To the Editor:
We sincerely appreciate the interest of Hsu and Lai1 in our recent publication in The Journal of Allergy and Clinical Immunology: In Practice titled “Impact of allergic rhinitis and asthma on COVID-19 infection, hospitalization, and mortality.”2 For the 2 main concerns raised in their correspondence, our clarifications are as follows.
In terms of the first concern regarding whether the effect of systemic and inhaled corticosteroids on COVID-19 could differ, in fact, we had initially analyzed the association between inhaled corticosteroids and the infection, severity, and mortality of COVID-19 among patients with allergic rhinitis and/or asthma, and the results were not significant (Table I and Figure 1, Figure 2, Figure 3 ). Because inhaled corticosteroids actually included oral inhaled corticosteroids and intranasal corticosteroids, we separated them in the subgroup analysis. Because the number of oral inhaled corticosteroid patients (n = 251) was significantly smaller than that in the nasal spray group (n = 12,579), we ultimately presented the results of corticosteroid nasal sprays instead of the inhaled corticosteroids. In addition, regarding the dose-response relationship, no detailed data on dose or duration information were collected in the UK Biobank, so no further analysis of these medications could be performed.
Table I.
The infection rate, hospitalization rate, and mortality of COVID-19 among participants who used long-term medications (antihistamine, glucocorticoids, inhaled corticosteroids, and β2-adrenoceptor agonists) to control allergic rhinitis (AR) or asthma
| Medication | Variable | COVID-19 infection (n = 2540/13,232) |
COVID-19 hospitalization (n = 945/2624) |
COVID-19 mortality (n = 122/2624) |
||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Number | RR (95% CI) | P value | Number | RR (95% CI) | P value | Number | RR (95% CI) | P value | ||
| Antihistamine | No | 11,732 | Reference | .656 | 2309 | Reference | .302 | 2309 | Reference | .891 |
| Yes | 847 | 1.04 (0.89-1.21) | 172 | 1.14 (0.89-1.45) | 172 | 0.95 (0.44-2.05) | ||||
| Systemic glucocorticoids | No | 10,904 | Reference | .922 | 2180 | Reference | .685 | 2180 | Reference | .726 |
| Yes | 1675 | 0.99 (0.88-1.12) | 301 | 0.96 (0.79-1.16) | 301 | 0.91 (0.55-1.52) | ||||
| Inhaled corticosteroids | No | 11,823 | Reference | .649 | 2348 | Reference | .328 | 2348 | Reference | .23 |
| Yes | 756 | 0.96 (0.81-1.14) | 133 | 0.85 (0.62-1.18) | 133 | 0.42 (0.1-1.72) | ||||
| β2-Adrenoceptor agonists | No | 11,689 | Reference | .104 | 2294 | Reference | .736 | 2294 | Reference | .321 |
| Yes | 890 | 1.13 (0.97-1.32) | 187 | 0.96 (0.77-1.21) | 187 | 1.31 (0.77-2.23) | ||||
Adjusted for sex, age, Townsend deprivation index, education, body mass index, ethnic background, smoking status (smoking experience and pack-year), drinking status, and pre-existing comorbidities (eg, diabetes, circulatory diseases, fracture, lower respiratory disease, upper gastrointestinal diseases, renal diseases, and dementia). Note that β2-adrenoceptor agonists were only prescribed for asthma, not AR.
CI, Confidence interval; RR, relative risk.
Figure 1.
Association between long-term control of allergic rhinitis (AR)/asthma medications (antihistamine, systemic glucocorticoids, inhaled corticosteroids, and β2-adrenoceptor agonists) and the infection of COVID-19 in patients with AR/asthma. Adjusted for sex, age, Townsend deprivation index, education, current employment status, body mass index, ethnic background, smoking status (pack-year) and drinking status, and pre-existing comorbidities (eg, diabetes, circulatory diseases, fracture, lower respiratory disease, upper gastrointestinal diseases, renal diseases, dementia, arthritis, and certain immune disorders). The x-axis indicates a log-scale.
Figure 2.
Association between long-term control of allergic rhinitis (AR)/asthma medications (antihistamine, systemic glucocorticoids, inhaled corticosteroids, and β2-adrenoceptor agonists) and the hospitalization of COVID-19 in patients with AR/asthma. Adjusted for sex, age, Townsend deprivation index, education, current employment status, body mass index, ethnic background, smoking status (pack-year) and drinking status, and pre-existing comorbidities (eg, diabetes, circulatory diseases, fracture, lower respiratory disease, upper gastrointestinal diseases, renal diseases, dementia, arthritis, and certain immune disorders). The x-axis indicates a log-scale.
Figure 3.
Association between long-term control of allergic rhinitis (AR)/asthma medications (antihistamine, systemic glucocorticoids, inhaled corticosteroids, and β2-adrenoceptor agonists) and the mortality of COVID-19 in patients with AR/asthma. Adjusted for sex, age, Townsend deprivation index, education, current employment status, body mass index, ethnic background, smoking status (pack-year) and drinking status, and pre-existing comorbidities (eg, diabetes, circulatory diseases, fracture, lower respiratory disease, upper gastrointestinal diseases, renal diseases, dementia, arthritis, and certain immune disorders). The x-axis indicates a log-scale.
Second, Hsu and Lai also highlighted the potential role of asthma severity in confounding or modifying the association between asthma and the outcome of COVID-19, as other studies3 , 4 have shown that patients with uncontrolled asthma had an increased risk of severe COVID-19 compared with those without asthma or with well-controlled asthma. We also agree that the confounding effects of asthma severity cannot be ignored, but, unfortunately, there are no relevant data on asthma severity in the UK Biobank, thus limiting the analysis of the impact of asthma severity on COVID-19 infection, hospitalization, and mortality in this study.
In conclusion, we concur that further research with more comprehensive data on medications and the severity of asthma is needed to reduce the confounding effects and better elucidate the relationship between asthma and COVID-19.
Footnotes
This work was supported by West China Hospital, Sichuan University (grant numbers 2019HXFH003, ZYJC21027, and 2019HXBH079); Sichuan University (grant numbers GSALK2020021 and 2020SCU12049); the Science and Technology Department of Sichuan Province (grant number 2022YFS0066); the Health Department of Sichuan Province (grant number 20PJ030); China Postdoctoral Science Foundation (grant number 2020M673250); and National Natural Youth Science Foundation of China (grant number 82002868).
Conflicts of interest: The authors declare that they have no relevant conflicts of interest.
UK Biobank ethical approval was from the North West Multi-centre Research Ethics Committee. The current analysis was approved under the UKB application (Applicant Number: 69718).
References
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