Differential impact of the pandemic first wave on chronic respiratory diseases in France

doi:10.21203/rs.3.rs-5287937/v1

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Differential impact of the pandemic first wave on chronic respiratory diseases in France

https://doi.org/10.21203/rs.3.rs-5287937/v1

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Background. The COVID-19 pandemic has been associated with dramatic changes in the management of patients with chronic respiratory diseases (CRD), resulting in varying clinical profile and use of healthcare services of patients hospitalised.

Patients and Methods. Using the French national hospital database, we analysed changes in epidemiological profile, clinical presentation at hospital referral and outcomes (ICU admission and in-hospital mortality) of CRD patients hospitalised for COVID-19 during the first wave of the pandemic in France.

Results. Non-interstitial CRD and pulmonary vascular diseases (PVD) were the most prevalent CRDs (95%). The part of CRD patients among the total inpatients was substantial, decreasing during lockdown (from 12.1% to 10.8%), then returning to the pre-lockdown level (12.4%). The CRD patients were older and comorbid males with higher rates of ICU admission and mortality, as compared with their counterparts. Half of them had a complicated clinical profile (CCP, ICU admission or death). Overall, in-hospital length of stay was 9 (4-16) days and mortality rate 19.8%. These characteristics varied along time. In multivariable analyses, PVD was associated with a CCP (aORs, from 1.36; CI 1.18-1.57 to 1.82; CI 1.56-2.12) and mortality (with aHRs >1.5 in the 18-55 and 56-75 age groups).

Conclusion. We report a substantial part of CRD patients hospitalised with COVID-19, characterized by a more severe initial clinical presentation and higher rates of complicated stays, with changes along time suggesting differential exposure and use of resources. The PVD subgroup was independently associated with CCP and mortality during the entire study period.

COVID19

Chronic respiratory diseases

In-hospital mortality

Healthcare services

National hospital database

The COVID-19 pandemic has been associated with dramatic changes in the management of patients with chronic diseases, among which chronic respiratory diseases (CRD) and their exacerbations are one of the leading causes of death and represent a major public health issue worldwide [1]. A low prevalence of CRD, particularly of asthma and chronic obstructive pulmonary disease (COPD), has been initially reported worldwide in inpatients diagnosed with COVID-19 [2]. Potential explanations for this unexpected observation included underdiagnosis or poor recognition of CRD in inpatients with COVID-19, or a potential protective effect of some long-term therapies, such as inhaled corticosteroids [3–6]. However, large studies have suggested that patients with CRD hospitalised for COVID-19 were at higher risk of developing severe COVID-19 and had higher rates of unfavourable outcomes, despite lower hospitalisation rates [7–10]. Therefore, the decrease in hospitalisation rates for acute exacerbations of CRD might have been related to a reduced access to healthcare resources and a shift from hospital care to community care during the pandemic [11, 12].

We hypothesized that the profile of patients with CRD hospitalised for COVID-19 varied during the course of the pandemic and following the implementation of control measures, and that patients with CRD may have had variable exposure and use of healthcare services. Using the French national hospital database, we aimed at describing and analysing the changes in the epidemiological profile, clinical presentation at hospital referral and outcomes (including ICU admission and in-hospital mortality) of CRD patients hospitalised for COVID-19 during the first wave of the pandemic in France.

Database

The SNDS database is a comprehensive anonymised database including the French National Hospital Database (Programme de Médicalisation des Systèmes d’Information, PMSI) chained with all French outpatient health care consumption, the Inter-Scheme consumption database refund (Données de Consommation Inter-Régimes), using a pseudo-anonymised identification number. This database enables to trace a given patient’s healthcare consumption and to identify some comorbidities [13] (Supplemental material (Sm) Appendix 1).

Study design and participants

During the first wave of the pandemic (from March 1st, 2020 to June 30, 2020), hospital admissions of adult patients (≥ 18 years old) for a symptomatic COVID-19 (ICD-10 code – 10th revision of the International Statistical Classification of Diseases and Related Health Problems, as a primary, PD; related, RD; or secondary associated diagnosis, SAD) to an acute-care facility in metropolitan France were identified from the PMSI database (Sm Appendix 2). Database cleaning are provided in Sm Appendix 3.

Three periods were identified: (i) pre-lockdown (from March, 1, to March, 16), during which COVID-19 patients and their contacts were identified and isolated for 14 days (“search and isolate” strategy); (ii) per-lockdown (from March, 17, to May, 10), during which patients were advised to stay at home and received home care as much as possible, while the most symptomatic or severe ones were referred to the hospital; and (iii) post-lockdown (from May, 11, to June, 30), during which the strategy returned to the “search and isolate” strategy, with patients being treated at home as much as possible.

Definitions and studied variables

Consistent with the initial description of different clinical patterns in SARS-CoV-2 patients [14], three clinical profiles were defined, according to the initial severity of COVID-19 and its progression with admission to the intensive care unit (ICU), including admission to any of the de novo dedicated units generated during the pandemic, and/or the need for the administration of life support therapies (Sm Appendix 2), or in-hospital mortality. A non-complicated clinical profile (NCp) corresponded to a patient not having ICU admission and surviving the hospital stay. A rapidly complicated clinical profile (RCp) was defined as ICU admission or death within the first 48 hours of COVID-19 clinical symptoms onset, whereas a slowly complicated clinical profile (SCp) was associated with ICU admission or death occurring more than 48 hours after symptoms onset. The onset of COVID-19 symptoms was estimated from the first event of any of the following within the 7 days preceding the inclusion stay: admission to a day-care center with COVID-19, previous stay for COVID-19, visit to the emergency department or outpatient visit (Sm Appendix 2).

Patients (gender, age, Charlson index [15], comorbidities with a particular attention on CRD) and corresponding hospital stays characteristics (admission source, ICU admission, hospital length of stay (LOS) and vital status at discharge) were recorded. CRD were first classified into six non-exclusive categories using ICD-10 codes (whether PD, RD, SAD) recorded during the inclusion stay and/or the preceding 12 months. However, as neuromuscular diseases and lung transplant categories accounted for few patients, neuromuscular diseases were grouped with non-interstitial CRD, while lung transplantation was grouped with interstitial CRD (Sm Appendix 4). Four categories of CRD were thus individualised: non-interstitial CRD and neuromuscular diseases, interstitial CRD and transplantation, thoracic oncology, and pulmonary vascular diseases (PVD). Of note, two diagnostic codes included within the Charlson index to define its “chronic pulmonary diseases” component were not selected by our CRD definition criteria (Sm Appendix 4).

Statistical analysis

Frequencies (percentages) were reported for discrete variables and median and interquartile range (IQR) for quantitative ones. As the data cover the national population, no confidence intervals were calculated for percentages. Comparisons between periods were conducted using Chi2 tests for discrete variables, and Kruskal-Wallis tests for quantitative variables or Chi2 test of homogeneity for distributions. A Cochran-Armitage test for trend was also used to assess changes across periods for binary variables. Patients and stays characteristics were compared, according to the CRD categories, using Chi2 tests.

Clinical profiles of patients were compared according to the two most prevalent CRD (non-interstitial CRD and PVD). Multinomial logistic regressions were performed to identify factors associated with clinical profiles. Since our definition of clinical profiles includes both ICU admission and death, Cox proportional hazard models were also conducted to analyse separately in-hospital mortality, accounting for ICU admission and its timing (within or after 48 hours of symptoms onset, respectively). Because ICU admission policies might differ according to age, interaction between age (18–55, 56–75, > 75) and ICU admission (none, within 48h, after 48h) was tested. Univariate analyses, including confounders and risk factors were performed. Variables for which the 99% CI of the crude odds ratio or hazard ratio (cOR/cHR) did not contain 1 and with a global P-value < 0.01 were included in the multivariable analyses. Since individual comorbidities were considered, the Charlson index was not included as well as its chronic pulmonary disease component, which was not independent from CRD. The final models were determined via a manual backward-selection procedure. As the data cover the national population, a conservative 2-tailed P-value < 0.01 in the multivariable models defined significance [16, 17].

COVID-19 Hospital admissions

During the four-month study period, 103,397 adults (≥18 yo) hospitalised for a symptomatic COVID-19 were identified, for a total of 137,174 in-hospital stays. Finally, after database cleaning, 91,028 patients and their corresponding stay were analysed (Figure 1).

The median age on admission was 72 (58-84) years, and 41,517 (46%) patients were women. The Charlson index was 1 (0-2). At least one predefined CRD was identified in 10,027 patients (11%). Most patients were admitted directly from home. Clinical profiles were non-complicated in 60% of cases, whereas CCP occurred in 40% of cases, with either rapid (16%) or late (24%) ICU referral or death, according to the symptoms onset. Whereas the proportion of NCp increased along the periods (50.6, 60.1 and 72.3%, respectively for pre-, per- and post-lockdown), the proportion of SCp decreased (34.1, 29.3 and 13.9%), while the proportion of RCp remained stable (15.4, 16.0 and 13.9%) (table 1). Overall, hospital LOS was 9 (4-16) days, and mortality rate was 19.8%. In addition to clinical profiles changes along time mentioned above, there were several differences in patients and stays characteristics (gender, age, comorbid conditions, admission source, ICU resources utilisation, hospital LOS and mortality) (table 1).

CRD Hospital admissions

Hospital admissions of patients with at least one predefined CRD decreased during lockdown (from 12.1% to 10.8%), then returned to its pre-lockdown level (to 12.4%, table 1). Non-interstitial lung diseases and PVD accounted for 95% of CRDs. As compared with their counterparts, the CRD patients were older males with a higher Charlson index (table 2). They had higher rates of ICU admission and hospital mortality, resulting in significantly different clinical profiles. Overall, half of patients with at least one CRD had a CCP (29.3% SCp, and 20.4% RCp) (table 2). Finally, hospital LOS were longer in CRD patients. The characteristics of the CRD patients varied along time (table 3): they were more often transferred from long-term facilities, were older with more comorbidities, with a decreasing male/female ratio, and an increasing part of PVD. The rates of NCp increased along time and the hospital LOS decreased. Of note, the part of RCp tended to increase along time in patients with PVD, in contrast with those having non-interstitial CRD (table 4).

Factors associated with complicated clinical profiles

The characteristics of the overall population of inpatients with COVID-19 and corresponding stays are detailed in table S1, according to their clinical profiles. Briefly, the patients with CCP were more often older men with more comorbidities, including at least one CRD. Non-interstitial CRD and PVD were more often associated with both SCp and RCp.

Among potential confounders or risk factors associated with CCP (table S2), only one comorbidity (connective tissue disease) was excluded from the multivariable analysis (table 5). As compared with NCp, women had a consistently lower risk of complicated disease than men, both for SCp (adjusted OR, aOR, 0.57; CI 0.55-0.60) and RCp (aOR, 0.60; CI 0.57-0.63). Whereas aORs for SCp increased gradually with age-group and reached a maximum of 4.76 (CI 3.90-5.82) for people over 85 years compared to younger patients (18-30 years), the highest aORs for RCp were reached for patients aged 56 to 75 yo, with aORs > 2. Regarding CRD, only the subgroup of PVD was associated with a CCP (vs. no CRD), specifically with SCp (aOR, 1.36; CI 1.18-1.57) and more so with RCp (aOR, 1.82; CI 1.56-2.12). Among other comorbidities, obesity, paraplegia or hemiplegia, cancer and congestive heart failure were associated with the highest aORs for SCp (table 5). Similar results were observed for RCp, except for cancer which became non-significant (P-value=0.02) and dementia which had an aOR significantly lower than 1. Finally, as compared with the pre-lockdown period, both the lockdown and the post-lockdown periods were associated with a lower risk of complicated disease, especially the latter period.

Factors associated with in-hospital mortality

A total of 18,051 (19.8%) patients died during their hospital stay, with decreasing mortality rates along the three periods (27.5, 19.6 and 13.1%, respectively), along with decreasing ICU admission rates (table 1). Of these deceased patients, 6,163 (34.1%) were admitted to the ICU, 52.5% of whom within the first 48 hours (table S3). As compared with deceased patients not admitted to the ICU, those with ICU admission were more often younger men (56-75 yo) with no comorbidity in 36% of cases, according to the Charlson index, despite a higher proportion of CRD (16.9% vs. 11.6%) and of obesity (21.2% vs. 9.1%).

Among potential confounders or risk factors for mortality, six comorbidities, including three CRDs, were excluded from the multivariable analysis (table S4). Because of a significant (P<.0001) interaction between age and ICU admission, the survival analysis was stratified by age group. After adjustment on the period, ICU admission was associated with the highest adjusted HR (aHR) in the 18-55 age-group, especially in the rapid admission group (aHRs, 3.60 and 2.32, respectively for ICU admission within and after 48h) (table 6). However, the strength of the association between ICU admission and its timing with mortality decreased with increasing age-group, and became similar in the group over 75 years. As compared with no CRD, PVD was associated with mortality, with aHRs >1.5 in the 18-55 and 56-75 age groups. Cancer had the highest associations with mortality, especially in the youngest age-group (aHR, 3.82; CI 3.00-4.86). Unexpectedly, aHRs associated with deficiencies and obesity were < 1 for all age-groups and for the over-55-year-old, respectively. Finally, female gender remained associated with a protective effect on mortality, except in the youngest age-group.

During the study period, CRD patients accounted for more than 10% of inpatients, and had a more severe initial clinical presentation and higher rates of complicated stays and mortality, as compared with their counterparts. These observations varied along time, with a decreasing part of CRD during lockdown, and a rate of NCp increasing progressively along time. However, the part of RCp increased in the subgroup of PVD, which remained associated with CCP and in-hospital mortality all along the first wave.

Despite the long-recognised role of respiratory viruses (including coronaviruses) as being one of the main causes of CRD exacerbations, CRDs have been unexpectedly under-represented worldwide during the first wave of the pandemic, averaging less than 5% among total hospitalised patients, especially when considering COPD [10, 18]. However, later reports suggested higher rates [7, 19]. In a nationwide French study of hospitalised COVID-19 patients, Beltramo et al. reported that CRD patients accounted for 16.3% of the total inpatients [7]. In the ISARIC-UK study conducted during the first wave, COPD was the third most prevalent comorbidity (18%) after chronic cardiac disease and uncomplicated diabetes [19]. We used a comprehensive definition of CRD to examine the burden of CRDs among the all inpatients with COVID-19, which may have contributed to the substantial part of CRD that we report, consistent with the latter studies.

Furthermore, more severe clinical presentation at hospital referral and poorer outcomes have been reported in CRD patients [7, 10, 20, 21]. Beltramo et al. reported that although CRD was significantly less common among patients with COVID-19 than in those with influenza pneumonia, patients with CRD were at higher risk of death than those without CRD or with influenza [7]. We confirm and expand those results by showing that CRD patients had more severe initial clinical presentation (age and associated comorbid conditions) and more CCPs (higher rates of ICU admission and mortality) over the entire first wave, as compared with their counterparts.

Control measures successively implemented during the pandemic, including the expansion of telemedicine and reduction of face-to-face visits, have changed the interactions between physicians and patients and available healthcare resources utilization [22]. These changes in practice may have in turn influenced the clinical profiles and hospital outcomes of CRD patients, depending on the underlying CRD and its baseline severity. Several studies looking at hospitalisation rates of patients with defined CRD have shown a marked reduction up to 50% in hospitalisation of patients with acute exacerbation of COPD [8,11,23], likely attributable in large part to the decline in the community viral burden during control measures of the pandemic. In our study, the proportion of CRD modestly decreased during lockdown, with a modification of the distribution of the admission sources, as shown by a higher part of long-term care and a lesser part of acute care and home admission sources. During post-lockdown, the CRD patients were older and more comorbid, but had less CCP than during the pre-lockdown period. However, this observation differed for the PVD subgroup, which had increasing rates of RCp. Altogether, these results suggest a greater intrinsic severity of CRD inpatients along time during the pandemic, and possibly limited ICU access for some patients during lockdown.

During the first wave in England, Aveyard et al. found that, as compared with people without CRD, those with CRD were at increased risks of hospital admission and death, especially those with COPD, interstitial lung disease and lung cancer [9]. In our study, there were few patients in the category of thoracic oncology, as reported by others [9, 20]. This might have been related to either under-recognition or under-coding of newly diagnosed lung cancer during the pandemic [24][25], while patients with a known lung cancer (either with active disease or under surveillance) were managed with reduced visits to hospitals, to minimize infection risk [26, 27].

The changes in clinical profiles of patients with PVD according to the three pandemic periods, and its consistent association with ICU admission and death are of particular interest given the pathophysiological singularity of SARS-CoV2 on the pulmonary vasculature [28]. This observation may illustrate the fact that the pandemic disrupted delivery of healthcare for some pathological conditions that require skilled resources for both diagnosis and management and are at high risk for decompensation [29]. An American College of Chest Physicians survey reported that the pandemic had caused significant disruption of care of patients with pulmonary hypertension, with decreased referrals for evaluation and increased use of telemedicine, mostly for selected low-risk patients [12].

Other patients’ characteristics than our predefined categories of CRDs were independently associated with hospital mortality in multivariable analyses. In addition to increasing age, and comorbid conditions such as cancer, chronic cardiovascular and renal diseases, as reported by others [20], female gender and obesity were associated with a lower risk of death. However, apart from cancer and ICU admission, other factors including gender were significantly associated with death only in the age groups beyond 55 yo, suggesting a major part of the intrinsic pathogenicity of the acute viral disease in the younger age group. That obesity was associated with a high risk of a CCP, but protective of mortality, points to the higher risk of hospitalisation and severe disease in obese patients, but suggests that the “obesity paradox” [30, 31] might be valid in patients with severe COVID-19.

Limitations

First, as our study focuses on the small fraction of patients hospitalised and severe forms of COVID-19, it does not provide information on estimates and effects of risk factors for hospitalisation for each category of CRD. Second, database research enables large population sets, but limits the possibility to get more granular analyses.

To summarize, during the first wave of the pandemic, patients with CRD accounted for more than 10% of total inpatients with COVID-19, non-interstitial CRD and PVD being the most prevalent ones. As compared with the non-CRD patients, they had more severe initial clinical presentation and higher rates of complicated stays and mortality, with changes along time suggesting differential exposure and use of healthcare services and resources. The subgroup of PVD was independently associated with CCP and mortality during the entire study period.

CRD

chronic respiratory disease

Ethics approval and consent to participate

The study analysis and data extraction were approved by the French Data Protection Agency (Commission Nationale de l'Informatique et des Libertés, CNIL, DR-2020-378). Informed consent is waived for the use of these anonymised secondary data, as mentioned in the Social Security Code, Article L161–28-1. All methods were performed in accordance with CNIL regulations and with STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines.

Consent for publication

Not applicable

Competing Interest

Muriel Fartoukh reports grants from La Fondation du Souffle for the submitted work; grants from La Fondation AP-HP, and from French Ministry of Health outside the submitted work; and personal fees from Pfizer, Fisher & Paykel, and BioMérieux outside the submitted work.

Laurence Watier reports personal fees from Pfizer, Sanofi and Heva outside of the submitted work.

Lucie Brolon, Marie Al Rahmoun, Alexandre Elabbadi, Didier Guillemot, and Christian Brun-Buissonhave no conflict of interest.

Funding

This work was supported by La Fondation du Souffle, l’Institut de Santé Publique de l’Inserm and by award F07REC02N - 20NAAP03 from the University of Versailles-Saint-Quentin.

Authors Contribution

MF, CBB and LW contributed to the conception and design of the work. LW, LB, AE and MAR were responsible for the acquisition and analysis of data. MF, CBB, LW, LB, AE and MAR were responsible for interpretation for the work. MF, CBB and LW drafted the article and all revised it critically for important intellectual content. All authors approved the final version of the manuscript.

Acknowledgments

We are grateful to the DATAD department of the French National Health Insurance (CNAM) for providing the data.

Availability of data and materials

The data that support the findings of this study are available from the French administrative health care database (SNDS) but restrictions apply to the availability of these data, which were used under the French Data Protection Agency approval, and so are not publicly available.

Soriano JB, Kendrick PJ, Paulson KR, et al. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020;8:585–96.
Halpin DMG, Faner R, Sibila O, et al. Do chronic respiratory diseases or their treatment affect the risk of SARS-CoV-2 infection? Lancet Respir Med. 2020;8:436–8.
Yamaya M, Nishimura H, Deng X, et al. Inhibitory effects of glycopyrronium, formoterol, and budesonide on coronavirus HCoV-229E replication and cytokine production by primary cultures of human nasal and tracheal epithelial cells. Respir Investig. 2020;58:155–68.
Jackson DJ, Busse WW, Bacharier LB, et al. Association of respiratory allergy, asthma, and expression of the SARS-CoV-2 receptor ACE2. J Allergy Clin Immunol. 2020;146:203–e2063.
Halpin DMG, Singh D, Hadfield RM. Inhaled corticosteroids and COVID-19: a systematic review and clinical perspective. Eur Respir J. 2020;55:2001009.
Beurnier A, Jutant E-M, Jevnikar M, et al. Characteristics and outcomes of asthmatic patients with COVID-19 pneumonia who require hospitalisation. Eur Respir J. 2020;56:2001875.
Beltramo G, Cottenet J, Mariet A-S, et al. Chronic respiratory diseases are predictors of severe outcome in COVID-19 hospitalised patients: a nationwide study. Eur Respir J. 2021;58:2004474.
Poucineau J, Delory T, Lapidus N, et al. Hospital admissions and mortality for acute exacerbations of COPD during the COVID-19 pandemic: A nationwide study in France. Front Med. 2022;9:995016.
Aveyard P, Gao M, Lindson N, et al. Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study. Lancet Respir Med. 2021;9:909–23.
Alqahtani JS, Oyelade T, Aldhahir AM, et al. Prevalence, Severity and Mortality associated with COPD and Smoking in patients with COVID-19: A Rapid Systematic Review and Meta-Analysis. PLoS ONE. 2020;15:e0233147.
Alsallakh MA, Sivakumaran S, Kennedy S, et al. Impact of COVID-19 lockdown on the incidence and mortality of acute exacerbations of chronic obstructive pulmonary disease: national interrupted time series analyses for Scotland and Wales. BMC Med. 2021;19:124.
Zhou CY, Sahay S, Shlobin O, et al. Effects of COVID-19 pandemic on the management of pulmonary hypertension. Respir Med. 2023;206:107061.
Tuppin P, Rudant J, Constantinou P, et al. Value of a national administrative database to guide public decisions: From the système national d’information interrégimes de l’Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S149–67.
Lescure F-X, Bouadma L, Nguyen D, et al. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis. 2020;20:697–706.
Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173:676–82.
Ioannidis JPA. What Have We (Not) Learnt from Millions of Scientific Papers with P Values? Am. Stat. Taylor & Francis; 2019; 73: 20–25.
Lin M, Lucas HC, Shmueli G. Research Commentary—Too Big to Fail: Large Samples and the p-Value Problem. Inf. Syst. Res. INFORMS; 2013; 24: 906–917.
Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62.
Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020;369:m1985.
Semenzato L, Botton J, Drouin J, et al. Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people. Lancet Reg Health Eur. 2021;8:100158.
Fedeli U, Barbiellini Amidei C, Marcon A, et al. Mortality Related to Chronic Obstructive Pulmonary Disease during the COVID-19 Pandemic: An Analysis of Multiple Causes of Death through Different Epidemic Waves in Veneto, Italy. Int J Environ Res Public Health. 2022;19:12844.
Halpin DMG, Criner GJ, Papi A, et al. Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee Report on COVID-19 and Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2021;203:24–36.
So JY, O’Hara NN, Kenaa B, et al. Population Decline in COPD Admissions During the COVID-19 Pandemic Associated with Lower Burden of Community Respiratory Viral Infections. Am J Med. 2021;134:1252–e12593.
Patt D, Gordan L, Diaz M et al. Impact of COVID-19 on Cancer Care: How the Pandemic Is Delaying Cancer Diagnosis and Treatment for American Seniors. JCO Clin Cancer Inf 2020;: 1059–71.
Kuderer NM, Choueiri TK, Shah DP, et al. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. Lancet. 2020;395:1907–18.
Spicer J, Chamberlain C, Papa S. Provision of cancer care during the COVID-19 pandemic. Nat Rev Clin Oncol. 2020;17:329–31.
Wang Q, Berger NA, Xu R. Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection. JAMA Oncol. 2021;7:220–7.
Ackermann M, Verleden SE, Kuehnel M, et al. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N Engl J Med. 2020;383:120–8.
Montani D, Certain M-C, Weatherald J, et al. COVID-19 in Patients with Pulmonary Hypertension: A National Prospective Cohort Study. Am J Respir Crit Care Med. 2022;206:573–83.
Zhang W, Wang Y, Li W, Wang J. Association Between Obesity and Short-And Long-Term Mortality in Patients With Acute Respiratory Distress Syndrome Based on the Berlin Definition. Front Endocrinol. 2020;11:611435.
Subramaniam A, Ling RR, Ridley EJ, Pilcher DV. The impact of body mass index on long-term survival after ICU admission due to COVID-19: A retrospective multicentre study. Crit Care Resusc. 2023;25:182–92.

Table 1. Characteristics of patients, corresponding stays and clinical profiles, overall and according to the pandemic periods

		Overall N=91,028				Pre-lockdown N=7,602				Per-lockdown N=76,972				Post-lockdown N=6,454
		n		%		n		%		n		%		n		%		P-value¹		P-value for trend²
*Patient’s characteristics*
Gender																		<.0001		<.0001
Male		49,511		54.4		4,238		55.8		42,023		54.6		3,250		50.4
Female		41,517		45.6		3,364		44.3		34,949		45.4		3,204		49.6
Age (years)																		<.0001		-
18-30		2,496		2.7		222		2.9		2,009		2.6		265		4.1
31-45		7,384		8.1		585		7.7		6,311		8.2		488		7.6
46-55		10,211		11.2		804		10.6		8,831		11.5		576		8.9
56-65		14,677		16.1		1,209		15.9		1,671		16.5		797		12.4
66-75		18,116		19.9		1,633		21.5		15,336		19.9		1,147		17.8
76-85		19,594		21.5		1,767		23.2		16,396		21.3		1,431		22.2
>85		18,550		20.4		1,382		18.2		15,418		20.0		1,750		27.1
Median (Q1-Q3)		72 (58-84)				72 (58-83)				71 (57-83)				75 (60-86)				<.0001		-
Charlson index																		<.0001
0		43,029		47.3		3,413		44.9		37,242		48.4		2,374		36.8
1-2		27,074		29.7		2,283		30.0		22,715		29.5		2,076		32.2
3-4		12,642		13.9		1,103		14.5		10,370		13.5		1,169		18.1
>5		8,283		9.1		803		10.6		6,645		8.6		835		12.9
Median (Q1-Q3)		1 (0-2)				1 (0-3)				1 (0-2)				2 (0-3)				<.0001		-
Comorbidities																				-
Cancer		9,555		10.5		956		12.6		7,676		10.0		923		14.3		<.0001		0.02
Congestive heart failure		17,077		18.8		1,547		20.4		13,871		18.0		1,659		25.7		<.0001		<.0001
Renal disease		11,938		13.1		1,146		15.1		9,838		12.8		954		14.8		<.0001		0.30
Chronic pulmonary disease		12,809		14.1		1,185		15.6		10,591		13.8		1,033		16.0		<.0001		0.82
Metastatic carcinoma		3,647		4.0		387		5.1		2,886		3.8		374		5.8		<.0001		0.16
Diabetes with chronic complications		5,841		6.4		560		7.4		4,815		6.3		466		7.2		<.0001		0.48
Paraplegia or hemiplegia		4,482		4.9		377		5.0		3,744		4.9		361		5.6		0.03		0.11
Dementia		10,246		11.3		671		8.8		8,614		11.2		961		14.9		<.0001		<.0001
Mild liver disease		2,722		3.0		191		2.5		2,254		2.9		277		4.3		<.0001		<.0001
Moderate or severe liver disease		905		1.0		87		1.1		720		0.9		98		1.5		<.0001		0.06
Connective Tissue Disease		1,461		1.6		145		1.9		1,182		1.5		134		2.1		<.01		0.62
AIDS		453		0.5		40		0.5		381		0.5		32		0.5		0.93		0.78
Obesity		14,375		15.8		1,142		15.0		12,376		16.1		857		13.3		<.0001		0.02
Deficiencies³		29,459		32.4		2,454		32.3		24,279		31.5		2,726		42.2		<.0001		<.0001
Chronic respiratory disease																				-
Non-interstitial CRD⁴		8,120		8.9		753		9.9		6,765		8.8		602		9.3		<.01		0.15
Interstitial CRD⁵		253		0.3		29		0.4		202		0.3		22		0.3		0.10		0.54
Thoracic oncology		273		0.3		34		0.5		219		0.3		20		0.3		0.05		0.10
Pulmonary vascular diseases		1,974		2.2		159		2.1		1,594		2.1		221		3.4		<.0001		<.0001
Patients with at least one CRD		10,027		11.0		921		12.1		8,306		10.8		800		12.4		<.0001		0.88
*Stay’s characteristics*
Admission source																		<.0001		-
Acute care		4,727		5.2		521		6.9		3,798		4.9		408		6.3
Long-term care		3,930		4.3		125		1.6		3,541		4.6		264		4.1
Home		82,371		90.5		6,956		91.5		69,633		90.5		5,782		89.6
Length of stay (days)																		<.0001		-
<7		34,296		37.7		2,315		30.5		29,158		37.9		2,823		43.7
7-14		31,485		34.6		2,349		30.9		26,910		35.0		2,226		34.5
15-30		17,965		19.7		1,875		24.7		14,969		19.5		1,121		17.4
>30		7,282		8.0		1,063		14.0		5,935		7.7		284		4.4
Median (Q1-Q3)		9 (4-16)				11 (5-21)				9 (4-15)				7 (4-13)				<.0001
ICU admission⁶																		<.0001		<.0001
Yes		24,408		26.8		2,536		33.4		20,675		26.9		1,197		18.6
No		66,620		73.2		5,066		66.6		56,297		73.1		5,257		81.5
Hospital discharge																		<.0001		-
Acute care⁷		4,940		5.4		400		5.3		3,888		5.1		652		10.1
Long-term care⁸		14,997		16.5		1,118		14.7		12,850		16.7		1,029		15.9
Home		53,040		58.3		3,993		52.5		45,120		58.6		3,927		60.9
Death		18,051		19.8		2,091		27.5		15,114		19.6		846		13.1
*Clinical Profile*																	<.0001		-
Non-complicated (NCp)	54,732		60.1		3,845		50.6		46,222		60.1		4,665		72.3
Slowly complicated (SCp)	21,880		24.0		2,589		34.1		18,397		23.9		894		13.9
Rapidly complicated (RCp)	14,416		15.8		1,168		15.4		12,353		16.1		895		13.9

1: Comparison between periods, P-value of corresponding test (Chi2, Chi2 for homogeneity or Kruskal-Wallis);

2: Trend between periods (Cochrane Armitage test);

3: Includes chronic anaemia, vitamin D deficiency and undernutrition;

4: Non-interstitial CRD and neuromuscular diseases;

5: Interstitial CRD and transplantation;

6: Admission to the intensive care unit (ICU) includes admission to any of the de novo dedicated units generated during the pandemic, and/or the need for the administration of life support therapies;

7: Transfer to an MSO or psychiatric unit;

8: Transfer to a rehabilitation unit, long-term care unit, or hospitalisation at home.

Table 2. Characteristics of patients, corresponding stays and clinical profiles, according to the chronic respiratory disease status

	Patients with CRD N=10,027 (11%)		Patients without CRD N=81,001 (89%)
	n	%	n	%	P-value¹
*Patient’s characteristics*
Gender					<.0001
Male	6,169	61.5	43,342	53.5
Female	3,858	38.5	37,659	46.5
Age (years)					<.0001
18-30	89	0.9	2,407	3.0
31-45	366	3.7	7,018	8.7
46-55	803	8.0	9,408	11.6
56-65	1,725	17.2	12,952	16.0
66-75	2,633	26.3	15,483	19.1
76-85	2,657	26.5	16,937	20.9
>85	1,754	17.5	16,796	20.7
Median (Q1-Q3)	73 (63-83)		71 (57-84)		<.0001
Charlson index²					<.0001
0	2,137	21.3	45,373	56.0
1-2	3,063	35.2	20,910	25.8
3-4	2,104	27.1	8,638	10.7
>5	1.250	16.4	6,080	7.5
Median (Q1-Q3)	2 (0-3)		0 (0-2)		<.0001
Comorbidities
Cancer	1,390	13.9	8,165	10.1	<.0001
Congestive heart failure	3,751	37.4	13,326	16.5	<.0001
Renal disease	2,362	23.6	9,576	11.8	<.0001
Chronic pulmonary disease²	4,082	40.7	8,727	10.8	<.0001
Metastatic carcinoma	455	4.5	3,192	3.9	<.01
Diabetes with chronic complications	1,367	13.6	4,474	5.5	<.0001
Paraplegia or hemiplegia	655	6.5	3,827	4.7	<.0001
Dementia	909	9.1	9,337	11.5	<.0001
Mild liver disease	460	4.6	2,262	2.8	<.0001
Moderate or severe liver disease	129	1.3	776	1.0	<.01
Connective Tissue Disease	240	2.4	1,221	1.5	<.0001
AIDS	55	0.6	398	0.5	0.44
Obesity	3,427	34.2	10,948	13.5	<.0001
Deficiencies³	4,351	43.4	25,108	31.0	<.0001
*Stay’s characteristics*
Admission source					<.0001
Acute care	641	6.4	4,086	5.0
Long-term care	515	5.1	3,415	4.2
Home	8,871	88.5	73,500	90.7
Length of stay (days)					<.0001
<7	3,009	30.0	31,287	38.6
7-14	3,466	34.6	28,019	34.6
15-30	2,318	23.1	15,647	19.3
>30	1,234	12.3	6,048	7.5
Median (Q1-Q3)	11 (6-19)		8 (4-15)		<.0001
ICU admission⁴					<.0001
Yes	3,469	34.6	20,154	24.9
No	6,558	65.4	60,847	75.1
Hospital discharge					<.0001
Acute care⁵	638	6.4	4,302	5.3
Long-term care⁶	1,972	19.7	13,025	16.1
Home	4,994	49.8	48,046	59.3
Death	2,423	24.2	15,628	19.3
*Clinical Profile*					<.0001
Non-complicated (NCp)	5,048	50.3	49,684	61.3
Slowly complicated (SCp)	2,935	29.3	18,945	23.4
Rapidly complicated (RCp)	2,044	20.4	12,372	15.3

1: Comparison between groups, P-value of corresponding test (Chi2, Chi2 for homogeneity or Kruskal-Wallis);

2: Two ICD-10 diagnostic codes included within the Charlson index to define its “chronic pulmonary diseases” component were not selected by our CRD definition criteria: J40-Bronchitis, not specified as acute or chronic and I278-Other specified pulmonary heart diseases. This results in some CRD-free patients categorized as having chronic pulmonary disease according to the Charlson index; conversely, some of our patients with CRD had no chronic pulmonary disease according to the Charlson index, and may thus have a Charlson index of 0;

3: Includes chronic anaemia, vitamin D deficiency and undernutrition;

4: Admission to the intensive care unit (ICU) includes admission to any of the de novo dedicated units generated during the pandemic, and/or the need for the administration of life support therapies;

5: Transfer to an acute-care facility (MSO: medicine surgery and obstetrics) or a psychiatric unit;

6: Transfer to a follow-up care or rehabilitation unit, long-term care unit, or hospitalisation at home.

Table 3. Characteristics of CRD patients, corresponding stays and clinical profiles, according to the pandemic periods

	Pre-lockdown N=921 (12.1%)		Per-lockdown N=8306 (10.8%)		Post-lockdown N=800 (12.4%)
	n	%	n	%	n	%		P-value¹		P-value for trend²
*Patient’s characteristics*
Gender								0.07		0.03
Male	583	63.3	5,121	61.7	465	58.1
Female	338	36.7	3,185	38.4	335	41.9
Age (years)								<.01		-
18-30	1	0.1	79	1.0	9	1.1
31-45	29	3.2	318	3.8	19	2.4
46-55	77	8.4	672	8.1	54	6.8
56-65	163	17.7	1,444	17.4	118	14.8
66-75	260	28.2	2,180	26.3	193	24.1
76-85	255	27.7	2,172	26.2	230	28.8
>85	136	14.8	1,441	17.4	177	22.1
Median (Q1-Q3)	73 (64-81)		73 (63-83)		76 (65-85)			<.0001		-
Charlson index								<.0001
0	170	18.5	1,867	22.5	100	12.5
1-2	326	35.4	2,952	33.5	250	31.3
3-4	268	29.1	2,192	26.4	258	32.3
>5	157	17.1	1,295	15.6	192	24.0
Median (Q1-Q3)	2 (1-4)		2 (1-4)		3 (1-4)			<.0001		-
Comorbidities										-
Cancer	151	16.4	1,083	13.0	156	19.5		<.0001		0.13
Congestive heart failure	354	38.4	3,006	36.2	391	48.9		<.0001		<.0001
Renal disease	235	25.5	1,912	23.0	215	26.9		0.02		0.63
Chronic pulmonary disease	399	43.3	3,313	39.9	370	46.3		<.01		0.32
Metastatic carcinoma	42	4.6	356	4.3	57	7.1		<.01		0.02
Diabetes with chronic complications	144	15.6	1,096	13.2	127	15.9		0.02		0.97
Paraplegia or hemiplegia	65	7.1	527	6.3	63	7.9		0.20		0.56
Dementia	68	7.4	754	9.1	87	10.9		0.04		0.01
Mild liver disease	25	2.7	384	4.6	51	6.4		<.01		<.01
Moderate or severe liver disease	18	2.0	95	1.1	16	2.0		0.02		0.92
Connective Tissue Disease	33	3.6	182	2.2	25	3.1		0.01		0.42
AIDS	6	0.7	44	0.5	5	0.6		0.85		0.91
Obesity	315	34.2	2,860	34.4	252	31.5		0.25		0.27
Deficiencies³	413	44.8	3,513	42.3	425	53.1		<.0001		<.01
Chronic respiratory disease										-
Non-interstitial CRD⁴	753	81.8	6,765	81.5	602	75.3		<.0001		<.01
Interstitial CRD⁵	29	3.2	202	2.4	22	2.8		0.38		0.54
Thoracic oncology	34	3.7	219	2.6	20	2.5		0.16		0.11
Pulmonary vascular diseases	159	17.3	1,594	19.2	221	27.6		<.0001		<.0001
*Stay’s characteristics*
Admission source								<.0001		-
Acute care	73	7.9	502	6.0	66	8.3
Long-term care	20	2.2	457	5.5	38	4.8
Home	828	89.9	7,347	88.5	696	87.0
Length of stay (days)								<.0001		-
<7	183	19.9	2,531	30.5	295	36.9
7-14	297	32.3	2,886	34.8	283	35.4
15-30	262	28.5	1,886	22.7	170	21.3
>30	179	19.4	1003	12.1	52	6.5
Median (Q1-Q3)	14 (8-26)		11 (5-19)		9 (4-16)			<.0001
ICU admission⁶								<.0001		<.0001
Yes	407	44.2	2,958	35.6	230	28.8
No	514	55.8	5,348	64.4	570	71.2
Hospital discharge								<.0001		-
Acute care⁷	57	6.2	499	6.0	82	10.3
Long-term care⁸	170	18.5	1,672	20.1	130	16.3
Home	388	42.1	4,146	49.9	460	57.5
Death	306	33.2	1,989	24.0	128	16.0
*Clinical Profile*							<.0001		-
Non-complicated (NCp)	359	39.0	4,190	50.5	499	62.4
Slowly complicated (SCp)	368	40.0	2,425	29.2	142	17.8
Rapidly complicated (RCp)	194	21.1	1,691	20.4	159	19.9

1: Comparison between periods, P-value of corresponding test (Chi2, Chi2 for homogeneity or Kruskal-Wallis);

2: Trend between periods, (Cochrane Armitage test);

3: Includes chronic anaemia, vitamin D deficiency and undernutrition;

4: Non-interstitial CRD and neuromuscular diseases;

5: Interstitial CRD and transplantation;

6: Admission to the intensive care unit (ICU) includes admission to any of the de novo dedicated units generated during the pandemic, and/or the need for the administration of life support therapies;

7: Transfer to an acute-care facility (MSO: medicine surgery and obstetrics) or a psychiatric unit;

8: Transfer to a follow-up care or readaptation unit, long-term care unit, or hospital-at-home care;

Abbreviation: CRD: chronic respiratory disease.

Table 4. Clinical profile of patients hospitalised for COVID-19 with non-interstitial CRD and PVD, overall and according to the pandemic periods

	Overall		Pre-lockdown		Per-lockdown		Post-lockdown
	n	%	n	%	n	%	n	%	P-value¹
*Non-interstitial CRD*
Clinical profile									<.0001
Non-complicated	4,204	51.8	297	39.4	3,513	51.9	394	65.5
Slowly complicated	2,332	28.7	293	38.9	1,938	28.7	101	16.8
Rapidly complicated	1,584	19.5	163	21.7	1,314	19.4	107	17.8
TOTAL	8,120	100.0	753	9.3	6,765	83.3	602	7.4
*Pulmonary vascular diseases*
Clinical profile									<.01
Non-complicated	852	43.2	59	37.1	682	42.8	111	50.2
Slowly complicated	610	30.9	65	40.9	496	31.1	49	22.2
Rapidly complicated	512	25.9	35	22.0	416	26.1	61	27.6
TOTAL	1,974	100.0	159	8.1	1,594	80.7	221	11.2

1: Comparison between periods, P-value of Chi2 test for homogeneity;

2: Trend between periods, Cochrane Armitage test.

Abbreviation: CRD: chronic respiratory disease; PVD: pulmonary vascular diseases.

Table 5. Factors associated with complicated clinical profiles: Multivariate multinomial logistic regression (reference class: non-complicated)

	Slowly complicated N=21,880			Rapidly complicated N=14,416
	n	aOR (99% CI)	P-value	n	aOR (99% CI)	P-value
Age (ref: 18-30 years)
31-45	988	1.66 (1.34-2.06)	<.0001	969	1.25 (1.03-1.52)	0.003
46-55	1,906	2.41 (1.97-2.96)	<.0001	1,668	1.64 (1.37-1.98)	<.0001
56-65	3,262	3.06 (2.51-3.74)	<.0001	2,861	2.12 (1.78-2.54)	<.0001
66-75	4,671	3.65 (2.99-4.45)	<.0001	3,558	2.24 (1.87-2.68)	<.0001
76-85	5,385	3.90 (3.20-4.76)	<.0001	2,872	1.71 (1.43-2.05)	<.0001
>85	5,469	4.76 (3.90-5.82)	<.0001	2,225	1.57 (1.30-1.89)	<.0001
Gender (ref: men)
women	8,351	0.57 (0.55-0.60)	<.0001	5,287	0.60 (0.57-0.63)	<.0001
Comorbidities (ref for each: no)
Cancer	3,220	1.53 (1.43-1.63)	<.0001	1,508	1.08 (0.99-1.17)	0.02
Congestive heart failure	5,245	1.29 (1.22-1.37)	<.0001	3,350	1.56 (1.46-1.67)	<.0001
Renal disease	3,683	1.16 (1.09-1.24)	<.0001	2,090	1.14 (1.06-1.23)	<.0001
Paraplegia or hemiplegia	1,406	1.56 (1.42-1.72)	<.0001	987	1.73 (1.56-1.92)	<.0001
Dementia	2,860	0.99 (0.93-1.06)	0.71	1,054	0.70 (0.64-0.77)	<.0001
Liver disease	848	1.19 (1.06-1.34)	<.0001	604	1.17 (1.03-1.33)	0.002
Obesity	4,107	1.69 (1.59-1.79)	<.0001	2,982	1.60 (1.50-1.71)	<.0001
Deficiencies¹	8,234	1.04 (1.00-1.10)	0.02	4,408	0.90 (0.85-0.96)	<.0001
Chronic respiratory disease (ref for each: none)
Pulmonary Vascular Diseases	610	1.36 (1.18-1.57)	<.0001	512	1.82 (1.56-2.12)	<.0001
Periods (ref: pre-lockdown)
Per-lockdown period	18,397	0.59 (0.55-0.63)	<.0001	12,353	0.89 (0.81-0.97)	0.0006
Post-lockdown period	894	0.26 (0.23-0.29)	<.0001	895	0.63 (0.56-0.72)	<.0001

aOR: adjusted Odds Ratio;

1: Includes chronic anaemia, vitamin D deficiency and undernutrition;

2: Non-interstitial CRD and Neuromuscular diseases;

3: Interstitial CRD and transplantation.

Abbreviation: CRD: chronic respiratory disease.

Table 6. Risk factors associated with in-hospital mortality: Multivariate survival analysis (Cox proportional hazard model stratified by age-group)

	Age-group
	18-55 years old N=20,091			56-75 years old N=32,793			>75 years old N=38,144
	Number of deceased	aHR (99% CI)	P-value	Number of deceased	aHR (99% CI)	P-value	Number of deceased	aHR (99% CI)	P-value
Gender (ref: men)
women	253	0.92 (0.75-1.12)	0.26	1,488	0.84 (0.78-0.91)	<.0001	5,531	0.76 (0.73-0.80)	<.0001
Comorbidities (ref for each: no)
Cancer	176	3.82 (3.00-4.86)	<.0001	1,312	1.98 (1.82-2.16)	<.0001	1,734	1.20 (1.12-1.28)	<.0001
Congestive heart failure	75	1.10 (0.79-1.54)	0.46	1,142	1.20 (1.10-1.32)	<.0001	4,048	1.08 (1.03-1.14)	0.0001
Renal disease	75	1.29 (0.92-1.80)	0.05	860	1.20 (1.08-1.34)	<.0001	2,764	1.13 (1.06-1.20)	<.0001
Diabetes*	49	1.31 (0.87-1.95)	0.09	627	1.27 (1.12-1.43)	<.0001	967	1.03 (0.94-1.12)	0.464
Dementia	8	1.87 (0.74-4.69)	0.08	288	1.70 (1.45-2.00)	<.0001	2,999	1.11 (1.05-1.17)	<.0001
Obesity	240	1.10 (0.89-1.36)	0.24	1,086	0.86 (0.78-0.94)	<.0001	1,065	0.87 (0.80-0.94)	<.0001
Deficiencies	228	0.78 (0.63-0.97)	0.003	1,741	0.67 (0.62-0.73)	<.0001	4,682	0.57 (0.55-0.60)	<.0001
Chronic respiratory disease (ref: no)
Pulmonary Vascular Diseases	21	1.76 (0.98-3.15)	0.013	219	1.54 (1.28-1.85)	<.0001	410	1.00 (0.88-1.15)	0.951
ICU admission (ref: No ICU admission)
Within 48h	287	3.60 (2.82-4.61)	<.0001	1,559	1.95 (1.78-2.13)	<.0001	1,392	1.24 (1.16-1.34)	<.0001
After 48h	264	2.32 (1.80-2.99)	<.0001	1,442	1.39 (1.26-1.52)	<.0001	1,219	1.10 (1.01-1.19)	0.0024
Period (ref: pre-lockdown)
Per-lockdown	632	0.76 (0.57-1.01)	0.013	4,285	0.85 (0.76-0.95)	0.0003	10,197	0.80 (0.75-0.86)	<.0001
Post-lockdown	34	0.72 (0.43-1.21)	0.10	222	0.76 (0.62-0.93)	0.0005	590	0.50 (0.44-0.57)	<.0001

aHR: adjusted Hazard Ratio; * with chronic complications;

Abbreviation: ICU: intensive care unit.

No competing interests reported.

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Differential impact of the pandemic first wave on chronic respiratory diseases in France

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Abstract

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Introduction

Patients and Methods

Database

Study design and participants

Definitions and studied variables

Statistical analysis

Results

COVID-19 Hospital admissions

CRD Hospital admissions

Factors associated with complicated clinical profiles

Factors associated with in-hospital mortality

Discussion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

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