Prostate-specific antigen testing in Tyrol, Austria: prostate cancer mortality reduction was supported by an update with mortality data up to 2008

Objectives: The objective of this study was to update an in-depth analysis of the time trend for prostate cancer (PCA) mortality in the population of Tyrol by 5 years, namely to 2008. In Tyrol, prostate-specific antigen (PSA) tests were introduced in 1988/89; more than three-quarters of all men in the age group 45–74 had at least one PSA test in the past decade. Methods: We applied the same model as in a previous publication, i.e., an age-period-cohort model using Poisson regression, to the mortality data covering more than three decades from 1970 to 2008. Results: For Tyrol from 2004 to 2008 in the age group 60+ period terms show a significant reduction in prostate cancer mortality with a risk ratio of 0.70 (95% confidence interval 0.57, 0.87) for Tyrol, and for Austria excluding Tyrol a moderate reduction with a risk ratio of 0.92 (95% confidence interval 0.87, 0.97), each compared to the mortality rate in the period 1989–1993. Conclusions: This update strengthens our previously published results, namely that PSA testing offered to a population at no charge can reduce prostate cancer mortality. The extent of mortality reduction is in line with that reported in the other recent publications. However, our data do not permit us to fully assess the harms associated with PCA screening, and no recommendation for PSA screening can be made without a careful evaluation of overdiagnosis and overtreatment

Importance of databases for technology assessment

The data indispensable for carrying out the comprehensive, multi-faceted process of medical technology assessment (MTA) should be collected from a variety of sources. The authors distinguish between type "A" general data, useful for assessment but collected without this specific aim, and type "B" data. Registries of health care procedures or of diseases, as well as clinical data bases are quoted as examples of type "B" data, specifically relating to MTA. Since demographic methods are of importance for the evaluation of long-term effects of medical technologies, examples of sources of type "A" data are presented. Their significance for health policy making is discussed