Adherence to non-pharmaceutical interventions following COVID-19 vaccination: a federated cohort study

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-09-10 DOI:10.1038/s41746-024-01223-4
Benjamin Rader, Neil K. R. Sehgal, Julie Michelman, Stefan Mellem, Marinanicole D. Schultheiss, Tom Hoddes, Jamie MacFarlane, Geoff Clark, Shawn O’Banion, Paul Eastham, Gaurav Tuli, James A. Taylor, John S. Brownstein
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Abstract

In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities. To address this challenge and demonstrate a framework for conducting observational research with sensitive data in a privacy-conscious manner, we employ a privacy-centric epidemiological study design: the federated cohort. This approach leverages recent computational advances to allow for distributed participants to contribute to a prospective, observational research study while maintaining full control of their data. We apply this strategy here to explore pandemic intervention adherence patterns. Participants (n = 3808) were enrolled in our federated cohort via the “Google Health Studies” mobile application. Participants completed weekly surveys and contributed empirically measured mobility data from their Android devices between November 2020 to August 2021. Using federated analytics, differential privacy, and secure aggregation, we analyzed data in five 6-week periods, encompassing the pre- and post-vaccination phases. Our results showed that participants largely utilized non-pharmaceutical intervention strategies until they were fully vaccinated against COVID-19, except for individuals without plans to become vaccinated. Furthermore, this project offers a blueprint for conducting a federated cohort study and engaging in privacy-preserving research during a public health emergency.

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接种 COVID-19 疫苗后坚持非药物干预措施:联合队列研究
在缓解大流行病方面,社会隔离和戴口罩等策略对于防止疾病复发至关重要。然而,由于个人可能不愿意与公共卫生机构分享行为数据,因此监测遵守情况具有挑战性。为了应对这一挑战,并展示一个以注重隐私的方式利用敏感数据开展观察研究的框架,我们采用了一种以隐私为中心的流行病学研究设计:联合队列。这种方法利用了最新的计算技术,允许分布式参与者为前瞻性观察研究做出贡献,同时保持对其数据的完全控制。我们在此采用这种策略来探索大流行病干预的坚持模式。参与者(n = 3808)通过 "谷歌健康研究 "移动应用程序加入我们的联合队列。在 2020 年 11 月至 2021 年 8 月期间,参与者每周完成一次调查,并通过他们的安卓设备提供经验测量的移动数据。利用联合分析、差异化隐私和安全聚合,我们分析了五个为期 6 周的数据,包括接种疫苗前后两个阶段的数据。我们的结果表明,除了没有计划接种疫苗的人之外,大部分参与者在完全接种 COVID-19 疫苗之前都采用了非药物干预策略。此外,该项目还为在公共卫生突发事件期间开展联合队列研究和保护隐私的研究提供了蓝图。
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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