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. 2021 Nov 22;102:108390. doi: 10.1016/j.intimp.2021.108390

Impact of asthma on COVID-19 mortality in the United States: Evidence based on a meta-analysis

Xueya Han a, Jie Xu a, Hongjie Hou a, Haiyan Yang a,⁎, Yadong Wang b
PMCID: PMC8611693  PMID: 34844871

Graphical abstract

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Abbreviations: COVID-19, coronavirus disease 2019; USA, the United States

Keywords: COVID-19, Asthma, Mortality, Meta-analysis, USA

Abstract

Objective

The aim of this study was to investigate the impact of asthma on the risk for mortality among coronavirus disease 2019 (COVID-19) patients in the United States by a quantitative meta-analysis.

Methods

A random-effects model was used to estimate the pooled odds ratio (OR) with corresponding 95% confidence interval (CI). I2 statistic, sensitivity analysis, Begg’s test, meta-regression and subgroup analyses were also performed.

Results

The data based on 56 studies with 426,261 COVID-19 patients showed that there was a statistically significant association between pre-existing asthma and the reduced risk for COVID-19 mortality in the United States (OR: 0.82, 95% CI: 0.74–0.91). Subgroup analyses by age, male proportion, sample size, study design and setting demonstrated that pre-existing asthma was associated with a significantly reduced risk for COVID-19 mortality among studies with age ≥ 60 years old (OR: 0.79, 95% CI: 0.72–0.87), male proportion ≥ 55% (OR: 0.79, 95% CI: 0.72–0.87), male proportion ＜ 55% (OR: 0.81, 95% CI: 0.69–0.95), sample sizes ≥ 700 cases (OR: 0.80, 95% CI: 0.71–0.91), retrospective study/case series (OR: 0.82, 95% CI: 0.75–0.89), prospective study (OR: 0.83, 95% CI: 0.70–0.98) and hospitalized patients (OR: 0.82, 95% CI: 0.74–0.91). Meta-regression did reveal none of factors mentioned above were possible reasons of heterogeneity. Sensitivity analysis indicated the robustness of our findings. No publication bias was detected in Begg’s test (P = 0.4538).

Conclusion

Our findings demonstrated pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality in the United States.

1. Introduction

It has been reported that the prevalence of comorbid asthma among coronavirus disease 2019 (COVID-19) patients varied greatly across countries or regions worldwide [1], [2], [3]. Previous meta-analyses have investigated the association between pre-existing asthma and COVID-19 mortality in the whole regions [1], [2], [3], but the conclusions were inconsistent, which might suffer limitations from substantial variation of asthma prevalence among different countries. Moreover, a previous meta-analysis by Sunjaya et al reported that COVID-19 patients with asthma had a significantly increased risk for mortality in Asia, but not in Europe, North America and South America [4]. Taken together, those urged us to investigate the association between pre-existing asthma and COVID-19 mortality in a specific country or region. To date, a number of individual studies have explored the association between pre-existing asthma and COVID-19 mortality in the United States with conflicting results [5], [6], [7], [8], [9], but no quantitative meta-analysis on this topic was conducted to address this issue. Therefore, we performed a quantitative meta-analysis to investigate the impact of asthma on the risk for COVID-19 mortality in the United States.

2. Methods

2.1. Search strategy and selection criteria

This meta-analysis strictly adhering to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was carried out [10]. We performed an extensive search of the literature in the online databases of PubMed, Wiley Library, Springer Link, Elsevier ScienceDirect, Web of Science, EMBASE, Scopus and Cochrane Library to identify all potential articles which were published from inception to October 30, 2021, using the following keywords: “COVID-19”, “coronavirus disease 2019”, “2019-nCoV”, “2019 novel coronavirus”, “SARS-CoV-2”, “severe acute respiratory syndrome coronavirus 2”, “asthma”, “asthmatic”, “mortality”, “fatality”, “death”, “non-survivor”, “deceased”, “US”, “USA”, “America”, “the United States” and “the United States of America”. The references of the included studies and relevant reviews were also searched to identify additional articles. The primary outcome of interest was mortality. The participants of exposure group were COVID-19 patients with asthma and those of control group were COVID-19 patients without asthma.

All studies were included in this meta-analysis when they fulfilled the following inclusion criteria: (1) studies reporting adult confirmed COVID-19 patients in the United States; (2) peer-reviewed articles which were written in English language; (3) studies with the sample sizes being more than fifteen cases; (4) studies with available data on the incidence of survivors and non-survivors among COVID-19 patients with asthma and without asthma or the effect size with 95% confidence interval (CI) regarding the association between asthma and COVID-19 mortality. We excluded case reports, review papers, repeated articles, preprints, errata and studies conducted in other than the United States accordingly. Literature search, study selection and data extraction were performed by two investigators independently. Any disagreement was resolved through discussion between the investigators. The extracted information is at list: first author (PMID), study design, country, sample size, the mean (standard deviation) or median (interquartile range) age respectively, proportion of males, available data on the incidence of survivors and non-survivors among COVID-19 patients with asthma and without asthma or the effect size with 95% CI, and setting.

2.2. Statistical analysis

The pooled odds ratio (OR) with corresponding 95% CI evaluating the association between asthma and COVID-19 mortality in the United States was calculated by a random-effects meta-analysis model [11], [12]. I2 statistic was applied to assess the heterogeneity among studies [13]. Sensitivity analysis by deleting one single study from overall pooled analysis each time was carried out to evaluate the robustness of the findings [2]. Begg’s rank correlation test was used to evaluate the potential publication bias [14]. The statistical analyses were performed with the package “meta” on R software (Version 4.1.1) [15]. Two tailed P value being less than 0.05 was considered statistically significant.

3. Results

3.1. Study selection

Yielding 5912 records from electronic databases and 10 records from hand-searching from the relevant studies or reviews in the cited lists. 2643 records were identified initially after removing duplications. After evaluating and assessing as much as 257 potential studies, 201 studies were removed due to outcome of interest being not available. In the end, what underlay this meta-analysis were eligible fifty-six articles with 426,261 COVID-19 patients [5], [6], [7], [8], [9], [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], [63], [64], [65], [66]. The detail of selection process is shown by a chart flow in Fig. 1 .

Fig. 1.

Fig. 1

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Flow chart of the process of study selection of PRISMA.

3.2. Study characteristics

A total of fifty-six eligible articles with 426,261 COVID-19 patients were included in our meta-analysis. The sample sizes among the included studies varied from 60 to 219,001 cases. There were forty-six retrospective studies, four prospective studies, three cohort studies, one cross-sectional study and one case series study. Forty studies reported the association between asthma and COVID-19 mortality among hospitalized patients. Most of studies (20/56) were conducted in New York. The summary information of included studies is presented in Table 1 .

Table 1.

General information of the eligible studies included in this meta-analysis.

Author (PMID) Study design Region Cases Male (%) Age Asthma
 No Asthma
 Setting
Non-survivor Survivor Non-survivor Survivor
Banoei MM (PMID: 34496940) Retrospective study Florida 250 56 62.75 ± 17.13 2 28 29 191 Hospitalized
Chou EH (PMID: 34546880) Retrospective study Texas 1788 50.2 54.6 (41.9–68.2) 9 116 188 1475 All patients
Kim D (PMID: 32950749) Retrospective study The USA 817 54.47 57.13 ± 14.57 10 78 111 618 Hospitalized
Garibaldi BT (PMID: 32960645) Retrospective study Maryland, Washington 832 53 63 (49–75) 8 71 123 630 Hospitalized
Kim TS (PMID: 33128848) Prospective study New York 10,861 59.6 NR Effect (95% CI): 0.81 (0.67–0.98) Hospitalized
Rustgi V (PMID: 33409033) Retrospective study New Brunswick 403 56.17 62.06 ± 18.62 4 21 86 292 Hospitalized
Suzuki A (PMID: 34444232) Cohort study Durham 22,777 NR NR 59 1254 1461 20,003 All patients
Pecina JL (PMID: 34452582) Retrospective study Minnesota 92 56.5 61 (50–74) Effect (95% CI): 10.0 (1.8–56.0) Hospitalized
Huang BZ (PMID: 34389242) Retrospective study California 61,338 46.08 43.97 ± 16.24 96 5430 901 54,911 All patients
Welder D (PMID: 34132393) Cohort study Texas 678 52.4 61.5 ± 16.7 6 92 50 530 All patients
Hou W (PMID: 33746590) Retrospective study New York 635 59.8 60 ± 11 3 38 79 515 Hospitalized
Forrest IS (PMID: 34089483) Retrospective study New York 688 63.52 67.22 ± 14.44 13 17 286 372 Hospitalized
Gupta YS (PMID: 33601125) Retrospective study New York 180 53 68 (59–80) 1 6 58 115 All patients
Jacobs JP (PMID: 34242641) Prospective study The USA 200 69 49.8 ± 12.1 19 14 91 76 All patients
Chhiba KD (PMID: 32554082) Retrospective study Chicago 1526 47 53.3 8 212 64 1242 All patients
Eggert LE (PMID: 34080210) Retrospective study California 605 47.8 50.68 ± 26.18 6 94 30 475 Hospitalized
Ho KS (PMID: 33647451) Retrospective study New York 4902 55.9 64.99 ± 16.92 54 179 1354 3315 Hospitalized
Lieberman-Cribbin W (PMID: 32522556) Retrospective study New York 6245 NR 57 45 227 1083 4890 Hospitalized
Lovinsky-Desir S (PMID: 32771560) Retrospective study New York 1298 41.3 52 9 154 101 1034 Hospitalized
Mather JF (PMID: 34143730) Retrospective study Hartford 1045 33.7 56.0 ± 17.58 7 81 157 800 Hospitalized
Robinson LB (PMID: 33650461) Retrospective study Boston 3248 72 51 ± 17 7 555 69 2617 All patients
Rosenthal JA (PMID: 33059035) Retrospective study Washington 727 NR 49.46 ± 17.93 10 95 51 571 All patients
Salacup G (PMID: 32617986) Retrospective study Pennsylvania 242 51 66 ± 14.75 0 18 52 172 Hospitalized
Shah P (PMID: 32620056) Retrospective study Georgia 522 41.8 63 (50–72) 11 57 81 373 Hospitalized
Miller J (PMID: 32945856) Retrospective study Michigan 2316 51.8 64.5 ± 16.3 31 186 402 1697 Hospitalized
Ioannou GN (PMID: 32965502) Retrospective study Washington 10,131 91 63.6 ± 16.2 58 687 1032 8354 All patients
Bahl A (PMID: 32970246) Prospective study Michigan 1461 52.7 62.0 (50.0–74.0) 30 124 297 1010 Hospitalized
Jackson BR (PMID: 32971532) Retrospective study Georgia 297 49.8 60 (45–69) 3 29 48 217 Hospitalized
Kim J (PMID: 33092732) Retrospective study New York 510 66 64 ± 14 Effect (95% CI): 0.93 (0.53–1.64) Hospitalized
Rechtman E (PMID: 33298991) Retrospective study New York 8770 54.3 60 (44–72) 43 341 1071 7315 All patients
Lundon DJ (PMID: 33324596) Cross-sectional study New York 8928 46.2 58.0 ± 18.8 45 358 1134 7391 All patients
Hobbs ALV (PMID: 33427149) Retrospective study Arkansas, Louisiana, Mississippi, North Carolina, and Tennessee 476 55.3 62 (49–71) 5 43 71 357 Hospitalized
Gupta R (PMID: 33461499) Retrospective study New York 475 NR NR Effect (95% CI): 2.77 (1.18–7.04) Hospitalized
Marmarchi F (PMID: 33469873) Retrospective study Georgia 288 55 63 ± 16 Effect (95% CI): 0.517 (0.189–1.409) Hospitalized
Mohamed NE (PMID: 33481113) Case series New York 7624 54.6 46.78 33 302 823 6466 Hospitalized
Muhammad R (PMID: 33538998) Retrospective study Washington 200 60.5 58.9 ± 15.1 3 17 42 138 Hospitalized
Lohia P (PMID: 33546658) Retrospective study Michigan 1871 51.6 64.11 ± 16 Effect (95% CI): 0.57 (0.38–0.87) Hospitalized
Cedano J (PMID: 33552409) Retrospective study New Jersey 132 59 63 (53–71) 6 1 86 39 Hospitalized
Mulhem E (PMID: 33827831) Retrospective study Michigan 3219 49 65.2 (52.6–77.2) 67 362 449 2341 Hospitalized
Kelly JD (PMID: 34106264) Cohort study New York 27,640 88.6 57.2 ± 16.6 Effect (95% CI): 0.78 (0.59–1.04) All patients
Ende VJ (PMID: 34397301) Retrospective study New York 294 68.7 62.61 ± 14.41 13 17 127 137 Hospitalized
Zerbo O (PMID: 34432371) NR California 219,001 47.3 37.21 (23.42–52.33) 287 31,057 1238 186,419 All patients
Roomi S (PMID: 33854659) Retrospective study Pennsylvania 1204 59.3 66 39 83 431 651 Hospitalized
Al Abbasi B (PMID: 33224386) Retrospective study Florida 257 52.53 63 ± 17 3 18 53 183 Hospitalized
Altonen BL (PMID: 33315929) Retrospective study New York 395 66.8 31.03 (27.79–34.73) 8 55 47 285 Hospitalized
Gayam V (PMID: 32672844) Retrospective study New York 408 56.62 67 (56–76) 16 38 116 238 Hospitalized
Morrison AR (PMID: 32646770) Retrospective study Michigan 81 69.1 64 (58–71) 5 6 30 40 Hospitalized
Gavin W (PMID: 32652252) Retrospective study Indiana 140 51.4 60 (48–72) 1 14 21 104 Hospitalized
Krishnan S (PMID: 32707517) Retrospective study Michigan 152 62.5 66 ± 13 16 9 76 51 Hospitalized
Li X (PMID: 33194455) Retrospective study New York 1022 56.46 62.13 ± 17.45 6 51 136 829 Hospitalized
Berry DA (PMID: 34329317) Retrospective study Texas 3123 60.36 63 (51–74) 58 218 637 2135 Hospitalized
Vu CA (PMID: 33353546) Retrospective study Florida 60 66.7 54 (26–87) 0 4 9 47 Hospitalized
Snider JM (PMID: 34428181) Retrospective study New York 90 53.3 62.3 2 5 28 55 Hospitalized
Mikami T (PMID: 32607928) Retrospective study New York 2820 57.1 65.33 ± 18.15 31 97 775 1917 All patients
Akama-Garren EH (PMID: 34089403) Retrospective study Massachusetts 835 48 64 (50–76) 15 66 134 620 All patients
Sulaiman I (PMID: 34465900) Prospective study New York 142 78.17 59.27 ± 18.89 1 1 33 107 Hospitalized
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Note: The age (years) was presented as mean ± standard deviation or median (interquartile range, IQR); CI, confidence interval; The USA, the United States ; NR, not clearly reported.

3.3. Asthma and mortality of COVID-19

Totally, this present meta-analysis showed that there was a statistically significant association between pre-existing asthma and the reduced risk for COVID-19 mortality in the United States (OR: 0.82, 95% CI: 0.74–0.91) (Fig. 2 ). Once the participants were only limited to hospitalized patients, we still observed that pre-existing asthma was associated with a significantly reduced risk for COVID-19 mortality (OR: 0.81, 95% CI: 0.74–0.88, Table 2 ). Subgroup analyses by age, male proportion, sample size and study design demonstrated that this significant association between asthma and the reduced risk for COVID-19 mortality did exist among studies with separated subgroup: age ≥ 60 years old (n = 34 studies, OR: 0.79, 95% CI: 0.72–0.87, Figure S1), male proportion ≥ 55% (n = 27 studies, OR: 0.79, 95% CI: 0.72–0.87, Figure S2), male proportion < 55% (n = 25 studies, OR: 0.81, 95% CI: 0.69–0.95, Figure S2), sample sizes ≥ 700 cases (n = 28 studies, OR: 0.80, 95% CI: 0.71–0.91, Figure S3), retrospective study/case series (n = 47 studies, OR: 0.82, 95% CI: 0.75–0.89, Figure S4) and prospective study (n = 4 studies, OR: 0.83, 95% CI: 0.70–0.98, Figure S4), but did not exist in the subgroups with age < 60 years old (n = 19 studies, OR: 0.87, 95% CI: 0.73–1.03, Figure S1) and sample sizes < 700 cases (n = 28 studies, OR: 0.88, 95% CI: 0.73–1.07, Figure S3). Chasing up the source of heterogeneity, further meta-regression did reveal none of factors mentioned above were possible reasons of heterogeneity (age: P value = 0.3917; male proportion: P value = 0.7489; sample size: P value = 0.4968; study design: P value = 0.6948; setting: P value = 0.4571) (Table 2).

Fig. 2.

Fig. 2

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Forest plot presents the relationship between COVID-19 mortality and asthma in the United States: pooled odds ratio (OR) with its 95% confidence interval (CI).

Table 2.

Subgroup analysis and meta-regression.

Variables No. of studies Meta-regression
 Subgroup analysis
 Heterogeneity
Tau2 Z-Value P value Pooled Effect (95% CI) I2 Tau2 P value
Age (years) 0.0314 – 0.3917
 ≥ 60 34 – −1.3669 0.1717 0.79 (0.72–0.87) 0% 0 0.76
 < 60 19 – – – 0.87 (0.73–1.03) 60% 0.0638 < 0.01
 NR 3 – −0.5549 0.5790 0.89 (0.58–1.37) 80% 0.0999 < 0.01
Male (%) 0.0400 – 0.7489
 ≥ 55 27 – −0.4003 0.6889 0.79 (0.72–0.87) 0% 0 0.80
 < 55 25 – – – 0.81 (0.69–0.95) 60% 0.0695 < 0.01
 NR 4 – 0.5062 0.6127 1.01 (0.64–1.58) 74% 0.1413 < 0.01
Sample size 0.0509 −0.6795 0.4968
 ≥ 700 28 – – – 0.80 (0.71–0.91) 66% 0.0587 < 0.01
 < 700 28 – – – 0.88 (0.73–1.07) 0% 0 0.46
Setting 0.0415 0.7436 0.4571
 All patients 16 – – – 0.85 (0.70–1.02) 74% 0.0860 < 0.01
 Hospitalized 40 – – – 0.81 (0.74–0.88) 0% 0 0.58
Study design 0.0416 – 0.6948
 Retrospective study/Case series 47 – −0.7816 0.4345 0.82 (0.75–0.89) 6% 0.0048 0.36
 Prospective study 4 – −0.1126 0.9104 0.83 (0.70–0.98) 0% 0 0.65
 Others 5 – – – 0.86 (0.58–1.26) 90% 0.1583 < 0.01
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Note: NR, not clearly reported; CI, confidence interval.

3.4. Sensitivity analysis and publication bias

The forest plot indicated that the pooled OR did not change significantly after deleting one single study each time (Fig. 3 ), which indicated the robustness of our findings.

Fig. 3.

Fig. 3

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Sensitivity analysis for pooled OR and 95% CI by deleting one single study from overall pooled analysis each time.

Fig. 4 showed rank correlation test of funnel plot asymmetry in Begg’s test. The statistics and asymmetry of funnel plot indicated that there was no evidence of publication bias (P = 0.4538).

Fig. 4.

Fig. 4

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Publication bias based on funnel plot.

4. Discussion

Our findings demonstrated that pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality in the United States based on fifty-six eligible articles with 426,261 COVID-19 patients. Taking the existence of heterogeneity into account, further meta-regression and subgroup analyses were conducted following by seeking the potential source of heterogeneity. None of factors in the further analyses can be used to explain the source of heterogeneity.

Asthma can be triggered exacerbation by respiratory viruses, inducing the severity of the infectious condition [67], but we found the association of asthma with the protective risk for mortality among coronavirus disease 2019 patients. At the same time, the detailed mechanisms of the association between asthma and the risk for COVID-19 mortality are unclear although several hypotheses were taken willingly to accept: (1) asthma in COVID-19 patients may take caution to build a fence to isolate themselves from the crowd and get more medical care from the paramedical practice; (2) the use of medicine to cope with asthma in convention, allergen immunotherapy, inhaled corticosteroids and biological agents, may resist the severe prognoses of COVID-19 in terms of suppressing viral replication and relieving inflammation [68]; (3) type 2 immune response modulating the expression of ACE2 and TMPRSS2 further supports an important role in inflammatory process in COVID-19 pathogenesis [69].

The prevalence of comorbid asthma among coronavirus disease 2019 patients varied greatly across countries or regions worldwide. Previous meta-analyses have reported the inconsistent association between asthma and COVID-19 mortality in the whole regions [1], [2], [3], which might be difficult in assessing the association on substantial variation of asthma prevalence among different countries. The strength of this study was that the included studies (56 eligible articles) with 426,261 cases were only conducted in the USA, which thought about the influences of this varied prevalence for asthma in regions among COVID-19 patients in the USA in terms of the relation between asthma and COVID-19 mortality. The meta-analysis only including studies conducted in the USA supported that pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality, which wards off the diversity of epidemiological characteristics and prevention and control measures in region, for the most part.

Undeniably, we indeed acknowledged that there were several limitations in this present meta-analysis. First, most of the included studies were retrospective, only four prospective studies were included, thus further meta-analyses on this topic based on prospective studies are warranted to confirm our results when more eligible data are available. Second, the pooled effect size was estimated on the crude effect sizes, which could not address the effects of certain confounders on the association between asthma and COVID-19 mortality. Therefore, further studies based on risk factors-adjusted estimates are warranted to verify our current findings. Third, this study could not address the effects of medications on the association between asthma and COVID-19 mortality, since most of the included studies did not provide the data. Forth, we noticed that the data of several studies were collected from multiple hospitals or centers, thus overlapping data might occur. In order to include more data as more as possible, we did not exclude the studies containing multiple hospitals or centers.

In conclusion, our findings demonstrated that pre-existing asthma was significantly associated with a reduced risk for COVID-19 mortality in the United States, further well-designed studies based on risk factors-adjusted estimates are warranted to confirm our findings. This study suggested that routine interventions and treatment for asthma patients with severe acute respiratory syndrome coronavirus 2 infection should be continued in the United States.

Author contribution

Haiyan Yang and Yadong Wang conceptualized the study. Xueya Han, Jie Xu, Hongjie Hou and Haiyan Yang performed literature search and data extraction. Xueya Han, Jie Xu and Hongjie Hou analyzed the data. Xueya Han and Yadong Wang wrote the manuscript. All the authors approved the final manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Acknowledgements

We would like to thank Yang Li, Peihua Zhang, Jian Wu, Xuan Liang, Wenwei Xiao, Ying Wang and Li Shi (All are from Department of Epidemiology, School of Public Health, Zhengzhou University) for their kind help in searching articles and collecting data, and valuable suggestions for analyzing data.

Data availability statement.

The data that support the findings of this study are included in this article and available from the corresponding author upon reasonable request.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.intimp.2021.108390.

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Supplementary data 1
mmc1.docx (6.5MB, docx)

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