Sharing of military Veterans’ mental health data across Canada: A scoping review

Journal of Military Veteran and Family Health ◽

10.3138/jmvfh-2021-0064 ◽

2022 ◽

Author(s):

Abraham Rudnick ◽

Dougal Nolan ◽

Patrick Daigle

Keyword(s):

Mental Health ◽

Information Sharing ◽

Scoping Review ◽

Information Sources ◽

Military Veterans ◽

Health Data ◽

Data Sources ◽

Data Sets ◽

Comprehensive Review ◽

Canadian Military

LAY SUMMARY Information on Canadian military Veterans’ mental health is needed to develop and improve mental health services. It is not clear to what extent such information is available and connected across its sources. A comprehensive review of scientific and other authorized publications was conducted to identify information sources related to Canadian Veteran mental health, connections between them, and related policies or guidelines. Ten data sources related to military Veterans’ mental health in Canada were found, but no policies or guidelines specifically addressing information sharing across these data sets were discovered. Secure, Accessible, eFfective, and Efficient (SAFE) information sharing across these sources was implied but not confirmed. The authors recommend consideration be given to establishing a repository of relevant data sets and policies and guidelines for information sharing and standardization across all relevant data sets.

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Patient-generated health data analytics on ambulatory behaviours of people living with T2D: A Scoping Review (Preprint)

10.2196/preprints.29027 ◽

2021 ◽

Author(s):

Meghan Shyama Nagpal ◽

Antonia Barbaric ◽

Diana Sherifali ◽

Plinio P Morita ◽

Joseph A Cafazzo

Keyword(s):

Scoping Review ◽

Health Behaviours ◽

Health Data ◽

Clinical Findings ◽

Machine Learning Algorithms ◽

Data Sources ◽

Critical Examination ◽

Online Forums ◽

Behavioural Patterns ◽

Health Data Analytics

BACKGROUND Complications due to Type 2 Diabetes (T2D) can be mitigated through proper self-management which can positively change health behaviours. Technological tools are available to help people living with T2D manage their condition and such tools provide a large repository for patient-generated health data (PGHD). Analytics can provide insights about the ambulatory behaviours of people living with T2D. OBJECTIVE The objective of this review was to investigate analytical insights can be derived through PGHD with respect to ambulatory behaviours of people living with T2D. METHODS A scoping review using the Arksey & O’Malley framework was conducted in which a comprehensive search of the literature was conducted by two reviewers. Three electronic databases (PubMed, IEEE, ACM) were searched using keywords associated with diabetes, behaviours, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted and studies were selected. Critical examination took place through a descriptive-analytical narrative method and data extracted from the studies was classified into thematic categories. These categories reflect the findings of this study as per our objective. RESULTS We identified 43 studies that met the inclusion criteria for this review. While 70% of the studies examined PGHD independently, 30% of the studies combined PGHD with other data sources. The majority of these studies used machine learning algorithms to perform their analysis. Themes identified through this review include 1) predicting diabetes / obesity, 2) factors that contribute to diabetes / obesity, 3) insights from social media & online forums, 4) predicting glycemia, 5) improved adherence / outcomes, 6) analysis of sedentary behaviours, 7) deriving behavioural patterns, 8) discovering clinical findings, and 9) developing design principles. CONCLUSIONS The increased volume and availability of PGHD has the potential to derive analytical insights regarding the ambulatory behaviours of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavioural patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, that would not be available through other data sources.

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Models of Mental Health Triage for Individuals Coming to the Attention of the Police Who May Be Experiencing Mental Health Crisis: A Scoping Review

Policing A Journal of Policy and Practice ◽

10.1093/police/paz050 ◽

2019 ◽

Author(s):

Alice Park ◽

Alison Booth ◽

Adwoa J Parker ◽

Arabella Scantlebury ◽

Kath Wright ◽

...

Keyword(s):

Mental Health ◽

Information Sharing ◽

Scoping Review ◽

Mental Distress ◽

Mental Health Crisis ◽

Personnel Training ◽

Health Crisis ◽

The Cost ◽

Do So

Abstract Police routinely encounter individuals experiencing mental distress, despite being ill-equipped to do so. Mental health triage aims to address these concerns. A range of approaches to triage has been introduced; however, no overview exists. We conducted a systematic scoping review of mental health triage co-responding schemes. Eleven databases were searched to identify the literature; each scheme was charted and described. Thirty-three studies describing 47 schemes were included. Intervention details were generally poorly reported, however, differences in personnel, training and information sharing were identified. There are multiple schemes in practice based on the co-responding model. Robust research into the cost and effectiveness of mental health triage is needed.

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Adequacy of Data Sources for Investigation of Tertiary Education Student’s Wellbeing in Australia: A Scoping Review

Healthcare ◽

10.3390/healthcare6040136 ◽

2018 ◽

Vol 6 (4) ◽

pp. 136 ◽

Cited By ~ 2

Author(s):

Stephanie Partridge ◽

Eloise Howse ◽

Gwynnyth Llewellyn ◽

Margaret Allman-Farinelli

Keyword(s):

Higher Education ◽

Young Adults ◽

Longitudinal Studies ◽

Scoping Review ◽

Tertiary Education ◽

Adult Population ◽

Data Sources ◽

Data Sets ◽

Policy And Practice ◽

Data Set

Young adulthood is a period of transition, which for many includes higher education. Higher education is associated with specific risks to wellbeing. Understanding the available data on wellbeing in this group may help inform the future collection of data to inform policy and practice in the sector. This scoping review aimed to identify the availability of data sources on the wellbeing of the Australian young adult population who are attending tertiary education. Using the methods of Arksey and O’Malley, data from three primary sources, i.e., Australian Bureau of Statistics, Australian Institute of Health and Welfare and relevant longitudinal studies, were identified. Data sources were screened and coded, and relevant information was extracted. Key data for eight areas related to wellbeing, namely, family and community, health, education and training, work, economic wellbeing, housing, crime and justice, and culture and leisure sources were identified. Forty individual data sets from 16 surveys and six active longitudinal studies were identified. Two data sets contained seven of the areas of wellbeing, of which one was specific to young adults in tertiary education, while the other survey was not limited to young adults. Both data sets lacked information concerning crime and justice variables, which have recently been identified as being of major concern among Australian university students. We recommend that government policy address the collection of a comprehensive data set encompassing each of the eight areas of wellbeing to inform future policy and practice.

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A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature

Animal Health Research Reviews ◽

10.1017/s1466252319000136 ◽

2019 ◽

Vol 20 (1) ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Zenhwa Ouyang ◽

Jan Sargeant ◽

Alison Thomas ◽

Kate Wycherley ◽

Rebecca Ma ◽

...

Keyword(s):

Big Data ◽

Veterinary Medicine ◽

Scoping Review ◽

Full Text ◽

Medical Literature ◽

Animal Health ◽

Genetic Data ◽

Health Data ◽

Data Sources ◽

Steady Increase

AbstractResearch in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

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A big data analytics platform to support simulation modeling for osteoarthritis care pathways

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.1024 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Shelly Vik ◽

Behnam Sharif ◽

Judy Seidel ◽

Deborah A Marshall

Keyword(s):

Big Data ◽

Simulation Models ◽

Health Data ◽

Data Cube ◽

Data Sources ◽

Multidimensional Data ◽

Technical Solution ◽

Data Sets ◽

Administrative Health Data ◽

Systems Research

IntroductionTechnical solutions have been used in industry settings for many years to facilitate efficient management and analyses of big data sources. An initiative to apply a business solution to support development of simulation models for health systems research using nearly two decades of provincial administrative health data is described. Objectives and ApproachAdministrative data including practitioner claims, hospitalizations and ambulatory care visits for patients with a diagnosis of osteoarthritis were obtained from Alberta Health for the period 1994/95 to 2012/13. These data were incorporated into a multidimensional data cube using Microsoft SQL Server Analysis Services. Initial steps required dimensional modeling to restructure the data into a star schema format. This involved appending several data sets and defining additional reference tables to contain stratification variables and denominator data for rate calculations. The modeling expert worked closely with the information technology team throughout the process and assessed validity of the output. ResultsDevelopment and validation of the multidimensional cube occurred in iterations over approximately 12 months. The final solution resulted in an analytics platform that compiled data from approximately 400 million records obtained from four different administrative data sources. Ten dimension tables containing 102 variables provided enhanced flexibility to conduct ad hoc stratified analyses in a fraction of the time that would be required using conventional methods. For example, some analyses that previously required a day of analyst time could be performed in less than 15 minutes. The efficiencies in analytic time were achieved by the pre-aggregated measures and slice and dice capability of the data cube, which negated many intermediary steps for data extraction and time consuming iterative analyses required for development of the simulation models. Conclusion/ImplicationsThis project demonstrated how a technical solution applied in industry can be utilized to address challenges encountered by researchers related to managing and analyzing large administrative health data sets. The methods could be applied in many other research settings to facilitate access to and analyses of information using big data.

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Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx130 ◽

2017 ◽

Vol 25 (1) ◽

pp. 47-53 ◽

Cited By ~ 27

Author(s):

Suranga N Kasthurirathne ◽

Joshua R Vest ◽

Nir Menachemi ◽

Paul K Halverson ◽

Shaun J Grannis

Keyword(s):

Public Health ◽

Mental Health ◽

Social Work ◽

Social Determinants Of Health ◽

Social Determinants ◽

Social Service ◽

Health Data ◽

Data Sources ◽

Decision Models ◽

Predictive Values

Abstract Introduction A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. Materials and Methods We integrated patient clinical data and community-level data representing patients’ social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Results Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. Discussion The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

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Segmenting or Summing the Parts? A Scoping Review of Male Suicide Research in Canada

The Canadian Journal of Psychiatry ◽

10.1177/07067437211000631 ◽

2021 ◽

Vol 66 (5) ◽

pp. 433-445

Author(s):

John L. Oliffe ◽

Mary T. Kelly ◽

Gabriela Gonzalez Montaner ◽

Paul S. Links ◽

David Kealy ◽

...

Keyword(s):

Mental Health ◽

Health Care ◽

Sexual Minority ◽

Scoping Review ◽

Suicide Risk ◽

Health Care Services ◽

Military Veterans ◽

Health Inequities ◽

Inclusion Criteria ◽

Care Services

Objective: Suicide in Canadian men is high and rising. Research consistently indicates increased suicide risk in male subgroups including sexual minority, Indigenous, middle-aged, and military men. The current scoping review addresses the research question: Among male subgroups featured in Canadian suicide research, what are the key findings to inform suicide prevention efforts?. Method: A scoping review was undertaken in accord with PRISMA-ScR guidelines. Structured searches were conducted in CIHAHL, Medline, PsychInfo, and Web of Science to identify studies reporting suicidality (suicidal ideation, plans and/or attempts) and suicide among men in Canada. Inclusion criteria comprised primary empirical studies featuring Canadian male subgroups published in English from 2009 to 2020 inclusive. Results: Sixty-eight articles met the inclusion criteria, highlighting significant rates of male suicidality and/or suicide in 3 categories: (1) health inequities ( n = 29); (2) age-specific ( n = 30); and (3) occupation ( n = 9). The health inequities category included sexual minority men, Indigenous, and other marginalized males (i.e., homeless, immigrant men, and men who use opiates). Age-specific men focused on adolescents and youth, and middle-aged and older males. Active military, veterans, and first responders featured in the occupation category. Studies compared at risk male subgroups to females, general male populations, and/or other marginalized groups in emphasizing mental health disparities and increased suicide risk. Some men’s suboptimal connections to existing mental health care services were also highlighted. Conclusion: While male subgroups who are vulnerable to suicidality and suicide were consistently described, these insights have not translated to tailored upstream suicide prevention services for Canadian boys and men. There may be some important gains through integrating social and mental health care services for marginalized men, implementing school-based masculinity programs for adolescent males, orientating clinicians to the potential for men’s mid-life suicide risks (i.e., separation, bereavement, retirement) and lobbying employers to norm help-seeking among activate military, veterans, and first responder males.

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