The decentralized structure of health care in the Unites States hinders population-based analysis of breast cancer screening. Our objectives are to model mammography in the United States as a whole, to identify the variables that most profoundly affect cost and efficacy, and to develop a strategy to improve mammography screening from a population perspective. A spreadsheet model was used to represent the variables of mammography screening in the United States. The population-based national screening program in Sweden provides a framework for comparison. The outcome measures are the aggregate cost and the number of cancers detected by mammography. We used deterministic sensitivity analysis to calculate the impact of variation in practice. Aggregate costs of screening in the United States are in the range of $3-$5 billion dollars. The percentage of women screened, cost per mammogram, cancer to biopsy ratio, recall rate, and cost of recall have the most profound effect on the quality and cost of a national screening program. Variance of these high-impact variables, based on the U.S. population, modifies the aggregate cost of screening by over $2 billion. As mammography screening in the United States increases to include all women over age 40, high-impact variables should be optimized to decrease costs and improve breast cancer detection. Our model establishes which parameters are most important.