Dementia epidemiology, health service utilization and treatment costs among American Indian and Alaska Native Elders

美洲印第安人和阿拉斯加原住民老年人的痴呆症流行病学、卫生服务利用和治疗费用

• 批准号: 10523628
• 负责人: Luohua Jiang
• 金额: $ 1.83万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10523628
• 关键词: Alaska Native; Alzheimer' s disease related dementia; American Indians; Caring; Data; Dementia; Diagnosis; Disease Outcome; Electronic Health Record; Epidemiology; Event; Health; Health system; Healthcare; Healthcare Systems; Left; Methodology; Methods; Modeling; Outcome; Patients; Pattern; Provider; Recurrence; Research; Research Personnel; Risk Factors; Selection Bias; Specific qualifier value; Statistical Methods; System; Time; Training; Treatment Cost; Work; care seeking; disorder risk; health data; health service use; improved; migration; mortality; native elder

Project Summary Electronic health records (EHRs) have grown in popularity for health research because they provide relatively easy access to large amounts of longitudinal health data in real­world healthcare scenarios. However, establishing causal relationships between potential disease risk factors and mortality is subject to multiple limitations. Among these are selection bias, misclassification bias, informed presence bias, and unmeasured confounding. Another potential, yet largely unstudied, bias arises from patients seeking care outside of the system being studied, what we refer to as system migration. By definition, system migration leads to intermittent missing data at the subject level. Further complicating this issue is the fact that most migration of patients is unknown to the researcher as there is generally no indication of a patient leaving one healthcare system and seeking care at another. This problem is particularly true in the Indian Health System (IHS), where it is common for patients to receive care by outside providers as well as the IHS. When modeling a time­to­event endpoint such as time to ADRD diagnosis, the resulting missingness due to system migration can be characterized by (potentially unobserved) left­, right­, or interval­censoring. My proposed training and research consider the implications of potentially unobserved intermittent missingness when modeling censored time­to­event outcomes and proposes methodological solutions to reduce bias in such cases. Specifically, we propose statistical methods that can be used to 1) more accurately estimate covariate effects on time­to­ event outcomes under unknown system migration patterns; 2) more accurately estimate covariate effects on time­to­event outcomes under a mis­specified model and unknown system migration patterns; and 3) improve assessment of prediction accuracy for recurrent event right­censored survival data. Our proposed work will provide the researchers with methods to better understand and minimize the impact of concerns related to system migration, thereby leading to increased validity and replicability of our research findings.

项目摘要 电子健康记录（EHR）在健康研究中越来越受欢迎，因为它们 提供对真实的虚拟世界医疗保健中的大量纵向健康数据的相对容易的访问 场景然而，建立潜在疾病风险因素之间的因果关系， 死亡率受到多种限制。其中包括选择偏差，错误分类偏差， 知情存在偏差和不可测量的混杂。另一个潜在的，但在很大程度上未被研究， 偏见来自于患者在所研究的系统之外寻求护理，我们称之为 系统迁移。根据定义，系统迁移导致受试者间歇性缺失数据 水平使这一问题进一步复杂化的是，大多数患者的迁移是未知的。 研究人员，因为通常没有迹象表明患者离开一个医疗保健系统， 寻求别人的照顾。这一问题在印度卫生系统（IHS）中尤为突出， 患者通常接受外部提供者以及IHS的护理。建模时 至ADRD事件发生的时间终点，如至ADRD诊断的时间，由于以下原因导致的缺失 系统迁移的特征可以是（可能未被观察到的）左移、右移或 区间截尾我提议的培训和研究考虑了潜在的 在建模删失时间间隔至事件结局时未观察到的间歇性缺失， 提出方法解决方案，以减少在这种情况下的偏见。具体来说，我们建议 可用于1）更准确地估计协变量对时间的影响的统计方法 未知系统迁移模式下的事件结局; 2）更准确地估计协变量 在错误指定的模型和未知系统迁移下，对事件结果的时间间隔的影响 模式;和3）改善复发事件右删失预测准确性的评估 生存数据。我们提出的工作将为研究人员提供更好地理解 最大限度地减少与系统迁移有关的问题的影响，从而提高 我们的研究成果的有效性和可复制性。