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Objectives: We analyze whether the prevalence of depressive symptoms differs among various migrant and non-migrant populations in Germany and to what extent these differences can be attributed to socioeconomic position (SEP) and social relations.Methods: The German National Cohort health study (NAKO) is a prospective multicenter cohort study (N = 204,878). Migration background (assessed based on citizenship and country of birth of both participant and parents) was used as independent variable, age, sex, Social Network Index, the availability of emotional support, SEP (relative income position and educational status) and employment status were introduced as covariates and depressive symptoms (PHQ-9) as dependent variable in logistic regression models.Results: Increased odds ratios of depressive symptoms were found in all migrant subgroups compared to non-migrants and varied regarding regions of origins. Elevated odds ratios decreased when SEP and social relations were included. Attenuations varied across migrant subgroups.Conclusion: The gap in depressive symptoms can partly be attributed to SEP and social relations, with variations between migrant subgroups. The integration paradox is likely to contribute to the explanation of the results. Future studies need to consider heterogeneity among migrant subgroups whenever possible.

OBJECTIVE: The aim of this study was to identify the occupational risk for a SARS-CoV-2 infection in a nationwide sample of German workers during the first wave of the COVID-19 pandemic (1 February–31 August 2020). METHODS: We used the data of 108 960 workers who participated in a COVID follow-up survey of the German National Cohort (NAKO). Occupational characteristics were derived from the German Classification of Occupations 2010 (Klassifikation der Berufe 2010). PCR-confirmed SARS-CoV-2 infections were assessed from self-reports. Incidence rates (IR) and incidence rate ratios (IRR) were estimated using robust Poisson regression, adjusted for person-time at risk, age, sex, migration background, study center, working hours, and employment relationship. RESULTS: The IR was 3.7 infections per 1000 workers [95% confidence interval (CI) 3.3–4.1]. IR differed by occupational sector, with the highest rates observed in personal (IR 4.8, 95% CI 4.0–5.6) and business administration (IR 3.4, 95% CI 2.8–3.9) services and the lowest rates in occupations related to the production of goods (IR 2.0, 95% CI 1.5–2.6). Infections were more frequent among essential workers compared with workers in non-essential occupations (IRR 1.95, 95% CI 1.59–2.40) and among highly skilled compared with skilled professions (IRR 1.36, 95% CI 1.07–1.72). CONCLUSIONS: The results emphasize higher infection risks in essential occupations and personal-related services, especially in the healthcare sector. Additionally, we found evidence that infections were more common in higher occupational status positions at the beginning of the pandemic.

Abstract Background At the end of secondary education, young people can either start vocational training, enter university, directly transition to employment or become unemployed. Research assumes that post-secondary pathways have immediate and/or long-term impacts on health and well-being, but empirical investigations on this are scarce and restricted to few countries. Therefore, this study traced the development of health and well-being throughout the highly institutionalised school-to-work transition (STWT) in Germany. Methods We used longitudinal data of the National Educational Panel Study (NEPS), a representative sample of 11,098 school-leavers (50.5% girls) repeatedly interviewed between 2011 and 2020. We estimated the effect of post-secondary transitions on self-rated health and subjective well-being by applying fixed-effects (FE) regression, eliminating bias resulting from time-constant confounding and self-selection into different pathways. A multiple-sample strategy was used to account for the increasing diversity of STWTs patterns. Models were controlled for age, as well as household and residential changes to minimise temporal heterogeneity. Results Findings indicate that leaving school was good for health and well-being. Compared with participants who did not find a training position after school, direct transitions to vocational training or university were linked to higher absolute levels of health and well-being, but also to a lower relative decline over time. Furthermore, upward transitions (e.g. to programs leading to better education or from unemployment to employment) were associated with improvements in health and well-being, while downward transitions were followed by deteriorations. Conclusion Findings suggest that school-leave is a sensitive period and that post-secondary pathways provide young people with different abilities to maintain health and well-being. Youth health interventions might benefit when setting a stronger focus on unsuccessful school-leavers.

Abstract Background The number of obese children is rising worldwide. Many studies have investigated single determinants of children’s body mass index (BMI), yet studies measuring determinants at different potential levels of influence are sparse. The aim of this study is to investigate the independent role of parental socioeconomic position (SEP), additional family factors at the micro level, as well as early childhood education and care (ECEC) centre characteristics at the meso level regarding BMI. Methods Analyses used the baseline data of the PReschool INtervention Study (PRINS) including up to 1,151 children from 53 ECEC centres. Multi-level models first estimated the associations of parental SEP indicators (parental school education, vocational training, and household income) with the children’s standard deviation scores for BMI (SDS BMI, standardised for age and gender). Second, structural (number of siblings), psychosocial (strained family relationships), and nutrition behavioural (soft-drink consumption, frequency of fast-food restaurant visits) family factors at the micro level were included. Third, characteristics of the ECEC centre at the meso level in terms of average group size, the ratio of overweight children in the group, ECEC centre type (all-day care), and the location of the ECEC centre (rural vs urban) were included. All analyses were stratified by gender and adjusted for age, migration background, and parental employment status. Results Estimates for boys and girls appeared to differ. In the full model, for boys the parental SEP indicators were not related to SDS BMI. Factors related to SDS BMI in boys were: two or more siblings; B = -.55; p = 0.045 [ref.: no sibling]), the characteristics of the ECEC centre in terms of average group size (20 – 25 children; B = -.54; p = 0.022 [ref.: 25 children; B = -.52; p = 0.037 [ref.:

Abstract Background Regional labour markets and their properties are named as potential reasons for regional variations in levels of SARS-CoV-2 infections rates, but empirical evidence is missing. Methods Using nationwide data on notified laboratory-confirmed SARS-CoV-2 infections, we calculated weekly age-standardised incidence rates (ASIRs) for working-age populations at the regional level of Germany’s 400 districts. Data covered nearly 2 years (March 2020 till December 2021), including four main waves of the pandemic. For each of the pandemic waves, we investigated regional differences in weekly ASIRs according to three regional labour market indicators: (1) employment rate, (2) employment by sector, and (3) capacity to work from home. We use spatial panel regression analysis, which incorporates geospatial information and accounts for regional clustering of infections. Results For all four pandemic waves under study, we found that regions with higher proportions of people in employment had higher ASIRs and a steeper increase of infections during the waves. Further, the composition of the workforce mattered: rates were higher in regions with larger secondary sectors or if opportunities of working from home were comparatively low. Associations remained consistent after adjusting for potential confounders, including a proxy measure of regional vaccination progress. Conclusions If further validated by studies using individual-level data, our study calls for increased intervention efforts to improve protective measures at the workplace, particularly among workers of the secondary sector with no opportunities to work from home. It also points to the necessity of strengthening work and employment as essential components of pandemic preparedness plans.

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.

Objectives The main objective was to systematically map evidence regarding the emergence of health inequalities in individuals aged 16–24 years during school-to-work and school-to-university transition (STWT). Second, we aimed to summarise the evidence on potential effects of contextual and compositional characteristics of specific institutional contexts entered during STWT on health and health behaviours.Design Scoping review.Study selection Relevant literature was systematically searched following the methodological framework proposed by Arksey and O’Malley. Ovid MEDLINE and Web of Science, and websites of the International Labour Organization and National Institute for Occupational Safety and Health were searched, using a predetermined search strategy. Articles in English or German published between 1 January 2000 and 3 February 2020 were considered.Data extraction To collect the main information from the selected studies, a data extraction spreadsheet was created. Data were summarised and grouped into five health outcomes and five institutional contexts (school, vocational training, university, work, unemployment).Results A total of 678 articles were screened for inclusion. To be able to draw a picture of the development of various health outcomes over time, we focused on longitudinal studies. Forty-six prospective studies mapping health-related outcomes during STWT were identified. Higher family socioeconomic position (SEP) was associated with higher levels of health behaviour and lower levels of health-damaging behaviour, but there was also some evidence pointing in the opposite direction. Disadvantaged family SEP negatively impacted on mental health and predicted an adverse weight development. There was limited evidence for the outcomes physical/somatic symptoms and self-rated health. Meso-level characteristics of the institutional contexts identified were not systematically assessed, only individual-level factors resulting from an exposure to these contexts, rendering an analysis of effects of contextual and compositional characteristics on health and health behaviours impossible.Conclusions This scoping review demonstrated a wide range of health inequalities during STWT for various health outcomes. However, knowledge on the role of the core institutional contexts regarding the development of health inequalities is limited.

ObjectiveAlthough health inequalities in adolescence are well documented, the underlying mechanisms remain unclear. Few studies have examined the role of the family in explaining the association between the family's socioeconomic position and adolescents' self-rated health. The current study aimed to explore whether the association between socioeconomic position and self-rated health was mediated by familial determinants.MethodsUsing data from wave 2 of the"German Health Interview and Examination Survey for Children and Adolescents" (KiGGS) (1,838 female and 1,718 male 11- to 17-year-olds), linear regression analyses were conducted to decompose the total effects of income, education, occupational status, socioeconomic position index and adolescents' subjective social status on self-rated health into direct effects and indirect effects through familial determinants (family cohesion, parental well-being, parental stress, parenting styles, parental obesity, smoking and sporting activity).ResultsA significant total effect of all socioeconomic position indicators on self-rated health was found, except for income in male adolescents. In female adolescents, more than 70% of the total effects of each socioeconomic position indicator were explained by familial mediators, whereas no significant direct effects remained. The most important mediator was parental well-being, followed by family cohesion, parental smoking and sporting activity. In male adolescents, the associations between income, parental education, the socioeconomic position index and subjective social status were also mediated by familial determinants (family cohesion, parental smoking, obesity and living in a single-mother family). However, a significant direct effect of subjective social status remained.ConclusionThe analysis revealed how a family's position of socioeconomic disadvantage can lead to poorer health in adolescents through different family practices. The family appears to play an important role in explaining health inequalities, particularly in female adolescents. Reducing health inequalities in adolescence requires policy interventions (macro-level), community-based strategies (meso-level) and programs to improve parenting and family functioning (micro-level).

Introduction School-to-work/university transition is a sensitive period that can have a substantial impact on health and health behaviour over the life course. There is some indication that health and health behaviour is socially patterned in the age span of individuals in this transition (16–24 years) and that there are differences by socioeconomic position (SEP). However, evidence regarding this phenomenon has not been systematically mapped. In addition, little is known about the role of institutional characteristics (eg, of universities, workplaces) in the development of health and possible inequalities in health during this transition. Hence, the first objective of this scoping review is to systematically map the existing evidence regarding health and health behaviours (and possible health inequalities, for example, differences by SEP) in the age group of 16–24 years and during school-to-work transition noted in Germany and abroad. The second objective is to summarise the evidence on the potential effects of contextual and compositional characteristics of specific institutions entered during this life stage on health and health behaviours. Third, indicators and measures of these characteristics will be summarised.Methods and analysis We will systematically map the evidence on health inequalities during school-to-work-transitions among young adults (aged 16–24 years), following the methodological framework proposed by Arksey and O’Malley. The literature search is performed in Ovid MEDLINE, Web of Science, International Labour Organization and National Institute for Occupational Safety and Health, using a predetermined search strategy. Articles published between January 2000 and February 2020 in English or German are considered for the review. The selection process follows a two-step approach: (1) screening of titles and abstracts, and (2) screening of full texts, both steps by two independent reviewers. Any discrepancies in the selection process are resolved by a third researcher. Data extraction will be performed using a customised data extraction sheet. The results will be presented in tabular and narrative form.Ethics and dissemination Ethical approval is not required for this scoping review. The results will be published in a peer-reviewed scientific journal and presented at international conferences and project workshops.

Unwanted sexual attention (UWSA) and sexual harassment (SH) are prevalent experiences for women in working life and often accompanied by poor health. Despite increasing numbers especially of young people working in insecure and irregular employment settings, there is little empirical evidence if such precarious arrangements are associated with UWSA or SH. To investigate this, we used a representative sample of the European working population consisting of 63,966 employees in 33 countries who participated in the European Working Conditions Survey in 2010 or 2015. Precarious employment (PE) was assessed on the basis of seven indicators and a formative index derived from them: temporary employment, contractual duration < 1 year, schedule unpredictability, involuntary part-time, low information on occupational health and safety risks (OSH), low pay (wage < 60%), and multiple job-holding. We measured self-reported experiences of workplace UWSA during the last month and SH during the last 12 months each using a single-item questionnaire. Multi-level Poisson regressions were used to estimate prevalence ratios for UWSA and SH according to PE adjusted for survey year, age, education, type of household, migration background, job tenure, weekly working hours, occupational position, working sector, company size, workplace gender ratio, and visiting customers or clients. 0.8% of men reported UWSA in the last month and 2.6% of the women. SH in the last year was reported by 0.4% of the men and 1.3% of the women. For both men and women, PE was significantly associated with elevated prevalence of UWSA and SH, in particular when reporting schedule unpredictability, multiple job-holding and low information on OSH. Our results suggest that precariously employed individuals may be more prone to experience unwanted sexual behaviour at the workplace compared with workers in non-precarious settings.