Evidence map›Paper›PMID 34682408›Full record

ArticleInternational journal of environmental research and public health2021

Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany.

Gabriele Doblhammer, Daniel Kreft, Constantin Reinke

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. [Geography and health-the example of the COVID-19 pandemic in Bremen].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gabriele DoblhammerDepartment of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany.ORCID 0000-0001-7746-0652
Daniel KreftDepartment of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany.ORCID 0000-0001-5153-0202
Constantin ReinkeDepartment of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany.ORCID 0000-0003-3228-1794

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Indexed as

COVID-19GermanyHumansIncidenceIncomeSARS-CoV-2boosting modelsincidencemachine learningmortalityShap values

Identifiers

PMID34682408
PMCPMC8535595

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.