Patterns and predictors of sick leave among Swedish non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic.

Healthcare and residential care workers represent two occupational groups that have, in particular, been at risk of Covid-19, its long-term consequences, and related sick leave. In this study, we investigated the predictors of prolonged sick leave among healthcare and residential workers due to non-hospitalized Covid-19 in the early period of the pandemic. This study is based on a patient register (n = 3209) and included non-hospitalized healthcare or residential care service workers with a positive RT- PCR for SARS-CoV-2 (n = 433) between March and August 2020. Data such as socio-demographics, clinical characteristics, and the length of sick leave because of Covid-19 and prior to the pandemic were extracted from the patient's electronic health records. Prolonged sick leave was defined as sick leave ≥ 3 weeks, based on the Swedish pandemic policy. A generalized linear model was used with a binary distribution, adjusted for age, gender, and comorbidity in order to predict prolonged sick leave. Of 433 (77% women) healthcare and residential care workers included in this study, 14.8% needed longer sick leave (> 3 weeks) due to Covid-19. Only 1.4% of the subjects were on sick leave because of long Covid. The risk of sick leave was increased two-fold among residential care workers (adjusted RR 2.14 [95% CI 1.31-3.51]). Depression/anxiety (adjusted RR 2.09 [95% CI 1.31-3.34]), obesity (adjusted RR 1.96 [95% CI 1.01-3.81]) and dyspnea at symptom onset (adjusted RR 2.47 [95% CI 1.55-3.92]), sick leave prior to the pandemic (3-12 weeks) (adjusted RR 2.23 [95% CI 1.21-4.10]) were associated with longer sick leave. From a public health perspective, considering occupational category, comorbidity, symptoms at onset, and sick leave prior to the pandemic as potential predictors of sick leave in healthcare may help prevent staff shortage.

Introduction

Healthcare and residential care workers had the highest rate of SARS-CoV-2 positive cases compared to other occupational groups, particularly in the first wave of the pandemic [1-4]. A large population study from the U.K. found a seven-fold increased risk of Covid-19 among healthcare and two-fold higher risk among residential care workers [5]. Risk factors for Covid-19 among healthcare and residential care workers included inadequate personal protective equipment, suboptimal handwashing, and working long hours in high-risk departments [6-9].

Even though the majority of healthcare personnel had mild disease [1,10], up to 10% might have had symptoms that persisted beyond 12 weeks, called long Covid, reducing their work ability [11]. Studies from the U.S. and Spain showed that healthcare workers also had an increased prevalence of sick leave during the pandemic [12,13], a major driver of high healthcare costs [14]. Sick leave has a multifactorial association between morbidity, education, working conditions, job satisfaction, general health, and family life–as an indicator of individuals’ well-being, but also a predictor of health consequences [15-17].

In Sweden, it was estimated that 3.4% of healthcare and 1.3% of residential care workers (per 100,000 people) had Covid-19 in the first wave of the pandemic [18]. Factors such as hospitalization, higher age, and sick leave prior to the pandemic were the risk factors of longer sick leave in the Swedish general population [5].

A recent editorial by Gohar suggested that staff shortage, increased work demand, and personal factors such as age, work experience, job role, health, history of previous sick leave, the organization safety, and employee job support were predictive factors for sick leave in healthcare [19]. In this study, we investigated predictors of prolonged sick leave after having a confirmed Covid-19 illness among non-hospitalized healthcare and residential care workers. The study covers the early period of the pandemic, when testing of both occupational categories was prioritized, providing valuable clinical and epidemiological information.

Materials and methods

Study population

This study is a part of COMBAT, a post Covid project investigating the long-term consequences of non-hospitalized Covid-19. It is is based on a patient register (n = 3209) containing information about symptomatic individuals who were tested at one Covid-19 testing outpatient center, created by the Department of Infectious Disease at the Uppsala University Hospital between March 10th and August 21st, 2020. This study population included symptomatic employees of Region Uppsala who tested positive for SARS-CoV-2 by reverse transcription–polymerase chain re-action (RT-PCR) on nasopharyngeal swab, not receiving in-patient care, and working in healthcare or residential care service. The included subjects were followed up after 8–12 months (until the end of April 2021). The Region Uppsala, being one of the regional governmental authorities responsible for public health and transportation, includes Uppsala University Hospital, primary healthcare, including residential care service, and primary care centers. Positive RT-PCR was the basis for a confirmed diagnosis of Covid-19 (U07.1), in accordance with the International Statistical Classification of Disease (ICD).

The study was approved by the institutional ethics committee of the University Hospital in Uppsala (2020–05707) and conducted in accordance with the Helsinki Declaration. All medical records were anonymized; only statistical information was used for research purposes.

Framework: Swedish sick leave policy during the pandemic

The information for this study was further collected from the electronic medical records that contain all information on patients, readily available for healthcare providers in the Uppsala Region. The sick leave due to Covid-19 was divided into two groups: ≤ 3 weeks and > 3 weeks. Prolonged sickness absence was defined as sick leave > 3 weeks, based on the Swedish pandemic policy on sick leave. In line with this policy, all suspected and confirmed Covid-19 cases were advised to self-isolate, having a paid sick leave up to 21 days (≤ 3 weeks) from the symptom onset. From day 22 (> 3 weeks), a doctor’s certificate was required for prolonged paid sick leave [20]. The group with sick leave > 3 weeks, defined as longer sick leave, included subjects who required a doctor’s certificate. Some subjects had more than one doctor’s certificate for their sick leave because of Covid-19, but there were no time gaps between them. Therefore, the length of sick leave in the group > 3 weeks was calculated as a period between a day of positive RT-PCR result and the last day of the doctor’s certificate. We also assessed sick leave > 12 weeks.

Potential predictors

Besides information on sick leave as a result of Covid-19, we collected the following data from the patient’s medical records:

Age (in years) and sex (woman/man), gathered from the Swedish national identification number.

Information on occupation, gathered by a nurse at the time of RT-PCR testing and categorized as physician, nurse/midwives, assistant nurse, psychologist/counselor, biomedical analytics, occupational- and physiotherapist, residential care workers, health supporting staff such as medical administrators, laboratory workers, and maintenance workers.

Symptom at onset, gathered by a nurse at the time of the RT-PCR testing, including fever, dyspnea, muscle and joint pain, impaired taste and smell, sore throat, headache, nasal symptoms, GI symptoms (including nausea, vomiting, diarrhea, stomach pain), fatigue resulting from Covid-19 illness, and pressure over chest/chest pain.

History of common chronic disease (based on diagnostic code only from the last 10 years) including diabetes, hypertension, other heart disease, chronic lung disease including asthma and obstructive lung disease, cancer/immunosuppressive treatment, hypo/hyperthyreosis, depression/anxiety.

BMI (kg/m2) measured by healthcare personnel in the last 5 years, classified as underweight < 18.5, normal 18.50 to 24.99, overweight between 25.0 and 29.99, and obesity > = 30.

Sick leave prior to the pandemic, defined as sick leave, with a doctor’s certificate from January 1, 2019 and February 28, 2020. In case of more than one sick leave period, the length was summed together and categorized as ≤ 3 weeks, 3–12 weeks, > 12 weeks of sick leave.

Statistical analysis

The categorical variables were presented as number and frequency using percentages, and the continuous variables were presented as means with standard deviation (SD). Differences between the groups (≤ 3 weeks and > 3 weeks sick leave) were assessed using Chi2 test or Fisher’s exact test (if the assumption for Chi2 test was not met). Multivariable generalized linear models, with adjustment for selected factors such as age, sex and different comorbidities were used to examine the factors predicting longer sick leave (> 3 weeks) in comparison to sick leave ≤ 3 weeks. We selected these three confounding factors based on the literature and that they had no missing values. The results of the regression analysis were presented as relative risk (RR) with 95% confidence intervals (CI). We included the following independent variables in the model: age (as continues variable), sex (women vs man), BMI (obesity vs normal weight), different occupational groups (all subgroups as yes vs no), comorbidities (all subgroups as yes vs no), symptom at onset (all subgroups as yes vs no), sick leave one year before the pandemic (as three subgroups <3,3–12,>12 weeks). All variables were included in the multivariable model without any statistical variable selection. P < 0.05 was considered statistically significant. Covariates included in the model were chosen based on clinical and theoretical reasoning. All analysis was managed in Excel and SAS 9.4.

Results

Socio-demographic and clinical characteristics

This study included 433 non-hospitalized subjects with detectable SARS-CoV-2 by RT-PCR, representing healthcare and residential care workers, of which 335 (77%) were women. Sixty individuals (13.8%) had longer sick leave (> 3 weeks) due to confirmed Covid-19. All basic and clinical characteristics of the study population are shown in Table 1. The length of the complementary doctor’s certificate was between 6–134 days (mean 43 days), S1 Fig. One subject was on sick leave at the end of the study period (241 days). Six subjects (1.4%) were on sick leave for > 12 weeks.

Table 1

Socio-demographic and clinical characteristics of the healthcare and home/service care workers divided into two groups as: Sick leave ≤3 weeks and sick leave >3 weeks.

	≤ 3 weeks n = 373	>3 weeks n = 60	P
Sex (women)	283 (84.5)	52 (15.5)	0.06
Age, mean (SD)	40.44 (13.30)	42.34 (12.08)	0.07
Occupational group
Physician	42 (82.3)	9 (17.7)	0.40
Nurse/Midwife	114 (90.5)	12 (9.5)	0.12
Assistant nurse	87 (89.7)	10 (10.3)	0.25
Physio- and occupational therapist	8 (80.0)	2 (20.0)	0.63
Psychologist/ curator	6 (85.7)	1 (14.3)	0.99
Biomedical analytics	6 (85.7)	1 (14.3)	0.99
No patient contact	54 (87.8)	6 (12.2)	0.42
Residential care	53 (74.6)	18 (25.4)	<0.01
Comorbidity
Hypertension	34 (87.2)	5 (12.8)	0.71
Diabetes	8 (80.0)	2 (20.0)	0.63
Hypo-/hyperthyroidism	29 (90.6)	3 (0.4)	0.59
Heart disease	18 (81.8)	4 (18.2)	0.52
Lung disease	35 (85.3)	6 (14.7)	0.87
Cancer/Immunosuppressive treatment	15 (100)	0	0
Depression/anxiety	87 (76.9)	26 (23.1)	<0.01
Comorbidity
No disease	208(88.5)	27(11.5)	0.12
One disease	111(83.5)	22(16.5)	0.27
Two or more diseases	54(83.1)	11(16.9)	0.42
BMI
Underweight <18.5	3 (60.0)	2 (40.0)	0.17
Normal weight 18.5–25	102 (87.2)	15 (12.8)	0.26
Overweight >25–30	62 (87.3)	9 (12.7)	0.44
Obesity >30	25 (73.5)	9 (26.5)	0.05
Symptom at onset
Fever	174 (84.5)	32 (15.5)	0.33
Dyspnea	65 (73.8)	23 (26.2)	<0.01
Muscle and joint pain	173 (83.2)	35 (16.8)	0.85
Impaired taste and smell	93 (94.9)	5 (5.1)	<0.01
Sore throat	110 (83.3)	22 (16.7)	0.29
Headache	157 (84.8)	28 (15.2)	0.50
Nasal symptoms	253 (85.2)	44 (14.8)	0.39
GI symptoms	23 (82.1)	5 (17.9)	0.56
Fatigue	90 (91.8)	8 (8.2)	0.06
Pressure over chest	24 (80.0)	6 (20.0)	0.31
The length of sick leave prior to the pandemic (January 2019–February 2020)
<3 week	39 (70.9)	16 (29.1)	0.49
3–12 weeks	27 (73.0)	10 (27.0)	0.24
>12 weeks	9 (75.0)	3 (25.0)	0.58

The P- values <0.05 of significance. BMI–missing data for 206 subjects. Occupation groups–missing data for 4 subjects.

Predictors of prolonged sick leave

Age (adjusted RR 1.00 [95% CI 0.98–1.02]) and sex (adjusted RR for women 1.08 [95% CI 1.00–1.17)] had no association with longer sick leave. Residential care workers had twice the risk of long sick leave because of Covid-19 (adjusted RR 2.14 [95% CI 1.31–3.51]), while the risk of sick leave was not increased in other healthcare categories. Among comorbidities, obesity (adjusted RR 1.96 [95% CI 1.01–3.81]) and depression/anxiety (adjusted RR 2.09 [95% CI 1.31–3.34]) were significantly associated with longer sick leave due to Covid-19. We did not consider the relative risk of being underweight since the number of samples in the group > 3 weeks was low. The presence of dyspnea at symptom onset (adjusted RR 2.47 [95% CI 1.55–3.92]) was the only symptom that predicted longer sick leave. Sick leave prior to the pandemic (3–12 weeks) led to an increased risk of sick absence as a result of Covid-19, Fig 1.

Fig 1

The predictors of longer sick leave >3 weeks was determined by relative risk (RR) with confidence interval (95% CI).

The RR values were adjusted for age, gender, and comorbidity.

Discussion

To the best of our knowledge, this is the first study investigating the pattern and predictors of sick leave among healthcare and residential care workers with non-hospitalized confirmed Covid-19 in the early period of the pandemic. Our main result was that working at residential care exhibited almost two-fold higher risk for longer sick leave (< 3 weeks), while other healthcare groups had no elevated risk of sick absence due to Covid-19. Our finding is in line with research conducted prior to the pandemic, showing that residential care workers had the highest number of cases with long-term sickness absence compared to other professional categories [21]. In those studies, identified risk factors included: having a stressful work environment, poor work support, shortage of staff, increased mental and physical fatigue, and low job control [19,22,23]. In Sweden, the media highlighted that at the beginning of the pandemic, the work demand was increased, and there was limited availability of protective equipment in residential care service [24,25].

In this study, we found that the relative risk for longer sick leave was not statistically different between the genders; however, women were more likely to take longer sick leave in comparison to men. This is in line with the previous research showing that women received more sickness benefits due to Covid-19 than men [26]. Also, in accordance with the Swedish report prior to the pandemic, more females, in general, took sick leave than males [27]. It was suggested that women more likely suffered long-term sequel after Covid-19, whereas men were at a higher risk for severe Covid-19 and its complications [28]. In addition, our study population mainly comprised women, especially among working categories of nurses, assistant nurses, and residential care personnel. These occupational groups have also been the most exposed while working closely with Covid-19 infected patients [1].

We found that depression and anxiety were predictors of prolonged sick leave. A study from China showed that approximately one-fifth of hospitalized Covid-19 patients had depression and anxiety [29]. A study conducted prior to the pandemic found that mental health disease was an important factor in longer and repeated sick leave, particularly in younger men and women [30,31].

As illustrated in our generalized linear model, obesity (> 30) was linked to a greater risk of longer sick absence. This finding is congruent with several previous studies showing that obese individuals have a higher likelihood of sickness absence compared to normal weight individuals [32,33]. Obesity was also associated with increased risk of hospitalization due to Covid-19, and critical outcomes from the infection, particularly in younger people [34-36].

Having dyspnea as a baseline symptom was a factor that predicted longer sick leave in our study. This is in line with a previous study where dyspnea at baseline was the only predictor affecting the recovery, both in outpatients and admitted patients [37]. The meta-analysis on Covid-19 showed that dyspnea as a symptom onset was strongly associated with progression of Covid-19 and risk of hospitalization [38].

Further, our study found that 3–12 weeks of sick leave prior to the pandemic increased the risk of prolonged sick absence due to a Covid-19 infection. This effect was not found in those with > 12 weeks of sick leave prior to the pandemic, which might be because of the low sample of subjects. A recent study on sick leave as a result of Covid-19 in the general population reported that sick leave prior to the pandemic was linked to a longer sick leave due to Covid-19 [26]. Also, studies conducted prior to the pandemic showed that healthcare workers who had been on previous sick leave were also at risk of future absenteeism [23,39,40].

In this study, we found that sick leave > 12 weeks was uncommon and affected less than 2% of the studied non-hospitalized healthcare and residential care workers. In contrast, a study on the Swedish general population, based on register data of both hospitalized and non-hospitalized patients, showed that long-term lingering symptoms following Covid-19 affected 13% of individuals [26]. Also, the previous study on Swedish healthcare showed that eight months after mild Covid-19, one in ten individuals still had at least one moderate to severe symptoms, so called long Covid, which negatively impacted their daily life and work ability [11].

The study’s strength is that it is based on all healthcare and residential care workers with confirmed Covid-19, tested during the first wave of the pandemic in the same testing center in Uppsala Sweden. Clinical characteristics were collected from the medical records, which reduces bias in comparison to self-reported information. By using patients’ medical records, instead of the Swedish Insurance System, sick leave information was assessed in real time (until the end of April 2021) without delay. However, collecting sick leave information in this way might bias the study, as Swedish inhabitants are free to seek healthcare in different regions. The various regions in Sweden have distinct patient records. Another limitation is that we have a limited number of predictors. In addition, BMI information was available in the electronic medical records for only 53% of the subjects. A previous study showed that health professionals mostly recorded information about BMI, for patients with deviating weight and if it was clinically relevant [41], which might also bias our study. Furthermore, the statistical power to examine some predictors, including occupational groups, was low. Therefore, we could only control for a limited number of confounders, such as age, gender, and comorbidity. It has previously been found that these factors significantly influence the occurrence of sickness absence [22].

Conclusions

In this study, we showed that working in residential care, obesity, depression/anxiety, and longer sick leave prior to the pandemic were predictors of sick leave in confirmed, non-hospitalized, Covid-19 cases. The pandemic is not yet over; from a public health perspective, identification of predictors of sick leave due to Covid-19 can be a coping strategy, preventing long sick leave or as an indicator of more severe disease. Thus, if front-line workers are affected, it may lead to staff shortage. Further studies with more sick leave predictors, comparing both hospitalized and non-hospitalized Covid-19 patients, are needed.

The length (days) of sick leave due to Covid-19 with doctor’s certificate in the group > 3 weeks.

(PDF)

Click here for additional data file.

2 Aug 2021

PONE-D-21-21612

Patterns and predictors of sick leave among non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic

PLOS ONE

Dear Dr. Kisiel,

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 Sep 16 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,

Huei-Kai Huang, 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. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

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.

4. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

5. Please include your table as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files

Additional Editor Comments:

The methodological issues raised by the reviewers should be appropriately addressed in your revised manuscript. Please note that additional reviewer comments could be found in the attached document.

[Note: HTML markup is below. Please do not edit.]

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 #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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: No

Reviewer #3: 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: Thank you for allowing me to review your study, “Patterns and predictors of sick leave among non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic”. The study examined predictors of sick leave among non-hospitalized healthcare & residential care workers from March to August 2020.

Overall, my impressions of this paper are positive. It offers meaningful data on a population exposed to various health risks and, naturally, higher than average sickness absence rates. I have included some remarks to help ensure the success of this paper.

• To improve this paper, I believe that more editing is required. I list some examples below.

++ “The study from the US and Spain showed that healthcare workers also had increased prevalence of sick leave during the pandemic….” It is unclear by stating “the study” that it was one study or several studies.

++ Issues with the structure of the sentence beginning with “A recent editorial by Gohar…”, specifically with “job support employees.” Do you mean “employee job support?”

++ Inconsistencies “healthcare” and “health care.”

++ “However, a limitation is that there might be a bias by collecting sick leave information in this way as Swedish inhabitants are free to seek health care in different regions that have distinct patients records BMI was missing in the substantial part of the study population.” I am unclear if these are supposed to be two separate sentences. If not, please clarify your point.

• It is indicated that multiple sick leave periods were summed together and later categorized based on total duration. Were there any measures taken for sick leaves that were uncommon (e.g., car accident)?

• How updated are medical records concerning variables such as BMI? If not updated regularly, I believe this should be described in the limitation section. Also, is BMI self-reported in these records? If it is self-reported, it is an important consideration to add as individuals typically underestimate their weight or BMI (which further magnifies the role of BMI and sick leave).

• It was mentioned that BMI was missing in a substantial part of the study population. Could this have compromised the integrity of the results?

• Age in previous studies showed inconsistencies with its relationship to sickness absence. However, some evidence suggests that sex (females) have higher rates of sick leaves. It might be worthy of hypothesizing why there was not a relationship. Could it be that COVID-19 affects male and female healthcare workers evenly? I would draw on the statistics (sex) of patients infected with COVID in the Uppsala region and contrast accordingly.

• Also missing from this study is its implications. By having this information, what should be done in terms of future research or from a practical standpoint?

• Mentioning the region or at least the country in the title is beneficial

My best wishes!

Reviewer #2: This is an interesting, important and generally well structured study. However, there is a fair amount of copy editing and revision for clarity of language that needs to be done before it can be published, in my opinion. I have attempted to make some suggestions along these lines in the attached document. My grammatical and wording edits are only suggestions, but I think they will add to the readability of the paper.

I also had some concerns about the description of the statistical methods and presentation of the results which I have also detailed in the attached document. These, I think, must be addressed.

Thanks you for sharing your work; I enjoyed reading it.

Reviewer #3: This is an interesting study. The manuscript is well-written. However, some methodologic issues should be further clarified. My specific comments are as follows:

1. The authors only mentioned that BMI data are missing for 206 subjects. How about the missing data regarding other variables (listed in Table 1) used in the prediction model? Please also clarify this in the manuscript.

2. The definitions of the variables (predictors) or how they were identified were not clearly described in the Method section. For instance, how the symptoms at onset were defined or identified (e.g., what GI symptoms? How to define fatigue?)? How were the comorbidities defined (e.g., using diagnostic codes only? any criteria for diagnosis times or the time period to identify the comorbidities?)? This section should be revised to provide more details.

3. The study sample size is only 433 (≤ 3 weeks, n=373; >3 weeks, n=60). The patient no. of many predictors is low; some were < 10 (as shown in Table 1). Whether the statistical power was enough to evaluate the effect of those predictors should be doubted.

4. According to the Statistical analysis section, the regression models only adjusted for age, gender, and comorbidities, but not other variables in Table 1, based on clinical and theoretical reasoning. However, to me, the reasons for choosing only age, gender, and comorbidities for adjustment seem arbitrary. Is it possible to perform additional analyses (sensitivity analyses) that applied other variable selection methods (e.g., stepwise selection method, LASSO regression)?

**********

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 #1: No

Reviewer #2: Yes: Matthew Groenewold

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.

Submitted filename: PONE-D-21-21612_mrg_review.docx

Click here for additional data file.

13 Oct 2021

Dear Academic Editor Dr. Huang,

Thank you for the constructive comments!

Below please find our responses to your comments and changes made in the manuscript as a result.

Reviewer #1: Thank you for allowing me to review your study, “Patterns and predictors of sick leave among non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic”. The study examined predictors of sick leave among non-hospitalized healthcare & residential care workers from March to August 2020.

Overall, my impressions of this paper are positive. It offers meaningful data on a population exposed to various health risks and, naturally, higher than average sickness absence rates. I have included some remarks to help ensure the success of this paper.

• To improve this paper, I believe that more editing is required. I list some examples below.

Answer: Thank you for the comment. An English proofreader has now reviewed the manuscript, and we have taken into account all the comments below as well.

++ “The study from the US and Spain showed that healthcare workers also had increased prevalence of sick leave during the pandemic….” It is unclear by stating “the study” that it was one study or several studies.

Answer: Thank you for the comment. We have now corrected this in the introduction; see the second paragraph.

++ Issues with the structure of the sentence beginning with “A recent editorial by Gohar…”, specifically with “job support employees.” Do you mean “employee job support?”

Answer: Thank you for this important comment. We have revised the introduction; see the fourth paragraph.

++ Inconsistencies “healthcare” and “health care.”

Answer: We apologize for this inconsistency. We have now used ‘healthcare’ consistently throughout the manuscript.

++ “However, a limitation is that there might be a bias by collecting sick leave information in this way as Swedish inhabitants are free to seek health care in different regions that have distinct patients records BMI was missing in the substantial part of the study population.” I am unclear if these are supposed to be two separate sentences. If not, please clarify your point.

Answer: We apologize for this inconsistency. These should be stated as two sentences (the last paragraph of the discussion section), which we have now corrected in the manuscript.

• It is indicated that multiple sick leave periods were summed together and later categorized based on total duration. Were there any measures taken for sick leaves that were uncommon (e.g., car accident)?

Answer: Thank you for this interesting comment. We understand that you are asking about sick leave prior to the pandemic. There were no uncommon reasons for sick leave; therefore, we find this reason as being unlikely to introduce a bias.

• How updated are medical records concerning variables such as BMI? If not updated regularly, I believe this should be described in the limitation section. Also, is BMI self-reported in these records? If it is self-reported, it is an important consideration to add as individuals typically underestimate their weight or BMI (which further magnifies the role of BMI and sick leave).

Answer: Thank you for this comment. Information on BMI was collected from the electronic medical records of patients. Height and weight used to be measured therefore by healthcare personnel during in- and outpatient visits. Therefore, there is a bias with regard to collection of BMI as this was related to healthcare contact. This is discussed now in Material and Methods (page 7) and the limitation section (page 12).

• It was mentioned that BMI was missing in a substantial part of the study population. Could this have compromised the integrity of the results?

Answer: This is an important comment. Please see the answer above.

• Age in previous studies showed inconsistencies with its relationship to sickness absence. However, some evidence suggests that sex (females) have higher rates of sick leaves. It might be worthy of hypothesizing why there was not a relationship. Could it be that COVID-19 affects male and female healthcare workers evenly?

Answer: Thank you for the comment. We have added a paragraph in the discussion, page 10.

I would draw on the statistics (sex) of patients infected with COVID in the Uppsala region and contrast accordingly.

Answer: Thank you for this interesting comment. We agree that this comparison would be relevant. However, this estimate in the population is unreliable because the accessibility for Covid-19 testing was low in the general population in the first part of the pandemic. The study covers the early period of the pandemic, where testing of both occupational categories was prioritized, providing valuable clinical and epidemiological information (see the introduction, page 5).

• Also missing from this study is its implications. By having this information, what should be done in terms of future research or from a practical standpoint?

Answer: Thank you for the comment. We have added implications of the study at the end of the last paragraph of the discussion, page 12, as well as in the conclusion, page 12.

• Mentioning the region or at least the country in the title is beneficial

Answer: Thank you for the comment. We have modified the title as you suggested.

My best wishes!

Reviewer #2: This is an interesting, important and generally well-structured study. However, there is a fair amount of copy editing and revision for clarity of language that needs to be done before it can be published, in my opinion. I have attempted to make some suggestions along these lines in the attached document. My grammatical and wording edits are only suggestions, but I think they will add to the readability of the paper.

I also had some concerns about the description of the statistical methods and presentation of the results which I have also detailed in the attached document. These, I think, must be addressed.

Thanks you for sharing your work; I enjoyed reading it.

Answer: Thank you for your suggestions for the manuscript. We have made changes according to your comments; please look at the manuscript:

Reviewer #3: This is an interesting study. The manuscript is well-written. However, some methodologic issues should be further clarified. My specific comments are as follows:

1. The authors only mentioned that BMI data are missing for 206 subjects. How about the missing data regarding other variables (listed in Table 1) used in the prediction model? Please also clarify this in the manuscript.

Answer: Thank you for this important comment. In the prediction model, we used age, gender, and comorbidity where we did not have missing data. Age and gender were determined from the Swedish national identification number, while comorbidity was gathered from the electronic medical records. However, underdiagnosis or that some diagnoses were not registered in the medical journals cannot be excluded.

2. The definitions of the variables (predictors) or how they were identified were not clearly described in the Method section. For instance, how the symptoms at onset were defined or identified (e.g., what GI symptoms? How to define fatigue?)? How were the comorbidities defined (e.g., using diagnostic codes only? any criteria for diagnosis times or the time period to identify the comorbidities?)? This section should be revised to provide more details.

Answer: Thank you for the comment. We have clarified this in the materials and methods, pages 6-7.

3. The study sample size is only 433 (≤ 3 weeks, n=373; >3 weeks, n=60). The patient no. of many predictors is low; some were < 10 (as shown in Table 1). Whether the statistical power was enough to evaluate the effect of those predictors should be doubted.

Answer: Thank you for this comment. Our study has several limitations, including the low number of some variables/predictors. Please see the discussion and the limitations, page 12.

4. According to the Statistical analysis section, the regression models only adjusted for age, gender, and comorbidities, but not other variables in Table 1, based on clinical and theoretical reasoning. However, to me, the reasons for choosing only age, gender, and comorbidities for adjustment seem arbitrary. Is it possible to perform additional analyses (sensitivity analyses) that applied other variable selection methods (e.g., stepwise selection method, LASSO regression)?

Answer: Thank you for this comment. We adjusted for age, gender, and comorbidities because they are the factors that were previously shown to influence sick leave (Allebeck et al., 2004). We did not adjust for factors that were missing in the substantial part of subjects (as BMI) or had a small sample size (as some occupational groups). Please see the discussion section and the limitations, page 12.

Based on the literature on mythos and misinterpretation in statics, we chose not to use the stepwise selection method or LASSO regression as these methods may complicate the analysis, in cases of variables with small sample size by invalidating common tools of statistical inference such as P-values and confidence intervals (Heize et al., Five myths about variable selection Transplant international, 30(1) 2016; Greenland et al., Statystical tests, P value, confidence intervals, and power: a guide to misinterpretation. Eur J Epidemiol, 31:337-350, 2016). We emphasized that our results should be interpreted with a caution, and we made bullet points for all the limitations in the last paragraph of the discussion section.

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 #1: No

Reviewer #2: Yes: Matthew Groenewold

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.

Accepting review changes in the manuscript:

Submitted filename: Answer to review.docx

Click here for additional data file.

15 Nov 2021

Patterns and predictors of sick leave among Swedish non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic

PONE-D-21-21612R1

Dear Dr. Kisiel,

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,

Huei-Kai Huang, M.D.

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

Reviewer #2: All comments have been addressed

Reviewer #3: 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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: I Don't Know

Reviewer #2: Yes

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 #1: Yes

Reviewer #2: Yes

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 #1: Yes

Reviewer #2: Yes

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 #1: The authors have adequately resolved all my comments in their resubmission. I believe that the paper is worthy of publication.

Reviewer #2: (No Response)

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 #1: No

Reviewer #2: No

Reviewer #3: No

1 Dec 2021

PONE-D-21-21612R1

Patterns and predictors of sick leave among Swedish non-hospitalized healthcare and residential care workers with Covid-19 during the early phase of the pandemic

Dear Dr. Kisiel:

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. Huei-Kai Huang

Academic Editor

PLOS ONE