Enhancing the ATra Black Box Matching Algorithm: Use of All Names for Deduplication Across Jurisdictions. (January 2023)

• Record Type: Journal Article
• Authors: Hamp, Auntré D.; Karn, Helen E.; Kwon, Frances Y.; Rhodes, Anne; Carrier, James; Bhattacharjee, Reshma; Flynn, Colin; Hsu, Trevor; McNeice, John; Anderson, Bridget J.; Chicoine, Joyce; Fridge, Jessica; King, Justice; Lum, Garret R.; Mishra, Tej; Kang, Alisa; Smart, J.C.
• Is Part Of: Public health reports. Volume 138:Number 1(2023)
• Page Start: 54
• Page End: 61
• Publication Date: 2023-01
• Subjects: HIV -- surveillance -- data sharing -- deduplication; Public health -- United States -- Periodicals; 614.0973
• DOI: 10.1177/00333549211066171
• Languages: English
• ISSNs: 0033-3549

Abstract

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. Methods: We implemented 2 matching algorithms for identifying potential duplicate case-pairs in ATra software. The Single Name Matching Algorithm examines only 1 name for a person, whereas the All Names Matching Algorithm examines all names in eHARS for a person. Six public health jurisdictions used the algorithms. We compared outputs for the overall number of potential matches and changes in matching level. Results: The All Names Matching Algorithm found more matches than the Single Name Matching Algorithm and increased levels of match. The All Names Matching Algorithm identified 9070 (4.5%) more duplicate matches than the Single Name Matching Algorithm (n = 198 828) and increased the total number of matches at the exact through high levels by 15.4% (from 167 156 to 192 932; n = 25 776). Conclusions: HIV data quality across multiple jurisdictions can be improved by using all known first and last names of people living with diagnosed HIV that match with eHARS rather than using only 1 first and last name.