Association between Screening for Osteoporosis and the Incidence of Hip Fracture

Context Although some groups, such as the U.S. Preventive Services Task Force and National Osteoporosis Foundation, advocate screening for osteoporosis, direct evidence that screening reduces fractures is lacking. Contribution In this observational study, receipt of bone density scans was associated with a lower hip fracture rate (4.8 per 1000 person-years vs. 8.2 per 1000 person-years), and this difference was statistically significant even after adjustment for sex and propensity to be screened. Surprisingly, differences in osteoporosis treatments between the screened and unscreened groups did not seem to account for all the differences in hip fractures, so the mechanism of the association is unclear. Cautions The association was tenuous, and a small, unmeasured confounder could explain it. However, this study presents the best available evidence for the effectiveness of osteoporosis screening. The Editors Population-based screening for osteoporosis, or low bone density, is controversial. Advocates for screening argue that osteoporosis is hazardous, because for every 1 SD that bone density decreases, the risk for hip fracture more than doubles (1). Hip fractures result in more than 340000 hospitalizations annually in the United States among adults 65 years of age and older, often leading to physical disability and social dependence (2, 3). Advocates for screening further argue that osteoporosis can be detected safely and accurately (4) and then be treated effectively, reducing the risk for hip fracture by 39% to 56% (5-9). However, opponents of screening argue that the chain of logic from screening to treatment to fracture prevention has not been proven because no studies have directly shown the effect of screening for osteoporosis on the incidence of hip fractures (10). Clinical guidelines reflect both sides of this debate. The National Osteoporosis Foundation and the U.S. Preventive Services Task Force recommend screening for osteoporosis in all women 65 years of age and older (11-13). However, the U.S. Preventive Services Task Force acknowledges that their guidelines were based on a synthesis of indirect evidence because no trials of screening had been published (13). The National Institutes of Health (NIH) argue that evidence is insufficient to recommend population-based screening for women (10). Two guidelines acknowledge the problem of osteoporosis in men but do not make screening recommendations (10, 11). Ideally, the effectiveness of screening for osteoporosis should be tested with a randomized, controlled trial, which has not been feasible to date. We present data from a nonrandomized but controlled study of osteoporosis screening. We sought to determine whether population-based screening for osteoporosis using dual-energy x-ray absorptiometry (DEXA) at the hip is associated with fewer incident hip fractures than usual medical care. Methods Study Design We conducted a nonconcurrent cohort study of participants in the Cardiovascular Health Study (CHS), which is a longitudinal study designed to determine risk factors for cardiovascular disease in adults 65 years of age and older (14). As part of a nested study on body composition and bone density, some participants were offered DEXA scans in 19941995 and others were not offered DEXA scans and received usual medical care. We followed participants over 6 years for the occurrence of hip fractures. All data were collected prospectively, but the hypothesis of an association between screening with DEXA scans and fewer hip fractures was generated after the data were collected. The institutional review boards at participating centers approved this research. Study Participants From 1989 to 1993, the CHS performed age- and sex-stratified random sampling of Medicare eligibility lists in 4 communities: Sacramento County, California; Washington County, Maryland; Forsyth County, North Carolina; and Pittsburgh (Allegheny County), Pennsylvania (14). The CHS excluded people who were institutionalized, were home-bound, were receiving treatment for cancer, could not give informed consent, or were planning to move within 3 years (15). Fifty-seven percent of those eligible agreed to participate and gave informed consent (15). Because the bone density scans were performed in 19941995, the present study included only those 4842 participants (82% of the total) who were alive and still enrolled that year. We excluded those who did not come to the study centers for their annual examinations in 19941995. We also excluded those who reported a history of osteoporosis, a history of hip fracture, or bisphosphonate use. Measurement of Bone Mineral Density and Communication of Test Results In 19941995, the CHS performed DEXA scans on participants in 2 of the 4 communities (Sacramento County and Pittsburgh [Allegheny County]). Scans were offered to participants in the order in which they came to the study centers for their annual visits until funding for these scans was exhausted. We excluded the participants in these communities who were eligible but were not offered scans. By using QDR-2000 bone densitometers (Hologic Inc., Bedford, Massachusetts), the CHS measured bone mineral density at the total hip according to a written protocol. Scans were performed locally in Sacramento County and Pittsburgh, with independent external quality assurance (16). Results were generated as a graph, with bone density on the y-axis, age on the x-axis, and the participant's value marked by an X. The normal range was shaded and defined as the mean bone density at each age with a 95% CI, as determined by the densitometer manufacturer's reference range (Hologic software, version 7.10, Hologic Inc.). The graph was given to the participants and was sent to their primary care providers. Text accompanying the graph explained that the X represented the participant's bone density value and that the shaded area represented the normal range; the text did not label the participant as normal, osteopenic, or osteoporotic and did not recommend any particular intervention. Collection of Data on Potential Confounders The CHS collected data on participants' demographic characteristics, medical histories, medication use, and physical examination findings. We focused on 31 measured variables that are known or potential risk factors for osteoporosis or hip fracture (11, 17-20). Data were collected through standardized questionnaires, reviews of medication bottles, and standardized physical examinations (14, 21-23). All variables were collected in 19941995 with the following exceptions: Sex, race, education, history of bilateral oophorectomy, and height were recorded at entry into the CHS, and physical activity was measured in 19921993. Self-report of radius or ulna fracture in 19941995 was supplemented with hospitalization data from the previous 5 years. We also generated a composite variable, frailty, which is a predictor of falls and is present when a participant has at least 3 of the following characteristics: unintentional weight loss (10 pounds in the previous year), weakness (poor grip strength compared with that of others of the same sex and body mass index), self-reported exhaustion, slow walking speed (compared with that of others of the same sex and height), and low physical activity (compared with that of others of the same sex) (24). Outcome Variable We defined hip fracture by a hospital discharge International Classification of Diseases, Ninth Revision (ICD-9), code of 820.xx without a concomitant code for motor vehicle accident (E810E819) or pathologic fracture (733.1x). The CHS conducted active and passive surveillance to capture all hospitalizations for all participants and checked the completeness of known hospitalizations against Medicare claims data. Deaths were captured through a similar surveillance system. Statistical Analysis We compared the characteristics of the screened and usual care groups at baseline (19941995) by using 2-tailed t-tests for continuous variables and chi-square tests for dichotomous variables. Physical activity was skewed, so subsequent analyses used the log of physical activity. Fewer than 5% of participants were missing data for any given characteristic except income (missing for 16%) and frailty (missing for 9%). We used multiple imputation to generate 5 complete versions of our data set, with some variation in the imputed values across versions (25, 26). To adjust for baseline differences between groups, we used propensity scores. Propensity scores are useful in observational studies in which 1) baseline characteristics differ between the exposed and unexposed groups, 2) exposure is relatively common, 3) the number of measured characteristics or potential confounders is relatively large, and 4) the number of events is relatively small (27). To generate propensity scores, a logistic regression model is first created in which the participant characteristics are independent variables and the exposure group is the dependent variable (28). Propensity scores are then calculated for each participant by applying the participant's values to the logistic model. Propensity scores range from 0 to 1 and reflect each participant's conditional probability of being exposed (that is, screened) given baseline characteristics (28). Scores close to 1 indicate participant characteristics associated with a high probability of being exposed (screened). In our study, 2 of the measured characteristics applied to women only: estrogen use and bilateral oophorectomy. We therefore derived propensity scores separately for women and for men in each imputed data set. We based the probability of a woman being screened on all measured characteristics, whereas we based the probability of a man being screened on all measured characteristics except estrogen use and bilateral oophorectomy. When we re-examined the baseline characteristics of the screened and usual care groups after adjustment for propensity score