Purpose: Among the known risk factors, smoking is clearly related to the incidence of lung cancer and alcohol consumption is to breast cancer. In this manuscript we modelled the potential benefits of reductions in smoking or alcohol prevalence for the burden of these cancers. Method: We used Prevent v.3.01 to assess the changes in incidence as a result of risk factor changes. Incidence of lung and breast cancer until 2050 was predicted under two scenarios: ideal (total elimination of smoking and reduction of alcohol intake to maximum 1 units/d for women) and optimistic (decreasing prevalence of risk factors because of a 10% increase in cigarette and alcohol beverage price, repeated every 5 years). Danish data from the household surveys, cancer registration and Eurostat were used. Results: Up to 49% less new lung cancer cases can be expected in 2050 if smoking were to be completely eliminated. Five-yearly 10% price increases may prevent 521 new lung cancer cases in 2050 (21% less cases). An intervention that immediately reduces population alcohol consumption to the recommended level (below 12 g/d) may lower breast cancer by 7%, preventing 445 out of the 6060 expected new cases in 2050. Five-yearly 10% price increases in alcoholic beverages achieved a reduction of half as expected by the ideal scenario, i.e. 4% (262) preventable cases in 2050. Conclusions: The future burden of lung and breast cancer could be markedly reduced by intervening in their risk factors. Prevent illustrates the benefit of interventions and may serve as guidance in political decision-making.

Breast cancer, Dynamic modelling, Intervention, Lung cancer, Population impact fraction, Prevent, Projection
dx.doi.org/10.1016/j.ejca.2010.07.051, hdl.handle.net/1765/28198
European Journal of Cancer
Erasmus MC: University Medical Center Rotterdam

Soerjomataram, I, de Vries, E, Engholm, G, Paludan-Müller, G, Brønnum-Hansen, H, Storm, H.H, & Barendregt, J.J.M. (2010). Impact of a smoking and alcohol intervention programme on lung and breast cancer incidence in Denmark: An example of dynamic modelling with Prevent. European Journal of Cancer, 46(14), 2617–2624. doi:10.1016/j.ejca.2010.07.051