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

Anthony Waruru; Agnes Natukunda; Lilly M Nyagah; Timothy A Kellogg; Emily Zielinski-Gutierrez; Wanjiru Waruiru; Kenneth Masamaro; Richelle Harklerode; Jacob Odhiambo; Eric-Jan Manders; Peter W Young

doi:10.2196/10436

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

JMIR Public Health Surveill. 2018 Dec 13;4(4):e10436. doi: 10.2196/10436.

Affiliations

¹ Division of Global HIV and TB, Centers for Disease Control and Prevention, Nairobi, Kenya.
² Global Programs for Research and Training, University of California San Francisco, San Francisco, CA, United States.
³ National AIDS and STI Control Program, Ministry of Health, Nairobi, Kenya.
⁴ Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, United States.
⁵ The Palladium Group, Nairobi, Kenya.
⁶ Division of Global HIV and TB, Centers for Disease Control and Prevention, Atlanta, GA, United States.

PMID: 30545805
PMCID: PMC6315226
DOI: 10.2196/10436

Abstract

Background: A 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.

Objective: The 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.

Methods: We 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.

Results: We 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.

Conclusions: Without 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 accuracy in monitoring HIV care clinical cascades.

Keywords: HIV case-based surveillance; deterministic matching; score-based matching; unique case identification; universal health care identifier.

©Anthony Waruru, Agnes Natukunda, Lilly M Nyagah, Timothy A Kellogg, Emily Zielinski-Gutierrez, Wanjiru Waruiru, Kenneth Masamaro, Richelle Harklerode, Jacob Odhiambo, Eric-Jan Manders, Peter W Young. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 13.12.2018.