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

点击翻译按钮获取中文摘要

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.