Enhancing the ATra Black Box Matching Algorithm: Use of All Names for Deduplication Across Jurisdictions.

Objectives: Achieving accurate, timely, and complete HIV surveillance data is complicated in the United States by migration and care seeking across jurisdictional boundaries. To address these issues, public health entities use the ATra Black Box-a secure, electronic, privacy-assuring system developed by Georgetown University-to identify and confirm potential duplicate case records, exchange data, and perform other analytics to improve the quality of data in the Enhanced HIV/AIDS Reporting System (eHARS). We aimed to evaluate the ability of 2 ATra software algorithms to identify potential duplicate case-pairs across 6 jurisdictions for people living with diagnosed HIV.

Improving HIV Surveillance Data by Using the ATra Black Box System to Assist Regional Deduplication Activities.

Background: Focused attention on Data to Care underlines the importance of high-quality HIV surveillance data. This study identified the number of total duplicate and exact duplicate HIV case records in 9 separate Enhanced HIV/AIDS Reporting System (eHARS) databases reported by 8 jurisdictions and compared this approach to traditional Routine Interstate Duplicate Review resolution.

Methods: This study used the ATra Black Box System and 6 eHARS variables for matching case records across jurisdictions: last name, first name, date of birth, sex assigned at birth (birth sex), social security number, and race/ethnicity, plus 4 system-calculated values (first name Soundex, last name Soundex, partial date of birth, and partial social security number).