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We thank van Tongeren et al for responding to our study on occupational disparities in SARS-CoV-2 infection risks during the first pandemic wave in Germany (1). The authors address the potential for bias resulting from differential testing between occupational groups and propose an alternative analytical strategy for dealing with selective testing. In the following, we want to discuss two aspects of this issue, namely (i) the extent and reasons of differential testing in our cohort and (ii) the advantages and disadvantages of different analytical approaches to study risk factors for SARS-CoV-2 infection. Our study relied on nationwide prospective cohort data including more than 100 000 workers in order to compare the incidence of infections between different occupations and occupational status positions. We found elevated infection risks in personal services and business administration, in essential occupations (including health care) and among people in higher occupational status positions (ie, managers and highly skilled workers) during the first pandemic wave in Germany (2). Van Tongeren's et al main concern is that the correlations found could be affected by a systematic bias because people in healthcare professions get tested more often than employees in other professions. A second argument is that better-off people could be more likely to use testing as they are less affected by direct costs (prices for testing) and the economic hardship associated with a positive test result (eg, loss of earnings in the event of sick leave). We share the authors' view that differential testing must be considered when analysing and interpreting the data. Thus, in our study, we examined the proportion of tests conducted in each occupational group as part of the sensitivity analyses (see supplementary figure S1, accessible at www.sjweh.fi/article/4037). As expected, testing proportions were exceptionally high in medical occupations (due to employer requirements). However, we did not observe systematic differences among non-medical occupations or when categorising by skill-level or managerial responsibility. This might be explained by several reasons. First, SARS-CoV-2 testing was free of charge during the first pandemic wave in Germany, but reporting a risk contact or having symptoms was a necessary condition for testing ( https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html (accessed 5 September 2022). The newspaper article cited by van Tongeren et al is misleading as it refers to a calendar date after our study period. Second, different motivation for testing due to economic hardship in case of a positive test result is an unlikely explanation, because Germany has a universal healthcare system, including paid sick leave and sickness benefits for all workers (3). Self-employed people carry greater financial risks in case of sickness. We therefore included self-employment in the multivariable analyses to address this potential source of bias. While the observed inverse social gradient may be surprising, it actually matches with findings of ecological studies from Germany (4, 5), the United States (6, 7) as well as Spain, Portugal, Sweden, The Netherlands, Israel, and Hong Kong (8), all of which observed higher infection rates in wealthier neighbourhoods during the initial outbreak phase of the pandemic. One possible explanation is the higher mobility of managers and better educated workers, who are more likely to participate in meetings and engage in business travel and holiday trips like skiing. Given the increasing number of studies providing evidence for this hypothesis, we conclude that the inverse social gradient in our study likely reflects different exposure probabilities and is not a result of systematic bias. This also holds true for the elevated infection risks in essential workers, which is actually corroborated by a large body of research (9-11). Regarding differential likelihood of testing, van Tongeren et al state that "[i]t is relatively simple to address this problem by using a test-negative design" (1). As van Tongeren et al describe, this is a case-control approach only including individuals who were tested (without considering those who were not tested). However, the proposed analytical strategy can lead to another (more serious) selection bias if testing proportions and/or testing criteria differ between groups (12). This can be easily illustrated when comparing the results based on a time-incidence design with those obtained by a test-negative design as shown in table 1 (see PDF). Both approaches show similar results in terms of vertical occupational differences. Infection was more common if individuals had a high skill level or had a managerial position, but associations were stronger in the time-incidence design and did not reach statistical significance in the test-negative design (as indicated by the confidence intervals overlapping "1"). Unfortunately, the test-negative approach relies on a strongly reduced sample size and thus results in greater statistical uncertainty and loss of statistical power (13). In contrast, the test-negative design yields a different picture when estimating the association between essential occupation and infection risk: In this analysis, essential workers did not differ from non-essential workers in their chance of being infected with SARS-CoV-2 (the test-negative design even exhibits a lower chance for essential workers). This is rather counter-intuitive and is not in accordance with what we know about the occupational hazards of healthcare workers during the pandemic (14). The main problem is that proportions of positive tests are highly unreliable when testing proportions and/or testing criteria differ between groups. As essential workers were tested more often without being symptomatic (due to employer requirements), a lower proportion of positive tests in this group does not necessarily correspond to a lower risk of infection. Consequently, we are not convinced that the test-negative design should be the 'gold standard' for studying risk factors for SARS-CoV-2 infections (15). Especially problematic is the loss of statistical power (increasing the probability of a type II error) and the low validity of the test-positivity when test criteria and/or test proportions differ between groups. References 1. van Tongeren M, Rhodes S, Pearce N. Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias. Scand J Work Environ Health 2022;48(7):586-587. https://doi.org/10.5271/sjweh.4052. 2. Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, et 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;48:446-56. https://doi.org/10.5271/sjweh.4037. 3. Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017;390:882-97. https://doi.org/10.1016/S0140-6736(17)31280-1. 4. Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener C, et al. Socioeconomic inequalities in the risk of SARS-CoV-2 infection - First results from an analysis of surveillance data from Germany. J Heal Monit 2020;5:18-29. https://doi.org/10.25646/7057. 5. Plümper T, Neumayer E. The pandemic predominantly hits poor neighbourhoods? SARS-CoV-2 infections and COVID-19 fatalities in German districts. Eur J Public Health 2020;30:1176-80. https://doi.org/10.1093/eurpub/ckaa168. 6. Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. Racial, Economic, and Health Inequality and COVID-19 Infection in the United States. J Racial Ethn Heal Disparities 2021;8:732-42. https://doi.org/10.1007/s40615-020-00833-4. 7. Mukherji N. The Social and Economic Factors Underlying the Incidence of COVID-19 Cases and Deaths in US Counties During the Initial Outbreak Phase. Rev Reg Stud 2022;52. https://doi.org/10.52324/001c.35255. 8. Beese F, Waldhauer J, Wollgast L, Pförtner T, Wahrendorf M, Haller S, et al. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic-A Scoping Review. Int J Public Health 2022;67:1-14. https://doi.org/10.3389/ijph.2022.1605128. 9. Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Heal 2020;5:e475-83. https://doi.org/10.1016/S2468-2667(20)30164-X. 10. Chou R, Dana T, Buckley DI, Selph S, Fu R, Totten AM. Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann Intern Med 2020;173:120-36. https://doi.org/10.7326/M20-1632. 11. Stringhini S, Zaballa M-E, Pullen N, de Mestral C, Perez-Saez J, Dumont R, et al. Large variation in anti-SARS-CoV-2 antibody prevalence among essential workers in Geneva, Switzerland. Nat Commun 2021;12:3455. https://doi.org/10.1038/s41467-021-23796-4. 12. Accorsi EK, Qiu X, Rumpler E, Kennedy-Shaffer L, Kahn R, Joshi K, et al. How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. Eur J Epidemiol 2021;36:179-96. https://doi.org/10.1007/s10654-021-00727-7. 13. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Editio. New York: Routledge; 2013. https://doi.org/10.4324/9780203771587. 14. The Lancet. The plight of essential workers during the COVID-19 pandemic. Lancet 2020;395:1587. https://doi.org/10.1016/S0140-6736(20)31200-9. 15. Vandenbroucke JP, Brickley EB, Pearce N, Vandenbroucke-Grauls CMJE. The Evolving Usefulness of the Test-negative Design in Studying Risk Factors for COVID-19. Epidemiology 2022;33:e7-8. https://doi.org/10.1097/EDE.0000000000001438.

Additional file 1: Table S1. ERI cross-sectional linear regression models selected with forward stepwise regression (factors shown in the order of selection) for four health and work related outcomes at the baseline (t0) or at the 5-year follow-up (t1). Table S2. COPSOQ cross-sectional linear regression models selected with forward stepwise regression (factors shown in the order of selection) for four health and work-related outcomes at the baseline (t0) or at the 5-year follow-up (t1). Table S3. Correlation matrix (Pearson correlation coefficients) for COPSOQ dimensions, outcomes (shaded in gray), age and sex at baseline. Table S4. Correlation matrix (Pearson correlation coefficients) for COPSOQ dimensions and outcomes (shaded in gray) between baseline and follow-up. Table S5. Correlation matrix (Pearson correlation coefficients) for ERI dimensions, outcomes (shaded in gray), age and sex at baseline. Table S6. Correlation matrix (Pearson correlation coefficients) for between ERI dimensions at baseline and follow-up. Table S7. ERI cross-sectional multivariable linear regression models (factors shown in the order of selection), and adjusted for age, sex, and socioeconomic status. Due to the model selection procedure, the models selected at t0 sometimes differ from the models selected at t1. Table S8. ERI prospective linear multivariable regression models (factors shown in the order of selection), and adjusted for age, sex, and socioeconomic status. Table S9. COPSOQ cross-sectional multivariable linear regression models (factors shown in the order of selection), and adjusted for age, sex, and socioeconomic status. Table S10. COPSOQ prospective linear multivariable regression models (factors shown in the order of selection), and adjusted for age, sex, and socioeconomic status.

Hintergrund und Ziel: Trotz niedriger Prävalenz der Hepatitis-B-Virus-(HBV-)Infektion in Deutschland ist es wichtig, vulnerable Gruppen und Ansatzpunkte für die Prävention zu identifizieren. In ersten Analysen der „Studie zur Gesundheit Erwachsener in Deutschland“ (DEGS1, 2008–2011) waren HBV-Infektion und -Impfung mit sozidemografischen Determinanten assoziiert. In dieser Arbeit werden die Ergebnisse im Detail untersucht. Material und Methoden: In DEGS1 lag für 7046 Teilnehmende (Alter: 18–79 Jahre) eine HBV-Serologie vor. Die stattgehabte HBV-Infektion war durch Antikörper gegen das Hepatitis-B-Core-Antigen (Anti-HBc) definiert, die impfinduzierte Immunität durch alleinigen Nachweis von Antikörpern gegen das Hepatitis-B-Surface-Antigen (Anti-HBs). Seroprävalenzen von HBV-Infektions- und -Impfstatus wurden geschlechtsstratifiziert geschätzt und Assoziationen mit Alter, Gemeindegröße, Einkommen, formaler Bildung, Krankenversicherung und Migrationsgeneration in logistischen Regressionen analysiert. Ergebnisse: Die HBV-Infektion war bei Männern und Frauen unabhängig mit den Altersgruppen 34–64 und ≥ 65 Jahre, erster Migrationsgeneration und Leben in größeren Gemeinden assoziiert, zudem bei Männern mit niedrigem Einkommen und bei Frauen mit niedriger Bildung. Die impfinduzierte Immunität war bei Männern und Frauen unabhängig mit den Altersgruppen 18–33 und 34–64 Jahre, mittlerer und hoher Bildung und hohem Einkommen assoziiert, darüber hinaus bei Männern mit mittlerem Einkommen und privater Krankenversicherung und bei Frauen mit fehlendem Migrationshintergrund. Diskussion: Die Berücksichtigung von Migrationsstatus, Einkommen und Bildung könnte zur zielgenauen Ausrichtung der HBV-Prävention beitragen. Background and objective: Even though the prevalence of hepatitis B virus (HBV) infection in Germany is low, it is important to identify vulnerable groups and targeted approaches for infection prevention. Previous analyses from the “German Health Interview and Examination Survey for Adults” (DEGS1, 2008–2011) have shown that HBV infections and vaccination are associated with sociodemographic determinants. This paper examines the results in detail. Materials and methods: In the DEGS1, HBV serology was available for 7046 participants aged 18–79 years. HBV infection was defined by antibodies to hepatitis B core antigen (anti-HBc), vaccine-induced immunity by antibodies to hepatitis B surface antigen (anti-HBs) in the absence of other markers. Seroprevalences of HBV infection and vaccine-induced immunity were estimated stratified by sex, and associations with age, municipality size, income, formal education, health insurance and migration generation were analysed by logistic regression. Results: In both sexes, HBV infection was independently associated with age groups 34–64 and ≥ 65 years, first migrant generation and living in larger municipalities as well as low income in men and low education in women. Vaccine-induced immunity was independently associated with age groups 18–33 and 34–64 years, middle and high education and high income in both sexes, middle income and private health insurance in men and having no migration background in women. Conclusions: HBV prevention measures should take into account migration status, income and education in order to focus prevention measures.

IntroductionWork-life conflicts (WLC) may impact health, but few studies prospectively consider the impact of WLC on objective outcomes such as cardiovascular disease. Using data from the Gutenberg Health Study (GHS), we examined if WLC at baseline was associated with an increased five-year incidence of cardiovascular events (myocardial infarct, stroke, atrial fibrillation, peripheral artery disease, coronary artery disease, chronic heart failure, sudden cardiac death). We also considered if WLC was associated with incident hypertension and arterial stiffness and if the effects of WLC on cardiovascular health differ for men and women.MethodsA working subsample of the 15,010 GHS cohort participants completed the Copenhagen Psychosocial Questionnaire, which included five "work-privacy conflict" questions at baseline and at the five-year follow-up. Relative risks for incident hypertension due to increased WLC at baseline (WLC scores exceeding 60 out of 100) were estimated with Poisson regression in the subgroup of participants without hypertension at baseline (n = 2426). Categories of WLC at baseline and follow-up were also used to examine the risk of hypertension due to chronic/recurrent WLC. In this subgroup, we also examined the association between WLC as a continuous score ranging from 0 to 100 with change to arterial stiffness after five years using linear regression. Hazard ratios were estimated for incident cardiovascular events in a larger subsample of participants without prevalent cardiovascular disease at baseline (n = 3698) using Cox regression. We used various multivariable regression models to adjust for sex, age, socioeconomic status, occupational, household, and cardiovascular risk factors.ResultsWe found no association between WLC and incident hypertension or increased arterial stiffness. The fully-adjusted relative risk for WLC >60 at baseline and hypertension was 0.93 (95% 0.74-1.17). The risk of hypertension due to chronic/recurrent WLC >60 was increased but not statistically significant (RR = 1.13, 95% CI 0.83-1.54). Overall, hazard ratios for incident cardiovascular events were also not increased. However, stratifying the results by sex resulted in a hazard ratio of 1.47 (95% CI 0.54-3.98) for incident cardiovascular disease among women in the fully adjusted model.ConclusionsAlthough our results were not statistically significant, they indicate that WLC is negatively impacting the cardiovascular health of women. While these results need to be confirmed with additional research and a longer follow-up, interventions to prevent WLC will promote health and could be especially beneficial for women.

Additional file 1: Supplementary Table 1. Baseline characteristics for the hypertension analysis. Supplementary Table 2. Sensitivity analysis using the five-item mobbing instrument. Supplementary Table 3. Associations of mobbing at baseline with change in arterial stiffness (AS). Supplementary Table 4. Associations of incident and recurrent mobbing with incidence of hypertension. Supplementary Table 5. Associations of incident and recurring mobbing with change in arterial stiffness (AS).