Spatiotemporal Associations Between the COVID-19 Pandemic and Cancer Diagnoses in Bavaria

Cancer

Cancer registration, Registry data, Population-based analysis, Incidence, COVID-19, Pandemics, Cancer

The COVID-19 pandemic has placed a significant strain on healthcare systems worldwide and has also impacted the diagnosis and treatment of cancer. In Germany, there was a notable decline in cancer diagnoses during the pandemic, partly due to delays in early detection and limited access to medical treatments. This study examines how the number of cancer cases in Bavaria changed before and during the pandemic, taking various factors into account, such as the socioeconomic status of different regions, the age of affected individuals, and the severity of the pandemic in each region. We aim to determine whether certain regions or specific types of tumors experienced greater declines in diagnoses, particularly for cancers with regular screening programs. The study seeks to provide a better understanding of how different social and geographical conditions influenced cancer diagnoses during the pandemic. These insights could help improve future preparedness for health crises like the pandemic and ensure that cancer treatment continues to function effectively even in times of crisis.

The COVID-19 pandemic has exerted significant pressure on healthcare systems worldwide, particularly impacting the diagnosis and treatment of non-communicable diseases such as cancer. In Germany, substantial declines in cancer diagnoses have been observed, primarily due to delays in early detection and limited access to medical services during the pandemic. This study aims to investigate the regional and temporal variations in cancer incidence in Bavaria before and during the pandemic, taking into account socio-economic, demographic factors, and the severity of the pandemic. This registry-based study utilizes data from the Bavarian Cancer Registry spanning from 2018 to 2023. The analysis adopts a spatiotemporal approach to examine changes in cancer incidence over time at the district level in Bavaria. A Bayesian spatiotemporal Poisson model with a Conditional Autoregressive (CAR) structure will be employed to account for both spatial and temporal variations, as well as spatial correlations between neighboring districts. The analysis will incorporate demographic characteristics, socio-economic status, tumor-specific data, and regional variations in the severity of the pandemic. It is hypothesized that the pandemic has led to regional disparities in cancer incidence, with socio-economically disadvantaged areas potentially being more severely affected. Tumors associated with established screening programs, such as breast, colon, and cervical cancer, may have experienced a greater reduction in diagnoses compared to other cancer types. The findings of this study will provide valuable insights into how geographical and socio-economic factors influenced cancer incidence during the pandemic and how these inequalities were exacerbated. The results could inform future health policies aimed at ensuring the continued accessibility of cancer detection and treatment, even during public health crises.

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Germany