Medication Adherence and Cardio-Metabolic Control Indicators among Adult American Indians Receiving Tribal Health Services

接受部落卫生服务的成年美洲印第安人的药物依从性和心脏代谢控制指标

基本信息

批准号：
10592441
负责人：
Lisa Scarton
金额：
$ 50万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-15 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10592441
关键词：
Adherence Adult Adverse effects Affect Age Algorithms American Indians Antihypertensive Agents Blood Pressure Body mass index Cardiovascular Diseases Cardiovascular system Caring Choctaw Nation of Oklahoma Chronic Kidney Failure Cohort Studies Communities Complications of Diabetes Mellitus Computerized Medical Record Data Data Sources Disease Outcome Distant Dyslipidemias Elderly Electronic Health Record Ethnic Population Female Future Glucose Glycosylated hemoglobin A Goals Health Health Services Healthcare Healthcare Systems Heart High Prevalence Hyperinsulinism Hypertension Insulin Resistance Intervention Interview Knowledge Lipids Location Logistic Regressions Longitudinal Studies Low-Density Lipoproteins Machine Learning Measures Medicare Metformin Modeling Nature Non-Insulin-Dependent Diabetes Mellitus Not Hispanic or Latino Obesity Oral Patients Pharmaceutical Preparations Pharmacy facility Population Poverty Prevalence Prevention program Recommendation Reporting Research Reservations Risk Risk Factors Role Rural Community Service setting System Techniques Translations Tribes United States Indian Health Service cardiometabolic risk cardiometabolism cardiovascular risk factor comorbidity copayment cost demographics depressive symptoms diabetes distress diabetes management electronic health record system experience forgetting health care settings health disparity human old age (65+)impaired glucose tolerance improved informant insight machine learning model male medication compliance mortality mortality risk population based predictive modeling prevent racial population random forest residence sex social health determinants tribal health tribal healthcare tribal lands virtual young adult

项目摘要

Project Summary/Abstract American Indians (AIs) have the highest prevalence of type 2 diabetes (T2D) of any racial or ethnic group and experience high rates of co-morbidities such as obesity, cardiovascular disease (CVD), and chronic kidney disease (CKD). Uncontrolled cardio-metabolic risk factors--insulin resistance resulting in impaired glucose tolerance, dyslipidemia, and hypertension (HTN)--increase mortality risk. Mortality is significantly reduced by glucose- and lipid-lowering, and antihypertensive medication adherence. Medication adherence is low among AIs living in non-Indian Health Services (IHS) healthcare settings. Virtually nothing is known about the nature and extent of medication adherence among reservation-dwelling AIs who primarily receive their medications without cost from IHS/tribal facilities. Electronic health records (EHR) offer a rich but underutilized data source about medication adherence and its potential to predict Cardio-Metabolic Control Indicators (C-MCI) such as HbA1c, LDL-C (Low Density Lipoprotein), SBP (Systolic Blood Pressure). With the support of Choctaw Nation of Oklahoma (CNO), we will address this oversight by using EHR data generated by this large, state-of- the-art tribal healthcare system to investigate C-MCI. The objective of our R01 application is to characterize the relationships among medication adherence (antihypertensive, glucose- and lipid-lowering drugs) and C- MCI (HbA1c ≤7%, LDL-C <100 mg/dL, and SBP <130 mm Hg), patient demographics (e.g., age, sex, SDOH, residence location) and co-morbidities (e.g., CVD, BMI>30, CKD) as well as the relationship of each C-MCI with patient demographics and co-morbidities from the tribe's EHR (2018-2021) for the 5,970 CNO patients who have T2D. Employing machine learning techniques, we will develop models to predict future (2019-2021) C-MCI based on the previous year medication adherence, patient demographics, co- morbidities, and common labs (e.g., lipid panel). Lastly, key informant interviews will explore facilitators of and barriers to medication adherence within the context of local social determinants of health (SDOH) that are not available in the EHR. Our specific aims are to: (1) Determine the bivariate relationships between (a) medication adherence and C-MCIs, demographics, and co-morbidities; (b) each C-MCI and demographics and co-morbidities; (2) Develop machine-learning models (e.g., random forest, nearest neighbors, others) for predicting future (2019-2021) C-MCI from the previous year medication adherence, demographics, co- morbidities, and common labs; and (3) Identify facilitators of and barriers to medication adherence within the context of SDOH, EHR-derived medication adherence (PDC) and C-MCI (at target, above target, and for HbA1c uncontrolled). We will share our findings with CNO leaders and other stakeholders, who will guide the translation of the results into recommendations for evaluating T2D management and complication prevention programs. Our findings will yield insights to improve medication adherence and C-MCI among AIs, consistent with CNO's State of the Nation's Health Report 2017 goal of reducing T2D and its complications.

项目摘要/摘要美国印第安人（AIS）的2型糖尿病（T2D）的患病率最高经历肥胖，心血管疾病（CVD）和慢性肾脏等合并症的高率疾病（CKD）。不受控制的心脏代谢危险因素 - 胰岛素抵抗，导致葡萄糖受损耐受性，血脂异常和高血压（HTN） - 增加死亡率风险。死亡率大大降低了葡萄糖和脂质降低，以及降压药依从性。药物依从性很低 AIS居住在非印度卫生服务（IHS）医疗保健环境中。几乎对自然知之甚少预订居民AIS中的药物依从性的程度，主要接受药物没有IHS/部落设施的费用。电子健康记录（EHR）提供了丰富但未充分利用的数据源关于药物依从性及其预测心脏代谢控制指标（C-MCI）的潜力作为HBA1C，LDL-C（低密度脂蛋白），SBP（收缩压）。在乔克托的支持下俄克拉荷马州（CNO），我们将使用此大型，最先进的EHR数据来解决此监督对C-MCI进行调查的部落医疗系统。我们R01应用的目的是表征药物依从性（降压，葡萄糖和降低脂质的药物）和C-之间的关系 MCI（HBA1C≤7％，LDL-C <100 mg/dl和SBP <130 mm Hg），患者人口统计学（例如，年龄，性别，性别，SDOH，居住地点）和合并症（例如CVD，BMI> 30，CKD）以及每个C-MCI的关系与Tribe的EHR（2018-2021）的患者人口统计学和共同症状有关5,970 CNO 患有T2D的患者。采用机器学习技术，我们将开发模型以预测未来（2019-2021）C-MCI基于上一年的药物依从性，患者人口统计学，共同病态和普通实验室（例如脂质面板）。最后，主要的线人访谈将探索在当地的卫生社会决定者（SDOH）的背景下，药物遵守的障碍 EHR中不可用。我们的具体目的是：（1）确定（a）之间的双变量关系药物依从性和C-MCI，人口统计和合并症；（b）每个C-MCI和人口统计以及合并症；（2）开发机器学习模型（例如随机森林，最近的邻居，其他邻居）预测未来（2019-2021）C-MCI上一年的药物依从性，人口统计学，共同病毒和普通实验室；（3）确定依靠药物依从性的促进者 SDOH，EHR衍生的药物依从性（PDC）和C-MCI的背景 HBA1C不受控制）。我们将与CNO领导人和其他利益相关者分享我们的发现，他们将指导将结果转换为评估T2D管理和预防并发症的建议程序。我们的发现将产生见解，以提高AIS中的药物依从性和C-MCI 随着CNO的《 2017年美国健康报告》的《减少T2D及其并发症》的目标。