Supporting COVID-19 Disparity Investigations with Dynamically Adjusting Case Reporting Policies

Data access limitations have stifled COVID-19 disparity investigations in the United States. Though federal and state legislation permits publicly disseminating de-identified data, methods for de-identification, including a recently proposed dynamic policy approach to pandemic data sharing, remain unproved in their ability to support pandemic disparity studies. Thus, in this paper, we evaluate how such an approach enables timely, accurate, and fair disparity detection, with respect to potential adversaries with varying prior knowledge about the population. We show that, when considering reasonably enabled adversaries, dynamic policies support up to three times earlier disparity detection in partially synthetic data than data sharing policies derived from two current, public datasets. Using real-world COVID-19 data, we also show how granular date information, which dynamic policies were designed to share, improves disparity characterization. Our results highlight the potential of the dynamic policy approach to publish data that supports disparity investigations in current and future pandemics.

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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%. CONCLUSION: Periodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.