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Background: COVID-19 pandemic has altered the work mode in long-term care facilities (LTCFs), but little is known about the mental health status of caregivers of older adults. Methods: A total of 672 formal caregivers of older adults in LTCFs and 1,140 formal patient caregivers in hospitals (comparison group) responded to an online survey conducted from March 25, 2022 to April 6, 2022. Five psychological scales, including Insomnia Severity Index (ISI), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), The 5-item World Health Organization Wellbeing Index (WHO-5) and Perceived Stress Scale-14 item (PSS-14), were applied to assess participants' mental health status. Factors, including sex, profession, marital status, economic conditions, length of working experience, frequent night shift beyond 1 day per week and having organic diseases, were included in logistic regression analysis to identify associated factors with mental health outcomes of formal caregivers of older adults in LTCFs. Results: Caregivers of older adults in LTCFs developed similar severe psychological symptoms with patient caregivers in hospital setting. For caregivers of older adults in LTCFs, unmarried status was a potent risk factor for insomnia, anxiety, impaired wellbeing and health risk stress, with odds ratios ranging from 1.91 to 3.64. Frequent night shift beyond 1 day per week was associated with higher risks of insomnia, depression and impaired wellbeing. Likewise, having organic disease or inferior economic condition, and being nurses appeared to be independent predictors for multiple mental health-related outcomes. Conclusion: During COVID-19 post-epidemic era, caregivers of older adults in LTCFs had a higher prevalence of psychological symptoms, especially those with particular risk factors. Special attention should be paid to promote their mental health.
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Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.