Anxiety and depression have been prevalent among Healthcare Workers (HCWs) amidst the COVID-19 pandemic. This study aims to evaluate the prevalence of anxiety and depression among HCWs amid the pandemic and their association with religious coping. A cross-sectional study design was applied. The scales utilized were Malay versions of the Brief Religious Coping Scale (Brief RCOPE M) and Hospital Anxiety and Depression Scale (HADS M). In total, 200 HCWs were recruited. HCWs scored higher in positive religious coping (mean: 21.33) than negative religious coping (mean: 10.52). The prevalence of anxiety and depression was 36.5% and 29.5%. Both positive and negative religious coping were significantly associated with anxiety (p < 0.01) and depression (p < 0.05, p < 0.01). Positive coping predicted reduction in anxiety (adjusted b = −0.15, p = 0.001) and log-transformed depression score (adjusted b = −0.019, p = 0.025). Negative coping predicted increment of anxiety (adjusted b = 0.289, p < 0.001) and log-transformed depression score (adjusted b = 0.052, p < 0.001). Positive religious coping is vital in reducing anxiety and depression among HCWs amid the pandemic. Strategies which increase positive religious coping and reduce negative religious coping must be emphasized to boost mental health among HCWs.

Introduction: Burnout is a syndrome occurring from an overwhelming workplace stress. The study was done at a large teaching hospital identified as a COVID‐19 treating hospital. During COVID‐19 pandemic, the country was in Movement Control Order since 18 March 2020 to contain the spread of the virus, and thus, it has increased the job workload and responsibility. The purpose of this study was to identify the prevalence of burnout among emergency healthcare worker in this hospital and to identify the factors contributed to the burnout.Methods: A cross‐sectional study was conducted using a questionnaire among emergency healthcare worker from 8 May 2020 to 15 May 2020 during the Conditional Movement Control Order. The questionnaire was adapted from Michelle Post, Public Welfare, Vol. 39, No. 1, 1981, American Public Welfare Association and distributed via Google Forms. It consisted of 28 questions and was rated based on a five‐point Likert‐type scale. The questions were then summed up to determine the burnout levels.Results: There were 216 respondents with 65.7% were nurses, 17.1% were doctors, and the rest were assistant medical officer. 51.3% of the respondents had burnout with 61.2% of nurses, 35.1% of doctors, and 29.6% of assistant medical officer. There was weak correlation (r = 0.148) with the years of experience working in emergency department and the level of burnout (p = 0.03). Among the burnout features were fatigue with 52.2% and frequent physical illness and feel unappreciated with 48.6% and 45.9%, respectively. The job‐related issues which predisposed to burnout were demand coping with an angry public with 70.2%, job overload 63.9%, lack clear guideline or rapid program changes 54%, and pay too little 53.1%.Conclusion: Emergency healthcare worker has a high rate of burnout especially among the nurses. The factors leading to burnout were frequent exposure to angry public, job overload, lack of clear guidelines, and perception of underpaid.

COVID-19,occupations,income,social class,economic status

Objective: Urine colorimetry using a digital image–based colorimetry is potentially an accessible hydration assessment method. This study evaluated the agreement between urine colorimetry values measured with different smartphone brands under various lighting conditions in patients with dengue fever. Methods: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. These images were analyzed using Adobe Photoshop to obtain urine Red, Green and Blue (RGB) values with and without colour correction. A commercially available colour calibration card was used for colour correction. Using intraclass correlation coefficient (ICC), inter-phone and intra-phone agreements of urine RGB values were analyzed. Results: Without colour correction, the various smartphones produced the highest agreement for Blue and Green values under the ‘daylight’ lighting condition. With colour correction, ICC values showed ‘exceptional’ inter-phone and intra-phone agreement for the Blue and Green values (ICC > 0.9). Red values showed ‘poor’ (ICC < 0.5) agreement with and without colour correction in all lighting conditions. Out of the five phones compared in this study, Phone 4 produced the lowest intra-phone agreement. Conclusions: Colour calibration using photo colour cards improved the reliability of smartphone-based urine colorimetry, making this a promising point-of-care hydration assessment tool using the ubiquitous smartphone.

Objective: Direct urine color assessment has been shown to correlate with hydration status. However, this method is subject to inter- and intra-observer variability. Digital image colorimetry provides a more objective method. This study evaluated the diagnostic accuracy of urine photo colorimetry using different smartphones under different lighting conditions, and determined the optimal cut-off value to predict clinical dehydration. Methods: The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve. Results: A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were “good” to “excellent” across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170. Conclusions: Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.