Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use.
Public Health Rep
; 136(5): 554-561, 2021.
Article
in English
| MEDLINE | ID: covidwho-1277841
ABSTRACT
OBJECTIVES:
Federal open-data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial partners. These initiatives advance understanding of health conditions and diseases by providing data to researchers, scientists, and policymakers for analysis, collaboration, and use outside the Centers for Disease Control and Prevention (CDC), particularly for emerging conditions such as COVID-19, for which data needs are constantly evolving. Since the beginning of the pandemic, CDC has collected person-level, de-identified data from jurisdictions and currently has more than 8 million records. We describe how CDC designed and produces 2 de-identified public datasets from these collected data.METHODS:
We included data elements based on usefulness, public request, and privacy implications; we suppressed some field values to reduce the risk of re-identification and exposure of confidential information. We created datasets and verified them for privacy and confidentiality by using data management platform analytic tools and R scripts.RESULTS:
Unrestricted data are available to the public through Data.CDC.gov, and restricted data, with additional fields, are available with a data-use agreement through a private repository on GitHub.com. PRACTICE IMPLICATIONS Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect the privacy of de-identified people allow for improved data use. Automating data-generation procedures improves the volume and timeliness of sharing data.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Centers for Disease Control and Prevention, U.S.
/
Confidentiality
/
Data Anonymization
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Public Health Rep
Year:
2021
Document Type:
Article
Affiliation country:
00333549211026817
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