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Background In the case of immigrant health and wellness, data are the key limiting factor, where comprehensive national knowledge on immigrant health and health service utilisation is limited. New data and data silos are an inherent response to the increase in technology in the collection and storage of data. The Health Data Cooperative (HDC) model allows members to contribute, store, and manage their health-related information, and members are the rightful data owners and decision-makers to data sharing (e g. research communities, commercial entities, government bodies). Objective This review attempts to scope the literature on HDC and fulfill the following objectives: 1) identify and describe the type of literature that is available on the HDC model; 2) describe the key themes related to HDCs; and 3) describe the benefits and challenges related to the HDC model. Methods We conducted a scoping review using the five-stage framework outlined by Arskey and O’Malley to systematically map literature on HDCs using two search streams: 1) a database and grey literature search; and 2) an internet search. We included all English records that discussed health data cooperative and related key terms. We used a thematic analysis to collate information into comprehensive themes. Results Through a comprehensive screening process, we found 22 database and grey literature records, and 13 Internet search records. Three major themes that are important to stakeholders include data ownership, data security, and data flow and infrastructure. Conclusions The results of this study are an informative first step to the study of the HDC model, or an establishment of a HDC in immigrant communities.

Introduction Hypertension is a common medical condition, affecting 1 in 5 Canadians, and is a major risk factor for heart attack, stroke, and kidney disease. Predicting the risk of developing incident hypertension may help to inform targeted preventive strategies. Objectives and Approach Identification of major risk factors and incorporation into a multivariable model for risk stratification may help to identify individuals who are at highest risk for developing incident hypertension and would potentially benefit most from intervention. The goal of the proposed research is to develop a robust hypertension prediction model for the general population using the Alberta Tomorrow Project (ATP) cohort data linked with Alberta’s administrative health data. ATP is Alberta's largest population health cohort, contains baseline data on socio-demographic characteristic, personal and family history of disease, medication use, lifestyle and health behavior, environmental exposures, physical measures and bio samples. Results Alberta’s administrative health data additionally provides information on health care utilization, enrollment, drugs, physician services, and hospital services. A prediction model for hypertension will be developed using logistic regression where information on candidate variables for the model will be gathered from ATP data and outcome (incident hypertension) will be ascertained from administrative health data (physicians/practitioner claim data and hospital discharge abstract data). Lacking follow-up information in current ATP data has laid the foundation of linking the two data sources through an anonymous unique person identifier (e.g. PHN) that will eventually provide follow-up information on ATP participants who are free of hypertension at baseline developed the disease as well as information on other potential variables. Conclusion/Implications The proposed prediction model will help to identify individuals at highest risk for developing hypertension and those who may benefit most from targeted healthy behavioral interventions and/or treatment. Such identification of high risk people may help prevent hypertension as well as the continuing costly cycle of managing hypertension and its complications.