Background Smoking is a major risk factor for chronic diseases causing early death and disability. Smoking prevalence over the past 25years has remained high in Switzerland. Evidence about the burden of disease and cost of illness attributable to smoking can support tobacco control. The aim of the present paper is to quantify from a societal perspective the mortality, disability-adjusted life years (DALYs), medical costs and productivity losses attributable to smoking in Switzerland in 2017. Methods Smoking attributable fractions (SAFs) were calculated based on the prevalence of current and former active smoking in the latest Swiss Health Survey from 2017 and relative risks from the literature. The SAFs were then multiplied with the number of deaths, DALYs, medical costs and productivity losses in the total population. Results In the Swiss population in 2017 smoking accounted for 14.4% of all deaths, for 29.2% of the deaths due to smoking-related diseases, 36.0% of the DALYs, 27.8% of the medical costs and 27.9% of productivity losses. Total costs amounted to CHF 5.0 billion which equals CHF 604 per capita per year. The highest disease burden in terms of mortality and DALYs attributable to smoking was observed for lung cancer and chronic obstructive pulmonary disease (COPD), whereas the highest cost of illness in terms of medical costs was observed for coronary heart diseases and lung cancer and in terms of productivity losses for COPD and coronary heart diseases. Sex and age group differences were found. Conclusions We provide an estimate of the burden of smoking on disease-specific mortality, DALYs, medical costs and productivity losses in Switzerland that could be prevented through evidence-based tobacco prevention and control policies as well as regular monitoring of tobacco consumption.

In 2020 2.3 million women were diagnosed with breast cancer. About 7.4% of women who have been diagnosed with primary breast cancer will have a second primary breast cancer within 10 years. This study builds a prediction model for second breast cancer for women who have had primary breast cancer. Readily available cancer registry data with machine learning methods for classification are employed. The best-performing model is selected based on the area under the receiver operator curve, and the key characteristics contributing to a high risk for second breast cancer are identified based on the prediction model. Using extreme gradient boosting (XGBoost) with limited patient features we find an area under the curve of 0.65-0.70 for the testing set. Among the most important features are days from incidence to treatment, size of primary tumor based on the pathology report, and oestrogen receptor status.This research is a step towards the development of a tool that will help doctors identify women very likely to develop second breast cancer, which will prioritize their follow-up or inform their course of treatment depending on their characteristics. Citation Format: Maria Eleni Syleouni, Nena Karavasiloglou, Laura Manduchi, Miriam Wanner, Dimitri Korol, Sabine Rohrmann. Predicting second breast cancers among women diagnosed with primary breast cancer using patient-level data and machine learning algorithms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2252.