Precise Bone Density Reference Ranges to Reduce Systematic Disparities in Osteoporosis Healthcare for Hispanic Women

项目总结/摘要 一些以高加索人为基础的临床指南已经成为种族和民族歧视的系统基础。 在医疗保健方面的平等。一个主要的例子是T评分骨质疏松症诊断标准， 在流行病学研究中仅建议用于绝经后白人女性。然而，T评分诊断- 目前使用NOSIS标准来帮助诊断所有患者的骨质疏松症。T-score方法定义 骨密度（BMD）的参考范围（RR）。尽管如此，以高加索人为基础的RR不能说明 种族/民族的正常变异性。因此，对少数种族/族裔适用这些基于高加索的种族规则， 这往往会导致诊断和风险评估的错误。随着少数民族人口的不断增长和日益多样化， 在美国，基于高加索人的临床指南有加剧种族和民族医疗保健的风险。 差距。BMD的现有RR是令人难以置信的争议，因为许多患者维持脆弱性， 骨折具有由常规T-评分方法定义的"正常" BMD。这种临床上使用的，高加索- 基于sian的方法是有缺陷的，因此造成系统性的保健差异。没有经验 专门为少数种族/民族确定BMD RR的方法。随后的种族/民族- 调整后的T分数阈值仍然存在争议，因为它们没有经过经验验证，因此， 他们在少数种族/族裔方面的表现不能令人满意。西班牙裔女性有类似的高 骨折风险与白人女性相同;然而，西班牙裔女性，作为最异质的群体， 骨质疏松症的误诊率与T评分诊断标准。随着日益增长的 多样化和老龄化的西班牙裔人口，西班牙裔妇女的骨质疏松性骨折正在迅速成为一个重要的， 重要的公共卫生问题。因此，本申请旨在为西班牙裔女性创建精确的BMD RR， 解决这个紧迫的问题。该应用程序的中心假设是，新的精确RR将对骨形成进行分类， 西班牙裔女性比传统阈值更准确。这一假设将通过追求来检验- 有三个具体目标：1）开发西班牙裔女性BMD标准的最佳模型; 2） 确定BMD的精确阈值; 3）评估西班牙裔女性的新RR。通过更换 传统的基于高加索人的阈值，新的，精确的RR将从根本上改变目前的研究 和临床护理模式的RR从一个固定的范围为每个病人更精确的阈值， 许多个体差异。该应用程序将利用西班牙裔妇女和创新的大样本 数据驱动的方法，以创建种族/民族特异性的BMD精确RR，从而系统地消除 西班牙裔女性医疗保健差异的相应根源。更重要的是， 在此获得的知识可用于为各种少数群体生成许多其他类型的精确RR。 新一代的RR将直接有助于改善少数民族的健康和减少医疗保健费用， 通过消除医疗保健系统中的这种系统性偏见来建立联系。

Project Summary / Abstract Some Caucasian-based clinical guidelines have become the systematic underpinnings of racial and ethnic dis- parities in healthcare. A prime example is the T-score osteoporosis diagnosis criteria, which the WHO initially proposed only for postmenopausal Caucasian women in epidemiological research. However, the T-score diag- nosis criteria are currently used to help with osteoporosis diagnosis for all patients. The T-score approach defines the reference range (RR) of bone mineral density (BMD). Nonetheless, Caucasians-based RRs cannot account for normal variability in race/ethnicity. As a result, applying these Caucasian-based RRs to racial/ethnic minorities often causes errors in diagnosis and risk assessment. With a growing and increasingly diverse minority popula- tion in the US, Caucasian-based clinical guidelines run the risk of exacerbating racial and ethnic healthcare disparities. The existing RRs of BMD are incredibly controversial because many patients who sustain fragility fractures have a “normal” BMD as defined by the conventional T-score approach. This clinically utilized, Cauca- sian-based approach is flawed and, as such, causes systematic healthcare disparities. There is no empirical approach specifically designed to define the RRs of BMD for racial/ethnic minorities. Subsequent race/ethnicity- adjusted T-score thresholds remain controversial because they are not empirically validated, so consequently, their performance has been unsatisfactory on racial/ethnic minorities. Hispanic women have a similarly high fracture risk as Caucasian women; however, Hispanic women, as the most heterogeneous group, have a higher osteoporosis misclassification rate with the T-score diagnosis criteria. With the rapid growth of an increasingly diverse and aging Hispanic population, osteoporotic fractures in Hispanic women are rapidly becoming a signif- icant public health issue. Hence, this application aims to create precise RRs of BMD for Hispanic women to address this pressing issue. This application's central hypothesis is that new precise RRs will classify osteopo- rosis in Hispanic women more accurately than conventional thresholds. This hypothesis will be tested by pursu- ing three specific aims: 1) developing the best-performing model for normative BMD in Hispanic women; 2) determining the precise thresholds of BMD; and 3) evaluating the new RRs in Hispanic women. By replacing the conventional Caucasian-based threshold, the novel, precise RRs will fundamentally shift the current research and clinical care paradigm in RR from one fixed range for every patient to more precise thresholds that account for many individual differences. This application will leverage large samples of Hispanic women and innovative data-driven approaches in creating race/ethnicity-specific, precise RRs of BMD, thus systematically eliminating the corresponding root cause of healthcare disparities for Hispanic women. Of increased significance, the knowledge gained herein can be utilized to generate many other types of precise RRs for various minority groups. The new generation of RRs will directly contribute to improving minority health and reducing healthcare dispari- ties by eliminating such systematic bias in the healthcare system.