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Aims: Productivity losses related to premature cancer mortality have been assessed for most developed countries but results for Russia are limited to cross-sectional reports. The aim of this study was to quantify productivity costs due to cancer mortality in Russia between 2001 and 2015 and project this to 2030. Methods: Cancer mortality data (2001–2015) were acquired from the State Cancer Registry, whereas population data, labour force participation rates and annual earnings were retrieved from the Federal State Statistics Service. Cancer mortality was projected to 2030 and the human capital approach was applied to estimate productivity losses. Results: The total annual losses increased from US6.5b in 2001–2005 to US$8.1b in 2011–2015, corresponding to 0.24% of the annual gross domestic product. The value is expected to remain high in 2030 (US$7.5b, 0.14% of gross domestic product). Productivity losses per cancer death are predicted to grow faster in women (from US$18,622 to US$22,386) than in men (from US$25,064 to US$28,459). Total losses were found to be highest for breast cancer in women (US$0.6b, 20% of overall losses in women) and lung cancer in men (US$1.2b, 24%). The absolute predicted change of annual losses between 2011–2015 and 2026–2030 was greatest for cervix uteri (+US$214m) in women and for lip, oral and pharyngeal cancers in men (+US$182m). Conclusions: In Russia, productivity losses due to premature cancer mortality are substantial. Given the expected importance especially for potentially preventable cancers, steps to implement effective evidence-based national cancer control policies are urgently required.

Background Nowadays, various simulation approaches for evaluation and decision making in cancer screening can be found in the literature. This paper presents an overview of approaches used to assess screening programs for breast, lung, colorectal, prostate, and cervical cancers. Our main objectives are to describe methodological approaches and trends for different cancer sites and study populations, and to evaluate quality of cancer screening simulation studies. Methods A systematic literature search was performed in Medline, Web of Science, and Scopus databases. The search time frame was limited to 1999–2018 and 7101 studies were found. Of them, 621 studies met inclusion criteria, and 587 full-texts were retrieved, with 300 of the studies chosen for analysis. Finally, 263 full texts were used in the analysis (37 were excluded during the analysis). A descriptive and trend analysis of models was performed using a checklist created for the study. Results Currently, the most common methodological approaches in modeling cancer screening were individual-level Markov models (34% of the publications) and cohort-level Markov models (41%). The most commonly evaluated cancer types were breast (25%) and colorectal (24%) cancer. Studies on cervical cancer evaluated screening and vaccination (18%) or screening only (13%). Most studies have been conducted for North American (42%) and European (39%) populations. The number of studies with high quality scores increased over time. Conclusions Our findings suggest that future directions for cancer screening modelling include individual-level Markov models complemented by screening trial data, and further effort in model validation and data openness.

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