45 | 45 | 56 | – | 38 (84.4%) | 12 (26.7%) |
Izquierdo-Domínguez, 2020 | Spain | Multi | Mar 21-Apr 18 | Cross-sectional | 143 | 846 | 56.8 | – | 454 (53.6%) | 43 (30.1%) |
Jalessi, 2020 | Iran | Single | Feb-Mar | Cohort | – | 92 | 52.9 | 62 (67.4%) | 22 (23.9%) | – |
Kai Chua, 2020 | Singapore | Single | Mar 23-Apr 4 | Cohort | 686 | 31 | – | – | 7 (22.6%) | 22 (3.2%) |
Kempker, 2020 | USA | Single | – | Cohort | 232 | 51 | – | 10 (19.6%) | 48 (94.1%) | 27 (11.6%) |
Kim, 2020 | Korea | Single | Mar 12-16 | Cross-sectional | – | 172 | 26 | 66 (38.4%) | 68 (39.5%) | – |
Klopfenstein, 2020 | France | Single | March 1-Mar 17 | Cohort | – | 114 | – | – | 54 (47.4%) | – |
Lechien (1), 2020 | 18 European hospitals | Multi | – | Cross-sectional | – | 417 | – | – | 357 (85.6%) | – |
Lechien (2), 2020 | Belgium | Single | – | Cross-sectional | – | 86 | 41.7 | 30 (34.9) | 53 (61.6%) | – |
Lechien (3), 2020 | 12 European hospitals | Multi | Mar 22-Apr 10 | Cross-sectional | – | 1420 | 39.2 | – | 997 (70.2%) | – |
Lechien (4), 2020 | Belgium | Single | Mar 20-Apr 16 | Cross-sectional | – | 47 | 58.8 | 22 (46.8%) | 13 (27.6%) | – |
Lee, 2020 | Canada | Single | Mar 16-Apr 15 | Cross-sectional | 71 | 56 | 38 | 23 (41.1%) | 31 (55.4%) | 3 (4.2%) |
Liang, 2020 | China | Single | Mar 16-Apr 12 | Cohort | – | 86 | 25.5 | 44 (51.2%) | 34 (39.5%) | – |
Magnavita, 2020 | Italy | Multi | Mar 27-Apr 30 | Cross-sectional | 513 | 82 | – | – | 35 (42.7%) | 4 (0.8%) |
Mao, 2020 | China | Multi | Jan 16 -Feb 19 | Cohort | – | 214 | – | – | 11 (5.1%) | – |
Martin-Sanz, 2020 | Spain | Single | Mar 1-Apr7 | Case-control | 140 | 215 | – | 44 (20.5%) | 138 (64.1%) | 30 (24.8%) |
Mishra, 2020 | India | Single | – | Cross-sectional | 74 | 74 | – | 43 (58.1%) | 11 (14.8%) | 1 (1.4%) |
Moein, 2020 | Iran | Single | March 21 - Apr 5 | Case-control | 60 | 60 | 46.6 | 40 (66.7%) | 59 (98.3%) | 11 (18.3%) |
Noh, 2020 | Korea | Single | NR | Cohort | – | 199 | 38 | 69 (34.7%) | 52 (26.1%) | – |
Paderno (1), 2020 | Italy | Single | Mar 27-Apr 1 | Cohort | – | 151 | 45 | 56 (37.1%) | 126 (83.4%) | – |
Paderno (2), 2020 | Italy | Single | Mar 27-Apr 1 | Cross-sectional | – | 508 | 55 | 284 (55.9%) | 283 (55.7%) | – |
Parente-Arias, 2020 | Spain | Single | Mar 3-Mar 24 | Cohort | – | 151 | – | 53 (35.1%) | 75 (49.7%) | – |
Patel, 2020 | UK | Single | Mar 1-Apr 1 | Cross-sectional | – | 141 | 45.6 | 83 (58.8%) | 80 (56.7%) | – |
Petrocelli, 2020 | Italy | Single | Apr 16-May 2 | Cohort | – | 300 | 43.6 | 75 (25.0%) | 184 (61.3%) | – |
Qiu, 2020 | China, France, Germany | Multi | Mar 15-Apr 5 | Cohort | – | 394 | – | – | 154 (40.9%) | – |
Romero-Sanchez, 2020 | Spain | Dual | Mar 1-Apr 1 | Cohort | – | 841 | 66.4 | 473 (56.2%) | 41 (64.1%) | – |
Sakalli, 2020 | Turkey | Single | – | Cross-sectional | – | 172 | 37.8 | 84 (48.8%) | 18 (10.4%) | – |
Sayin, 2020 | Turkey | Single | – | Cross-sectional | 64 | 64 | 37.8 | 25 (39.1%) | 41 (64.1%) | 13 (20.3%) |
Tostmann, 2020 | Netherlands | Single | Mar 10 -Mar 29 | Cross-sectional | 190 | 79 | – | – | 37 (46.8%) | 7 (3.7%) |
Tsivgoulis, 2020 | Greece | Single | Mar 19- Apr 8 | Case-control | 22 | 22 | 55 | 6 (54.5%) | 17 (77.3%) | 8 (36.4%) |
Vaira (1), 2020 | Italy | Single | Mar 31 - Apr 6 | Cross-sectional | – | 72 | – | – | 60 (83.3%) | – |
Vaira (2), 2020 | Italy | Mutli | – | Cohort | – | 345 | 48.5 | 146 (42.3%) | 241 (69.9%) | – |
Yan (1), 2020 | USA | Single | Mar 3 -Mar 29 | Cross-sectional | 203 | 59 | – | 29 (49.2%) | 40 (67.8%) | 33 (16.3%) |
Yan (2), 2020 | USA | Single | Mar 3 - Apr 8 | Cohort | – | 128 | – | – | 75 (59.6%) | – |
Zayet, 2020 | France | Single | Feb 26-Mar 14 | Cohort | 54 | 70 | 50.4 | 29 (41.4%) | 37 (54.2%) | 9 (16.7%) |
Discussion
We summarized the overall prevalence of “loss of smell” for COVID-19 patients and compared with control patients i.e. those without laboratory confirmation of COVID-19 from the same study period. The overall prevalence of “loss of smell” was significantly higher for the COVID-19 group compared to control group. In addition, “loss of smell” had a lower association with severe COVID-19 compared to COVID-19 patients without “loss of smell”.
Olfactory and gustatory changes are one of the most underreported symptoms in COVID-19 and can sometimes be only presenting symptoms in these patients.3 As demonstrated in our study, “loss of smell” was associated with somewhat favorable prognosis of the disease and hence careful screening should be undertaken to identify potential patients with COVID-19. These patients should undergo testing to rule out COVID-19. This will help in preventing the spread of the virus
We noted significant variations in the reporting of symptoms (i.e., dysosmia/anosmia/hyposmia/microsmia) in the studies. Mao et al. noted “loss of smell” in 5.1% of their cohort, while Moein et al. noted that roughly 98% of patients had “loss of smell”.2 , 18 Earlier studies such as by Mao et al. relied on the retrospective data collection and questionnaire based survey. As the olfactory symptoms became well-recognized, the newer studies might have assessed these patients specifically for these symptoms, resulting in a higher prevalence of olfactory symptoms. Further, only few studies objectively evaluated the “loss of smell” using validated tools.18 , 20 , 25 , 35 , 42 , 44 , 60 , 61 The objective methods used in literature to assess “loss of smell” included: “Sniffin Sticks test”, “The University of Pennsylvania Smell Identification Test (UPSIT)”, “Quick Smell Identification Test (Q-SIT)”, and “Connecticut Chemosensory Clinical Research Center Test (CCCRC test)”. We feel that the actual prevalence of olfactory symptoms could be much higher than what is reported as we have combined data from relatively older studies as well. Our results should be interpreted as such keeping in mind this important limitation.
Only 7 studies compared the disease severity in patients with “loss of smell” versus those without “loss of smell”. Although our results are limited due to the very small sample size, “loss of smell” was characterized by the less severe disease compared to those without this symptom. This finding is noteworthy and needs to be further explored in more extensive studies. The limitation of our analysis is the observational nature of the studies with significant variations in the reporting of symptoms and follow-up. A temporospatial association of the disease severity and the symptom was not possible. However, our study is novel as we performed a pooled analysis combining the statistical power and further compared and demonstrated the prevalence in the control group.
In conclusion, we demonstrate here that alteration in smell is prevalent in COVID-19 and should be included as one of the essential symptoms to screen the population. Further larger studies are urgently needed to evaluate the utility of olfactory dysfunction in patients with COVID-19, as demonstrated in our study. Therefore, alteration in the sense of smell should be added as a screening question to identify not only the symptomatic disease but also possible healthy (or presumed asymptomatic) carriers of the disease.
Author contributions
Conception and design: Muhammad Aziz, Hemant Goyal, Literature search: Wade M. Lee-Smith, First draft: Muhammad Aziz, Critical revision and editing: All authors, Final approval: All authors.
Footnotes
Conflict of Interest and Ethical statement: The authors have no commercial associations or sources of support that might pose a conflict of interest.
Source of funding: None.
Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j.amjms.2020.09.017.
Appendix. SUPPLEMENTARY MATERIALS
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