With screening guidelines and financial coverage varying among health systems and insurers – sometimes dramatically – the 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, ScD, a biostatistician at Dana-Farber who developed the model along with Marvin Zelen, PhD, 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 will describe 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 Sunday, Feb. 20, 8:30 am (Marriott Wardman Park Hotel, Lobby Level, Maryland Suite C). She also will present that data at a press briefing later that day at 2 pm (Marriott Wardman Park Hotel, Mezzanine Level).
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.
At present, American Cancer Society guidelines recommend that women age 40 and older have a screening mammogram every year and that they "should continue to do so for as long as they are in good health."
But payors differ in their coverage for the tests: in Great Britain, said Zelen, the National Health System pays for mammograms only at three-year intervals and doesn't cover any screening whatsoever for women younger than 50, when the incidence of breast cancer is lower and mammograms are effective.
The model can be helpful to women, he said, by eliminating unnecessary screening exams when the chance of detecting an unknown breast cancer is too low to warrant them.
"It's clear that the more mammograms you give, the more able you are to locate disease that a person didn't know about," Zelen says. But, testing with increasing frequency has diminishing returns, while boosting the odds of "false positives" that can be traumatic to women and lead to unneeded biopsies that drive up health costs.
Lee and Zelen, along with Hui Huang, MS, of Dana-Farber, described the model in 2004 in Statistical Methods in Medical Research. Among their conclusions:
Women who have a higher breast cancer risk because of their family history are advised to begin mammography at an early age. Using the model, say the researchers, health care providers can determine when to schedule mammograms depending on the amount of a woman's extra risk.
The model also provides estimates of the relative costs incurred by screening populations of women at greater or lesser intervals – an important issue for health policymakers.
Dana-Farber Cancer Institute (www.danafarber.org) is a principal teaching affiliate of the Harvard Medical School and is among the leading cancer research and care centers in the United States. It is a founding member of the Dana-Farber/Harvard Cancer Center (DF/HCC), designated a comprehensive cancer center by the National Cancer Institute.
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