Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.
J Am Med Inform Assoc. 2022 Apr 13;29(5):853-863. doi: 10.1093/jamia/ocac011.
Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data.
The framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the PK11 threshold of 0.01.
When sharing COVID-19 county-level case data across all US counties, the framework's approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%.
Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.
在大流行期间支持公共卫生研究和公众的态势感知需要不断传播传染病监测数据。立法，如 1996 年的《健康保险流通与责任法案》和最近的州级法规，允许共享去识别个人级别的数据；然而，目前的去识别方法是有限的。也就是说，它们效率低下，依赖于回顾性的披露风险评估，并且不能随着感染率或人口统计数据的变化而灵活调整。在本文中，我们介绍了一个框架，用于为实时共享个人级别的监测数据动态调整去识别。
该框架利用模拟机制，能够在任何地理级别应用，以预测在广泛的泛化策略下共享数据的重新识别风险。这些估计为每周的前瞻性政策选择提供信息，以维持 PK11（记录对应于 11 人以下的群体大小的比例）等于或低于 0.1 的比例。在每周开始时固定政策，便于及时更新数据集，并支持共享粒度日期信息。我们使用约翰霍普金斯大学和疾病控制与预防中心 2020 年 8 月至 2021 年 10 月的案例数据来演示该框架在维持 0.01 的 PK11 阈值方面的有效性。
当在所有美国县共享 COVID-19 县一级的案例数据时，该框架的方法满足 96.2%的每日数据发布的阈值，而基于当前去识别技术的政策满足 32.3%的阈值。
定期调整数据发布政策在保护隐私的同时，通过及时更新和共享流行病学关键特征，增强公共卫生的效用。
