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

After 2 years of the COVID-19 pandemic, the evidence on occupational determinants of SARS-CoV-2 infection risks is still limited. Among the existing studies, many focus on essential occupations and reveal that risks of infections are generally higher among health care workers, transport workers, teachers, child care workers, postal service workers, kitchen staff or workers in the logistic sector—but rather low for workers in the media sector (e.g., journalists), lawyers, scientist or workers in the financial sector [1‐11]. These studies are instrumental as they help to describe infection risks for specific occupations. However, far-reaching conclusions on high-risk groups and corresponding policy implications remain limited, mainly because most studies focus on rather homogenous occupational groups without allowing for systematic comparisons of risks between different occupations. In addition, it is still not clear how findings based on individual-level data translate into regional differences of SARS-CoV-2 infections and corresponding incidence rates. Yet, knowledge on regional differences and the role of labour markets and their properties is instructive, as infections tend to cluster regionally and many decisions for pandemic intervention measures are made at regional levels [12]. Therefore, research based on individual data should be supplemented by ecological studies that examine levels of infections in relation to characteristics of regional labour markets—not as a substitute for individual studies (in case individual-level data is missing), but as a helpful supplement with immediate relevance for local and targeted policy decisions [13].

In fact, ecological studies on regional differences of COVID-19 have experienced increased attention during the pandemic [14, 15]. For example, studies from Germany [16‐19], the UK [20], and the US [21, 22], suggest that levels of SARS-CoV-2 infections and COVID-19 mortality rates are comparatively higher in regions with high poverty rates or low income levels, or in regions that are generally socioeconomically disadvantaged. Interestingly, studies that also compare associations between different phases of the pandemic in Germany demonstrate that these socioeconomic differences are less pronounced (or even inversed) in the early first wave of the pandemic (with lower infection rates and mortality in more disadvantaged regions) [19, 23, 24]. Such an inverse association during the early phase of the pandemic was also found for the US [25] and France [26]. These findings highlight that socio-economic differences can also vary by different phases of the pandemic and that pandemic phases should be considered within the analyses. One possible explanation for these differences (and the reversion in the course of the pandemic) are different compositions of regional workforces and respective exposure to the virus. Higher infection rates at the beginning of the pandemic are possibly due to more employed people with international business travel, transregional commuting and more overall mobility (incl. holidays). In the course of the pandemic, though, these were exactly the occupational groups that were able to reduce their mobility with opportunities of working from home, while workers in less advantaged occupations were more exposed to the virus at their workplaces. Additionally, because of pre-existing health differences [27], workers in less advantaged occupations were then also more susceptible to an infection due to underlying health conditions.

Despite being mentioned as potential explanations, the role of regional labour markets and their properties, however, remains unclear. One exception is a recent study from Toronto that documents that infections and COVID-19 mortality rates (though for the overall population) are higher in neighbourhoods with a high proportion of people working in essential occupations [28]. Therefore, a comprehensive study of regional differences in levels of infections among the working-age population (those most likely to be affected by labour markets) that explicitly focus on regional labour markets and their properties (instead of socioeconomic factors) and takes changing patterns over time into account is still missing. It is the overall objective of this ecological study to fill this gap of knowledge for Germany, and to investigate how regional labour markets are related to levels of infections in the working population.

In the current ecological study, we rely on weekly nationwide data on notified laboratory-confirmed SARS-CoV-2 infections at the regional level for Germany (as an indicator of actual levels of infection in the population), and combine this data with regional labour market indicators to investigate differences in age-standardised incidence rates (ASIRs) of SARS-CoV-2 infections according to three labour market indicators. As previous studies show that associations between regional indicators and incidence rates vary by phases of the pandemic in Germany, we study associations separately for the first four main pandemic waves (ranging from March 2020 until December 2021). The three labour market indicators are: the overall extent of employment in a region, the composition of the workforce by economical job sectors, and existing opportunities of working from home. We also explicitly focus on the working-age population, as well as we apply a statistical approach (spatial panel analysis) which allows to address spatial autocorrelation—meaning that neighbouring regions are usually interrelated and not independent (see “Methods” for details).

This ecological 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, 42]. 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 [28], or relies on studies that use cumulative infection rates across an extended observation period as outcome without focussing on labour market factors [43, 44]. 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, clearly asking for refined analyses to investigate if certain occupations within the sectors are driving the varying incidence rates. But our findings give good reasons to believe that people who work in regions where more people work in the secondary sector are generally more likely to be exposed to the virus (at least for the studied periods of the pandemic). This is also supported by studies relying on individual data that show that essential workers in the secondary sector (e.g., food production) have higher rates and that many jobs of the tertiary sector (e.g., financial services or media) have lower rates (with the exception 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 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, as well as in models where labour market indicators are included simultaneously. 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 [45, 46]. 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 [27], air pollution [47, 48] (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) [49]. 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 [50, 51]. 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. Future extensions of this study, therefore, should study if our findings are confirmed at a more granular regional scale (with possibly more pronounced associations), as well as on the basis of individual-level data. Another limitation relates to testing strategies in the regions. While our study is based on official data on notified laboratory-confirmed infections based on widely accessible testing opportunities and identical notification procedures throughout Germany, we still may ask if some occupational groups could be more likely to be tested in some regions than others (and thus to be detected and notified as cases). Yet, because 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 because respective policy changes are decided at the federal level for all regions (irrespective of the workforce composition of a region), it is unlikely that the found differences by regions are affected by changes of testing strategies during the observation period. In a similar way, it is known that health seeking behaviour varies by occupation [52], meaning that some infections are possibly more likely to remain undetected for some occupational groups. A recent German study, for example, compared nationwide findings from a SARS-CoV-2 seroepidemiological study with data on notified infections and estimated that up till 45% of SARS-CoV-2 infections remained undetected [53], with slightly higher values in socioeconomically disadvantaged regions. Along these lines, we may speculate if our study underestimates the association with ASIRs in the cases of the secondary sector and low capacities to work from home, as these are possibly jobs with more people working in less advantaged occupations.

Another limitation relates to the generalisation of results. Albeit our study covers nearly 2 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.