ArticlePublic health reports (Washington, D.C. : 1974)
Enhancing the ATra Black Box Matching Algorithm: Use of All Names for Deduplication Across Jurisdictions.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Using the ATra Black Box to Improve Public Health Data Linkages and Analytics in the DC Cohort Longitudinal HIV Study: Viewpoint on the Process and Findings.Journal of medical Internet research · 2025Article
- No ground truth? No problem: Improving administrative data linking using active learning and a little bit of guile.PloS one · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAchieving 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.
methodsWe 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.
resultsThe 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).
conclusionsHIV 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.
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