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AbstractFrom every avenue of concern comes the pressure today to accelerate the environmental cleanup of military installations while the available federal budget dollars decrease. The Navy has applied the principles of formalized partnering, adopted from the construction industry, and successfully accelerated many of its Defense Installation Restoration Program projects. Working together with federal, state, and local regulators, former adversarial relationships are being eliminated through committed team building centered on trust, open communications, and long‐term relationships. Top management is actively supporting environmental partnering with their own personal time and scarce departmental resources because they have experienced the improved working relationships that result in projects being completed in less time and often at or under budget. This article presents the elements of environmental partnering and provides two case studies in which it has been successful in producing real and measurable results.

ObjectiveA recently established data research network identified the need to address Inclusion, Diversity, Equity, and Accessibility and established a team committed to IDEA informed change. The Team identifies, develops, and implements IDEA strategies in distributed research network. We will share the process of establishing this Team and outline identified coordinated opportunities moving forward.
ApproachAdministrative data and analyses are not neutral. Colonialism, racism, gender discrimination, ableism, and other forms of oppression have shaped the data that are available and the research processes that are used to analyze and evaluate data. It is imperative that health data research adopts and develops methods that embed IDEA. An informal survey of a health data research network identified four areas for action including capacity building, culture shifts, information sharing, and coordinated development of policies, practices, and tools. A multi-regional team was established to move forward with this work with an emphasis on operations and research practices.
ResultsIn the fall of 2021 IDEA Team members were recruited from data centres across the network, including IDEA professionals, researchers, data collection and curation specialists, human resource professionals, and public/patient engagement specialists. The first IDEA Team meetings focused on team buildings and building shared purpose by shaping the Terms of Reference and creating principles for working together. Next steps will include an environmental audit to assess network capacity and a consensus oriented decision-making process to identify priorities. The Team includes over 20 members with a broad range of knowledge and expertise, making facilitation a key operational component. Establishing a baseline of knowledge and defining priorities will encompass the first year of the Teams work, navigating the distinct needs of a distributed network, the diverse needs of data centres, and the breadth of data that flows through the network.
ConclusionThis multi-regional inter-disciplinary team is crucial to adopting, developing and embedding IDEA in a distributed data network and could serve as a model for other data research organizations. The Team will work towards capacity building, creating an internal culture shift and coordinating the development of new tools and resources.

IntroductionThe Manitoba Centre for Health Policy has provided international leadership in organizing and accessing administrative databases, linking and analyzing data and translating the findings of research into policy for three decades. During this period, MCHP has addressed numerous challenges in each of these areas.
Objectives and ApproachLinked data research is expanding rapidly in terms of access to new data sources, different types of data, sharing of data across jurisdictions, and advances in data analytics. Technical advances such as computing power and artificial intelligence support these developments while governance structures and ethical issues challenge them. This presentation will describe some of the challenges MCHP has met with a view to gaining insight into how solutions evolved and how experience can guide the future of linked data research.
ResultsThe scaling up of linked data research will need to address specific challenges including de-identification of free text, accessing and linking data from private enterprise such as wearables, and interdisciplinary collaboration to incorporate new techniques developed by computer scientists. Cross-jurisdictional data analysis presents challenges in addressing differences in data architecture. Inter-jurisdictional and international data sharing create ethical and governance challenges. Experience has demonstrated the critical role that relationship building plays in addressing each of these. These relationships are different depending on the partners. They are all based on the development of common use of language, understanding the motivation and concerns of each party, clearly articulating the benefits of the relationship and data use and attention to the cultural and political environment.
Conclusion/ImplicationsLessons from the past can guide us in addressing challenges posed by the exciting opportunities available to us all. While many of these challenges will be solved with technical solutions, we should not overlook the importance of human relationships in building a culture of trust and collaboration as we move

IntroductionIndigenous populations are known to have poor health and health outcomes in many countries. Indigenous peoples continue to be the subjects of unethical research. Research that is undertaken without their consent, involvement in the design, delivery and interpretation of results that perpetuates negative stereotypes ignoring the historical and ongoing impacts of colonialism.
Objectives and ApproachIn order to understand the health status and health system use of First Nations people in Manitoba Canada we developed a partnership between the First Nations Social Secretariat of Manitoba and researchers to link First Nations identifiers with administrative data. This partnership was based on long-standing relationships with researchers who were affiliated with the Manitoba Centre for Health Policy.
ResultsA tripartite data sharing agreement set out the parameters of sharing data that supported the linkage of the Federal Registered First Nations database to the Manitoba Population Research Data Repository. The DSA facilitated direct First Nations input into the indicators chosen, the reporting cohorts, the interpretation of results and the language of the report.
Conclusion / ImplicationsDSAs can be used as a tool to facilitate partnerships with Non-indigenous researchers and Indigenous Nations that lead to meaningful partnerships and lay the foundation for respectful and ethical research. The research presents findings the health of First Nations and shines a light on the underlying colonialism and racism that contributes to the health inequities. These findings have the potential to influence health and well-being of First Nation peoples in Manitoba. This model of collaboration can be used a model in other jurisdictions.

Introduction
Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Data quality can impact the usefulness of UTD for research. UTD are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. Different NLP methods are used to preprocess UTD and may affect data quality.

Objective
Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases.

Methods
A scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the study objective. Information extracted from the studies included article characteristics (i.e., year of publication, journal discipline), data characteristics, types of preprocessing methods, and data quality topics. Study data were presented using a narrative synthesis.

Results
A total of 41 articles were included in the scoping review; over 50% were published between 2016 and 2021. Almost 20% of the articles were published in health science journals. Common preprocessing methods included removal of extraneous text elements such as stop words, punctuation, and numbers, word tokenization, and parts of speech tagging. Data quality topics for articles about EMR data included misspelled words, security (i.e., de-identification), word variability, sources of noise, quality of annotations, and ambiguity of abbreviations.

Conclusions
Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. There are similarities in the data quality dimensions used to characterize structured data and UTD. While a few general-purpose measures of data quality that do not require external data; most of these focus on the measurement of noise.

For more than 30 years, the Manitoba Centre for Health Policy has been conducting research and evaluation to provide timely and critical evidence to answer real-world policy questions. Our experienced team of research scientists, analysts and other staff work extensively with policy-makers at the macro, meso and micro levels of government to support evidence-informed policy and program development in an effort to ensure that policy initiatives provide the greatest benefit possible to individuals and society as a whole. Using the widely recognized whole-population Manitoba Population Research Data Repository, which comprises approximately 100 different datasets from multiple sectors, we employ sophisticated and state-of-the-art research methods and data science technologies, and then translate the results into meaningful insights or recommendations for policy-makers.
Our long and productive history of working with policy-makers has taught us much about making our research relevant to policy-makers. In this article, we outline some examples of how research evidence has been used to influence policy in Manitoba, and the key lessons we have learned about what makes relationships between researchers and policy-makers work. In essence, policy-makers have supported the growth of the Repository over the last 30 years, because researchers have "closed the loop" by sharing valuable and policy-relevant research results with them. This ability to inform policies, programs and service delivery with scientific evidence continues to benefit individuals, communities and our society as a whole.

ABSTRACTObjectivesTo determine the relationship between known social complexity and model of primary care service deliveryApproachThe impacts of the social determinants of health are well described. To understand the contribution of specific factors on primary care service use we linked social data in the Population Health Research Data Repository at the Manitoba Centre for Health Policy to health system data. We included all patients visiting a Winnipeg clinic at least three times between 2010 and 2013. We allocated each participant to the primary care provider providing the majority of their care; and each provider was assigned to the model of care where they provided the majority of their clinical care. We developed eleven new indicators to describe social complexity such as: children in care, low income quintile, income assistance (welfare), high residential mobility, and involvement with the justice system. Results The cohort included 626,264 unique individuals of whom 53.1% were female. The majority of participants received their care from the fee for service (FFS) model (511,763) followed by 76,261 assigned to "reformed FFS". 16,536 and 12,178 were assigned to the 2 team-based care alternative funded models and 9,526 to the teaching clinic model. Patients with social complexities, except for newcomers, were more likely to attend the alternative funded clinics. The patients these clinics served were generally very complex with over 15% having more than 5 complexities compared to less than 5% of those attending the FFS models. Twice as many patients in the FFS models (60%) had no complexities compared to the alternative funded models.ConclusionThe availability of social data in population health repositories provides new opportunities to understand the distribution of these social factors amongst care providers and the impact of each on the health of populations. This new understanding can support focused interventions to address specific social risk factors and provide the evidence to support different models of primary care service delivery.

ABSTRACTObjectives To determine the relationships between five models of primary care service delivery and quality of care indicators in an urban population. Two fee-for-service (FFS) and three alternative-funded models of primary care service delivery were studiedApproach We allocated all Manitoba residents who had at least three visits to any primary care provider (PCP) at any Winnipeg clinic between 2010-2013 to the most responsible PCP (N = 626,264). We then allocated each PCP to a model of primary care service delivery. We created general linear mixed models to describe the relationship between each model of primary care and the dominant, traditional fee-for-service model for health services use, while controlling for a variety of PCP and patient factors, including patient social complexity.Results Patient social complexity was associated with poorer crude rates for many of the indicators. There were no differences among the models for hospital readmission within 30 days or specialist referral by the assigned PCP. Hospitalizations for ACSC were higher for one alternative funded model (1.98 OR, 1.38-2.83 95% CI), while non-indicated low back X-rays were lower for a different alternative funded model (0.14 OR, 0.03-0.59 95% CI). Ambulatory care visits to any PCP were lower for all three alternative funded models than the two FFS models. The family medicine academic teaching sites had lower rates of continuity of care (p< 0.5)Conclusion Overall, no model of primary care consistently outperformed the others. FFS models had higher rates of visits, but appeared to satisfy patient needs better because they had less use of telehealth services following visits. Teaching sites appeared to sacrifice continuity of care potentially to support other academic activities. Controlling for social complexity was associated with a reduction in the differences between models in indicator outcomes.

ObjectivePublicly funded healthcare delivery systems use projections to ensure the availability of adequate future service delivery. Planning cycles need to consider infrastructure, human resources, and other essential requirements with an adequate lead time. Projections are fraught with challenges due to multiple unknowns but new developments in modeling may be useful.
ApproachWe explored the available data to determine the best approach to modeling surgical demand. The Manitoba Population Research Data Repository includes 90+ databases linkable at the person level over time. These include the population registry which includes all Manitobans registered for the universal healthcare benefit. Hospital discharge abstracts include over 20 relevant diagnoses (ICD10) and procedure codes for each admission. Medical services claims include all physician services provides with ICD 9CM codes. Fee-for-service physicians are paid based on these and alternate funded physicians are required to submit shadow claims.
ResultsWe found 349,171 orthopedic procedures of which 18.1% were absent from the Medical claims files and 551,508 medical claims of which 27.5% lacked a corresponding hospital abstract. We also identified 230,717 ophthalmologic procedures in the hospital data of which 2.5% had no corresponding medical claim; of the 648,826 medical claims 66.2% had no matching hospital abstract. Resource requirements of procedures are reflected in the number and complexity of each procedure performed. Historical changes over time reflect changing demand (population growth and aging) balanced by available resources. Available resources cannot be predicted via modelling. The best fit based on the validation dataset was a Seasonal Autoregressive Integrated Moving Average model with a Mean Absolute Percentage Error (MAPE) of 5.327%. which translates to 94.7% accuracy.
ConclusionDespite the limitations of modeling based on past behavior, we were able to predict surgical demand with 95% accuracy. These projections are valid partly due to the persistence of historical constraints through the validation period. Policies that address these service provision limitations would precipitate a need to adjust the model.

Objectives: This article articulates the complexity of modeling in First Nations, Metis, and Inuit contexts by providing the results of a modeling exercise completed at the request of the First Nations Health and Social Secretariat of Manitoba.
Methods: We developed a model using the impact of a previous pandemic (the 2009 H1N1) to generate estimates.
Results: The lack of readily available data has resulted in a model that assumes homogeneity of communities in terms of health status, behaviour, and infrastructure limitations. While homogeneity may be a reasonable assumption for province-wide planning, First Nation communities and Tribal Councils require more precise information in order to plan effectively. Metis and urban Inuit communities, in contrast, have access to much less information, making the role of Indigenous organizations mandated to serve the needs of these populations that much more difficult.
Conclusion: For many years, Indigenous organizations have advocated for the need to have access to current and precise data to meet their needs. The COVID-19 pandemic demonstrates the importance of timely and accurate community-based data to support pandemic responses.

For more than 30 years, the Manitoba Centre for Health Policy has been conducting research and evaluation to provide timely and critical evidence to answer real-world policy questions. Our experienced team of research scientists, analysts and other staff work extensively with policy-makers at the macro, meso and micro levels of government to support evidence-informed policy and program development in an effort to ensure that policy initiatives provide the greatest benefit possible to individuals and society as a whole. Using the widely recognized whole-population Manitoba Population Research Data Repository, which comprises approximately 100 different datasets from multiple sectors, we employ sophisticated and state-of-the-art research methods and data science technologies, and then translate the results into meaningful insights or recommendations for policy-makers. Our long and productive history of working with policy-makers has taught us much about making our research relevant to policy-makers. In this article, we outline some examples of how research evidence has been used to influence policy in Manitoba, and the key lessons we have learned about what makes relationships between researchers and policy-makers work. In essence, policy-makers have supported the growth of the Repository over the last 30 years, because researchers have "closed the loop" by sharing valuable and policy-relevant research results with them. This ability to inform policies, programs and service delivery with scientific evidence continues to benefit individuals, communities and our society as a whole.

IntroductionOn their 18th birthday children in custody of provincial Child and Family Services (CFS) age out, and are adults in control of their own care. An additional extended transitional services program was introduced several years ago to address gaps in the provicion of, and access to, adult social services during this change.
Objectives and ApproachUsing linked population based data from the Manitoba Population Research Data Repository, children in the custody of CFS who turned 18 during a 10 year study period were compared to children not in custody. For those in custody of CFS, we also compared individuals who participated in the extended transitional care services to those who opted out. Outcomes included use of health services and prescription drugs, social assistance, involvment with the justice system, living in social housing, and mental health outcomes. For most outcomes, the two year period prior to the 18th birthday and the two year period after were measured.
ResultsDuring the study period, 4656 children in care of CFS turned 18 while in custody. There were 2811 permanent wards, of which 1663 participated in the extended transitional services program. An additional 1845 non-permanent wards also turned 18 during the study period. Permanent wards were much more likely to be long term wards (greater than six years, ~65\%) compared non permanent wards (~17\%). Opioid prescription rates more than doubled in the two years after their 18th birthday and were about 6 times greater than prescription rates for those not in care of CFS. Criminal accusation rates did not change after their 18th birthday, were about equal for permanent and non-permanent wards. For the majority of outcomes, the transitional services program appeared to have little impact.
Conclusion/ImplicationsCompared to children not in care of CFS, rates of most outcomes were considerably higher for wards. Not all outcomes demonstrated a significant change over the transition period. By linking data from so many different government departments, extra attention can be focused on areas likely to have the greatest impact.

IntroductionFrailty is a state of vulnerability to diverse stressors emphasizing the importance of identifying the frail to support them. The burden of frailty in Canada is steadily growing. Today, approximately 25% of people over age 65 and 50% past age 85 – over one million Canadians – are medically frail.
Objectives and ApproachTo develop an administrative data definition of frailty to facilitate clinical and health system planning. We will validate the definition by linking the administrative data to electronic medical records (EMR) data. The EMR definition is based on a Machine Learning binarized frailty flag for patients with a Rockwood Clinical Frailty Score > 5 on physician chart audit. The sensitivity of the Machine Learning was disappointing: 28% (95% CI: 21% to 36%).specificity was: 94% (95% CI: 93% to 96%), positive predictive value: 53% (95% CI: 42% to 64%), negative predictive value: 86% (95% CI: 83% to 88%).
ResultsThere was little overlap between the EMR and administrative data definitions using the same population. Of the 29,382 eligible administrative data community dwelling patients over 65 years old, with a linkable EMR record, 2398 (8.15%) were identified as frail using the administrative data definition, but only 16.1% of these were frail according to the EMR definition. Of the 2396 who were identified as frail in EMR data, only 375 (15.7%) were identified as frail using the administrative data definition.
Conclusion/ImplicationsWe are not yet able to develop a reliable administrative data definition of frailty to identify community living individuals to support health service planning. The lack of agreement between the results obtained from EMR and administrative data definitions suggests that further refinement is necessary. Identification of frailty remains complex.