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Objective: To describe an efficient, generalizable approach to validate probabilistic record linkage results, in particular by a model-guided detection of linking errors, and to apply this approach to validate linkage of admissions of newborns.Methods: Our double-blind validation procedure consisted of three steps: sample selection, data collection and data analysis. The linked Dutch national newborn admission registry contained 30,082 records for 2001 including readmissions (7.4%) and twins (9.7%). A highly informative sample was selected from the linked file by oversampling uncertain links based on model-derived linking weight. Four hundred and eight fax forms with minimal registry information (admissions of 191 children) were sent out to different pediatric units. The pediatricians were asked to create a short detailed patient history from independent sources. The linkage status and additional record data was validated against this external information.Results: Response rate was 97% (395/408 faxes). Accuracy of the linkage of singleton admissions was high: except for some expected errors in the uncertain area (0.02% of record pairs), linkage was error-free. Validation of multiple birth readmissions showed 37% linkage errors due to low data quality of the multiple birth variables. The quality of the linked registry file was still high; only 1.7% of the children were from a multiple birth with multiple admissions, resulting in less than 1% linking error.Conclusions: Our external validation procedure of record linkage was feasible, efficient, and informative about identifying the source of the errors. [ABSTRACT FROM AUTHOR]

OBJECTIVES This study sought to examine the differences between ignoring (naïve) and incorporating dependency (nonnaïve) among linkage variables on the outcome of a probabilistic record linkage study. DESIGN AND MEASUREMENTS We used the outcomes of a previously developed probabilistic linkage procedure for different registries in perinatal care assuming independence among linkage variables. We estimated the impact of ignoring dependency by re-estimating the linkage weights after constructing a variable that combines the outcomes of the comparison of 2 correlated linking variables. The results of the original naïve and the new nonnaïve strategy were systematically compared for 3 scenarios: the empirical dataset using 9 variables, the empirical dataset using 5 variables, and a simulated dataset using 5 variables. RESULTS The linking weight for agreement on 2 correlated variables among nonmatches was estimated considerably higher in the naïve strategy than in the nonnaïve strategy (16.87 vs. 13.55). Therefore, ignoring dependency overestimates the amount of identifying information if both correlated variables agree. The impact on the number of pairs that was classified differently with both approaches was modest in the situation in which there were many different linking variables but grew substantially with fewer variables. The simulation study confirmed the results of the empirical study and suggests that the number of misclassifications can increase substantially by ignoring dependency under less favorable linking conditions. CONCLUSION Dependency often exists between linking variables and has the potential to bias the outcome of a linkage study. The nonnaïve approach is a straightforward method for creating linking weights that accommodate dependency. The impact on the number of misclassifications depends on the quality and number of linking variables relative to the number of correlated linking variables. [ABSTRACT FROM AUTHOR]

Objective: To describe the technical approach and subsequent validation of the probabilistic linkage of the three anonymous, population-based Dutch Perinatal Registries (LVR1 of midwives, LVR2 of obstetricians, and LNR of pediatricians/neonatologists). These registries do not share a unique identification number., Study Design and Setting: A combination of probabilistic and deterministic record linkage techniques were applied using information about the mother, delivery, and child(ren) to link three known registries. Rewards for agreement and penalties for disagreement between corresponding variables were calculated based on the observed patterns of agreement and disagreements using maximum likelihood estimation. Special measures were developed to overcome linking difficulties in twins. A subsample of linked and nonlinked pairs was validated., Results: Independent validation confirmed that the procedure successfully linked the three Dutch perinatal registries despite nontrivial error rates in the linking variables., Conclusions: Probabilistic linkage techniques allowed the creation of a high-quality linked database from crude registry data. The developed procedures are generally applicable in linkage of health data with partially identifying information. They provide useful source date even if cohorts are only partly overlapping and if within the cohort, multiple entities and twins exist.

This paper presents a refinement of the probabilistic medical record linking algorithm. We introduced "close agreement" to account for typical errors in administrative variables used for record linkage. Linking data on early pregnancy determinants with data on late child outcomes was used as a case study. We analyzed whether the addition of close agreement resulted in a higher discriminating power of the linking key reflected ina reduction of the number of links with an uncertain linking status. Incorporating close agreement for postal code and date of birth in the record linking algorithm resulted in a reduction of 95% of the number of pairs in the uncertain region. We showed that the extension of a third outcome"close" when comparing values of corresponding linking variables led to a major improvement in our probabilistic record linkage study. Similar improvements are likely in other studies because the frequency, nature, and type of errors in other large databases will not be substantially different.

This paper describes the linkage of data from three Dutch Perinatal Registries: the Dutch National Midwife Registry, the Dutch National Obstetrics Registry and the Dutch National Pediatrics Registry, for the year of 2001. All these registries are anonymous and lack a common identifier. We used probabilistic and deterministic record linkage techniques to combine data from the mother, delivery and child involving to the same pregnancy. Records of singleton and twin pregnancies were linked separately. We have developed a probabilistic close method based on maximum likelihood methods to estimate the weights of individual linking variables and the threshold value for the overall weight. Probabilistic linkage identified 80% more links than a full deterministic linkage approach. External validation revealed an error rate of less than 1%. Our method is a flexible and powerful method to link anonymous registries in the absence of a gold standard.