scholarly journals Physical health and health risk factors in a population of long-stay psychiatric patients

2005 ◽  
Vol 29 (4) ◽  
pp. 153-153 ◽  
Author(s):  
G. L. Milner
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Physical Health ◽  
Psychiatric Patients ◽  
Health Risk Factors

scholarly journals Physical health and health risk factors in a population of long-stay psychiatric patients

2005 ◽  
Vol 29 (1) ◽  
pp. 18-20 ◽  
Author(s):  
Irene Cormac ◽  
Michael Ferriter ◽  
Ram Benning ◽  
Carol Saul
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Physical Health ◽  
Psychiatric Patients ◽  
Cross Sectional Survey ◽  
Health Risk Factors ◽  
Cross Sectional ◽  
Case Register ◽  
Care Services ◽  
Waist Size

Aims and MethodTo evaluate the physical health and health risk factors in long-stay psychiatric patients in a high secure psychiatric hospital. A cross-sectional survey of consenting patients was undertaken using a semi-structured questionnaire, a brief physical examination and review of patient case notes. A comparison was made with data collected on admission and held on the Special Hospitals' Case Register.ResultsMain findings were: a mean increase in weight since admission, in men of 10.62 kg and in women of 12.74 kg; high rates of smoking, obesity and large waist size; 54% of patients had one or more health problems.Clinical ImplicationsThe study's profile of the physical health of psychiatric in-patients indicates the need for health promotion initiatives in such hospitals and the need for primary care services.

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.

scholarly journals Prediction Algorithm of Young Students’ Physical Health Risk Factors Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xianping Yin
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Physical Health ◽  
Human Body ◽  
Vascular System ◽  
Overweight And Obesity ◽  
Prediction Algorithm ◽  
Fundus Images ◽  
Physical And Mental Health ◽  
Health Risk Factors

Young people’s physical and mental health is the foundation of society’s overall development and the key to improving people’s health quality. Middle school students’ physical examinations and monitoring work are a surefire way to ensure their healthy development. Poor vision, dental caries, overweight and obesity, and high blood pressure are the most common adverse health outcomes of students caused by adolescent health risk behavior factors. Researchers have been concerned about the retinal fundus vascular system, which is the only internal vascular system that can be observed in a noninvasive state of the human body. Fundus images contain a wealth of disease-related information. Fundus images have been widely used in the field of medical auxiliary diagnosis because many important systemic diseases of the human body cause specific reactions in the fundus. Aiming to solve the problem of inseparable tiny blood vessels, this paper proposes a model of retinal vessel segmentation based on attention mechanisms. In light of the retinal arteriovenous division of discontinuous challenges, the topological structure of the constraint system along with overcoming the network and topology restrictions is monitored. Finally, simulation experiments were conducted on two publicly available datasets. The findings show that the proposed method is reliable, effective, and accurate in predicting physical health risk factors in adolescent students.

scholarly journals Bidirectional associations between COVID-19 and psychiatric disorder: a study of 62,354 COVID-19 cases

2020 ◽  
Author(s):  
Maxime Taquet ◽  
Sierra Luciano ◽  
John R Geddes ◽  
Paul J Harrison
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Physical Health ◽  
Psychiatric Disorders ◽  
Sensitivity Analyses ◽  
Severe Illness ◽  
Psychiatric Diagnoses ◽  
Health Risk Factors ◽  
Bidirectional Associations ◽  
Tract Infections

Background: Adverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. Methods: We addressed both questions using cohort studies derived from an electronic health records (EHR) network of 69 million patients including over 62,000 cases of COVID-19. Propensity score matching was used to control for confounding by risk factors for COVID-19 and for more severe illness. Findings: In patients with no prior psychiatric history, COVID-19 was associated with an increased incidence of psychiatric diagnoses in the three months after infection compared to 6 other health events (hazard ratio [95% CI] 2.1 [1.8-2.5] compared to influenza; 1.7 [1.5-1.9] compared to other respiratory tract infections; 1.6 [1.4-1.9] compared to skin infection; 1.6 [1.3.1-9] compared to cholelithiasis; 2.2 [1.9-2.6] compared to urolithiasis, and 2.1 [1.9-2.5] compared to fracture of a large bone; all p<0.0001). The increase was greatest for anxiety disorders but also present for depression, insomnia, and dementia. The results were robust to several sensitivity analyses. There was a ~30% reduction in psychiatric diagnoses in the total EHR population over the same period. A psychiatric diagnosis in the previous year was associated with a 65% higher incidence of COVID-19 (relative risk 1.65, 95% CI: 1.59-1.71, p<0.0001). This was independent of known physical health risk factors for COVID-19. Interpretation: COVID-19 infection has both psychiatric sequelae and psychiatric antecedents. Survivors have an increased rate of new onset psychiatric disorders, and prior psychiatric disorders are associated with a higher risk of COVID-19. The findings have implications for research into aetiology and highlight the need for clinical services to provide multidisciplinary follow-up, and prompt detection and treatment.

A Study of the Effects of Exercise on Stress, Depression Levels, and Health Risk Factors

2000 ◽  
Author(s):  
Paul A. Thomas ◽  
Jen Hanley ◽  
Christy Tomczak ◽  
Jennifer Wuchteil ◽  
Nathan Underwood ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Health Risk Factors

Behavioral Health Risk Factors for Nonmedical Use of Prescription Opioids in Adolescence

2020 ◽  
Author(s):  
Junhan Cho ◽  
Lorraine I. Kelley-Quon ◽  
Jessica L. Barrington-Trimis ◽  
Afton Kechter ◽  
Sarah Axeen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Risk ◽  
Behavioral Health ◽  
Prescription Opioids ◽  
Health Risk Factors ◽  
Nonmedical Use
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