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1.
J Acquir Immune Defic Syndr ; 82 Suppl 1: S13-S19, 2019 09 01.
Article in English | MEDLINE | ID: mdl-31425390

ABSTRACT

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.


Subject(s)
Data Collection/standards , HIV Infections/epidemiology , Population Surveillance , Humans , United States/epidemiology
2.
Article in English | MEDLINE | ID: mdl-27227157

ABSTRACT

BACKGROUND: The National HIV/AIDS Strategy calls for active surveillance programs for human immunodeficiency virus (HIV) to more accurately measure access to and retention in care across the HIV care continuum for persons living with HIV within their jurisdictions and to identify persons who may need public health services. However, traditional public health surveillance methods face substantial technological and privacy-related barriers to data sharing. OBJECTIVE: This study developed a novel data-sharing approach to improve the timeliness and quality of HIV surveillance data in three jurisdictions where persons may often travel across the borders of the District of Columbia, Maryland, and Virginia. METHODS: A deterministic algorithm of approximately 1000 lines was developed, including a person-matching system with Enhanced HIV/AIDS Reporting System (eHARS) variables. Person matching was defined in categories (from strongest to weakest): exact, very high, high, medium high, medium, medium low, low, and very low. The algorithm was verified using conventional component testing methods, manual code inspection, and comprehensive output file examination. Results were validated by jurisdictions using internal review processes. RESULTS: Of 161,343 uploaded eHARS records from District of Columbia (N=49,326), Maryland (N=66,200), and Virginia (N=45,817), a total of 21,472 persons were matched across jurisdictions over various strengths in a matching process totaling 21 minutes and 58 seconds in the privacy device, leaving 139,871 uniquely identified with only one jurisdiction. No records matched as medium low or low. Over 80% of the matches were identified as either exact or very high matches. Three separate validation methods were conducted for this study, and they all found ≥90% accuracy between records matched by this novel method and traditional matching methods. CONCLUSIONS: This study illustrated a novel data-sharing approach that may facilitate timelier and better quality HIV surveillance data for public health action by reducing the effort needed for traditional person-matching reviews without compromising matching accuracy. Future analyses will examine the generalizability of these findings to other applications.

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