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

全国具有代表性的大范围健康数据的推广应用 精准公共卫生的数据科学方法论。种族--少数民族，社会经济 弱势群体和其他未得到充分服务的人群经历不成比例的不良健康后果 尽管几十年的研究将社会决定因素(SD)与健康结果的变化联系在一起。许多公众 卫生保健方法使用人口平均值来创建“一刀切”的干预措施，以增加这种可能性 为普通人实现最好的结果，但在数量上受到人口异质性的限制， 抑郁的大小、相互作用和放大。精准公共卫生(PPH)利用数字技术应运而生 (DTS)针对特定人群的独特需求制定干预措施，以改善健康和减少 差距。对DTS生成的海量、精确、连续和纵向数据的分析很有说服力 随着智能手机、物联网和可穿戴传感器变得无处不在，产生 关于环境、交通、地理位置、饮食、锻炼、社交和日常活动的数据。这些 个人生成的健康数据(PGHD)具有前所未有的潜力，可以为日常人类添加丰富的洞察力 对传统健康研究的行为。虽然PGHD在临床上的应用还处于早期阶段，但很快就会出现 在开发数字健康指标方面取得进展，提供了几乎无限的潜力。因为PGHD是 他们通常在受控研究环境之外捕获，受到非传统数据的挑战，这些数据 阻碍了它们在整个医疗保健生态系统中的接受和使用。首先，PGHD很容易受到投入偏差的影响 因为消费者DT的用户是一个自我选择的群体。第二，PGHD内部数据质量较差， 由于不总是在各个个体上平均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)。输入偏差和糟糕的数据质量共同导致 外部效度差，从PGHD得出的分析不能推广到更广泛的人群。这个 兰德公司和Eviation Health之间的这一合作伙伴关系的目标是提高推广能力 PGHD的数据科学方法，允许代表所有人口群体，包括历史上的 服务不足。我们将通过三个目标实现这一目标：(I)从一个具有全国代表性的人产生PGHD 了解健康DT用户参与度的社交分布的美国人的概率样本 睡眠健康状况不佳；(Ii)制定一种方法，描述PGHD内缺失的数据，并选择 适当的归罪策略(现有的和新的)优化，以减少模型偏差和社会 人口投入差异；以及，(3)建立基于倾向得分的统计加权方法，以 提高源自非随机、自选和/或已有方法的有效性和适用性 在服务不足的人群中收集了PGHD。这项工作将使未来的数字识别和应用成为可能 考虑到所有人口的卫生干预指标，这是数字化PPH的关键第一步。

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.