(2024) 22:360
Chuang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03589-4

BMC Medicine

RESEARCH

Open Access

New‑onset obstructive airway disease
following COVID‑19: a multicenter retrospective
cohort study
Min‑Hsiang Chuang1, Wei Hsu2, Ya‑Wen Tsai3,4, Wan‑Hsuan Hsu2, Jheng‑Yan Wu5, Ting‑Hui Liu6,
Po‑Yu Huang2 and Chih‑Cheng Lai7,8*

Abstract
Background The study assessed the association between COVID-19 and new-onset obstructive airway diseases,
including asthma, chronic obstructive pulmonary disease, and bronchiectasis among vaccinated individuals recover‑ing from COVID-19 during the Omicron wave.
Methods This multicenter retrospective cohort study comprised 549,606 individuals from the U.S. Collaborative
Network of TriNetX database, from January 8, 2022, to January 17, 2024. The hazard of new-onset obstructive airway
diseases between COVID-19 and no-COVID-19 groups were compared following propensity score matching using
the Kaplan–Meier method and Cox proportional hazards model.
Results After propensity score matching, each group contained 274,803 participants. Patients with COVID-19 exhib‑
ited a higher risk of developing new-onset asthma than that of individuals without COVID-19 (adjusted hazard ratio(aHR), 1.27; 95% CI, 1.22–1.33; p < 0.001). Stratified analyses by age, SARS-CoV-2 variant, vaccination status, and infec‑
tion status consistently supported this association. Non-hospitalized individuals with COVID-19 demonstrated
a higher risk of new-onset asthma (aHR, 1.27; 95% CI, 1.22–1.33; p < 0.001); however, no significant differences wereobserved in hospitalized and critically ill groups. The study also identified an increased risk of subsequent bronchi‑
ectasis following COVID-19 (aHR, 1.30; 95% CI, 1.13–1.50; p < 0.001). In contrast, there was no significant differencein the hazard of chronic obstructive pulmonary disease between the groups (aHR, 1.00; 95% CI, 0.95–1.06; p = 0.994).
Conclusion This study offers convincing evidence of the association between COVID-19 and the subsequent onsetof asthma and bronchiectasis. It underscores the need for a multidisciplinary approach to post-COVID-19 care,with a particular focus on respiratory health.
Keywords Asthma, Bronchiectasis, COPD, COVID-19, Obstructive airway diseases*Correspondence:
Chih‑Cheng Lai
dtmed141@gmail.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Chuang et al. BMC Medicine(2024) 22:360

Background
Over the past 3 years, severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2) has infected more than
773 million individuals globally [1]. Despite coronavirus
disease (COVID-19) being responsible for over sevenmillion fatalities, the case fatality rate stood at a mere
0.9% [1], suggesting that more than 99% of patients with
COVID-19 have survived the acute phase of SARS-CoV-2
infection. Nevertheless, a significant number of COVID19 survivors develop post-COVID-19 conditions, also
termed long COVID, which present a grave threat to
global health [2–4]. Reports have documented a broad
range of persistent symptoms linked to post-COVID-19
conditions, impacting the cardiovascular and pulmonary
systems, gastrointestinal tract, neurocognitive functions,
psychological well-being, and musculoskeletal system[3–7].
The respiratory system is frequently involved in postCOVID-19 conditions, as demonstrated by clinical andradiologic findings, along with pulmonary function tests[8, 9]. Predominantly, studies have concentrated on the
restrictive ventilatory defect, which is marked by lungfibrosis, a diminished diffusion capacity of the lung forcarbon monoxide, and reduced lung volume [8–11]. In
addition, residual abnormalities on computed tomography for patients with COVID-19 are not uncommon.
One meta-analysis showed that fibrotic-like changes
had the highest event rates of 0.44 and 0.38 during both
short-term (1–6 months) and long-term (12–24 months)
follow-up periods [12]. Moreover, patients with severe
COVID-19 exhibited significantly higher rates of various abnormalities, including bronchiectasis, fibroticlike changes, and reticulation, at long-term follow-ups
compared to those in the non-severe subgroup [12].
Conversely, although prior research has identified a link
between respiratory virus infections and the subsequent
onset of asthma [13–15], the evaluation of obstructive
airway disease risk following COVID-19 has been limited[16–18].
Kim et al. using the Korean National Health Insurance claim-based database demonstrated that COVID19 could be associated with a higher risk of new-onset
asthma (adjusted hazard ratio (aHR), 2.14; 95% CI, 1.88–
2.45) [16]. However, the study was conducted between
2020 and June 30, 2021, in South Korea and focused specifically on asthma. Their findings were thus limited to
the Asian population and the non-Omicron wave. Since
then, the virulence, epidemiology, and disease severity of
the currently prevalent Omicron variant have changed
significantly from those of the early strains [19, 20]. An
updated investigation among non-Asian populations is
also needed to provide a broader understanding of theassociation between COVID-19 and obstructive airway

Page 2 of 10

diseases. Therefore, we conducted this study using the
TriNetX network to assess the association between
COVID-19 and new-onset obstructive airway diseases,
particularly asthma, following COVID-19 during theOmicron wave.

Results
Characteristics of study subjects

Among the vaccinated individuals without pre-existing
asthma, COPD, or bronchiectasis, the study included
274,820 enrollees in the COVID-19 group and 2,214,332
in the no-COVID-19 group (Fig. 1). Prior to PSM, theaverage age of patients with COVID-19 was 55.5 years,
compared to 54.8 years for those without. The COVID19 group was 57.4% female, while the no-COVID-19
group was 58.9% female; 64% of the COVID-19 group
and 67.5% of the no-COVID-19 group were Caucasians.
Age, sex, and race did not differ significantly between thegroups (all standardized differences < 0.1). However, the
COVID-19 group showed a higher prevalence of comorbidities, including DM, dyslipidemia, hypertension,ischemic heart disease, heart failure, cerebrovasculardisease, CKD, neoplasm, anemia, and systemic connective tissue disease, as well as allergic rhinitis, comparedto their counterparts (Table 1). Additionally, a greater
percentage of the COVID-19 group had histories oftobacco use/nicotine dependence, a BMI of ≥ 30 kg/m2,
and socioeconomic status (SES)- or psychosocial-related
health hazards. Regarding medication, a higher proportion of the COVID-19 group had received systemic glucocorticoids, antihistamines, beta-blockers, aspirin, and
NSAIDs within 1 year preceding the index date (Table 1).
After PSM, each group consisted of 274,803 patients.
Characteristics for all the considered covariates, including demographics, comorbidities, and medications, werebalanced between the two groups with standardized differences < 0.1 (Table 1). The group with COVID-19 was
designated as the study group, whereas the group without COVID-19 was designated as the control group.
Primary outcome

During the follow-up period, 4216 new asthma cases
were diagnosed in the study group, compared with 3352
in the control group. The study group had a significantly
higher risk of developing new-onset asthma than thecontrol group (aHR, 1.27; 95% CI, 1.22–1.33; p < 0.001;
see Table 2). Additionally, Kaplan–Meier analysis showeda lower probability of event-free survival for the study
group relative to the control group (log-rank p < 0.001;
see Fig. 2).
Stratified analyses by age group, SARS-CoV-2 variant,vaccine status, and infection status consistently demonstrated similar trends. Patients with COVID-19 exhibited

Chuang et al. BMC Medicine(2024) 22:360

Page 3 of 10

Fig. 1 Flowchart of cohort construction. BMI body mass index, COVID-19 coronavirus disease 2019, FEV1 forced expiratory volume in the firstsecond, FVC forced vital capacity, HCO healthcare organization, COPD chronic obstructive pulmonary diseasea higher risk of new-onset asthma, as illustrated in Fig. 3.
Notably, there were no significant differences in effectsize estimates across the analyses, except for those basedon disease severity (p for interaction = 0.011). A significantly higher risk of new-onset asthma was observed only
in non-hospitalized patients with COVID-19, whereas no
significant differences were observed between hospitalized and critically ill groups (Fig. 3).
Secondary outcomes

We also identified that patients with COVID-19 had ahigher risk of bronchiectasis (aHR, 1.30; 95% CI, 1.13–
1.50, p < 0.001, Table 2). There was a trend towards anincreased risk of bronchiectasis in patients with critical
COVID-19 compared to those not hospitalized, but nostatistically significant between-subgroup heterogeneity was observed for bronchiectasis as evidenced by theinteraction tests (all p values for interaction > 0.05) andthe largely overlapping confidence intervals (Fig. 4). On
the other hand, there was no significant difference in theincidence of COPD between the groups (aHR, 1.00; 95%
CI, 0.95–1.06, p = 0.994, Table 2).
Sensitivity analysis

Our sensitivity analysis using different cutoff landmarks(2, 3, and 6 months after the index date) to define newonset obstructive airway diseases and the start of followup, showed consistent results. These results suggested
that COVID-19 is associated with an increased risk of

new-onset asthma and bronchiectasis, but not COPD
(Additional file 1: Table S4).
Regarding the negative and positive control outcomes,
we observed a positive association between COVID19 and symptoms characteristic of the post-COVID-19
condition, as expected, compared to the no-COVID-19
group. No significant associations were observed with
the negative control outcomes, as depicted in Additional
file 1: Fig. S1.

Discussion
This large-scale study evaluated the respiratory sequelae
of COVID-19, including asthma, COPD, and bronchiectasis. We found a potential link between COVID-19
and an increased risk of new-onset asthma. Specifically,
patients with COVID-19 had a 27% higher risk of developing asthma during the follow-up period compared tothose without COVID-19. Further analyses, stratified byage, SARS-CoV-2 variant, vaccination status, and infection status, as well as sensitivity analyses using different landmarks, consistently supported these findings.
In summary, our study presents compelling evidence
regarding the association between COVID-19 and thesubsequent development of asthma, highlighting thepotential long-term respiratory impacts of SARS-CoV-2
infection and enhancing our knowledge of the disease’s
progression beyond the acute phase.
Stratified analysis by disease severity revealed an
intriguing nuance. Although the overall risk of new-onsetChuang et al. BMC Medicine(2024) 22:360

Page 4 of 10

Table 1 Baseline characteristics
Variables

Before matching

After matching

COVID-1 (n = 274,820)

No COVID-19 (n= 2,214,322)

Std. diff.

COVID-19 (n = 274,803)

No COVID-19(n = 274,803)

Std. diff.

Age (mean ± SD)

55.5 ± 18.5

54.8 ± 18.8

0.037

55.5 ± 18.5

55.8 ± 18.3

0.014

Female161,968 (58.9)

1,271,264 (57.4)

0.031

161,954 (58.9)

161,511 (58.5)

0.003

White

185,452 (67.5)

1,417,970 (64.0)

0.073

185,438 (67.5)

185,767 (67.6)

0.003

African American

31,209 (11.4)

291,711 (13.2)

0.055

31,208 (11.4)

31,720 (11.1)

0.006

Asian

21,816 (7.9)

184,713 (8.3)

0.015

21,816 (7.9)

21,761 (7.7)

0.001

Diabetes mellitus

47,704 (17.4)

208,628 (9.4)

0.235

47,691 (17.4)

47,293 (17.1)

0.004

Dyslipidemia

105,886 (38.5)

460,850 (20.8)

0.395

105,870 (38.5)

110,171 (38.3)

0.032

Hypertension

107,739 (39.2)

494,876 (22.4)

0.371

107,723 (39.2)

110,598 (39.3)

0.021

Ischemic heart diseases

27,825 (10.1)

109,008 (4.9)

0.198

27,815 (10.1)

26,617 (10.1)

0.015

Heart failure

12,402 (4.5)

41,265 (1.9)

0.151

12,395 (4.5)

10,877 (4.4)

0.027

Peripheral vascular disease

6,295 (2.3)

22,709 (1.0)

0.099

6,289 (2.3)

5,680 (2.2)

0.015

Cerebrovascular diseases

14,040 (5.1)

52,719 (2.4)

0.144

14,032 (5.1)

12,813 (5.5)

0.021

Dementia

3,446 (1.3)

10,705 (0.5)

0.083

3,443 (1.3)

3,131 (1.1)

0.010

Alzheimer’s disease

1,938 (0.7)

7,280 (0.3)

0.053

1,937 (0.7)

1,825 (0.0)

0.005

Chronic kidney disease

23,891 (8.7)

86,687 (3.9)

0.198

23,884 (8.7)

22,449 (8.8)

0.019

Inflammatory liver diseases

3,907 (1.4)

12,246 (0.6)

0.088

3,904 (1.4)

3,328 (1.1)

0.018

Liver cirrhosis

3,057 (1.1)

11,227 (0.5)

0.068

3,057 (1.1)

2,608 (1.1)

0.016

Neoplasms

61,041 (22.2)

272,071 (12.3)

0.265

61,026 (22.2)

61,983 (22.2)

0.008

Anemia25,937 (9.4)

85,755 (3.9)

0.225

25,928 (9.4)

23,833 (9.9)

0.027

Systemic CTDs

7,369 (2.7)

27,394 (1.2)

0.104

7,365 (2.7)

6,643 (2.2)

0.017

Rheumatoid arthritis

4,177 (1.5)

15,896 (0.7)

0.076

4,170 (1.5)

3,873 (1.1)

0.009

Atopic dermatitis

1,509 (0.6)

6,073 (0.3)

0.043

1,509 (0.6)

1,346 (0.0)

0.008

Allergic rhinitis

19,379 (7.1)

69,874 (3.2)

0.178

19,371 (7.1)

19,381 (7.7)

0.000

Tobacco use

11,697 (4.3)

38,852 (1.8)

0.147

11,688 (4.3)

10,181 (4.4)

0.028

Nicotine dependence

12,913 (4.7)

60,134 (2.7)

0.105

12,908 (4.7)

12,503 (4.4)

0.007

Alcohol-related disorders

5,919 (2.2)

20,583 (0.9)

0.099

5,913 (2.2)

5,185 (2.2)

0.019

SES and psychosocial-related 9,816 (3.6)
health hazards

28,838 (1.3)

0.148

9,805 (3.6)

8,539 (3.3)

0.026

Demographics

Comorbidities

HIV

2,797 (1.0)

14,075 (0.6)

0.042

2,797 (1.0)

2,501 (1.1)

0.011

BMI ≥30 kg/m2

39,113 (14.2)

168,388 (7.6)

0.214

39,100 (14.2)

40,247 (14.1)

0.012

Glucocorticoids

88,008 (32.0)

371,437 (16.8)

0.361

88,992 (32.0)

88,114 (32.3)

0.001

Antihistamines

55,601 (20.2)

217,363 (9.8)

0.295

55,587 (20.2)

55,187 (20.2)

0.004

Beta blockers

53,298 (19.4)

258,025 (11.7)

0.215

53,287 (19.4)

52,790 (19.1)

0.005

Aspirin

26,372 (9.6)

112,237 (5.1)

0.174

26,366 (9.6)

24,916 (9.9)

0.018

25,176 (9.2)

109,365 (4.9)

0.165

25,166 (9.2)

24,976 (9.9)

0.002

Medications

BMI Body mass index, COVID-19 Coronavirus disease 2019, CTD Connective tissue disease, HIV Human immunodeficiency virus, NSAID Non-steroidal anti-inflammatory
drugs, SD Standard deviation, SES Socioeconomic status, Std. diff Standardized difference

asthma was significantly higher in the COVID-19 group,
this association was particularly pronounced in nonhospitalized individuals. The absence of a significant
difference in hospitalized and critically ill groups might
be partly due to a smaller patient count in these categories. Progression to viral pneumonia could indicate more

severe inflammation of the lower airway and could be
associated with the risk of subsequent obstructive airway diseases, but its occurrence, the chronological relationship with SARS-CoV-2 infection, and the etiologies
could not be ascertained in the current database, thereby
precluding further analysis on this variable. However, theChuang et al. BMC Medicine(2024) 22:360

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Table 2 Adjusted hazard ratios of primary and secondary
outcomes
Outcomes

No. of patients withoutcome
COVID19 (n =274,803)

Adjustedhazard ratio(95% CI)

p-value3352

1.27 (1.22–
1.33)*<0.001No COVID-19(n = 274,803)

Primary outcome
Asthma4216

Secondary outcomes
Bronchiec‑
tasis

438

345

1.30 (1.13–1.50)*<0.001

COPD

2180

1964

1.00 (0.95–
1.06)*

0.994

CI Confidence interval, COPD Chronic obstructive pulmonary disease*

Proportionality (Schoenfeld test p >0.05)

interaction test suggested a significant difference, indicating a complex relationship between COVID-19 disease
severity and the risk of obstructive airway diseases thatwarrants further exploration.
Our findings are consistent with those reported byKim et al., who used the Korean National Health Insurance claim-based database. They found that 1.6% of the
COVID-19 cohort and 0.7% of the matched cohort developed new-onset asthma, with an aHR of 2.14 (95% CI,
1.88–2.45) [16]. However, unlike the Korean study [16]conducted during 2020–2021, our analyses were basedon data collected after 2022 and feature a more ethnicallydiverse population from the TriNetX platform. Consequently, our results provided updated insights during theOmicron wave and are likely to be more generalizable
and relevant to the current context.
Respiratory tract viral infections, including respiratory syncytial virus and measles, have been identified
as potential causes of bronchiectasis [21], but the link
between SARS-CoV-2 and post-infectious bronchiectasis is not well established. Although a meta-analysis by
Guinto et al. [22] reported a 16.8% prevalence of bronchiectasis (95% CI, 9.10–26.1%) based on imaging studies during post-COVID-19 follow-up, our study is thefirst to demonstrate an increased risk of subsequent
bronchiectasis following COVID-19, with an aHR of
1.30 (95% CI, 1.13–1.50). These findings suggest that
bronchiectasis may be a respiratory sequela of COVID19. Respiratory viruses were found more frequently inbronchiectasis exacerbations and the proposed mechanisms included disturbances in host-defense responses,
heightened inflammation, and changes in bacterial virulence [23], which could be involved in the vicious cycle
model of development and progression of bronchiectasis,
for which the primary insult is often unknown [24]. Ourfindings accordingly supported that clinicians should be
vigilant for this potential complication after COVID-19.
The current study aligns with previous research [16,22] highlighting the respiratory system’s vulnerability to
post-COVID-19 conditions. Notably, the increased incidence of new-onset asthma and bronchiectasis observedin the study group suggests a potential link between
SARS-CoV-2 infection and the development of obstructive airway diseases. This represents a contradiction withthe results of previous studies [25, 26] which primarily
focused on restrictive ventilatory defects associated withlung fibrosis. However, further investigation is crucial to

Fig. 2 Kaplan–Meier curves of event-free survival for new-onset asthma (log-rank test p < 0.001)

Chuang et al. BMC Medicine(2024) 22:360

Page 6 of 10

Fig. 3 Subgroup analysis of new-onset asthma between COVID-19 and no-COVID-19 groups

Fig. 4 Subgroup analysis of new-onset bronchiectasis between COVID-19 and no-COVID-19 groups

validate these findings and elucidate the potential mechanisms involved.
This study boasts several strengths. First, the analyses
utilized the TriNetX platform, a sizable database, whichfacilitated the inclusion of a substantial patient cohort.

Second, we conducted numerous stratification analyses and sensitivity tests, with the majority yielding consistent results. Third, although patients with COVID-19
exhibited a higher prevalence of comorbidities such as
diabetes mellitus, cardiovascular diseases, and systemicChuang et al. BMC Medicine(2024) 22:360

connective tissue disorders—conditions potentiallyelevating the risk of obstructive airway diseases—thesefactors were meticulously adjusted in relation to study
outcomes. The application of PSM effectively equalizedbaseline characteristics between the COVID-19 and control groups, thus bolstering the study’s internal validity.
Despite the study’s strengths, we must acknowledge
certain limitations. The reliance on electronic healthrecords and administrative data raises the possibility of
misclassification and underreporting. Furthermore, thestudy does not investigate potential mechanisms behindthe observed associations, which leaves a gap for future
research to clarify the pathophysiological connections
between SARS-CoV-2 infection and obstructive airway
diseases. Long-term patient follow-up is also essential to comprehend the persistence and progression ofthese respiratory conditions over time. Meanwhile, ­FEV1
and disease staging data for asthma or COPD were notdocumented for most patients in the TriNetX database,
thereby introducing potential information bias and precluding further analysis on these variables. Finally, thepossibility of residual confounding could not be fully
eliminated though we had accounted for numerous clinically significant confounders in our analyses.

Conclusions
This study provides essential insights into the long-term
respiratory consequences of COVID-19, highlighting theneed for continued monitoring and care for individuals
recovering from SARS-CoV-2 infection. The associations
found with new-onset asthma and bronchiectasis underscore the importance of a multidisciplinary approach topost-COVID-19 care, with a particular focus on respiratory issues. Further research is necessary to investigate
the underlying mechanisms and to develop therapeuticstrategies to mitigate the effects of SARS-CoV-2 on the
respiratory system.
Methods
Data source

The present study utilized data from the US Collaborative Network of the TriNetX database, which gatheredde-identified patient-level information from electronic
health records. A health care organization (HCO) typically referred to an academic healthcare center thatcompiled data from its associated facilities, including
the main and satellite hospitals, and outpatient clinics.
The collected data encompassed patient demographics,
clinical diagnoses (coded using ICD-10-CM), medical
procedures (categorized according to ICD-10-PCS or
Current Procedural Terminology), medications (coded
based on the Veterans Affairs Drug Classification

Page 7 of 10

System and RxNorm medication codes), laboratory
tests (organized with Logical Observation Identifiers
Names and Codes), and records of healthcare utilization. The US Collaborative Network contained datafrom over 100 million patients across 61 HCOs in the
United States. Analysis of patient-level data was conducted on the TriNetX platform, and the results were
provided to researchers in a summarized format. The
TriNetX database performs intensive data preprocessing procedures to minimize missing values and maps
the data to a consistent framework. Details regarding
the database can be found online [21].
The current study’s use of the TriNetX databasereceived ethical approval from the Chi-Mei Hospital’s
Institutional Review Board (no: 11202–002). We conducted the study in accordance with the Declarationof Helsinki and reported our findings following theStrengthening the Reporting of Observational Studies
in Epidemiology (STROBE) guidelines.
Study population

Participants of this study were ≥ 18-year-olds and visited HCOs ≥ 3 times after January 8, 2022. This datecoincides with the emergence of the Omicron variantas the predominant strain in the United States [26],ensuring that the data for our analysis were collected
within a comparable timeframe. We limited our inclusion to those vaccinated against COVID-19 to reflect
the high vaccination rates among U.S. adults, which
had surpassed 90% in U.S. adults [27], and to minimize biases related to health-seeking behavior using
vaccination status as a proxy. To avoid misinterpretation, we excluded individuals with pre-existing diagnoses of asthma, chronic obstructive pulmonary disease(COPD), bronchiectasis, or forced expiration in thefirst second (FEV1)/forced vital capacity (FVC) < 70%
before the index date. The index date was defined as thedate of COVID-19 diagnosis for the COVID-19 groupand the date of the first visit to HCOs during the inclusion period for the no-COVID-19 group.
The enrollees were further divided into those diagnosed with COVID-19 (the COVID-19 group) during
the study timeframe and those who never had COVID19 (the no-COVID-19 group). One-to-one propensity score matching (PSM) was conducted, involving
34 variables, including demographics, comorbidities,
and medication usage, to balance the two groups. Bothgroups were followed for a maximum of 2 years or until
the date of data analysis on January 17, 2024. Details
regarding the codes used to identify demographics,
diagnoses, procedures, and medications are provided in
Additional file 1: Table S2.

Chuang et al. BMC Medicine(2024) 22:360

Covariates

In the current analysis, we considered the following
variables to balance baseline characteristics between the
COVID-19 and non-COVID-19 groups: age, sex, race,
diabetes mellitus (DM), dyslipidemia, cardiovasculardiseases (hypertension, ischemic heart diseases, heart
failure, peripheral vascular disease, cerebrovascular diseases), dementia, chronic kidney disease (CKD), hepatitis, cirrhosis, autoimmune diseases (systemic connective
tissue disorders, rheumatoid arthritis), anemia, neoplasms, human immunodeficiency virus (HIV) disease,atopic dermatitis, allergic rhinitis, tobacco use/nicotine
dependence, alcohol-related disorders, potential healthhazards related to socioeconomic and psychosocial circumstances, and BMI (≥ 30 kg/m2). We also included
medications that could alter allergic or inflammatory
responses or are potentially associated with bronchospasm (glucocorticoids, antihistamines, beta-adrenergic
blockers, aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs)). The codes used to define thecovariates are provided in Additional file 1: Table S3.
Prespecified outcomes

The primary outcome of this study was the hazard of
new-onset asthma during the follow-up period, which
commenced 4 months after the index date to preclude
the confounding effects of post-viral bronchial hyperreactivity syndrome. Secondary outcomes encompassed
the hazard of bronchiectasis and COPD within the same
follow-up timeframe. To ascertain the specificity of ourresults, we incorporated negative control outcomes,
including traumatic intracranial injury, skin cancer, schizophrenia, and cataract. Conversely, the positive control
outcome was the post-COVID-19 condition, characterized by a collection of symptoms such as fatigue, headache, dizziness, myalgia, sleep disturbance, emotional
distress, cognitive impairment, palpitation, shortnessof breath, and changes in bowel habits [28]. Additional
file 1: Table S4 contains the definitions and codes pertinent to these outcomes.
Statistical analysis

Baseline characteristics for the COVID-19 and noCOVID-19 groups are presented as means with standard
deviations (SD) or as counts and percentages. We compared categorical variables using the χ2 test and assessed
continuous variables with the independent two-sample
t-test. We conducted one-to-one PSM using the greedy
nearest neighbor algorithm with a caliper of 0.1 pooled
standardized differences to balance baseline characteristics. Variables were considered adequately matchedpost-PSM if the standardized difference between groups

Page 8 of 10

was less than 0.1. We computed survival probabilities
using the Kaplan–Meier method and calculated adjusted
hazard ratios (aHR) for the outcomes using the Cox proportional hazards model, including corresponding 95%
confidence intervals (CI) and p-values. We tested the
proportional hazards assumption with the generalized
Schoenfeld residuals method. Outcome variables werecategorized as present or absent; thus, missingness was
not applicable. We excluded cases lost to follow-up to
reduce potential biases or inaccuracies from incompletedata.
We conducted sensitivity analyses using different cutoff
landmarks (2, 3, and 6 months post-index date) to initiate follow-up and define new-onset asthma. To investigate potential differences in effect sizes among clinically
relevant subgroups, we performed pre-specified subgroup analyses based on age (≥ 18 to < 40, ≥ 40 to < 65,or ≥ 65 years), variants (BA.1.1, BA.2, BA.2.12.1, BA.5,
or XBB, corresponding to periods when a variant predominated in the U.S. [27]), booster vaccination status,
COVID-19 severity (not admitted, hospitalized, or critical, the latter defined by the need for endotracheal intubation, mechanical ventilation, extracorporeal membrane
oxygenation, or intensive care unit admission), and infection status (primary infection or reinfection). All tests
were two-sided with a significance threshold of 0.05. Statistical analyses were performed using TriNetX analytics
tools and R (version 4.2.2; R Foundation for StatisticalComputing, Vienna, Austria).
Abbreviations
aHR	Adjusted hazard ratio
BMI	Body mass index
CI	Confidence interval
COPD	Chronic obstructive pulmonary disease
COVID-19	Coronavirus disease 2019
CKD	Chronic kidney diseaseDM	Diabetes mellitus
FEV1	Forced expiration in the first second.
FVC	Forced vital capacity
HCO	Health care organizationHIV	Human immunodeficiency virus
ICD-10-CM	International Classification of Diseases, Tenth Revision, Clinical
Modification
ICD-10-PCS	International Classification of Diseases, Tenth Revision, Proce‑dure Coding System
NSAID	Non-steroidal anti-inflammatory drugs
PSM	Propensity score matching
SARS-CoV-2	Severe acute respiratory syndrome coronavirus 2
SD	Standard deviation

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03589-4.
Additional file 1: Table S1. Sensitivity analysis for primary and secondary
outcomes with different cutoff landmarks to define new-onset obstruc‑tive airway diseases and the start of follow-up. Table S2. Demographic,
diagnostic, procedural, medication, visit, and laboratory codes used inChuang et al. BMC Medicine(2024) 22:360

the definition of the cohort. Table S3. Demographic, diagnostic, labora‑
tory, and medication codes used in the definition of covariates. Table S4.
Diagnostic codes used in the definition of outcomes. FigS1. Results fornegative and positive control outcomes.
Acknowledgements
We express our gratitude to Chung-Han Ho for his consultation on statisticalanalysis.
Authors’ contributions
MHC conceptualized, designed the study performed the data analysis anddrafted the manuscript. WH, YWT, WHH, JYW, THL and PYH assisted data cor‑
rection and created figures. MHC, and CCL contributed to project design andedited the manuscript. MHC and CCL was responsible for the data interpreta‑tion. MHC and CCL finalized the manuscript. All authors approved the final
version of the manuscript.
Funding
No specific funding was received from any bodies in the public, commercial,or not-for-profit sectors to carry out the work described in this article.
Availability of data and materials
All data generated or analyzed during this study are included in this publishedarticle and will be available upon request to CCL.
Data availability
No datasets were generated or analysed during the current study.

Page 9 of 10

4.
5.
6.

7.
8.
9.

10.
11.
12.
13.

Declarations

14.

Ethics approval and consent to participate
The Institutional Review Board of the Chi Mei Medical Center approved thestudy protocol (no. 11202–002). Written informed consent was not required
because TriNetX contains anonymized data.

15.

Consent for publication
None.

16.

Competing interests
None.

17.

Author details
1
Division of Nephrology, Department of Internal Medicine, Chi Mei Medical
Center, Tainan, Taiwan. 2 Division of Cardiology, Department of Internal Medi‑cine, Chi Mei Medical Center, Tainan, Taiwan. 3 Center for Integrative Medicine,
Chi Mei Medical Center, Tainan, Taiwan. 4 Department of Medical Laboratory
Sciences and Biotechnology, Fooyin University, Kaohsiung, Taiwan. 5 Depart‑
ment of Nutrition, Chi Mei Medical Center, Tainan, Taiwan. 6 Departmentof Psychiatry, Chi Mei Medical Center, Tainan, Taiwan. 7 Department of Intensive
Care Medicine, Chi Mei Medical Center, Tainan, Taiwan. 8 School of Medicine,College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Received: 27 April 2024 Accepted: 27 August 2024

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22.
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