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

项目摘要 拉美裔育龄妇女受II型糖尿病的影响不成比例。从1996年到2014年，拉丁裔女性 经年龄调整后的妊娠期II型糖尿病患病率增加了125.2%，相比之下， 非拉丁裔白人女性。患有孕前糖尿病的妇女的孕产妇和新生儿结局更差。 这是由生物、文化和社会因素推动的，包括获得护理的机会。预防母婴 健康并发症需要在孕前密切跟踪。通过将护理延伸到诊所以外的地方， 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，以增加获得护理的机会，并有可能改善对拉丁裔的护理 患有II型糖尿病的育龄妇女。我的K23家长补助金的这项补充的目的就是为了解决这个问题 迫切需要评估远程医疗对#年拉美裔妇女糖尿病结局的吸收、使用和影响 患有II型糖尿病的生育年龄。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美裔育龄妇女在获得远程医疗方面的差异 对患有II型糖尿病的年龄进行评估，评估远程医疗对糖尿病预后的影响。拟议的项目将测试 中心假设有以下三个具体目标。目标1将审查以下人员获取和使用远程医疗的情况 患有II型糖尿病的育龄拉丁裔妇女。目标2将评估远程医疗对血糖控制的影响 在患有II型糖尿病的育龄妇女中。目标3将利用无监督机器学习 一种基于患者人口统计、临床和社会因素来发现潜在亚群，然后评估其 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，这项建议解决了重叠的卫生信息技术和 拉丁裔育龄II型糖尿病患者的糖尿病差异。 拟议的研究辅之以严格的培训计划和经验丰富的指导和 协作团队将确保我完成此补充内容。培训计划的重点是生物统计学 研究方法、机器学习和指导技能。这项建议将进一步支持我的K23，它构成了 R01应用的基础，以更大的临床试验为中心，测试实施干预措施的效果 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.