There is consensus that women with osteoporosis require intervention to reduce future fracture risk, but the situation is less clear among women with BMD that is low but not yet osteoporotic. Using data from a population of 57 421 postmenopausal white women with "osteopenia," as well as baseline risk factor information and 1-year follow up data on incident fractures, Miller et al developed an algorithm for predicting which women were at increased risk for fracture. A total of 32 risk factors for fracture were examined. Prior fracture, peripheral T score of −1.8 or less, poor health, and poor mobility were the most important determinants. Women with a prior fracture had a 1-year fracture risk similar to that of women with T score of −2.5 or less. Women with either T score of −1.8 or less or poor health or poor mobility had a fracture risk approximately twice that of women with normal BMD, and osteopenic women without any of the 4 determinants had a 1-year fracture risk that was similar to that of the women with T scores greater than −1.0 in the cohort. This algorithm offers an approach to identifying those women with less severe levels of low bone mass.