Investigating Psychological Differences between Nurses and Other Healthcare Workers from Asia-Pacific Region during the Early Phase of the Coronavirus Disease 2019 (COVID-19): A Machine Learning Approach (Preprint)

BACKGROUND As the pandemic evolves, frontline work challenges continue to impose significant psychological impact on nurses. However, there is a lack of data how nurses fared compared to other healthcare workers in Asia-Pacific region. OBJECTIVE This study aims to investigate 1) psychological differences between nurses, doctor and non-medical healthcare workers, and 2) psychological outcome characteristics of nurses from different Asia-Pacific countries. METHODS Decision-tree type machine learning models (LIghtGBM, Gradientboost, and RandomForest) were adopted to predict psychological impact on nurses. The SHAP (SHapley Additive exPlanations) values of these models were extracted to identify the distinctive psychological distress characteristic. RESULTS Nurses had relatively higher percentages of normal or no-change in psychological distress symptoms relative to other healthcare workers (86.3% - 96.8% vs 80.7% - 92.3%). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and non-medical healthcare workers (40.8%, 25.8%, and 33.4%, respectively). CONCLUSIONS Different contexts, cultures, and points in pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all healthcare workers practise self-care and render peer support to bolster psychological resilience for effective coping. CLINICALTRIAL Not applicable

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Assessing the risk of spread of COVID-19 to the Asia Pacific region

10.1101/2020.04.09.20057257 ◽

2020 ◽

Cited By ~ 3

Author(s):

Freya M. Shearer ◽

James Walker ◽

Nefel Tellioglu ◽

James M. McCaw ◽

Jodie McVernon ◽

...

Keyword(s):

Early Phase ◽

Emerging Disease ◽

Asia Pacific ◽

Pacific Region ◽

Asia Pacific Region ◽

Outbreak Response ◽

Case Definitions ◽

Early Stages ◽

Travel Restrictions ◽

Importation Risk

AbstractDuring the early stages of an emerging disease outbreak, governments are required to make critical decisions on how to respond appropriately, despite limited data being available to inform these decisions. Analytical risk assessment is a valuable approach to guide decision-making on travel restrictions and border measures during the early phase of an outbreak, when transmission is primarily contained within a source country. Here we introduce a modular framework for estimating the importation risk of an emerging disease when the direct travel route is restricted and the risk stems from indirect importation via intermediary countries. This was the situation for Australia in February 2020. The framework was specifically developed to assess the importation risk of COVID-19 into Australia during the early stages of the outbreak from late January to mid-February 2020. The dominant importation risk to Australia at the time of analysis was directly from China, as the only country reporting uncontained transmission. However, with travel restrictions from mainland China to Australia imposed from February 1, our framework was designed to consider the importation risk from China into Australia via potential intermediary countries in the Asia Pacific region. The framework was successfully used to contribute to the evidence base for decisions on border measures and case definitions in the Australian context during the early phase of COVID-19 emergence and is adaptable to other contexts for future outbreak response.

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Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.752870 ◽

2022 ◽

Vol 12 ◽

Author(s):

Liana C. L. Portugal ◽

Camila Monteiro Fabricio Gama ◽

Raquel Menezes Gonçalves ◽

Mauro Vitor Mendlowicz ◽

Fátima Smith Erthal ◽

...

Keyword(s):

Mental Health ◽

Machine Learning ◽

Social Isolation ◽

Healthcare Workers ◽

Intervention Programs ◽

Depression Symptoms ◽

Learning Approach ◽

Professional Recognition ◽

Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

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Asian-Pacific perspective on the psychological well-being of healthcare workers during the evolution of the COVID-19 pandemic

BJPsych Open ◽

10.1192/bjo.2020.98 ◽

2020 ◽

Vol 6 (6) ◽

Cited By ~ 1

Author(s):

Nicholas W. S. Chew ◽

Jinghao Nicholas Ngiam ◽

Benjamin Yong-Qiang Tan ◽

Sai-Meng Tham ◽

Celine Yan-Shan Tan ◽

...

Keyword(s):

Healthcare Workers ◽

Physical Symptoms ◽

Psychological Outcomes ◽

Asia Pacific ◽

Pacific Region ◽

Medical Conditions ◽

Asia Pacific Region ◽

Factors Associated ◽

Prevalence Of Depression ◽

The Impact

Background The coronavirus disease 2019 (COVID-19) pandemic has led to significant strain on front-line healthcare workers. Aims In this multicentre study, we compared the psychological outcomes during the COVID-19 pandemic in various countries in the Asia-Pacific region and identified factors associated with adverse psychological outcomes. Method From 29 April to 4 June 2020, the study recruited healthcare workers from major healthcare institutions in five countries in the Asia-Pacific region. A self-administrated survey that collected information on prior medical conditions, presence of symptoms, and scores on the Depression Anxiety Stress Scales and the Impact of Events Scale-Revised were used. The prevalence of depression, anxiety, stress and post-traumatic stress disorder (PTSD) relating to COVID-19 was compared, and multivariable logistic regression identified independent factors associated with adverse psychological outcomes within each country. Results A total of 1146 participants from India, Indonesia, Singapore, Malaysia and Vietnam were studied. Despite having the lowest volume of cases, Vietnam displayed the highest prevalence of PTSD. In contrast, Singapore reported the highest case volume, but had a lower prevalence of depression and anxiety. In the multivariable analysis, we found that non-medically trained personnel, the presence of physical symptoms and presence of prior medical conditions were independent predictors across the participating countries. Conclusions This study highlights that the varied prevalence of psychological adversity among healthcare workers is independent of the burden of COVID-19 cases within each country. Early psychological interventions may be beneficial for the vulnerable groups of healthcare workers with presence of physical symptoms, prior medical conditions and those who are not medically trained.

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A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy

Annals of Vascular Surgery ◽

10.1016/j.avsg.2020.07.001 ◽

2020 ◽

Author(s):

Jinyun Tan ◽

Qi Wang ◽

Weihao Shi ◽

Kun Liang ◽

Bo Yu ◽

...

Keyword(s):

Machine Learning ◽

Carotid Endarterectomy ◽

Early Phase ◽

Learning Approach ◽

Machine Learning Approach ◽

Postoperative Hypertension

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Mass-Casualty Triage Training for International Healthcare Workers in the Asia-Pacific Region Using Manikin-Based Simulations

Prehospital and Disaster Medicine ◽

10.1017/s1049023x00006828 ◽

2009 ◽

Vol 24 (3) ◽

pp. 206-213 ◽

Cited By ~ 14

Author(s):

Dale S. Vincent ◽

Benjamin W. Berg ◽

Keiichi Ikegami

Keyword(s):

Formative Assessment ◽

Healthcare Workers ◽

Healthcare Providers ◽

Tension Pneumothorax ◽

Educational Activity ◽

Asia Pacific ◽

Pacific Region ◽

Asia Pacific Region ◽

Simulation Based ◽

Primary Triage

AbstractIntroduction:More than half of the world's disasters occur in the Asia-Pacific region. A simulation-based exercise to teach healthcare workers prehospital triage, tagging, and treatment methods was used to link disaster management theory to practice with a student-centered, hands-on educational activity. Various strategies for teaching disaster health education have been advocated, and best-practice disaster education models continue to be sought.Methods:A manikin-based, primary triage and treatment course was adapted for international healthcare providers in the Asia-Pacific region using symbolic representations of triage categories and physical findings. The pedagogical construct that was used was an interactive, formative assessment in which faculty members mediated learner information gathering and interpretation during four simulation scenarios. After establishing a multi-casualty disaster context, a wireless, audience response system anonymously collected learner responses to four clinical situations: (1) leg wound (hemorrhagic shock/immediate); (2) chest wound (tension pneumothorax/immediate); (3) head wound (traumatic brain injury/expectant); and (4) limb trauma (leg fracture/delayed).Results:There were 182 healthcare providers from eight Asia-Pacific countries (including the US) that participated in four simulation seminars. The simulation sessions were successfully tailored to groups of learners that varied in size and professional composition.Expectant and delayed triage categories posed the greatest challenge to learners. In one of two groups that were queried, learner self-confidence in applying principles of triage and treatment improved significantly. At the conclusion of the simulation sessions, learners strongly agreed that manikin-based simulation improved their understanding of triage, and should be used to teach principles of primary triage and treatment.Conclusions:Simulation training represents an opportunity to engage learners regardless of language and cultural barriers. Simulation-based training can be effective in introducing healthcare professionals to principles of primary triage and treatment in an effective and culturally sensitive manner.The characteristics of the course with respect to planned formative assessment and culturally competent scholarship were reviewed.

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