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

• 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
• 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

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)在任何种族或民族中2型糖尿病(T2D)的患病率最高， 患有肥胖症、心血管疾病(CVD)和慢性肾脏病等并发症的几率很高 疾病(CKD)。不受控制的心脏代谢危险因素--胰岛素抵抗导致血糖受损 耐受性、血脂异常和高血压(HTN)--增加死亡风险。死亡率显著降低， 降血糖和降血脂，以及抗高血压药物依从性。服药依从性较低的 AIS生活在非印度健康服务(IHS)医疗保健环境中。事实上，人们对大自然一无所知 以及主要接受药物治疗的保留居所人工智能机构的用药依从性程度 不包括IHS/部落设施的费用。电子健康记录(EHR)提供了丰富但未得到充分利用的数据源 关于用药依从性及其预测心脏代谢控制指标的潜力 测定糖化血红蛋白(HbA1c)、低密度脂蛋白(LDL-C)、收缩压(SBP)。在乔克托的支持下 俄克拉何马州(CNO)，我们将使用由这一大型状态生成的EHR数据来解决这一疏忽 最先进的部落医疗系统来调查C-MCI。我们R01应用程序的目标是 药物依从性(降压、降糖、降脂药物)与C-反应蛋白的关系 MCI(HbA1c≤7%，低密度脂蛋白胆固醇100 mg/dL，血压130毫米汞柱)，患者人口统计(例如，年龄，性别，SDOH， 居住地)和共病(例如，CVD、BMI和GT；30、CKD)以及每个C-MCI之间的关系 5970名CNO的患者人口统计数据和部落EHR(2018-2021年)的合并症 有T2D的患者。利用机器学习技术，我们将开发预测未来的模型 (2019-2021)C-MCI基于前一年的服药依从性、患者人口统计数据、共同 疾病，和公共实验室(例如，脂类小组)。最后，主要的线人访谈将探讨 以及在当地社会健康决定因素(SDOH)的背景下坚持用药的障碍 在电子病历中不可用。我们的具体目标是：(1)确定(A)之间的二元关系 服药依从性和C-MCIS、人口统计学和并存；(B)每个C-MCI和人口统计学和 共病；(2)开发机器学习模型(例如，随机森林、最近邻居、其他) 预测未来(2019-2021)C-MCI与前一年的服药依从性、人口统计学、共同 疾病和公共实验室；以及(3)确定药物依从性的促进因素和障碍 SDOH、EHR衍生的药物依从性(PDC)和C-MCI的背景(达到目标、高于目标和 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 将结果转化为评估T2D治疗和并发症预防的建议 程序。我们的发现将为改善AIS中的用药依从性和C-MCI提供见解，一致 CNO的2017年国家健康报告的目标是减少T2D及其并发症。