Suchergebnisse

We write to you, here in the pages of the International Journal of Population Data Science, for the second time in our capacity of co-directors of the International Population Data Linkage Network (IPDLN – www.ipdln.org). Time has certainly passed quickly since our first communication, where we introduced ourselves, and discussed planned initiatives for our tenure as leads of the IPDLN. Our network's scientific community is steadily growing and thriving in an era of heightened interest around all things 'data'. Indeed, there is great enthusiasm for all initiatives that explore ways of harnessing information systems and multisource data to enhance collective knowledge of health matters so that better decisions can be made by governments, system planners, providers, and patients. Never before have such initiatives attracted more attention.
It is in this context of heightened interest and relevance around IPDLN and its science that we prepare to convene in Banff, Alberta, Canada for the 5th biennial IPDLN Conference – September 11-14. The conference, to be held at the inspiring Banff Centre (www.banffcentre.ca), is almost sold out, with only limited space remaining for late registrants. A tremendous program has been created through the oversight of Scientific Program co-chairs, Drs. Astrid Guttman and Hude Quan. A compelling roster of plenary lectures from Drs. Diane Watson, Jennifer Walker, and Osmar Zaïane is eagerly anticipated, as are topical panel discussions, an entertaining Science Slam session, and a terrific social program. These sessions will be surrounded by rich scientific oral and poster presentations arising from the more than 450 scientific abstracts submitted for review. We are so pleased to see this vibrant scientific engagement from the IPDLN membership and students, and look forward to hosting all delegates in Banff.
The Banff conference will also be the venue at which we announce the new Directorship of the IPDLN for the next two years (2019 and 2020). As co-directors, we engaged with a number of individuals and organizations with interest in leading the IPDLN. In the end, two compelling Directorship applications were submitted – one a joint bid from Australia's Population Health Research Network and the South Australia Northern Territory DataLink, and the other from the US-based Actionable Intelligence for Social Policy. IPDLN members submitted votes on these strong leadership bids through an online voting process, and while the excellence and appeal of both bids was apparent in strong voter support for both, a winning bid has been confirmed, and it will (as mentioned) be announced at the upcoming September conference.
As we look forward to the Banff meeting with great anticipation, we are compelled to acknowledge the growing IPDLN legacy created by past directors. We are particularly indebted to our immediate predecessor, Dr. David Ford, and his team at Swansea University. Their work in hosting the 2016 IPDLN conference has been an inspiration to us in the planning of this year's conference, and their crucial and foundational work in creating an IT platform for the IPDLN website, the membership database, and the new International Journal for Population Data Science has brought the IPDLN to a new level of organizational sophistication. Over the last 18 months, our co-directorship teams from the Institute for Clinical Evaluative Sciences in Ontario and the O'Brien Institute for Public Health at the University of Calgary have built on the foundation established by prior directors to update/enhance the IPDLN website and membership database. The IPDLN has more members than ever before representing a greater number of countries, and we have a more formalized governance structure with the creation of an Executive Committee that will include immediate past-Directors in order to better ensure continuity. A new Executive Committee will be elected by the IPDLN membership following the Banff conference.
The waiting is almost over and IPDLN 2018 is upon us! Our scientific domain has never had the prominence or level of anticipation that we currently see. And the IPDLN has grown in its size, vibrancy and scientific scope. The opportunities for us are boundless, and the timing of our upcoming conference could not be better. We are honoured, with our respective organizations, to have had this opportunity to serve as co-directors over the past two years, and look forward to seeing many of you very soon. For those of you who are unable to travel to Canada's Rocky Mountains this year, we look forward to connecting with you at a later time in the IPDLN's continuing upward journey.

ABSTRACT
ObjectiveCanada's large single payer health systems have created provincial centres with rich, population-wide health and social data holdings that are linkable at the person level. Complementary federal data include a subset of standardized and enriched administrative healthcare data at the Canadian Institute for Health Information, Statistics Canada's extensive array of survey- and census-based social and geography-based information, and large national datasets that are being created through pan-Canadian initiatives. Unfortunately, these data assets are rarely combined in multi-province or pan-Canadian studies, often because data are not directly comparable from one province to another, or cannot be shared due to legislative or other barriers. There is now growing interest in enabling multi-province studies — even when data are not directly comparable — and in sharing experiences to make more effective use of linked or linkable administrative data across Canada.
ApproachNine provincial and national organizations have created a detailed Implementation Plan for the new PRHDN distributed data network. Without requiring that record-level data leave provincial boundaries, the PRHDN will create shared core research data infrastructure: (i) validated algorithms that implement case definitions applicable across provinces, (ii) harmonized common data and (iii) common analytic protocols. The PRHDN will also establish complementary infrastructure including dedicated personnel to assist researchers and decision makers, joined-up training and capacity building sessions, opportunities to share learning and experience related to linking new datasets such as electronic medical records and omic datasets, and forums for knowledge translation and exchange with decision makers.
ResultsThe PRHDN is already bringing together expertise from across Canada as researchers, decision makers and data custodians begin to identify opportunities for enhanced use of health data in Canadian research, policy making and practice. A simple PRHDN website has been created (https://www.prhdn.ca/) and more than 200 researchers and policy/decision makers have become members of the PRHDN consortium. Surveys of consortium members are identifying priorities for the first algorithm validation work (to-date the top four priorities are mental health, cardiovascular disease, diabetes and respiratory disease) and the PRHDN Leads Team is functioning as a decision-making body, e.g., in discussions with Statistics Canada.
ConclusionsWorking together, provincial and national organizations across Canada have identified concrete steps that can be taken to enable multi-province and pan-Canadian studies based on administrative data within one year of the start of funding. The PRHDN Leads Team is currently discussing the PRHDN vision and Implementation Plan with potential funders.

Population data science [1] researchers are not alone in recognizing the value of health and health-related data. In the era of big data, and with advent of machine learning and other artificial intelligence methods, organizations around the world are actively working to turn data into knowledge, and, in some cases, profit. The media and members of the public have taken notice, with high profile news stories about data breaches and privacy concerns [2-4] alongside some stories that call for increased use of data [5,6]. In response, public and private sector data-holding organizations and jurisdictions are turning their attention to policies, processes and regulations intended to ensure that personal data are used in ways that that the public supports. In some cases, these efforts include involving "publics" in decisions about data, such as using patient and lay person advice and other inputs to help shape policies [7-10].

Many countries around the world are creating COVID-19 trial datasets and databases of COVID-19-related data such as test results that cover entire populations. It is our strong recommendation, and sincere hope, that these data assets will be brought together through record linkage so that their scientific value and impact for society can be amplified. We call on trialists, data stewards and research funders to work together so that prospective linkage of trial data to medical databases becomes the norm, starting with COVID-19 trials.

This article explores the approach that ICES (formerly the Institute for Clinical Evaluative Sciences)uses to encourage public engagement at both the research study and corporate level. ICES is anindependent not-for-profit research institute in the province of Ontario, Canada. This article wasco-written by ICES' public engagement team and four members of the ICES Public AdvisoryCouncil (PAC). As part of the process of writing this article PAC members provided theirreflections on why they got involved, what worked well and the limitations and challenges of ICES'approach.
ICES described the development of its public engagement strategy to inform how the institutionwould capture and incorporate the values of Ontarians in ICES activities and research. ICES provideddetails on two key elements of its strategy: the formation of a PAC to advise its leadership, andthe creation of resources and supports to encourage researchers to incorporate public engagementin their projects.
PAC members and ICES provided perspectives on what impact they perceive as a result ofthe public engagement strategy. PAC members expressed that ICES has demonstrated listening toand using their input, but it is too early to evaluate if their feedback has changed the way ICESconducts its work. ICES discussed the challenges and successes in building and implementing thepublic engagement strategy, including recruiting a diverse council, aligning with public prioritiesand creating a culture of engagement. As a result of public input, ICES has restructured theway the institution explains its privacy and cybersecurity approach to build trust and confidence.ICES has also seen an increase in researchers using public engagement resources, and early datasuggests that in 2019 about 20% of scientists included some form of public engagement in theirprojects.
ICES' journey to public engagement resulted in important changes to processes and activitiesat the institution, but there is much more that needs to be done. PAC members advocate thatpublic members should be engaged in health data research and hope that public input will bea core element in health data research in the future. ICES will continue its efforts to addresspublic priorities and will seek to further evaluate the impact of public engagement across theorganisation.

ABSTRACTObjectivesThere is a growing need to broaden access to administrative health data in order to support decision making and planning by health system stakeholders. An initiative funded by the Ontario Ministry of Health and Long-Term Care, the Applied Health Research Question (AHRQ) portfolio leverages the linked administrative health data holdings and the scientific and clinical expertise at ICES to answer questions generated by stakeholders that will have a direct impact on health care policy, planning or practice.
ApproachEligible requesters include government ministries, health care providers and planners. Requests detail the purpose of the research question, the related scientific literature, and the planned use and intended impact of the research findings. An internal review team meets monthly to adjudicate; requests demonstrably needing research findings rapidly are adjudicated on an ad hoc basis. Eligible requests are those that aim to inform evidence-based decision making, do not advocate for a particular answer and are feasible in terms of data availability. All projects are reviewed by the internal privacy office to ensure that use of the administrative health data is in accordance with both data sharing agreements and legislation governing use of personal health information. At no cost to the requesting organization, ICES scientists and research staff formulate the analysis plan, conduct the analysis and prepare the research product (data tables, a slide deck and/or a written report); and, may opt to publish noteworthy findings. All research products must be cleared for risk of re-identification prior to being shared externally.
ResultsRequests have steadily increased from 43 submissions in fiscal year 2012/13, to 59 in 2014/15 and 74 to date in 2015/16. In fiscal year 2014/15, provincial government and government agencies were the most frequent requesters (39%), followed by hospitals and other health care providers (19%), disease advocacy groups (12%) and professional associations (10%). Requests include assessment of health care utilization; health system performance and evaluation; and chronic disease prevalence and treatment. Time to complete reports varies from 5 days to 24 months, depending on project complexity and requirements. Requesters report that AHRQ research findings have influenced decision-making, policy development and health care practice; and have inspired future research.
ConclusionThis initiative demonstrates the value and feasibility of using the linked administrative health data to answer questions to meet the unique needs of health planners and policymakers, and presents an opportunity for collaboration beyond the academic research community.

ICES was founded in 1992 to study the health care system and promote effective, efficient and equitable health care. Over 27 years later, the goal remains largely unchanged, though the institute has grown in size and impact. Created as an independent not-for-profit research institute and given what was, at the time, unprecedented access to administrative health data records for the population of Ontario, ICES' initial focus was to better understand the delivery of hospital services and translate its findings into better health care and policy. From modest beginnings with a handful of researchers located in a few hospital offices, ICES has grown to encompass a community of almost 500 scientists and staff across a network of seven physical sites in Ontario. The original focus on hospital-based services has expanded significantly and now includes research and analysis of community-based health services, health policy, Indigenous health, social determinants of health, and data science.

IntroductionThe importance of Indigenous data sovereignty and Indigenous-led research processes is increasingly being recognized in Canada and internationally. For First Nations in Ontario, Canada, access to routinely-collected demographic and health systems data is critical to planning and measuring health status and outcomes in their populations. Linkage of this data with the Indian Register (IR), under First Nations data governance, has unlocked data for use by First Nations organizations and communities.
ObjectivesTo describe the linkage of the IR database to the Ontario Registered Persons Database (RPDB) within the context of Indigenous data sovereignty principles.
MethodsDeterministic and probabilistic record linkage methods were used to link the IR to the RPDB. There is no established population of First Nations people living in Ontario with which we could establish a linkage rate. Accordingly, several approaches were taken to determine a denominator that would represent the total population of First Nations we would hope to link to the RPDB.
ResultsOverall, 201,678 individuals in the national IR database matched to Ontario health records by way of the RPDB, of which 98,562 were female and 103,116 were male. Of those First Nations individuals linked to the RPDB, 90.2% (n=181,915) lived in Ontario when they first registered with IR, or were affiliated with an Ontario First Nation Community. The proportion of registered First Nations people linking to the RPDB improved across time, from 62.8% in the 1960s to 94.5% in 2012.
Conclusions This linkage of the IR and RPDB has resulted in the creation of the largest First Nations health research study cohort in Canada. The linked data are being used by First Nations communities to answer questions that ultimately promote wellbeing, effective policy, and healing.

Objective To compare performance and factors predicting failure to reach Ontario and Australian government time targets between a Canadian (Sunnybrook Hospital) and an Australian (Austin Health) academic tertiary-level hospitals in 2012, and to assess for change of factors and performance in 2016 between the same hospitals. Methods This was a retrospective, observational study of patient administrative data in two calendar years. The main outcome measure was reaching Ontario and Australian ED time targets for admissions, high and low urgency discharges. Secondary outcomes were factors predicting failure to reach these targets. Results Between 2012 and 2016, Sunnybrook and Austin experienced increased patient volume of 10.2% and 19.2%, respectively. Bed capacity decreased at Sunnybrook (-10.8%) but increased at the Austin (+30.3%). For both years, Austin failed to achieve the Australian time target, but succeeded for all Ontario targets except for low urgency discharges. Sunnybrook failed all targets irrespective of year. The top factors for failing Ontario ED length-of-stay targets for both hospitals in 2012 and 2016 were bed request greater than 6 h, access block greater than 1 h, use of cross-sectional imaging, consultation and waiting for the emergency physician greater than 2 h. Conclusion Austin outperformed Sunnybrook for Ontario and Australian government time targets. Both hospitals failed the Australian targets. Factors predicting failure to achieve targets were different between hospitals, but were mainly clinical resources. Sunnybrook focussed on increasing human resources. Austin focussed on increasing human resources, observation unit and hospital beds. Intrinsic hospital characteristics and infrastructure influenced target success. ; Peer reviewed

INTRODUCTION: The importance of Indigenous data sovereignty and Indigenous-led research processes is increasingly being recognized in Canada and internationally. For First Nations in Ontario, Canada, access to routinely-collected demographic and health systems data is critical to planning and measuring health status and outcomes in their populations. Linkage of this data with the Indian Register (IR), under First Nations data governance, has unlocked data for use by First Nations organizations and communities. OBJECTIVES: To describe the linkage of the IR database to the Ontario Registered Persons Database (RPDB) within the context of Indigenous data sovereignty principles. METHODS: Deterministic and probabilistic record linkage methods were used to link the IR to the RPDB. There is no established population of First Nations people living in Ontario with which we could establish a linkage rate. Accordingly, several approaches were taken to determine a denominator that would represent the total population of First Nations we would hope to link to the RPDB. RESULTS: Overall, 201,678 individuals in the national IR database matched to Ontario health records by way of the RPDB, of which 98,562 were female and 103,116 were male. Of those First Nations individuals linked to the RPDB, 90.2% (n=181,915) lived in Ontario when they first registered with IR, or were affiliated with an Ontario First Nation Community. The proportion of registered First Nations people linking to the RPDB improved across time, from 62.8% in the 1960s to 94.5% in 2012. CONCLUSION: This linkage of the IR and RPDB has resulted in the creation of the largest First Nations health research study cohort in Canada. The linked data are being used by First Nations communities to answer questions that ultimately promote wellbeing, effective policy, and healing

ObjectiveStewards of population-level data have an obligation to serve the public interest. As one such publicly funded data steward, our Public Advisory Council (PAC) co-led and determined the focus of an analysis project using population-level administrative health data. We describe our engagement strategies herein.
ApproachThe 20-member PAC was involved in each stage of the project, from research question formulation to knowledge translation planning over 18 months, consisting of 12 meetings with both large and smaller groups. Our approach to engagement applied the International Association for Public Participation's Framework as the foundation for an 'empowerment' level of engagement and adapted approaches from the James Lind Alliance and the 'Plan-Do-Study-Act' cycle.
ResultsThe PAC chose to focus their analysis project on factors related to mental health and addiction service use. Four strategies were used and co-designed by members to foster engagement throughout: providing education and guidance, shared and guided brainstorming, building consensus, and responsiveness to feedback and evaluation. PAC members directed how and when these strategies were used, with challenges and lessons learned currently being co-developed into a publicly accessible report.
ConclusionsOur work demonstrates the importance and value of public-driven research outputs and the feasibility of integrating public members in the work of data stewards.
ImplicationsThe insights and practical strategies generated from this project will be used to guide effective engagement of the public for future analysis projects and to improve trust and social license for other initiatives using population-level data.

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.

Background: The government of Malawi is committed to the broad rollout of antiretroviral treatment in Malawi in the public health sector; however one of the primary challenges has been the shortage of trained health care workers. The Practical Approach to Lung Health Plus HIV/AIDS in Malawi (PALM PLUS) package is an innovative guideline and training intervention that supports primary care middle-cadre health care workers to provide front-line integrated primary care. The purpose of this paper is to describe the lessons learned in implementing the PALM PLUS package. Methods: A clinical tool, based on algorithm- and symptom-based guidelines was adapted to the Malawian context. An accompanying training program based on educational outreach principles was developed and a cascade training approach was used for implementation of the PALM PLUS package in 30 health centres, targeting clinical officers, medical assistants, and nurses. Lessons learned were identified during program implementation through engagement with collaborating partners and program participants and review of program evaluation findings. Results: Key lessons learned for successful program implementation of the PALM PLUS package include the importance of building networks for peer-based support, ensuring adequate training capacity, making linkages with continuing professional development accreditation and providing modest in-service training budgets. The main limiting factors to implementation were turnover of staff and desire for financial training allowances. Conclusions: The PALM PLUS approach is a potential model for supporting mid-level health care workers to provide front-line integrated primary care in low and middle income countries, and may be useful for future task-shifting initiatives.

The accurate interpretation of mortality surveys in humanitarian crises is useful for both public health responses and security responses. Recent examples suggest that few medical personnel and researchers can accurately interpret the validity of a mortality survey in these settings. Using an example of a mortality survey from the Democratic Republic of Congo (DRC), we demonstrate important methodological considerations that readers should keep in mind when reading a mortality survey to determine the validity of the study and the applicability of the findings to their settings.