Open AccessDescribing the linkage between administrative social assistance and health care databases in Ontario, Canada
Author(s) -
Claire de Oliveira,
Evgenia Gatov,
Laura Rosella,
Simon Chen,
Rachel Strauss,
Mahmoud Azimaee,
Elizabeth Paterno,
Astrid Guttmann,
Nelson W. Chong,
Peter Ionescu,
Sean Ji,
Alexander Kopp,
Annie Lan,
Charlotte Ma,
Miranda Pring,
Priyanka Raj,
Steve Ryan,
Refik Saskin,
Fiona Y. Wong
Publication year - 2022
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v7i1.1689
Subject(s) - record linkage , database , linkage (software) , representativeness heuristic , population , service (business) , medicine , christian ministry , computer science , business , environmental health , psychology , political science , social psychology , biochemistry , chemistry , marketing , law , gene
BackgroundThe 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.ObjectiveThe 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.MethodsDeterministic 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.ResultsThere 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).ConclusionsAdditional techniques to account for sub-optimal linkage rates may be required to address potential biases resulting from this data linkage. Nonetheless, the linkage between administrative social assistance and health care data will provide important findings on the social determinants of health.