[The cartographic depiction of regional variation in morbidity : Data analysis options using the example of the small-scale cancer atlas for Schleswig-Holstein]

Population-Based Clinical Cancer Registration in Germany

INTRODUCTION

In 2013, a new federal law obligated all German federal states to collect additional clinical data in population-based cancer registries as an active tool for monitoring and improving the quality of cancer care, increasing transparency and promoting health research. Now, 10 years later, the current status of the expanded cancer registration is presented, including current figures on cancer in Germany.

METHODS

Reporting of cancer is mandatory for physicians, and about 5 to 10 reports from different healthcare providers are expected for each case. A uniform national dataset of about 130 items is used, and reports are usually sent electronically to the registry. We used the most recent data available from cancer registries up to the year of diagnosis in 2019. We calculated incidence rates and 5-year relative survival (5YRS) for common cancers. Data on clinical outcomes and benchmarking based on quality indicators (QIs) from guidelines were provided by the Cancer Registry Schleswig-Holstein (CR SH).

RESULTS

All federal state cancer registries met most of the previously defined national eligibility criteria. Approximately 505,000 cancer cases were registered in 2019, with breast, prostate, colorectal and lung cancer being the most common cancers. The age-standardised cancer incidence has slightly decreased during the last decade. and spatial heterogeneity can be observed within Germany. 5YRS for all cancers was 67% and 63% for women and men, respectively. Therapy data for rectal cancer in 2019-2021 from the CR SH are shown as an example: 69% of the registered patients underwent surgery, mostly with curative intent (84%) and tumour-free resection (91%). Radiotherapy was given to 33% of the patients, and chemotherapy was given to 40%. Three selected QIs showed differences between involved healthcare providers.

DISCUSSION

The implementation of population-based clinical cancer registration can be considered a success. Comprehensive recording of diagnosis, treatment and disease progression and the use of registry data for quality assurance, benchmarking and feedback have been implemented.

[Mapping Regional Differences in Infection Rates for the Coronavirus (COVID-19): Results of a Bayesian Approach to Administrative Districts of Bavaria]

BACKGROUND

Since the beginning of the COVID-19 pandemic, thematic maps showing the spread of the disease have been of great public interest. From the perspective of risk communication, those maps can be problematic, since random variation or extreme values may occur and cover up the actual regional patterns. One potential solution is applying spatial smoothing methods. The aim of this study was to show changes in incidence ratios over time in Bavarian districts using spatially smoothed maps.

METHODS

Data on SARS-CoV-2 were provided by the Bavarian Health and Food Safety Authority on 29.10.2021 and 17.02.2022. The demographic data per district are derived from the Statistical Report of the Bavarian State Office for Statistics for 2019. Four age groups per sex (<18, 18-29, 30-64,>64 years) divided into 16 time periods (01/28/2020 to 12/31/2021) were included. Maps show standardized incidence ratios (SIR) spatially smoothed by Bayesian hierarchical modelling.

RESULTS

The SIR varied remarkably between districts. Variations occurred for each time period, showing changing regional patterns over time.

CONCLUSION

Smoothed health maps are suitable for showing trends in incidence ratios over time for COVID-19 in Bavaria and offer the advantage over traditional maps in giving more realistic estimates by including neighborhood relationships. The methodological approach can be seen as a first step to explain the regional heterogeneity in the pandemic, and to support improved risk communication.

Area Deprivation and COVID-19 Incidence and Mortality in Bavaria, Germany: A Bayesian Geographical Analysis

Background

Area deprivation has been shown to be associated with various adverse health outcomes including communicable as well as non-communicable diseases. Our objective was to assess potential associations between area deprivation and COVID-19 standardized incidence and mortality ratios in Bavaria over a period of nearly 2 years. Bavaria is the federal state with the highest infection dynamics in Germany and demographically comparable to several other European countries.

Methods

In this retrospective, observational ecological study, we estimated the strength of associations between area deprivation and standardized COVID-19 incidence and mortality ratios (SIR and SMR) in Bavaria, Germany. We used official SARS-CoV-2 reporting data aggregated in monthly periods between March 1, 2020 and December 31, 2021. Area deprivation was assessed using the quintiles of the 2015 version of the Bavarian Index of Multiple Deprivation (BIMD 2015) at district level, analyzing the overall index as well as its single domains.

Results

Deprived districts showed higher SIR and SMR than less deprived districts. Aggregated over the whole period, the SIR increased by 1.04 (95% confidence interval (95% CI): 1.01 to 1.07, p = 0.002), and the SMR by 1.11 (95% CI: 1.07 to 1.16, p < 0.001) per BIMD quintile. This represents a maximum difference of 41% between districts in the most and least deprived quintiles in the SIR and 110% in the SMR. Looking at individual months revealed clear linear association between the BIMD quintiles and the SIR and SMR in the first, second and last quarter of 2021. In the summers of 2020 and 2021, infection activity was low.

Conclusions

In more deprived areas in Bavaria, Germany, higher incidence and mortality ratios were observed during the COVID-19 pandemic with particularly strong associations during infection waves 3 and 4 in 2020/2021. Only high infection levels reveal the effect of risk factors and socioeconomic inequalities. There may be confounding between the highly deprived areas and border regions in the north and east of Bavaria, making the relationship between area deprivation and infection burden more complex. Vaccination appeared to balance incidence and mortality rates between the most and least deprived districts. Vaccination makes an important contribution to health equality.

[Regional and Temporal Trends in SARS-CoV-2-Associated Mortality in Bavaria: An Age-Stratified Analysis Over 5 Quarters for Persons Aged 50 and Older]

OBJECTIVE

The aim of this study was to investigate the influence of regional factors such as incidence rate, hospitalizations, socio-economic status and nursing homes on the regional and temporal heterogeneity of SARS-CoV-2-associated mortality in Bavaria.

METHODOLOGY

Official Bavarian SARS-CoV-2 reporting data were considered for three age groups (50-64, 65-74,>74 years) between March 2020 and April 2021. Maps of regional standardized mortality rates were spatially smoothed using a Bayesian hierarchical model.

RESULTS

The picture of regional mortality was heterogeneous with an increasing gradient toward the northeast. Adjustment for standardized incidence rates, hospitalizations of infected persons, and availability of care homes for the elderly levelled the heterogeneity.

CONCLUSION

The north-east gradient in Bavarian SARS-CoV-2-specific mortality rates is clearly explained by the comparable gradient in regional incidence rates. Other regional factors show a less clear influence.

[Relevance of health geographic research for dermatology]

The association between geographic and medical aspects is a well-known phenomenon, which also occurs in dermatological research. This article reviews the field of health geography, the history of the association between spatial location and health, and focuses on current areas of research. Research focusing on explaining regional variations in health refer to individual aspects and needs, population factors, environmental factors, and health care delivery structures in specific regions, as well as the interaction between them. Regional healthcare research is primarily concerned with access to health services and on the utilisation of those services. Methodologically, the analysis of geodata and the application of geographic information systems (GIS) and spatial modelling play a major role in this field. Dermatological research and dermatological practice can benefit from the findings of the regional analysis of access, utilisation, and variations in order to obtain a more detailed picture of care and thus to optimise care.

[Regional monitoring of infections by means of standardized case fatality rates using the example of SARS-CoV-2 in Bavaria]

BACKGROUND

Maps of the temporal evolution of the regional distribution of a health-related measure enable public health-relevant assessments of health outcomes.

OBJECTIVES

The paper introduces the concept of standardized case fatality rate (sCFR). It describes the ratio of the regional variation in mortality to the regional variation in the documented infection process. The regional sCFR values are presented in maps and the time-varying regional heterogeneity observed in them is interpreted.

MATERIALS AND METHODS

The regional sCFR is the quotient of the regional standardized mortality and case rate. It is estimated using a bivariate model. The sCFR values presented in maps are based on SARS-CoV‑2 reporting data from Bavaria since the beginning of April 2020 until the end of March 2021. Four quarters (Q2/20, Q3/20, Q4/20, and Q1/21) are considered.

RESULTS

In the quarters considered, the naïve CFR values in Bavaria are 5.0%, 0.5%, 2.5%, and 2.8%. In Q2/20, regional sCFR values are irregularly distributed across the state. This heterogeneity weakens in the second wave of the epidemic. In Q1/21, only isolated regions with elevated sCFR (> 1.25) appear in southern Bavaria. Clusters of regions with sCFR > 1.25 form in northern Bavaria, with Oberallgäu being the region with the lowest sCFR (0.39, 95% credibility interval: 0.25-0.55).

CONCLUSIONS

In Bavaria, heterogeneous regional SARS-CoV-2-specific sCFR values are shown to change over time. They estimate the relative risk of dying from or with COVID-19 as a documented case. Strong small-scale variability in sCFR suggests a preference for regional over higher-level measures to manage the incidence of infection.

[Geographic methods for health monitoring]

The interest in using geographic methods for health monitoring has grown strongly over the last two decades. Through these methods, analysis and visualization of health data can be more focused and target-group specific. The application in health monitoring is possible mostly due to broader technical possibilities and more available datasets. In this article, we show which geographic aspects are adapted in health monitoring at different levels (federal, state, municipality).For example, at the federal level, surveillance methods are used; at the state level health atlases are created; and on the municipality level geographic analyses are performed for possible public health interventions.Methods range from simple maps on different levels of aggregation to more complex methods like space-temporal visualization or spatial-smoothing methods. While the technical possibilities are in place, a broader implementation of geographic methods is mostly hindered by missing data access to small-area information and data protection policies. Better access to data could especially improve the possibility for geographic methods in health monitoring and could inform the population and decision makers to inform and improve population health or healthcare.