- Research note
- Open Access
Socioeconomic inequalities in the prevalence and perceived dangerousness of SARS-CoV-2 infections in two early German hotspots: findings from a seroepidemiological study
BMC Research Notes volume 14, Article number: 375 (2021)
Evidence on socioeconomic inequalities in infections with the novel coronavirus (SARS-CoV-2) is still limited as most of the available studies are ecological in nature and individual-level data is sparse. We therefore analysed individual-level data on socioeconomic differences in the prevalence and perceived dangerousness of SARS-CoV-2 infections in local populations. Data were obtained from a population-based seroepidemiological study of adult individuals in two early German SARS-CoV-2 hotspots (n = 3903). Infection was determined by IgG antibody ELISA, RT-PCR testing and self-reports on prior positive PCR tests. The perceived dangerousness of an infection and socioeconomic position (SEP) were assessed by self-reports. Logistic and linear regression were applied to examine associations of multiple SEP measures with infection status and perceptions of dangerousness.
We found no evidence of socioeconomic inequalities in SARS-CoV-2 infections by education, occupation, income and subjective social status. Participants with lower education and lower subjective social status perceived an infection as more dangerous than their better-off counterparts. In successfully contained local outbreaks of SARS-CoV-2 in Germany, infections may have been equally distributed across the socioeconomic spectrum. But residents in disadvantaged socioeconomic groups might have experienced a higher level of mental distress due to the higher perceived dangerousness of an infection.
Socioeconomic inequalities in ill-health are well described for many non-communicable diseases in various countries . Similar inequalities may emerge in the communicable coronavirus disease 2019 (COVID-19), as the risk for an infection with the novel coronavirus (SARS-CoV-2) may be increased among people in poorer living and working conditions. For instance, living in crowded households may increase the risk for a SARS-CoV-2 infection . Essential workers, e.g. health-care workers or those in the logistic, retailing or public transport sector, might not have the opportunity to effectively protect themselves against virus transmissions. Those workers generally tend to fall into the middle to low income groups , whereas the possibility to work from home, a recommended measure to reduce virus transmissions, is more often available to people on higher salaries and with higher qualifications . A modelling study from the USA found that a higher COVID-19 incidence in people from lower-income areas might partly be explained by less opportunities to reduce mobility during the pandemic—presumably due to the lack of opportunity to work from home. Additionally, people from lower-income areas tended to visit more crowded places and were therefore more exposed to potential virus transmissions . Considering that people in lower socioeconomic groups have a higher burden of pre-existing health conditions and may have reduced immune function, they might have an increased susceptibility to SARS-CoV-2 and an increased risk for a severe course of COVID-19 [6, 7].
Empirical evidence of socioeconomic inequalities in the current COVID-19 pandemic is emerging but still sparse [7, 8]. The available evidence is predominantly based on ecological studies from the USA and the UK and suggests that socioeconomically disadvantaged groups have a higher risk of infection with SARS-CoV-2 and severe COVID-19 . But the exclusive use of area-level socioeconomic indicators in most of these studies might underestimate inequalities and findings may be subject to potential ecological fallacy. To date, only few studies can draw on data including socioeconomic information on an individual level . Our analysis therefore used data from a German seroepidemiological study with individual-level data on both infection status and socioeconomic indicators. The study was conducted in the municipalities of Kupferzell in the German federal state of Baden-Wurttemberg and Bad Feilnbach in Bavaria . In Kupferzell, most transmissions of SARS-CoV-2 started with infected inhabitants returning from Northern Italy at the end of February 2020 subsequently visiting a local church concert in early March resulting in a SARS-CoV-2 outbreak. In Bad Feilnbach, early infections were related to local festivities and an outbreak in a nursing home in spring 2020. We analysed if there were socioeconomic inequalities in (1) infections with SARS-CoV-2 and (2) the inhabitants’ perceived dangerousness of an infection.
The Corona Monitoring Local (CoMoLo) study investigates the seroprevalence of SARS-CoV-2 antibodies in different hotspots in Germany. For this cross-sectional study, a population register-based random sample of the adult population of each municipality was invited to visit a local study centre, where a blood sample and an oropharyngeal swab were taken to examine for antibody prevalence (IgG-ELISA) and acute infection (RT-PCR). Participants answered a short paper-based questionnaire on-site and a detailed questionnaire later on, online or by telephone. Overall response rates were 63% for Kupferzell (20.05.–09.06.2020) and 59% for Bad Feilnbach (23.06.–04.07.2020). Further details can be found elsewhere [10, 11].
We defined an infection with SARS-CoV-2 if either the participant was tested seropositive for SARS-CoV-2 IgG antibodies (Euroimmun SARS-CoV-2-S1 IgG-ELISA: ratio ≥ 1.1), had a positive SARS-CoV-2-RT-PCR swab test during the study or self-reported a positive PCR test prior to the study. The perceived dangerousness of a SARS-CoV-2 infection was assessed by asking the participants: ‘How would you perceive an infection with the novel coronavirus for yourself?’ with a 7-point response scale from 1 (‘completely harmless’) to 7 (‘extremely dangerous’). Individual information on education, occupation, income and subjective social status (SSS) were used as indicators of SEP. Using the 2011 version of International Standard Classification of Education (ISCED), participants’ highest school and vocational qualifications were classified into low (ISCED 1–2), middle (ISCED 3–4) and high (ISCED 6–8) educational levels. Occupational status was assessed with the International Socio-Economic Index of Occupational Status (ISEI) . Equivalised disposable household income was calculated using the OECD-modified equivalence scale. SSS was measured using the MacArthur Scale, a pictorial presentation of a ten rung social ladder on which participants rate their position in society .
Prevalence of SARS-CoV-2 infection and mean values of perceived dangerousness ratings were calculated among the 3,903 participants who responded to the detailed questionnaire after their on-site study participation. Logistic and linear regression models were fitted to examine associations of each socioeconomic indicator with infection status and perceived dangerousness. Adjustments were made for age, sex, (education), migrant status, household size and municipality. Weighting factors were used to adjust the net samples of each municipality by age, sex and education to match the official populations statistics. The analysis was performed using Stata version 15.1 (StataCorp LP, College Station, TX, USA) survey data commands.
Table 1 shows the characteristics of the study population. Population-weighted mean age was 50.0 years (Kupferzell: 49.2; Bad Feilnbach: 50.9). The overall prevalence of a previous or active SARS-CoV-2 infection was 10.4% (95%-CI 9.3–11.6), with 11.8% (95%-CI 10.2–13.6) in Kupferzell and 9.0% (95%-CI 7.6–10.7) in Bad Feilnbach. We found no evidence for differences in the prevalence and adjusted odds ratios (OR) of a SARS-CoV-2 infection between socioeconomic groups, irrespective of the SEP measure used (Table 2): In the low education group, the prevalence was 10.6% (95%-CI 7.5–14.8%) compared to 10.2% (95%-CI 8.6–12.1%) in the high education group (low income 9.6% (95%-CI 7.2–12.7%) vs. high income 9.9% (95%-CI 7.6–12.9%); low occupational status 11.9% (95%-CI 9.0–15.4%) vs. high occupational status 10.5% (95%-CI 8.1–13.6%); low SSS 9.4% (95%-CI 7.0–12.4%) vs. high SSS 9.9% (95%-CI 8.2–11.8%). When only focusing on the results for Bad Feilnbach, we found that for each SEP measure the low SEP groups had slightly higher prevalences than the high SEP groups (Table 2). However, there was no evidence against the null hypothesis of there being no difference.
The perceived dangerousness of a SARS-CoV-2 infection differed according to education, income and SSS, with those in the low educational, lower income and lower SSS groups perceiving an infection as more dangerous than those in the high SEP groups (Table 3). The associations with education and SSS were found in both hotpots, that with income was only evident for Kupferzell.
This is the first study from Germany that analysed associations of multiple SEP indicators with SARS-CoV-2 infections and perceptions of dangerousness on an individual level. In two population-based random samples from successfully contained early hotspots, we found no evidence for a socioeconomic gradient in SARS-CoV-2 infections. However, local residents with lower education and lower SSS perceived a SARS-CoV-2 infection as more dangerous than their better-off counterparts.
These findings are in line with seroepidemiological studies in the German city of Bonn and the German hotspot region of Tirschenreuth, which reported no educational differences in the risk of SARS-CoV-2 infection [14, 15], and with nationwide seroepidemiological results from Spain that showed no differences in infections according to census-tract income . But they contrast with results from England and the USA that found higher seropositivity among people living in more deprived areas  and those with lower education . However, as these results are based on samples of the general population with a much lower prevalence, the comparability to our findings is limited. In contrast to our results, ecological studies that used area-level socioeconomic indicators have found a higher infection risk in districts with less socioeconomic deprivation in Germany  for the early stages of the pandemic in March and April 2020 but higher infection risks in more deprived districts during the second wave of the pandemic .
Virus spread in early hotspots started in particular social networks, such as visitors of a church concert in Kupferzell, their families and friends. These initial transmissions led to new subsequent infections within the community that affected a broader social spectrum of the local population. However, these transmission networks might as well be more socioeconomically homogenous.
Little is known so far about socioeconomic inequalities in the perceived dangerousness of a SARS-CoV-2 infection. Perceiving an infection as more dangerous might lead to higher stress levels and therefore potentially higher susceptibility to an infection. Furthermore, higher perceived dangerousness might lead to higher levels of depression and anxiety and hence aggravate socioeconomic inequalities in mental health . In contrast, a higher perceived dangerousness might lead to an increased engagement in preventive behaviours , and thereby reduce the infection risk. Further research is needed to better understand the possible effects of perceived dangerousness of a SARS-CoV-2 infection on physical and mental health and health disparities.
A strength of this study is that SARS-CoV-2 infections were not only assessed by self-reports, but were also determined by serological and PCR testing, which enabled the detection of both known and yet undetected infections. This is highly relevant, because infections are often mild or asymptomatic and thus may be unknown to infected individuals.
Altogether, the findings suggest that—in early and successfully contained local outbreaks of SARS-CoV-2—infections may be equally distributed across the socioeconomic spectrum, but residents with lower SEP may have experienced a higher burden of mental distress. In order to get a more comprehensive understanding of the social determinants of SARS-CoV-2 infections, data including various measures of individual SEP is needed, ideally for different waves and stages of the epidemic and on a national level.
As our study population was restricted to adult residents in two rural municipalities in Southern Germany with certain events of viral overdispersion in the early stages of the pandemic, the results should be interpreted exclusively within this context and cannot be generalised to the German general population. It should further be considered that these early hotspots were located in socioeconomically less-deprived regions and that their local populations were relatively homogeneous in socioeconomic terms. Findings might differ in other settings and at the national level. Moreover, our findings are limited to the infection risk and the perceived dangerousness of an infection. Even if the infection risk seems to be equally distributed across different socioeconomic groups in our study settings, other outcomes like hospitalisations and mortality linked to COVID-19 could still be associated with a lower SEP, as it is known that risk factors are more frequent in socioeconomically more disadvantaged groups . Further research is needed to better understand these associations and arrive at a more comprehensive picture of health inequalities during the COVID-19 pandemic.
Availability of data and materials
The data used in this study are not publicly available at this time due to data protection regulations. However, access can be requested for scientific reasons. Requests should be submitted to the Research Data Centre at the Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany. E-mail: firstname.lastname@example.org.
Coronavirus disease 2019
Enzyme-linked immunosorbent assay
International standard classification of education
International Socio-Economic Index of Occupational Status
Organisation for Economic Co-operation and Development
Polymerase chain reaction
Reverse transcription polymerase chain reaction
Severe acute respiratory syndrom coronavirus type 2
Subjective social status
McNamara CL, Balaj M, Thomson KH, Eikemo TA, Solheim EF, Bambra C. The socioeconomic distribution of non-communicable diseases in Europe: findings from the European Social Survey (2014) special module on the social determinants of health. Eur J Public Health. 2017;27(suppl_1):22–6.
Millett GA, Jones AT, Benkeser D, Baral S, Mercer L, Beyrer C, Honermann B, Lankiewicz E, Mena L, Crowley JS, et al. Assessing differential impacts of COVID-19 on black communities. Ann Epidemiol. 2020;47:37–44.
World Economic Situation and Prospects: April 2020 Briefing, No. 136. https://www.un.org/development/desa/dpad/publication/world-economic-situation-and-prospects-april-2020-briefing-no-136/.
Bonacini L, Gallo G, Scicchitano S. Working from home and income inequality: risks of a ‘new normal’ with COVID-19. J Popul Econ. 2021;34(1):303–60.
Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, Leskovec J. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2020;589:82–7.
Bambra C, Riordan R, Ford J, Matthews F. The COVID-19 pandemic and health inequalities. J Epidemiol Commun Health. 2020;74:964–8.
Khalatbari-Soltani S, Cumming RC, Delpierre C, Kelly-Irving M. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health. 2020;74(8):620–3.
Cornejo Müller A, Wachtler B, Lampert T: Digital Divide – Soziale Unterschiede in der Nutzung digitaler Gesundheitsangebote. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 2020.
Niedzwiedz CL, O’Donnell CA, Jani BD, Demou E, Ho FK, Celis-Morales C, Nicholl BI, Mair FS, Welsh P, Sattar N, et al. Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK Biobank. BMC Med. 2020;18(1):160.
Santos-Hövener C, Busch MA, Koschollek C, Schlaud M, Hoebel J, Hoffmann R, Wilking H, Haller S, Allen J, Wernitz J et al: Seroepidemiological study on the spread of SARS-CoV-2 in populations in especially affected areas in Germany—study protocol of the CORONA-MONITORING lokal study. Journal of Health Monitoring 2020(S5/20):1–18.
Santos-Hövener C, Neuhauser HK, Schaffrath Rosario A, Busch M, Schlaud M, Hoffmann R, Gößwald A, Koschollek C, Hoebel J, Allen J, et al. Serology- and PCR-based cumulative incidence of SARS-CoV-2 infection in adults in a successfully contained early hotspot (CoMoLo study), Germany, May to June 2020. Eurosurveillance. 2020;25(47):2001752.
Ganzeboom HGB, De Graaf PM, Treimann DJ. A standard international socioeconomic index of occupational status. Soc Sci Res. 1992;21(1):1–56.
Hoebel J, Müters S, Kuntz B, Lange C, Lampert T. Measuring subjective social status in health research with a German version of the MacArthur Scale. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2015;58(7):749–57.
Aziz NA, Corman VM, Echterhoff AKC, Richter A, Schmandke A, Schmidt ML, Schmidt TH, De Vries FM, Drosten C, Breteler MMB: Seroprevalence and correlates of SARS-CoV-2 neutralizing antibodies: Results from a population-based study in Bonn, Germany. medRxiv 2020:2020.2008.2024.20181206.
Wagner R, Peterhoff D, Beileke S, Guenther F, Berr M, Einhauser S, Schütz A, Niller HH, Steininger P, Knöll A et al: Estimates and determinants of SARS-CoV-2 seroprevalence and infection fatality ratio using latent class analysis: the population-based Tirschenreuth study in the hardest-hit German county in spring 2020. medRxiv 2021:2021.2003.2029.21254343.
Pollán M, Pérez-Gómez B, Pastor-Barriuso R, Oteo J, Hernán MA, Pérez-Olmeda M, Sanmartín JL, Fernández-García A, Cruz I, Fernández de Larrea N, et al. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. Lancet. 2020;396(10250):535–44.
Ward H, Cooke G, Atchison C, Whitaker M, Elliott J, Moshe M, Brown JC, Flower B, Daunt A, Ainslie K et al: Declining prevalence of antibody positivity to SARS-CoV-2: a community study of 365,000 adults. medRxiv 2020:2020.2010.2026.20219725.
Vassallo RR, Bravo MD, Dumont LJ, Hazegh K, Kamel H: Seroprevalence of Antibodies to SARS-CoV-2 in US Blood Donors. medRxiv 2020:2020.2009.2017.20195131.
Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener C, Lampert T, Hoebel J. Socioeconomic inequalities in the risk of SARS-CoV-2 infection—first results from an analysis of surveillance data from Germany. J Health Monit. 2020;5(S7):18–29.
Hoebel J, Michalski N, Wachtler B, Diercke M, Neuhauser H, Wieler LH, Hövener C. Socioeconomic differences in the risk of infection during the second SARS-CoV-2 wave in Germany. Dtsch Arztebl Int. 2021;118(15):269–70.
Jaspal R, Breakwell GM: Socio-economic inequalities in social network, loneliness and mental health during the COVID-19 pandemic. Int J Soc Psychiatry, 0(0):0020764020976694.
Yıldırım M, Geçer E, Akgül Ö. The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol Health Med. 2021;26(1):35–43.
We would like to thank the communities of Kupferzell and Bad Feilnbach for supporting the study. We would also like to thank all the people involved in the data collection and preparation. We are very grateful to all participants who took part in this study
Open Access funding enabled and organized by Projekt DEAL. The Corona Monitoring Local Study was funded by the German Federal Ministry of Health (reference number ZMVI1-2520COR402) and this analysis was supported by grants from the German Research Foundation (project number HO 6565/2-1). The funders had no role in the data collection and analysis, decision to publish, or preparation of the manuscript. All authors are employees of the RKI. None of the authors received specific funding for this work.
Ethics approval and consent to participate
This study was approved by the ethics committee of the Berlin Chamber of Physicians (Berliner Ärztekammer, reference number Eth-11/20), and the data commissioner of the Robert Koch Institute. All participants gave written informed consent.
Consent for publication
All authors declare no competing interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Wachtler, B., Müters, S., Michalski, N. et al. Socioeconomic inequalities in the prevalence and perceived dangerousness of SARS-CoV-2 infections in two early German hotspots: findings from a seroepidemiological study. BMC Res Notes 14, 375 (2021). https://doi.org/10.1186/s13104-021-05784-5
- Social determinants
- Social epidemiology
- Seroepidemiological study