Original article

Scand J Work Environ Health 2024;50(3):168-177    pdf

https://doi.org/10.5271/sjweh.4144 | Published online: 12 Feb 2024, Issue date: 01 Apr 2024

Education and pandemic SARS-CoV-2 infections in the German working population – the mediating role of working from home

by Wachtler B, Beese F, Demirer I, Haller S, Pförtner T-K, Wahrendorf M, Grabka MM, Hoebel J

Objectives SARS-CoV-2 infections were unequally distributed during the pandemic, with those in disadvantaged socioeconomic positions being at higher risk. Little is known about the underlying mechanism of this association. This study assessed to what extent educational differences in SARS-CoV-2 infections were mediated by working from home.

Methods We used data of the German working population derived from the seroepidemiological study “Corona Monitoring Nationwide – Wave 2 (RKI-SOEP-2)” (N=6826). Infections were assessed by seropositivity against SARS-CoV-2 antigens and self-reports of previous PCR-confirmed infections from the beginning of the pandemic until study participation (November 2021 – February 2022). The frequency of working from home was assessed between May 2021 and January 2022.We used the Karlson-Holm-Breen (KHB) method to decompose the effect of education on SARS-CoV-2 infections.

Results Individuals with lower educational attainment had a higher risk for SARS-CoV-2 infection (adjusted prevalence ratio of low versus very high = 1.76, 95% confidence interval 1.08–2.88; P=0.023). Depending on the level of education, between 27% (high education) and 58% (low education) of the differences in infection were mediated by the frequency of working from home.

Conclusions Working from home could prevent SARS-CoV-2 infections and contribute to the explanation of socioeconomic inequalities in infection risks. Wherever possible, additional capacities to work remotely, particularly for occupations that require lower educational attainment, should be considered as an important measure of pandemic preparedness. Limitations of this study are the observational cross-sectional design and that the temporal order between infection and working from home remained unclear.

This article refers to the following texts of the Journal: 2022;48(1):52-60  2022;48(5):380-390  2022;48(6):446-456  2022;48(7):530-539  2023;49(4):259-270  2023;49(6):386-394

The outbreak of the newly emerged severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in the early days of 2020, later declared a pandemic by the World Health Organization (WHO) on 11 March 2020, has challenged societies worldwide on a historic scale. Since the beginning of the outbreak, hundreds of millions of individuals have been infected worldwide and almost 7 million deaths due to the new coronavirus disease 2019 (COVID-19) were reported until the WHO declared the end of the global health emergency on 5 March 2023 (1). Already during the early phases of the COVID-19 pandemic, evidence emerged that people in a lower socioeconomic position (SEP) might have been infected more frequently with SARS-CoV-2 than people in higher SEP (2). Today, there is plenty of evidence from numerous studies worldwide demonstrating that the SARS-CoV-2 infection risk was unequally distributed across societies during the pandemic, mostly to the disadvantage of those with lower SEP (35).

But how these socioeconomic inequalities in SARS-CoV-2 infections exactly arose is still not fully understood. There are different synopses available that differentiate possible pathways through which socioeconomic inequalities in SARS-CoV-2 infections might be explained, but empirical evidence remains limited so far. For instance, Clare Bambra described four main interconnected pathways (unequal exposure, unequal transmission, unequal susceptibility and unequal treatment) whereby existing social inequalities shape the spread and distribution of newly emerged infectious diseases (6). For some of these pathways, empirical evidence exists but still remains inconclusive. One major driver of inequalities in SARS-CoV-2 infections that is mentioned frequently is the unequal exposure as a result of unequal working conditions (6, 7).

It has been described that some occupations, such as healthcare workers (810), indoor trade and process and plant workers (11, 12) had higher risks of infection during the early phases of the pandemic. Beale et al. found that these differences were partly due to differences in work-related close contacts (11) and Reuter et al. found that the SARS-CoV-2 infection risk was generally twice as high for essential workers than for non-essential workers (9). Attendance at the workplace during the COVID-19 pandemic seems generally to be a risk factor for a SARS-CoV-2 infection (13, 14). In line with these findings, some studies found that the possibility to work from home was associated with a lower infection risk (1518). However, the evidence is inconsistent here, as some studies found no associations between the possibility to work from home and SARS-CoV-2 infection risk (19, 20) or even associations to the disadvantage of those who were working remotely (21). The employees’ position in the occupational system is strongly shaped by their educational attainment, and studies have shown that highly qualified employees generally have more opportunities to work from home, which was especially evident during the COVID-19 pandemic (22, 23).

In order to slow down the spread of the virus, access restrictions to the workplace were in place in multiple countries and working from home was often recommended, at least during some periods of the pandemic. Some countries, including Germany, implemented legislation that made it mandatory for employers to offer the possibility to work from home whenever working remotely was regarded as feasible. But in the case of Germany, this legislation was only effective between January and June 2021 (24). However, offering the possibility to work from home appears to be an interesting potential intervention to prevent infection, with limited negative effects on the wider economy.

But the possibility to work from home is unequally distributed within the workforce (25, 26). Individuals with higher educational attainments more frequently tend to have the possibility to work remotely than socially more disadvantaged individuals (22, 23) therefore the recommendations or orders to work from home might have discriminated individuals in lower SEP and might at least partly explain the observed socioeconomic inequalities in SARS-CoV-2 infection risk. To our best knowledge, no study has examined and quantified how much of these inequalities can be explained by inequalities in the possibility to work from home. This knowledge is needed in order to evaluate the recommendation to work remotely whenever possible and to identify those who are not yet able to work from home and increase the efforts to make working from home more available to those who could not yet benefit from these recommendations.

We therefore conducted this study in order to address the following research questions: Was the association between educational attainment as a measure of SEP and pandemic SARS-CoV-2 infection risk in the working population mediated by the possibility to work from home? How much of the association between educational attainment and pandemic SARS-CoV-2 infection risk was mediated by the unequal possibility to work from home?


Study design and population

We used data from the German population-based seroepidemiological study “Corona Monitoring Nationwide – Wave 2 (RKI-SOEP-2)” and the Socio-Economic Panel (SOEP) study. The RKI-SOEP-2 study was based on the SOEP, a dynamic cohort based on nationwide random samples of individuals living in private households across Germany. All individuals who participated in the SOEP survey wave in 2021 and their household members aged ≥14 years were invited to participate in the RKI-SOEP-2 study. Data collection took place between November 2021 and February 2022. A questionnaire and test-kit for self-sampling capillary blood to detect immunoglobulin G (IgG) antibodies against the spike protein (anti-S-antigen) and the nucleocapsid protein of SARS-CoV-2 (anti-N-antigen) was sent to each target person. In order to achieve a high participation rate, respondents were informed that they would receive the results of their blood test and a monetary incentive (€10 for adults and €5 for adolescents) if all the documents were completed and returned. The questionnaire covered topics such as previously experienced SARS-CoV-2 infection, COVID-19 vaccination status, the willingness to be vaccinated and questions on general health status and health behaviors. The Ethics Committee of the Berlin Chamber of Physicians approved the study (Eth-33/20) in compliance with the Declaration of Helsinki. A detailed study description, including detailed information on response rates and non-response analyses, can be found elsewhere (27, 28).

Outcome: SARS-CoV-2 infections

SARS-CoV-2 infection status was assessed by using individual self-reports and serological assays for SARS-CoV-2 antibodies (29). The Euroimmun enzyme-linked immunosorbent assays (ELISA) anti-SARS-CoV-2-QuantiVac and anti-SARS-CoV-2-NCP were used for the detection of anti-S (S1 domain of the spike protein) and anti-N (nucleocapsid protein, NCP) antibodies. To determine seropositivity for anti-N antibodies in dried blood samples, the ratio cut point for serum samples provided by the manufacturer was adapted from 1.1 to 0.95 (30). A positive SARS-CoV-2 infection status was defined as either having self-reported a previous PCR-confirmed infection or seropositivity for anti-N antibodies or, in the absence of self-reported vaccination, seropositivity for anti-S antibodies (29). Infections might have occurred between the onset of the pandemic in Germany and the date of individual study participation.

Exposure: educational attainment

Self-reported educational attainment from the SOEP wave in 2020 (or the latest available data from earlier waves) was used. Education was classified with the 2011 version of the International Standard Classification of Education (ISCED) (31). Participants’ highest school and vocational qualifications were classified as low (lower secondary education or below, ISCED levels 0–2), medium (upper secondary or post-secondary education, ISCED levels 3 and 4), high (lower tertiary education: Bachelor’s degree or equivalent, ISCED levels 5 and 6) and very high (upper tertiary education: Master’s degree or equivalent or doctorate ISCED levels 7 and 8).

Mediator: working from home

The frequency of working from home was assessed in SOEP core wave 2021 (data collection between May 2021 and January 2022). Participants were asked how often they were working from home. We coded the variable into four categories (i): not working from home (ii); every two weeks or less frequently (iii); several times a week; and (iv) daily. For sensitivity analyses, the variable was also dichotomized into working from home and not working from home.

Confounding variables

A directed acyclic graph (32) was drawn to identify a minimal sufficient adjustment set to estimate the total effect of education on infection risk and the mediation of this association by the frequency of working from home (supplementary material, URL, 1 and 2). Age, sex, migration experience, federal state and urban versus rural residence [based on characteristics of the district of residence according to official statistics (33)] were identified as the minimal sufficient adjustment set using DAGitty version 3.1 (34). Household composition is known to be a predictor of SARS-CoV-2 infection but was regarded as a potentially mediating variable between educational attainment and SARS-CoV-2 infection risk here and was therefore not included into the minimal sufficient adjustment set. A sensitivity analysis was conducted by including household composition (“single household”, “multiple persons without children under 16” and “multiple persons with children under 16”) into the adjustment set of the analysis.

Statistical analysis

Descriptive results are presented as proportions and prevalences with 95% confidence intervals (95% CI). In order to establish mediation, the following conditions must hold according to the traditional approach by Baron & Kenny (35): (i) the exposure variable (education) must be significantly associated with the mediator (frequency of working from home); (ii) exposure (education) must be significantly associated with the outcome (SARS-CoV-2 infection); and (iii) the mediator (frequency of working from home) must be significantly associated with the outcome (SARS-CoV-2 infection). If all these conditions hold in the predicted direction, the association of the exposure variable on the outcome must become weaker when the mediator variable is added to the regression model. In addition, significant exposure–mediator interaction must be ruled out.

The association between educational attainment and risk of SARS-CoV-2 infection, as well as between frequency of working from home and risk of SARS-CoV-2 infection, was estimated by calculating prevalence ratios (PR) with 95% CI and P-values using Poisson regressions with household-clustered standard errors. The models were adjusted for age, sex, migration experience, federal state and urban versus rural residence. In the next step, a third model that included both the exposure and mediator variables was calculated using the same adjustment set. An additional regression model with an interaction term between education and frequency of working from home was calculated to assess the exposure–mediator interaction while adjusting for the minimal sufficient adjustment set.

We conducted a decomposition analysis of the effect of education on SARS-CoV-2 infection risk by using the Karlson-Breen-Holm (KHB) method (36, 37). The KHB method allows to recover the degree to which a variable mediates the relationship between the exposure and the outcome and is unaffected by the rescaling or attenuation bias that might arise in simple cross-model comparisons in non-linear models (38). The method was implemented by using the Stata khb package and household-clustered robust standard errors were calculated (38).

The analyses were calculated using weighting factors that compensated for systematic panel attrition, systematic non-response and adjusted the sample to match the official German population statistics by age, sex, citizenship (German versus non-German), federal state, household type and size, as well as owner-occupied housing (27, 28). Details of the weighting procedures and non-response can be found elsewhere (28). The sample population was restricted to the working population aged 18–67 years. All analyses were conducted using the survey data functionality of Stata version 17.0 (StataCorp LLC 2021, College Station, TX, USA). The reporting of this study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (39).


A total of 11 162 individuals from 6760 households took part in the RKI-SOEP-2 study between November 2021 and February 2022. A majority of 74% of the sample population participated in November and December 2021. The overall response rate was 53.7%. We included 6826 individuals into this study who were 18–67 years old and described themselves as “working”. The description of the sample population according to the SARS-CoV-2 infection status is shown in table 1.

Table 1

Description of the sample population by SARS-CoV-2 infection status.

  Total a   No SARS-CoV-2 infection   SARS-CoV-2 infection b
  N c % d   N c % d   N c % d
    6826 100   6018 89   805 11
  Female 3171 53.6   3214 51.5   365 43.9
  Male 3655 46.5   2804 48.5   440 56.1
Age (years)
  18–34 1466 30.2   1284 30.4   181 22.5
  35–49 2276 32.1   1964 30.6   311 38.6
  49–67 3084 37.7   2770 39   313 38.9
  Very high 1170 14.8   1074 15.4   96 9.9
  High 1667 23.1   1472 23   195 23.8
  Medium 3092 49.2   2718 48.9   373 51
  Low 534 8.3   445 8   88 10.2
  Missing 363 4.7   309 4.6   53 5.0
Possibility to work from home
  No possibility 3282 51.3   2852 50.3   429 58.5
  Every two weeks or less frequently 700 10   616 10   84 9.1
  Several times per week 1200 16.4   1096 17   104 11.1
  Daily 967 15.2   898 15.9   69 9.5
  Missing 677 7.3   556 6.8   119 11.8
Migration experience
  Yes 819 82.2   670 16.3   148 26
  No 5959 17.4   5304 83.3   653 74
  Missing 48 4   44 0.4   4 0.3
Residential area
  Urban 4545 69.4   4046 70   496 64.7
  Rural 2204 29.3   1906 28.9   298 32.9
  Missing 77 1.3   66 1.1   11 2.3

a The numbers in the table do not fully add up to the totals of n = 6,826 because the infection status values for three participants could not be retrieved. b Self-reported previous PCR-confirmed infection or seropositivity for anti-N antibodies or, in the absence of self-reported vaccination, seropositivity for anti-S antibodies against SARS-CoV-2. c Unweighted number of participants. d Weighted proportions.

A total of 805 individuals or 11% of the sample population had a SARS-CoV-2 infection and the proportion of SARS-CoV-2-infected individuals was higher among those with lower formal education. While 13.6% (95% CI 9.4–19.4) in the low educated group had a previous SARS-CoV-2 infection, only 7.4% (95% CI 5.2–10.4) in the very highly educated group had a previous SARS-CoV-2 infection. Furthermore, a larger proportion of the highly educated group reported working more frequently from home than those with a lower formal education (table 2). The exposure (educational attainment) was associated with the mediator (possibility to work from home): those with a very high education had a 9.45-fold (95% CI 5.49–16.28; P<0.0001) higher probability to work from home several times per week in comparison to those with low educational attainment after adjustment for age, sex, migration experience, federal state and urban versus rural residence. Educational attainment (exposure) and the possibility to work from home (mediator) were both associated with the SARS-CoV-2 infection status (outcome), as shown in table 3. The prevalence of a SARS-CoV-2 infection was 1.76 times higher in the group with low education in comparison to those in the group with very high education (95% CI 1.08–2.88; P=0.023) after adjusting for age, sex, migration experience, federal state and urban versus rural residence. When the frequency of working from home was added to the model, the total effect of education on infection risk was diminished to a prevalence ratio of 1.27 (95% CI 0.78–2.09; P=0.338) between the groups with low and very high educational attainment (table 3). We found no significant exposure–mediator interaction (supplementary material 3).

Table 2

Possibility to work from home by educational attainment. [CI=confidence interval]

  No possibility   ≤ Every two weeks   Several times per week   Daily   Missing
N a (%) b 95% CI   N a (%) b 95% CI   N a (%) b 95% CI   N a (%) b 95% CI   N a (%) b 95% CI
Total 3282 (51.3) 49.4–53.1   700 (9.9) 8.9–11   1200 (16.4) 15.1–17.7   967 (15.2) 13.9–16.6   677 (7.3) 6.5–8.3
Education Very high 225 (17.2) 14.2–20.7   151 (12.2) 9.8–15.2   380 (33.5) 29.5–37.8   342 (32.6) 28.5–37   72 (4.5) 2.9–6.8
High 633 (39.4) 35.8–43.1   234 (13.6) 11.3–16.2   419 (24.7) 21.8–27.9   271 (17) 14.4–20.0   110 (5.3) 3.9–7.2
Medium 1950 (64.2) 61.6–66.6   258 (8.6) 7.3–10.2   338 (10) 8.5–11.6   301 (11.2) 9.7–13.1   245 (6) 5.0–7.3
Low 335 (71.3) 65.3–76.6   27 (4.2) 2.3–7.5   14 (2.3) 1.1–4.7   13 (3.4) 1.5–7.3   145 (18.9) 14.4–24.3
Missing 139 (46.8) 38.1–55.7   30 (7.7) 4.4–13.1   49 (12.7) 8.1–19.2   40 (13.2) 8.3–20.2   105 (19.7) 14.3–26.5

a Unweighted totals. b Weighted percentages.

Table 3

Relative risk of SARS-CoV-2 infection by educational attainment and frequency of working from home. [PR=prevalence ratio; CI = confidence interval; ref.=reference category].

  Model 1 a   Model 2 a   Model 3 a
  PR 95% CI P-value   PR 95% CI P-value   PR 95% CI P-value
  Very high (ref.) (ref.) (ref.) (ref.)           (ref.) (ref.) (ref.)
  High 1.51 1.01–2.26 0.043           1.34 0.89–2.02 0.16
  Medium 1.66 1.15–2.39 0.007           1.34 0.92–1.96 0.126
  Low 1.76 1.08–2.88 0.023           1.27 0.78–2.09 0.338
Possibility to work from home
  No possibility         (ref.) (ref.) (ref.)   (ref.) (ref.) (ref.)
  ≤ Every two weeks         0.85 0.60–1.19 0.337   0.88 0.63–1.22 0.436
  Several times per week         0.61 0.43–0.86 0.005   0.65 0.45–0.92 0.017
  Daily         0.55 0.39–0.79 0.001   0.59 0.41–0.86 0.006

a Models 1–3 = Poisson regression models adjusted for age, sex, migration experience, federal state and urban versus rural residence.

The results of the decomposition analysis according to the KHB method are shown in table 4. The association of education with SARS-CoV-2 infection risk was mediated by the frequency of working from home. The proportion of the total effect that was mediated by the frequency of working from home varied between the education groups: 27% of the effect of education on SARS-CoV-2 infection risk was mediated by the frequency of working from home in the highly educated group, while it was 42% in the group with medium and 58% in the group with low education (table 4).The sensitivity analysis, which included the household composition into the adjustment set of the analyses showed that the results varied only marginally (supplementary material 4).

Table 4

Decomposition of the total effect of education on SARS-CoV-2 infection risk according to the Karlson-Holm-Breen (KHB) method. [SE=standard error; CI=confidence interval; ref.=reference category.

Education Effect Model 1 a   Model 2 b
    Coefficient (SE) 95% CI P-value Mediation % c   Coefficient (SE) 95% CI P-value Mediation % c
Very High (ref.) (ref.) (ref.) (ref.) (ref.)   (ref.) (ref.) (ref.) (ref.)
High Total 0.54 (0,23) 0.09–0.99 0.018     0.47 (0.23) 0.02–0.92 0.041  
Direct 0.39 (0.23) -0.07–0.85 0.096 28.1   0.34 (0.24) -0.11–0.80 0.146 26.8
Indirect 0.15 (0.06) 0.16–49.57 0.011     0.13 (0.06) 0.02–0.23 0.024  
Medium Total 0.60 (0.21) 0.20–1.01 0.004     0.58 (0.21) 0.17–0.99 0.005  
Direct 0.31 (0.22) -0.11–0.73 0.149 48.5   0.34 (0.22) 0.09–0.75 0.119 42
Indirect 0.29 (0.09) 0.12–0.46 0.001     0.24 (0.08) 0.08–0.40 0.003  
Low Total 0.80 (0.29) 0.23–1.36 0.006     0.67 (0.28) 0.11–1.22 0.020  
Direct 0.34 (0.30) -0.24–0.92 0.256 57.7   0.28 (0.29) -0.30–0.86 0.343 58.1
Indirect 0.46 (0.11) 0.24–0.68 <0.0001     0.39 (0.11) 0.18–0.60 <0.0001  

a Model 1 = adjusted for age and sex b Model 2 = adjusted for age, sex, migration experience, federal state and urban versus rural residence c Mediation % = (Indirect effect / Total effect) × 100


This is the first study to examine the mediating effect of working from home during the COVID-19 pandemic on educational inequalities in pandemic SARS-CoV-2 infection among Germany’s working population. We found that individuals with lower educational attainment had a higher risk for SARS-CoV-2 infection during the pandemic than those with higher education. At the same time, individuals with lower educational attainment were less frequently able to work from home and those with fewer possibilities to work from home had higher risks for infection with SARS-CoV-2. Our decomposition analysis showed that a substantial proportion of the educational inequality in SARS-CoV-2 infection risk was mediated by differences in the frequency of working from home between educational groups, with the highest contribution to explanation of the differences being among those with low formal education.

Our results are in line with previous findings that showed an association between the possibility to work from home and SARS-CoV-2 infection risk. The majority of these studies showed that those who were not able to work remotely had a higher risk of infection during the pandemic (1518, 4046). In contrast, one study among employees of a big Swiss company found that those who worked from home most of the time during the year 2020 had higher odds for a SARS-CoV-2 infection in comparison to colleagues who only sometimes or never worked from home (21). However, these results were not statistically significant, the sample was very small and the study design might have been particularly prone to selection bias. In contrast to our results, another study from Cologne, Germany, found no statistically significant association between working from home and SARS-CoV-2 infection risk after adjusting for multiple other variables (47). However, the sample was relatively small and restricted to the population of just one German city. Our results are also in line with most of the available evidence on socioeconomic inequalities in SARS-CoV-2 infection risk that showed higher SARS-CoV-2 infection risks for individuals in lower SEP. By using the level of educational attainment as a measure of SEP, our study adds to the previously existing evidence that a substantial part of the socioeconomic inequalities in pandemic SARS-CoV-2 infection risk can be explained by the frequency of working from home.

A literature search in “LitCovid”, a literature hub for tracking scientific research papers on COVID-19 provided by the US National Institute of Health (48), revealed that no study investigating the mediating effect of working from home on the association between formal education and SARS-CoV-2 infection risk was published in the PubMed database until 1 August 2023. We therefore assume that this is the first study – not only in Germany but internationally – to assess the role of working from home in explaining these inequalities. By using data from a population-based seroepidemiological study, we were able to include both previously known and unknown infections with SARS-CoV-2. Another strength of this study is the high external validity to the German working population living in private households. We used a directed acyclic graph to systematically identify potentially confounding variables and adjusted our analyses to the minimal sufficient adjustment set. The KHB method was used to minimize the rescaling problem that arises when comparing regression coefficients between same-sample nested models. We tested for exposure–mediator interactions and found no significant interaction. Furthermore, we did not see a strong correlation between the error terms of the mediated and non-mediated model, which points towards the absence of relevant residual confounding in our analysis (49).

Despite these strengths, our study has some limitations that should be kept in mind when interpreting the results. Firstly, our data came from a cross-sectional observational study, which may to a certain degree have been affected by selection bias. The detailed analyses of the reasons for non-response showed that among other factors, lower educational attainment was associated with higher levels of non-response (28). This might have led to an underestimation of educational inequalities in SARS-CoV-2 infection risk despite the fact that we used weights to correct our analyses for systematic non-response. In addition, the information on the frequency of working from home came from the SOEP survey wave in 2021 (study period May 2021 to January 2022), whereas our outcome was assessed in the RKI-SOEP-2 study (study period November 2021 to February 2022). Infections might have occurred prior to the assessment of the frequency of working from home as infections were partly assessed retrospectively when participants were asked about previous PCR confirmed infections. This and the observational design of our study means that causal interpretations of our results are impossible. The possibility to work remotely might have changed between the two timepoints, leading to the possibility of some misclassification and the knowledge of a previous infection might have influenced the recollection of the possibility to work from home. Additionally, we do not know exactly when the SARS-CoV-2 infections occurred and were not able to differentiate between first infections and subsequent re-infections. As we know that individuals with lower educational attainment have a higher SARS-CoV-2 infection risk, we might have missed re-infections that are more probable in individuals with low education. This may have led to an underestimation of educational inequalities and the protective effect of working from home by not counting these additional subsequent infections. In this study, we have focused on the mediating effect of working from home, but educational inequalities might also arise through other mechanisms that were not assessed in this study. It cannot be ruled out that some of these unobserved alternative explanations, such as differences in protective behavior, inequalities in housing conditions and on the neighborhood level, etc., might also have influenced our findings. We conducted a sensitivity analysis and additionally included the household composition as potentially confounding variable and found that our results did not change. Another limitation of our study is, that while we used the most appropriate data source available with a high number of participants, we were still not able to stratify our analyses for gender while simultaneously stratifying by education and sufficiently adjusting for potentially confounding variables. As the gender distribution differs across different occupations, and the infection risk within occupations may differ by gender (50), further gender-specific analyses are desirable.

Concluding remarks

We analyzed, for the first time, what effect working from home has in the explanation of educational inequalities in SARS-CoV-2 infection risk and found that working from home explained 27–58% of these differences. However, limitations of this study were the cross-sectional design and the impossibility to establish a clear temporal order between the mediator and the outcome that make a causal interpretation impossible. The results of our study suggest that enabling as many individuals as possible to work from home during outbreaks of acute respiratory diseases might be promising for public health, pandemic preparedness planning and politics because it could reduce overall infections and possibly also socioeconomic inequalities in infection risk. It should particularly be considered if more jobs that require lower levels of education could be done remotely in order to reduce health inequalities during epidemics. In addition, those who are not able to work from home should be prioritized during the distribution of effective protective equipment and preventive interventions such as vaccination campaigns. Those industries where remote working is not possible should have effective infection prevention and control plans in place. Enabling a growing proportion of the population to work from home should further be planned and realized not only during but particularly between infectious disease epidemics in order to be prepared for the next outbreak. The further health consequences of working from home are the recent focus of many international studies and the effect of working from home on both the individual’s mental and physical health and productivity is still at the center of an ongoing debate (5153). Working from home might, on the one hand, be associated with negative side effects such as higher levels of musculoskeletal pain (54), depression or alcohol consumption (55, 56), less physical activity, etc (57). On the other hand, working from home might lead to a reduction of contacts with infected individuals, longer sleeping times, more time for physical activity, an enhanced possibility to arrange working and personal life and better productivity (58, 59). Further research with a health equity focus is needed to fully understand the effect of working from home on individuals’ health and wellbeing. A better understanding of the various effects of working from home could help to improve the work environment at home and reduce potential adverse health impacts if it becomes necessary again to limit human mobility and physical contacts on a large scale during the next epidemic.

Ethics approval and consent to participate

The Ethics Committee of the Berlin Chamber of Physicians approved this study (Eth-33/20) in compliance with the Declaration of Helsinki. Informed consent to participate was obtained from all individual participants included in the study.

Availability of data and materials

The data cannot be made publicly available because informed consent from participants did not cover the public deposition of data. However, the data underlying the analysis in this article is archived in the SOEP Research Data Centre in Berlin and can be accessed on site upon reasonable request (www.diw.de/en/diw_01.c.601584.en/data_access.html).

Competing interests

The authors declare no conflicts of interest.


The German Research Foundation funded this study (grant number 458531028). The German Federal Ministry of Health funded the RKI-SOEP-2 study (reference number ZMI1-2521COR305).


We would like to thank all our colleagues at the Robert Koch Institute (RKI), the Socio-Economic Panel (SOEP) at the German Institute for Economic Research (DIW Berlin), the Institute for Employment Research (IAB) and the Research Centre of the Federal Office for Migration and Refugees (BAMF-FZ) for their support and cooperation. Special thanks go to the staff of DIW Econ GmbH for carrying out the weighting. We also thank the employees of the ‘infas – Institute for Applied Social Sciences’ who contributed to the planning and implementation of fieldwork and data collection. We sincerely thank all study participants for their willingness to participate.



Wise J. Covid-19: WHO declares end of global health emergency. BMJ 2023 May;381:1041. [CrossRef] [PubMed]


Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener Cet al. Socioeconomic inequalities and COVID-19 - A review of the current international literature. J Health Monit 2020 Oct;5 Suppl 7:3–17.[PubMed]


Khanijahani A, Iezadi S, Gholipour K, Azami-Aghdash S, Naghibi D. A systematic review of racial/ethnic and socioeconomic disparities in COVID-19. Int J Equity Health 2021 Nov;20(1):248. [CrossRef] [PubMed]


Benita F, Rebollar-Ruelas L, Gaytán-Alfaro ED. What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review. Sustain Cities Soc 2022 Nov;86:104158. [CrossRef] [PubMed]


Beese F, Waldhauer J, Wollgast L, Pförtner TK, Wahrendorf M, Haller Set al. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic-A Scoping Review. Int J Public Health 2022 Aug;67:1605128. [CrossRef] [PubMed]


Bambra C. Pandemic inequalities: emerging infectious diseases and health equity. Int J Equity Health 2022 Jan;21(1):6. [CrossRef] [PubMed]


Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Community Health 2020 Nov;74(11):964–8.[PubMed]


Wachtler B, Neuhauser H, Haller S, Grabka MM, Zinn S, Schaade Let al. The Risk of Infection with SARS-CoV-2 Among Healthcare Workers During the Pandemic. Dtsch Arztebl Int 2021 Dec;118(49):842–3. [CrossRef] [PubMed]


Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell Set al. Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany. Scand J Work Environ Health 2022 Sep;48(6):446–56. [CrossRef] [PubMed]


Würtz AM, Kinnerup MB, Pugdahl K, Schlünssen V, Vestergaard JM, Nielsen Ket al. Healthcare workers’ SARS-CoV-2 infection rates during the second wave of the pandemic: follow-up study. Scand J Work Environ Health 2022 Sep;48(7):530–9. [CrossRef] [PubMed]


Beale S, Patel P, Rodger A, Braithwaite I, Byrne T, Fong WLet al.; Virus Watch Collaborative. Occupation, work-related contact and SARS-CoV-2 anti-nucleocapsid serological status: findings from the Virus Watch prospective cohort study. Occup Environ Med 2022 Apr;79(11):729–35. [CrossRef] [PubMed]


Boucher E, Cao C, D’Mello S, Duarte N, Donnici C, Duarte Net al.; SeroTracker Consortium. Occupation and SARS-CoV-2 seroprevalence studies: a systematic review. BMJ Open 2023 Feb;13(2):e063771. [CrossRef] [PubMed]


Eekhout I, van Tongeren M, Pearce N, Oude Hengel KM. The impact of occupational exposures on infection rates during the COVID-19 pandemic: a test-negative design study with register data of 207 034 Dutch workers. Scand J Work Environ Health 2023 May;49(4):259–70. [CrossRef] [PubMed]


Torén K, Albin M, Bergström T, Murgia N, Alderling M, Schiöler Let al. Occupational risks associated with severe COVID-19 disease and SARS-CoV-2 infection - a Swedish national case-control study conducted from October 2020 to December 2021. Scand J Work Environ Health 2023 Sep;49(6):386–94. [CrossRef] [PubMed]


Galmiche S, Charmet T, Schaeffer L, Paireau J, Grant R, Chény Oet al. Exposures associated with SARS-CoV-2 infection in France: A nationwide online case-control study. Lancet Reg Health Eur 2021 Aug;7:100148. [CrossRef] [PubMed]


Matthay EC, Duchowny KA, Riley AR, Thomas MD, Chen Y-H, Bibbins-Domingo K, Glymour MM. Occupation and Educational Attainment Characteristics Associated With COVID-19 Mortality by Race and Ethnicity in California. JAMA Network Open. 2022;5(4):e228406-e.


Wahrendorf M, Reuter M, Hoebel J, Wachtler B, Höhmann A, Dragano N. Regional disparities in SARS-CoV-2 infections by labour market indicators: a spatial panel analysis using nationwide German data on notified infections. BMC Infect Dis 2022 Jul;22(1):661. [CrossRef] [PubMed]


Shah MM, Spencer BR, Feldstein LR, Haynes JM, Benoit TJ, Saydah SHet al. Occupations Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Vaccination, US Blood Donors, May 2021-December 2021. Clin Infect Dis 2023 Apr;76(7):1285–94. [CrossRef] [PubMed]


Lastrucci V, Lorini C, Del Riccio M, Gori E, Chiesi F, Sartor Get al. SARS-CoV-2 Seroprevalence Survey in People Involved in Different Essential Activities during the General Lock-Down Phase in the Province of Prato (Tuscany, Italy). Vaccines (Basel) 2020 Dec;8(4):778. [CrossRef] [PubMed]


Billingsley S, Brandén M, Aradhya S, Drefahl S, Andersson G, Mussino E. COVID-19 mortality across occupations and secondary risks for elderly individuals in the household: A population register-based study. Scand J Work Environ Health 2022 Jan;48(1):52–60. [CrossRef] [PubMed]


Schmid A, Anker D, Dubois J, Bureau-Franz I, Piccardi N, Colombo Mottaz Set al. SARS-CoV-2 infection among employees working from home and on site: an occupational study in Switzerland. Front Public Health 2022 Sep;10:980482. [CrossRef] [PubMed]


Atchison C, Bowman LR, Vrinten C, Redd R, Pristerà P, Eaton Jet al. Early perceptions and behavioural responses during the COVID-19 pandemic: a cross-sectional survey of UK adults. BMJ Open 2021 Jan;11(1):e043577. [CrossRef] [PubMed]


Office for National Statistics. Is hybrid working here to stay? 2022 [updated 23.05.2022. Available from: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/ishybridworkingheretostay/2022-05-23


Die Bundesregierung. Corona-Arbeitsschutzverordnung - Homeoffice-Regelung verlängert [Corona Occupational Health and Safety Ordinance - home office regulation extended] Online2021. Available from: https://www.bundesregierung.de/breg-de/themen/coronavirus/verordnung-zu-homeoffice-1841120


Nwosu CO, Kollamparambil U, Oyenubi A. Socio-economic inequalities in ability to work from home during the coronavirus pandemic. Econ Labour Relat Rev 2022;33(2):290–307. [CrossRef]


Asfaw A. Racial and Ethnic Disparities in Teleworking Due to the COVID-19 Pandemic in the United States: A Mediation Analysis. Int J Environ Res Public Health 2022 Apr;19(8):4680. [CrossRef] [PubMed]


Bartig S, Brücker H, Butschalowsky H, Danne C, Gößwald A, Goßner Let al. Corona Monitoring Nationwide (RKI-SOEP-2): Seroepidemiological Study on the Spread of SARS-CoV-2 Across Germany. Jahrb Natl Okon Stat 2022.


Danne C, Priem M, Steinhauer HW. SOEP-Core - 2021: Sampling, nonresponse, and weighting in wave 2 of living in Germany - Nationwide Corona-Monitoring (RKI-SOEP2). Berlin; 2022. Report No.: 1175.


Mercuri E, Schmid L, Poethko-Müller C, Schlaud M, Kußmaul C, Ordonez-Cruickshank Aet al. Nationwide population-based infection- and vaccine-induced SARS-CoV-2 seroprevalence in Germany at the end of 2021. medRxiv. 2023:2023.10.30.23297594. [CrossRef]


Robert Koch Institute. Application of the Euroimmun Anti-SARS-CoV-2-NCP (IgG) and Anti-SARS-CoV-2-QuantiVac-ELISA (IgG) antibody tests to dried blood spots. 2023.


UNESCO Institute for Statistics. International standard classification of education: ISCED 2011. Montreal2012.


Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999 Jan;10(1):37–48. [CrossRef] [PubMed]


Federal Institute for Research on Building UAaSD. Siedlunggstruktureller Kreistyp [settlement-structural type of district] 2024 [cited 2024. Available from: https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html


Textor J, van der Zander B, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol 2016 Dec;45(6):1887–94.[PubMed]


Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986 Dec;51(6):1173–82. [CrossRef] [PubMed]


Karlson KB, Holm A. Decomposing primary and secondary effects: A new decomposition method. Res Soc Stratification Mobility 2011;29(2):221–37. [CrossRef]


Karlson KB, Holm A, Breen R. Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and Probit:A New Method. Sociol Methodol 2012;42(1):286–313. [CrossRef]


Kohler U, Karlson KB, Holm A. Comparing coefficients of nested nonlinear probability models. Stata J 2011;11(3):420–38. [CrossRef]


Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJet al.; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med 2007 Oct;4(10):e297. [CrossRef] [PubMed]


Fisher KA, Olson SM, Tenforde MW, Feldstein LR, Lindsell CJ, Shapiro NIet al.; IVY Network Investigators; CDC COVID-19 Response Team. Telework Before Illness Onset Among Symptomatic Adults Aged ≥18 Years With and Without COVID-19 in 11 Outpatient Health Care Facilities - United States, July 2020. MMWR Morb Mortal Wkly Rep 2020 Nov;69(44):1648–53. [CrossRef] [PubMed]


Miller S, Wherry LR, Mazumder B. Estimated Mortality Increases During The COVID-19 Pandemic By Socioeconomic Status, Race, And Ethnicity. Health Aff (Millwood) 2021 Aug;40(8):1252–60. [CrossRef] [PubMed]


Ha J, Lee S. Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties. Cities 2022 Dec;131:103892. [CrossRef] [PubMed]


Lim S, Dominianni C, Alroy KA, Baquero M, Crossa A, Gould LH. Racial and Ethnic Disparities in COVID-19-Like Illness and Impacts of Social Distancing and Working from Home. Ethn Dis 2022 Apr;32(2):123–30. [CrossRef] [PubMed]


Speaker SL, Doherty CM, Pfoh E, Dunn A, Hair B, Daboul Let al. Social Behaviors Associated With a Positive COVID-19 Test Result. Cureus 2021 Feb;13(2):e13064. [CrossRef] [PubMed]


Alishaq M, Nafady-Hego H, Jeremijenko A, Al Ajmi JA, Elgendy M, Al Ansari NAet al. Seroprevalence of SARS-CoV-2 Infection Among Working Women and Impact of Workplace Restrictions. Infect Drug Resist 2022 Jul;15:3871–9. [CrossRef] [PubMed]


Rosengren A, Söderberg M, Lundberg CE, Lindgren M, Santosa A, Edqvist Jet al. COVID-19 in people aged 18-64 in Sweden in the first year of the pandemic: key factors for severe disease and death. Glob Epidemiol 2022 Dec;4:100095. [CrossRef] [PubMed]


Oberste M, Schnörch N, Shah-Hosseini K, Asenova T, Dewald F, Lehmann Cet al. Results of the Cologne Corona Surveillance (CoCoS) study - a cross-sectional study: survey data on risk factors of SARS-CoV-2 infection in adults. BMC Public Health 2023 Feb;23(1):260. [CrossRef] [PubMed]


Chen Q, Allot A, Leaman R, Wei CH, Aghaarabi E, Guerrerio JJet al. LitCovid in 2022: an information resource for the COVID-19 literature. Nucleic Acids Res 2023 Jan;51 D1:D1512–8. [CrossRef] [PubMed]


Imai K, Keele L, Yamamoro T. Identification, Inference and Sensitivity Analysis for Causal Mediation Effects. Stat Sci 2010;25(1):51–71. [CrossRef]


Wahrendorf M, Schaps V, Reuter M, Hoebel J, Wachtler B, Jacob Jet al. Berufsbedingte Unterschiede bei COVID-19-Morbidität und -Mortalität in Deutschland. Eine Analyse von Krankenkassendaten von 3,17 Mio. Versicherten [Occupational differences of COVID-19 morbidity and mortality in Germany. An analysis of health insurance data from 3.17 million insured persons]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2023 Aug;66(8):857–68. [CrossRef] [PubMed]


Hall CE, Davidson L, Brooks SK, Greenberg N, Weston D. The relationship between homeworking during COVID-19 and both, mental health, and productivity: a systematic review. BMC Psychol 2023 Jun;11(1):188. [CrossRef] [PubMed]


Blank L, Hock E, Cantrell A, Baxter S, Goyder E. Exploring the relationship between working from home, mental and physical health and wellbeing: a systematic review. Public Health Res (Southampt) 2023 May;11(4):1–100. [CrossRef] [PubMed]


Lunde LK, Fløvik L, Christensen JO, Johannessen HA, Finne LB, Jørgensen ILet al. The relationship between telework from home and employee health: a systematic review. BMC Public Health 2022 Jan;22(1):47. [CrossRef] [PubMed]


Oakman J, Kinsman N, Lambert K, Stuckey R, Graham M, Weale V. Working from home in Australia during the COVID-19 pandemic: cross-sectional results from the Employees Working From Home (EWFH) study. BMJ Open 2022 Apr;12(4):e052733. [CrossRef] [PubMed]


Mojica-Perez Y, Livingston M, Pennay A, Callinan S. Examining the relationship between alcohol consumption, psychological distress and COVID-19 related circumstances: an Australian longitudinal study in the first year of the pandemic. Addict Behav 2022 Dec;135:107439. [CrossRef] [PubMed]


Nielsen MB, Christensen JO, Knardahl S. Working at home and alcohol use. Addict Behav Rep 2021 Dec;14:100377. [CrossRef] [PubMed]


Loef B, van Oostrom SH, van der Noordt M, Proper KI; Lifelines Corona Research initiative. Working from home during the COVID-19 pandemic and its longitudinal association with physical activity and sedentary behavior. Scand J Work Environ Health 2022 Jul;48(5):380–90. [CrossRef] [PubMed]


Antunes ED, Bridi LR, Santos M, Fischer FM. Part-time or full-time teleworking? A systematic review of the psychosocial risk factors of telework from home. Front Psychol 2023 Feb;14:1065593. [CrossRef] [PubMed]


Furuya Y, Nakazawa S, Fukai K, Tatemichi M. Health impacts with telework on workers: A scoping review before the COVID-19 pandemic. Front Public Health 2022 Oct;10:981270. [CrossRef] [PubMed]

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