Large-Scale Nationally Representative Person-Generated Health Data for Development of Generalizable Data Science Methodologies for Precision Public Health

大规模的全国代表性个人生成的健康数据，用于开发精准公共卫生的通用数据科学方法

• 批准号: 10591527
• 负责人: Ritika Ratnam Chaturvedi
• 金额: $ 25.42万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591527
• 关键词: Achievement; Algorithms; American; Back; Behavior; Behavioral; Cellular Phone; Clinical; Communities; Data; Data Collection; Data Element; Data Science; Data Set; Development; Diet; Disparity; Ecosystem; Effectiveness; Elements; Environment; Equity; Exercise; Failure; Feedback; Future; Goals; Health; Health Policy; Health Professional; Health Services; Health Status; Healthcare; Human; Individual; Institution; Internet of Things; Intervention; Knowledge; Lead; Life; Methodology; Methods; Modeling; Monitor; Nature; Outcome; Participant; Patient Representative; Persons; Population; Population Group; Population Heterogeneity; Probability; Probability Samples; Provider; Public Health; Research; Research Personnel; Sampling; Series; Social Interaction; Social stratification; Surveys; Testing; Time; Transportation; Underrepresented Populations; Underserved Population; Variant; Work; battery life; clinical application; cost; data quality; digital; digital health; digital technology; disparity elimination; disparity reduction; efficacy evaluation; ethnic minority; experience; fitbit; health data; health disparity; improved; individual variation; innovation; insight; member; novel; personalized intervention; poor sleep; racial minority; sleep health; social; social determinants; sociodemographic group; sociodemographics; socioeconomic disadvantage; tool; virtual; wearable sensor technology

Large-Scale Nationally Representative Patient-generated Health Data for Development of Generalizable Data Science Methodologies for Precision Public Health. Racial-ethnic minorities, socioeconomically disadvantaged, and other underserved populations experience disproportionate adverse health outcomes despite decades of research correlating social determinants (SDs) to variations in health outcomes. Many public health approaches use population averages to create “one-size-fits-all” interventions to increase the probability of achieving the best outcomes for the average person, but are limited by population heterogeneity in number, magnitude, interplay, and amplification of SDs. Precision public health (PPH) emerged to use digital technologies (DTs) to develop interventions targeting unique needs of specific populations to improve the health and reduce disparities. Analysis of voluminous, precise, continuous, and longitudinal data generated by DTs holds great promise for PPH as smartphones, Internet of Things, and wearable sensors are becoming ubiquitous, generating data on environment, transportation, geolocation, diet, exercise, social interactions, and daily activities. These person-generated health data (PGHD) have unprecedented potential to add rich insight on everyday human behaviors to traditional health research. Though clinical PGHD applications are in early stages, there is rapid progress in development of digital indicators of health, offering virtually limitless potential. Because PGHD are typically captured outside of controlled research settings, they suffer from challenges of non-traditional data that impede their acceptance and use across the healthcare ecosystem. First, PGHD are vulnerable to input biases as users of consumer DTs are a self-selected group. Second, PGHD suffer from poor internal data quality due to high variability in completeness for reasons that are not always equally distributed across individuals (e.g., connectivity issues, battery, user forgetfulness, user error). Together, input bias and poor data quality lead to poor external validity, where analytics derived from PGHD are not generalizable to the broader population. The objective of this partnership between the RAND Corporation and Evidation Health is to improve generalizability of data science methods for PGHD, allowing for representation of all population groups, including the historically underserved. We will accomplish this goal via three aims: (i) generate PGHD from a nationally representative probability sample of Americans to understand the social distribution of user engagement with health DTs and poor sleep health; (ii) develop a methodology that characterizes missing data within PGHD and selects appropriate imputation strategies (existing and novel) optimized for reduction in model bias and socio- demographic input disparities; and, (iii) create a propensity-score based statistical weighting methodology to improve the effectiveness and applicability of methods derived from non-random, self-selected, and/or already collected PGHD in underserved populations. This work will enable future identification and application of digital indicators for health interventions that account for all populations, a critical first step for digital PPH.

大规模全国代表性患者生成的健康数据，用于开发可推广的 精准公共卫生的数据科学方法。社会经济上的少数民族 弱势群体和其他得不到充分服务的人群经历了不成比例的不良健康后果 尽管数十年来的研究将社会决定因素（SDs）与健康结果的变化联系起来。许多公共 健康方法使用人口平均数来创建“一刀切”的干预措施，以增加 为普通人实现最佳结果，但受到人口数量异质性的限制， SD的大小、相互作用和放大。精准公共卫生（PPH）开始使用数字技术 (DTs)针对特定人群的独特需求制定干预措施，以改善健康， 差距。对DT产生的大量、精确、连续和纵向数据进行分析， 随着智能手机、物联网和可穿戴传感器变得无处不在，PPH的前景越来越好， 关于环境、交通、地理位置、饮食、锻炼、社会互动和日常活动的数据。这些 个人生成的健康数据（PGHD）具有前所未有的潜力，可以为日常人类提供丰富的洞察力 传统的健康研究。虽然PGHD的临床应用尚处于早期阶段， 数字健康指标的发展取得进展，提供了几乎无限的潜力。因为PGHD是 通常在受控研究环境之外捕获，它们遭受非传统数据的挑战， 阻碍了它们在整个医疗生态系统中的接受和使用。首先，PGHD容易受到输入偏差的影响 因为消费者DT的用户是自我选择的群体。其次，PGHD遭受内部数据质量差， 由于不总是在个体之间均匀分布的原因而导致完整性的高度可变性（例如， 连接问题、电池、用户健忘、用户错误）。输入偏差和较差的数据质量共同导致 外部有效性差，其中来自PGHD的分析无法推广到更广泛的人群。的 兰德公司和Evidation Health之间的这种伙伴关系的目标是提高普遍性 PGHD的数据科学方法，允许代表所有人口群体，包括历史上 服务不足。我们将通过三个目标来实现这一目标：（一）从全国代表中产生PGHD 美国人的概率样本，以了解用户参与健康DT的社会分布， 睡眠健康状况不佳;（ii）开发一种方法，描述PGHD中缺失的数据，并选择 适当的插补策略（现有的和新的）优化，以减少模型偏倚和社会影响， 人口输入差异;以及（iii）创建基于倾向分数的统计加权方法， 提高方法的有效性和适用性，这些方法是从非随机的、自我选择的和/或已经 在服务不足的人群中收集PGHD。这项工作将使未来的识别和应用数字 考虑到所有人群的健康干预措施指标，这是数字PPH的关键第一步。