scholarly journals Mental health risk among children during COVID‐19 lockdown

2021 ◽  
Author(s):  
Marium Asif ◽  
Irfan Ullah ◽  
Usha Kumari ◽  
Saqib Munir ◽  
Muhammad Junaid Tahir ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Risk

scholarly journals Health risk assessment and countermeasure analysis of the elderly population exposed to PM2.5 microenvironment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Yan Xu ◽  
Wantian Cui
Keyword(s):  
Mental Health ◽  
Health Risk ◽  
Elderly Population ◽  
Mental Health Problems ◽  
Negative Impact ◽  
The Elderly ◽  
Exposure Concentration ◽  
Physical Diseases ◽  
The Impact

BACKGROUND: China’s atmospheric PM2.5 pollution is serious, and PM2.5 exerts a negative impact on the human respiratory system, cardiovascular, and mental health, and even more serious health risk for the elderly with weak immunity. OBJECTIVE: This work aims to analyse the impacts of PM2.5 microenvironment exposure on the health of the elderly and provide corresponding countermeasures. METHODS: The survey subjects are 118 retired elderly people in the community. PM2.5 exposure concentrations are monitored in summer (June 10 ∼ July 10, 2019) and winter (November 25 ∼ December 25, 2019). RESULTS: The exposure concentration in winter is higher than that in summer, with statistical difference (P <  0.05). Under the impact of PM2.5 microenvironment exposure, smoking in the elderly can increase the concentration of PM2.5, and long-term exposure to PM2.5 in the elderly can cause mental health problems. CONCLUSION: Long-term exposure of the elderly to the PM2.5 microenvironment leads to physical diseases and even psychological problems, which requires attention.

Cannabis use among early adolescents and transdiagnostic mental health risk factors

2021 ◽  
pp. 135910452199463
Author(s):  
Sara Moreno-Mansilla ◽  
Jorge J Ricarte ◽  
David J Hallford
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Health Risk ◽  
Suicide Attempts ◽  
Intolerance Of Uncertainty ◽  
Cannabis Use ◽  
Health Risk Factors ◽  
Symptoms Of Depression ◽  
Modifiable Factors ◽  
Wide Range

Introduction: Cannabis is the most widely used psychoactive substance among adolescents worldwide, and the age at which consumption begins to decrease. Cannabis use in adolescents is associated with a wide range of adverse consequences in adulthood including increased vulnerability to psychosis and other mental disorders, as well as suicidal ideation and attempt. The aim of this study is to extend understanding of the link between cannabis use and mental illness by examining whether cannabis use at early ages predicts transdiagnostic variables that are precursors to severe clinical diagnoses. Methods: A descriptive cross-sectional study was conducted. The sample was made up of 605 adolescents from 7th to 9th grades, with a mean age of 13.2 years ( SD = 1.0, 47% girls). The variables evaluated were: anomalous perception of reality, intolerance of uncertainty, rumination, suicide attempt, hopelessness, and symptoms of depression and anxiety. The administration of the questionnaires was carried out in groups of 20 participants under the supervision of a researcher in a unique session of 1 hour. Results: Adolescent cannabis users scored higher on all variables assessed: anomalous perception of reality (Cohen’s d = .60), rumination ( d = .48), intolerance of uncertainty ( d = .11), suicidal attempt (affirmative answer: 25.9% of users vs 7.7% of non-users), hopelessness ( d = .85), symptoms of depression ( d = .80), and anxiety ( d = .39). A binary logistic regression showed that the only variable uniquely related to cannabis use was hopelessness (Wald = 4.560, OR: 1.159, p = .033). Conclusions: Among some mental health risk factors, hopelessness appears uniquely related to cannabis use. Adolescents may use cannabis as a coping strategy for negative thoughts and emotions, or it may be a consequence of cannabis use. Future prevention programs should focus on preventing/treating modifiable factors such as hopelessness, and delaying cannabis use in specific subgroups of adolescents who experience pathologies such as depression or suicide attempts.

scholarly journals Clustering effects of health risk behavior on mental health and physical activity in Chinese adolescents

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiangren Yi ◽  
Zongyu Liu ◽  
Wenzhen Qiao ◽  
Xiuye Xie ◽  
Nuo Yi ◽  
...  
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Health Risk ◽  
Risk Behavior ◽  
Chinese Adolescents ◽  
Health Risk Behavior

Danish-like Regulations May Improve Postabortion Mental Health Risk

2019 ◽  
Vol 76 (1) ◽  
pp. 99
Author(s):  
D. Paul Sullins ◽  
Donna Harrison
Keyword(s):  
Mental Health ◽  
Health Risk

scholarly journals Beta Inflated Regression Models on the Physical and Mental Health of Nigerian Stroke Survivors

2021 ◽  
Vol 7 (3) ◽  
pp. 302-312
Author(s):  
KS Oritogun ◽  
OO Oyewole
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Blood Pressure ◽  
Health Risk ◽  
Physical Health ◽  
Regression Models ◽  
Outcome Variable ◽  
Stroke Survivors ◽  
Physical And Mental Health ◽  
Health Risk Factors

Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions. Therefore, the Beta Regression models family for data between zero and one will be appropriate. Objectives: To identify a suitable model and the likely risk factors of physical and mental health of stroke survivors. Method: Secondary data of stroke survivors from two tertiary health Institutions in Ogun State, Nigeria, were analysed. Inflated Beta (BEINF) and Inflated-at-one-Beta (BEINF1) models were compared using Deviance (DEV), Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) for model selection. The model with minimum DEV, AIC and BIC was considered to be better. Results: The deviance (-86.0604,), AIC (-46.0604) and BIC (6.4391) values of the BEINF1 model for physical health and the deviance (-20.1217), AIC (19.8783) and BIC (72.3778) values of BEINF1 model for mental health were smaller than BEINF models. Therefore, BEINF1 was the better model to identify the health risk factors of stroke survivors. Age, marital status, diastolic blood pressure, disability duration and systolic blood pressure had a significant association with physical health, while BMI had a significant positive association with mental health.  Conclusion: The beta-inflated-at-one (BEINF1) model is suitable for identifying health risk factors of stroke survivors when the outcome variable is a proportion. Both demographic and clinical characteristics were significantly associated with the health of stroke survivors. This study would assist researchers in knowing the appropriate model for analysing proportion or percentage response variables.

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