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Objectives: Numerous studies have used maternally linked birth records to investigate perinatal outcomes, maternal behaviors, and the quality of vital records birth data. Little attention has been given to assessing errors in the linkages and to understanding how such errors affect estimates derived from the linked data. The author developed a framework for conceptualizing maternal linkage error and measures for quantifying it, and examined the behavior of the new measures in a maternally linked file., Methods: Linkage errors were conceptualized as misclassification, with the classes being the maternal sets (records classified as representing different births to the same woman). The true linkage proportion, analogous to sensitivity, was used to capture the degree to which all of a woman's births were assigned to a single maternal set; the false linkage proportion, analogous to specificity, was used to capture the degree to which the assigned maternal sets combined births from different women. The behavior of the two proportions was examined by introducing increasing degrees of linkage error into a maternally linked file., Results: Both measures indicated greater misclassification with increasing simulated linkage errors., Conclusions: The new measures may be a useful tool for assessing the quality of maternally linked data, as well as other types of linked records where the linkages are within a single file. This is a necessary step towards developing methods for addressing misclassification bias in studies of maternally linked records through sensitivity analysis, adjustment, and other means.

Background: Multiplication of data sources within heterogeneous healthcare information systems always results in redundant information, split among multiple databases. Our objective is to detect exact and approximate duplicates within identity records, in order to attain a better quality of information and to permit cross-linkage among stand-alone and clustered databases. Furthermore, we need to assist human decision making, by computing a value reflecting identity proximity., Methods: The proposed method is in three steps. The first step is to standardise and to index elementary identity fields, using blocking variables, in order to speed up information analysis. The second is to match similar pair records, relying on a global similarity value taken from the Porter-Jaro-Winkler algorithm. And the third is to create clusters of coherent related records, using graph drawing, agglomerative clustering methods and partitioning methods., Results: The batch analysis of 300,000 "supposedly" distinct identities isolates 240,000 true unique records, 24,000 duplicates (clusters composed of 2 records) and 3,000 clusters whose size is greater than or equal to 3 records., Conclusion: Duplicate-free databases, used in conjunction with relevant indexes and similarity values, allow immediate (i.e. real-time) proximity detection when inserting a new identity.