Impact of Telemedicine on Blood Glucose Control and Ophthalmic Screenings for Patients with Diabetes in Remote Areas During the COVID-19 Pandemic: A Real-World Study in Northern Taiwan.

Introduction: The purpose of this study was to assess the impact of telemedicine on ophthalmic screening and blood glucose control for patients with diabetes in remote areas of Northern Taiwan during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Telemedicine was implemented in Shiding and Wanli Districts using a 5G platform from April 2021 to December 2022. Patients with poorly controlled diabetes received real-time consultations from endocrinologists at Far Eastern Memorial Hospital, 50 km away, for medication adjustment, diet control, and lifestyle recommendations. The study also provided cloud-upload blood glucose meters for self-monitoring and regular medical advice from hospital nurses. Ophthalmic screenings included fundus imaging, external eye image, and intraocular pressure measurement, with instant communication and diagnosis by ophthalmologists through telemedicine. A satisfaction questionnaire survey was conducted. Results: The study enrolled 196 patients with diabetes. Blood glucose and glycosylated hemoglobin levels were significantly reduced after applying telemedicine (p = 0.01 and p = 0.005, respectively). Ophthalmic screenings led to hospital referrals for 16.0% with abnormal fundus images, 15.6% with severe cataract or anterior segment disorders, and 27.9% with ocular hypertension or glaucoma. Fundus screening rates remained high at 86.3% and 80.4% in 2022, mainly using telemedicine, comparable with the traditional screening rate in the past 5 years. The overall satisfaction rate was 98.5%. Conclusions: Telemedicine showed effectiveness and high satisfaction in managing diabetes and conducting ophthalmic screenings in remote areas during the COVID-19 pandemic. It facilitated early diagnosis and treatment of ocular conditions while maintaining good blood glucose control and fundus screening rates.

Association of Childhood Attention Deficit/Hyperactivity Disorder With Adolescent Traditional and Cyberbullying Victimization in the Digital Generation: A Population-Based Cohort Study.

PURPOSE: This population-based cohort study aimed to examine the association with childhood attention deficit/hyperactivity disorder (ADHD) and bullying experiences during adolescence among Digital Generation individuals, exploring both traditional and cyberbullying. METHODS: This study included data from 15,240 participants, collected from the Taiwan Adolescent to Adult Longitudinal Study project. Participants, initially in seventh and 10th grade in 2015, were selected through a multistage stratified sampling approach. Self-report questionnaires assessed traditional and cyberbullying victimization experiences during adolescence, with 5-year longitudinal follow-up. Childhood ADHD diagnoses were identified by linking data to Taiwan's National Health Insurance Research Database from 2000 to 2015. Logistic regression models were employed to examine the relationship between childhood ADHD and bullying victimization while controlling for relevant covariates. RESULTS: Individuals diagnosed with childhood ADHD exhibited a significantly higher likelihood of experiencing bullying during adolescence (adjusted odds ratio (aOR) = 1.52, 95% confidence interval (CI): 1.28-1.80). This association extended to various forms of bullying, including physical (aOR = 1.42, 95% CI: 1.20-1.68), verbal (aOR = 1.42, 95% CI: 1.20-1.67), relational (aOR = 1.45, 95% CI: 1.22-1.71), and cyber (aOR = 1.35, 95% CI: 1.14-1.61). Additional factors positively associated with bullying victimization included male, binge drinking, and depression, while a positive campus atmosphere was protective against bullying. However, there is no evidence for interactions between these factors and ADHD in their associations with bullying. DISCUSSION: Childhood ADHD increases the risk of both traditional and cyberbullying during adolescence. Recognizing this risk is essential for targeted interventions and further research on underlying mechanisms.

Early detection of nasopharyngeal carcinoma through machine-learning-driven prediction model in a population-based healthcare record database.

OBJECTIVE: Early diagnosis and treatment of nasopharyngeal carcinoma (NPC) are vital for a better prognosis. Still, because of obscure anatomical sites and insidious symptoms, nearly 80% of patients with NPC are diagnosed at a late stage. This study aimed to validate a machine learning (ML) model utilizing symptom-related diagnoses and procedures in medical records to predict nasopharyngeal carcinoma (NPC) occurrence and reduce the prediagnostic period. MATERIALS AND METHODS: Data from a population-based health insurance database (2001-2008) were analyzed, comparing adults with and without newly diagnosed NPC. Medical records from 90 to 360 days before diagnosis were examined. Five ML algorithms (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting [XGB], Multivariate Adaptive Regression Splines [MARS], Random Forest [RF], and Logistics Regression [LG]) were evaluated for optimal early NPC detection. We further use a real-world data of 1 million individuals randomly selected for testing the final model. Model performance was assessed using AUROC. Shapley values identified significant contributing variables. RESULTS: LGB showed maximum predictive power using 14 features and 90 days before diagnosis. The LGB models achieved AUROC, specificity, and sensitivity were 0.83, 0.81, and 0.64 for the test dataset, respectively. The LGB-driven NPC predictive tool effectively differentiated patients into high-risk and low-risk groups (hazard ratio: 5.85; 95% CI: 4.75-7.21). The model-layering effect is valid. CONCLUSIONS: ML approaches using electronic medical records accurately predicted NPC occurrence. The risk prediction model serves as a low-cost digital screening tool, offering rapid medical decision support to shorten prediagnostic periods. Timely referral is crucial for high-risk patients identified by the model.

Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

BACKGROUND: Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. METHODS: This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. RESULTS: The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. CONCLUSIONS: This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Comparing Low- or Standard-Dose Alteplase in Endovascular Thrombectomy: Insights From a Nationwide Registry.

BACKGROUND: Timely intravenous thrombolysis and endovascular thrombectomy are the standard reperfusion treatments for large vessel occlusion stroke. Currently, it is unknown whether a low-dose thrombolytic agent (0.6 mg/kg alteplase) can offer similar efficacy to the standard dose (0.9 mg/kg alteplase). METHODS: We enrolled consecutive patients in the multicenter Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke who had received combined thrombolysis (within 4.5 hours of onset) and thrombectomy treatment from January 2019 to April 2023. The choice of low- or standard-dose alteplase was based on the physician's discretion. The outcomes included successful reperfusion (modified Thrombolysis in Cerebral Infarction score, 2b-3), symptomatic intracerebral hemorrhage, 90-day modified Rankin Scale score, and 90-day mortality. The outcomes between the 2 groups were compared using multivariable logistic regression and inverse probability of treatment weighting-adjusted analysis. RESULTS: Among the 2242 patients in the Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke, 734 (33%) received intravenous alteplase. Patients in the low-dose group (n=360) were older, had more women, more atrial fibrillation, and longer onset-to-needle time compared with the standard-dose group (n=374). In comparison to low-dose alteplase, standard-dose alteplase was associated with a lower rate of successful reperfusion (81% versus 87%; adjusted odds ratio, 0.63 [95% CI, 0.40-0.98]), a numerically higher incidence of symptomatic intracerebral hemorrhage (6.7% versus 3.9%; adjusted odds ratio, 1.81 [95% CI, 0.88-3.69]), but better 90-day modified Rankin Scale score (functional independence [modified Rankin Scale score, 0-2], 47% versus 31%; adjusted odds ratio, 1.91 [95% CI, 1.28-2.86]), and a numerically lower mortality rate (9% versus 15%; adjusted odds ratio, 0.73 [95% CI, 0.43-1.25]) after adjusting for covariates. Similar results were observed in the inverse probability of treatment weighting-adjusted models. The results were consistent across predefined subgroups and age strata. CONCLUSIONS: Despite the lower rate of successful reperfusion and higher risk of symptomatic intracerebral hemorrhage with standard-dose alteplase, standard-dose alteplase was associated with a better functional outcome in patients receiving combined thrombolysis and thrombectomy.

Beyond the waves: Unraveling pandemic outcomes with genomic insights and immunity analysis - Evidence from 14 countries.

OBJECTIVE: Although the World Health Organization and many governments have recategorized COVID-19 as a generally mild to moderately severe disease, consecutive pandemic waves driven by immune escape variants have underscored the need for timely and accurate prediction of the next outbreak. Nevertheless, little attention has been paid to translating genomic data and infection- and vaccine-induced immunity into direct estimates. METHODS: We retrieved epidemiologic and genomic data shortly before pandemic waves across 14 developed countries from late 2021 to mid-2022 and examined associations between early-stage variant competition, infection- and vaccine-induced immunity, and the time intervals between wave peaks. We applied regression analysis and the generalized estimating equation method to construct an inferential model. RESULTS: Each per cent increase in the proportion of a new variant was associated with a 1.0% reduction in interpeak intervals on average. Curvilinear associations between vaccine-induced immunity and outcome variables were observed, suggesting that reaching a critical vaccine distribution rate may decrease the caseload of the upcoming wave. CONCLUSIONS: By leveraging readily accessible pre-outbreak genomic and epidemiologic data, our results not only substantiate the predictive potential of early variant fractions but also propose that immunity acquired through infection alone may not sufficiently mitigate transmission. Conversely, a rapid and widespread vaccination initiative appears to be correlated with a decrease in disease incidence.

Uncovering a dose-response relationship between positive fecal immunochemical test (FIT) and all-cause, cardiovascular and cancer-related mortality.

BACKGROUND: Fecal immunochemical test (FIT) is for colorectal cancer (CRC) screening. Its association with non-CRC mortality has been overlooked. Given the quantitative FIT values, its dose-response relationships with different causes of deaths and years of life shortened were assessed. METHODS: This retrospective study included 546,214 adults aged ≥ 20 who attended a health surveillance program from 1994 to 2017 and were followed up until the end of 2020. FIT ≥ 20 µg Hb/g was defined as positive. The Cox model was used to assess adjusted hazard ratios (aHR). RESULTS: Positive FIT was associated with increased all-cause mortality (aHR: 1.34, 95 % CI: 1.25-1.44) and all-cancer mortality (aHR: 1.71, 95 % CI: 1.55-1.89), with a reduction of life expectancy by 4 years. The association remained even with CRC excluded. With each 10 µg Hb/g increase in FIT above 20 µg Hb/g, life expectancy was reduced by one year, and mortality increased by 4 %. About 18.6 % of deaths with positive FIT were attributed to cardiovascular disease (CVD), followed by CRC (13.5 %) and upper gastrointestinal (GI) cancers (4.5 %). The all-cause mortality rate after excluding CRC for positive FIT was 3.56/1,000 person-year, comparable to the all-cause mortality rate of 3.69/1,000 person-year for hypertension. CONCLUSION: Positive FIT was associated with increased mortality in a dose-response manner and shortened life expectancy by 4 years, an overlooked risk comparable to hypertension, even with CRC excluded. After a negative colonoscopy, subjects with positive FIT should undergo a workup on CVD risk factors and look for other upper GI cancers.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank.

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

Exploring concepts and trends in informal caregiver burden: systematic review using citation network and content analysis.

BACKGROUND: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern. OBJECTIVE: This study aimed to comprehensively depict the concept of "informal caregiver burden" through bibliometric and content analyses. METHODS: We searched the Web of Science (WoS) database to obtain bibliometric data and included only papers published between 2013 and 2022. We used content analysis to extract and identify the core concepts within the text systematically. RESULTS: Altogether, 934 papers were included in the bibliometric analysis, from which we selected 19 highly impactful papers for content analysis. The results indicate that researchers have focused on exploring the factors that impact informal caregiver burden. Meanwhile, there has been a widespread discussion regarding the caregiver burden among those caring for recipients with specific illnesses, such as dementia, Alzheimer's disease, and cancer, as these illnesses can contribute to varying levels of burden on informal caregivers. In addition, questionnaires and interviews emerged as the predominant methods for data collection in the realm of informal caregiver research. Furthermore, we identified 26 distinct assessment tools specifically tailored for evaluating burden, such as caregiver strain index (CSI). CONCLUSION: For future studies, we suggest considering the intersectionality of factors contributing to the burden on informal caregivers. This approach could enhance the well-being of both caregivers and older care recipients.

Interregional mobility in different age groups is associated with COVID-19 transmission in the Taipei metropolitan area, Taiwan.

Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15-59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases.

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.

BACKGROUND: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. OBJECTIVE: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. METHODS: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. RESULTS: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs. CONCLUSION: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.

Low-to-Moderate Arsenic Exposure and Urothelial Tract Cancers with a Long Latent Period of Follow-Up in an Arseniasis Area.

BACKGROUND: Arsenic exposure can cause adverse health effects. The effects of long-term low-to-moderate exposure and methylations remain unclear. OBJECTIVE: This study aims to examine the association between low-to-moderate arsenic exposure and urothelial tract cancers while considering the effects of methylation capacity. METHODS: In this study, 5,811 participants were recruited from an arseniasis area in Taiwan for inorganic arsenic metabolite analysis. This follow-up study was conducted between August 1995 and December 2017. We identified 85 urothelial tract cancers in these participants, including 49 bladder and 36 upper urothelial tract cancer cases. A Cox proportional hazards model was employed. RESULTS: The analyses revealed a significant association between concentrations of inorganic arsenic in water > 100 ug/L and bladder cancer occurrence, with a hazard ratio (HR) of 4.88 (95% CI 1.35-17.61). A monotonic trend was observed between concentrations of inorganic arsenic in water (from 0 to > 100 ug/L) and the incidence of urothelial tract cancer, including bladder cancer (p < 0.05) and upper urothelial tract cancers (p < 0.05). Participants with a lower primary methylation index or higher secondary methylation index had a prominent effect. CONCLUSIONS: Rigorous regulations and active interventions should be considered for populations with susceptible characteristics.

Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study.

BACKGROUND: Although vaccination has been shown to be one of the most important interventions, COVID-19 vaccine hesitancy remains one of the top 10 global public health challenges worldwide. OBJECTIVE: The objective of this study is to investigate (1) major determinants of vaccine hesitancy, (2) changes in the determinants of vaccine hesitancy at different time periods, and (3) the potential factors affecting vaccine acceptance. METHODS: This study applied a mixed methods approach to explore the potential determinants contributing to vaccine hesitancy among the Taiwanese population. The quantitative design of this study involved using Google Trends search query data. We chose the search term "" (vaccine), selected "" (Taiwan) as the location, and selected the period between December 18, 2020, and July 31, 2021. The rising keywords related to vaccine acceptance and hesitancy were collected. Based on the responses obtained from the qualitative study and the rising keywords obtained in Google Trends, the 3 most popular keywords related to vaccine hesitancy were identified and used as search queries in Google Trends between December 18, 2020, and July 31, 2021, to generate relative search volumes (RSVs). Lastly, autoregressive integrated moving average modeling was used to forecast the RSVs for the 3 keywords between May 29 and July 31, 2021. The estimated RSVs were compared to the observed RSVs in Google Trends within the same time frame. RESULTS: The 4 prevailing factors responsible for COVID-19 vaccine acceptance and hesitancy were doubts about the government and manufacturers, side effects, deaths associated with vaccination, and efficacy of vaccination. During the vaccine observation period, "political role" was the overarching consideration leading to vaccine hesitancy. During the peak of the pandemic, side effects, death, and vaccine protection were the main factors contributing to vaccine hesitancy. The popularity of the 3 frequently searched keywords "side effects," "vaccine associated deaths," and "vaccine protection" continued to rise throughout the pandemic outbreak. Lastly, the highest Google search queries related to COVID-19 vaccines emerged as "side effects" prior to vaccination, deaths associated with vaccines during the period when single vaccines were available, and "side effects" and "vaccine protection" during the period when multiple vaccines were available. CONCLUSIONS: Investigating the key factors influencing COVID-19 vaccine hesitancy appears to be a fundamental task that needs to be undertaken to ensure effective implementation of COVID-19 vaccination. Google Trends may be used as a complementary infoveillance tool by government agencies for future vaccine policy implementation and communication.

Health of migrants and other vulnerable populations across Asian countries: Build Forward Better beyond the COVID-19 pandemic.

Global migration has been increasing since before the COVID-19 pandemic. The pandemic has clearly shown a lack of preparedness for the next public health emergency when it comes to vulnerable populations including migrants. To include the issues of migration and health in the current global health agenda, it is important to establish/strengthen a network for collaboration among various stakeholders from both the migrant-sending and host countries of migrants especially in the Asian-Pacific region. As the initial step for networking in Asia, in March 2023, a hybrid style international symposium was held in Japan and agreed on a goal and five pillars: surveillance and monitoring, risk communications, community engagement, access to health and social protection services, and supportive environments. Considering the transition of context from the COVID-19 crisis to 'Build Forward Better', through the Asian network, we will envisage the better world, where vulnerable populations including migrants will not be left behind from health security.

Prevalence and predictive modeling of undiagnosed diabetes and impaired fasting glucose in Taiwan: a Taiwan Biobank study.

INTRODUCTION: We investigated the prevalence of undiagnosed diabetes and impaired fasting glucose (IFG) in individuals without known diabetes in Taiwan and developed a risk prediction model for identifying undiagnosed diabetes and IFG. RESEARCH DESIGN AND METHODS: Using data from a large population-based Taiwan Biobank study linked with the National Health Insurance Research Database, we estimated the standardized prevalence of undiagnosed diabetes and IFG between 2012 and 2020. We used the forward continuation ratio model with the Lasso penalty, modeling undiagnosed diabetes, IFG, and healthy reference group (individuals without diabetes or IFG) as three ordinal outcomes, to identify the risk factors and construct the prediction model. Two models were created: Model 1 predicts undiagnosed diabetes, IFG_110 (ie, fasting glucose between 110 mg/dL and 125 mg/dL), and the healthy reference group, while Model 2 predicts undiagnosed diabetes, IFG_100 (ie, fasting glucose between 100 mg/dL and 125 mg/dL), and the healthy reference group. RESULTS: The standardized prevalence of undiagnosed diabetes for 2012-2014, 2015-2016, 2017-2018, and 2019-2020 was 1.11%, 0.99%, 1.16%, and 0.99%, respectively. For these periods, the standardized prevalence of IFG_110 and IFG_100 was 4.49%, 3.73%, 4.30%, and 4.66% and 21.0%, 18.26%, 20.16%, and 21.08%, respectively. Significant risk prediction factors were age, body mass index, waist to hip ratio, education level, personal monthly income, betel nut chewing, self-reported hypertension, and family history of diabetes. The area under the curve (AUC) for predicting undiagnosed diabetes in Models 1 and 2 was 80.39% and 77.87%, respectively. The AUC for predicting undiagnosed diabetes or IFG in Models 1 and 2 was 78.25% and 74.39%, respectively. CONCLUSIONS: Our results showed the changes in the prevalence of undiagnosed diabetes and IFG. The identified risk factors and the prediction models could be helpful in identifying individuals with undiagnosed diabetes or individuals with a high risk of developing diabetes in Taiwan.

Outcomes after surgery in patients with and without recent influenza: a nationwide population-based study.

Background: The influence of recent influenza infection on perioperative outcomes is not completely understood. Method: Using Taiwan's National Health Insurance Research Data from 2008 to 2013, we conducted a surgical cohort study, which included 20,544 matched patients with a recent history of influenza and 10,272 matched patients without. The main outcomes were postoperative complications and mortality. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for the complications and for mortality in patients with a history of influenza within 1-14 days or 15-30 days compared with non-influenza controls. Results: Compared with patients who had no influenza, patients with influenza within preoperative days 1-7 had increased risks of postoperative pneumonia (OR 2.22, 95% CI 1.81-2.73), septicemia (OR 1.98, 95% CI 1.70-2.31), acute renal failure (OR 2.10, 95% CI 1.47-3.00), and urinary tract infection (OR 1.45, 95% CI 1.23-1.70). An increased risk of intensive care admission, prolonged length of stay, and higher medical expenditure was noted in patients with history of influenza within 1-14 days. Conclusion: We found that there was an association between influenza within 14 days preoperatively and the increased risk of postoperative complications, particularly with the occurrence of influenza within 7 days prior to surgery.

Mortality rate and its determinants among people with dementia receiving home healthcare: a nationwide cohort study.

People with dementia (PwD) who receive home healthcare (HHC) may have distressing symptoms, complex care needs and high mortality rates. However, there are few studies investigating the determinants of mortality in HHC recipients. To identify end-of-life care needs and tailor individualized care goals, we aim to explore the mortality rate and its determinants among PwD receiving HHC. We conducted a retrospective cohort study using a Taiwanese national population database. People with new dementia diagnosis in 2007-2016 who received HHC were included. We calculated the accumulative mortality rate and applied Poisson regression model to estimate the risk of mortality for each variable (adjusted risk ratios, aRR) with a 95% confidence interval (CI). We included 95,831 PwD and 57,036 (59.5%) of them died during the follow-up period (30.5% died in the first-year). Among comorbidities, cirrhosis was associated with the highest mortality risks (aRR 1.65, 95% CI 1.49-1.83). Among HHC-related factors, higher visit frequency of HHC (> 2 versus ≦1 times/month, aRR 3.52, 95% CI 3.39-3.66) and higher level of resource utilization group (RUG, RUG 4 versus 1, aRR = 1.38, 95% CI 1.25-1.51) were risk factor of mortality risk. Meanwhile, HHC provided by physician and nurse was related to reduced mortality risk (aRR 0.79, 95% CI 0.77-0.81) compared to those provided by nurse only. Anticipatory care planning and timely end-of life care should be integrated in light of the high mortality rate among PwD receiving HHC. Determinants associated with increased mortality risk facilitate the identification of high risk group and tailoring the appropriate care goals. Trial registration number: ClinicalTrials.gov Identifier is NCT04250103 which has been registered on 31st January 2020.

Association between clustering of unhealthy behaviors and depressive symptom among adolescents in Taiwan: A nationwide cross-sectional survey.

Background: Among Taiwanese adolescents, how the clustering of unhealthy behaviors, including insufficient physical activity, screen-based sedentary behavior and frequent sugar-sweetened beverage consumption affecting depressive symptom remains unclear. This study aims to examine the cross-sectional association between clustering of unhealthy behaviors and depressive symptom. Methods: We analyzed 18,509 participants from the baseline survey of the Taiwan Adolescent to Adult Longitudinal Survey in 2015. The outcome was depressive symptoms, and the main exposures were insufficient physical activity, screen-based sedentary behaviors and frequent sugar-sweetened beverage consumption. Generalized linear mixed models were performed to find key factor associated with depressive symptom. Results: Depressive symptoms were common among participants (31.4%), particularly in female and older adolescents. After adjustments for covariates including sex, school type, other lifestyle factors and social determinants, individuals exhibiting clustering of unhealthy behaviors were more likely (aOR = 1.53, 95% CI: 1.48-1.58) to exhibit depressive symptoms than those who have no or only one unhealthy behavior. Conclusions: Clustering of unhealthy behaviors is positively associated with depressive symptom among Taiwanese adolescents. The findings highlight the importance of strengthening public health interventions to improve physical activity and decrease sedentary behaviors.

Perceptions towards the COVID-19 Pandemic during Different Lockdown Levels among International Students in Taiwan.

International students face many impediments under the COVID-19 pandemic. The objectives of this study are to assess the association between the perceptions of international students and the lockdown policy for COVID-19. In 2021, three different levels of lockdown policy were enforced, including level I from January to April, level III from May to July, and level II from August to December. We conducted three surveys for international graduate students using a validated questionnaire during the different lockdown levels. We collected 185, 119, and 83 valid questionnaires in level I, II, and III, respectively. There were linear trends in the correlations of lockdown policy with the knowledge (p = 0.052), attitudes (p = 0.002), and practices (p < 0.001) of COVID-19. In brief, the stricter the lockdown policy, the better the students adhered to sufficient knowledge, positive attitudes, and healthy practices. Furthermore, there were significant linear correlations of lockdown policy with the transportation, school study, leisure, family life, and diet behavior. In conclusion, lockdown policy had important impacts on the knowledge, attitudes, practices, and daily lives of international students. The findings indicated that the lockdown system and its corresponding measures appear to affect perceptions in a positive way.

Identifying differential trajectories and predictors for depressive symptoms in adolescents using latent class growth analysis: A population-based cohort study.

INTRODUCTION: This study investigated the differential trajectories and relevant determinants of depressive symptoms in adolescents by following cohorts that included junior, senior, and vocational high school adolescents, over a 3-year period in Taiwan. METHODS: Longitudinal data were obtained from 575 adolescents who participated in the Taiwan Adolescent to Adult Longitudinal Study. Data analysis included latent class growth with time-varying covariate, univariate, and multivariate analysis. RESULTS: A three-class ("low but increasing trajectory," "moderate and stable trajectory," and "high but decreasing trajectory") model fit the data of the cohort. Our findings indicated that 29%, 38%, and 33% of the adolescents were in the low but increasing, moderate and stable, and high but decreasing trajectories, respectively. After confounders were controlled for, bullying experiences were identified as a risk factor for depressive symptoms. The protective factors against depressive symptoms included resilience and peer and social support. CONCLUSIONS: The transitions between different educational stages critically influence the depressive symptoms of adolescents, and the adolescents follow different depressive trajectories, that have different etiology. Therefore, identifying adolescents at high risk for depression and designing student-centered intervention programs through individualized and multidimensional assessment of depressive symptoms are crucial for adolescents.