BACKGROUND: We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. METHODS: We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. RESULTS: The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. CONCLUSIONS: This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.
BACKGROUND: We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. METHODS: We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. RESULTS: The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. CONCLUSIONS: This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.
Authors: Thomas Iftner; Sven Becker; Klaus-Joachim Neis; Alejandra Castanon; Angelika Iftner; Barbara Holz; Annette Staebler; Melanie Henes; Katharina Rall; Juliane Haedicke; Claus Hann von Weyhern; Andreas Clad; Sara Brucker; Peter Sasieni Journal: J Clin Microbiol Date: 2015-05-27 Impact factor: 5.948
Authors: Amelia Acera; Ana Rodriguez; Marta Trapero-Bertran; Pilar Soteras; Norman Sanchez; Josep M Bonet; Josep M Manresa; Pablo Hidalgo; Pere Toran; Gemma Prieto Journal: BMC Health Serv Res Date: 2011-10-19 Impact factor: 2.655
Authors: Andrzej Nowakowski; Marek Cybulski; Andrzej Śliwczyński; Arkadiusz Chil; Zbigniew Teter; Przemysław Seroczyński; Marc Arbyn; Ahti Anttila Journal: BMC Cancer Date: 2015-04-14 Impact factor: 4.430