Verena Bade, Hendrik Schmitz, Beatrice Baaba Tawiah
Abstract
Vaccination willingness against COVID-19 is generally perceived as low. Moreover, there is large heterogeneity across and within countries. As a whole, Germany has average vaccination rates compared to other industrialized countries. However, vaccination rates in the 16 different German federal states differ by more than 20 percentage points. We describe variation in vaccination rates on the level of the 400 German counties using data on all vaccinations carried out until December 2022. Around 52-72% of that variation can be explained by regional differences in demographic characteristics, housing, education and political party preferences. We find indications that the remaining part may be due to differences in soft factors such as risk aversion, trust in the German government, trust in science, and beliefs in conspiracy theories regarding the origins of the Corona virus. We conclude that improving the trust in science and the fight against conspiracy theories may possibly be effective tools to improve vaccination rates and effectively fight pandemics.
Introduction
In order to curb the SARS-CoV-2 virus, vaccines were developed as quickly as possible in 2020 and enormous vaccination campaigns started by the end of 2020 in the industrialized countries. In the first months of 2021, undersupply of vaccines was the major problem of these vaccination campaigns. Yet, this changed throughout the year and, later, too low vaccination willingness was considered the major challenge to yield herd immunity [1–3]. Herd immunity, in turn, was among the most important health policy goals in most countries in order to overcome the Corona crisis. However, vaccination willingness against COVID-19 is generally perceived as low [4–6]. While several mutations of the virus made the original vaccinations less effective—but also reduced the severity of the virus [7].
Methods
Vaccination rates
Our main dependent variable is the share of individuals with at least two vaccinations against the Corona virus. It is based on freely accessible and continuously updated data from the German Robert Koch Institute (RKI) [47] that publishes data on the universe of daily vaccinated doses per age group on the level of the 400 German counties. German counties can either be rural areas (Landkreis, N = 293) or single larger cities (Kreisfreie Stadt, N = 107). The RKI vaccination data are assigned to the county where the vaccination took place and not where the vaccinated individual lives. In principle, individuals can freely choose where to get vaccinated and it may be possible that they get vaccinated in neighbouring counties. In particular, it seems to be frequently done that individuals in rural areas get vaccinations in the next bigger cities where larger vaccination centers had been installed.
Results
Baseline results
Table 1 provides the descriptive statistics of the pool of variables on the level of the 400 counties. Table 2 shows the regression results of the model with the highest predictive power according to the spatial regression model outlined in Section Spatial interdependencies and the variable selection procedure outlined in Section Basic approach. The table includes the chosen variables and interactions. Both ρ and λ are negative. This most likely reflects the definition of the outcome variable, capturing vaccinations where they took place, not where individuals live. This possibly induces a negative correlation in vaccination rates across neighboring counties. The spatial model takes this into account. However, ρ is not significantly different from zero and λ is small in magnitude. All in all, spatial correlations do not seem to play a large role in this application.
Discussion
In terms of Corona vaccination rates, Germany ranks only in the midfield among the industrialized countries. This is partly due to some regions within Germany that have very low vaccination rates. In this study we seeked to understand the determinants of the regional variation in Corona vaccination in Germany. It turned out that differences in demographic status are able to explain a considerable share of this regional variation. For instance, regions with a higher share of individuals older than 65 have higher vaccination rates. All in all, differences in demographics alone account for 48 percent of the variation in Corona vaccination on the federal states level. Arguably, differences in vaccination rates due to differences in demographics seem to be justified, for instance given that older individuals belong to the high-risk group and should have higher vaccination rates. Thus, parts of the regional differences observed in Figs 1 and 2 are something policy does not need to worry about or even address. Yet, this refers to only half of the regional variations.
Conclusion
High vaccination rates have repeatedly been stressed by many scholars as a decisive factor to fight pandemics such as the COVID-19 pandemic. However, vaccination rates differ strongly across and within countries where some regions have vaccination rates that are generally perceived too low. In this study we analyzed regional variations of COVID-19 vaccination and their determinants in Germany. A highly flexible spatial-econometric model came to the result that around 52% of the county-level differences and 72% of the federal state-level differences can be explained by different county structure such as demographics, housing, economy, education, and party preferences. Around 50% of the differences on federal state level are due to differences in demographics. Thus, arguably, up to 50% of the regional variations may be regarded as justified. However, in future pandemics, policymakers may want to reduce the other 50%. We find suggestive evidence that soft factors on preferences like risk attitudes and trust might be important additional explanatory factors.
Citation: Bade V, Schmitz H, Tawiah BB (2024) Regional variations in vaccination against COVID-19 in Germany. PLoS ONE 19(4): e0296976. https://doi.org/10.1371/journal.pone.0296976
Editor: Simon Porcher, Universite Paris Pantheon-Assas, FRANCE
Received: March 3, 2023; Accepted: December 25, 2023; Published: April 18, 2024
Copyright: © 2024 Bade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the main results presented in the study are available from Mendeley data (https://data.mendeley.com/drafts/83sb8nxw4f). Data for the supplementary analyses cannot be made available due to data protection and data security laws. Data are available from the European Social Survey’s (ESS) data portal (https://ess-search.nsd.no/), and the German Socio- Economic Panel’s (SOEP) Data Access/Ethics Committee (https://www.diw.de/en/diw_01.c.601584.en/data_access.html) for researchers who meet the criteria for access.
Funding: HS acknowldges funding from the Deutsche Forschungsgemeinschaft (DFG, https://www.dfg.de/) with project number 492351283. HS acknowledges support for the publication cost by the Open Access Publication Fund of Paderborn University.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296976#abstract0
