The following HTML text is provided to enhance online
readability. Many aspects of typography translate only awkwardly to HTML.
Please use the page image
as the authoritative form to ensure accuracy.
Saving Women’s Lives: Strategies for Improving Breast Cancer Detection and Diagnosis
Poorly designed studies have impeded the development of more refined models of risk stratification. In an attempt to develop a model for breast cancer risk, in 2001 AHRQ reviewed 500 studies involving more than 30,000 women. Unfortunately, poorly collected data and insufficient evidence prevented the inclusion of all factors except age. Age was the only risk factor that definitively showed clinical significance. Problems with the meta-analysis included a lack of standardization of risk factor reporting, lack of standard reporting formats, and failure to link risk factors to an eventual diagnosis of breast cancer.6 Because improving the early detection of breast cancer requires the development of better models to assess risk, critical attention must be given to improving the quality of clinical trials.
Population Measure of Cancer Status
There are three major measures of cancer status in a population: incidence, survival, and mortality. Cancer incidence represents the occurrence of cancer in the population and is often reported as a rate. Most cancer registries report cancer incidence in units of number of cases per 100,000 population per year. Calculations of short-term cancer incidence rates can be distorted by the extent to which a population is subjected to tests that might lead to cancer detection. Because studies of cancer screening are designed to do just that, these studies inevitably lead to major perturbations in the “reported” incidence, rendering cancer incidence an invalid endpoint for evaluating the real impact of the screening intervention.
Survival is the term used for the time interval from diagnosis to death from cancer, in patients who contract the disease. Since many patients will not die of their cancer, the survival experience must be calculated actuarially, using methods such as the life table, or the Kaplan-Meier method (Box 6-3). Although such calculations are definitive and unambiguous, the duration of survival is heavily dependent on the time of incidence of the cancer, and, as indicated, this can be strongly influenced in an artifactual way by the intervention under study (for example, screening). Although survival of cancer patients is the critical endpoint for studies of cancer therapies, it has little utility in studies of cancer prevention.
Mortality (or cancer-specific mortality) is the term used to describe the rate at which subjects die of the disease in the population targeted for the cancer prevention intervention; that is, it is the cancer death rate in the population under study. Mortality is the fundamental endpoint for cancer prevention studies, and to the extent that other endpoints—such as detection of cancer—are employed, they are used in lieu of mortality. Mortality is the only endpoint among these three that is valid for studies of cancer screening.
Screening is a form of secondary prevention, which is the control of