After record linkage was complete, identifiers (e.g., names) were removed and these anonymized datasets were used to calculate linkage rates and prevalence estimates for linked and unlinked datasets. We examined the number of records linked by deterministic and probabilistic record linkage in each step of the process, as well as the linkage rates over time. The prevalence rates of socio-demographic and geographic characteristics were calculated for the records that did and did not link to the RPDB population (i.e. where an ICES unique identifier could not be attached to the record). Given the very large sample sizes, p-values were not used for statistical testing; instead, prevalence estimates between the linked and unlinked samples were compared using standardized differences to assess systematic bias as suggested by Cohen [31 ], with 0.2, 0.5, and 0.8 representing small, moderate, and large standardized differences, respectively. Data elements of interest in the ORG-VSD data included age at death, sex, cause of death and fiscal year of death. Cause of death was categorized into broad categories of death based on ICD-9 codes. Data elements of interest in the IRCC-PR database included immigrant class, sex, marital status, and age at landing, year of entry into Ontario, as well as geographical attributes such as country of birth. The geographic attributes were grouped into 4 main world regions and 18 sub-regions according to the Standard Classification of Countries and Areas of Interest.
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