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. 2022 Jan 7;17(1):e0261756. doi: 10.1371/journal.pone.0261756

COVID-19 outbreaks in nursing homes: A strong link with the coronavirus spread in the surrounding population, France, March to July 2020

Muriel Rabilloud 1,2,*, Benjamin Riche 1,2, Jean François Etard 3,4, Mad-Hélénie Elsensohn 1,2, Nicolas Voirin 5, Thomas Bénet 6, Jean Iwaz 1,2, René Ecochard 1,2, Philippe Vanhems 7,8
Editor: Giordano Madeddu9
PMCID: PMC8741027  PMID: 34995290

Abstract

Background

Worldwide, COVID-19 outbreaks in nursing homes have often been sudden and massive. The study investigated the role SARS-CoV-2 virus spread in nearby population plays in introducing the disease in nursing homes.

Material and methods

This was carried out through modelling the occurrences of first cases in each of 943 nursing homes of Auvergne-Rhône-Alpes French Region over the first epidemic wave (March-July, 2020). The cumulative probabilities of COVID-19 outbreak in the nursing homes and those of hospitalization for the disease in the population were modelled in each of the twelve Départements of the Region over period March-July 2020. This allowed estimating the duration of the active outbreak period, the dates and heights of the peaks of outbreak probabilities in nursing homes, and the dates and heights of the peaks of hospitalization probabilities in the population. Spearman coefficient estimated the correlation between the two peak series.

Results

The cumulative proportion of nursing homes with COVID-19 outbreaks was 52% (490/943; range: 22–70% acc. Département). The active outbreak period in the nursing homes lasted 11 to 21 days (acc. Département) and ended before lockdown end. Spearman correlation between outbreak probability peaks in nursing homes and hospitalization probability peaks in the population (surrogate of the incidence peaks) was estimated at 0.71 (95% CI: [0.66; 0.78]).

Conclusion

The modelling highlighted a strong correlation between the outbreak in nursing homes and the external pressure of the disease. It indicated that avoiding disease outbreaks in nursing homes requires a tight control of virus spread in the surrounding populations.

Introduction

In December 2019, a severe respiratory syndrome due to a novel coronavirus named subsequently Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) was identified in Wuhan, China [1, 2]. The disease named COronaVIrus Disease 2019 (COVID-19) has then spread worldwide and, on 11 March 2020, the World Health Organization declared the SARS-Cov-2 outbreak a pandemic. The main mode of transmission of the virus is via the droplets expelled during face-to-face exposure [3]. Prolonged unprotected exposure to symptomatic patients presents the highest risk of transmission. However, pre-symptomatic and asymptomatic individuals can also transmit the virus and are major contributors to the spread of the disease. The most common COVID-19 symptoms are fever, dry cough, shortness of breath, fatigue, digestive symptoms, and myalgia. Anosmia or ageusia are also frequent and can be the sole symptoms. Among the factors associated with the disease severity such as diabetes, hypertension, or obesity, advanced age seems to be a major prognostic factor.

In 2020, elderly people were particularly affected by COVID-19. Worldwide, during the first wave of the pandemic, COVID-19 outbreak levels were notably high in nursing homes. This was reported by a number of large studies. In a large-scale survey carried out in England between May 26 and June 20, 2020, nearly half of nursing homes reported at least one confirmed case [4]. By June 30, 2020, among 13,709 nursing homes of the USA, 39% reported at least one case [5]; this proportion reached even 60% in 1,146 nursing homes of Massachusetts, Georgia, and New Jersey [6]. By May 2020, across Europe, long-term care facilities (that include nursing homes) registered 26 to 66% of all COVID-19 deaths [7]. By November 2020, in France, 44% of COVID-19 deaths occurred in nursing homes and, in these homes, the mean COVID-19 case fatality rate was 20% [8]. Similar high fatality rates were observed in nursing homes of other European countries [9, 10].

During the first epidemic wave, the surge and spread of the disease in nursing homes were favoured by the presence of asymptomatic and pre-symptomatic residents or persons from surrounding communities (staff, visitors, etc.) [10–14] and further aggravated by shortage of protective equipment, tests, and staff [15–17]. This led to recommend testing firstly residents and staff with suggestive symptoms and then contact persons rather than carrying out systematic screening [18]. Thus, the outbreak of COVID-19 in nursing homes might be strongly linked with the spread of the coronavirus in the surrounding population.

To check this hypothesis, we modelled during the first epidemic wave the extent and the dynamics of the disease in the nursing homes of all Départements of Auvergne-Rhône-Alpes (ARA) Region. The change in the hospitalization probability in same Départements’ populations was also modelled over the same period to assess the link between COVID-19 outbreak in the nursing homes and the external pressure of the disease.

Methods

Characteristics of ARA Region

ARA Region is the second most important of the 13 Regions of Metropolitan France in terms of population (8,032,377 inhabitants in 2020, 12% of the French population)). It includes twelve Départements whose populations range between 142,811 (Cantal) and 1,876,051 people (Rhône, Département where Lyon metropole is home to nearly 1,400,000 people) (Table 1, Fig 1).

Table 1. COVID-19 cumulative outbreaks in nursing homes and cumulative incidence of inhabitant hospitalizations for COVID-19 in the Départements of Auvergne-Rhône-Alpes Region, France, March 1–July 31, 2020.

Nursing homes Population
Département Total number Nursing homes with outbreak Cumulative percentage % Inhabitantsa Cumulative incidence of hospitalizationsb Cumulative incidence of hospitalizations per 100,000
Cantal 40 9 22 142,811 73 51
Allier 48 17 35 331,315 265 80
Puy-de-Dôme 99 38 38 660,240 264 40
Savoie 57 26 46 432,548 515 119
Drôme 71 35 49 520,560 730 140
Loire 112 57 51 764,737 1,391 182
Haute-Loire 49 26 53 226,901 158 70
Isère 108 57 53 1,264,979 954 75
Ardèche 65 38 58 326,875 674 206
Ain 67 41 62 656,955 640 97
Rhône 161 100 62 1,876,051 4,488 239
Haute-Savoie 66 46 70 828,405 1,084 131
All Dept. 943 490 52 8,032,377 11,236 140
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a Source INSEE projections for 2020.

b Number of inhabitants hospitalized for COVID-19 during the study period.

Fig 1. Locations of the nursing homes of Auvergne-Rhône-Alpes Region, France, March 1–July 31, 2020.

Fig 1

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Black and white circles indicate, respectively, the nursing homes with and without COVID-19 outbreaks.

Nursing home characteristics and collected data

The residents of the nursing homes under study were people aged ≥60 years with low to high degree of dependency. In those homes, care is provided by nurses and care assistants under the supervision of a coordinating physician.

The study period was between March 1 and July 31, 2020; this corresponded roughly to the duration of the first epidemic wave.

The data on the epidemic outbreak were extracted from a database designed by Santé Publique France to monitor the surge and spread of COVID-19 in all medico-social facilities, including nursing homes (Cf. https://www.gouvernement.fr/info-coronavirus/carte-et-donnees). This nationwide surveillance system of COVID-19 cases required the declaration of a suspected or confirmed case and then the provision of daily data on the number of cases, deaths, etc.

An outbreak of the disease in a nursing home was declared as soon as the first suspected or confirmed case in a resident or staff member was reported to the surveillance system [19, 20]. The date of this outbreak was that of symptom onset in that first case. A suspected case was defined by the presence of fever, respiratory symptoms, or other clinical symptoms considered by a physician as compatible with COVID-19. A confirmed case was defined as one with a positive SARS-CoV-2 RT-PCR test result.

Imputation of missing dates of symptom onsets in first cases

The date of symptom onset in the first case was missing in 44% of the 490 nursing homes that reported COVID-19 cases during the study period. For each nursing home with missing date, a nearest neighbour imputation algorithm was developed to identify the ten nearest homes in terms of date of first report, number of reported cases, and number of deaths. The delay between symptom onset in the first case and the date of first report was sampled from the ten nearest neighbours and used to impute the outbreak date. This sampling was repeated ten times to build ten datasets for analysis.

Hospitalization data

The daily number of hospitalized persons in each Département of ARA Region was extracted from the national database on COVID-19-related hospitalizations (https://www.gouvernement.fr/info-coronavirus/carte-et-donnees). The population of each Département was obtained from projections for 2020 of national census data (https://www.insee.fr/fr/statistiques/2859843).

Characteristics of the outbreaks in the nursing homes and the population

The outbreaks were characterized by the duration of the active outbreak period, the date and height of the peak of outbreak probability in nursing homes, and the date and height of the peak of hospitalization probability in the population. Estimating these characteristics required modelling the observed data.

Modelling

The cumulative proportions of COVID-19 outbreaks in the nursing homes over the study period were modelled in each Département using the five-parameter non-linear model of Brain and Cousens (an extension of the four-parameter logistic model) [21]. The first and second derivatives of each predicted cumulative probability curve allowed extracting: i) T1: the delay to acceleration of the probability increase; ii) T2: the delay to the probability peak; and, iii) T3: the delay to deceleration of the probability decrease (Fig 2).

Fig 2. Curve of cumulative probabilities of COVID-19 outbreak in the nursing homes of Département Ain, Auvergne-Rhône-Alpes Region, France, March 1–June 31, 2020.

Fig 2

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T1: delay to acceleration of the probability increase. T2: delay to the peak of outbreak probability. T3: delay to deceleration of the probability decrease, expressed in days since March 1, 2020.

T1, T2, and T3 were expressed in number of days elapsed since March 1, 2020. The duration of the active outbreak period was calculated as the lag time (in days) between T1 and T3. The mean cumulative curves were built using the mean of parameter estimates over the ten datasets (S1A Fig). A sampling from the distribution of each parameter estimates was used to determine the 95% confidence interval of each indicator. The same analysis strategy was used to model the cumulative hospitalization probabilities (S1B Fig).

The correlation between the ranks of the peaks of outbreak probability in nursing homes and the ranks of the peaks of hospitalization probability was estimated using Spearman correlation coefficient.

The analysis was carried out with R statistical software, version 3.6.1. [22].

Ethics

The study was conducted in agreement with the European General Data Protection Regulation and approved by the institutional research ethics committee (Comité d’Éthique du Centre Hospitalier Universitaire de Lyon, N° 20–81).

The use of anonymized and aggregated data obviated the need for participants’ consents.

Results

The cumulative proportion of nursing homes of ARA Region that reported COVID-19 outbreaks was 52% (490 / 943; range: 22–70% according to the Département) (Table 1, Fig 3). The cumulative proportion of hospitalizations for COVID-19 in the Region was 140 per 100,000 inhabitants (range: 40–239 according to the Département).

Fig 3. Modelled curves of daily outbreak probabilities of COVID-19 in the nursing homes of each Département (solid line) and daily hospitalization probabilities in each Département population (dotted line) according to the time elapsed since March 1, 2020, France.

Fig 3

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The curves are ranked in an increasing order of height of the peaks of outbreak probabilities in nursing homes. The left-hand y-axis corresponds to the daily probability of outbreak. The right-hand y-axis corresponds to the daily hospitalization probability per 100,000 inhabitants.

The active outbreak period in the nursing homes (i.e., T1 to T3) lasted 11 to 21 days, according to the Département, and ended before the end of the lockdown on May 11 (Table 2, Fig 3, and S1C Fig).

Table 2. Estimated durations of the active periods of COVID-19 outbreaks in nursing homes, estimated delays and values of the outbreak probability peaks in the nursing homes and the hospitalization probability peaks for COVID-19 in the populations of Auvergne-Rhône-Alpes Region Départements, France, March 1–July 31, 2020.

Outbreaks in nursing homes Hospitalizations in Département populations
Département Active period durationa (T1-T3) Delay to the peak since March 1, 2020 (T2) Value of the peak % Delay to the peak since March 1, 2020 (T2) Value of the peak, per 100,000 inhabitants Population densityb
Cantal 17 [15;21] 32 [30;34] 0.91 [0.76;1.01] 36 [35;37] 1.34 [1.29;1.39] 24.9
Puy-de-Dôme 21 [19;24] 29 [27;30] 1.11 [1.02;1.19] 33 [32;34] 1.29 [1.23;1.36] 82.8
Allier 17 [14;20] 32 [29;34] 1.43 [1.23;1.62] 29 [27;31] 1.88 [1.80;1.97] 45.1
Haute Loire 21 [18;25] 30 [28;32] 1.62 [1.46;1.83] 39 [39;40] 2.24 [2.16;2.33] 51.7
Loire 17 [16;18] 26 [25;27] 1.89 [1.77;1.98] 28 [28;29] 5.88 [5.80;5.96] 160.0
Savoie 13 [11;14] 27 [26;29] 2.09 [1.90;2.43] 31 [30;32] 3.92 [3.81;4.02] 71.8
Isère 13 [11;15] 24 [23;25] 2.39 [2.17;2.71] 30 [29;31] 1.90 [1.87;1.92] 170.2
Drôme 11 [9;13] 24 [22;26] 2.68 [2.31;3.08] 29 [28;29] 5.78 [5.69;5.87] 79.7
Rhône 14 [12;21] 22 [21;23] 2.71 [2.56;2.94] 28 [28;29] 6.76 [6.68;6.85] 577.4
Ain 12 [10;14] 30 [29;31] 2.93 [2.53;3.63] 36 [36;37] 2.89 [2.83;2.95] 114.0
Ardèche 12 [10;14] 24 [23;24] 3.13 [2.72;3.78] 30 [22;34] 4.23 [3.93;4.56] 59.1
Haute Savoie 13 [11;14] 26 [25;27] 3.22 [2.90;3.53] 29 [28;29] 4.16 [4.10;4.21] 188.8
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Whenever applicable, values are followed by their [95% confidence intervals].

The durations and the delays are expressed in number of days.

a Delay between the date of acceleration of outbreak probability increase (T1) and the date of deceleration of outbreak probability decrease (T3).

b Number of inhabitants per km2 according to the French INSEE projections for 2020.

The peak of the outbreak probability in the nursing homes occurred 7 to 17 days after the beginning of the lockdown (i.e., March 15) and before the peak of hospitalization probability in all Départements but one (with two to nine days delay between the two peaks) (Table 2, Fig 3). The geographic distribution of the outbreaks in the nursing homes was heterogeneous, especially at the beginning of the first epidemic wave (S1C Fig). The peak of hospitalization probabilities occurred between March 28 and 31 in eight Départements out of twelve. In the four remaining Départements with hospitalization probability peaks occurring after March 31, the heights of the peaks were lower (Table 2).

The correlation between the ranks of the Départements according to the outbreak probability peak in the nursing homes and their ranks according to the hospitalization probability peak was estimated at 0.71 (95% CI: [0.66; 0.78]). This indicated a strong association between COVID-19 outbreak in the nursing homes and the spread of the disease in the surrounding population (Fig 4).

Fig 4. Correlation between Département ranks according to the outbreak probability peak in nursing homes and Département ranks according to the hospitalization probability peak in the population in the Auvergne-Rhône-Alpes Region, France, March 1–July 31, 2020.

Fig 4

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The four Départements with the lowest outbreak probability peaks in nursing homes were also the Départements with the lowest hospitalization probability peaks; three of which had the lowest population densities. However, Département Rhône (that includes Lyon metropole) that had the highest hospitalization probability peak and population density was not the one that had the highest outbreak probability peak in nursing homes. In Département Ardèche (that has a low population density), the outbreak and hospitalization probability peaks were high. In Départements Isère and Ain, the outbreak probability peaks were high, whereas the hospitalization probability peaks were low (Fig 3). The duration of the active outbreak period was shorter in Départements with outbreak probability peaks > 2% than in other Départements (Table 2).

Discussion

In the French ARA Region, as in other settings, the cumulative proportion of nursing homes that reported COVID-19 outbreaks during the first epidemic wave was high [4–6, 23]. Fifty-two percent of nursing homes declared at least one suspected or confirmed case in a resident or staff member. However, there was an important heterogeneity between Départements that may be explained by the heterogeneity of the disease incidence in the Départements’ populations. Indeed, the analyses showed a strong positive correlation between the ranks of the outbreak probability peaks in the nursing homes of the Départements and the ranks of the hospitalization probability peaks in the same Départements (the latter being indirect measurements of incidence peaks). This correlation was already mentioned in other studies [5, 6, 24–26]. In a study by Sun et al. [6], in 1,146 nursing homes of Massachusetts, Georgia, and New Jersey, the strongest predictors of the probability of presence of at least one COVID-19 case were the infection rate in the county and the number of care units in the nursing homes. A third predictor was the population density, which was positively linked with the outbreak probability in the nursing homes. According to Sugg et al. [5], COVID-19 transmission in 13,709 nursing homes in the USA was positively associated with the corresponding county infection rate and the population density. Similarly, the present study showed that the Départements with the lowest peaks of hospitalization probability were also the Départements with the lowest population densities. Another indicator of the strong link between coronavirus spread in the general population and the risk of disease outbreak in nursing homes was the concurrent strong negative impacts of the lockdown on the hospitalization probability in the population and on the outbreak probability in the nursing homes.

The study results support the following recommendations for protecting the vulnerable residents in nursing homes: i) reduce the virus spread in the general population; ii) test regularly all staff members of nursing homes located in areas with high levels of virus spread in the population (Cf. the ECDC guide for surveillance and control in long-term care facilities) [7]; iii) acquire and use personal protective equipment; and, iv) comply with the universal barrier measures soon after an outbreak alert in the general population.

In addition, the study found that, in most Départements, the peak of outbreak probability in nursing homes preceded the peak of hospitalization probability. This suggests that a decreasing risk of outbreak in nursing homes might indicate a shift toward a decrease in hospitalization rates. This shift may be used to guide hospital bed management.

Limitations of the study

First, the incidence of hospitalization allowed an indirect quantification of the disease incidence in the general population (obtained with a 13.5 day delay, on average) [27]. In each Département, as the delay between the peak of outbreak probability in the nursing homes and the peak of hospitalization probability in the surrounding population was estimated at two to nine days, the peak in the nursing homes should closely follow the peak of disease incidence in the population. Second, the incidence of hospitalization at a Département level was an imperfect measure of the extent of the virus spread in the surrounding populations. This may explain the discrepancy between the heights of the outbreak probability peak in the nursing homes and the hospitalization probability peak in some Départements (Isère or Ain). Third, the study did not explore other characteristics of the nursing homes that could be linked with the outbreak probability. This will be the object of a future analysis.

In conclusion, this study highlighted that the outbreak of COVID-19 in nursing homes occurred soon after its outbreak in the general population and was highly correlated with the incidence of the disease in the surrounding populations. Consequently, avoiding or limiting outbreaks of the disease in nursing homes requires a tight control of virus spread in the surrounding populations and a quick implementation of virus screening and barrier measures in those homes soon after outbreak alerts in the nearby populations.

Supporting information

S1 Fig. Observed and modelled cumulative curves of COVID-19 outbreaks in the nursing homes of each Département of Auvergne-Rhône-Alpes Region over the study period (March 1 –July 31, 2020).

(PDF)

Click here for additional data file. (517.1KB, pdf)
S2 Fig. Observed and modelled cumulative curves of hospitalization for COVID-19 in the Départements of Auvergne-Rhône-Alpes Region over the study period (March 1 –July 31, 2020).

(PDF)

Click here for additional data file. (514.5KB, pdf)
S3 Fig. Weekly dynamics of COVID-19 outbreaks in the nursing homes of Auvergne-Rhône-Alpes Region over the study period (943 nursing homes; March 1 –July 31, 2020).

(PDF)

Click here for additional data file. (558.9KB, pdf)
S1 Dataset. This file in csv format contains the data allowing to model the outbreak in the nursing homes of each Département.

Description of the dataset: 943 lines (one line per nursing home) and 3 columns (Number, Département, DateOutbreak). Number: number of the nursing home. Département: number of the Département. DateOutbreak: date of the beginning of the outbreak.

(CSV)

Click here for additional data file. (14KB, csv)
S2 Dataset. This file in csv format contains the data allowing to model the hospitalization incidence in each Département.

Description of the dataset: 135 lines per Département and four columns (Département, DateHospitalization, NumberHosp, NumberPopDpt). Département: number of the Département. DateHospitalization: Date of hospitalization. NumberHosp: number of inhabitants hospitalized the given day. NumberPopDpt: population size of the Département.

(CSV)

Click here for additional data file. (39.3KB, csv)

Acknowledgments

The authors would like to thank the healthcare professionals of the nursing homes in Auvergne-Rhône-Alpes Region who helped carrying out the survey and the agents of Agence Régionale de Santé Auvergne-Rhône-Alpes who were involved in the implementation of the control measures.

The authors also thank the following epidemiologists involved in the surveillance system: Delphine Casamatta, Philippe Pépin, Christine Saura, Garance Terpant, Mélanie Yvroud (Santé Publique France Auvergne-Rhône-Alpes), Kostas Danis, Scarlett Georges, Côme Daniau, Laure Fonteneau (Département des maladies infectieuses, Santé Publique France).

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The study was funded by the Agence Nationale de la Recherche (Flash COVID-19 program, ANR-20-COVI-000) and the Fondation de France (Engagement 105969). The funders had no role in the study design; data collection and analysis; the preparation of the manuscript; or the decision to publish it.

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Decision Letter 0

Giordano Madeddu

4 Aug 2021

PONE-D-21-20188

COVID-19 outbreaks in nursing homes: a strong link with the coronavirus spread in the surrounding population, France, March to July 2020

PLOS ONE

Dear Dr. Rabilloud,

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PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Rabilloud et al aimed to evaluate the link between COVID-19 in nursing homes and SARS.CoV.2 spread on surrounding population.

There is a rising interest on nursing homes, giving their particular settings and their relationship with SARS-CoV-2 spread. It follows the paper focus is interesting and suitable to be shared with the scientific community.

However, there are some points to address before the paper would be ready for publication.

Introduction

Introduction is quite short and would benefit from generalities adding before introducing nursing homes importance. Please, to make it more complete, add some more data on virus importance and most frequent clinical features. As example:

- Generalities: In December 2019, a new severe respiratory syndrome was identified in Wuhan, China. On January 2020, a new Coronavirus was detected and called SARS-CoV-2. On March 2021, the WHO declared COVID-19 as a public health emergency. (Commission WMH. Available: http://wjw.wuhan.gov.cn/front/web/showDetail/2019123108989; New-type coronavirus causes pneumonia in Wuhan: expert - Xinhua | English.news.cn. Available: http://www.xinhuanet.com/english/2020-01/09/c_138690570.htm)

- Pathophysiology and transmission: e.g. https://doi.org/10.1186/s40779-020-00240-0; https://doi.org/10.1001/jama.2020.12839; https://doi.org/10.26355/eurrev_202101_24424

- Most common clinical features: e.g. https://doi.org/10.26355/eurrev_202007_22291; https://doi.org/10.1002/hed.26269; https://doi.org/10.1016/S1473-3099(20)30402-3.

Then, introduce nursing homes importance for SARS-CoV-2 spread.

Methods

- May you move data on ARA Region from Methods to Introduction? It is just descriptive and is not exactly a ‘Method’. Furthermore, with a slightly better description and references the table could be avoided.

- Study population. This should be slightly better defined. It is not clear the level of medical/nursing assistance needed in the setting (low, medium, high level of patient’s dependency). Are they sheltered care, residential care home residents, or nursing home residents?. It would be useful to better understand the settings and, as consequence, the level of contacts with visitors and healthcare providers.

Discussion

Discussion seems to be well constructed. However, there are at least other two recent manuscripts regarding nursing home in the same Journal. Please, check and comment: https://doi.org/10.1371/journal.pone.0255141; https://doi.org/10.1371/journal.pone.0248009

Limitations

Please, provide limitations in a paragraph apart with ‘Limitations of the study’ heading.

Language

Language should be slightly revised, in order to make the paper more clear (e.g. some parts may be rephrased to make the sentence even shorter).

In conclusion, with some modifications, the article will be an added value for the actual knowledge on SARS-CoV-2.

Reviewer #2: Title: SARS due to COVID-19: predictors of death and profile of patients in the state of Rio de Janeiro, 2020 Manuscript number PONE-D-21-19718

Review by Mastewal Arefaynie /Assistant professor in public health)

Wollo University

Dessie, Ethiopia

General comment

There are several topological and grammar usage errors that need extensive proof reading for revisions.

Specific comments

Abstract

1. In the introduction part you state simply the objective of the study. But it needs the justification of the research (the identified gap).

2. Material and method part it is enough to say method. So remove material. Try to include the software you used for analysis and the type logistic regression you were used.

3. Result: are you using all SARS case or COVID-19 patients only? Try to focus only on the latter case.

4. Line “32” comorbidity was risk factor for death. But you state comorbidity like kidney disease in line “32-34”. But preferred to use each comorbidity factor by removing their comorbidity.

5. Immunodepression change to immunosuppression

6. The conclusion part Line “36” by using odds ratio, you try to conclude to factors. But it is advisable to include more predictors with direction.

Introduction

Generally it is good. But need some justification.

7. The justification to do the research is not well described for scholars.

Methods

8. Change “Materials and Methods” to “methods”

9. “Line 82”, immediate notification of SARS cases was. Change cases to case or was to were.

10. “Line 86” during your description of the outcome variable, you say none death for hospitalization, and hospitalization in an intensive care unit patients. Why not declare their outcome? Unless miss-classification may be there. Your dependent variable should be cure and death.

11. You are using general linear logistic regression. But your outcome variable is dichotomous so binary logistic regression is appropriate. Also your result is expressed in OR.

Result

Unless you were doing comparative study among COVID-19 SARS and SARS, write the result only for you interest. Or compare them.

Discussion

Needs justification for factors

Wish you the luckiest!

Mastewal Arefaynie.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 Jan 7;17(1):e0261756. doi: 10.1371/journal.pone.0261756.r002

Author response to Decision Letter 0

1 Oct 2021

Dear Sir or Madam,

Please find below the answers to the points raised by the Academic Editor and Reviewer 1 regarding our manuscript untitled “COVID-19 outbreaks in nursing homes: a strong link with the coronavirus spread in the surrounding population, France, March to July 2020”.

In an e-mail sent on 23 August, we have informed the editorial office of the journal that the comments of Reviewer 2 do concern another manuscript written by another team (precisely, manuscript PONE-D-21-19718 “SARS due to COVID-19: predictors of death and profile of patients in the state of Rio de Janeiro, 2020). After your reply on 24 August, we received no other review and considered there were no additional comments to deal with.

We hope the following answers will prove satisfactory and that the revised manuscript will be soon accepted.

Best regards,

Muriel Rabilloud

Answers to the Academic Editor comments

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

We have made these checks.

2. Thank you for stating the following financial disclosure:

“PV, MR, RE, JFE, NV

ANR-20-COVI-000

Agence Nationale de la Recherche, programme Flash COVID-19

Engagement 105969

Fondation de France”

Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

The statement was checked and the lack of roles of the funders added to the main text (See section ‘Funding’): “The funders had no role in the study design; data collection and analysis; the preparation of the manuscript; or the decision to publish it.”

3. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

We have prepared twol files in csv format for the data that allow modeling respectively the outbreaks in nursing homes and incidence of hospitalization for COVID-19 in each of the twelve Départements of Auvergne-Rhône-Alpes Region. The first file consists of 942 lines (one per nursing home) and three columns (number of nursing homes, Département number, and date of outbreak). The second file consists of 135 lines per Département and four columns (Département number, date, number of hospitalisations, population size). The files will be submitted with the revised manuscript as Supporting Information files.

4. We note that Figure 1 and S3 Figure in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

Figures 1 and S3 are not copyrighted. We built the maps of Auvergne-Rhône-Alpes Region using R software and the GPS coordinates of the nursing homes.

5. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

We have reviewed the reference list and added five references in response to the reviewer’s queries (see below).

Answers to Reviewer 1 comments

We thank Reviewer 1 for his interest in our work and hope the following answers will prove satisfactory.

Introduction

Introduction is quite short and would benefit from generalities adding before introducing nursing homes importance. Please, to make it more complete, add some more data on virus importance and most frequent clinical features.

As required by the reviewer, we have added generalities on the pandemic due to SARS-Cov-2 infection (+ three references) before introducing the importance of the disease in the nursing homes (lines 27 to 39).

Methods

May you move data on ARA Region from Methods to Introduction? It is just descriptive and is not exactly a ‘Method’. Furthermore, with a slightly better description and references the table could be avoided.

The authors have examined the point and deemed Table 1 can hardly be avoided because it presents the observed data used to model the outbreaks in the nursing homes and hospitalization incidence. Thus, they believe these data may remain in section ‘Methods’ because they show the material and the setting of the study.

- Study population. This should be slightly better defined. It is not clear the level of medical/nursing assistance needed in the setting (low, medium, high level of patient’s dependency). Are they sheltered care, residential care home residents, or nursing home residents? It would be useful to better understand the settings and, as consequence, the level of contacts with visitors and healthcare providers.

We have added information on the characteristics of the residents in the nursing homes and on the type of care provided in these facilities (lines 96 to 99).

Discussion

Discussion seems to be well constructed. However, there are at least other two recent manuscripts regarding nursing home in the same Journal. Please, check and comment: https://doi.org/10.1371/journal.pone.0255141; https://doi.org/10.1371/journal.pone.0248009

The above-cited manuscripts are now mentioned in the Introduction as supplementary references highlighting the severity of the COVID-19 for the residents of nursing homes (references 9 and 10, lines 51-52).

The aim of the paper of De Vito et al. was to assess the spread of the infection in nursing homes where at least one infected person was present, and identify the predictors of developing symptoms and die. The aim of the paper of Meis-Pinheiro et al. was to describe the clinical characteristics and the prognosis of the disease in a cohort of residents of long-term nursing homes who were infected during the first wave of the pandemic. One aim of this study was also to assess the link between the organization of the nursing homes and the incidence of the disease. However, these two studies did not explore the link between the outbreak of COVID-19 in nursing homes and the spread of the coronavirus in the surrounding population. Thus, the two papers present interesting results but we did not feel we have to include them in our Discussion.

Limitations

Please, provide limitations in a paragraph apart with ‘Limitations of the study’ heading.

We mention now the limitations of the study in a separate paragraph.

Language

Language should be slightly revised in order to make the paper more clear (e.g., some parts may be rephrased to make the sentence even shorter).

The whole text has been revised to clarify or simplify the expression of long sentences.

Decision Letter 1

Giordano Madeddu

10 Dec 2021

COVID-19 outbreaks in nursing homes: a strong link with the coronavirus spread in the surrounding population, France, March to July 2020

PONE-D-21-20188R1

Dear Dr. Rabilloud,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Giordano Madeddu

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors addressed the most part of my comments and in a satisfying way. I think the paper may actually be published.

**********

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Acceptance letter

Giordano Madeddu

31 Dec 2021

PONE-D-21-20188R1

COVID-19 outbreaks in nursing homes: a strong link with the coronavirus spread in the surrounding population, France, March to July 2020

Dear Dr. Rabilloud:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Giordano Madeddu

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Observed and modelled cumulative curves of COVID-19 outbreaks in the nursing homes of each Département of Auvergne-Rhône-Alpes Region over the study period (March 1 –July 31, 2020).

    (PDF)

    Click here for additional data file. (517.1KB, pdf)
    S2 Fig. Observed and modelled cumulative curves of hospitalization for COVID-19 in the Départements of Auvergne-Rhône-Alpes Region over the study period (March 1 –July 31, 2020).

    (PDF)

    Click here for additional data file. (514.5KB, pdf)
    S3 Fig. Weekly dynamics of COVID-19 outbreaks in the nursing homes of Auvergne-Rhône-Alpes Region over the study period (943 nursing homes; March 1 –July 31, 2020).

    (PDF)

    Click here for additional data file. (558.9KB, pdf)
    S1 Dataset. This file in csv format contains the data allowing to model the outbreak in the nursing homes of each Département.

    Description of the dataset: 943 lines (one line per nursing home) and 3 columns (Number, Département, DateOutbreak). Number: number of the nursing home. Département: number of the Département. DateOutbreak: date of the beginning of the outbreak.

    (CSV)

    Click here for additional data file. (14KB, csv)
    S2 Dataset. This file in csv format contains the data allowing to model the hospitalization incidence in each Département.

    Description of the dataset: 135 lines per Département and four columns (Département, DateHospitalization, NumberHosp, NumberPopDpt). Département: number of the Département. DateHospitalization: Date of hospitalization. NumberHosp: number of inhabitants hospitalized the given day. NumberPopDpt: population size of the Département.

    (CSV)

    Click here for additional data file. (39.3KB, csv)

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

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