Corona Absences Variations Across Germany's Regions
When examining corona-related absences in the 46th week, Bavaria and Baden-Württemberg emerged as regions with the lowest rates, affecting 57 and 71 individuals per 10,000 insured people entitled to sick pay respectively. On the other hand, Saxony-Anhalt and Thuringia had the highest rates, with 541 and 499 sick individuals per 10,000 Barmer policyholders in mid-November. Interestingly, Lower Saxony recorded significant corona-related absences, affecting 167 individuals per 10,000 insured people in the same period (1).
The Agency Française de Presse (AFP) reported that Barmer highlighted regional variations in coronavirus-related absences, citing potential influences from local policies and health measures (2). However, the data showed that even regions with lower absentee rates, such as Bavaria and Baden-Württemberg, still encountered issues with missed time and sick leave, emphasizing the importance of continuous monitoring and support (3).
A strong correlation between the number of absences and the implementation of containment measures was observed in certain parts of Saxony-Anhalt, indicating that stricter measures might lead to a decrease in absences (4). Barmer's data reveals substantial differences in coronavirus-related absences between regions such as Saxony-Anhalt and Bavaria. Understanding these disparities is crucial for developing targeted, effective solutions (5).
The organization also underlined the importance of collaboration between stakeholders, including employers, healthcare providers, and government institutions, to overcome the ongoing challenge of coronavirus-related absences (6).
Factors contributing to observed regional disparities in corona-related absences include:
- Socioeconomic Status: Higher mobility of high SES groups early in pandemic waves, followed by lower SES groups' increased risks due to mitigation measures and pandemic seriousness beliefs.
- Geographic Location: Clusters of high-incidence counties in the south of Germany and less connected rural areas with associated higher incidence rates.
- Urban vs. Rural Areas: Lower population density and connectivity in rural regions facilitate virus spread.
- Migration and Population Characteristics: Counties with higher percentages of foreign populations and migrant dynamics experiencing distinct virus dynamics.
- Mobility and Contact Restrictions: Mobility and contact restrictions contributing to regional differences during the initial pandemic wave.
Sources: (1) (2) AFP (3-6) Barmer's latest report (7) Enrichment Data
Disclaimer: This article utilizes enrichment data to provide a more comprehensive understanding of coronavirus-related absences in Germany. The data is incorporated sparingly, ensuring it enhances the content without dominating it.
