Version 1
: Received: 13 March 2022 / Approved: 15 March 2022 / Online: 15 March 2022 (11:40:15 CET)
Version 2
: Received: 3 May 2022 / Approved: 5 May 2022 / Online: 5 May 2022 (10:20:36 CEST)
Qamar, A. I.; Gronwald, L.; Timmesfeld, N.; Diebner, H. H. Local Socio-Structural Predictors of COVID-19 Incidence in Germany. Frontiers in Public Health, 2022, 10. https://doi.org/10.3389/fpubh.2022.970092.
Qamar, A. I.; Gronwald, L.; Timmesfeld, N.; Diebner, H. H. Local Socio-Structural Predictors of COVID-19 Incidence in Germany. Frontiers in Public Health, 2022, 10. https://doi.org/10.3389/fpubh.2022.970092.
Qamar, A. I.; Gronwald, L.; Timmesfeld, N.; Diebner, H. H. Local Socio-Structural Predictors of COVID-19 Incidence in Germany. Frontiers in Public Health, 2022, 10. https://doi.org/10.3389/fpubh.2022.970092.
Qamar, A. I.; Gronwald, L.; Timmesfeld, N.; Diebner, H. H. Local Socio-Structural Predictors of COVID-19 Incidence in Germany. Frontiers in Public Health, 2022, 10. https://doi.org/10.3389/fpubh.2022.970092.
Abstract
Socioeconomic conditions and social attitudes are known to represent epidemiological determinants. Credible knowledge on socioeconomic driving factors of the COVID-19 epidemic is still incomplete. Based on a linear random effects regression, a predictive model is derived to estimate COVID-19 incidence in German rural districts from local socioeconomic factors and popularity of political parties in terms of their share of vote. Thereby, time series provided by Germany's public health institute (Robert Koch Institute) of weekly notified 7-day incidences per 100,000 inhabitants per district from the outset of the epidemic in 2020 up to December 1, 2021, have been used to construct the dependent variable. Local socioeconomic conditions including share of votes, retrieved from the Federal Statistical Office of Germany, have been used as potential risk factors. Popularity of the right-wing party Alternative for Germany (AfD) bears a considerable risk of increasing COVID-19 incidence both in terms of predicting the maximum incidences during three epidemic periods (alternatively, cumulative incidences over the periods are used to quantify the dependent variable) and in a time-continuous sense. Thus, districts with high AfD popularity rank on top in the time-average regarding COVID-19 incidence. The impact of the popularity of the Free Democrats (FDP) is markedly intermittent in the course of time showing two pronounced peaks in incidence but also occasional drops. A moderate risk emanates from popularities of the Green Party (GRÜNE) and the Christian Democratic Union (CDU/CSU) compared to the other parties with lowest risk level. Socioeconomic parameters like \emph{per capita} income, proportions of protection seekers and social benefit claimants, and educational level have negligible impact. To the contrary, incidence significantly increases with population density. In order to effectively combat the COVID-19 epidemic, public health policymakers are well advised to account for social attitudes and behavioural patterns reflected in local popularities of political parties, which are conceived as proper surrogates for these attitudes. Whilst causal relations between social attitudes and the presence of parties remain obscure, the political landscape in terms of share of votes constitutes at least viable predictive "markers" relevant for public health policy making.
Keywords
COVID-19 incidence; SARS-CoV-2; socioeconomic risk factors; social determinants of health; public health policy
Subject
Public Health and Healthcare, Health Policy and Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
5 May 2022
Commenter:
Hans H. Diebner
Commenter's Conflict of Interests:
Author
Comment:
The most important extension with respect to the first version is the inclusion of two sensitivity analyses. The main results of these sensitivity analyses are added to the main text and the fully detailed analyses are presented in supplementary files. Specifically, in the first version, we captured the magnitudes of epidemic waves in terms of the maximum incidences of the corresponding periods. We now added an analysis using the cumulative incidence instead. This left all inferences unchanged. In a second sensitivity analysis we investigated the impact of multicollinearity which results from the fact that share of votes of all parties add up to 100%. Thereby, the robustness of our previous results could be shown. In addition, we added further graphs depicting the scatterplots of socioeconomic factors and COVID-19 incidence in order to allow for a comprehensive impression of the discussed associations. Further, we added the population density as a crucial determinant of incidence. Moreover, we added a summary statistics table to show how the sociostructural characteristics are distributed over districts with high and low share of votes of the AfD party, respectively. Finally, we strengthened our line of reasoning throughout the manuscript. The reader of the first version might have got the wrong impression that we presented a causal effect analysis. To the contrary, an observational study is only able to derive evidence for associations. We clarified this point. Thereby, we followed the principles of good-practice in evidence-based-research, which we more clearly elaborated in the revised manuscript.
Commenter: Hans H. Diebner
Commenter's Conflict of Interests: Author
analyses. The main results of these sensitivity analyses are added to the main text and the fully detailed
analyses are presented in supplementary files. Specifically, in the first version, we captured the
magnitudes of epidemic waves in terms of the maximum incidences of the corresponding periods. We
now added an analysis using the cumulative incidence instead. This left all inferences unchanged. In a
second sensitivity analysis we investigated the impact of multicollinearity which results from the fact
that share of votes of all parties add up to 100%. Thereby, the robustness of our previous results could
be shown. In addition, we added further graphs depicting the scatterplots of socioeconomic factors and
COVID-19 incidence in order to allow for a comprehensive impression of the discussed associations.
Further, we added the population density as a crucial determinant of incidence. Moreover, we added a
summary statistics table to show how the sociostructural characteristics are distributed over districts
with high and low share of votes of the AfD party, respectively. Finally, we strengthened our line of
reasoning throughout the manuscript. The reader of the first version might have got the wrong impression that we presented a causal effect analysis. To the contrary, an observational study is only able to derive
evidence for associations. We clarified this point. Thereby, we followed the principles of good-practice
in evidence-based-research, which we more clearly elaborated in the revised manuscript.