Record linkage for routinely collected health data in an African health information exchange

Abstract

Abstract Introduction The Patient Master Index (PMI) plays an important role in management of patient information and epidemiological research, and the availability of unique patient identifiers improves the accuracy when linking patient records across disparate datasets. In our environment, however, a unique identifier is seldom present in all datasets containing patient information. Quasi identifiers are used to attempt to link patient records but sometimes present higher risk of over-linking. Data quality and completeness thus affect the ability to make correct linkages. Aim This paper describes the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, and assesses its output to date. Methods We apply a stepwise deterministic record linkage approach to link patient data that are routinely collected from health information systems in the Western Cape province of South Africa. Variables used in the linkage process include South African National Identity number (RSA ID), date of birth, year of birth, month of birth, day of birth, residential address and contact information. Descriptive analyses are used to estimate the level and extent of duplication in the provincial PMI. Results The percentage of duplicates in the provincial PMI lies between 10% and 20%. Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with the first names and surname being different for the same individual in approximately 22% of duplicates. The RSA ID is the variable mostly affected by poor completeness with less than 30% of the records having an RSA ID. The current linkage algorithm requires refinement as it makes use of algorithms that have been developed and validated on anglicised names which might not work well for local names. Linkage is also affected by data quality-related issues that are associated with the routine nature of the data which often make it difficult to validate and enforce integrity at the point of data capture.