Where No Universal Health Care Identifier Exists: Comparison and Determination of the Utility of Score-Based Persons Matching Algorithms Using Demographic Data

Abstract

BackgroundA universal health care identifier (UHID) facilitates the development of longitudinal medical records in health care settings where follow up and tracking of persons across health care sectors are needed. HIV case-based surveillance (CBS) entails longitudinal follow up of HIV cases from diagnosis, linkage to care and treatment, and is recommended for second generation HIV surveillance. In the absence of a UHID, records matching, linking, and deduplication may be done using score-based persons matching algorithms. We present a stepwise process of score-based persons matching algorithms based on demographic data to improve HIV CBS and other longitudinal data systems.ObjectiveThe aim of this study is to compare deterministic and score-based persons matching algorithms in records linkage and matching using demographic data in settings without a UHID.MethodsWe used HIV CBS pilot data from 124 facilities in 2 high HIV-burden counties (Siaya and Kisumu) in western Kenya. For efficient processing, data were grouped into 3 scenarios within (1) HIV testing services (HTS), (2) HTS-care, and (3) within care. In deterministic matching, we directly compared identifiers and pseudo-identifiers from medical records to determine matches. We used R stringdist package for Jaro, Jaro-Winkler score-based matching and Levenshtein, and Damerau-Levenshtein string edit distance calculation methods. For the Jaro-Winkler method, we used a penalty (р)=0.1 and applied 4 weights (ω) to Levenshtein and Damerau-Levenshtein: deletion ω=0.8, insertion ω=0.8, substitutions ω=1, and transposition ω=0.5.ResultsWe abstracted 12,157 cases of which 4073/12,157 (33.5%) were from HTS, 1091/12,157 (9.0%) from HTS-care, and 6993/12,157 (57.5%) within care. Using the deterministic process 435/12,157 (3.6%) duplicate records were identified, yielding 96.4% (11,722/12,157) unique cases. Overall, of the score-based methods, Jaro-Winkler yielded the most duplicate records (686/12,157, 5.6%) while Jaro yielded the least duplicates (546/12,157, 4.5%), and Levenshtein and Damerau-Levenshtein yielded 4.6% (563/12,157) duplicates. Specifically, duplicate records yielded by method were: (1) Jaro 5.7% (234/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.4% (308/6993) within care, (2) Jaro-Winkler 7.4% (302/4073) within HTS, 0.5% (6/1091) in HTS-care, and 5.4% (378/6993) within care, (3) Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care, and (4) Damerau-Levenshtein 6.4% (262/4073) within HTS, 0.4% (4/1091) in HTS-care, and 4.2% (297/6993) within care.ConclusionsWithout deduplication, over reporting occurs across the care and treatment cascade. Jaro-Winkler score-based matching performed the best in identifying matches. A pragmatic estimate of duplicates in health care settings can provide a corrective factor for modeled estimates, for targeting and program planning. We propose that even without a UHID, standard national deduplication and persons-matching algorithm that utilizes demographic data would improve a...

Epidemiological profile of individuals diagnosed with HIV: results from the preliminary phase of case-based surveillance in Kenya

“…Additionally, newly diagnosed persons are not assigned unique identifiers (a comprehensive care clinic number) until they receive care, and the current unique identifier is facility based, making matching and deduplication of cases more difficult, hence some repeat testers may have been missed. However, in the absence of unique identifiers, we utilized Jaro-Winkler score-based persons matching algorithm to match and de-duplicate records (Waruru et al, 2018). Limitations of the pilot system were data abstraction occurring six months after initial entry at testing and care facilities, which limits the public health response, and that data abstraction from paper records is resource intensive.…”

“…Matching and de-duplication of cases were performed using a probabilistic matching algorithm (Waruru et al, 2018). Variables used were Soundex of first name (a phonetic algorithm for indexing names by sound as the way they are pronounced in English), secondary double metaphone of the middle name (where the middle name was available), secondary double metaphone of last name, the first character of sex at birth, and year of birth.…”

Feasibility and acceptability of an iris biometric system for unique patient identification in routine HIV services in Kenya

“…Text-based matching is often associated with multiple registration: one patient may have multiple IDs or one ID may be associated with multiple individuals or incomplete registration. Misidentification not only compromises patient care at the individual level but also limits utilization of surveillance data from routine programme data due to inaccuracies [20]. Double-registration could lead to overestimation of HIV incidence, with HIV re-testers being counted as newly diagnosed cases.…”

Re-thinking Linkage to Care in the Era of Universal Test and Treat: Insights from Implementation and Behavioral Science for Achieving the Second 90

“…Instead, employing longitudinal information, programs should adopt sequential time-defined patient-level “cohort” reporting, which can be used to assess LTC changes over time and to monitor LTC in populations who may be less likely to achieve full LTC (e.g., adolescents) [69]. Where universal national health identifiers are not available, cohort-based reporting within a geographic area (e.g., district or province) may be assisted by electronic health record data capture at all service entry points along the LTC pathway [70] and the use of probabilistic patient matching algorithms to identify silent transfers, “side door” LTC pathway entry [47], and minimize duplicate patient records [71].…”