# Vulnerability of hospitalized patients during community disease outbreaks: Lessons from the early COVID-19 pandemic

Eliana Wassermann, Claude Bachmeyer, Guillaume Lemaitre, Mélodie Bernaux, Christel Daniel, Judith Leblanc, Olivier Steichen

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## ► To cite this version:

Eliana Wassermann, Claude Bachmeyer, Guillaume Lemaitre, Mélodie Bernaux, Christel Daniel, et al. Vulnerability of hospitalized patients during community disease outbreaks: Lessons from the early COVID-19 pandemic. La Revue de Médecine Interne, 2025, 10.1016/j.revmed.2025.01.003. hal-04939011

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Submitted on 10 Feb 2025

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Original article

# Vulnerability of hospitalized patients during community disease outbreaks: Lessons from the early COVID-19 pandemic

## Vulnérabilité des patients hospitalisés pendant les épidémies communautaires : leçons du début de la pandémie COVID-19

Eliana Wassermann<sup>a</sup>, Claude Bachmeyer<sup>b</sup>, Guillaume Lemaitre<sup>c</sup>, Mélodie Bernaux<sup>d</sup>, Christel Daniel<sup>e</sup>, Judith Leblanc<sup>a,f</sup>, Olivier Steichen<sup>b,f,*</sup>, the AP-HP/Universities/Inserm COVID-19 research collaboration, AP-HP COVID CDR Initiative

## INFO ARTICLE

Historique de l'article :
Disponible sur Internet le xxx

Keywords :

COVID-19

Hospitalization

Cross infection

Intensive care units

Hospital mortality

## ABSTRACT

**Objectives.** – Hospital-acquired infections (HAIs) during community disease outbreaks threaten vulnerable hospitalized patients. This study compares the outcomes of hospitalized patients who had COVID-19 as either a HAI or a community-acquired infection (CAI).

**Methods.** – We conducted a retrospective cohort study involving adult patients hospitalized across 39 greater Paris University hospitals between January 27th, 2020, and April 21st, 2021, who tested positive for SARS-CoV-2 PCR during their stay. Patients were classified as CAI if they tested positive within 72 hours of admission and HAI if they tested negative within 72 hours but later positive. HAI was subclassified as possible (first positive test between days 4–7), probable (days 8–13), or definite (day 14 onward). Patients with probable or definite HAI were matched 1:3 to CAI patients for age, sex, and comorbidities, to compare intensive care unit (ICU) transfer and in-hospital death between both groups.

**Results.** – Of 10,831 patients, 506 (4.7%) were classified as HAI. They were older and had more comorbidities. After matching, the 333 patients with probable or definite HAI were less likely to be transferred to the ICU (hazard ratio [HR] 0.57, 95% CI 0.38–0.85) compared to their 999 CAI controls and had a higher risk for in-hospital death (HR 1.58, 95% CI 1.16–2.14).

**Conclusion.** – Patients with COVID-19 as a HAI face a higher risk of death compared to patients hospitalized with COVID-19 acquired in the community and are less likely to be admitted to the ICU. Strict infection control measures are needed during community outbreaks to protect hospitalized patients.

© 2025 Les Auteurs. Publié par Elsevier Masson SAS au nom de Société Nationale Française de Médecine Interne (SNFMI). Cet article est publié en Open Access sous licence CC BY (http://creativecommons.org/licenses/by/4.0/).

## RÉSUMÉ

**Objectifs.** – Les infections nosocomiales affectent des patients plus vulnérables que la population générale. Cette étude rétrospective a comparé le pronostic des patients hospitalisés ayant une COVID-19, selon son origine communautaire ou nosocomiale.

Mots clés :

COVID-19

Hospitalisation

Infection croisée

Unités de soins intensifs

Mortalité hospitalière

<footnote><sup>a</sup> Clinical Research Platform, hôpital Saint-Antoine, AP-HP, Paris, France</footnote>

<footnote><sup>b</sup> DMU 3ID, Internal Medicine Department, hôpital Tenon, AP-HP, Sorbonne université, Paris, France</footnote>

<footnote><sup>c</sup> INRIA, CEA, AP-HP, université Paris-Saclay, Palaiseau, France</footnote>

<footnote><sup>d</sup> Strategy and Transformation Department, AP-HP, Paris, France</footnote>

<footnote><sup>e</sup> WIND Department, AP-HP, Paris, France</footnote>

<footnote><sup>f</sup> Institut Pierre-Louis d'épidémiologie et de santé publique, IPLESP, Sorbonne université, Inserm, Paris, France</footnote>

<footnote>* Corresponding author: Service de médecine interne, hôpital Tenon, 4, rue de la Chine, 75020 Paris, France.
Adresse e-mail: olivier.steichen@aphp.fr (O. Steichen).</footnote>
**Méthodes.** – L'étude incluait les adultes hospitalisés dans 39 hôpitaux universitaires de la région parisienne entre le 27 janvier 2020 et le 21 avril 2021, avec une PCR positive pour le SARS-CoV-2 durant leur séjour. Les infections étaient classées communautaires (test positif dans les 72 heures suivant l'admission) ou nosocomiales (négatif initialement mais positif ensuite). Les infections nosocomiales étaient subdivisées en possibles (premier test positif entre les jours 4 et 7), probables (jours 8 à 13), ou certaines (à partir du jour 14). Les patients avec une infection nosocomiale probable ou certaine étaient appariés 1:3 avec des patients ayant une infection communautaire pour comparer les transferts en réanimation et les décès hospitaliers.

**Résultats.** – Sur 10 831 patients inclus, 506 (4,7 %) avaient une infection nosocomiale. Ils étaient plus âgés et avaient plus de comorbidités. Après appariement, les 333 patients avec une infection nosocomiale probable ou certaine étaient moins souvent transférés en réanimation que leurs 999 témoins avec infection communautaire (hazard ratio [HR] 0,57, IC 95 % 0,38-0,85) mais décédaient davantage (HR 1,58, IC 95 % 1,16-2,14).

**Conclusion.** – Les patients atteints de COVID-19 nosocomiale ont un risque de décès plus élevé que ceux avec une COVID-19 communautaire et sont moins souvent admis en réanimation, soulignant l'importance des mesures de contrôle en milieu hospitalier lors des épidémies communautaires.

© 2025 Les Auteurs. Published by Elsevier Masson SAS au nom de Société Nationale Française de Médecine Interne (SNFMI). Cet article est publié en Open Access sous licence CC BY (http://creativecommons.org/licenses/by/4.0/).

# 1. Introduction

Hospital-acquired infections (HAIs) present a significant challenge during community disease outbreaks. The influx of infected patients and staff, combined with the concentration of individuals hospitalized for other conditions, creates an environment where contagious infections can spread. This can turn hospitals into inadvertent hot spots for the transmission of infectious diseases to vulnerable individuals. Indeed, hospitalized adults are generally older than the general adult population and, within any given age group, hospitalized individuals are frail and have more comorbidities than the general population. Furthermore, the acute medical conditions requiring inpatient care may also increase the risk of poor outcomes in case of concurrent HAI. Worse outcomes of HAIs compared to community-acquired infections (CAIs) have been consistently documented for influenza in adults [1,2] and for respiratory syncytial virus infection in children [3,4].

Regarding COVID-19, hospital-acquired cases may account for 7 to 11% of all COVID-19 cases in hospitals [5]. Although SARS-CoV-2 screening on admission to hospital has gradually become mandatory since mid-2020, this proportion remained similar during the pandemic and the number of HAI increased during peaks of CAIs [6].

A meta-analysis of 21 studies from 8 countries found that 1513 patients who developed COVID-19 as a HAI had a higher risk of death compared to 6738 patients admitted with CAI [7]. However, most of these studies were small and did not ascertain the absence of asymptomatic COVID-19 at admission.

This study aims to compare the risk of intensive care unit (ICU) admission and in-hospital death between patients who developed COVID-19 as a HAI after being admitted for another acute condition and those hospitalized with COVID-19 as a CAI. Through this comparison, we aim to illustrate the heightened risks associated with HAIs during a community disease outbreak.

# 2. Patients and methods

This report complies with the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement [8].

## 2.1. Study design and population

We conducted a retrospective multicenter cohort study considering for inclusion all adult patients hospitalized through the Emergency Departments (EDs) across the 39 greater Paris University hospitals (GPUH) between January 27th, 2020, and April 21st, 2021, who had a positive SARS-CoV-2 PCR test during their hospital stay. In the case of multiple stays with a positive test, only the first one was analyzed.

## 2.2. Classification of infections

Patients were classified based on the timing of their first positive SARS-CoV-2 PCR test after admission to the ED. Those with a positive test within 72 hours of admission were classified as CAI, serving as the reference group. Patients with a negative test within the first 72 hours and a subsequent positive test later during their stay were classified as having HAI. Patients not tested during the first 72 hours were excluded due to their uncertain infection status. Consistent with the European Centre for Disease Prevention and Control definitions [9] and with previous studies [10], HAIs were subclassified as possible HAI (positive result from day 4 to day 7 after admission), probable HAI (day 8 to day 13), or definite HAI (day 14 onwards).

## 2.3. Outcomes

The two main outcomes are the times from the first positive PCR to in-hospital death and to ICU transfer. These outcomes were chosen due to their relevance in assessing the severity of disease in hospitalized patients. They can be applied when studying the impact of HAI due to other pathogens.

## 2.4. Data source

Age, sex, comorbidities, date of admission, and outcomes were retrieved from the GPUH COVID Clinical Data Warehouse, which aggregates administrative, demographic, clinical, and biological data from patients cared for in the GPUH [11]. These hospitals, which encompass over 22,000 beds and 1.5 million hospitalizations annually, use the same electronic health record system for patient care. Comorbidities (cardiovascular diseases, chronic respiratory diseases, chronic kidney diseases, cancer, diabetes, hypertension,
<figure><img src="image_3.png" /><figcaption>Fig. 1. Patient flowchart.</figcaption></figure>

obesity) were retrieved from the index hospital stay and previous stays ending within 12 months before the first PCR test result.

## 2.5. Matching

Patients with probable or definite HAI were matched 1:3 to CAI patients using a propensity score based on age, sex, and all comorbidities. This matching controls for available confounding factors, and allows for a direct comparison of outcomes between HAI and CAI groups. Due to their uncertain true status, patients with possible HAI were not included in these matched analyses. The effectiveness of the matching was assessed by the computation of the standardized mean differences (SMD) between cases and controls. SMD less than 0.1 are considered to reflect good balance [12].

## 2.6. Statistical analysis

Cox proportional hazard models adjusted for age, sex, and all comorbidities were used to study the time from the first positive PCR to in-hospital death (first model) or ICU transfer (second model). Analyses were censored at 30 days, as later death or ICU transfer were unlikely to be directly related to COVID-19. To provide accurate hazard ratios (HR), alive discharge was considered as a competing event in both models, and non-ICU in-hospital death was considered as a competing event for ICU transfer. The proportional hazards assumption was graphically checked. Weighted Cox regression was performed with robust covariance estimate to provide unbiased estimates of average hazard ratios also in case of non-proportional hazards (R function *coxphw*) [13]. Analyses were performed with R version 3.6.3 (R Foundation).

## 2.7. Ethics

The data warehouse was authorized by the National Freedom and Informatics Commission (CNIL number: 1980120). The study was approved by the AP-HP Clinical Data Warehouse Institutional Review Board (IRB: 00011591) on April 29, 2020.

# 3. Results

A total of 13,545 patients were admitted through the ED and had a positive SARS-CoV-2 test during the study period (Fig. 1). Among them, 10,831 had a test during the first 72 hours and were classified as CAI (10,325; 95.3%) or HAI (506; 4.7%). Patients with possible, probable, or definite HAI were older than patients with CAI, more frequently women, and had a higher prevalence of comorbidities such as cardiovascular diseases, chronic respiratory conditions, chronic kidney disease, and cancer, although they were less frequently obese (Table 1).

Out of the 10,325 CAI patients, 1,460 (14.1%) died during their hospital stay after a median of 7 days from the first positive test (first-third quartiles: 4–13), whereas 97 of the 506 HAI patients (19.2%) died after a similar median time of 7 days (first-third quartiles: 5–13). ICU transfer rates differed widely: 2,072 CAI patients (20.1%) were transferred to the ICU after a median of one day from the first positive test (first-third quartiles: 0–2), compared to only 40 HAI patients (7.9%) who were transferred after a median of 3 days (1–6).

Characteristics of patients with probable or definite HAI and their matched CAI controls are reported in Table 2. Patients with HAI remained more frequently obese (10,2%) than their matched CAI controls (6,9%, SMD 0.109). Patients with a HAI had a higher risk for in-hospital death (HR 1.58, 95% CI: 1.16–2.14, *P*=0.003, Fig. 2), but a lower likelihood of transfer in the ICU (HR 0.57, 95% CI: 0.38–0.85, *P*=0.006, Fig. 3). Hazards of in-hospital death (Fig. 2) and out-of-ICU hospital death (Fig. 3) were not proportional over the entire follow-up but they were from day 1 to day 5 after the first positive test (with similar hazards of death in both groups), and then from day 6 to day 30 (with higher hazards of deaths in HAI). The computed HRs of death, which are weighted averages of the true HRs over the entire follow-up period, therefore underestimate the increased risk of death from day 6 to day 30.

Additional risk factors of in-hospital death were male sex (HR 1.82, 95% CI: 1.34–2.47, *P*<0.001) and older age (HR 1.03, 95% CI: 1.02–1.05, *P*<0.001). Additional risk factors of ICU transfer were male sex (HR 1.76, 95% CI: 1.26–2.46, *P*<0.001), underlying cardiovascular diseases (HR 1.69, 95% CI: 1.16–2.45, *P*=0.006), diabetes (HR 1.66, 95% CI: 1.18–2.33, *P*=0.004), and obesity (HR 2.11, 95% CI: 1.34–3.33, *P*=0.001).
<table><caption>**Table 1**

Characteristics of the study population.</caption><thead><tr><th></th><th>Patients with CAI<br>n = 10,325</th><th>Patients with possible HAI<br>n = 173</th><th>Patients with probable HAI<br>n = 152</th><th>Patients with definite HAI<br>n = 181</th><th>P-value<sup>a</sup></th></tr></thead><tbody><tr><td>Women</td><td>4036 (39.1%)</td><td>76 (43.9%)</td><td>76 (50.0%)</td><td>98 (54.1%)</td><td>&lt; 0.001</td></tr><tr><td>Age</td><td>66 (54–78)</td><td>72 (59–86)</td><td>77 (67–87)</td><td>81 (71–88)</td><td>&lt; 0.001</td></tr><tr><td>Cardiovascular<br>diseases</td><td>2144 (20.8%)</td><td>66 (30.1%)</td><td>77 (50.7%)</td><td>83 (45.9%)</td><td>&lt; 0.001</td></tr><tr><td>Chronic respiratory<br>diseases</td><td>767 (7.4%)</td><td>23 (13.3%)</td><td>19 (12.5%)</td><td>23 (12.7%)</td><td>&lt; 0.001</td></tr><tr><td>Cancer</td><td>732 (7.1%)</td><td>25 (14.5%)</td><td>33 (21.7%)</td><td>32 (17.7%)</td><td>&lt; 0.001</td></tr><tr><td>Diabetes</td><td>2691 (26.1%)</td><td>38 (22.0%)</td><td>51 (33.5%)</td><td>47 (26.0%)</td><td>0.135</td></tr><tr><td>Hypertension</td><td>3674 (35.6%)</td><td>84 (48.5%)</td><td>77 (50.6%)</td><td>82 (45.3%)</td><td>&lt; 0.001</td></tr><tr><td>Chronic kidney<br>diseases</td><td>1082 (10.5%)</td><td>35 (20.2%)</td><td>38 (25.0%)</td><td>41 (22.6%)</td><td>&lt; 0.001</td></tr><tr><td>Obesity</td><td>1534 (14.9%)</td><td>17 (9.8%)</td><td>18 (11.8%)</td><td>16 (8.8%)</td><td>0.040</td></tr></tbody></table>

Results provided as number (percentage) or median (first quartile–third quartile). CAI: community-acquired infection; HAI: hospital-acquired infection.

<table><caption>**Table 2**

Characteristics of patients included in the matched outcome analysis.</caption><thead><tr><th></th><th>Patients with probable or definite HAI (n = 333)</th><th>Patients with CAI (n = 999)</th><th>SMD<sup>a</sup></th></tr></thead><tbody><tr><td>Women</td><td>174 (52.2%)</td><td>514 (51.4%)</td><td>0.016</td></tr><tr><td>Age</td><td>79 (68–88)</td><td>80 (68–88)</td><td>0.006</td></tr><tr><td>Cardiovascular diseases</td><td>160 (48.0%)</td><td>493 (49.3%)</td><td>0.026</td></tr><tr><td>Chronic respiratory diseases</td><td>42 (12.6%)</td><td>93 (9.3%)</td><td>0.099</td></tr><tr><td>Cancer</td><td>65 (19.5%)</td><td>191 (19.1%)</td><td>0.010</td></tr><tr><td>Diabetes</td><td>98 (29.4%)</td><td>267 (26.7%)</td><td>0.059</td></tr><tr><td>Hypertension</td><td>159 (47.7%)</td><td>463 (46.3%)</td><td>0.028</td></tr><tr><td>Chronic kidney diseases</td><td>79 (23.7%)</td><td>216 (21.6%)</td><td>0.049</td></tr><tr><td>Obesity</td><td>34 (10.2%)</td><td>69 (6.9%)</td><td>0.109</td></tr></tbody></table>

Results provided as number (percentage) or median (first quartile–third quartile). CAI: community-acquired infection; HAI: hospital-acquired infection; SMD: standardized mean difference.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Cumulative incidence of hospital death and alive discharge according to time from the first positive test, estimated with a competing risks model. Full line: hospital-acquired infection; dotted line: community-acquired infection; black lines: hospital death; grey lines: alive discharge.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Cumulative incidence of Intensive Care Unit (ICU) transfer and non-ICU hospital death according to time from the first positive test, estimated with a competing risks model also including alive discharge (not plotted for clarity). Full line: hospital-acquired infection; dotted line: community-acquired infection; black lines: non-ICU hospital deaths; gray lines: ICU transfer.</figcaption></figure>

## 4. Discussion

In this hospital-based cohort study of more than 10,000 patients hospitalized with COVID-19 between January 2020 and April 2021, about 5% were classified as having HAI. HAI patients were older and had more comorbidities compared to patients hospitalized with CAI. They were less frequently transferred to the ICU and died more

often than CAI patients. Our results are consistent with those of a meta-analysis including studies from the first year of the pandemic [7], which computed a pooled relative risk of death of 2.14 (95% CI: 1.76–2.61) in HAI patients compared to CAI patients, and a relative risk of ICU admission of 0.70 (95% CI: 0.48–1.03).

<footnote><sup>a</sup> Qualitative variables were compared across the four groups using a Chi² test and quantitative ones with a Kruskal-Wallis test.</footnote>

<footnote><sup>a</sup> A SMD less than 0.1 is considered to reflect good balance.</footnote>
## 4.1. Interpretation

During the first epidemic wave in 2020, about 15% of infected patients developed a severe disease requiring hospitalization, 2 to 5% needed intensive care unit (ICU) admission, and 2 to 5% eventually died [14]. In our study as in those included in the meta-analysis, patients hospitalized with CAI were a mix of two types of patients: those hospitalized for another reason with asymptomatic or non-severe SARS-CoV-2 infection screened at admission, who are similar to those later affected by HAI, and those hospitalized for severe CAI, in whom the outcomes could have been expected to be worse. By design, the CAI group was therefore biased toward vulnerable patients and severe forms of COVID-19. The even higher risk of death in patients with COVID-19 as a HAI is therefore noteworthy.

Risk factors for poor outcomes in CAI include older age, male sex, and comorbidities [14]. Our results cannot be explained by imbalance in these variables, which were matched and adjusted for. We had no information on vaccination status and administered treatments, which could affect outcomes. The death of some patients with HAI may have been the direct result from the acute medical condition prompting hospitalization. However, the comparisons between HAI and CAI only involved patients with probable or definite HAI, who had at least survived their first seven days as inpatients. The worse outcome of HAI may also result from the infection affecting patients with an already unstable clinical state due to the acute medical condition prompting hospitalization, and more likely to be frail [15]. Frailty, which is not fully captured by age and comorbidities, could also explain why, despite being more severe as reflected by the higher mortality risk, patients with HAI were less frequently admitted to the ICU. However, we were unable to explore this hypothesis due to the lack of data on frailty.

The mortality curve for patients with HAI roughly overlaps with that of patients with CAI up to day 5 after the first positive PCR test, and then diverges proportionally through day 30. This pattern suggests that mortality in patients with HAI is driven by the primary cause of hospitalization up to day 5, with an additional risk of death emerging beyond that point. This is consistent with the progression to severe COVID-19, where respiratory distress typically develops between 5 and 10 days after symptom onset [16]. Further studies comparing patients with HAI to patients with similar primary causes of hospitalization but without HAI are needed to confirm this hypothesis and properly estimate the proportion of death attributable to COVID-19, to the primary cause of hospitalization, or to their interaction.

In CAI, the median time from the first positive PCR test (in the ED) and ICU transfer was one day, consistent with previous findings. By contrast, the time between the first positive test (in the ward) and ICU transfer was three days in HAI, much less than the typical 8 to 11 days between symptom onset and ICU transfer for severe COVID-19 [16]. Again, further studies with more detail on the cause of ICU transfer are needed to clarify the role of COVID-19, the primary cause of hospitalization, and their interaction.

## 4.2. Limitations

Our study has other limitations. First, some CAI may have been misclassified as HAI. To limit this risk, we required a negative SARS-CoV-2 test during the first 72 hours of admission and we excluded patients with a first positive test result from day 4 to day 7 after admission (possible HAI). Second, while death and ICU transfer are highly relevant patient-oriented outcomes, we did not study other impacts of HAIs that are important from the provider perspective, such as increased length-of-stay or increased health costs, which would have required a different comparison group [17]. Third, we did not match HAI and CAI cases based on the date of infection. Since COVID-19 outcomes improved over time [18], this

could have biased the results if the proportion of HAI had changed during the study period. Specifically, an increasing proportion of hospital-acquired COVID-19 might skew the results toward apparently better outcomes for these cases, and vice-versa. However, the consistency of our findings with previous studies suggests that such bias, if present, was likely limited. Fourth, patients with HAI were slightly more often obese than matched patients with CAI. However, time-to-event analyses were adjusted for this imbalance. Fifth, our results reflect the early stages of the pandemic and cannot be generalized to the current impact of hospital-acquired COVID-19, because the virulence of SARS-CoV-2 variants has decreased and the level of acquired immunity from vaccines and prior infections has increased, and because treatment is more effective. However, other studies suggest that morbidity and mortality are still significantly increased by HAI due to Omicron variant, although less than with previous variants [17].

## 4.3. Generalizability

The lessons learned from the COVID-19 pandemic are broadly applicable to future emergent or common community disease outbreaks. While the specific pathogens involved may change, the disproportionate impact that infections can have on hospitalized patients and the fundamental strategies for preventing HAIs remain the same [1]. Key measures to protect the vulnerable hospital population during community outbreaks include vaccinating healthcare workers and patients, adhering to rigorous hand hygiene, using face masks, conducting effective virological screening and diagnosis, and isolating infected patient [6,19-22]. On the long run, increasing the availability of single-occupancy rooms in hospitals, beyond enhancing patient privacy and improving the overall care experience, significantly reduces the risk of HAI during community outbreaks [23-26].

In conclusion, the findings from this study highlight the universal risk posed by HAIs during community disease outbreaks: they affect vulnerable patients and result in a disproportionate risk of fatal outcomes. HAIs during community disease outbreaks should not be regarded as benign and unavoidable events. Patients, families, and hospital staff must carefully adhere to rigorously crafted prevention policies to limit this burden.

## Protection of human and animal rights

The authors declare that the conduct of the research described in this article is in accordance with the World Medical Association's Declaration of Helsinki, revised in 2013, concerning experiments on patients or subjects, as well as with the European Directive 2010/63/EU on animal experimentation.

## Informed consent and data sharing statement

The authors declare that the article does not contain any personal data that could identify the patient or subject.

## Disclosure of interest

The authors declare that they have no competing interest.

## Funding

This work was supported by a grant from the Fondation de France to EW.
## Contribution

Conceptualization: O.S., E.W. and J.L.; methodology: O.S., E.W. and J.L.; investigation: G.L., M.B., C.D., O.S. and C.B.; data curation: E.W. and J.L.; formal analysis: E.W.; project administration: J.L.; supervision: O.S. and J.L.; validation: C.B., G.L., M.B. and C.D.; visualization: E.W.; writing – original draft: O.S.; writing – review and editing: E.W., J.L., C.B., G.L., M.B. and C.D.

## Acknowledgments

The authors thank the members of the AP-HP Covid Clinical Data Warehouse Initiative, listed on https://eds.aphp.fr/covid-19, who contributed to the implementation of the EDS-COVID database and operating solutions for this database.

## Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.revmed.2025.01.003.

## Références

[1] Li Y, Wang L-L, Xie L-L, Hou W-L, Liu X-Y, Yin S. The epidemiological and clinical characteristics of the hospital-acquired influenza infections: a systematic review and meta-analysis. Medicine (Baltimore) 2021;100:e25142.

[2] Fullana Barceló MI, Asensio Rodriguez J, Artigues Serra F, Ferre Beltran A, Salva D'agosto P, Almodovar Garcia M, et al. Epidemiological and clinical characteristics of community-acquired and nosocomial influenza cases and risk factors associated with complications: a four season analysis of all adult patients admitted in a tertiary hospital. Influenza Other Respir Viruses 2021;15:352-60.

[3] Langley JM, LeBlanc JC, Wang EE, Law BJ, MacDonald NE, Mitchell I, et al. Nosocomial respiratory syncytial virus infection in Canadian pediatric hospitals: a Pediatric Investigators Collaborative Network on Infections in Canada Study. Pediatrics 1997;100:943-6.

[4] Simon A, Müller A, Khurana K, Engelhart S, Exner M, Schildgen O, et al. Nosocomial infection: a risk factor for a complicated course in children with respiratory syncytial virus infection—results from a prospective multicenter German surveillance study. Int J Hyg Environ Health 2008;211:241-50.

[5] Read JM, Green CA, Harrison EM, Docherty AB, Funk S, Harrison J, et al. Hospital-acquired SARS-CoV-2 infection in the UK's first COVID-19 pandemic wave. Lancet 2021;398:1037-8.

[6] Hatfield KM, Baggs J, Maillis A, Warner S, Jernigan JA, Kadri SS, et al. Assessment of hospital-onset SARS-CoV-2 infection rates and testing practices in the US, 2020-2022. JAMA Netw Open 2023;6:e2329441.

[7] Ponsford MJ, Ward TJC, Stoneham SM, Dallimore CM, Sham D, Osman K, et al. A systematic review and meta-analysis of inpatient mortality associated with nosocomial and community COVID-19 exposes the vulnerability of immunosuppressed adults. Front Immunol 2021;12:744696.

[8] Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med 2015;12:e1001885.

[9] Surveillance definitions for COVID-19. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/covid-19/surveillance/surveillance-definitions (accessed January 3, 2022).

[10] Abbas M, Zhu NJ, Mookerjee S, Bolt F, Otter JA, Holmes AH, et al. Hospital-onset COVID-19 infection surveillance systems: a systematic review. J Hosp Infect 2021;115:44-50.

[11] Entrepôt de données de santé AP-HP. https://eds.aphp.fr/covid19 (accessed January 19, 2022).

[12] Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083-107.

[13] Schemper M, Wakounig S, Heinze G. The estimation of average hazard ratios by weighted Cox regression. Stat Med 2009;28:2473-89.

[14] Stokes EK, Zambrano LD, Anderson KN, Marder EP, Raz KM, El Burai Felix S, et al. Coronavirus disease 2019 case surveillance – United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:759-65.

[15] Ponsford MJ, Jefferies R, Davies C, Farewell D, Humphreys IR, Jolles S, et al. Burden of nosocomial COVID-19 in Wales: results from a multicentre retrospective observational study of 2508 hospitalised adults. Thorax 2021;76:1246-9.

[16] Oda T, Hagiwara E, Yamaya T, Ogura T. Timeline of oxygen demand in patients with COVID-19 pneumonia. Intern Med 2022;61:3651-8.

[17] Klompas M, McKenna CS, Kanjilal S, Pak T, Rhee C, Chen T. Morbidity and mortality of hospital-onset SARS-CoV-2 infections due to omicron versus prior variants: a propensity-matched analysis. Ann Intern Med 2024;177:1078-88.

[18] Lam B, Stepanova M, Venkatesan C, Garcia I, Reyes M, Mannan A, et al. Outcomes of hospitalized patients with COVID-19 during the course of the pandemic in a fully integrated health system. PLoS One 2022;17:e0263417.

[19] Bridges CB, Kuehnert MJ, Hall CB. Transmission of influenza: implications for control in health care settings. Clin Infect Dis 2003;37:1094-101.

[20] Blanco N, Eisenberg MC, Stillwell T, Foxman B. What transmission precautions best control influenza spread in a hospital? Am J Epidemiol 2016;183:1045-54.

[21] Ngandu NK, Mmotsa TM, Dassaye R, Thabetha A, Odendaal W, Langdown N, et al. Hospital acquired COVID-19 infections amongst patients before the rollout of COVID-19 vaccinations, a scoping review. BMC Infect Dis 2022;22:140.

[22] Petrie JG, Talbot TR. Health care-acquired viral respiratory diseases. Infect Dis Clin North Am 2021;35:1055-75.

[23] van de Glind I, de Roode S, Goossensen A. Do patients in hospitals benefit from single rooms? A literature review. Health Policy 2007;84:153-61.

[24] Taylor E, Card AJ, Piatkowski M. Single-occupancy patient rooms: a systematic review of the literature since 2006. HERD 2018;11:85-100.

[25] Munier-Marion E, Bénet T, Régis C, Lina B, Morfin F, Vanhems P. Hospitalization in double-occupancy rooms and the risk of hospital-acquired influenza: a prospective cohort study. Clin Microbiol Infect 2016;22:461.e7-9.

[26] Jo HJ, Choe PG, Kim JS, Lee M, Lee M, Bae J, et al. Risk of nosocomial coronavirus disease 2019: comparison between single- and multiple-occupancy rooms. Antimicrob Resist Infect Control 2024;13:95.