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IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories.
Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data.
ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience.
Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.

IntroductionThe Ontario Brain Institute has developed Brain-CODE, an informatics platform designed to support the collection, storage, federation, sharing and analysis of different neuroscience research data types across several brain disorders. Linking such "deep" research data with "broad" health administrative data allows for improved characterization of disorders and supports the development of related health and social policies (Anderson et al., 2015). A privacy preserving record linkage protocol, developed through the Indoc Consortium, has been used to facilitate such linkages between Brain-CODE and administrative data holdings at the Institute for Clinical Evaluative Sciences (ICES; e.g., emergency department use, inpatient records, prescription drug utilization) (Gee et al., 2018).
Objectives and ApproachThree linkage pilots in the areas of neurodevelopmental disorders, epilepsy, and stroke research have been completed with >99% success match rates across all projects. However, each of these projects required a significant amount of human and computational resources to complete. With other similar data linkages being planned, it was determined that a more permanent solution was required rather than completing linkages on a project-by-project basis. The governance and technical elements to support the creation and maintenance of such a crosswalk between Brain-CODE and ICES were reviewed with an implementation plan subsequently developed.
Results:A methodology for creating a crosswalk between Brain-CODE and ICES has been established. The same privacy preserving record linkage protocol, as used in the previous linkage pilots, will support the creation of this crosswalk. A plan has been established to update this crosswalk annually to account for new study participants on Brain-CODE.
Conclusion / ImplicationsThe creation of this crosswalk will allow for a more streamlined approach of data linkage between Brain-CODE and ICES. Such an approach can significantly reduce overall resourcing requirements, enable more efficient data linkages, and contribute to the coupling of "broad" and "deep" data.

IntroductionResearch data combined with administrative data provides a robust resource capable of answering unique research questions. However, in cases where personal health data are encrypted, due to ethics requirements or institutional restrictions, traditional methods of deterministic and probabilistic record linkages are not feasible. Instead, privacy-preserving record linkages must be used to protect patients' personal data during data linkage.
ObjectivesTo determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data.
MethodsFeasibility was measured by the number of records that successfully matched via direct identifiers. Validity was measured by the number of records that matched with multiple indirect identifiers. The threshold for feasibility and validity were both set at 95%. The datasets shared a single, direct identifier (health card number) and multiple indirect identifiers (sex and date of birth). Direct identifiers were encrypted in both datasets and then transferred to a third-party server capable of linking the encrypted identifiers without decrypting individual records. Once linked, the study team used indirect identifiers to verify the accuracy of the linkage in the final dataset.
ResultsWith a combination of manual and automated data transfer in a sample of 8,128 individuals, the privacy-preserving data linkage took 36 days to match to a population sample of over 3.2 million records. 99.9% of the records were successfully matched with direct identifiers, and 99.8% successfully matched with multiple indirect identifiers. We deemed the linkage both feasible and valid.
ConclusionsAs combining administrative and research data becomes increasingly common, it is imperative to understand options for linking data when direct linkage is not feasible. The current linkage process ensured the privacy and security of patient data and improved data quality. While the initial implementations required significant computational and human resources, increased automation keeps the requirements within feasible bounds.