Socioeconomic inequalities in pandemic-induced psychosocial stress in different life domains among the working-age population.

BACKGROUND: Psychosocial stress is considered a risk factor for physical and mental ill-health. Evidence on socioeconomic inequalities with regard to the psychosocial consequences of the COVID-19 pandemic in Germany is still limited. We aimed to investigate how pandemic-induced psychosocial stress (PIPS) in different life domains differed between socioeconomic groups. METHODS: Data came from the German Corona-Monitoring nationwide study - wave 2 (RKI-SOEP-2, November 2021-February 2022). PIPS was assessed using 4-point Likert scales with reference to the following life domains: family, partnership, own financial situation, psychological well-being, leisure activity, social life and work/school situation. Responses were dichotomised into "not stressed/slightly stressed/rather stressed" (0) versus "highly stressed" (1). The sample was restricted to the working-age population in Germany (age = 18-67 years, n = 8,402). Prevalence estimates of high PIPS were calculated by sex, age, education and income. Adjusted prevalence ratios (PRs) were estimated using Poisson regression to investigate the association between education/income and PIPS; high education and income were the reference groups. RESULTS: The highest stress levels were reported in the domains social life and leisure activity. Women and younger participants reported high stress levels more frequently. The highest inequalities were found regarding people's own financial situation, and PIPS was higher in low vs. high income groups (PR 5.54, 95% CI 3.61-8.52). Inequalities were also found regarding partnerships with higher PIPS in low vs. high education groups (PR 1.68, 95% CI 1.13-2.49) - and psychological well-being with higher PIPS in low vs. high income groups (PR 1.52, 95% CI 1.14-2.04). CONCLUSION: Socioeconomic inequalities in PIPS were found for different life domains. Generally, psychosocial support and preventive interventions to help people cope with stress in a pandemic context should be target-group-specific, addressing the particular needs and circumstances of certain socioeconomic groups.

[Socioeconomic deprivation and premature mortality in Germany, 1998-2021 : An ecological study with what-if scenarios of inequality reduction]. / Sozioökonomische Deprivation und vorzeitige Sterblichkeit in Deutschland 1998­2021 : Eine ökologische Studie mit What-if-Szenarien der Ungleichheitsreduktion.

BACKGROUND: Earlier mortality in socioeconomically disadvantaged population groups represents an extreme manifestation of health inequity. This study examines the extent, time trends, and mitigation potentials of area-level socioeconomic inequalities in premature mortality in Germany. METHODS: Nationwide data from official cause-of-death statistics were linked at the district level with official population data and the German Index of Socioeconomic Deprivation (GISD). Age-standardized mortality rates before the age of 75 were calculated stratified by sex and deprivation quintile. A what-if analysis with counterfactual scenarios was applied to calculate how much lower premature mortality would be overall if socioeconomic mortality inequalities were reduced. RESULTS: Men and women in the highest deprivation quintile had a 43% and 33% higher risk of premature death, respectively, than those in the lowest deprivation quintile of the same age. Higher mortality rates with increasing deprivation were found for cardiovascular and cancer mortality, but also for other causes of death. Socioeconomic mortality inequalities had started to increase before the COVID-19 pandemic and further exacerbated in the first years of the pandemic. If all regions had the same mortality rate as those in the lowest deprivation quintile, premature mortality would be 13% lower overall. DISCUSSION: The widening gap in premature mortality between deprived and affluent regions emphasizes that creating equivalent living conditions across Germany is also an important field of action for reducing health inequity.

[Occupational differences of COVID-19 morbidity and mortality in Germany. An analysis of health insurance data from 3.17 million insured persons]. / Berufsbedingte Unterschiede bei COVID-19-Morbidität und -Mortalität in Deutschland. Eine Analyse von Krankenkassendaten von 3,17 Mio. Versicherten.

INTRODUCTION: During the COVID-19 pandemic, occupation was assumed to play a central role in the occurrence of infection and disease. For Germany, however, there are only a few studies that analyse occupational differences in risk of COVID-19, COVID-19-associated hospitalisation, and mortality. METHODS: The study uses longitudinal health insurance data from the research database of the Institute for Applied Health Research (InGef) with information on 3.17 million insured persons aged 18-67 years (1,488,452 women; 1,684,705 men). Outcomes (morbidity, hospitalisation, and mortality) were determined on the basis of submitted COVID-19 diagnoses between 1 January 2020 and 31 December 2021. Occupations were classified according to four groupings of the official German classification of occupations. In addition to cumulative incidences, relative risks (RR) were calculated - separately for men and women. RESULTS: There is an increased risk of disease in personal service occupations, especially in health care, compared to other occupations (RR for women 1.46; for men 1.30). The same applies to social and cultural service occupations (but only for women) and for manufacturing occupations (only for men). In addition, the risks for hospitalisation and mortality are increased for cleaning occupations and transport and logistics occupations (especially for men). For all three outcomes, the risks are higher in non-managerial occupations and differ by skill level (highest for unskilled jobs and lowest for expert positions). CONCLUSION: The study provides important findings on work- and gender-related differences in COVID-19 morbidity and mortality in Germany, which indicate starting points for structural infection protection measures.

Regional disparities in SARS-CoV-2 infections by labour market indicators: a spatial panel analysis using nationwide German data on notified infections.

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.

Socioeconomic differences in the reduction of face-to-face contacts in the first wave of the COVID-19 pandemic in Germany.

BACKGROUND: The COVID-19 pandemic has led to physical distancing measures to control the spread of SARS-CoV-2. Evidence on contact dynamics in different socioeconomic groups is still sparse. This study aimed to investigate the association of socioeconomic status with private and professional contact reductions in the first COVID-19 wave in Germany. METHODS: Data from two especially affected municipalities were derived from the population-based cross-sectional seroepidemiological CORONA-MONITORING lokal study (data collection May-July 2020). The study sample (n = 3,637) was restricted to working age (18-67 years). We calculated the association of educational and occupational status (low, medium, high) with self-reported private and professional contact reductions with respect to former contact levels in the first wave of the pandemic. Multivariate Poisson regressions were performed to estimate prevalence ratios (PR) adjusted for municipality, age, gender, country of birth, household size, contact levels before physical distancing measures, own infection status, contact to SARS-CoV-2 infected people and working remotely. RESULTS: The analyses showed significant differences in the initial level of private and professional contacts by educational and occupational status. Less private contact reductions with lower educational status (PR low vs. high = 0,79 [CI = 0.68-0.91], p = 0.002; PR medium vs. high = 0,93 [CI = 0.89-0.97], p = 0.001) and less professional contact reductions with lower educational status (PR low vs. high = 0,87 [CI = 0.70-1.07], p = 0.179; PR medium vs. high = 0,89 [CI = 0.83-0.95], p = 0.001) and lower occupational status (PR low vs. high = 0,62 [CI = 0.55-0.71], p < 0.001; PR medium vs. high = 0,82 [CI = 0.77-0.88], p < 0.001) were observed. CONCLUSIONS: Our results indicate disadvantages for groups with lower socioeconomic status in private and professional contact reductions in the first wave of the pandemic. This may be associated with the higher risk of infection among individuals in lower socioeconomic groups. Preventive measures that a) adequately explain the importance of contact restrictions with respect to varying living and working conditions and b) facilitate the implementation of these reductions especially in the occupational setting seem necessary to better protect structurally disadvantaged groups during epidemics.

Moderating or mediating effects of family characteristics on socioeconomic inequalities in child health in high-income countries - a scoping review.

BACKGROUND: By explaining the development of health inequalities, eco-social theories highlight the importance of social environments that children are embedded in. The most important environment during early childhood is the family, as it profoundly influences children's health through various characteristics. These include family processes, family structure/size, and living conditions, and are closely linked to the socioeconomic position (SEP) of the family. Although it is known that the SEP contributes to health inequalities in early childhood, the effects of family characteristics on health inequalities remain unclear. The objective of this scoping review is to synthesise existing research on the mediating and moderating effects of family characteristics on socioeconomic health inequalities (HI) during early childhood in high-income countries. METHODS: This review followed the methodology of "Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews". To identify German and English scientific peer-reviewed literature published from January 1st, 2000, to December 19th, 2019, the following search term blocks were linked with the logical operator "AND": (1) family structure/size, processes, living conditions, (2) inequalities, disparities, diversities, (3) income, education, occupation, (4) health and (5) young children. The search covered the electronic databases PubMed, PsycINFO, and Scopus. RESULTS: The search yielded 7,089 records. After title/abstract and full-text screening, only ten peer-reviewed articles were included in the synthesis, which analysed the effects of family characteristics on HI in early childhood. Family processes (i.e., rules /descriptive norms, stress, parental screen time, parent-child conflicts) are identified to have mediating or moderating effects. While families' living conditions (i.e., TVs in children's bedrooms) are suggested as mediating factors, family structure/size (i.e., single parenthood, number of children in the household) appear to moderate health inequalities. CONCLUSION: Family characteristics contribute to health inequalities in early childhood. The results provide overall support of models of family stress and family investment. However, knowledge gaps remain regarding the role of family health literacy, regarding a wide range of children's health outcomes (e.g., oral health, inflammation parameters, weight, and height), and the development of health inequalities over the life course starting at birth.

[Social inequalities in the regional spread of SARS-CoV-2 infections]. / Soziale Ungleichheit in der regionalen Ausbreitung von SARS-CoV-2.

BACKGROUND AND OBJECTIVE: It has not been adequately answered whether the spread of SARS-CoV­2 is influenced by social and economic factors. Earlier studies generally looked at cumulative incidences up to the analysis date and did not take into account the development of the spread over time. This study therefore focuses on the regional dynamic of new infections and their relationship to socioeconomic factors. Based on the literature we describe the state of knowledge and present our own analyses of administrative data from Germany. METHODS: For this study, we examined regional progress data of reported COVID-19 cases for 401 cities and counties in Germany and associated them with socioeconomic characteristics of the areas. Age-standardized weekly incidence rates were calculated for the period from 3 February 2020 to 28 March 2021. Macroindicators were added from the INKAR database (e.g., income, employment rate, and crowding). RESULTS: While areas with higher incomes and lower poverty had higher incidences in the first and at the beginning of the second wave of the pandemic, they increased significantly in low-income regions from December 2020 on. Regions with a high proportion of gainfully employed people in general and especially those in the manufacturing sector had high incidences, especially in the second wave and at the beginning of the third wave. A low mean living space per inhabitant was related to higher incidence rates since November 2020. CONCLUSION: The regional temporal course of the pandemic correlates with social and economic indicators. A differentiated consideration of these differences could provide information on target group-specific protection and test strategies and help to identify social factors that generally favor infections. An English full-text version of this article is available at SpringerLink as Supplementary Information.

[Social Inequalities and COVID-19: Social-Epidemiological Perspectives on the Pandemic]. / Soziale Ungleichheit und COVID-19: Sozialepidemiologische Perspektiven auf die Pandemie.

The new Coronavirus Disease (COVID-19) hits specific population groups harder than others. However, social-epidemiological patterns of the pandemic which go beyond differences by age and gender have hardly been addressed in Germany. First reports from other western industrialised countries indicate that people living in socioeconomically deprived areas and people of color have an increased risk of severe COVID-19 progression and mortality. Social inequalities in the risk of infection due to different living and working conditions, and social inequalities in the susceptibility and risk factors for severe COVID-19, particularly in pre-existing medical conditions, might play an important role in explaining those findings. Such inequalities are well established for Germany, as well. It can therefore be assumed that also in Germany people with a lower socioeconomic status might be more affected during the further course of the pandemic. In addition, the interventions to contain the pandemic might have unequal social, economic and psychological impacts on different social groups. Hence, the COVID-19 pandemic has the overall potential to increase social and health inequalities. Social-epidemiological research into COVID-19 is therefore needed to advance measures of health protection and infection control in an evidence-based, targeted and socially equitable manner.

[Digital divide-social inequalities in the utilisation of digital healthcare]. / Digital Divide ­ Soziale Unterschiede in der Nutzung digitaler Gesundheitsangebote.

Today, health inequalities are one of the most important issues in public health worldwide. The digitalisation of healthcare is frequently attributed with the potential to reduce health inequalities. At the same time, effective internet usage is a prerequisite of the successful utilisation of digital health interventions. This might be a new obstacle for those who lack the necessary material resources or individual skills.Evidence on how exactly digital health interventions affect health inequalities is scarce. The aim of this study was to present a narrative review of the available literature. The majority of studies showed an association between the usage of digital health interventions and sociodemographic factors. The utilisation was generally higher among younger people and those with higher education and higher income. Only few studies showed no association. Other studies reported higher utilisation among those with higher levels of health literacy while health literacy showed a social gradient to the disadvantage of those in lower socioeconomic position. With a low overall level of evidence, there is currently no indication that digital health interventions are reducing health inequalities.The studies analysed in this review indicate that existing inequalities persist in the digital realm. Further evidence is needed to create a better understanding of the importance of sociodemographic factors for digital health interventions.