Evaluating the quality of anonymous record linkage using deterministic procedures with the New York state aids registry and a hospital discharge file

Abstract

Linkage of same-person records across multiple databases relies on high-quality, uniformly available identifying information. These data quality issues become increasingly important when personal names are not available for record linkage. Using deterministic decision criteria, we linked records from two population-based files in the absence of personal names. The sensitivity of anonymous record linkage procedures ranged from 32 to 85 per cent for the two years studied, and the positive predictive value (PPV) ranged from 14 to 99 per cent. Decreasing sensitivity and PPV were primarily attributed to (1) errors in computerized identifying information and (2) the deterministic decision criteria specified for record linkage. An evaluation of the contribution of personal names to the quality of record linkage found no measurable impact.

Perceived stress and mortality in a Taiwanese older adult population

“…Linkage of one database to another creates newly generalizable information about potential cause-and-effect relationships and provides additional, more specific, information about the study participants. The citizen ID was matched with the Department of Health death certificate file in a manner that maintained the confidentiality of individual participant information (Muse, Mickl, Smith, 1995; Neutel, Johansen, Walop, 1991). All protocols were approved by the Institutional Review Boards at Princeton University, Georgetown University, and the Bureau of Health Promotion, Department of Health, Taiwan.…”

Practical introduction to record linkage for injury research

“…In this case, we must be quite sure that records from different sources truly refer to the same person (high PPV), and might favor a deterministic method. 70 However, probabilistic methods with careful clerical review may also be useful. 41 A few studies have compared deterministic and probabilistic methods, using human review or artificially withheld identifying information as a ''gold standard''.…”

Conditions of Non-Unique Identifiers in Record Linkage Using Japanese Cohort Dataset

“…The sensitivity of using total identifiers (hospital identification code, data of hospitalization, birth date, sex, surname, street name, county of residence and Zip code) was 81.9% in 1987 and 84.7% in 1988. However, the sensitivity of using the identifiers of only birth date and sex is 81.7% in 1987 and 84.3% in 1988, which could predict fairly similar with the one using total identifiers [20]. Other linkage research was to determine the proportion of persons receiving Public Health Nursing Services (n = 5,749) who also received income assistance from region (n = 38,800) or the province (n = 16,741) comparing with three different models.…”