scholarly journals The effects of mental health on recurrent falls among elderly adults, based on Korean Community Health Survey data

2020 ◽  
Vol 42 ◽  
pp. e2020005
Author(s):  
Kyung Hee Jo ◽  
Jong Park ◽  
So Yeon Ryu
Keyword(s):  
Mental Health ◽  
Community Health ◽  
Health Survey ◽  
Multinomial Logistic Regression ◽  
Treatment Strategies ◽  
Elderly Adults ◽  
Chi Square ◽  
Multinomial Logistic Regression Analysis ◽  
Recurrent Falls ◽  
Community Health Survey

OBJECTIVES: This study aimed to identify the effect of mental health on frequency of falls (single and recurrent falls) among elderly adults.METHODS: Data were drawn from the 2015 Korean Community Health Survey. A chi-square test was conducted to compare differences in fall frequency according to health-related behaviors, chronic diseases, and mental health. Subsequently, multinomial logistic regression analysis was used to identify the effects of mental health on single and recurrent falls based on variables found to be significant in the chi-square test.RESULTS: Recurrent falls were found to be more risky than single falls. Depression was significantly related to single falls (odds ratio [OR], 1.27; 95% confidence interval [CI], 1.12 to 1.44). Depression (OR, 1.56; 95% CI, 1.38 to 1.76), sleep disorder (5 hours or less: OR, 1.12; 95% CI, 1.02 to 1.23; more than 9 hours: OR, 1.24; 95% CI, 1.07 to 1.44, respectively), and subjective stress (OR, 2.30; 95% CI, 1.90 to 2.78) were significantly related to recurrent falls.CONCLUSIONS: The study’s findings suggest that specialized fall prevention programs are needed to address different types of falls in elderly adults. To prevent recurrent falls, systematic treatment strategies and rehabilitation training must improve physical function and mental health.

Immigration as a social determinant of troubled sleep in Canada: some evidence from the Canadian Community Health Survey–Mental Health

Sleep Health ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Yujiro Sano ◽  
Roger Antabe ◽  
Emmanuel Kyeremeh ◽  
Eugena Kwon ◽  
Jonathan Amoyaw
Keyword(s):  
Mental Health ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Social Determinant ◽  
Community Health Survey

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.

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