Suchergebnisse

IntroductionIntegrating health and social services data is critical to understanding social determinants of health and responding to public expectations for evidence-based policies amidst changing demographics and fiscal constraint.
While academia has long understood the importance of social determinants of health, real and perceived obstacles have slowed their evaluation in Ontario.
Objectives and ApproachThis report describes how the Institute for Clinical Evaluative Sciences (ICES) and the Ministry and Community and Social Services (MCSS) have partnered to bring social services data and health data together to better understand the Ontario population and better support decision makers across various sectors.
We present how ICES and MCSS tackled barriers to data access and cultural challenges to data sharing in the Ontario context, provide an overview of their unique data and research partnership - including the new collaboration research and data access platforms created, highlight research findings to date, and identify key topics of interest moving forward.
ResultsOver the last decade, ICES and MCSS have led the way in Ontario linking health administrative and social services data. An initial single year linkage enabled the success of the Health Care Access Research and Developmental Disabilities project. This cross-sectoral initiative provided a clearer sense of how people with developmental disabilities experienced health care in Ontario.
Building on this work, ICES and MCSS recently expanded their partnership bringing together 15 years of social services and health data through a broader data sharing agreement. This agreement allows greater data access to researchers. In addition, ICES and MCSS have been successful in creating a new integrated research platform that will increase the depth and quality of health and social services research and policy evaluation in Ontario.
Conclusion/ImplicationsA broader collaborative research community will now be able to answer questions of interest, do self-directed integrated data analytics and leverage respective program data expertise to tackle joint research projects. Importantly, MCSS analytics teams will now also have access to linked data on this platform to conduct their own research.

IntroductionThe importance of the determinants of health to health outcomes has long been established. Historically, data from each of these sectors has been captured in disparate, often siloed, sources. Attempts to integrate these data have faced a number of challenges including technical, legislative and interpretative barriers, creating inefficiencies and inhibiting knowledge sharing. Despite this, there have been notable successes where intersectoral data and health data have been brought together in a meaningful way. The establishment of strong partnerships, with academia, governments, privacy and legal sectors, and other bodies, across sectors has been key to this success. These partnerships ensure data are integrated, analyzed, and interpreted accurately and appropriately, while also leveraging existing investments and expertise.
Objectives and ApproachThe objective of this session is to explore the role of partnerships throughout the data integration life cycle, from initial discussions, to data integration, through to connecting research output to policy impact. Each of the presenters will discuss the successes, barriers and mitigation strategies they have experienced across different jurisdictions using real world examples.
ResultsHealth research institutes globally are increasingly able to access routinely collected intersectoral data from non-health sectors. In each institute, data are unique, complex and have been collected in a manner consistent with the needs of the sector. As health research institutes work to understand the data structures and determine the best way to link, use and interpret the information according to national and international best practice guidelines, it has become clear that it is critical to undertake this in partnership with experts from each sector, who understand how the data was collected and can guide appropriate interpretation. In addition, these partnerships have enabled the connection of policy priorities in other sectors with research done in the health sector using intersectoral data. For example, in addition to supporting government health departments, health research institutes have collaborated with other government ministries including immigration, social services, and education. This session will present real world examples from local (provincial), national and international contexts, and highlight a novel data platform, being developed to minimize barriers to data access and use across sectors and jurisdictions.
Conclusion / ImplicationsThe participants on this panel will demonstrate the importance of partnership throughout the data integration life cycle when working with intersectoral data using real world examples. Collaboration increases the value of integrated data to both health and non-health sectors, through the connection of policy priorities and support of research across the determinants of health.

ObjectiveThere is growing recognition of the importance of community data governance to build accountability of research institutions to communities. Our organization, a steward of health and administrative population-level data, has previously implemented community governance structures for Indigenous data. This scoping initiative explores development and implementation of an additional community governance structure for race-related data.
ApproachWe conducted a scoping review of peer-reviewed and grey literature to identify existing practices of community data governance. We also conducted key informant interviews with thirteen racialized community stakeholders, who addressed open-ended questions on potential co-design processes as well as governance mandates, scopes, barriers, and facilitators.
ResultsThe scoping review identified eight community data governance examples. Two of these pertained to race-related data, while the remaining six pertained to other data that identified "community" geographically, by disease condition, life stage, and/or economic circumstance. Governance structures were diverse, ranging from one-time crowd-design of a data-sharing agreement to quarterly meetings of a governing board to review project-level data requests. Key informant interviews provided four themes to guide implementation in the context of our organization: exploring organizational readiness, considering who should be involved, defining the scope and mandate, and drafting an approach and process.
ConclusionWe are committed to implementing a community governance structure for race-related, population-level data. However, there are limited examples of similar structures in the existing literature.
ImplicationsThe identified examples and the advice of community stakeholders will guide co-design of a preliminary structure, scope, and mandate for community governance of race-related, population-level data.

ObjectiveThe James and Hudson Bay Region, consisting of six remote Indigenous communities, have experienced barriers accessing regional health data. To inform local health planning, the Minomathasowin Healthy Living department in the Weeneebayko Area Health Authority (WAHA), ICES and Laurentian University developed a Collaboration, to co-create enhanced Indigenous data stewardship.
ApproachThe Collaboration combines expertise in Indigenous knowledge with quantitative and qualitative analyses to develop relevant data intended for public dissemination. Through a community-driven and strength-based approach, local knowledge guides the direction of the research. Indigenous data governance principles are applied, supporting local data ownership, and supplementing local knowledge on population health issues. This ensures the development of research projects that have meaningful impacts. The Collaboration is part of a larger partnership and is continually engaging local Indigenous stakeholders. Protocols ensure research is done in a manner that respects and reflects community well-being and is undertaken in a good way.
ResultsThe Collaboration is an ongoing, living initiative and has enabled WAHA to become a local hub for Indigenous stakeholders to obtain health data for their respective communities. It adheres to the importance of following protocols within Indigenous communities, acknowledging qualitative research activities can be undertaken at the community-level. Projects from this Collaboration identify and prioritize the most pressing health issues impacting the Region including mental health and addictions, COVID-19 surveillance, hospitalization trends, and the prevalence of lupus. The success of the Collaboration is demonstrated through increased requests from the Region to WAHA for support on health planning and decision-making. Data access barriers in the Region are being addressed through the combined expertise of the Collaboration and local knowledge. This approach is enhancing Indigenous data stewardship.
ConclusionsThe Collaboration advocates for Indigenous-led and -driven research that recognizes the value of combining local knowledge with quantitative and qualitative data analyses to put communities first. The Collaboration supports equitable data access and the development of relevant research projects. This is leading to sustainable, impactful health planning for the Region.

IntroductionHealth care systems have faced unprecedented challenges due to the COVID-19 pandemic. Access to timely population-based data has been vital to informing public health policy and practice.
MethodsWe describe how ICES, an independent not-for-profit research and analytic institute in Ontario, Canada, pivoted existing research infrastructure and engaged health system stakeholders to provide near real-time population-based data and analytics to support Ontario's COVID-19 pandemic response.
ResultsSince April 2020, ICES provided the Ontario COVID-19 Provincial Command Table and public health partners with regular and ad hoc reports on SARS-CoV-2 testing and COVID-19 vaccine coverage. These reports: 1) helped identify congregate care/shared living settings that needed testing and prevention efforts early in the pandemic; 2) provided early indications of inequities in testing and infection in marginalized neighbourhoods, including areas with higher proportions of immigrants and visible minorities; 3) identified areas with high test positivity, which helped Public Health Units target and evaluate prevention efforts; and 4) contributed to altering the province's COVID-19 vaccine roll-out strategy to target high-risk neighbourhoods and helping Public Health Units and community organizations plan local vaccination programs. In addition, ICES is a key component of the Ontario Health Data Platform, which provides scientists with data access to conduct COVID-19 research and analyses.
Discussion and ConclusionICES was well-positioned to provide rapid analyses for decision-makers to respond to the evolving public health emergency, and continues to contribute to Ontario's pandemic response by providing timely, relevant reports to health system stakeholders and facilitating data access for externally-funded COVID-19 research.