Evaluating the Use and Impact of Telemedicine Among Latina Women of Reproductive Age with Type II Diabetes

项目摘要 拉丁裔育龄妇女不成比例地受到II型糖尿病的影响。从1996年到2014年， 经历了125.2%的增加，年龄调整的患病率孕前II型糖尿病相比，只有13.6%， 非拉丁裔白色女性。患有妊娠前糖尿病的妇女经历更差的孕产妇和新生儿结局。 这是由生物、文化和社会因素驱动的，包括获得护理的机会。预防母婴 健康并发症需要在怀孕前密切跟踪。通过将护理延伸到诊所之外， 远程医疗为应对这些挑战提供了机会。远程医疗为患者提供视频和电话 从远程设置访问。它提供了一个机会，以增加获得护理，并有可能改善护理拉丁美洲 患有II型糖尿病的育龄妇女。我的K23父母补助金的补充目标是解决这个问题 迫切需要评估远程医疗的吸收、使用和对拉丁美洲妇女糖尿病结局的影响， 2型糖尿病的治疗方法我的中心假设是，通过应用严格和创新的统计和 机器学习方法，我们可以确定拉丁妇女生殖健康中获得远程医疗的差距 年龄与II型糖尿病和评估远程医疗对糖尿病结果的影响。拟议项目将测试 中心假设，有以下三个具体目标。目标1将审查远程医疗的获取和使用情况， 患有II型糖尿病的拉丁裔育龄妇女。目标2将评估远程医疗对血糖控制的影响 在患有II型糖尿病的育龄拉丁妇女中。Aim 3将利用无监督机器学习 一种基于患者人口统计学、临床和社会因素检测潜在亚组的方法， 远程医疗的临床应用。通过应用先进的生物统计学和机器学习 在社区参与的支持下，该提案解决了重叠的卫生信息技术问题， 拉丁裔育龄II型糖尿病患者的糖尿病差异。 拟议的研究得到了严格的培训计划和经验丰富的导师的补充， 这支团队将确保我完成这一补充。培训计划侧重于生物统计学 研究方法、机器学习和指导技能。这一建议将进一步支持我的K23， R01申请的基础，以测试实施干预对以下方面的影响的大型临床试验为中心 拉丁裔糖尿病患者的临床结局。

PROJECT ABSTRACT Latina women of reproductive age are disproportionately affected by type II diabetes. From 1996 to 2014, Latina women experienced a 125.2% increase in age-adjusted prevalence of pregestational type II diabetes compared to only 13.6% for non-Latina White women. Women with pregestational diabetes experience worse maternal and neonatal outcomes. This is driven by biological, cultural, and social factors, including, including access to care. Preventing maternal and infant health complications requires close follow-up in the pregestational period. By extending care beyond the clinic, telemedicine presents an opportunity to address these challenges. Telemedicine offers patients video and telephone visits from remote settings. It presents an opportunity to increase access to care and potentially improve care for Latina women of reproductive age with type II diabetes. The goal of this supplement to my K23 parent grant is to address this critical need by to evaluating the uptake, use, and impact telemedicine on diabetes outcomes for Latina women of reproductive age with type II diabetes. My central hypothesis is that by applying rigorous and innovative statistical and machine learning approaches we can identify disparities in access to telemedicine among Latina women of reproductive age with type II diabetes and assess the impact of telemedicine on diabetes outcomes. The proposed project will test the central hypothesis with the following 3 specific aims. Aim 1 will examine the access and use of telemedicine among Latina women of reproductive age with type II diabetes. Aim 2 will assess the impact of telemedicine on glycemic control among Latina women of reproductive age with type II diabetes. Aim 3 will utilize an unsupervised machine learning approach to detect latent subgroups based on patient demographic, clinical, and social factors and then evaluate their clinical applications in telemedicine use. Through the application of advanced biostatistical and machine learning methods supported by community engagement, this proposal addresses overlapping health information technology and diabetes disparities among Latina patients of reproductive age with type II diabetes. The proposed research is complemented by a rigorous training plan and a highly experienced mentorship and collaboration team that will ensure my completion of this supplement. The training plan focuses on biostatistical research methods, machine learning, and mentorship skills. This proposal will further support my K23 which forms the basis for an R01 application centering on a larger clinical trial testing the effect of the implementation intervention on clinical outcomes among Latino patients with diabetes.