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

Abstract

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.

Social care data and its fitness for integrated health and social care service governance: an exploratory qualitative analysis in the Dutch context

“…Thus, linking social care data to healthcare data shows potential for integrated governance, but there are also known pitfalls. A Canadian study linking administrative social assistance data to healthcare data warns for potential biases due to linkage errors 17. A study in Dundee, Scotland found that linkage between health and social care data faced challenges around data linkage (eg, use of a shared identifier across organisations), data analysis (eg, understanding missing data) and the need for tacit knowledge (eg, people understanding the data, its limitations and what it represents) limiting the full exploitation 18.…”

“…A Canadian study linking administrative social assistance data to healthcare data warns for potential biases due to linkage errors. 17 A study in Dundee, Scotland found that linkage between health and social care data faced challenges around data linkage (eg, use of a shared identifier across organisations), data analysis (eg, understanding missing data) and the need for tacit knowledge (eg, people understanding the data, its limitations and what it represents) limiting the full exploitation. 18 The Integrated Children’s System in the UK initially followed a top-down managerial approach emphasising on accountability of institutional risk instead of user-centredness which had a detrimental effect on professional autonomy.…”