Empirical aspects of record linkage across multiple data sets using statistical linkage keys: the experience of the PIAC cohort study

Abstract

BackgroundIn Australia, many community service program data collections developed over the last decade, including several for aged care programs, contain a statistical linkage key (SLK) to enable derivation of client-level data. In addition, a common SLK is now used in many collections to facilitate the statistical examination of cross-program use. In 2005, the Pathways in Aged Care (PIAC) cohort study was funded to create a linked aged care database using the common SLK to enable analysis of pathways through aged care services.Linkage using an SLK is commonly deterministic. The purpose of this paper is to describe an extended deterministic record linkage strategy for situations where there is a general person identifier (e.g. an SLK) and several additional variables suitable for data linkage. This approach can allow for variation in client information recorded on different databases.MethodsA stepwise deterministic record linkage algorithm was developed to link datasets using an SLK and several other variables. Three measures of likely match accuracy were used: the discriminating power of match key values, an estimated false match rate, and an estimated step-specific trade-off between true and false matches. The method was validated through examining link properties and clerical review of three samples of links.ResultsThe deterministic algorithm resulted in up to an 11% increase in links compared with simple deterministic matching using an SLK. The links identified are of high quality: validation samples showed that less than 0.5% of links were false positives, and very few matches were made using non-unique match information (0.01%). There was a high degree of consistency in the characteristics of linked events.ConclusionsThe linkage strategy described in this paper has allowed the linking of multiple large aged care service datasets using a statistical linkage key while allowing for variation in its reporting. More widely, our deterministic algorithm, based on statistical properties of match keys, is a useful addition to the linker's toolkit. In particular, it may prove attractive when insufficient data are available for clerical review or follow-up, and the researcher has fewer options in relation to probabilistic linkage.

Determinação de um ponto de corte para a identificação de pares verdadeiros pelo método probabilístico de linkage de base de dados

“…Além disso, as variáveis de confirmação do linkage, como "nome da mãe" e "endereço de residência", também apresentavam baixa completitude, respectivamente, 0,5 e 23,6%. No estudo de Karmel et al 25 , que objetivou descrever os aspectos empíricos da técnica de linkage, foi observado que a presença de valores ignorados diminuiu a probabilidade de identificar um par verdadeiro.…”

Examining the quality of name code record linkage: what is the impact on death and cancer risk estimates? A validation study

“…However, the use of an SLK in addition to person-level data items such as area of usual residence and date of health care episode has been shown, using a stepwise deterministic matching algorithm, to achieve 0.5% false positive links between aged care and residential/community care databases. 19 This approach also achieved a PPV of 97% and a sensitivity of 95% for linkage between hospitalisation and residential aged care records. 20 A previous study found that the use of alias names may affect the quality of name data held on the various registers for these individuals.…”

“…Similarly, the relative accuracy of simple deterministic record linkage using a statistical linkage key, such as the SLK‐581, which incorporates five letters of the name (three letters of the family name and two letters of the given name), the eight digit date of birth, and sex of the registrant, is uncertain. However, the use of an SLK in addition to person‐level data items such as area of usual residence and date of health care episode has been shown, using a stepwise deterministic matching algorithm, to achieve 0.5% false positive links between aged care and residential/community care databases 19 . This approach also achieved a PPV of 97% and a sensitivity of 95% for linkage between hospitalisation and residential aged care records 20…”

Building a National Perinatal Data Base without the Use of Unique Personal Identifiers

“…Previous work [18] suggested at least two widely used and suitable encryption methods for privacy preserving record linkage in the given context: The Swiss Anonymous Linking Code (Swiss ALC [16]) and the Encrypted Statistical Linkage Key (Australian ALC [17]). The Swiss ALC concatenates the Soundex codes of first and last name, sex and date of birth.…”

“…After the phonetic encoding, the resulting codes are usually combined with additional identifiers, such as date of birth and sex, which are finally encrypted with keyed HMACs. Examples for these kind of linkage keys are given by [16] and [17]. Statistical linkage keys usually produce acceptable results and seem to be safe against most cryptographic attacks.…”