Evaluating the measurement of mental health service accessibility, acceptability, and availability in the Canadian Community Health Survey.

2015 ◽  
Vol 85 (3) ◽  
pp. 238-242 ◽  
Author(s):  
Katherine P. Elliott ◽  
John Hunsley
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Health Service ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Service Accessibility ◽  
Community Health Survey ◽  
Health Service Accessibility

scholarly journals The Canadian Community Health Survey: Mental Health and Well-Being

2005 ◽  
Vol 50 (10) ◽  
pp. 573-579 ◽  
Author(s):  
Ronald Gravel ◽  
Yves Béland
Keyword(s):  
Mental Health ◽  
Data Collection ◽  
Community Health ◽  
Health Survey ◽  
Well Being ◽  
Canadian Community Health Survey ◽  
Computer Assisted ◽  
Community Health Survey ◽  
Interviewer Training ◽  
Health And Well Being

As part of the Canadian Community Health Survey (CCHS) biennial strategy, the provincial survey component of the first CCHS cycle (Cycle 1.2) focused on different aspects of the mental health and well-being of Canadians living in private dwellings. Moreover, the survey collected data on prevalences of specific mental disorders and problems, use of mental health services, and economic and personal costs of having a mental illness. Data collection began in May 2002 and extended over 8 months. More than 85% of all interviews were conducted face-to-face and used a computer-assisted application. The survey obtained a national response rate of 77%. This paper describes several key aspects of the questionnaire content, the sample design, interviewer training, and data collection procedures. A brief overview of the CCHS regional component (Cycle 1.1) is also given.

scholarly journals Prediction of Suicidal Ideation in the Canadian Community Health Survey - Mental Health Component Using Deep Learning

2019 ◽  
Author(s):  
Sneha Desai ◽  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Eleanor Brown ◽  
...  
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Feature Model ◽  
Model Parameters ◽  
Community Health Survey ◽  
Patients At Risk

AbstractIntroductionSuicidal ideation (SI) is prevalent in the general population, and is a prominent risk factor for suicide. However, predicting which patients are likely to have SI remains a challenge. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete psychiatric datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.MethodsUsing the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to predict lifetime SI on an individual patient basis. Models were created to predict both lifetime and last 12 month SI. We reduced 582 possible model parameters captured by the survey to 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI respondents; validation was done on held-out data.ResultsAUC was used as the main model metric. For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.75. For SI in the last 12 months the 96 feature model had an AUC of 0.76 and the 21 feature model had an AUC of 0.69. DL outperformed random forest classifiers.DiscussionAlthough requiring further study to ensure clinical relevance and sample generalizability, this study is a proof-of-concept for the use of DL to improve prediction of SI. This kind of model would help start conversations with patients which could lead to improved care and, it is hoped, a reduction in suicidal behavior.

scholarly journals Regional variation in multimorbidity prevalence in British Columbia, Canada: a cross-sectional analysis of Canadian Community Health Survey data, 2015/16

2020 ◽  
Vol 40 (7/8) ◽  
pp. 225-234
Author(s):  
C. Andrew Basham
Keyword(s):  
British Columbia ◽  
Sensitivity Analysis ◽  
Health Service ◽  
Service Delivery ◽  
Community Health ◽  
Health Survey ◽  
Health Service Delivery ◽  
Canadian Community Health Survey ◽  
Valuable Source ◽  
Community Health Survey

Introduction Multimorbidity represents a major concern for population health and service delivery planners. Information about the population prevalence (absolute numbers and proportions) of multimorbidity among regional health service delivery populations is needed for planning for multimorbidity care. In Canada, health region–specific estimates of multimorbidity prevalence are not routinely presented. The Canadian Community Health Survey (CCHS) is a potentially valuable source of data for these estimates. Methods Data from the 2015/16 cycle of the CCHS for British Columbia (BC) were used to estimate and compare multimorbidity prevalence (3+ chronic conditions) through survey-weighted analyses. Crude frequencies and proportions of multimorbidity prevalence were calculated by BC Health Service Delivery Area (HSDA). Logistic regression was used to estimate differences in multimorbidity prevalence by HSDA, adjusting for known confounders. Multiple imputation using chained equations was performed for missing covariate values as a sensitivity analysis. The definition of multimorbidity was also altered as an additional sensitivity analysis. Results A total of 681 921 people were estimated to have multimorbidity in BC (16.9% of the population) in 2015/16. Vancouver (adj­OR = 0.65; 95% CI: 0.44–0.97) and Richmond (adj­OR = 0.55; 95% CI: 0.37–0.82) had much lower prevalence of multimorbidity than Fraser South (reference HSDA). Missing data analysis and sensitivity analysis showed results consistent with the main analysis. Conclusion Multimorbidity prevalence estimates varied across BC health regions, and were lowest in Vancouver and Richmond after controlling for multiple potential confounders. There is a need for provincial and regional multimorbidity care policy development and priority setting. In this context, the CCHS represents a valuable source of information for regional multimorbidity analyses in Canada.

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