gms | German Medical Science

15. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie

Gesellschaft für Arzneimittelforschung und Arzneimittelepidemiologie

20.11. - 21.11.2008, Bonn

Age distributions for costs in drug prescription and benchmark methods in drug economy

Altersverteilungen für Arzneimittelverordnungskosten und Benchmark-Methoden in der Arzneimittelökonomie

Meeting Abstract

Suche in Medline nach

  • corresponding author Reinhard Schuster - MDK Nord, Medizinischer Dienst der Krankenversicherung, Lübeck, Germany

Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA). 15. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie. Bonn, 20.-21.11.2008. Düsseldorf: German Medical Science GMS Publishing House; 2008. Doc08gaa26

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Veröffentlicht: 6. November 2008

© 2008 Schuster.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen ( Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.



Background and aim: We analyze age-dependent fractions of patients with costs above a threshold value in dependency of that value. We compare the results of different German regions and different statutory insurances. The outcome of age-dependency of costs is highly important in respect to demographic changes. Further on we compare several methods of drug economy on the basis of the international ATC-DDD-System with German specifications.

Material and method: We will discuss difference measures to compare costs related to different German regions or different statutory insurances. We use convex or concave functions in order to fit given data. We consider two-dimensional data and use a smoothing procedure by the application of a linear optimization problem. As a benchmark method we first compare the physicians by the mean amount of generic drug with a lower price position in aut-idem price listings. Secondly we consider the mean amount of guidance drugs.

With respect to the mentioned parameters we use a benchmark analysis with respect to physicians of a German region. On this basis goal values are determined.

Results: If millions of data sets are available, one has to extract the adequate information content, therefore the question of modeling is a point of central importance. Coursed by the large amount of data sets, the data of different years and regions are significantly different. It is much more interesting, to get stable measures of difference which allow health economic interpretations. We consider cost fractions above a threshold value. The used threshold value is taken for a time period of one year. The statistically reported data basis for age-dependent costs is poor in general with respect to specific details, especially if a (pseudonymized) patient identifier is necessary. We state a stable non-parametric model with high resolution.

There are yearly "goal agreements" in Schleswig-Holstein between Statutory Health Insurances and the National Association of Statutory Health Insurance Accredited Physicians orientated on principals given by national law. Given by federal guidelines there are determined guidance drugs for certain groups of drugs defined in terms of the ATC-system. Two essential goals are given by the fraction of prescribed DDD in the low price third and by the DDD-based fraction of guidance drugs. The ATC-DDD model is well adapted to health economic goals, if it is used appropriately. Benchmark models on the physician level are relevant for individual decisions. The benchmark models in Schleswig-Holstein include much more regional information than federal guidelines benchmark models. One can calculate the financial potential, if the regional goals are reached. By the way, the effect reached by positive motivation is including all those physicians, which are under the trifle-border with respect to malus regulations. The summation of those small amounts results in a substantial contribution. In Schleswig-Holstein malus regulations on the basis of unproved quarterly data were excluded in contrast to the federal guidelines. The regional analysis is offering much more constructive elements for the physician in the reflection of his individual prescription behavior.

Conclusion: Demographic changes are important for a large range of induced implications. Often it is implicated that the costs are strictly increasing with age. If this turns out not to be true in general, costs are depending much more sensible on the exact (demographically changing) age distribution of the population, which should be analyzed in that direction. For goal agreements between Statutory Health Insurances and the National Association of Statutory Health Insurance Accredited Physicians the regional analysis may offer much more constructive elements for the physician in the reflection of his individual prescription behavior. It provides initial information for discussions between physicians and with other groups on the basis of data analysis.