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. 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.

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). RESULTS: In approximately 11 hours, this study matched 290,482 cases from 799,326 uploaded records, including 55,460 exact case pairs. Top case pair overlaps were between NYC and NYS (51%), DC and MD (10%), and FL and NYC (6%), followed closely by FL and NYS (4%), FL and NC (3%), DC and VA (3%), and MD and VA (3%). Jurisdictions estimated that they realized a combined 135 labor hours in time efficiency by using this approach compared with manual methods previously used for interstate duplication resolution. DISCUSSION: This approach discovered exact matches that were not previously identified. It also decreased time spent resolving duplicated case records across jurisdictions while improving accuracy and completeness of HIV surveillance data in support of public health program policies. Future uses of this approach should consider standardized protocols for postprocessing eHARS data.