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IntroductionPrivacy preserving record linkage (PPRL) using encoded or hashed data has potential to enable large-scale record linkage of previously inaccessible data. With limited real-world evaluation and implementation of PPRL at scale it is challenging for linkage practitioners to judiciously balance data protection with the accuracy and usability of linked datasets.
Objectives and ApproachWe evaluated the performance of PPRL techniques using Bloom filters for linkage of data across primary and secondary care settings. This technique limits the need to disclose personal information for linkage activities. Primary care data included 272,202 records from 16 general practices in NSW. This was linked to 42.8 million records from a 7 year series of emergency presentations, hospitalisations and death registrations. For the purpose of evaluation, personal information was encoded within the data linkage centre. The quality of PPRL linkage was assessed against the true match status based on a gold standard probabilistic linkage using full personal identifiers.
ResultsCompared to the gold standard probabilistic linkage using full personal identifiers, the PPRL techniques produced quality metrics of precision, recall and F measure in excess of 0.90. When configured to leverage pre-existing links between emergency department, hospital and mortality data, quality metrics around 0.98-0.99 were achieved. Lower rates of linkage quality were associated with missing demographic information and some residual variation in linkage quality across practices was observed.
Conclusion/ImplicationsPPRL using Bloom filters is a promising technique for achieving high quality linkage across primary and secondary care in Australia. Further evaluation will assess scalability and quality in Australia but international collaborations are encouraged to more rapidly develop the evidence base and tactical approaches to support real world implementations.

IntroductionMortality inequalities by income and education levels have historically been estimated using an area-based approach in Canada. Although useful in measuring socioeconomic inequalities overtime, this method underestimates the level of inequality and only allows the examination of a single dimension at a time.
Objectives and ApproachTo create a series of census linked datasets that allowed for the examination of health inequalities across different socioeconomic dimensions. Specifically, five census cycles (beginning with the 1991 Census) were probabilistically and deterministically linked to different health outcomes (mortality, cancer, hospitalization) to create the Canadian Census Health and Environment Cohort (CanCHEC). Each dataset was created using a similar methodological approach which allowed for the measurement of these health inequalities over time. Mortality inequalities by both income and education level (including multidimensional) for all causes and cause-specific groups were examined.
ResultsFive census linked datasets were constructed that followed mortality for a period of up to 20 years. The 1991 CanCHEC includes 2.6 million adults, the 1996 and 2001 CanCHECs include 3.5 million adults respectively, and the 2006 and 2011 CanCHECs include 5.9 and 6.5 million people respectively. Findings revealed a stair-stepped gradient in all-cause and cause-specific mortality by educational attainment and income quintile across each time period. The lowest mortality rates were among the university educated and richest income quintile and highest mortality rates among those with less than high school graduation and the poorest income quintile. The gradient differed by cause of death groupings. Over the 25-year time period, the mortality gradient trend varied by socioeconomic dimension and cause of death.
Conclusion/ImplicationsThese data show clear mortality inequalities by socioeconomic position across the different time periods. These linked datasets can help advance knowledge in understanding health inequalities in Canada as well as provide a tool for on-going surveillance of health inequalities by different socioeconomic dimensions.