Background: Lower socioeconomic groups and disadvantaged populations across the world suffer disproportionately from the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to examine the impact of health- and social-inequality–related factors on well-being in order to further distinguish each of their effects during the pandemic. Methods: A nationally-representative sample of 5077 UK respondents aged 18 years or older was recruited through an online survey panel during the COVID-19 pandemic. Their subjective well-being was measured using the 11-point Cantril Ladder of Life Scale. The impact of inequality-related health and social factors (pre-existing medical conditions, household size and occupation), as well as COVID-19–related risk factors (symptoms, confirmed infections, and social distancing behaviours) on well-being were analysed using multiple linear regression models. The associations between the COVID-19–related risk factors and well-being according to the respondents’ household size and occupation were modelled in order to test the differences by their socioeconomic profile. Results: We identified inverted V-shaped associations between household size and subjective well-being during the COVID-19 pandemic. Compared to single-person households, respondents from households of two to four persons showed better well-being (β = 0.57; CI (0.44, 0.72)), whereas living in crowded households of five persons or more was associated with decreased well-being (β = −0.48; CI (−0.71, −0.25)). Furthermore, lower-skilled occupations (elementary occupations: β = −0.31; CI (−0.58, −0.03); logistics and transport services: β = −0.37; CI (−0.74, −0.01)) and chronic medical conditions (cardiometabolic or respiratory diseases: β = −0.25; CI (−0.41, −0.1); and mental health conditions: β = −1.12; CI (−1.28, −0.96)) were factors associated with reduced well-being during the pandemic. Interactions between a positive COVID-19 diagnosis, symptoms, and crowded households were identified (β = −0.95; CI (−1.76, −0.14) and β = −4.74; CI (−9.87, −1.61), respectively). Conclusions: In a national sample, the levels of general subjective well-being during the COVID-19 pandemic and lockdowns were disproportionately distributed across different groups within society. Preventive policies should explicitly focus on reaching lower socioeconomic groups; more emphasis should be placed on the coordination of multisectoral support in order to tackle existing health and social inequalities.

Objectives: Target populations with persistent polypharmacy should be identified prior to implementing strategies against inappropriate medication use, yet limited information regarding such populations is available. The main objectives were to explore the trends of excessive polypharmacy, whether transient or persistent, at the individual level. The secondary objectives were to identify the factors associated with persistently excessive polypharmacy and to estimate the probabilities for repeatedly excessive polypharmacy. Methods: Retrospective cohort analyses of excessive polypharmacy, defined as prescription of ≥ 10 medicines at an ambulatory visit, from 2001 to 2013 were conducted using a nationally representative claims database in Taiwan. Survival analyses with log-rank test of adult patients with first-time excessive polypharmacy were conducted to predict the probabilities, stratified by age and sex, of having repeatedly excessive polypharmacy. Results: During the study period, excessive polypharmacy occurred in 5.4% of patients for the first time. Among them, 63.9% had repeatedly excessive polypharmacy and the probabilities were higher in men and old people. Men versus women, and old versus middle-aged and young people had shorter median excessive polypharmacy-free times (9.4 vs. 5.5 months, 5.3 vs. 10.1 and 35.0 months, both p < 0.001). Overall, the probabilities of having no repeatedly excessive polypharmacy within 3 months, 6 months, and 1 year were 59.9, 53.6, and 48.1%, respectively. Conclusion: Although male and old patients were more likely to have persistently excessive polypharmacy, most cases of excessive polypharmacy were transient or did not re-appear in the short run. Systemic deprescribing measures should be tailored to at-risk groups.

Background: Although more and more women are becoming physicians, their decisions regarding pregnancy may be affected by the lengthy period of medical education and postgraduate training. The aim of this study was to explore the birth trends among female physicians in Taiwan; Methods: Retrospective analyses of maternal ages at delivery from 1996 to 2013, both for physicians and the general population, were conducted using a nationwide dataset called National Health Insurance Research Database; Results: During the study period, 8540 female physicians were identified. The physicians delivered a total of 4940 births in that time, with a rise from 210 in 1996 to 440 in 2013. In addition, the mean maternal age of the physicians at delivery increased from 32.19 years (standard deviations (SD) 2.80) in 1996 to 33.61 (SD 3.21) in 2013, values significantly higher than those for non-physicians of 27.81 (SD 4.74) in 1996 (p < 0.001) and 31.36 (SD 4.78) in 2013 (p < 0.001); Conclusion: Female physicians usually gave birth at an older age than non-physicians, but the discrepancy between the two groups gradually declined over the 18-year course of the study. The establishment of a maternity-friendly environment for female physicians should be considered by those who determine healthcare system policies.

The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488–0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582–0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068–0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician’s model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

Perioperative rehabilitation is crucial for patients receiving surgery in order to reduce complications and mortality. Conventional methods such as verbal instructions and pre-recorded video are commonly used, but several disadvantages exist. Therefore, we developed an augmented reality (AR) app that includes respiration training, resistance muscle training, and walking training for surgery preparation. The aim of this pilot study was to compare the effects of AR-based training rehabilitation programs with conventional (non-AR-based) programs considering the objective pulmonary function and subjective feasibility and potency in orthopedic patients. This prospective study was conducted in a medical center in Taiwan between 2018 to 2021. Sixty-six patients undergoing elective orthopedic surgery were allocated with a 1:1 ratio to non-AR or AR groups according to their wishes. After training, the inspiratory flow rate of the AR group was higher than that of the non-AR group pre-operatively. As for the subjective assessment, the feasibility (level of confidence and anxiety reduction) and potency (cooperation and educative effect) were superior in AR-based training, compared with the conventional training model. Our study showed that patients using our AR app had better subjective and objective outcomes compared with a conventional model for perioperative rehabilitation.