Burden of disease in patients hospitalised with COVID-19 during the first and second pandemic wave in Switzerland: a nationwide cohort study

DOI: https://doi.org/https://doi.org/10.57187/smw.2023.40068

Claudia Gregorianoa, Kris Rafaiszab, Philipp Schuetzab, Beat Muellerab, Christoph A. Fuxac, Anna Conenac, Alexander Kutzad

a Medical University Department, Division of General Internal and Emergency Medicine, Cantonal Hospital Aarau, Aarau, Switzerland

b Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland

cDepartment of Infectious Diseases and Infection Prevention, Cantonal Hospital Aarau, Aarau, Switzerland

d Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

* Equally contributing first authors

Summary

AIM OF THE STUDY: The first and second waves of the COVID-19 pandemic led to a tremendous burden of disease and influenced several policy directives, prevention and treatment strategies as well as lifestyle and social behaviours. We aimed to describe trends of hospitalisations with COVID-19 and hospital-associated outcomes in these patients during the first two pandemic waves in Switzerland.

METHODS: In this nationwide retrospective cohort study, we used in-hospital claims data of patients hospitalised with COVID-19 in Switzerland between January 1st and December 31st, 2020. First, stratified by wave (first wave: January to May, second wave: June to December), we estimated incidence rates (IR) and rate differences (RD) per 10,000 person-years of COVID-19-related hospitalisations across different age groups (0–9, 10–19, 20–49, 50–69, and ≥70 years). IR was calculated by counting the number of COVID-19 hospitalisations for each patient age stratum paired with the number of persons living in Switzerland during the specific wave period. Second, adjusted odds ratios (aOR) of outcomes among COVID-19 hospitalisations were calculated to assess the association between COVID-19 wave and outcomes, adjusted for potential confounders.

RESULTS: Of 36,517 hospitalisations with COVID-19, 8,862 (24.3%) were identified during the first and 27,655 (75.7%) during the second wave. IR for hospitalisations with COVID-19 was highest during the second wave and among patients above 50 years (50–69 years: first wave: 31.49 per 10,000 person-years; second wave: 62.81 per 10,000 person-years; RD 31.32 [95% confidence interval [CI]: 29.56 to 33.08] per 10,000 person-years; IRR 1.99 [95% CI: 1.91 to 2.08]; ≥70 years: first wave: 88.59 per 10,000 person-years; second wave: 228.41 per 10,000 person-years; RD 139.83 [95% CI: 135.42 to 144.23] per 10,000 person-years; IRR 2.58 [95% CI: 2.49 to 2.67]). While there was no difference in hospital readmission, when compared with the first wave, patients hospitalised during the second wave had a lower probability of death (aOR 0.88 [95% CI: 0.81 to 0.95], ARDS (aOR 0.56 [95% CI: 0.51 to 0.61]), ICU admission (aOR 0.66 [95% CI: 0.61 to 0.70]), and need for ECMO (aOR 0.60 [95% CI: 0.38 to 0.92]). LOS was –16.1 % (95% CI: –17.8 to –14.2) shorter during the second wave.

CONCLUSION: In this nationwide cohort study, rates of hospitalisations with COVID-19 were highest among adults older than 50 years and during the second wave. Except for hospital readmission, the likelihood of adverse outcomes was lower during the second pandemic wave, which may be explained by advances in the understanding of the disease and improved treatment options.

Introduction

In 2020, Switzerland faced two waves of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic in spring and winter, respectively. At the beginning of the first wave, many European countries reached their capacity limits of acute and/or intensive care beds for COVID-19 affected patients requiring inpatient care [1]. Therefore, hospitals throughout Europe were forced to restore their capacity by postponing elective treatments and non-emergency surgeries [2, 3]. To address the threat, Swiss authorities introduced public health demands and restrictions to limit the spread of the new virus. While during the first wave, policy interventions included non-pharmacological mitigation measures such as national closures of borders, schools, non-essential stores and businesses with a nationwide lockdown mid of March 2020, [4] during the second wave, the main non-pharmacological measures included social distancing, increased testing, and restricted mobility [5, 6].

With cumulating evidence during the second wave, the antiviral remdesivir was prescribed more frequently [7–9]. In addition, dexamethasone was prescribed to most hospitalised patients needing oxygen therapy during the second wave [10]. Moreover, different strategies were applied regarding the hospitalisation of infected people. While, during the first wave, most infected people were hospitalised, even with only minor symptoms, this was no longer the case during the second wave, where criteria for hospitalisation were far more restrictive and based on clinical parameters [11]. Regardless, given the many infected people, the absolute number of hospitalisations during the second wave were higher.

As both the number of hospitalisations with COVID-19 and treatment options varied substantially between the first and second wave [12], we sought to evaluate epidemiological trends of COVID-19 related hospitalisations and corresponding in-hospital outcomes across different age groups using clinical routine data from Switzerland.

Methods

Study design

This analysis was conducted using a nationwide cohort of patients hospitalised with COVID-19 in Switzerland between January 1st and December 31st 2020. Hospitalisation data was provided by the Swiss Federal Statistical Office (FSO, Neuchâtel, Switzerland), based on a nationwide compulsory full census of Swiss hospitals. The dataset includes all Swiss inpatient discharge records from general hospitals for acute somatic care. Individual-level data on patient demographics, healthcare utilisation, hospital typology, medical diagnoses, clinical procedures, and in-hospital patient outcomes were provided. A multi-step anonymisation procedure ensured patient confidentiality, and an unique patient identifier was used to ascertain rehospitalisations. Medical diagnoses were coded using the International Classification of Disease version 10, German Modification (ICD-10-GM) codes. Open-source census data from the FSO on the Swiss population size, stratified by age and year, and data from the Swiss Federal Office of Public Health (FOPH) on the number of positive tests for SARS-CoV-2, stratified by age and period were obtained to calculate population-based incidence rates (IR) of hospitalisation with COVID-19. The institutional review board of Northwestern and Central Switzerland (EKNZ) waived the need for an ethical authorisation due to the use of exclusively anonymised data (EKNZ Project-ID: Req-2021-01397). This study adheres to the “Strengthening The Reporting of Observational Studies in Epidemiology (STROBE)” statement [13].

Case ascertainment and study variables

For this analysis, we included all hospitalisations that were treated with or for COVID-19. Hospitalisations in special clinics (psychiatric clinics, rehabilitation clinics, and other special clinics) were excluded. To identify hospitalisations with COVID-19, we used the following International Statistical Classification of Diseases and Related Health Problems, German Modification (ICD-10-GM) discharge codes at any position: U07.1 and U07.2. Details on all ICD-10-GM codes and Swiss operation classification (CHOP) codes used for the analysis are summarised in Table S1-S3 in the appendix. Comorbidities were measured using the Elixhauser Comorbidity Index [14], and frailty was measured using the Hospital Frailty Score [15].

Outcomes

The primary outcome was the IR of hospitalisation with COVID-19 per 10,000 person-years (PY) with corresponding 95% confidence intervals (CIs) during the first and second waves across the age spectrum. Secondary outcomes comprised of the occurrence of the following in-hospital outcomes during the first and second wave: all-cause in-hospital mortality, acute respiratory distress syndrome (ARDS), length of hospital stay (LOS), intensive care unit (ICU) admission, length of ICU stay, mechanical ventilation, duration of mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and 30-day all-cause hospital readmission. Hospitalisation with COVID-19 and ARDS was defined using ICD-10-GM codes, and the need for ECMO was defined using CHOP codes (table S1-S3 in the appendix). The remaining outcomes were applicable in the dataset provided by the FSO. Analyses of hospital outcomes were stratified by different age groups (0–9, 10–19, 20–49, 50–69, and ≥70 years).Out-of-hospital mortality data was not available in the dataset and could not be linked to the national death registry.

Statistical analysis

Descriptive statistic was calculated for patient demographics, including age, sex, nationality, and insurance status. All baseline characteristics are expressed as mean (standard deviation [SD]), median (interquartile range [IQR]), or frequency (%). Graphical illustration of hospitalisation IR over age spectrum was performed using locally estimated scatterplot smoothing (LOESS). Stratified by waves (first wave: January to May, second wave: June to December), we estimated IR per 10,000 PY with 95% CI, rate differences (RD), and incidence rate ratios (IRR). IR was calculated, allowing multiple hospitalisations for a single person (more than 95% of the patients were hospitalised only once) [16]. These estimates were calculated as the number of individuals with a COVID-19 hospitalisation divided by the sum of “person-time” population at risk in Switzerland, represented by the population size multiplied by the 5-month follow-up during the first wave and by the 7-month follow-up, according to age. To assess the IR of hospitalisation among the overall Swiss population, the denominator was the standard population in 2020 during the first or second waves, respectively. We used the number of people living in Switzerland at the end of 2020 as a surrogate. This information is publicly available and published by the FSO [17]. We aggregated COVID-19 hospitalisations per year of patient age. In detail, we counted the number of COVID-19 hospitalisations for each patient age stratum paired with the published number of persons during the respective wave in Switzerland. Thus, we assumed 5/12 of one person-year for every resident during the first wave and 7/12 of one person-year for every resident during the second wave, ignoring people dying or moving in and out of the country. IR of hospitalisation among those who had a proven SARS-CoV-2 infection were calculated using the overall positively tested population in the denominator. These analyses were performed within the above-mentioned age groups, and the first wave was denoted as a reference.

To explore differences in binary hospital outcomes between the waves and by age groups, we estimated adjusted odds ratios (aOR) and corresponding 95% CIs using a multivariable logistic regression model. For continuous right-skewed outcomes, we assessed changes in percentage and corresponding 95% CIs using a multiple linear generalised log-gamma regression model. All models were adjusted for age, sex and Elixhauser comorbidity index. The selection of covariates was based on the research question at hand and on knowledge such as what was important to guide hospitalisation criteria during the waves in Switzerland.

We evaluated for heterogeneity in OR estimates across age groups using the Wald test for homogeneity. We performed a risk factor analysis for mortality using univariable and multivariable analyses. All p-values are two-sided and have not been adjusted for multiple testing. Results were considered statistically significant at p <0.05. Statistical analyses were performed with STATA 15.1 (STATA Corp., College Station, TX, USA).

Results

Characteristics of the cohort

From January 1st to December 31st 2020, we identified 36,517 hospitalisations with COVID-19, 8862 (24.3%) during the first and 27,655 (75.7%) during the second wave. Baseline characteristics of the eligible study population stratified by age groups are shown in table 1. Baseline characteristics stratified by waves are summarised in table S4 in the appendix. Overall, the median age was 72 years (IQR, 58 to 82), 57.3% were male, 74.8% were Swiss residents, and 13.8% had supplementary health care insurance. The majority of hospitalisations was observed in patients aged older than 45 years. Among those, we observed an overall high burden of comorbidities, with similar distribution during the first and second waves.

Table 1Baseline characteristics stratified by age groups.

   <10 years 10–19 years 20–49 years 50–69 years ≥70 years
Hospitalisations, n 297 275 4,338 11,278 20,329
Wave, n (%) First wave 73 (24.6) 72 (26.2) 1333 (30.7) 2974 (26.4) 4410 (21.7)
Second wave 224 (75.4) 203 (73.8) 3005 (69.3) 8304 (73.6) 15,919 (78.3)
Demographics Age, median (IQR) [years] 0 (0, 2) 16 (14, 18) 40 (32, 46) 61 (56, 65) 80 (75, 86)
Male sex, n (%) 161 (54.2) 133 (48.4) 2321 (53.5) 7244 (64.2) 11,052 (54.4)
Swiss nationality, n (%) 181 (60.9) 178 (64.7) 2277 (52.5) 7622 (67.6) 17,058 (83.9)
Supplementary insurance, n (%) 20 (6.7) 32 (11.6) 308 (7.1) 1248 (11.1) 3432 (16.9)
Admission data Emergency admission, n (%) 215 (72.4) 221 (80.4) 3692 (85.1) 9831 (87.2) 16,576 (81.5)
Admission from home, n (%) 277 (93.3) 246 (89.5) 3857 (88.9) 9617 (85.3) 14,675 (72.2)
Admission to tertiary care hospital: university hospital, n (%) 116 (39.1) 122 (44.4) 1472 (33.9) 2803 (24.9) 4507 (22.2)
Admission to tertiary care hospital: non-university hospital, n (%) 168 (56.6) 119 (43.3) 2190 (50.5) 6338 (56.2) 11,657 (57.3)
Admission to secondary care hospital, n (%) 13 (4.4) 34 (12.4) 676 (15.6) 2137 (18.9) 4165 (20.5)
Comorbidities, n (%) Hypertension 2 (0.7) 4 (1.5) 444 (10.2) 4596 (40.8) 12,481 (61.4)
Dyslipidemia 0 (0.0) 0 (0.0) 128 (3.0) 1731 (15.3) 4119 (20.3)
Obesity (BMI ≥ 30.0 kg/m2) 2 (0.7) 6 (2.2) 197 (4.5) 612 (5.4) 559 (2.7)
Coronary artery disease 2 (0.7) 1 (0.4) 63 (1.5) 1220 (10.8) 4336 (21.3)
Atrial fibrillation 0 (0.0) 1 (0.4) 31 (0.7) 820 (7.3) 5269 (25.9)
Congestive heart failure 6 (2.0) 9 (3.3) 64 (1.5) 476 (4.2) 3094 (15.2)
Peripheral arterial disease 0 (0.0) 0 (0.0) 5 (0.1) 181 (1.6) 1012 (5.0)
Cerebrovascular disease 1 (0.3) 2 (0.7) 43 (1.0) 357 (3.2) 1233 (6.1)
Chronic obstructive pulmonary disease 0 (0.0) 0 (0.0) 17 (0.4) 582 (5.2) 1889 (9.3)
Bronchial asthma 9 (3.0) 12 (4.4) 247 (5.7) 557 (4.9) 618 (3.0)
Obstructive sleep apnoea syndrome 1 (0.3) 1 (0.4) 95 (2.2) 669 (5.9) 889 (4.4)
Chronic kidney disease stage 3 & 4 0 (0.0) 1 (0.4) 36 (0.8) 458 (4.1) 4066 (20.0)
Chronic kidney disease stage 5 & hemodialysis 1 (0.3) 1 (0.4) 42 (1.0) 294 (2.6) 495 (2.4)
Solid organ transplant recipient 1 (0.3) 4 (1.5) 64 (1.5) 199 (1.8) 84 (0.4)
Solid tumour 5 (1.7) 3 (1.1) 88 (2.0) 560 (5.0) 1296 (6.4)
Liver disease, including cirrhosis 2 (0.7) 6 (2.2) 202 (4.7) 693 (6.1) 660 (3.2)
Diabetes mellitus type 2 0 (0.0) 1 (0.4) 231 (5.3) 2584 (22.9) 5172 (25.4)
Diabetes mellitus type 1 1 (0.3) 11 (4.0) 27 (0.6) 57 (0.5) 45 (0.2)
Haematological malignancy 8 (2.7) 7 (2.5) 60 (1.4) 216 (1.9) 367 (1.8)
Rheumatoid arthritis 0 (0.0) 1 (0.4) 22 (0.5) 131 (1.2) 324 (1.6)
Human immunodeficiency virus infection 0 (0.0) 0 (0.0) 31 (0.7) 78 (0.7) 32 (0.2)
Elixhauser comorbidity index, median (IQR) 0 (0, 1) 0 (0, 1) 1 (0, 2) 2 (1, 3) 3 (2, 4)
Hospital frailty score, n (%) <5 points 275 (92.6) 249 (90.5) 3870 (89.2) 8390 (74.4) 9647 (47.5)
5–15 points 21 (7.1) 26 (9.5) 445 (10.3) 2642 (23.4) 8987 (44.2)
>15 points 1 (0.3) 0 (0.0) 23 (0.5) 246 (2.2) 1695 (8.3)
Outcomes In-hospital mortality, n (%) 2 (0.7) 0 (0.0) 39 (0.9) 526 (4.7) 3725 (18.3)
ICU admission, n (%) 23 (7.7) 29 (10.5) 479 (11.0) 2012 (17.8) 2080 (10.2)
ICU LOS, median (IQR) [days] 3.5 (1.7, 7.6) 2.1 (0.8, 5.8) 3.8 (1.5, 10.5) 6.9 (2.5, 15.7) 4.9 (1.6, 12.6)
Need for mechanical ventilation, n (%) 14 (4.7) 11 (4.0) 282 (6.5) 1484 (13.2) 1430 (7.0)
Duration of mechanical ventilation, median (IQR) [days] 2.2 (1.0, 7.5) 2.0 (1.0, 6.3) 7.0 (2.7, 12.3) 8.7 (3.7, 16.0) 6.7 (2.0, 14.3)
30-days rehospitalisation, n (%) 17 (5.7) 13 (4.7) 203 (4.7) 485 (4.3) 1049 (5.2)

BMI: body mass index; ICU: intensive care unit; IQR: interquartile range; LOS: length of stay

Hospitalisation rates of patients with COVID-19

While IR of hospitalisations during the first and second waves were generally low in children and adolescents, they increased at around the age of 40 years, with a peak in older patients during both waves (figure 1a).

Figure 1a Age-dependent incidence rates for hospitalisations for COVID-19 among the overall Swiss population per 10,000 person-years during the first wave (dark blue) and second wave (light blue), using locally estimated scatterplot smoothing (LOESS).

Figure 1b Age-dependent incidence rates for hospitalisations for COVID-19 among the overall SARS-CoV-2 positive tested population per 10,000 person-years during the first wave (dark blue) and second wave (light blue), using locally estimated scatterplot smoothing (LOESS).

Considering the overall standard population in the denominator, IR were higher during the second wave compared with the first wave. COVID-19 hospitalisation rates were highest in patients above 50 years (50–69 years: first wave: 31.49 per 10,000 person-years; second wave: 62.81 per 10,000 person-years; RD 31.32 [95% CI: 29.56 to 33.08] per 10,000 person-years; ≥70 years: first wave: 88.59 per 10,000 person-years; second wave: 228.41 per 10,000 person-years; RD 139.83 [95% CI: 135.42 to 144.23] per 10,000 person-years) with a strong predominance during the second wave (table 2).

Table 2Differences in absolute and relative risk between the first and second COVID-19 waves across age groups.

    Age 0–9 years Age 10–19 years Age 20–49 years Age 50–69 Age ≥70 p of interaction
First wave Second wave First wave Second wave First wave Second wave First wave Second wave First wave Second wave
Hospitalisations, n 73 224 72 203 1333 3005 2974 8304 4410 15,919
Person-years 365,335 511,468 353,958 495,541 1,451,184 2,031,657 944,334 1,322,068 497,815 696,941
Incidence rate per 10,000 PY 1.99 4.38 2.03 4.10 9.19 14.79 31.49 62.81 88.59 228.41
Incidence rate difference (95% CI) Ref. 2.38 (1.65 to 3.12) Ref. 2.06 (1.32 to 2.80) Ref. 5.61 (4.88 to 6.33) Ref. 31.32 (29.56 to 33.08) Ref. 139.83 (135.42 to 144.23) p <0.001
Incidence rate ratio (95% CI) Ref. 2.19 (1.65 to 3.12) Ref. 2.01 (1.53 to 2.67) Ref. 1.61 (1.51 to 1.72) Ref. 1.99 (1.91 to 2.08) Ref. 2.58 (2.49 to 2.67) p <0.001

CI: confidence interval; PY: person-years; Ref: reference

Illustration of hospitalisation IR across the age spectrum among those who were tested positive for SARS-CoV-2 showed different patterns for the first and second wave, with a first peak of hospitalisations among children aged 0 to 9 year and a second peak with a continuous increase of hospitalisations among adults beyond age 50 (figure 1b). While rates of hospitalisation among the overall Swiss population were higher during the second wave (figure 1a), the proportion of hospitalised people among positively tested individuals was higher during the first wave (figure 1b).

In-hospital outcomes between the first and second wave

Among the overall population, 1028 (11.6%) died in hospital during the first wave and 3264 (11.8%) during the second wave, corresponding to lower odds of in-hospital mortality during the second wave (aOR 0.88 [95% CI: 0.81 to 0.95]). Similarly, compared with the first wave, we observed lower numbers of patients with ARDS (1056 [11.9%] vs. 1945 [7.0%], aOR 0.56 [95% CI: 0.51 to 0.61]), ICU admission (1463 [16.5%] vs. 3160 [11.4%], aOR 0.66 [95% CI: 0.61 to 0.70]), in need for mechanical ventilation (1101 [12.4%] vs. 2120 [7.7%], aOR 0.67 [95% CI: 0.58 to 0.78]), and in need for ECMO (38 [0.4%] vs. 46 [0.2%], aOR 0.60 [95% CI: 0.38 to 0.92]) during the second wave. The odds for 30-day all-cause hospital readmission remained similar during the first and second wave (427 [4.8%] vs. 1340 [4.9%], aOR of 0.99 [95% CI: 0.88 to 1.10]) (figure 2). Compared with the first wave, LOS (7.4 [SD 2.7] vs. 6.7 [SD 2.5]) and ICU LOS (6.2 [SD 4.0] vs. 4.1 [SD 3.9]) were shorter during the second wave with a reduction of –16.1% (95% CI: –17.8 to –14.2) and –32.7% (95% CI: –37.2 to –27.9), respectively (figure 3). There was no evidence for effect modification by age for all hospital outcomes of interest (p of interaction >0.05).

Figure 2 Odds ratios and 95% confidence intervals for adverse outcomes in prespecified age subgroups. First wave was chosen as a reference. There was no evidence for effect modification by age for all hospital outcomes of interest (p of interaction >0.05).

* adjusted for age, sex and Elixhauser comorbidity Index

ARDS: acute respiratory distress syndrome; CI: confidence interval; ECMO: extracorporeal membrane oxygenation; ICU: intensive care unit; N/A: not applicable; no: number; OR: odds ratio

Figure 3 Changes in frequency for adverse outcomes in prespecified age subgroups.

First wave was chosen as a reference. There was no evidence for effect modification by age for all hospital outcomes of interest (p of interaction >0.05).

* adjusted for age, sex and Elixhauser comorbidity Index

CI: confidence interval; ICU: intensive care unit; SD: standard deviation

Predictors for mortality

Table 3 and 4 show baseline risk factors for in-hospital mortality during the first and second waves. Among the adjusted regression analysis, the main risk factors were the prevalence of chronic kidney disease stage 5 and/or hemodialysis (first wave: aOR 3.26 [95% CI: 2.47 to 4.31), p <0.001]; second wave: aOR 4.02 [95% CI: 3.31 to 4.89], p <0.001) and haematological malignancy (first wave: aOR 3.03 [95% CI: 1.97 to 4.66)], p <0.001; second wave: aOR 2.39 [95% CI: 1.92 to 2.98], p <0.001).

Table 3Risk factor analysis for in-hospital mortality during the first pandemic wave.

Factor Survivors (n = 7,834) Non-survivors (n = 1,028) Unadjusted OR (95% CI), p-value Adjusted OR* (95% CI), p-value
Demographics Age, median (IQR) [years] 67 (54,78) 81 (73,86) 1.07 (1.06, 1.08), p <0.001 1.07 (1.06, 1.08), p <0.001
Male sex, n (%) 4493 (57.4) 700 (68.1) 1.59 (1.38, 1.82), p <0.001 1.96 (1.69, 2.28), p <0.001
Swiss nationality, n (%) 5750 (73.4) 836 (81.3) 1.58 (1.34, 1.86), p <0.001 0.97 (0.81, 1.16), p = 0.728
Supplementary insurance, n (%) 952 (12.2) 109 (10.6) 0.86 (0.70, 1.06), p = 0.151 0.74 (0.60, 0.93), p = 0.008
Admission data Emergency admission, n (%) 6410 (81.8) 883 (85.9) 1.35 (1.12, 1.63), p = 0.001 1.68 (1.38, 2.04), p <0.001
Admission from home, n (%) 5988 (76.4) 724 (70.4) 0.73 (0.64, 0.85), p <0.001 1.16 (1.00, 1.36), p = 0.054
Admission to tertiary care hospital: University hospital, n (%) 2520 (32.2) 287 (27.9) 0.82 (0.71, 0.94), p = 0.006 0.84 (0.72, 0.98), p = 0.027
Admission to tertiary care hospital: Non-university hospital, n (%) 3789 (48.5) 562 (54.7) 1.28 (1.12, 1.46), p <0.001 1.29 (1.12, 1.48), p <0.001
Admission to secondary care hospital, n (%) 1516 (19.4) 179 (17.4) 0.88 (0.74, 1.04), p = 0.137 0.84 (0.70, 1.00), p = 0.055
Comorbidities, n (%) Hypertension, n (%) 3437 (43.9) 579 (56.3) 1.65 (1.45, 1.88), p <0.001 0.67 (0.57, 0.78), p <0.001
Dyslipidemia, n (%) 1256 (16.0) 178 (17.3) 1.10 (0.92, 1.30), p = 0.294 0.74 (0.61, 0.88), p = 0.001
Obesity (BMI ≥30.0 kg/m2), n (%) 241 (3.1) 34 (3.3) 1.08 (0.75, 1.55), p = 0.688 1.39 (0.94, 2.07), p = 0.100
Coronary artery disease, n (%) 971 (12.4) 253 (24.6) 2.31 (1.97, 2.70), p <0.001 1.21 (1.02, 1.43), p = 0.030
Atrial fibrillation, n (%) 1043 (13.3) 296 (28.8) 2.63 (2.27, 3.06), p <0.001 1.15 (0.97, 1.37), p = 0.104
Congestive heart failure, n (%) 604 (7.7) 223 (21.7) 3.32 (2.80, 3.93), p <0.001 1.47 (1.21, 1.80), p <0.001
Peripheral arterial disease, n (%) 210 (2.7) 56 (5.4) 2.09 (1.55, 2.83), p <0.001 0.89 (0.64, 1.23), p = 0.488
Cerebrovascular disease, n (%) 305 (3.9) 81 (7.9) 2.11 (1.64, 2.72), p <0.001 1.35 (1.03, 1.77), p = 0.029
Chronic obstructive pulmonary disease, n (%) 467 (6.0) 114 (11.1) 1.97 (1.59, 2.44), p <0.001 1.20 (0.95, 1.51), p = 0.132
Bronchial asthma, n (%) 379 (4.8) 19 (1.8) 0.37 (0.23, 0.59), p <0.001 0.52 (0.32, 0.85), p = 0.008
Obstructive sleep apnoea syndrome, n (%) 361 (4.6) 68 (6.6) 1.47 (1.12, 1.92), p = 0.005 1.37 (1.03, 1.83), p = 0.029
Chronic kidney disease stage 3 & 4, n (%) 649 (8.3) 196 (19.1) 2.61 (2.19, 3.11), p <0.001 1.02 (0.84, 1.24), p = 0.851
Chronic kidney disease stage 5 & hemodialysis, n (%) 212 (2.7) 93 (9.0) 3.58 (2.78, 4.61), p <0.001 3.26 (2.47, 4.31), p <0.001
Solid organ transplant recipients,  n (%) 65 (0.8) 8 (0.8) 0.94 (0.45, 1.96), p = 0.864 1.42 (0.65, 3.13), p = 0.389
Solid tumor, n (%) 328 (4.2) 97 (9.4) 2.38 (1.88, 3.02), p <0.001 1.87 (1.44, 2.42), p <0.001
Liver disease including cirrhosis, n (%) 427 (5.5) 85 (8.3) 1.56 (1.23, 1.99), p <0.001 1.86 (1.43, 2.42), p <0.001
Diabetes mellitus type 2, n (%) 1447 (18.5) 265 (25.8) 1.53 (1.32, 1.78), p <0.001 0.97 (0.82, 1.15), p = 0.758
Diabetes mellitus type 1, n (%) 27 (0.3) 4 (0.4) 1.13 (0.39, 3.23), p = 0.821 1.75 (0.56, 5.44), p = 0.335
Haematological malignancy, n (%) 98 (1.3) 34 (3.3) 2.70 (1.82, 4.01), p <0.001 3.03 (1.97, 4.66), p <0.001
Rheumatoid arthritis, n (%) 81 (1.0) 16 (1.6) 1.51 (0.88, 2.60), p = 0.133 1.45 (0.82, 2.56), p = 0.203
Human immunodeficiency virus infection, n (%) 37 (0.5) 2 (0.2) 0.41 (0.10, 1.71), p = 0.221 0.67 (0.16, 2.91), p = 0.597
Elixhauser comorbidity index, median (IQR) 2 (1, 4) 3 (2, 5) 1.27 (1.24, 1.31), p <0.001 1.13 (1.10, 1.17), p <0.001
Hospital frailty score <5 points, n (%) 5061 (64.6) 449 (43.7) 0.42 (0.37, 0.48), p <0.001 0.92 (0.79, 1.08), p = 0.304
5–15 points, n (%) 2377 (30.3) 507 (49.3) 2.23 (1.96, 2.55), p <0.001 1.22 (1.06, 1.41), p = 0.006
>15 points, n (%) 396 (5.1) 72 (7.0) 1.41 (1.09, 1.83), p = 0.009 0.64 (0.49, 0.85), p = 0.002

* adjusted for age, sex and Elixhauser comorbidity Index

BMI: body mass index; IQR: interquartile range; OR: odds ratio

Table 4Risk factor analysis for in-hospital mortality during the second pandemic wave.

Factor Survivors (n = 24,391) Non-Survivors (n = 3,264) unadjusted OR (95% CI), p-value adjusted OR* (95% CI), p-value
Demographics Age, median (IQR) [years] 71 (58,81) 82 (75, 87) 1.07 (1.06, 1.07), p <0.001 1.07 (1.06, 1.07), p <0.001
Male sex, n (%) 13,595 (55.7) 2123 (65.0) 1.48 (1.37, 1.59), p <0.001 1.79 (1.65, 1.94), p <0.001
Swiss nationality, n (%) 18,069 (74.1) 2661 (81.5) 1.54 (1.41, 1.69), p <0.001 0.94 (0.85, 1.04), p = 0.238
Supplementary insurance, n (%) 3448 (14.1) 531 (16.3) 1.18 (1.07, 1.30), p = 0.001 0.94 (0.85, 1.05), p = 0.310
Admission data Emergency admission, n (%) 20,420 (83.7) 2822 (86.5) 1.24 (1.12, 1.38), p <0.001 1.48 (1.33, 1.66), p <0.001
Admission from home, n (%) 19,609 (80.4) 2351 (72.0) 0.63 (0.58, 0.68), p <0.001 0.91 (0.83, 0.99), p = 0.031
Admission to tertiary care hospital
University hospital, n (%) 5555 (22.8) 658 (20.2) 0.86 (0.78, 0.94), p = 0.001 0.92 (0.84, 1.01), p = 0.093
Non-university hospital, n (%) 14,087 (57.8) 2025 (62.0) 1.20 (1.11, 1.29), p <0.001 1.16 (1.07, 1.25), p <0.001
Admission to secondary care hospital, n (%) 4749 (19.5) 581 (17.8) 0.90 (0.81, 0.99), p = 0.023 0.87 (0.79, 0.96), p = 0.006
Comorbidities, n (%) Hypertension, n (%) 11,700 (48.0) 1811 (55.5) 1.35 (1.26, 1.46), p <0.001 0.58 (0.53, 0.63), p <0.001
Dyslipidemia, n (%) 4008 (16.4) 536 (16.4) 1.00 (0.91, 1.10), p = 0.998 0.69 (0.62, 0.76), p <0.001
Obesity (BMI ≥ 30.0 kg/m2), n (%) 968 (4.0) 133 (4.1) 1.03 (0.85, 1.24), p = 0.771 1.08 (0.89, 1.33), p = 0.430
Coronary artery disease, n (%) 3563 (14.6) 835 (25.6) 2.01 (1.84, 2.19), p <0.001 1.13 (1.03, 1.25), p = 0.008
Atrial fibrillation, n (%) 3703 (15.2) 1079 (33.1) 2.76 (2.54, 3.00), p <0.001 1.27 (1.16, 1.39), p <0.001
Congestive heart failure, n (%) 2050 (8.4) 772 (23.7) 3.38 (3.08, 3.70), p <0.001 1.63 (1.46, 1.81), p <0.001
Peripheral arterial disease, n (%) 752 (3.1) 180 (5.5) 1.83 (1.55, 2.17), p <0.001 0.90 (0.75, 1.07), p = 0.224
Cerebrovascular disease, n (%) 1015 (4.2) 235 (7.2) 1.79 (1.54, 2.07), p <0.001 1.11 (0.95, 1.30), p = 0.184
Chronic obstructive pulmonary disease, n (%) 1528 (6.3) 379 (11.6) 1.97 (1.74, 2.21), p <0.001 1.26 (1.11, 1.43), p <0.001
Bronchial asthma, n (%) 962 (3.9) 83 (2.5) 0.64 (0.51, 0.80), p <0.001 0.67 (0.53, 0.85), p = 0.001
Obstructive sleep apnoea syndrome, n (%) 1068 (4.4) 158 (4.8) 1.11 (0.94, 1.32), p = 0.229 0.98 (0.82, 1.18), p = 0.870
Chronic kidney disease stage 3 & 4, n (%) 2891 (11.9) 825 (25.3) 2.52 (2.30, 2.75), p <0.001 1.07 (0.97, 1.18), p = 0.201
Chronic kidney disease stage 5 & hemodialysis, n (%) 328 (1.3) 200 (6.1) 4.79 (4.00, 5.73), p <0.001 4.02 (3.31, 4.89), p <0.001
Solid organ transplant recipients, n (%) 252 (1.0) 27 (0.8) 0.80 (0.54, 1.19), p = 0.270 1.21 (0.80, 1.84), p = 0.368
Solid tumor, n (%) 1201 (4.9) 326 (10.0) 2.14 (1.88, 2.44), p <0.001 1.61 (1.40, 1.85), p <0.001
Liver disease including cirrhosis, n (%) 861 (3.5) 190 (5.8) 1.69 (1.44, 1.99), p <0.001 1.88 (1.57, 2.25), p <0.001
Diabetes mellitus type 2, n (%) 5366 (22.0) 910 (27.9) 1.37 (1.26, 1.49), p <0.001 0.95 (0.87, 1.05), p = 0.321
Diabetes mellitus type 1, n (%) 104 (0.4) 6 (0.2) 0.43 (0.19, 0.98), p = 0.045 0.61 (0.26, 1.43), p = 0.253
Haematological malignancy, n (%) 402 (1.6) 124 (3.8) 2.36 (1.92, 2.89), p <0.001 2.39 (1.92, 2.98), p <0.001
Rheumatoid arthritis, n (%) 324 (1.3) 57 (1.7) 1.32 (0.99, 1.75), p = 0.055 1.07 (0.80, 1.44), p = 0.655
Human immunodeficiency virus infection, n (%) 98 (0.4) 4 (0.1) 0.30 (0.11, 0.83), p = 0.020 0.56 (0.20, 1.56), p = 0.271
Elixhauser comorbidity index, median (IQR) 2 (1, 4) 3 (2, 5) 1.27 (1.25, 1.29), p <0.001 1.16 (1.14, 1.18), p <0.001
Hospital frailty score <5 points, n (%) 15,645 (64.1) 449 (43.7) 0.36 (0.33, 0.39), p <0.001 0.70 (0.64, 0.76), p <0.001
5–15 points, n (%) 7551 (31.0) 1686 (51.7) 2.38 (2.21, 2.57), p <0.001 1.40 (1.29, 1.51), p = 0.006
>15 points, n (%) 1195 (4.9) 302 (9.3) 1.98 (1.73, 2.26), p <0.001 0.97 (0.84, 1.12), p = 0.678

* adjusted for age, sex and Elixhauser comorbidity Index.

BMI: body mass index; IQR: interquartile range; OR: odds ratio

Discussion

This nationwide cohort study of hospitalised COVID-19 patients in Switzerland revealed several key findings: First, hospitalisation rates in patients with COVID-19 were highest among adults older than 50 years and more pronounced during the second wave. Second, while the hospitalisation rate was higher among the overall Swiss population during the second wave, it was lower among positively tested individuals during the same period. Third, except for readmission, the risk for hospital-associated adverse outcomes was lower during the second wave, irrespective of patient age.

The highest hospitalisation rates among the overall Swiss population were observed in patients aged 50 years and older, both during the first and second wave. However, the peak of hospitalisations during the second wave was much higher than the first wave. These results are plausible since we observed significantly more infections during the second wave, resulting in more hospitalisations. Although data must be interpreted carefully, as hospitalisation and test criteria differed between the two waves, we observed a lower proportion of hospitalised people among the overall positively tested population during the second wave.

Improvement of patient management was a further important key component which was achieved during the second wave. Overall, the odds of adverse outcomes decreased during the second wave, except for hospital readmission. The expected decrease of in-hospital adverse events during the second wave is probably explained by the improved understanding of the treating physicians and nursing staff about the management of COVID-19 patients [18]. Importantly, the widespread administration of dexamethasone during the second wave may have contributed to a stronger reduction in in-hospital mortality, as already shown by previous studies [19]. Consistent with other studies [20–24], we observed a lower risk of in-hospital mortality during the second wave (aOR 0.88 [95% CI: 0.81 to 0.95]). While, as compared with findings from clinical trials, a relative risk reduction of 12% may seem small, hospitalised patients during the second wave tended to be older and more comorbid, both characteristics known to independently increase the risk of COVID-19-related mortality [25–28].

Similarly, corticosteroids may also have reduced the rate of progression to severe COVID-19 due to an attenuation of the inflammation, resulting in lower rates of ARDS, admission to ICU, need for mechanical ventilation and ECMO support as well as a 16% shorter LOS of 7.4 vs. 6.7 days [29]. In addition, the reduction in LOS during the second wave could also be due to improved discharge processes and earlier transfer to rehabilitation facilities. This was possible since many rehabilitation facilities were obligated to unburden acute care hospitals by accepting still-infectious COVID-19 patients [30].

We did not observe a change in readmission rates between the two waves. This can be explained by the fact that COVID-19 is an acute disease and may not lead to readmissions per se, whereas the burden of comorbidities as a potential driver for readmission tended to be higher during the second wave and may have diluted any beneficial effect on hospital readmission. These data must be interpreted in the context of the study design. As COVID-19 hospitalisations were identified using ICD-10-GM codes used for billing purposes, thus, misclassification and underreporting are possible, especially during the beginning of the pandemic when no specific ICD-10 codes were available. In line, the database provided by the FSO revealed larger numbers of hospitalisations as compared with data from the CH-SUR database. Since algorithms based on the U07.1 code had high sensitivity among hospitalised patients but at the expense of low specificity [31], it is likely that the number of hospitalisation with COVID-19 as provided by the FSO might be overestimated. However, it can also not be excluded that some hospitalisations with COVID-19 in the FSO-database were not lab-confirmed during the hospital stay but diagnosed based on an out-of-hospital test or clinical features. Second, unmeasurable confounding like smoking status, genetic susceptibility or home medication must be considered, as the used dataset does not include any information in this regard. Third, due to the study’s retrospective design, no causal inference is possible. Fourth, we did not account for potential within-patient/hospital clustering. However, we do not think that accounting for clustering relevantly changes the conclusion of our manuscript, as the number of clusters (hospitals, hospital admissions per patient) were comparable between the first and second waves of the pandemic. Fifth, medical treatments received during the hospitalisation for COVID-19 could not be analysed, as this information is missing in the analysed dataset. However, there are several strengths of note. This analysis was based on nationwide hospital care data with high external validity, strong statistical power and high generalisability across all regions in Switzerland. Moreover, this study provides insights into different age groups that were not comprehensively addressed in earlier studies in Switzerland.

Conclusion

In this nationwide cohort study, hospitalisation rates of COVID-19 patients were highest among adults older than 50 years and during the second wave. Except for readmission, the risk of hospital adverse outcomes was decreased during the second wave, regardless of the patient`s age.

Data sharing statement

The data supporting this study’s findings are available upon request from the Swiss Federal Statistical Office (Neuchâtel, Switzerland). Restrictions apply to the availability of these data, which were used under license for this study. Data are available as part of the data on “Medizinische Statistik der Krankenhäuser” with the permission of the Swiss Federal Statistical Office, Section Health Services and Population Health.

Acknowledgement

We thank the Swiss Federal Statistics Office (Neuchâtel, Switzerland) for the acquisition and provision of data.

Notes

Potential conflicts of interest

All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. No potential conflict of interest related to the content of this manuscript was disclosed.

Claudia Gregoriano, PhD

Medical University Department of Medicine

Kantonsspital Aarau

Tellstrasse 25

CH-5001 Aarau

c.gregoriano[at]gmail.com

References

1. Verelst F , Kuylen E , Beutels P . Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020. Euro Surveill. 2020 Apr;25(13):2000323. https://doi.org/10.2807/1560-7917.ES.2020.25.13.2000323

2. Winkelmann J , Webb E , Williams GA , Hernández-Quevedo C , Maier CB , Panteli D . European countries’ responses in ensuring sufficient physical infrastructure and workforce capacity during the first COVID-19 wave. Health Policy. 2022 May;126(5):362–72. https://doi.org/10.1016/j.healthpol.2021.06.015

3. Webb E , Hernández-Quevedo C , Williams G , Scarpetti G , Reed S , Panteli D . Providing health services effectively during the first wave of COVID-19: A cross-country comparison on planning services, managing cases, and maintaining essential services. Health Policy. 2022 May;126(5):382–90. https://doi.org/10.1016/j.healthpol.2021.04.016

4. Zimmermann BM , Fiske A , McLennan S , Sierawska A , Hangel N , Buyx A . Motivations and Limits for COVID-19 Policy Compliance in Germany and Switzerland. Int J Health Policy Manag. 2021 Apr;11(8):1342–53. https://doi.org/10.34172/ijhpm.2021.30

5. Salathé M , Althaus CL , Neher R , Stringhini S , Hodcroft E , Fellay J , et al.  COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation. Swiss Med Wkly. 2020 Mar;150:w20225. https://doi.org/10.4414/smw.2020.20225

6. Flaxman S , Mishra S , Gandy A , Unwin HJ , Mellan TA , Coupland H , et al.; Imperial College COVID-19 Response Team . Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020 Aug;584(7820):257–61. https://doi.org/10.1038/s41586-020-2405-7

7. Cao B , Wang Y , Wen D , Liu W , Wang J , Fan G , et al.  A Trial of Lopinavir-Ritonavir in Adults Hospitalized with Severe Covid-19. N Engl J Med. 2020 May;382(19):1787–99. https://doi.org/10.1056/NEJMoa2001282

8. Boulware DR , Pullen MF , Bangdiwala AS , Pastick KA , Lofgren SM , Okafor EC , et al.  A Randomized Trial of Hydroxychloroquine as Postexposure Prophylaxis for Covid-19. N Engl J Med. 2020 Aug;383(6):517–25. https://doi.org/10.1056/NEJMoa2016638

9. Wang Y , Zhang D , Du G , Du R , Zhao J , Jin Y , et al.  Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. Lancet. 2020 May;395(10236):1569–78. https://doi.org/10.1016/S0140-6736(20)31022-9

10. Horby P , Lim WS , Emberson JR , Mafham M , Bell JL , Linsell L , et al.; RECOVERY Collaborative Group . Dexamethasone in Hospitalized Patients with Covid-19. N Engl J Med. 2021 Feb;384(8):693–704. https://doi.org/10.1056/NEJMoa2021436

11. Biswas M , Rahaman S , Biswas TK , Haque Z , Ibrahim B . Association of Sex, Age, and Comorbidities with Mortality in COVID-19 Patients: A Systematic Review and Meta-Analysis. Intervirology. 2020 Dec;1–12.  

12.   Bundesamt für Statistik . Auswirkungen der Covid-19-Pandemie auf die Gesundheitsversorgung im Jahr 2020. 2021. 

13. von Elm E , Altman DG , Egger M , Pocock SJ , Gøtzsche PC , Vandenbroucke JP ; STROBE Initiative . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007 Oct;335(7624):806–8. https://doi.org/10.1136/bmj.39335.541782.AD

14. Elixhauser A , Steiner C , Harris DR , Coffey RM . Comorbidity measures for use with administrative data. Med Care. 1998 Jan;36(1):8–27. https://doi.org/10.1097/00005650-199801000-00004

15. Gilbert T , Neuburger J , Kraindler J , Keeble E , Smith P , Ariti C , et al.  Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018 May;391(10132):1775–82. https://doi.org/10.1016/S0140-6736(18)30668-8

16. Bundesamt für Gesundheit (BAG) . Coronavirus: Monitoring [Available from: https://www.bag.admin.ch/bag/de/home/krankheiten/ausbrueche-epidemien-pandemien/aktuelle-ausbrueche-epidemien/novel-cov/situation-schweiz-und-international/monitoring.html#19385953 ], last access: 23.12.2022. 

17. Bundesamt für Statistik . Permanent resident population by age, sex and category of citizenship, 2010-2021 2010-2021 [Available from: https://www.bfs.admin.ch/bfs/de/home/statistiken/bevoelkerung/stand-entwicklung/alter-zivilstand-staatsangehoerigkeit.assetdetail.23064709.html ], last access: 23.12.2022. 

18.   World Health Organization (WHO) . Clinical management of COVID-19: Living guideline, 23 June 2022 2022 [Available from: WHO-2019-nCoV-Clinical-2022.1-eng.pdf.], last access, 23.12.2022. 

19. van Paassen J , Vos JS , Hoekstra EM , Neumann KM , Boot PC , Arbous SM . Corticosteroid use in COVID-19 patients: a systematic review and meta-analysis on clinical outcomes. Crit Care. 2020 Dec;24(1):696. https://doi.org/10.1186/s13054-020-03400-9

20. Oladunjoye O , Gallagher M , Wasser T , Oladunjoye A , Paladugu S , Donato A . Mortality due to COVID-19 infection: A comparison of first and second waves. J Community Hosp Intern Med Perspect. 2021 Nov;11(6):747–52. https://doi.org/10.1080/20009666.2021.1978154

21. Saito S , Asai Y , Matsunaga N , Hayakawa K , Terada M , Ohtsu H , et al.  First and second COVID-19 waves in Japan: A comparison of disease severity and characteristics. J Infect. 2021 Apr;82(4):84–123. https://doi.org/10.1016/j.jinf.2020.10.033

22. Di Fusco M , Shea KM , Lin J , Nguyen JL , Angulo FJ , Benigno M , et al.  Health outcomes and economic burden of hospitalized COVID-19 patients in the United States. J Med Econ. 2021;24(1):308–17. https://doi.org/10.1080/13696998.2021.1886109

23. Mallow PJ , Belk KW , Topmiller M , Hooker EA . Outcomes of Hospitalized COVID-19 Patients by Risk Factors: Results from a United States Hospital Claims Database. J Health Econ Outcomes Res. 2020 Sep;7(2):165–74. https://doi.org/10.36469/jheor.2020.17331

24. Iftimie S , López-Azcona AF , Vallverdú I , Hernández-Flix S , de Febrer G , Parra S , et al.  First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain. PLoS One. 2021 Mar;16(3):e0248029. https://doi.org/10.1371/journal.pone.0248029

25. Hothorn T , Bopp M , Günthard H , Keiser O , Roelens M , Weibull CE , et al.  Assessing relative COVID-19 mortality: a Swiss population-based study. BMJ Open. 2021 Mar;11(3):e042387. https://doi.org/10.1136/bmjopen-2020-042387

26. Williamson EJ , Walker AJ , Bhaskaran K , Bacon S , Bates C , Morton CE , et al.  Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020 Aug;584(7821):430–6. https://doi.org/10.1038/s41586-020-2521-4

27. Bravi F , Flacco ME , Carradori T , Volta CA , Cosenza G , De Togni A , et al.  Predictors of severe or lethal COVID-19, including Angiotensin Converting Enzyme inhibitors and Angiotensin II Receptor Blockers, in a sample of infected Italian citizens. PLoS One. 2020 Jun;15(6):e0235248. https://doi.org/10.1371/journal.pone.0235248

28. Maximiano Sousa F , Roelens M , Fricker B , Thiabaud A , Iten A , Cusini A , et al.; Ch-Sur Study Group . Risk factors for severe outcomes for COVID-19 patients hospitalised in Switzerland during the first pandemic wave, February to August 2020: prospective observational cohort study. Swiss Med Wkly. 2021 Jul;151(2930):w20547. https://doi.org/10.4414/smw.2021.20547

29. Ye Z , Wang Y , Colunga-Lozano LE , Prasad M , Tangamornsuksan W , Rochwerg B , et al.  Efficacy and safety of corticosteroids in COVID-19 based on evidence for COVID-19, other coronavirus infections, influenza, community-acquired pneumonia and acute respiratory distress syndrome: a systematic review and meta-analysis. CMAJ. 2020 Jul;192(27):E756–67. https://doi.org/10.1503/cmaj.200645

30. Liebl ME , Gutenbrunner C , Glaesener JJ , Schwarzkopf S , Best N , Lichti G , et al.  Early Rehabilitation in COVID-19 – Best Practice Recommendations for the Early Rehabilitation of COVID-19 Patients. Phys Med Rehabilmed Kurortmed. 2020 Jun;30(3):129–34. https://doi.org/10.1055/a-1162-4919

31. Brown CA , Londhe AA , He F , Cheng A , Ma J , Zhang J , et al.  Development and Validation of Algorithms to Identify COVID-19 Patients Using a US Electronic Health Records Database: A Retrospective Cohort Study. Clin Epidemiol. 2022 May;14:699–709. https://doi.org/10.2147/CLEP.S355086   

Appendix

Table S1ICD-10 codes for inclusion criteria.

Name ICD-10 diagnosis code Code position
COVID-19, virus confirmed U071 Any
COVID-19, virus not confirmed U072 Any

Table S2ICD-10 codes for baseline characteristics (comorbidities) and outcomes.

Name of group ICD-10 diagnosis code Code position: comorbidities
Hypertension I10–I13, I15, I674 Any
Dyslipidemia E78 Any
Obesity (BMI ≥ 30.0 kg/m2) E65–E66 Any
Coronary artery disease I20–I25 Any
Congestive heart failure I50, I110, I130, I132 Any
Peripheral arterial disease I702 Any
Cerebrovascular disease I60–I67, I69 Any
Chronic obstructive pulmonary disease J44 Any
Bronchial asthma J45–J46 Any
Obstructive sleep apnoea syndrome G473 Any
Acute respiratory distress syndrome J80 Any
Chronic kidney disease stage 3 & 4 N183–N184 Any
Chronic kidney disease stage 5 & hemodialysis N185, T824, T8571,Z491–Z492, Z992 Any
Solid organ transplant recipients T861–T864, T8681–T8682, T8688, T869, Z940–Z944, Z9488, Z949 Any
Solid tumor C0–C7 Any
Liver disease including cirrhosis K7, K703, K717, K744, K746 K7470–K7472 Any
Diabetes mellitus type 1 E10 Any
Diabetes mellitus type 2 E11 Any
Haematological malignancy C81–88, C90–C96) Any
Rheumatoid arthritis M05–M06, M08 Any
Human immunodeficiency virus infection B20–B24, F024, O987, U60–U61, U85, Z21 Any

Table S3CHOP codes baseline characteristics (comorbidities) and outcomes.

Name CHOP code Code position
Haemo- and peritoneal dialysis 3895, 3927, 3942–3943, 39951–39954, 3995I1–3995I2, 5498 Any
Solid organ transplant 0091*–0093*,335*–336*, 3751*,4194*,4697*,505*,528*,556* Any
Installation and revision of portosystemic shunt 391100, 391199 Any
Extracorporeal membrane oxygenation 37698*, 3769A*, 376A61*– 376A62*, 376A71*–376A73*, 376B61*– 376B62*, 376B71*–376B73*, 376C61*, 376C62*, 376C71*–376C73*, 376D*, 376D31*, 376D41* Any

Table S4Baseline characteristics stratified by waves.

  First wave Second wave p-value
Hospitalisations, n 8862 27,655
Demographics Age, median (IQR) [years] 69 (55, 80) 73 (60, 82)  <0.001
Age groups, n (%)
<10 years 73 (0.8) 224 (0.8)  <0.001
10–19 years 72 (0.8) 203 (0.7)
20–49 years 1333 (15.0) 3005 (10.9)
50–69 years 2974 (33.6) 8304 (30.0)
≥70 years 4410 (49.8) 15,919 (57.6)
Male sex, n (%) 5,193 (58.6) 15,718 (56.8) 0.004
Swiss nationality, n (%) 6586 (74.3) 20,730 (75.0) 0.23
Supplementary insurance, n (%) 1061 (12.0) 3979 (14.4) <0.001
Admission data Emergency admission, n (%) 7293 (82.3) 23,242 (84.0) <0.001
Admission from home, n (%) 6712 (75.7) 21,960 (79.4) <0.001
Admission to tertiary care hospital, n (%) <0.001
University hospital 2807 (31.7) 6213 (22.5)
Non-university hospital 4360 (49.2) 16,112 (58.3)
Admission to secondary care hospital, n (%) 1695 (19.1) 5330 (19.3)
Comorbidities, n (%) Hypertension 4016 (45.3) 13,511 (48.9) <0.001
Dyslipidemia 1434 (16.2) 4544 (16.4) 0.58
Obesity (BMI ≥ 30.0 kg/m2) 275 (3.1) 1101 (4.0) <0.001
Coronary artery disease 1224 (13.8) 4398 (15.9) <0.001
Atrial fibrillation 1339 (15.1) 4782 (17.3) <0.001
Congestive heart failure 827 (9.3) 2822 (10.2) 0.017
Peripheral arterial disease 266 (3.0) 932 (3.4) 0.090
Cerebrovascular disease 386 (4.4) 1250 (4.5) 0.52
Chronic obstructive pulmonary disease 581 (6.6) 1907 (6.9) 0.27
Bronchial asthma 398 (4.5) 1,045 (3.8) 0.003
Obstructive sleep apnea syndrome 429 (4.8) 1226 (4.4) 0.11
Chronic kidney disease stage 3 & 4 845 (9.5) 3716 (13.4) <0.001
Chronic kidney disease stage 5 & hemodialysis 305 (3.4) 528 (1.9) <0.001
Solid organ transplant recipients 73 (0.8) 279 (1.0) 0.12
Solid tumor 425 (4.8) 1527 (5.5) 0.008
Liver disease including cirrhosis 512 (5.8) 1051 (3.8) <0.001
Diabetes mellitus type 2 1712 (19.3) 6276 (22.7) <0.001
Diabetes mellitus type 1 31 (0.3) 110 (0.4) 0.53
Haematological malignancy 132 (1.5) 526 (1.9) 0.011
Rheumatoid arthritis 97 (1.1) 381 (1.4) 0.041
Human immunodeficiency virus infection 39 (0.4) 102 (0.4) 0.35
Elixhauser comorbidity index, median (IQR) 2 (1, 4) 2 (1, 4) <0.001
Hospital frailty score, n (%) <5 points 5510 (62.2) 16,921 (61.2) 0.25
5–15 points 2884 (32.5) 9237 (33.4)
>15 points 468 (5.3) 1497 (5.4)
Outcomes In-hospital mortality, n (%) 1028 (11.6) 3264 (11.8) 0.61
ICU admissions, n (%) 1463 (16.5) 3160 (11.4) <0.001
ICU LOS, median (IQR) [days] 8.3(2.1, 18.3) 4.9 (1.8, 11.7) <0.001
Need for mechanical ventilation, n (%) 1101 (12.4) 2120 (7.7) <0.001
Duration of mechanical ventilation, median (IQR) [days] 10.7 (5.0, 18.3) 6.3 (2.0, 13.0) <0.001
30-days rehospitalisation, n (%) 427 (4.8) 1340 (4.8) 0.92

BMI: body mass index; ICU: intensive care unit; IQR: interquartile range; LOS: length of hospital stay.