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Describing the linkage between administrative social assistance and health care databases in Ontario, Canada

Title: Describing the linkage between administrative social assistance and health care databases in Ontario, Canada
Authors: Claire de Oliveira; Evgenia Gatov; Laura Rosella; Simon Chen; Rachel Strauss; Mahmoud Azimaee; Elizabeth Paterno; Astrid Guttmann; Ministry of Children; Community and Social Services-ICES Working Group; Nelson Chong; Peter Ionescu; Sean Ji; Alexander Kopp; Annie Lan; Charlotte Ma; Miranda Pring; Priyanka Raj; Steven Ryan; Refik Saskin; Fiona Wong
Source: International Journal of Population Data Science, Vol 7, Iss 1 (2022)
Publisher Information: Swansea University
Publication Year: 2022
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: data linkage; administrative social assistance data; administrative health care data; Ontario; Demography. Population. Vital events; HB848-3697
Description: Background The linkage of records across administrative databases has become a powerful tool to increase information available to undertake research and analytics in a privacy protective manner. Objective The objective of this paper was to describe the data integration strategy used to link the Ontario Ministry of Children, Community and Social Services (MCCSS)-Social Assistance (SA) database with administrative health care data. Methods Deterministic and probabilistic linkage methods were used to link the MCCSS-SA database (2003-2016) to the Registered Persons Database, a population registry containing data on all individuals issued a health card number in Ontario, Canada. Linkage rates were estimated, and the degree of record linkage and representativeness of the dataset were evaluated by comparing socio-demographic characteristics of linked and unlinked records. Results There were a total of 2,736,353 unique member IDs in the MCCSS-SA database from the 1st January 2003 to 31st December 2016; 331,238 (12.1%) were unlinked (linkage rate = 87.9%). Despite 16 passes, most record linkages were obtained after 2 deterministic (76.2%) and 14 probabilistic passes (11.7%). Linked and unlinked samples were similar for most socio-demographic characteristics (i.e., sex, age, rural dwelling), except migrant status (non-migrant versus migrant) (standardized difference of 0.52). Linked and unlinked records were also different for SA program-specific characteristics, such as social assistance program, Ontario Works and Ontario Disability Support Program (standardized difference of 0.20 for each), data entry system, Service Delivery Model Technology only and both Service Delivery Model Technology and Social Assistance Management System (standardized difference of 0.53 and 0.52, respectively), and months on social assistance (standardized difference of 0.43). Conclusions Additional techniques to account for sub-optimal linkage rates may be required to address potential biases resulting from this data linkage. Nonetheless, the ...
Document Type: article in journal/newspaper
Language: English
Relation: https://ijpds.org/article/view/1689; https://doaj.org/toc/2399-4908; https://doaj.org/article/5ec94fbbb0d044b585e130ac96ddc630
DOI: 10.23889/ijpds.v7i1.1689
Availability: https://doi.org/10.23889/ijpds.v7i1.1689; https://doaj.org/article/5ec94fbbb0d044b585e130ac96ddc630
Accession Number: edsbas.249C87EE
Database: BASE