Description and analysis of representative COVID-19 cases-A retrospective cohort study.

BACKGROUND: Most data on COVID-19 was collected in hospitalized cases. Much less is known about the spectrum of disease in entire populations. In this study, we examine a representative cohort of primarily symptomatic cases in an administrative district in Southern Germany.
METHODS: We contacted all confirmed SARS-CoV-2 cases in the administrative district. Consenting participants answered a retrospective survey either via a telephone, electronically or via mail. Clinical and sociodemographic features were compared between hospitalized and non-hospitalized patients. Additionally, we assessed potential risk factors for hospitalization and time to hospitalization in a series of regression models.
RESULTS: We included 897 participants in our study, 69% out of 1,305 total cases in the district with a mean age of 47 years (range 2-97), 51% of which were female and 47% had a pre-existing illness. The percentage of asymptomatic, mild, moderate (leading to hospital admission) and critical illness (requiring mechanical ventilation) was 54 patients (6%), 713 (79%), 97 (11%) and 16 (2%), respectively. Seventeen patients (2%) died. The most prevalent symptoms were fatigue (65%), cough (62%) and dysgeusia (60%). The risk factors for hospitalization included older age (OR 1.05 per year increase; 95% CI 1.04-1.07) preexisting lung conditions (OR 3.09; 95% CI 1.62-5.88). Female sex was a protective factor (OR 0.51; 95% CI 0.33-0.77).
CONCLUSION: This representative analysis of primarily symptomatic COVID-19 cases confirms age, male sex and preexisting lung conditions but not cardiovascular disease as risk factors for severe illness. Almost 80% of infection take a mild course, whereas 13% of patients suffer moderate to severe illness. TRIAL REGISTRATION: German Clinical Trials Register, DRKS00022926. URL: https://www.drks.de/drks_web/setLocale_EN.do.

Introduction

SARS-CoV-2 has affected the entire globe with millions of confirmed cases, leading to increasing fatalities [1]. Germany is among the most affected countries worldwide [2].

Previous studies focused mostly on the clinical features and potential risk factors for a severe course of COVID-19 in hospitalized cases. The majority of infected patients, however, remain asymptomatic or suffer mild symptoms and recover in home-quarantine. This group of patients is underrepresented in most studies. Furthermore, few countries other than Germany offered the extensive testing capacity to identify almost all symptomatic infections. Thus a high number of unreported cases has to be presumed based on the observed COVID-19 specific and excess mortality in studies from countries like Italy, the United Kingdom or United States of America [3, 4]. The paucity of representative cohorts makes it difficult to draw conclusions in regard to the typical sociodemographic and clinical features of COVID-19 as well as risk factors for severe disease.

The few available studies examining whole cohorts were linked to distinct settings with selected patient populations (e.g. homeless shelter, cruise ship) [5-7]. The proportion of persons infected developing severe disease in these and other large cohort studies was estimated to be around 16–24% [6, 8–10]. Identified risk factors for severe disease in hospitalized patients include advanced age, male sex and comorbidities such as hypertension, cardiovascular disease, diabetes or COPD [11-15].

In our study presented here, we describe clinical features, demographics, epidemiological characteristics and assess potential risk factors of severe disease for a cohort of persons infected with SARS-CoV2 in an entire administrative district in Southern Germany. Due to the widespread case finding and testing capabilities in Germany and the region especially, our study represents a near-complete population cohort reflecting the entire spectrum of disease [2].

Methods

Study design and participants

The study took place in the Rhein-Neckar region of Germany from March 19, 2020 until June 30, 2020. The Rhein-Neckar-Region inhabits approximately 710,000 people, constituting an administrative district of Germany [16, 17]. One of the largest University hospitals in Germany, the Heidelberg University Hospital is located in this district with an approximate of 2000 beds including 156 beds with mechanical ventilation. At no point during the pandemic were the capacities of the University Hospital and surrounding hospitals exceeded. A national lock-down in Germany was announced on March 15, 2020 and lasted until April 19th.

Persons of all age groups who tested positive for SARS-CoV-2 using RT-PCR nasopharyngeal swabs, identified through the registry of the public health authority in the Rhein-Neckar-Region in Germany between February 7 and June 30, 2020 were screened and asked for consent. At the time of the study, testing was performed based on clinical suspicion, i.e. presence of symptoms or high risk contact. If a participant was not able to give written consent due to death or legal care, we asked first degree relatives or guardians to fill out the survey on behalf of the participant. Consenting participants were contacted after they had completed two weeks of quarantine and were asked to fill out a survey developed by infectious disease clinicians based on findings in the literature (S1 File). Overall data on number of cases and number of cases hospitalized and COVID-related deaths were available from the public health authority. Data protection was in line with the German data protection laws and the General Data Protection Regulation of the European Union.

Data collection

After confirming consent, participants were invited to participate via a phone interview or an electronical or paper-based questionnaire was sent to them to fill out. All data used in this study was collected with the retrospective survey, no other sources were used.

The Research Electronic Data Capture (REDCapTM, www.prorect-redcap.org) hosted at the University Hospital Heidelberg was used for data management. REDCapTM is a secure, web-based application, which provides audit trails for tracking data manipulation and export procedures [18].

Data, variables and definitions

Variables

We collected sociodemographic, clinical variables and outcome indicators. The questionnaire and translation of assessed information is available in the supplement (S1 File).

Outcome indicators

For the descriptive analysis we stratified our study population by a five-level categorical outcomes variable: (1) Asymptomatic cases were defined as patients with confirmed SARS-CoV-2 infection who did not report any symptoms or clinical signs over the course of their duration of quarantine. (2) Symptomatic outpatients requiring no hospital admission were considered mild cases. (3) Hospitalized participants and patients admitted to the intensive care unit without requiring mechanical ventilation were defined as moderate cases. (4) Participants were considered critical cases if they required mechanical ventilation or extracorporeal membrane oxygenation (ECMO) due to respiratory failure. (5) All patients who died as a result of the infection were classified as deceased cases.

For the purpose of the regression analysis we chose to dichotomize the outcome variable given the small numbers in some of the outcome categories. Consequently, we pooled all cases that were moderate, critical or deceased as hospitalized cases. Vice versa all asymptomatic and mild cases were pooled into the non-hospitalized group for the regression analysis.

Statistics

The analyses were performed using the R statistical language (version 4.0 or higher) on Windows and macOS, and Microsoft Excel 2018 (version 16.16.14 or higher). The statistical analysis plan is available upon request.

We generated a detailed descriptive summary of the study population structure and subgroups with the appropriate measures of central tendency and spread. We compared the demographic structure of our study population to the structure of alle recorded cases in the study area using Pearson’s Chi-squared test to assess the representativeness of our study. Unfortunately, recorded cases were only available through the public health authority with information on age, not sex. Our subgroup analyses were constructed around disease severity which is described in the section above. We performed a post-hoc analysis of age distribution before and after the lock-down in which we decided to set the cut-off value at April 1st, 2020, because the German borders were closed on March 15, 2020. Considering an incubation period of 14 days, the cut-off date of April 1st will likely have excluded all travelers in the second group.

For the inferential statistics, we constructed a multivariable logistic regression model to identify potential risk factors of hospitalization. We identified the potential predictors for the multivariable model through a series of univariate logistic regression models and selected those predictors for the multivariate model that appeared to have a low probability of error (p < 0.1) in the univariate models. All univariate predictors with p < 0.1 were included in the final multivariate model. As predictors we assessed age as a continuous variable, sex, smoking as a continuous variable using pack years, living with children (age <18), hypertension (yes/no), coronary heart disease (CHD; yes/no), diabetes (type 1 or type 2; yes/no) and lung conditions (yes/no). Lung conditions were defined as a combined variable of either COPD, asthma treated with medications, any other lung disease or previously performed lung surgery. We decided to assess the variable age in a linear relation to allow for easier interpretation and dissemination of the results and because the focus of this paper is primarily of exploratory nature and not predictive.

Secondly, we estimated the influence of the same covariates on the time from symptom onset to hospitalization with a Cox proportional hazard ratio (HR) model. We manually censored all non-hospitalized patients at 14 days after symptom onset, since the majority of patients get hospitalized within 7 days [19].

Study approval

The institutional ethics board of the University Hospital in Heidelberg approved this study (S-179/2020). Prior to the inclusion in the study, written informed consent was received from each participant. For data protection purposes, all participants were assigned a study ID to ensure pseudonymization.

Results

By June 30th, 2020, the public health authority in Heidelberg had registered 1,293 SARS-CoV-2 cases in the region with completed quarantine. From these registered cases 166 were hospitalized and a total of 47 patients had died as a result of the infection [20].

Of the registered patients, 142 either refused to give informed consent to the study or were not responding to our inquiry. Thus, our study included 1,151 participants with laboratory-confirmed SARS-CoV-2 infection. Subsequently, 254 participants either withdrew consent or did not submit the questionnaire, leaving a total of 897 patients in the final analysis (69.4% of confirmed cases; Fig 1).

Fig 1

Study diagram indicating the recruitment process.

Depicting the recruitment process of participants leaving a total of 897 (69.4%) data sets in the final analysis.

We present clinical and demographic characteristics in Tables 1 and 2. Most patients had from mild symptoms (713, 79.5% of the cohort; 63.3% of all patients not hospitalized among 1,293 cases in the district overall took part in the study). Only a minority of patients remained asymptomatic (54, 6.0%). Altogether, 97 participants (10.8%) were admitted to a hospital without requiring mechanical ventilation (moderate cases), and 16 participants (1.8%) were hospitalized requiring mechanical ventilation (critical cases). In total, 78.3% of all hospitalized cases in the district took part in the study. We observed 17 (1.9%) deaths in the study due to COVID-19 (this made up 36.2% of the total 47 deaths among all 1,293 cases observed in the district). Aside from the age group 70–79 which was slightly underrepresented, (5.8% in our study vs. 8.4% among all infected) our study sample represented the age distribution of all SARS-CoV-2 infections in the district at the time of the study (S1 Table).

Table 1

Demographics and clinical characteristics.

	All patients	Asymptomatic patients	Mild (symptomatic outpatients)	Moderate (hospitalized)	Critical (ventilation)	Deceased
n (%)	897 (100)	54 (6.0)	713 (79.5)	97 (10.8)	16 (1.8)	17 (1.9)
Age (years)
Mean (SD)–yr	47.03 (17.5)	52.3 (20.7)	44.0 (16.0)	58.1 (15.9)	62.2 (13.3)	79.1 (11.8)
Distribution n (%)
0–17	17 (1.9)	0 (0.0)	17 (2.4)	0 (0.0)	0 (0.0)	0 (0.0)
18–49	460 (52.3)	23 (42.6)	410 (57.5)	23 (23.7)	3 (18.8)	1 (5.9)
50–59	202 (22.5)	7 (13.0)	162 (22.7)	28 (28.9)	5 (31.3)	0 (0.0)
60–69	112 (12.5)	9 (16.7)	78 (10.9)	21 (21.7)	3 (18.8)	1 (5.9)
70–79	68 (7.6)	10 (18.5)	34 (4.8)	17 (17.5)	3 (18.8)	4 (23.5)
>80	36 (4.0)	5 (9.3)	10 (1.4)	8 (8.3)	2 (12.5)	11 (64.7)
Sex n (%)
Female	453 (50.5)	30 (55.6)	376 (52.7)	40 (41.2)	4 (25.0)	3 (17.7)
Male	441 (49.2)	24 (44.4)	334 (46.8)	57 (58.8)	12 (75.0)	14 (82.3)
No answer	3 (0.3)	0 (0.0)	3 (0.4)	0 (0.0)	0 (0.0)	0 (0.0)
Participants living with children <18 years n (%)	269 (30.0)	11 (20.4)	236 (33.1)	18 (18.6)	4 (25.0)	0 (0.0)
Source of Transmission/ Exposure n (%)
1 Not sure	215 (24.0)	18 (33.3)	155 (21.8)	31 (32.0)	7 (43.8)	4 (23.5)
2 Social contact	210 (23.4)	11 (20.4)	184 (25.8)	12 (12.4)	2 (12.5)	1 (5.9)
3 Work	180 (20.1)	8 (14.8)	154 (21.6)	18 (18.6)	0 (0)	0 (0.0)
4 University, school, kindergarten	25 (2.8)	0 (0.0)	24 (3.4)	1 (1.0)	0 (0)	0 (0.0)
5 Travel	83 (9.3)	6 (11.1)	68 (9.5)	8 (8.3)	0 (0)	1 (5.9)
6 Other	146 (16.3)	9 (16.7)	125 (17.5)	10 (10.3)	2 (12.5)	0 (0)
No answer	38 (4.2)	2 (3.7)	3 (0.4)	17 (17.5)	5 (31.3)	11 (64.7)

Baseline characteristics of 897 participants with coronavirus disease 19 stratified by level of severity.

Table 2

Clinical course of disease.

	All patients	Asymptomatic patients	Mild (symptomatic outpatients)	Moderate (hospitalized)	Critical (ventilation)	Deceased
n (%)	897 (100)	54 (6.0)	713 (79.5)	97 (10.8)	16 (1.8)	17 (1.9)
Symptoms#
n (%)
1. Fever	481 (53.6)	Asymptomatic	374 (52.5)	82 (84.5)	13 (81.3)	12 (70.6)
2. Cough	552 (61.5)		457 (64.1)	72 (74.2)	9 (56.3)	14 (82.4)
3. Sputum	79 (8.8)		75 (10.5)	10 (10.3)	3 (18.8)	1 (5.9)
4. Sore throat	306 (32.1)		269 (37.7)	32 (33.0)	5 (31.3)	1 (5.9)
5. Dyspnea	181 (20.1)		113 (15.8)	50 (51.5)	10 (62.5)	8 (47.1)
6. Muscle pain	279 (31.1)		242 (33.9)	27 (27.8)	8 (50.0)	2 (11.8)
7. Limb pain	432 (48.1)		373 (52.3)	47 (48.5)	10 (62.5)	2 (11.8)
8. Fatigue	586 (65.3)		524 (73.5)	64 (66.0)	9 (56.3)	11 (64.7)
9. Headache	513 (57.2)		450 (63.1)	53 (54.6)	8 (50.0)	2 (11.8)
10. Runny nose	270 (30.1)		253 (35.5)	15 (15.5)	2 (12.5)	0 (0.0)
11. Chest pain	154 (17.1)		135 (18.9)	15 (15.5)	2 (12.5)	2 (11.8)
12. Diarrhea	212 (23.6)		173 (24.3)	29 (29.9)	5 (31.3)	5 (29.4)
13. Nausea	96 (10.7)		77 (10.8)	14 (14.4)	3 (18.8)	2 (11.8)
14. Change in taste	537 (59.8)		480 (67.3)	50 (51.5)	6 (37.5)	1 (5.9)
15. Other	304 (33.9)		259 (36.3)	20 (20.6)	4 (25.0)	2 (11.8)
Comorbidities n (%)
Cardiac disease	197 (22.0)	14 (25.9)	119 (16.7)	39 (40.2)	12 (75.0)	13 (76.5)
Hypertension	152 (17.0)	9 (16.7)	98 (13.7)	29 (29.9)	6 (37.5)	10 (58.8)
CHD	19 (2.1)	2 (3.7)	7 (1.0)	2 (2.1)	2 (12.5)	6 (35.3)
Lung disease	124 (13.8)	8 (14.8)	87 (12.2)	22 (22.7)	4 (25.0)	3 (17.7)
Kidney disease	24 (2.7)	3 (5.6)	13 (1.8)	4 (4.1)	2 (12.5)	2 (11.8)
Liver disease	13 (1.5)	1 (1.9)	6 (0.8)	5 (5.2)	1 (6.3)	0 (0.0)
Diabetes mellitus	44 (4.9)	4 (7.4)	25 (3.5)	9 (9.3)	4 (25.0)	2 (11.8)
Autoimmune disorder	52 (5.8)	3 (5.6)	40 (5.6)	8 (8.3)	1 (6.3)	0 (0.0)
HIV positive	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
Malignancy	49 (5.5)	5 (9.3)	26 (3.7)	14 (14.4)	1 (6.3)	3 (17.7)
Other	168 (19)	8 (14.8)	129 (18.1)	27 (27.8)	5 (31.3)	9 (52.9)
Medication n (%)
Any	346 (38.6)	20 (37.0)	271 (38.0)	40 (41.2)	10 (62.5)	5 (29.4)
NSAR	71 (7.9)	7 (13.0)	51 (7.2)	8 (8.3)	2 (12.5)	3 (17.7)
ACEI	52 (5.8)	4 (7.4)	30 (4.2)	12 (12.4)	5 (31.3)	1 (5.9)
AT1-Inhibitors	82 (9.1)	6 (11.1)	60 (8.4)	13 (13.4)	3 (18.8)	0 (0.0)
Immunosuppressants	42 (4.7)	1 (1.9)	32 (4.5)	9 (9.3)	0 (0.0)	0 (0.0)
Chemotherapy	1 (0.1)	0 (0.0)	0 (0.0)	1 (1.0)	0 (0.0)	0 (0.0)
Smoking n (%)
Never	533 (59.4)	40 (74.1)	442 (62.3)	43 (44.3)	5 (31.3)	3 (17.7)
Former	246 (27.4)	8 (14.8)	198 (27.8)	32 (33.0)	6 (37.5)	2 (11.8)
Current	82 (9.1)	5 (9.3)	71 (10.0)	5 (5.2)	0 (0.0)	1 (5.9)
Missing	36 (4.0)	1 (1.9)	2 (0.3)	17 (17.5)	5 (31.3)	11 (64.7)
Days from illness onset to diagnosis Mean (SD)	6 (4)	x	6 (4)	6 (4)	8 (2)	7 (4)
Days from illness onset to recovery Mean (SD)	14 (8)	x	14 (7)	19 (9)	19 (8)	x

Clinical characteristics and history of 897 participants with coronavirus disease 19 stratified by level of severity. Data are either numbers (percentages) or means (standard deviations) as indicated in the table. Given symptoms are pooled symptoms comprising of initial and developed symptoms. See S2 Table for further details.

The mean age of the study population was 47 years (SD 17.5) with a range from 2 to 97. The age average did not change before and after the national lock-down starting on March 15, 2020 [21]. Age increased with rising severity of disease from 52.3 years (SD 20.7) in the mild group to 62.2 years (SD 13.3) in the critical group and 79.1 years (SD 11.8) among the deceased. A substantially higher proportion of participants aged ≥ 70 was admitted to a hospital (28.3%) in comparison to 5.5% of participants below the age of 50. Participants suffering moderate illness were on average 14 years older than participants showing a mild course of disease. Critical cases were on average 18 years older than patients with mild disease.

Around half (453, 50.5%) of the participants were female but the hospitalized patients were predominantly of male sex (58.8% in moderate, 75.0% in critical cases) (Table 1, Fig 2). Smokers or former smokers comprised 36.6% of participants.

Fig 2

Severity and age distribution.

Patients with COVID-19 stratified by age and sex. Female n = 453; Male n = 441 The left-hand side (green) shows male and the right-hand side (blue) female participants. The shades of color represent different level of disease severity. The severity of disease increases with darker shades of color. Further details on the group´s composition can be found in Tables 1 and 2.

The most commonly reported symptoms were fatigue (65.3%), followed by cough (61.5%) and dysgeusia (59.8%) (Table 2; S2 Table). Dyspnea was substantially more frequent in moderate and critical cases than mild ones (51.5%, 62.5% versus 15.8%).

Nearly half (46.6%) of the study population reported at least one underlying comorbidity, the most common was hypertension (152, 17%) and 38.6% (346 patients) of the study population was on regular medication. The presence of comorbidities including heart disease, lung disease and diabetes was more common among patients with a moderate/ critical course of disease versus a mild course (mild 16.7% versus moderate/critical 40.3% / 75.0%; 12.2% versus 22.7% / 25.0%; 3.5% versus 9.3% / 25.0%, respectively; Table 1).

The mean duration of disease from symptom onset to resolution of symptoms was 14 days (SD of 8 days) (Table 2). Among the hospitalized population, the mean time from symptom onset to hospitalization was 8 (SD 4), 8 (SD 3) and 7 (SD 4) days for moderate, critical and deceased participants, respectively. The mean length of hospital stay was 10 days (SD 9) in moderate and 24 days (SD 20) in critical cases.

The multivariable logistic regression model identified potential risk factors for disease severity that led to hospitalization (Table 3). The strongest predictors for hospitalization included greater age with every year increase conferring a higher risk (OR 1.05; 95% CI 1.04–1.07) and the presence of lung disease (OR 3.09; 95% CI 1.6–5.9). Female sex (OR 0.51; 95% CI 0.3–0.8), on the other hand, was identified as a protective factor for disease progression. The presence of coronary heart disease and diabetes showed a trend towards being associated with an increased risk of hospitalization with an increased OR of 1.44 (95% CI 0.51–4.07) and OR 1.38 (95% CI 0.65–2.91) of hospitalization, but confidence interval crossed 1. Hypertension was not associated with hospitalization (OR 0.98, 95% CI 0.59–1.65). Living with children (< 18 years of age) in a household demonstrated a trend towards a protective effect (OR 0.80; 95% CI 0.46–1.37) (Table 3).

Table 3

Risk factors associated with hospital admission.

Variables	Non-hospitalized (n = 767)	Hospitalized and deceased (n = 130)	OR	95% Cis
Age Mean (SD)	44.6 (16.3)	61.4 (15.0)	1.05	1.04–1.07
Sex n (%)
Female	406 (52.9)	47 (36.2)	0.51	0.33–0.77
Lung disease incl. n (%)
• Any lung disease	95 (12.4)	29 (22.3)	3.09	1.62–5.88
• Chronic Asthma with medication
• Any lung surgery
Smoking history n (%)	282 (36.8)	46 (35.4)	1.00	0.98–1.01
Coronary heart disease n (%)	9 (1.2)	10 (7.7)	1.44	0.51–4.07
Hypertension n (%)	107 (14.0)	45 (34.6)	0.98	0.59–1.65
Diabetes mellitus Type 1 or 2 n (%)	29 (3.8)	15 (11.5)	1.38	0.65–2.91
Living with children n (%)	247 (32.2)	22 (16.9)	0.80	0.46–1.37

Multivariable logistic regression was conducted to display the effect of potential risk factors (age, sex, comorbidities, smoking history and children living in household) for hospitalization. All moderate, severe and deceased cases were pooled as hospitalized in the context of the regression analysis. All asymptomatic and mild cases were pooled for the outpatient group. n = 897.

The multivariable cox regression was built around the same covariates as the logistic regression model to estimate their influence on the time from symptom onset to hospitalization. This cox regression, however, yielded similar results to the logistic regression and considering the limited time frame of the study, offered no added value to our analysis (S3 Table).

Discussion

This retrospective cohort study provides a comprehensive picture of clinical features and factors associated with severe disease in a primarily symptomatic population infected with SARS-CoV-2 in an administrative district in Southern Germany. The study shows 6% asymptomatic cases, a low percentage of hospitalization (12.9%) and confirms older age, lung conditions and male sex to be associated with greater disease severity.

The study provides a representative picture of COVID-19 in symptomatic patients in the general population in Germany in a district that was moderately affected by the disease (cumulative prevalence 0.18%) [16, 17]. However, we have to acknowledge that we very likely under-sampled asymptomatic patients, given the difficulty in detecting them despite extensive case finding efforts. Furthermore, we included less than half of the cases with fatal outcomes due to difficulties with gathering information from their close relatives in times of grief and severe stress. We therefore decided to use hospitalization with or without intubation and subsequent death as our main outcome in the analysis.

Previous studies primarily described characteristics of hospitalized patients [6, 11, 13, 14, 22–31] leading to limited data regarding mildly affected or asymptomatic patients. While the percentages of mild cases in our study were similar to the results of Wu and McGoogan, who described 72,314 cases (80% mild versus 79% in our study) [10], asymptomatic cases were reported in only 1% of their findings and severe and critical cases in 19% (as opposed to 6% and 4% in our study, respectively). This may be attributed to the fact that the study population of Wu and McGoogan mostly included patients from Hubei province and the rapid spread led to exhausted health care resources causing asymptomatic and less severely affected patients to be underrepresented in their report.

While the number of asymptomatic cases in our study is larger compared to the Wu and McGoogan study, it is likely that the actual percentage in the population is even higher based on other studies of complete cohorts and also modelling exercises [6, 32]. Also, while the number of hospitalizations was lower in our study compared to Wu and McGoogan and others, we postulate that Germany´s well-established health care infrastructure combined with the uncertainty about the novel disease may have allowed for an increased hospitalization and ICU admission out of precaution beyond of what would be considered necessary with a known disease.

The case fatality ratio was 3.6% in the Rhein-Neckar-Region [33]. This case fatality ratio was, however, lower than the overall death rate in Germany reported at 4.6% [34]. While differences are small, the discrepancy could be due to the patient populations in Southern Germany versus the whole of Germany as in Southern Germany more young people who returned from skiing in endemic regions were infected early in the epidemic. However, in our analysis of age before and after the lock down, we found no substantial differences to support this theory (S1 Fig).

Consistent with other studies, we identified fatigue (65.3%) and cough (61.5%) as the most predominant symptoms in patients suffering from COVID-19 [27, 31, 35]. Additionally, we found headache (57.2%) and dysgeusia (59.8%) among the most common symptoms. However, no symptom or symptom constellation appears to be frequent and specific enough to consider it for screening. Further studies considering advanced analyses of large datasets with symptoms of COVID-19 patients and randomly sampled cohorts of patients without COVID-19 at different times of the year should be considered to develop screening algorithms.

Identifying risk factors of patients prone to develop severe disease would help to focus medical surveillance and apply treatment at an early stage [13]. We identified older age, male sex as well as lung conditions as potential risk factors for a severe course of infection in our logistic regression. Our findings are consistent with previous studies who have described age, male sex, and lung disease as risk factors [14, 23, 27]. However, in contrast to other studies, we did not identify hypertension and diabetes mellitus as significant independent risk factor of a severe course of COVID-19, although odds ratios were increased. An increased risk would be supported by the pathophysiology of the virus. SARS-CoV-2 uses ACE2 receptors as cellular key entries. The virus then causes a down regulation of the same receptor leading to an increased permeability of the pulmonal vascular system [36]. As a result, pulmonary injuries may exacerbate and induce a more severe course of disease in patients suffering from hypertension or diabetes mellitus.

Unfortunately, the we were unable to collect reliable data on weight in our study. However, another study importantly demonstrated the relevance of obesity towards an increased risk of severe disease, hospital admission and mortality which we could not account for in our study [37].

Interestingly enough, the presence on children in a household showed and odds ratio below 1. Even though our correlation was not statistically significant, a recent publication observed similar findings. In a cohort study from Scotland among 300,000 adults living in a household, the risk of testing positive for SARS-CoV-2 was slightly lower for individuals living with young children after adjusting for potential confounders [38]. However, large data sets from other countries need to be analyzed to address this with more sincerity as these findings could be of importance for informing policy on school openings.

While our study is able to provide a comprehensive overview of the disease and the predictors of severe disease among Sars-CoV-2 infected patients, it also has several limitations. First, due to the retrospective study design, some patients filled out the questionnaire several weeks after being tested positive or showing symptoms which may lead to a recall bias. Second, patients not speaking German were not able to be included in the study due to the informed consent being available only in German. Third, relatives of deceased patients and nursing home residents were harder to reach and less open to participate in the study which leads to an underrepresentation of these groups. Fourth, patients suffering severe courses of disease were hospitalized for an extended period of time and rehabilitation measures were applied afterwards, making these patients difficult to reach. This may have led to a selection bias. Besides disease severity, hospitalization is also influenced by factors such as compliance of patients, hospital management or affordability of the health care system tailored to the respective setting which may have affected event occurrence as well. Fifth, 30% of patients in the registry could not be reached, withdrew consent or did not return the survey leading to missing data.

Conclusion

This analysis of representative COVID-19 cases confirms age, male sex and preexisting lung conditions but not cardiovascular disease as risk factors for severe illness.

Health care systems should prepare for about 15% of patients to suffer moderate or critical illness.

Data comparison of study population vs. overall infected population.

To assess the representativeness of our study population in comparison with the overall population infected with SARS-CoV-2, we compared the age groups sorted by age groups using the Chi-Square test.

(PDF)

Click here for additional data file.

Initial and subsequent symptoms.

Participants were asked to specify symptoms they suffered in the initial phase of the infection and symptoms which developed during the course of disease. n = 897.

(PDF)

Click here for additional data file.

Cox regression, influence of variables on time to hospitalization.

Multivariable Cox proportional hazard model analyzing age, sex, comorbidities and smoking history (in pack years). n = 897.

(PDF)

Click here for additional data file.

Age distribution among study population.

In the early part of the epidemic many cases returned from skiing holidays. We display the age distribution before and after the border closure considering 14 days of an incubation period (i.e. April 1st, 2020). n = 897.

(PNG)

Click here for additional data file.

Questionnaire.

Copy of the questionnaire distributed to consenting participants.

(PDF)

Click here for additional data file.

Additional methods.

Description of variables.

(PDF)

Click here for additional data file.

10 May 2021

PONE-D-21-06695

Description and analysis of representative COVID-19 cases – a retrospective cohort study

PLOS ONE

Dear Dr. Denkinger,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jun 24 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Tai-Heng Chen, M.D.

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Funding Section of your manuscript:

[This work was supportedby internal COVID funds of the Heidelberg University Hospital]

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

[The authors received no specific funding for this work.]

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Reviewers' comments:

Reviewer's Responses to Questions

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 #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: 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 #2: Yes

Reviewer #3: Yes

Reviewer #4: 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 #2: Yes

Reviewer #3: Yes

Reviewer #4: 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 #2: The authors conducted a retrospective survey on the COVID-19 patients in Southern German, described clinical features and assessed potential risk factors for hospitalization. Overally, the article is well written and well designed. In my opinion, it need only some minor revisions.

1.Statistics: what kind of multivariate logistic regression model was performed, backward stepwise or others？

2.I thinck hospitalization is influenced potentially by many factors, e.g., disease severity, compliance of patients, the affordability of health care system, and even management policy in each country. For example, in China, even asymptomatic and mild COVID-19 patients are hospitalized. So, I suggest this should be discussed in section of limitation.

3.P value is not a trend. So the sentences should be expressed in a more appropriate way (Page 20, line 358, line 364).

Reviewer #3: 1. This is an interesting study but the findings are not of sufficient originality and there is a lack of novelty as compared to a number of similar studies already published on the topic. In addition, there are some concerns need to be thought carefully.

2. Patients enrolled from February 7 to June 30 were included in the follow-up, and the paper needs to explain when the retrospective investigation was conducted.

3. There are major problems with the study design. The study design of this paper is not a retrospective cohort study, not based on the exposure of certain risk factors, but divided the outcomes of the cases into different study levels and collected relevant data retrospectively, which should be a cross-section investigation design and then do case-control study and risk factors analysis.

4. In the study, 30% cases lost to follow-up may affect the distribution of the pathogen of the disease, making the results less reliable. So, suggest to analysis the basic situation of the lost.

5. Regarding to the risk factor analysis, it is more appropriate for the authors to conduct univariate analysis firstly to find meaningful variate and then do multiple logistic regression.

6. The authors should notice that all COVID-19 including are required to hospital for isolation in China.

7. Line 212-213, page 19, please check the number of percent and its corresponding type of patients. It is inconsistent with the figure presented in Table 2.

Reviewer #4: General Comments

Thank you very much for choosing me as a reviewer of this manuscript. I think this article is well written and it looks into an important and concerning problem nowadays as is COVID-19. This study is a demographic and epidemiological analysis of a German cohort representative of an administrative district. They found that age, male sex and pre-existing lung conditions were associated with severe illness. The statistical analysis is well done and well discussed.

Although is difficult to assess the real incidence of SARS-CoV-2 infection in this cohort and asymptomatic patients’s rate could be infra estimated, they analyzed 69% of all COVID-19 registered cases with an important underrepresentation of the group of severe disease. All these limitations are reported at the discussion by the authors.

Finally, I have provided some specific comments point by point.

Discussion

Data regarding BMI index is missing in the study and it has also been related to worse outcomes in other large community-based cohort study (Gao M, Piernas C, Astbury NM, Hippisley-Cox J, O'Rahilly S, Aveyard P, Jebb SA. Associations between body-mass index and COVID-19 severity in 6•9 million people in England: a prospective, community-based, cohort study. Lancet Diabetes Endocrinol. 2021 Apr 28:S2213-8587(21)00089-9). Please, add this comment to the discussion section.

Supplementary material

Please translate the questionnaire into English

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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 #2: Yes: Liang Chen

Reviewer #3: No

Reviewer #4: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

22 May 2021

Journal comments to the author:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

Response: thank you for this comment. We have made appropriate adaptions throughout the document.

2. Thank you for stating the following in the Funding Section of your manuscript:

[This work was supportedby internal COVID funds of the Heidelberg University Hospital]

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

[The authors received no specific funding for this work.]

Response: we excluded the funding statement in the manuscript and wish to keep the following statement

(“This work was supported by internal COVID funds of the Heidelberg University Hospital.”)

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Response: thank you for this remark. Appropriate changes have been incorporated throughout.

Reviewer 1:

1. The authors conducted a retrospective survey on the COVID-19 patients in Southern German, described clinical features and assessed potential risk factors for hospitalization. Overally, the article is well written and well designed. In my opinion, it need only some minor revisions.

Response: we appreciate the reviewer’s comment.

2. Statistics: what kind of multivariate logistic regression model was performed, backward stepwise or others？

Response: thank you for this comment! We did not use any stepwise model selection algorithms but selected the variables for the multivariate analysis from a series of univariate screening models.

We clarified this in lines 134-138

3. I think hospitalization is influenced potentially by many factors, e.g., disease severity, compliance of patients, the affordability of health care system, and even management policy in each country. For example, in China, even asymptomatic and mild COVID-19 patients are hospitalized. So, I suggest this should be discussed in section of limitation.

Response: we appreciate the reviewer’s comment and have added this to the limitation section (line 443-446)

4. P value is not a trend. So the sentences should be expressed in a more appropriate way (Page 20, line 358, line 364)

Response: thank you for this comment, we have changed the sentences (line 417-423)

Reviewer 3:

1. Patients enrolled from February 7 to June 30 were included in the follow-up, and the paper needs to explain when the retrospective investigation was conducted.

Response: thank you for your remark. We added information on the period of conduct (line 60-61). Data collection started on March 19, 2020 and concluded on June 30, 2020.

2. There are major problems with the study design. The study design of this paper is not a retrospective cohort study, not based on the exposure of certain risk factors, but divided the outcomes of the cases into different study levels and collected relevant data retrospectively, which should be a cross-section investigation design and then do case-control study and risk factors analysis.

Response: we appreciate the reviewer’s valuable feedback. A case control study or prospective cohort study might have been of higher value. However, given the dynamics of the early pandemic this was not feasible.

We believe that or study fills all the formal criteria for a retrospective cohort study in that it:

- Describes a cohort selected by the occurrence of an outcome

- Retrospectively evaluates risk factors that might have contributed to said diseases, i.e. accumulated significantly in our population

Retrospectively assesses said risk factors contribution to the severity of the disease (thus stratifying the populations into groups, e.g. age, smoking status and using this stratification as case-control setup)

3. In the study, 30% cases lost to follow-up may affect the distribution of the pathogen of the disease, making the results less reliable. So, suggest to analysis the basic situation of the lost.

Response: thank you for this comment. We highlighted this limitation of the study at the end of our discussion. (line 454-455) In addition, we compared the age structure of our study population to the overall population in the district tested positive for SARS-CoV-2 to identify potential underrepresentation of age groups. (line 129-131; line 194-197)

4. Regarding to the risk factor analysis, it is more appropriate for the authors to conduct univariate analysis firstly to find meaningful variate and then do multiple logistic regression.

Response: we appreciate the reviewer’s comment and have revised the appropriate methods section to indicate that we had done a univariate analysis first. The section should be clearer now. (Line 139-143)

5. The authors should notice that all COVID-19 including are required to hospital for isolation in China.

Response: we are thankful for this information and have acknowledged this. This is not the case in Germany.

6. Line 212-213, page 19, please check the number of percent and its corresponding type of patients. It is inconsistent with the figure presented in Table 2

Response: we appreciate that the reviewer pointed this out. We have identified the mistake and included appropriate changes but could not find inconsistencies on page 19.

Reviewer 4:

1. Discussion

Data regarding BMI index is missing in the study and it has also been related to worse outcomes in other large community-based cohort study (Gao M, Piernas C, Astbury NM, Hippisley-Cox J, O'Rahilly S, Aveyard P, Jebb SA. Associations between body-mass index and COVID-19 severity in 6•9 million people in England: a prospective, community-based, cohort study. Lancet Diabetes Endocrinol. 2021 Apr 28:S2213-8587(21)00089-9). Please, add this comment to the discussion section.

Response: thank you for this helpful comment! Unfortunately, the data collected on weight in our study was not reliable, which led us to exclude the information. However, we added this risk factor to our discussion (line 417-424).

2. Supplementary material

Please translate the questionnaire into English

Response: we appreciate the reviewer’s feedback and have added supplement tables that depict the assessed variables of the questionnaire.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

16 Jun 2021

PONE-D-21-06695R1

Description and analysis of representative COVID-19 cases – a retrospective cohort study

PLOS ONE

Dear Dr. Denkinger,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 31 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Tai-Heng Chen, M.D.

Academic Editor

PLOS ONE

Journal Requirements:

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.

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 #3: (No Response)

**********

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 #3: Yes

**********

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

Reviewer #3: 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 #3: 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 #3: 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 #3: In my opinion, cohort study means classification is made according to exposure factor, generally one factor. For example, if we want to study the association between smoking (exposure factor) and lung cancer (outcome variable), the study population are classified as smoking group and non-smoking group according to their smoking history status. And then follow them, observe whether they develop lung cancer or not after a period of times. Usually, only one exposure factor is used as the classified variable. Conversely, the outcome variables may be more than one. In the above mentioned example, aside from lung cancer, the outcome variables may be hypertention, stroke or others. Here, we calculate the relative ration (RR) rather than the odds ratio (OR) to show the degree of association between the two factors (here is smoking and lung cancer). So, if the author declared that they used a cohort study, please explain what the exposure variable and outcome variable were in their study.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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 #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

5 Jul 2021

Journal Comments to the Author

1. 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.

Response:

We have checked our references and ensured only appropriate references were included. We did not include retracted publications. We revised two preprint references because both studies had undergone peer-review and were published in journals. The first reference titled “A demographic scaling model for estimating the total number of COVID-19 infections” by Bohk-Ewald et al. was published in the International Journal of Epidemiology. The second one named “Prevalence of SARS-CoV-2 Infection in Residents of a Large Homeless Shelter in Boston” by Baggett et al was published in JAMA.

Reviewer 3 Comments to the Author

1. In my opinion, cohort study means classification is made according to exposure factor, generally one factor. For example, if we want to study the association between smoking (exposure factor) and lung cancer (outcome variable), the study population are classified as smoking group and non-smoking group according to their smoking history status. And then follow them, observe whether they develop lung cancer or not after a period of times. Usually, only one exposure factor is used as the classified variable. Conversely, the outcome variables may be more than one. In the above mentioned example, aside from lung cancer, the outcome variables may be hypertention, stroke or others. Here, we calculate the relative ration (RR) rather than the odds ratio (OR) to show the degree of association between the two factors (here is smoking and lung cancer). So, if the author declared that they used a cohort study, please explain what the exposure variable and outcome variable were in their study.

Response:

We appreciate the reviewer’s feedback and generally agree with the raised points and assessment. Coherent with the design of a cross-sectional study, our study was constructed to show correlations between an exposure and outcome variable. Due to the retrospective design, data for both were collected at the same time and the Odds ratio was calculated based on a logistic regression model. Nonetheless, we still are of the opinion that this study could also be described as a cohort study due to the reasons mentioned below.

In their textbook on medical statistics, Aviva and Sabin define a (retrospective) cohort study as follows: “A cohort study takes a group of individuals and usually follows them forward in time, the aim being to study whether exposure to a particular aetiological factor will affect the incidence of a disease outcome in the future. If so, the factor is generally known as a risk factor for the disease outcome. […] Although most cohort studies are prospective, historical cohorts are occasionally used: these are identified retrospectively and relevant information relating to outcomes and exposures of interest up to the present day ascertained using medical records and memory.”

(Petrie, Aviva, and Caroline Sabin. Medical Statistics at a Glance, John Wiley & Sons, Incorporated, 2009. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/ub-heidelberg/detail.action?docID=1561073.

Created from ub-heidelberg on 2021-06-24 15:30:09.)

We believe that this definition of a historical or retrospective cohort applies to our study because a defined group (in this case patients who tested positive for SARS-CoV-2) was followed backwards in time to identify whether the exposure of an aetiological factor (i.e. age, sex heart disease, lung disease, diabetes, smoking history, living with children) had an effect on the defined outcome (i.e. hospitalization, severity of disease).

Instead of classifying by exposure variables (i.e. age, sex heart disease, lung disease, diabetes, smoking history, living with children) as usually done in cohort studies, we decided to classify the population by the outcome variable (hospitalization and no hospitalization) due to the relatively large number of variables considered in the analysis. This in our opinion allowed a better and more structured way of presenting our results to the reader without defying the essence of a cohort study.

Because of the complex demographic of our study population, we needed to adjust for confounding factors. To do so, we created a logistic regression model for our (binary) exposure variables and therefore, present the results as odds ratios.

Even though we, alongside other reviewers, believed to have fulfilled the criteria for a retrospective cohort study, we acknowledge that our study also meets criteria for a cross-sectional study design. If deemed necessary by the editor, we will change the title and methods to cross-sectional instead of cohort.

Submitted filename: Response to reviewers_R2.docx

Click here for additional data file.

19 Jul 2021

Description and analysis of representative COVID-19 cases – a retrospective cohort study

PONE-D-21-06695R2

Dear Dr. Denkinger,

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,

Tai-Heng Chen, M.D.

Academic Editor

PLOS ONE

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 #3: (No Response)

**********

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 #3: (No Response)

**********

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

Reviewer #3: (No Response)

**********

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 #3: (No Response)

**********

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 #3: (No Response)

**********

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 #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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 #3: No

23 Jul 2021

PONE-D-21-06695R2

Description and analysis of representative COVID-19 cases – a retrospective cohort study

Dear Dr. Denkinger:

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. Tai-Heng Chen

Academic Editor

PLOS ONE