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Real-time surveillance of COVID-19 in large-scale community outbreaks presents challenges. Simple counts of the daily confirmed cases can be misleading due to constraints from bottlenecks in access to care or laboratory testing. This study aimed to investigate the role of the SARS-CoV-2 antigen rapid diagnostic test (Ag-RDT) in addressing these challenges for real-time COVID-19 surveillance. This study included the results of 86,933 SARS-CoV-2 Ag-RDT and real-time reverse transcription polymerase chain reaction (RT-PCR) tests. These were conducted at four community testing stations within the Taipei metropolitan area during a community COVID-19 outbreak spanning from May 17, 2021, to August 9, 2021. We examined the correlation between the positive rates of Ag-RDT tests and the epidemic curve of laboratory-confirmed COVID-19 cases by onset date to examine its role in real-time surveillance. During the 85-day study period, the trend of Ag-RDT test positive rates paralleled that of the epidemic curve. The correlation between the Ag-RDT positive rate and the number of cases (Pearson correlation coefficient: 0.968) is comparable to that of the RT-PCR positive rate (Pearson correlation coefficient: 0.964). The Ag-RDT positive rate exhibited a more advanced leading trend, with Ag-RDT leading by 3 days in comparison to the 2-day lead for RT-PCR. The positive rate of SARS-CoV-2 Ag-RDT tests at community testing stations serves as a good surrogate for assessing virus activity within the community and a useful tool for real-time COVID-19 surveillance. It is a robust indicator of the outbreak trend and near-term numbers of cases. This finding may facilitate the management of subsequent outbreaks of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is common, with claims that it is associated with an increased risk for suicide. A nationwide cohort study conducted in Taiwan assessed suicide risk in patients with PCOS after adjustment for multiple variables, including age, demographic characteristics, comorbid conditions, income, and frequency of medical visits. Visual Abstract. Suicide Attempts After a Diagnosis of Polycystic Ovary Syndrome: Polycystic ovary syndrome (PCOS) is common, with claims that it is associated with an increased risk for suicide. A nationwide cohort study conducted in Taiwan assessed suicide risk in patients with PCOS after adjustment for multiple variables, including age, demographic characteristics, comorbid conditions, income, and frequency of medical visits. Background: Limited evidence exists about suicide risk in persons with polycystic ovary syndrome (PCOS). Objective: To assess suicide risk in persons with PCOS, accounting for psychiatric comorbid conditions and age group. Design: Cohort study. Setting: Data from the Taiwanese nationwide database from 1997 to 2012. Patients: A cohort of 18 960 patients diagnosed with PCOS, each matched with control participants in a 1:10 ratio on the basis of age, psychiatric comorbid conditions, urbanization level, and income. Suicide attempts were evaluated using Cox regression models. Measurements: Suicide risk with hazard ratios (HRs). Results: Participants with PCOS had a notable 8.47-fold increase in risk for suicide attempt compared with the control group (HR, 8.47 [95% CI, 7.54 to 9.51]), after adjustment for demographic characteristics, psychiatric comorbid conditions, Charlson Comorbidity Index scores, and frequency of all-cause clinical visits. The elevated risk was evident across the adolescent (HR, 5.38 [CI, 3.93 to 7.37]), young adult (<40 years; HR, 9.15 [CI, 8.03 to 10.42]), and older adult (HR, 3.75 [CI, 2.23 to 6.28]) groups. Sensitivity analyses involving the exclusion of data from the first year or the first 3 years of observation yielded consistent results. Limitation: Potential underestimation of PCOS and mental disorder prevalence due to use of administrative claims data; lack of clinical data, such as body mass index and depressive symptoms; and no assessment of a confounding effect of valproic acid exposure. Conclusion: This study underscores the heightened risk for suicide attempt that persons with PCOS face, even after adjustment for demographics, psychiatric comorbid conditions, physical conditions, and all-cause clinical visits. This suggests the importance of routine monitoring of mental health and suicide risk in persons diagnosed with PCOS. Primary Funding Source: Taipei Veterans General Hospital, Yen Tjing Ling Medical Foundation, and Ministry of Science and Technology of Taiwan. [ABSTRACT FROM AUTHOR]

This paper empirically studied how Airbnb supply and business cycles affected the price elasticity of room demand (PED) in the hotel industry. Economic theory points out that demand becomes more elastic as there exist more close substitutes in the market. Moreover, market elasticities can vary either procyclically or countercyclically across the business cycles. Using monthly operation data of Taipei's international tourist hotels (ITHs) from 2009 (debut of Airbnb in Taipei) to 2016 and exploiting a Markov regime-switching model, this study finds that total listings of Airbnb were positively associated with PED during the peaks (i.e. high price elasticity of demand). Thus, the Airbnb supply posed a substitution threat to the hotel industry in Taipei. For the effect of business cycles, this study found that elasticities of room demand exhibited a countercyclical nature in the Taipei ITH market. [ABSTRACT FROM AUTHOR]

This paper describes the curriculum implementation and teaching effects of a life education course integrated with COVID-19 issues. A total of 43 university students in Taipei who had taken the life education course in the second semester of the academic year 108 were included. The life education course focused on spiritual cultivation and consisted of four teaching units. COVID-19-related issues were integrated into each teaching unit. The study collected students' interactive feedback, learning sheets, homework, activity reflection, body-- mind--spirit wellness diary, body-mind-spirit wellness behavior questionnaire responses, and self-reflections for data analysis. The results indicated that students were able to realize the vulnerability and limitlessness of life, enhance self-reflection on the meaning and purpose of life, analyze the ethical dilemma and care for those affected by the pandemic, experience the joy of social contribution and cultivate altruism, experience the healing power of green plants and enhance spiritual connectedness to nature, cherish and be grateful during the pandemic, meditate for resting body and mind, and implement plans to improve the body-mind-spirit wellness. On the basis of the study results, we provided recommendations for improving the post-pandemic life education curriculum and studies. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM 2.5 ) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM 2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM 2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM 2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%). [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to investigate the factors influencing the rate of Pneumococcal Conjugate Vaccine (PCV) vaccination of children under 5 years of age in Taipei City during 2012. Methods: Based on data from the National Immunization Information System (NIIS), we selected families with children under 5 years of age in Taipei City as the study population. We adopted the stratified random sampling method to sample 12 administrative districts in Taipei City and allocated samples proportionally according to the number of families in each district. In December 2012, a computer assisted telephone interview was conducted. We used chi-square tests, point-biserial correlations, and a multiple logistic regression model to analyze the 707 valid samples. Results: The PCV vaccination rate of children under 5 years of age in Taipei City exceeded 80%. After multiple logistic regression analysis, we found that the statistically significant factors influencing PCV vaccination were: father's age and primary caregiver's acknowledgement of health beliefs such as perceived susceptibility to invasive pneumococcal disease (IPD) and PCV's effectiveness. Conclusions: In order to raise primary caregivers' acknowledgement of perceived susceptibility to IPD and the effectiveness of PCV, we suggest that the health authorities provide more comprehensive information for the public. Campaign materials might include educational posters and brochures in which the severity of IPD and the ingredients and protective effects of PCV shots are explained. We also suggest further research on vaccination and herd immunity based on the PCV vaccination rate in Taipei city. [ABSTRACT FROM AUTHOR]

Background: The study was conducted to investigate the diagnostic performance of infrared (IR) imaging of the breast using an interpretive model derived from a scoring system. Methods: The study was approved by the Institutional Review Board of our hospital. A total of 276 women (mean age = 50.8 years, SD 11.8) with suspicious findings on mammograms or ultrasound received IR imaging of the breast before excisional biopsy. The interpreting radiologists scored the lesions using a scoring system that combines five IR signs. The ROC (receiver operating characteristic) curve and AUC (area under the ROC curve) were analyzed by the univariate logistic regression model for each IR sign and an age-adjusted multivariate logistic regression model including 5 IR signs. The cut-off values and corresponding sensitivity, specificity, Youden's Index (Index = sensitivity+specificity-1), positive predictive value (PPV), negative predictive value (NPV) were estimated from the age-adjusted multivariate model. The most optimal cut-off value was determined by the one with highest Youden's Index. Results: For the univariate model, the AUC of the ROC curve from five IR signs ranged from 0.557 to 0.701, and the AUC of the ROC from the age-adjusted multivariate model was 0.828. From the ROC derived from the multivariate model, the sensitivity of the most optimal cut-off value would be 72.4% with the corresponding specificity 76.6% (Youden's Index = 0.49), PPV 81.3% and NPV 66.4%. Conclusions: We established an interpretive age-adjusted multivariate model for IR imaging of the breast. The cutoff values and the corresponding sensitivity and specificity can be inferred from the model in a subpopulation for diagnostic purpose. Trial Registration: NCT00166998. [ABSTRACT FROM AUTHOR]

To better understand events related to going home to die (GHTD) from the intensive care unit (ICU), with the hope that this information might improve the palliative care of ICU patients. This retrospective observational study was performed at a tertiary medical center—the National Taiwan University Hospital. All surgical ICU mortality cases between 1 January 2003 and 31 December 2007 were included in this study. The rate of GHTD from the ICU declined annually, but has reached a plateau of around 25% in recent years. Multivariate logistic regression analysis found independently significant factors associated with GHTD, including older age (OR: 1.013; P = 0.001), married status (OR: 2.128; P < 0.001), lower educational level (OR: 1.799; P = 0.001), and lack of DNR consent (OR: 1.499; P = 0.006). When treatment intensity was compared on the date of death, GHTD patients in general received more treatments and diagnostic procedures than those who died in the ICU. Univariate analysis showed that GHTD patients received significantly more advanced antibiotics, more chest radiography, greater use of sedatives, greater use of analgesics, and more transfusions, but less FiO2 and mechanical circulatory support than patients who died in the ICU. GHTD from the ICU is a special phenomenon in the Chinese cultural area, representing a cultural tradition rather than a form of palliative care. [ABSTRACT FROM AUTHOR]

This study was undertaken to determine whether there is an association between exposure to air pollutants levels and number of hospital admissions for asthma in Taipei, Taiwan. Hospital admissions for asthma and ambient air pollution data for Taipei were obtained for the period from 1996 through 2003. The relative risk of hospital admission for asthma was estimated using a case-crossover approach, controlling for weather variables, day of the week, seasonality, and long-term time trends. In the single-air-pollutant model, on warm days (≥25°C) statistically significant positive associations were found for SO2, NO2, and CO levels with an increase in asthmatic admissions. On cool days (<25°C), all air pollutants were significantly associated with elevated asthma admissions except SO2. For the two-air-pollutant model, CO significantly increases hospital admissions for asthma in combination with each of the other four pollutants on warm days. On cool days, NO2 and O3 significantly elevated asthma rates in all the two-air-pollutant models. This study provides evidence that higher levels of ambient air pollutant concentrations increase the risk of hospital admissions for asthma. [ABSTRACT FROM AUTHOR]

A historical investigation is a kind of inquiry-based learning, aiming to guide students' reading and analyzing processes of historical text to enhance the historical thinking. For this reason, a Digital Humanities Research Platform for Mr. Lo Chia-Lun's Writings (DHRP-MLCLW) with Perspective Change and Chronological Division Tool (PCCDT) is used for assisting senior high school students in inquiry-based learning activities of history course. It is expected to help learners understand historical investigation issues from different perspectives and aspects as well as enhance learners' understanding and inquiry abilities of interpreting historical text and context. With a pre-experimental research design, a total of 13 Grade 10 students in a senior high school in Taipei City, Taiwan, were recruited as the research participants for DHRP-MLCLW with PCCDT supported history inquiry course to examine whether learners significantly promote inquiry-based learning performance and to investigate students' learning satisfaction. Furthermore, lag sequential analysis (LSA) is applied to explore the effective behavior transition mode of learners with high learning performance. Semi-structured interview is also utilized for understanding learners' perception and suggestions about using the DHRP-MLCLW with PCCDT for history inquiry course. The research results show that DHRP-MLCLW with PCCDT supported history inquiry course could effectively enhance learners' learning performance and led to highly positive learning satisfaction. Moreover, the LSA results reveal that compared with the learners with low learning performance, the behavioral patterns among those with high learning performance using the DHRP-MLCLW with PCCDT for inquiry-based historical investigation better reflect the spirit of inquiry-based learning. [ABSTRACT FROM AUTHOR]

To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission. Retrospective cohort study. Single university teaching hospital in Taipei, Taiwan. 6238 critically ill patients from August 2020 to August 2021. Data were extracted, pre-processed, and split into training and testing datasets based on the time period. Eligible variables included demographic characteristics, Glasgow Coma Scale, vital signs parameters, treatments, and laboratory data. The predicted outcome was delirium, defined as any positive result (a score ≥ 4) of the Intensive Care Delirium Screening Checklist that was assessed by primary care nurses in each 8-h shift within 48 h after ICU admission. We trained models to predict delirium upon ICU admission (ADM) and at 24 h (24H) after ICU admission by using logistic regression (LR), gradient boosted trees (GBT), and deep learning (DL) algorithms and compared the models' performance. Eight features were extracted from the eligible features to train the ADM models, including age, body mass index, medical history of dementia, postoperative intensive monitoring, elective surgery, pre-ICU hospital stays, and GCS score and initial respiratory rate upon ICU admission. In the ADM testing dataset, the incidence of ICU delirium occurred within 24 h and 48 h was 32.9% and 36.2%, respectively. The area under the receiver operating characteristic curve (AUROC) (0.858, 95% CI 0.835–0.879) and area under the precision-recall curve (AUPRC) (0.814, 95% CI 0.780–0.844) for the ADM GBT model were the highest. The Brier scores of the ADM LR, GBT, and DL models were 0.149, 0.140, and 0.145, respectively. The AUROC (0.931, 95% CI 0.911–0.949) was the highest for the 24H DL model and the AUPRC (0.842, 95% CI 0.792–0.886) was the highest for the 24H LR model. Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission. • A certain proportion of critically ill patients develop delirium within the first 24h after ICU admission. • Traditional delirium prediction models make a prediction time of 24h after ICU admission cannot predict early delirium. • Developing machine learning model to predict delirium upon ICU admission is crucial for early intervention. • Make a second prediction at 24h after ICU admission can improve the prediction of delirium occurred after 24h. [ABSTRACT FROM AUTHOR]