Aktives Monitoring kleinräumiger Krebshäufungen

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

The cancer registry in Germany collects area-wide small-area data that can be presented in themes (disease mapping). Because of the occurrence of random extreme values of rates, mapping without prior spatial-statistical data analysis is problematic from a methodological and risk-communicative viewpoint - the extreme values easily mislead the card reader and obscure actual spatial patterns.The problem of data instability can generally be met by aggregation or by smoothing. The cancer atlas for Schleswig-Holstein is based on data from 1142 municipalities (median population: 721) for the diagnostic years 2001-2010. Maps for incidence (as a standardized incidence ratio), mortality (as a standardized mortality ratio), and relative survival (as a relative excess risk) were smoothed by using a Bayesian method (BYM model). The maps show that spatial differences can be made visible by smoothing.Data aggregation is the methodically simpler way, but means a loss of information. The atlas shows that small-scale mapping is possible while preserving the entire spatial information. The method of smoothing is complex, but useful for generating hypotheses. The spatial patterns found are complex, difficult to interpret, and require the collaboration of specialists from different professions, because of the diverse influencing factors (data collection, lifestyle factors, early detection, risk factors, etc.). The effort required to explain the methodology in a language easy to understand should not be underestimated.

Sequential tests for monitoring methods to detect elevated incidence - a simulation study

Background

Common cancer monitoring practice is seldom prospective and rather driven by public requests. This study aims to assess the performance of a recently developed prospective cancer monitoring method and the statistical tools used, in particular the sequential probability ratio test in regard to specificity, sensitivity, observation time and heterogeneity of size of the geographical unit.

Methods

A simulation study based on a predefined selection of cancer types, geographical unit and time period was set up. Based on the population structure of Lower Saxony the mean number of cases of three diagnoses were randomly assigned to the geographical units during 2008–2012. A two-stage monitoring procedure was then executed considering the standardized incidence ratio and sequential probability ratio test. Scenarios were constructed differing by the simulation of clusters, significance level and test parameter indicating a risk to be elevated.

Results

Performance strongly depended on the choice of the test parameter. If the expected numbers of cases were low, the significance level was not fully exhausted. Hence, the number of false positives was lower than the chosen significance level suggested, leading to a high specificity. Sensitivity increased with the expected number of cases and the amount of risk and decreased with the size of the geographical unit.

Conclusions

The procedure showed some desirable properties and is ready to use for a few settings but demands adjustments for others. Future work might consider refinements of the geographical structure.

Inhomogeneous unit size could be addressed by a flexible choice of the test parameter related to the observation time.