In Canada, universal health care is delivered through provincial and territorial publicly funded health care systems, which, in turn, collect administrative data that reflect patients’ interactions with the health care system across multiple sectors (e.g., inpatient and ambulatory care) and over time. In Ontario, Canada’s most populous province with over 14.5 million residents, the Ontario Ministry of Health collects data on the health care utilisation of all legal residents eligible for public health care insurance. Under section 45 of Ontario’s Personal Health Information Protection Act (PHIPA) [1], ICES, an independent non-profit research institute, is a prescribed entity whose legal status under Ontario’s health information privacy law allows it to collect, use, and disclose personal health information from health information custodians, without consent, for the evaluation, planning and/or monitoring of the health system. To ensure the privacy and protection of data, ICES implements a series of physical and logical controls to govern access to information, like the use of secure zones within ICES facilities, complex passwords, and encryption. The use of these data has enabled scientists to answer important policy-relevant questions across different disciplines such as health services research, health economics, epidemiology and public health [2–5].

In recent years, there has been a growing interest in examining social determinants of health [6] defined by the World Health Organization as “the conditions in which people are born, grow, live, work and age” [7], which lie outside of the health care sector. The social and economic conditions of an individual are known to substantially impact health outcomes [8, 9]. Within this context, researchers have been interested in understanding the relationship between the receipt of social assistance, typically provided to an economically disadvantaged segment of the population, and their health (such as injuries and substance use) and health care use [10, 11]. However, the lack of reliable and comprehensive data in most regions in Canada has made it difficult to examine the characteristics and outcomes of social assistance recipients. Most Canadian research on social assistance has relied on self-reported population survey data, such as the Survey of Labour and Income Dynamics and the Canadian Community Health Survey [12]. These data are limited by poor response rates, potentially unreliable responses due, in part, to social desirability bias, and biased samples, due to possible underrepresentation of respondents with lower socioeconomic status who may not have the means to participate in surveys [13]. At least one other Canadian province, Manitoba, has been successful in linking administrative health care data to administrative social assistance data [14], which has resulted in work characterising health outcomes in social assistance recipients [15] and the evaluation of a number of health care programs in this population, such as the impact of an unconditional prenatal benefit initiative [16].

The linkage of records across administrative databases has become a powerful tool to increase the amount of information available on individuals for research and analytics, going beyond any individual data source in isolation [17, 18]. For example, in Ontario the linkages of administrative health care databases to the Immigration, Refugees and Citizenship Canada permanent residents data [19], the Office of the Registrar General’s Vital Statistics Death Registry [19], and the federal Indian Register [20] have produced important evidence that can be used to inform policy. The Ontario Ministry of Children, Community and Social Services, which administers social assistance programs in the province, partnered with ICES to address the need for more comprehensive data to support decision-making, policy development, and service provision relevant to the health and well-being of individuals living in Ontario.

In this paper, we describe the data integration strategy used to prepare the Ministry of Children, Community and Social Services (MCCSS)-Social Assistance (SA) database for research through record linkage with the administrative health care databases held at ICES. We further evaluate the degree of record linkage and the representativeness of the dataset by comparing the socio-demographic characteristics of linked and unlinked records.

There were a total of 2,736,353 unique member IDs in MCCSS-SA dataset from the 1^st January 2003 to 31^st December 2016, where 331,238 (12.1%) unique member IDs were unlinkable, for a total linkage rate of 87.9% (Table 1). Three quarters (76.2%) of records were obtained through deterministic linkage while the remaining 11.7% of records was obtained through probabilistic linkage (Table 2). The vast majority of records were obtained after two deterministic passes (76.2%) and fourteen probabilistic passes (11.7%). In total, sixteen passes were required to obtain the total number of records (2,405,115), using a variety of matching and block schemes (e.g., surnames, given names, sex, date of birth, residential postal code).

On average, standardized differences between linked and unlinked samples were less than 0.1 for most socio-demographic characteristics (i.e., sex, age and rural dwelling), except migrant status (standardized difference of 0.52) (Table 1). The individuals that were successfully linked were different from those that were not for program-specific characteristics, such as program, where there were more individuals in the unlinked group enrolled in the OW and less enrolled in the ODSP compared to the linked group (standardized difference of 0.20 for each), data entry system, SDMT only, or both SDMT and SAMS (standardized difference of 0.53 and 0.52, respectively), and months on social assistance (standardized difference of 0.43) (Table 1). Compared to linked member ID, unlinked individuals were more likely to be on OW, in the SDMT system only, and to have a shorter duration of social assistance.

The deterministic linkage rates within the SDMT system ranged from 74.2% in 2003 to 79.8% in 2014, while these rates in the SAMS system were 79.3% in 2015 and 77.8% in 2016 (Figure 3). The probabilistic linkage rates within the SDMT system ranged from 11.6% in 2009 to 12.2% in 2003 while in the SAMS system they were 11.4% in 2015 and 11.2% in 2016 (Figure 3). The proportion of unlinked records was typically higher in the SDMT system (Figure 3).

We also looked at these differences by program (OW/ODSP) and by year; the differences were quite consistent (see Appendix Tables A1, A2 in the Appendix).

The linkage between the MCCSS-SA and the Registered Persons Database resulted in a high number of MCCSS-SA participants successfully linked to health administrative records (linkage rate = 87.9%) and the linked sample was reasonably representative of the original MCCSS-SA sample. However, there were large differences in the linked and unlinked samples with regard to migrant status such that there was a greater proportion of migrants (i.e., immigrants and refugees) in the population receiving SA that were unable to be linked. There were smaller differences in the linked and unlinked samples by program and system entry characteristics as well as by duration on social assistance, such that the OW records were more likely to be unlinked as was the case for individuals with SDMT records only, and unlinked individuals were on social assistance for a shorter period of time.

The difference in the linked and unlinked samples for migrants may be due to a number of reasons. For example, some refugees, such as asylum seekers, are eligible for social assistance prior to obtaining permanent resident status and provincial health care benefits; this could explain why these individuals had a record in the MCCSS-SA data but not in the ICES data. This difference could also be due to cases where names were recorded incorrectly. The OW records were more likely to be unlinked likely because these records are of shorter duration. This may also be related to the fact that shorter spells of SA had poorer linkage. In addition, individuals with SDMT records only were more likely to be unlinked. This is not surprising given that the SDMT, the old data entry system, was likely not as good at recording information on the variables used for linkage; for example, it includes more free text fields, which can introduce recording errors. In terms of the SA program variables, while there was not complete representativeness, to date there has been more interest in individuals with developmental disabilities [32, 33] and the ODSP data, which provides information on SA provided to these individuals. The extent to which these differences may introduce selection bias will depend on the specific question of study and the specific population generated from the database. The high linkage rate may help mitigate against some of the impacts of these biases, though this will be dependent on the amount of bias and how focused it is. Thus, it is important to acknowledge the potential for selection bias and researchers should seek to address this in their analyses, if/where applicable.

The linkage between the Registered Persons Database and the MCCSS-SA data linkage has a few limitations. The data transferred to ICES are currently only available from 2003 onwards, which limits the how far back researchers can go to examine SA. We were not able to examine the representativeness of all variables (e.g., education), as these were not entered reliably in the SA data (they are not mandatory for entry). Furthermore, given the switch in data systems, researchers should be aware that some comparisons over time may not be possible, in particular when undertaking longitudinal analyses, which cover the transition year (i.e., from 2014 to 2015). Finally, while there are currently limitations on the use of the SA data, there are proposed legislative changes that will enable their broader use in Ontario [34].

Nonetheless, despite these limitations, this high linkage rate will enable scientists to examine one of the many social determinants of health and answer a series of questions that have not been possible until now. Few jurisdictions have been able to undertake this type of data linkage; examples include the Canadian province of Manitoba [35, 36], Scotland [37] and Scandinavian countries, such as Sweden [38]. Future work will seek to examine the health service use of SA clients and their characteristics using ICES data as well as explore the inclusion of additional MCCSS variables into the SA minimum dataset available to researchers.

We found a high linkage rate between the MCCSS-SA and ICES administrative health care databases; furthermore, the linkage was fairly representative of the population of social assistance recipients. However, given sub-optimal linkage rates for migrant recipients of social assistance, there is potential for selection bias. Nonetheless, this linkage represents a significant advancement in understanding the social determinants of health and will enable scientists to answer relevant research questions in the future, recognising the limitations of the data.