With screening guidelines and financial coverage varying among health systems and insurers – sometimes dramatically – a new mathematical model provides quantitative predictions of the mortality benefits, on average, in populations of women over the course of 40 years. “We’re not advocating any particular interval for mammography screening,” says Sandra Lee, a biostatistician at Dana-Farber who developed the model along with Marvin Zelen of Dana-Farber and the Harvard School of Public Health. “This is a preliminary tool to show policymakers the kind of information they can draw on to help them make decisions.” Lee described the development of the mathematical model, which made use of data from several past clinical trials of mammography screening and from cancer databases, in a presentation at the annual meeting of the American Association for the Advancement of Science on Feb. 20. The mathematical tool generates comparative information that’s impossible to obtain in the real world, say the scientists, because clinical trials would require hundreds of thousands of volunteers following a variety of schedules over many years to demonstrate small mortality differences – and would be prohibitively expensive. Moreover, adds Lee, such trials would be ethically questionable because of the need for unscreened control groups.