scholarly journals Lifestyles, Depression, Anxiety, and Stress as Risk Factors in Nursing Apprentices: A Logistic Regression Analysis of 1193 Students in Lima, Peru

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Jessica Diaz-Godiño ◽  
Luz Fernández-Henriquez ◽  
Florencia Peña-Pastor ◽  
Patricia Alfaro-Flores ◽  
Gloria Manrique-Borjas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Nursing Students ◽  
Marital Status ◽  
Depression And Anxiety ◽  
Number Of Children ◽  
Dependent Variables ◽  
Different Types ◽  
Health Promoting ◽  
Normal States

Currently, it is considered that mental disorders are related to different types of chronic pathologies; for this reason, efforts to improve general health should also focus on preserving mental health. Therefore, the objective of this study was to determine through logistic regression if the independent variables (risk factors) such as (X1) age, (X2) sex, (X3) marital status, (X4) number of children, and (X5) occupation have influence on the dependent variables such as lifestyles, depression, anxiety, and stress in Peruvian nursing students. The research study was descriptive, transversal, and prospective; 1193 nursing students from Chorrillos, Ica, and Chincha were evaluated, which constituted the total population for the 2018 semester. The Health Promoting Life Profile-II (HPLP-II) and the Depression and Anxiety Stress Scale-21 (DASS-21) were used as instruments. 53.9% of nursing students had unhealthy lifestyles; however, they presented moderate (19.7%), slight (14.2%), severe (2.5%), and extremely severe (2.4%) anxiety. With respect to depression, it was found that 61.2% and 59.9% of affected students were stressed. A significant association was found only between depression and age (p=0.040) and OR = 2.0 (95% CI 1.3–3.1), anxiety and marital status (p=0.043) and OR = 1.7 (95% CI 1.0–2.6), and lifestyles and sex of the students (p=0.003) and OR = 1.1 (95% CI 1.1–2.3). Finally, it is concluded that Peruvian nursing students showed levels of anxiety ranging from moderate to extremely severe, while most of them had “normal” states of depression and stress and also showed unhealthy lifestyles.

scholarly journals The Association between ADHD and the Severity of COVID-19 Infection

2021 ◽  
pp. 108705472110036
Author(s):  
Eugene Merzon ◽  
Margaret D. Weiss ◽  
Samuele Cortese ◽  
Ann Rotem ◽  
Tzipporah Schneider ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Health Services ◽  
Logistic Regression Analysis ◽  
Significant Risk ◽  
Increased Risk ◽  
Dependent Variables ◽  
Male Sex ◽  
Analysis Models

Objective: Patients with ADHD are at increased risk of acquiring COVID-19. The present study assessed the possibility that ADHD also increases the risk of severe COVID-19 infection. Method: We assessed 1,870 COVID-19 positive patients, aged 5 to 60 years, registered in the database of Leumit Health Services (LHS, Israel), February to -June 2020, of whom 231 with ADHD. Logistic regression analysis models evaluated the association between ADHD and the dependent variables of being symptomatic/referral to hospitalization, controlling for demographic and medical variables. Results: Age, male sex, and BMI were confirmed to be significant risk factors for increased COVID-19 severity. ADHD was found to be associated with increased severity of COVID-19 symptoms ( OR = 1.81, 95% CI [1.29, 2.52], p < .05) and referral to hospitalization ( OR =1.93, 95% CI [1.06, 3.51], p = .03). Conclusion: ADHD is associated with poorer outcomes in COVID-19 infection.

Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Wu Q ◽  
◽  
Zhao W ◽  
Yang X ◽  
Tan H ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Marital Status ◽  
Prediction Accuracy ◽  
Learning Approaches ◽  
Never Married ◽  
Factors Associated ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
And Control

Objective: Explore the risk factors related to the recurrence of MDD and provide a basis for the prevention and control of MDD. Methods: Patients with MDD were extracted from two large, multi-center clinical datasets. The inpatients and outpatients between January 2000 and December 2015 were collected. Eligible patients were 18-90 years-old and had a diagnosis of MDD. The MDD were identified based on the MDD-related ICD-9- CM diagnosis codes; and MDD-related ICD-10-CM diagnosis codes. Eventually, 140,497 patients were qualified for further analysis, including 69.2% female patients. Among of 140,497, 20, 078 patients (14.3%) had no comorbidities. Logistic regression, SVM, and LSTM were employed to predict the key risk factors associated with MDD recurrence. Results: The MDD patients with married /life partners had a lower prevalence rate (9.2%) of MDD recurrence than the patients with single marital status (11.8%). The primary MDD patients had a higher MDD recurrent rate (11.7%) than secondary MDD patients (10.5%). Primary MDD was associated with MDD recurrence (OR 2.49, 95% CI 1.53-3.96) via logistic regression analysis. Insomnia, anxiety and single marital status were also top-ranked risk factors for the MDD recurrence. The prediction accuracy of logistic regression, SVM and LSTM were 0.736, 0.791 and 0.834, respectively. Conclusions: Building statistical models by mining existing EHR data can explore the risk factors associated with MDD recurrence. Our results indicated that primary MDD, never married, anxiety symptoms, and insomnia were risk factors for MDD recurrence. The prediction accuracy of the LSTM model was higher than the other two approaches.

scholarly journals Prevalence and risk factors of depression and anxiety for inpatients with lung cancer in China: A hospital-based cross-sectional study

2022 ◽  
Author(s):  
Xueqian Wang ◽  
Xuejiao Ma ◽  
Mo Yang ◽  
Yan Wang ◽  
Yi Xie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression Model ◽  
Depression And Anxiety ◽  
Multivariate Logistic Regression ◽  
Cross Sectional

Abstract Background Lung cancer was often accompanied by depression and anxiety. Nowadays, most investigations for depression and anxiety were concentrated in western medical hospitals, while few related studies have been carried out in the tradition Chinese medicine (TCM) ward. It was necessary to understand the prevalence and risk factors of depression and anxiety in the inpatients with lung cancer in TCM hospital. Methods This study adopted cross-sectional research method, which enrolled a total of 222 inpatients with lung cancer in TCM hospital. PHQ-9 and GAD-7 scales were used to assess depression and anxiety for the inpatients, respectively. Demographic and clinical data were also collected. Statistical methods of the univariate analysis and the multivariate logistic regression model were used. Results The prevalence of depression and anxiety in the inpatients with lung cancer were 58.1% and 34.2%, respectively. Multivariate logistic regression analysis prompted that the common risk factor of depression and anxiety was the symptom of insomnia. Constipation and gender were the two anther risk factors of depression. Conclusion Depression and anxiety were common for the inpatients with lung cancer in TCM hospital. Gender, insomnia and constipation were risk factors for depression, and insomnia was risk factor for anxiety. Therefore, medical workers should pay close attention to the emotional changes of these high-risk patients and intervene the symptoms as early as possible.

scholarly journals Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

10.2196/16374 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e16374
Author(s):  
Subendhu Rongali ◽  
Adam J Rose ◽  
David D McManus ◽  
Adarsha S Bajracharya ◽  
Alok Kapoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Outcome Prediction ◽  
Network Models ◽  
Neural Network Models ◽  
Latent Space ◽  
Different Types

Background Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). Objective This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. Methods We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. Results Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (<0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. Conclusions Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality.

Inverse Correlation between Age and Risk of Lymph Node Metastasis in Bladder Cancer

2020 ◽  
Author(s):  
Wenchao Ma ◽  
Tiantian Wang ◽  
Yadong Guo ◽  
Ruiliang Wang ◽  
Ji Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bladder Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Marital Status ◽  
Inverse Correlation ◽  
Factors Affecting ◽  
Node Metastasis

Abstract Background Bladder cancer (BCa) is the most common malignant tumor in humans and brings about a huge burden on the international community and on the families of those it affects. Lymph node metastasis (LNM) is an important factor affecting the prognosis of BCa. This study aimed to investigate the risk factors affecting LNM.Patients and Methods This study involved 5517 patients who underwent BCa-related surgery between 2006 and 2015. The multivariate logistic regression analysis was used to evaluate the association between age and LNM. The overall survival (OS) and cancer-specific survival (CSS) were analyzed using the Kaplan–Meier method. The multivariable Cox regression model was used to evaluate independent risk factors affecting OS and CSS.Results We retrieved 5517 cases from SEER database, including 148 patients aged 40-49 years, 726 aged 50-59 years, 1541 aged 60-69 years, 1538 aged 70-79 years and 1564 aged 80+ years. The rates of LNM were 20.27%, 16.94%, 11.94%, 9.95% and 6.46% for patients aged 40-49, 50-59, 60-69, 70-79 and 80+ years. We found an inverse correlation between age at diagnosis and risk of LNM from the logistic regression analysis in three modules(Module 1: P-value for trend, crude, no adjustment < 0.001; Module 2: P-value for trend adjusted for sex, race, insurance status, and marital status < 0.001; Module 3: P-value for trend adjusted for sex, race, insurance, marital status, size, grade, and metastasis < 0.001). Compared with patients aged 40–49 years, patients aged 50–59 years (OR = 0.752; 95% CI, 0.470–1.204; P = 0.236), 60–69 years (OR = 0.517; 95% CI, 0.329–0.815; P = 0.004), 70–79 years (OR = 0.375; 95% CI, 0.237–0.595; P < 0.001), and 80+ years (OR = 0.248; 95% CI, 0.154–0.398; P < 0.001) had a lower risk of LNM.ConclusionsYounger age at diagnosis was associated with a higher risk of LNM in patients with BCa. Excepting this, grade and metastasis were also risk factors for LNM.

Health risk factors and health promoting behavior of medical, dental and nursing students

1995 ◽  
Vol 48 (6) ◽  
pp. 841-849 ◽  
Author(s):  
G.Reza Najem ◽  
Marian Rose Catherine Passannante ◽  
James D. Foster
Keyword(s):  
Risk Factors ◽  
Nursing Students ◽  
Health Risk ◽  
Health Risk Factors ◽  
Health Promoting

scholarly journals Effects of Non-intubated Video-Assisted Thoracic Surgery on Patients With Pulmonary Dysfunction

2022 ◽  
Vol 8 ◽  
Author(s):  
Shiyu Deng ◽  
Yanyi Cen ◽  
Long Jiang ◽  
Lan Lan
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Thoracic Surgery ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Pulmonary Dysfunction ◽  
Video Assisted Thoracic Surgery ◽  
Video Assisted ◽  
Different Types

Background: Non-intubated video-assisted thoracic surgery (NIVATS) can be safely performed in lung volume reduction surgery for patients with severe pulmonary dysfunction. However, there is still no cohort observation on the effects of NIVATS on patients with pulmonary dysfunction undergoing different types of thoracic procedures. This retrospective study aimed to observe the effects of NIVATS for this kind of patients.Methods: Three hundred and twenty-eight patients with moderate to severe obstructive pulmonary dysfunction, who underwent video-assisted thoracic surgery (VATS), were retrospectively collected from June 1st, 2017 to September 30th, 2019. Patients in NIVATS were case-matched with those in intubated video-assisted thoracic surgery (IVATS) by a propensity score-matched analysis. The primary outcome was the comparison of perioperative values, the secondary outcome was the risk factors for postoperative clinical complications (PCP) which were identified by binary logistic regression analysis.Results: After being matched, there were no differences in demographics and preoperative values of pulmonary function between NIVATS and IVATS groups. The duration of surgery and anesthesia had no difference (P = 0.091 and P = 0.467). As for the postoperative recovery, except for the mean intensive care unit (ICU) stay was longer in the IVATS group than in the NIVATS group (P = 0.015), the chest tube removal time and the postoperative hospital stay had no difference (P = 0.394 and P = 0.453), and the incidence of PCP also had no difference (P = 0.121). The binary logistic regression analysis revealed that the history of pulmonary disease, anesthesia method, and surgical location were risk factors of PCP.Conclusion: For patients with pulmonary dysfunction when undergoing different types of thoracic procedures, the NIVATS can be performed as effectively and safely as the IVATS, and can reduce the ICU stay.

scholarly journals Bidirectional, Unidirectional, and Nonviolence: A Comparison of the Predictors Among Partnered Young Adults

2010 ◽  
Vol 25 (5) ◽  
pp. 617-630 ◽  
Author(s):  
Lisa A. Melander ◽  
Harmonijoie Noel ◽  
Kimberly A. Tyler
Keyword(s):  
Risk Factors ◽  
Intimate Partner Violence ◽  
Logistic Regression ◽  
Depressive Symptoms ◽  
Partner Violence ◽  
Illicit Drug ◽  
Multinomial Logistic Regression ◽  
Intimate Partner ◽  
Relationship Aggression ◽  
Different Types

In order to more fully understand the context and impact of intimate partner violence (IPV), it is important to make distinctions between different types of relationship aggression. As such, the current study longitudinally examines the differential effects of childhood, adolescent, and demographic factors on three different partner violence groups: those who experience bidirectional IPV, those who experience unidirectional IPV, and those who do not experience either form of IPV. Multinomial logistic regression results reveal that depressive symptoms and lower partner education predict bidirectional when compared to unidirectional IPV and nonviolence. In contrast, other risk factors such as illicit drug use are found to be predictors of unidirectional violence only, which reveals that the correlates of violence vary depending upon the type of IPV examined.

scholarly journals Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation (Preprint)

2019 ◽  
Author(s):  
Subendhu Rongali ◽  
Adam J Rose ◽  
David D McManus ◽  
Adarsha S Bajracharya ◽  
Alok Kapoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Outcome Prediction ◽  
Network Models ◽  
Neural Network Models ◽  
Latent Space ◽  
Different Types

BACKGROUND Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). OBJECTIVE This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient’s mortality using their longitudinal EHR data. METHODS We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient’s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians’ input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. RESULTS Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models (&lt;0.86). In addition, physicians’ agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. CONCLUSIONS Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality. CLINICALTRIAL

scholarly journals Depression and Anxiety in Healthcare Staffs in A Tertiary University Hospital in Luoyang, Henan Province: A Cross-Sectional Observational Study

2019 ◽  
Author(s):  
Hao Wang ◽  
Xiaochun Qing ◽  
Li Li ◽  
Lijun Zhang ◽  
Jing Ruan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Sleep Quality ◽  
Workplace Violence ◽  
University Hospital ◽  
Depression And Anxiety ◽  
Cross Sectional ◽  
Surgical Department ◽  
Working Length ◽  
Prevalence Of Depression

Abstract ObjectiveMental health condition of medical professionals in China is under-recognized. The current study aimed to determine the prevalence of depression and anxiety among healthcare professionals and explore the potential influence factors.MethodThe study employed a cross-sectional design. All employees were surveyed in the first week of September 2017. General information included gender, age, workload, workplace violence, sleep quality and so on. Depression and anxiety were evaluated using PHQ-9 and GAD-7, respectively. SPSS 22.0 was used for data analysis. Logistic regression was conducted to explore risk factors contributed to metal health.ResultsA total of 1,950 questionnaires were delivered, and 1,864 were returned with a response rate of 95.6%. The prevalence of depression and anxiety were 24.1% and 28.9%. As for workload, the average number of beds in charge per month is 65.97±95.58 beds, among which internal medicine department and surgical department endure more workloads. Though workers in ER and ICU manage fewer beds, they bear the longest nonstop working length in the previous month (19.18±10.82 h). There were 78.0% had suffered WPV in the preceding year. Staffs in ER and ICU are at higher risk to physical violence, especially doctors. Regarding to sleep quality, only 9.2% participants reported that they sleep well. Logistic regression indicated that workplace violence and sleep quality were independent risk factors for both anxiety and depression.ConclusionThe current study revealed the devastating conditions of mental disorders in medical workers and associated factors. Effective interventions are necessary to improve this situation.

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