National Cheng Kung University

Sociodemographic factors and individual attitudes may influence individuals' decisions for vaccination against infectious diseases. The present study aimed to identify how these factors were associated with COVID-19 and influenza vaccination experiences among Taiwanese individuals. Using a cross-sectional design, 914 individuals (female = 58%; aged 50 years or above = 43.1%) completed an online survey between October and November 2024. Chi-squares and odds ratios (ORs) derived from logistic regression were used to examine the associations and perform predictive analysis. Results showed that 54% were undecided (those who answered 'not sure') or reluctant (those who answered 'no') about receiving a COVID-19 booster vaccine, while 65% were willing to get the next influenza vaccine. Factors such as age, employment, and education were significantly associated with awareness and experience for both types of vaccination. Older people had a higher awareness of the vaccination and were more likely to receive the vaccines. Individuals who worked in health-related settings or had an education in a related field, along with those with higher education, showed increased awareness and experience of vaccination. After controlling for sociodemographic factors, attitude (i.e., vaccination readiness and conspiracy belief) predicted vaccination willingness for both vaccines (pseudo-R² = 0.28-0.33). Moreover, factors such as age, education, occupation, and attitudes (readiness, conspiracy beliefs) may be associated with awareness, experience, and willingness to receive COVID-19 and influenza vaccines among Taiwanese individuals. Developing awareness programs for younger and less educated people, and those who work in non-health-related sectors, may be helpful to encourage individuals to get vaccinated.

Systemic sclerosis (SSc) is a chronic autoimmune disease characterized by fibrosis and immune dysregulation, often involving pulmonary and cardiovascular systems. Limited data exist regarding its impact on outcomes among patients hospitalized with influenza.

Using the US Nationwide Inpatient Sample (2016-2020), we identified adults hospitalized with a principal diagnosis of influenza using ICD-10 codes. Patients were categorized into SSc and non-SSc groups. Propensity score matching (1:4) was applied to balance baseline characteristics. Logistic and linear regression analyses estimated adjusted odds ratios (aORs) or coefficients with 95% confidence intervals (CIs) for in-hospital mortality, complications, length of stay (LOS), and adverse discharge outcomes.

After matching, 1,265 patients (253 with SSc, 1,012 without SSc) were included, representing 6,248 hospitalizations nationwide after weighting. Compared with non-SSc patients, those with SSc had significantly higher risks of secondary bacterial or fungal infections (aOR = 1.42; 95% CI: 1.05-1.92; p = 0.025) and bronchiectasis (aOR = 3.91; 95% CI: 1.77-8.65; p < 0.001). Among patients aged ≥60 years, SSc was associated with increased risks of bronchiectasis (aOR = 4.67; 95% CI: 2.13-10.24) and respiratory failure requiring mechanical ventilation (aOR = 1.47; 95% CI: 1.06-2.02). Smokers with SSc had a higher risk of shock, while non-smokers with SSc remained at elevated risk for secondary infections, bronchiectasis, and respiratory failure.

SSc was associated with excess risks of severe in-hospital complications among influenza patients, particularly in older adults and smokers, underscoring the need for targeted prevention and early intervention strategies.

Depression is a major global health concern, still individuals with depressive tendencies remain undetected in outpatient settings due to the limitations of self-report, stigma, and reporting bias. Advances in artificial intelligence (AI) offer opportunities for objective, scalable, and unobtrusive methods of early detection. This study examined predictive accuracy of two AI-driven systems, the iSeeME facial-expression model and the Event-Driven Depression Tendency Warning System (EDDTW-V2) in identifying depressive symptoms among high-risk outpatients in Taiwan.

A cross-sectional study was conducted with 62 outpatients recruited from psychiatric and surgical-oncology clinics at a medical center in southern Taiwan. Participants completed standardized depression assessments, including the Hamilton Depression Rating Scale (HDRS), Beck Depression Inventory-II (BDI-II), and Patient Health Questionnaire-9 (PHQ-9). Facial-expression data were analyzed using iSeeME, while narrative transcripts were processed with EDDTW-V2. Predictive validity was assessed through accurate metrics, correlations with clinical scales, and k-means cluster analysis.

Of the 62 participants, 39 were clinically depressed (HDRS ≥7). The iSeeME system achieved precision of 0.761, recall of 0.854, F1-score of 0.805, and accuracy of 0.770, showing stronger correlations with BDI-II (r = 0.442, p < 0.01) and PHQ-9 (r = 0.335, p < 0.01) than EDDTW-V2. Cluster analysis revealed a high-distress subgroup characterized by younger age, single status, fewer children, and predominance of psychiatric patients.

Both AI systems showed potential as complementary tools to traditional assessments. iSeeME was more sensitive to affective-behavioral symptoms, while EDDTW-V2 captured cognitive-linguistic features. They support earlier detection, monitoring, and targeted interventions in outpatient settings.