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The Final Report of the Royal Commission into Violence, Abuse, Neglect and Exploitation of People with Disability has heightened national focus on the experiences of people with disabilities. The Commission made important progress in recognising that violence, abuse and neglect occurs in all the places that people live their lives. Attention is now on how governments and communities will prevent and respond to entrenched and difficult problems. The recommendations may not go far enough to dismantle the siloed approaches that cause abuse and make it difficult to respond effectively for people with intellectual disabilities, who face particular barriers. This article uses research on domestic and family violence experienced by children and young people with intellectual disabilities to illustrate this risk. Without transformative responses by governments and communities, the systems and structures that enable violence and abuse across sectors will not be adequately addressed. [ABSTRACT FROM AUTHOR]

The objective of this project is to implement a harmonized artificial intelligence (AI)-based de-identification of free-text medical data across multiple Canadian jurisdictions. This federated learning approach will allow these jurisdictions to leverage each other’s data and resources while no individual-level data leaves the jurisdiction. Federated Learning enables health data centers in different jurisdictions to collaborate in training machine learning models without sharing individual-level data. This approach will significantly reduce privacy and cybersecurity risks and barriers that are involved in sharing and moving data across different jurisdictions. In a federated learning environment, machine learning models are trained on multiple data sources available in local data centers; local data are not shared to a central computing/analysis environment. Instead, parameters (such as model weights) are shared between these local data centers to generate a global model that will be shared and used by all participating data centers. In this case study, four health research data centers in different Canadian provinces will take part in deployment of an AI-based application for de-identification of free-text data. The data centers are members of Health Data Research Network (HDRN) Canada. The deployment will include: • harmonized annotation and labeling of local data, • local training of entity recognition algorithms, • integrating model weights from each data centers to create a global model • development of license agreements between the participating data centers to allow sharing model weights This is an ongoing project. The talk will demonstrate learning experiences, advantages, and challenges in a federated learning environment and explore the feasibility of transporting this approach to other multi-jurisdiction research networks.

Objectives People with disability represent 1 in 6 Australians and use services provided across multiple levels of government. Data is siloed between different governments and service systems, limiting visibility and ability to improve outcomes. Our objective was to progress people-centred data sharing across jurisdictions at scale, focused on people with disability. Approach More than 140 engagements with the disability community, governments, researchers, and service providers were conducted to ascertain user and data needs; systematically map enablers and barriers to data use; and outline the requisite technical capabilities and social license for a disability asset. Five demonstration projects tested novel linkage approaches, data flows and access models to inform technical aspects of the design. We adopted an iterative try-test-learn approach and forged consensus through extensive dialogue across all stakeholders to collaboratively design a system for enduring data linkage. The final design was reviewed by external experts to ensure best practice and build trust. Results A national disability-focused data asset requires linking data across all human services portfolios, as will be required for most national assets seeking to improve outcomes for particular communities or in particular domains. The proposed approach is three building blocks: national data linkage and integration infrastructure, national data system governance, and streamlined data sharing agreements for multiple uses. Current project-by-project approaches to linkage are proposed to be replaced with new enduring national keys to connect disparate state-based infrastructure and enable streamlined, reusable data curation processes. Collaboration between jurisdictions will be embedded through joint governance of the new infrastructure. Finally, a new approach to data sharing agreements between Australian governments will be developed to create safe parameters for streamlined access within existing legislation and authorisation pathways. Conclusion By designing to meet information needs across human services, this approach can be leveraged for multiple linked, longitudinal enduring data asset, beyond disability. It multiplies the benefits of upfront efforts to share and improve data, reflects a shared understanding among stakeholders, and moves Australia towards scaled use of people-centred data.

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research.

Aim: To describe the process by which the 12 community-based primary health care (CBPHC) research teams worked together and fostered cross-jurisdictional collaboration, including collection of common indicators with the goal of using the same measures and data sources. Background: A pan-Canadian mechanism for common measurement of the impact of primary care innovations across Canada is lacking. The Canadian Institutes for Health Research and its partners funded 12 teams to conduct research and collaborate on development of a set of commonly collected indicators. Methods: A working group representing the 12 teams was established. They undertook an iterative process to consider existing primary care indicators identified from the literature and by stakeholders. Indicators were agreed upon with the intention of addressing three objectives across the 12 teams: (1) describing the impact of improving access to CBPHC; (2) examining the impact of alternative models of chronic disease prevention and management in CBPHC; and (3) describing the structures and context that influence the implementation, delivery, cost, and potential for scale-up of CBPHC innovations. Findings: Nineteen common indicators within the core dimensions of primary care were identified: access, comprehensiveness, coordination, effectiveness, and equity. We also agreed to collect data on health care costs and utilization within each team. Data sources include surveys, health administrative data, interviews, focus groups, and case studies. Collaboration across these teams sets the foundation for a unique opportunity for new knowledge generation, over and above any knowledge developed by any one team. Keys to success are each team's willingness to engage and commitment to working across teams, funding to support this collaboration, and distributed leadership across the working group. Reaching consensus on collection of common indicators is challenging but achievable. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To investigate the relationship between area‐level deprivation and risk of cognitive dysfunction. DESIGN: Cross‐sectional analysis. SETTING: The Trinity, Ulster, and Department of Agriculture (TUDA) study from 2008 to 2012. PARTICIPANTS: Community‐dwelling adults aged 74.0 ± 8.3 without dementia (N = 5,186; 67% female). MEASUREMENTS: Adopting a cross‐jurisdictional approach, geo‐referenced address‐based information was used to map and link participants to official socioeconomic indicators of deprivation within the United Kingdom and the Republic of Ireland. Participants were assigned an individual deprivation score related to the smallest administrative area in which they lived. These scores were categorized into comparable quintiles, that were then used to integrate the datasets from both countries. Cognitive health was assessed using the Mini‐Mental State Examination (MMSE); cognitive dysfunction was defined as a MMSE score of 24 or less. RESULTS: Approximately one‐quarter of the cohort resided within the most‐deprived districts in both countries. Greater area‐level deprivation was associated with significantly lower MMSE scores; fewer years of formal education; greater anxiety, depression, smoking and alcohol use, and obesity; and more adverse outcomes, including higher blood pressure and diabetes risk. After adjustment for relevant covariates, area deprivation was associated with significantly higher risk of cognitive dysfunction (odds ratio = 1.40, 95% confidence interval = 1.05–1.87, P = .02, for most vs least deprived). CONCLUSION: This analysis combining data from two health systems shows that area deprivation is an independent risk factor for cognitive dysfunction in older adults. Adults living in areas of greatest socioeconomic deprivation may benefit from targeted strategies aimed at improving modifiable risk factors for dementia. Further cross‐national analysis investigating the impact of area‐ level deprivation is needed to address socioeconomic disparities and shape future policy to improve health outcomes in older adults.© 2018 American Geriatrics Society and Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project., Competing Interests: Statement on conflicts of Interest: Natalie Wray, Kate Miller and Felicity Flack were employed by the University of Western Australia with funding from the Australian Government’s National Collaborative Research Infrastructure Strategy via the Population Health Research Network during the development and implementation of the OAS.

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research., Competing Interests: Statement on conflicts of interest: The authors declare there are no conflicts of interest

Background: Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout the metropolitan area, which includes Maryland and Virginia. To address gaps in HIV surveillance data, health departments of DC, Maryland, and Virginia have established HIV surveillance data sharing agreements. Although the Black Box (a privacy data integration tool external to the health departments) facilitates the secure exchange of data between DC, Maryland, and Virginia, its previous iterations were limited by the frequency and scope of information exchanged. The health departments of DC, Maryland, and Virginia engaged in data sharing to further improve HIV surveillance estimates. Objective: This study assessed the impact of cross-jurisdictional data sharing on the estimation of people living with HIV in DC and reduction of cases in the RIDR process. Methods: Data sharing agreements established in 2014 allowed for the exchange of HIV case information (eg, current residential address) and laboratory information (eg, test types, result dates, and results) from the enhanced HIV/AIDS Reporting System (eHARS). Regular data exchanges began in 2017. The participating jurisdictions transferred data (via secure file transfer protocol) for individuals having a residential address in a partnering jurisdiction at the time of HIV diagnosis or evidence of receiving HIV-related services at a facility located in a partnering jurisdiction. The DC Department of Health compared the data received to DC eHARS and imported updated data that matched existing cases. Evaluation of changes in current residential address and HIV prevalence was conducted by comparing data before and after HIV surveillance data exchanges. Results: After the HIV surveillance data exchange, an average of 396 fewer cases were estimated to be living in DC each year from 2012 to 2016. Among cases with a residential status change, 66.4% (1316/1982) had relocated to Maryland and 19.8% (392/1982) to Virginia; majority of these had relocated to counties bordering DC. Relocation in and out of DC differed by mode of transmission, race and ethnicity, age group, and gender. After data exchange, the volume of HIV cases needing RIDR decreased by 74% for DC-Maryland and 81% for DC-Virginia. Conclusions: HIV surveillance data exchange between the public health departments of DC, Maryland, and Virginia reduced the number of cases misclassified as DC residents and reduced the number of cases needing RIDR. Continued data exchanges will enhance the ability of DC Department of Health to monitor the local HIV epidemic. [ABSTRACT FROM AUTHOR]

Background: Accurate HIV surveillance data are essential to monitor trends to help end the HIV epidemic. Owing to strict policies around data security and confidentiality, HIV surveillance data have not been routinely shared across jurisdictions except a biannual case-by-case review process to identify and remove duplicate cases (Routine Interstate Duplicate Review, RIDR). HIV surveillance estimates for the District of Columbia (DC) are complicated by migration and care seeking throughout the metropolitan area, which includes Maryland and Virginia. To address gaps in HIV surveillance data, health departments of DC, Maryland, and Virginia have established HIV surveillance data sharing agreements. Although the Black Box (a privacy data integration tool external to the health departments) facilitates the secure exchange of data between DC, Maryland, and Virginia, its previous iterations were limited by the frequency and scope of information exchanged. The health departments of DC, Maryland, and Virginia engaged in data sharing to further improve HIV surveillance estimates., Objective: This study assessed the impact of cross-jurisdictional data sharing on the estimation of people living with HIV in DC and reduction of cases in the RIDR process., Methods: Data sharing agreements established in 2014 allowed for the exchange of HIV case information (eg, current residential address) and laboratory information (eg, test types, result dates, and results) from the enhanced HIV/AIDS Reporting System (eHARS). Regular data exchanges began in 2017. The participating jurisdictions transferred data (via secure file transfer protocol) for individuals having a residential address in a partnering jurisdiction at the time of HIV diagnosis or evidence of receiving HIV-related services at a facility located in a partnering jurisdiction. The DC Department of Health compared the data received to DC eHARS and imported updated data that matched existing cases. Evaluation of changes in current residential address and HIV prevalence was conducted by comparing data before and after HIV surveillance data exchanges., Results: After the HIV surveillance data exchange, an average of 396 fewer cases were estimated to be living in DC each year from 2012 to 2016. Among cases with a residential status change, 66.4% (1316/1982) had relocated to Maryland and 19.8% (392/1982) to Virginia; majority of these had relocated to counties bordering DC. Relocation in and out of DC differed by mode of transmission, race and ethnicity, age group, and gender. After data exchange, the volume of HIV cases needing RIDR decreased by 74% for DC-Maryland and 81% for DC-Virginia., Conclusions: HIV surveillance data exchange between the public health departments of DC, Maryland, and Virginia reduced the number of cases misclassified as DC residents and reduced the number of cases needing RIDR. Continued data exchanges will enhance the ability of DC Department of Health to monitor the local HIV epidemic., (©Auntre D Hamp, Rupali K Doshi, Garret R Lum, Adam Allston. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 13.08.2018.)