This study provides evidence that regional labour markets and their properties are related to regional patterns of SARS-CoV-2 infections in the working-age population in Germany. In detail, for all four phases under study we find that regions with higher proportions of people in employment have generally higher weekly age-standardised incidence rates, and that regions where more people work in the secondary sector or with low capacities to work from home (mainly in waves 3 and 4 though) have higher rates as well. Furthermore, findings indicate that these latter regions also experience a steeper increase of infection rates in the course of the four waves under study. Findings are based on spatial models that account for spatial autocorrelation and remained stable after adjusting for potential confounders at the regional level. And – in cases of wave 3 and wave 4 – the reported findings were also found when additionally adjusting for a proxy measure of vaccination progress.
Overall, the observed associations are in line with previous studies, specifically ecological studies that investigate socioeconomic deprivation in conjunction with SARS-CoV-2 infection rates or COVID-19 mortality [23, 39]. Yet, by focussing on working-age populations and conducting refined spatial panel analyses of trajectories of weekly age-standardised incidence rates that consider both regional clustering and potential regional confounders, we provide evidence that adds to existing research in at least two ways: First, by including an indicator that measures the general amount of employment at the regional level, we highlight that work and employment could be key factors for infection transmissions in a region, and thus, that the workplace may be an important entry point for interventions. Second, the finding of higher infections rates in regions with a large secondary sector (or conversely a small tertiary sector) adds to current knowledge that is either limited to smaller geographical areas , or relies on studies that use cumulative infection rates across an extended observation period as outcome without focussing on labour market factors [40, 41]. Sure, the considered occupations of the secondary sector (i.e. all occupations involved in the production or the construction of goods) must be considered as heterogeneous in our case, but our findings give good reasons to believe that people who work in the secondary sector are generally more likely to be exposed to the virus (at least for the studied periods of the pandemic). Overall, this is also supported by studies based on individual data with more refined measures of occupations that show that essential workers in the secondary sector have higher rates and that many jobs of the tertiary sector have lower rates (with except of health care workers) [2, 8]. On the one hand, we may speculate that protective measures in the secondary sector (e.g. social distancing or use of face masks) are less established and less effective (e.g. when working in a large manufacturing hall compared with office work). Also, the number of co-workers in close proximity at work is possibly higher compared with office work. On the other hand, though, our results suggest show that opportunities of working from home, including the possibility to reduce work-related mobility (e.g. public transport to the workplace), are much smaller in jobs of the secondary sector. The latter idea is also supported by our finding of a negative correlation between size of the secondary sector and capacity to work from home. The named aspects (i.e. transmission risks, mitigation measures, work from home) are also important components of recent efforts to estimate potential SARS-CoV-2 infection risks and to develop a respective job exposure matrix [42, 43]. Another reason, though, may be that the sectors were differently affected by closures as part of the non-pharmaceutical interventions implemented to contain the pandemic. In fact, while large parts of the tertiary sectors were affected through closures of several businesses (the gastronomy, cultural institutions, or shops), many industries of the secondary sectors remained open.
On a more general note, our study illustrates the necessity of extending the growing evidence on socioeconomic differences in infection risks to factors that are considered as potential explanations. Future ecological studies also need to focus on other potential explanations of socioeconomic differences, such as pre-existing health conditions , air pollution [44, 45] (as two potential reasons for greater vulnerability), or on measures capturing different access and use of medical care in a region (incl. adherence to NPI and vaccination coverage) . Here, the included measure of vaccination rate as part of our sensitivity analyses must clearly be seen as a preliminary measure that deserves more methodological refinements (assuring that we know where vaccinated people live) and more analyses.
The study has several limitations: First, we again need to consider that work and employment are possibly one – though it is not the only factor that may explain regional variations of infection rates, and other factors equally deserve attention in future studies to understand varying infection risks [47, 48]. Beside those just named above, these may also be more general aspects not necessarily related to socioeconomic deprivation, such as meteorological information (e.g. number of raining days or average temperature), sanitation or hygiene, public transport systems or policy interventions at the regional level. Second, albeit we maintain that ecological studies are instrumental, and well-suited to supplement individual data (because of the direct relevance for potential interventions), we need to be very careful when drawing conclusion from the regional to the individual level. At this point, we need to remember that – albeit being the smallest level available – our study relies on rather large areas. We therefore must consider the risk of an ecological fallacy including potential heterogeneity of workers within regions. To be clear, we cannot guarantee that those who are employed in the secondary sector of a region are also those who are infected. Another limitation relates to testing strategies in the regions. While our study is based on official notification data on laboratory-confirmed infections with identical notification procedures throughout Germany, testing strategies may still vary by regions, with a potential bias as some occupational groups are more likely to be tested in some regions than others (and thus to be detected and notified as cases). Yet, in the case of Germany, testing opportunities (i.e. antigen rapid tests with subsequent PCR-test in case of positive result) were free of charge throughout the whole observation period of this study and respective policy changes are decided at the federal level (without variations between states). Furthermore, even if changes may exist across time, all regions were equally affected by these changes – thus making it unlikely that the found differences by regions are biased. Likewise, it is known that health seeking behaviour varies by occupation , meaning that some infections are possibly more likely to remain undetected for some occupational groups. Another limitation relates to the generalisation of results. Albeit our study covers nearly two years of the pandemic and distinguishes four periods, insights into subsequent waves with other dominant virus variants are not possible. Results also need to be replicated for other countries. Finally, although our study firstly allows to compare regional variations in infection rates focusing on working-age populations, we still need to question if the observed differences for working-age populations are also translated into differences at the population level. Because infections are likely to be transmitted within families, and because the overall regional disparities correspond to what is observed for the general population [16, 23], there are good reasons to think that this is the case.
In conclusion, our study extends current knowledge, by analysing regional variations of SARS-CoV-2 infection risks across four waves of the pandemic by regional labour markets and their properties. This underlines the importance of work and employment as key domains and places for transmission risks. In doing so, it points to the necessity of strengthening these factors as essential component of pandemic preparedness plans and to amend workplace interventions, particularly among workers of the secondary sector without opportunities of working from home.