Endocrine Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States.
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35233, United States.
J Bone Miner Res. 2024 May 24;39(5):517-530. doi: 10.1093/jbmr/zjae048.
Using race and ethnicity in clinical algorithms potentially contributes to health inequities. The American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee convened the ASBMR Task Force on Clinical Algorithms for Fracture Risk to determine the impact of race and ethnicity adjustment in the US Fracture Risk Assessment Tool (US-FRAX). The Task Force engaged the University of Minnesota Evidence-based Practice Core to conduct a systematic review investigating the performance of US-FRAX for predicting incident fractures over 10 years in Asian, Black, Hispanic, and White individuals. Six studies from the Women's Health Initiative (WHI) and Study of Osteoporotic Fractures (SOF) were eligible; cohorts only included women and were predominantly White (WHI > 80% and SOF > 99%), data were not consistently stratified by race and ethnicity, and when stratified there were far fewer fractures in Black and Hispanic women vs White women rendering area under the curve (AUC) estimates less stable. In the younger WHI cohort (n = 64 739), US-FRAX without bone mineral density (BMD) had limited discrimination for major osteoporotic fracture (MOF) (AUC 0.53 (Black), 0.57 (Hispanic), and 0.57 (White)); somewhat better discrimination for hip fracture in White women only (AUC 0.54 (Black), 0.53 (Hispanic), and 0.66 (White)). In a subset of the older WHI cohort (n = 23 918), US-FRAX without BMD overestimated MOF. The Task Force concluded that there is little justification for estimating fracture risk while incorporating race and ethnicity adjustments and recommends that fracture prediction models not include race or ethnicity adjustment but instead be population-based and reflective of US demographics, and inclusive of key clinical, behavioral, and social determinants (where applicable). Research cohorts should be representative vis-à-vis race, ethnicity, gender, and age. There should be standardized collection of race and ethnicity; collection of social determinants of health to investigate impact on fracture risk; and measurement of fracture rates and BMD in cohorts inclusive of those historically underrepresented in osteoporosis research.
在临床算法中使用种族和民族因素可能会导致健康不平等。美国骨骼与矿物质研究学会（ASBMR）专业实践委员会召集了 ASBMR 骨折风险临床算法工作组，以确定在美国骨折风险评估工具（US-FRAX）中调整种族和民族因素的影响。工作组聘请明尼苏达大学循证实践核心部门进行了一项系统评价，调查了 US-FRAX 在预测亚洲、黑种人、西班牙裔和白种人个体 10 年内骨折事件的表现。符合条件的有来自妇女健康倡议（WHI）和骨质疏松性骨折研究（SOF）的六项研究；队列仅包括女性，且主要为白种人（WHI>80%，SOF>99%），数据并未始终按种族和民族进行分层，且当按种族和民族分层时，黑人和西班牙裔女性的骨折病例较少，使得曲线下面积（AUC）估计值不太稳定。在较年轻的 WHI 队列（n=64739）中，不包括骨密度（BMD）的 US-FRAX 对主要骨质疏松性骨折（MOF）的区分度有限（AUC 0.53（黑人）、0.57（西班牙裔）和 0.57（白人））；仅对白人女性的髋部骨折有较好的区分度（AUC 0.54（黑人）、0.53（西班牙裔）和 0.66（白人））。在 WHI 较年长队列的一个子集中（n=23918），不包括 BMD 的 US-FRAX 高估了 MOF。工作组得出结论，几乎没有理由在估算骨折风险时同时考虑种族和民族调整因素，并建议骨折预测模型不包括种族或民族调整因素，而是基于人群，并反映美国人口统计学特征，包括关键的临床、行为和社会决定因素（适用时）。研究队列在种族、民族、性别和年龄方面应具有代表性。应标准化收集种族和民族信息；收集健康社会决定因素，以调查其对骨折风险的影响；并在包括骨质疏松症研究中代表性不足的人群的队列中测量骨折率和 BMD。
