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This study aimed to explore the association between bone mineral density and physical frailty including nutrition, muscle mass, and oral function. We included participants aged 35–80 years and examined their bone mineral density, serum albumin level, body composition, and variance of hue (VOH) of two-colored gum. We also used the geriatric oral health assessment index (GOHAI). These data were used to calculate the geriatric nutritional risk index (GNRI) and skeletal muscle index (SMI). Multinomial logistic regression was performed to assess the relationship between GNRI, SMI, VOH, GOHAI, and bone mineral density after adjusting for comorbidities, including hypertension, diabetes mellitus, and previous bone fracture. We included 228 participants and classified their bone mineral density as normal, osteopenic, or osteoporotic. Older age (odds ratio (OR) 1.15, 95% confidence interval (CI) [1.08, 1.23]), low GNRI (OR 0.90, 95% CI [0.83, 0.98]), low SMI (OR 0.43, 95% CI [0.27, 0.68]), and high VOH (OR 1.08, 95% CI [1.01, 1.17]) were significantly associated with osteoporosis. Older age (OR 1.08, 95% CI [1.04, 1.11]) and low GNRI (OR 0.93, 95% CI [0.87, 0.99]) were significantly associated with osteopenia. GNRI, SMI, and VOH were significantly associated with osteoporosis among male participants. Although the multinomial logistic regression analysis indicated that GNRI, SMI, VOH, and GOHAI were not significantly associated with osteoporosis or osteopenia among female participants, the demographic distribution showed that older age, low GNRI, and low SMI were significantly associated with bone mineral density decline. Physical frailty, including nutritional decline, muscle mass loss, and poor oral status, is associated with low bone density. This easy-to-use tool can be used to detect osteoporosis early and to prevent osteoporosis and osteoporosis-related fractures. [ABSTRACT FROM AUTHOR]

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality. [ABSTRACT FROM AUTHOR]

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution. [ABSTRACT FROM AUTHOR]

Strict and repeated lockdowns have caused public fatigue regarding policy compliance and had a large impact on several countries' economies. We aimed to evaluate the effectiveness of a soft lockdown policy and the strategy of active community screening for controlling COVID-19 in Taiwan. We used village-based daily confirmed COVID-19 statistics in Taipei City and New Taipei City, between May 2, 2021, and July 17, 2021. The temporal Gi* statistic was used to compute the spatiotemporal hotspots. Simple linear regression was used to evaluate the trend of the epidemic, positivity rate from community screening, and mobility changes in COVID-19 cases and incidence before and after a level three alert in both cities. We used a Bayesian hierarchical zero-inflated Poisson model to estimate the daily infection risk. The cities accounted for 11,403 (81.17%) of 14,048 locally confirmed cases. The mean effective reproduction number (Re) surged before the level three alert and peaked on May 16, 2021, the day after the level three alert in Taipei City (Re = 3.66) and New Taipei City (Re = 3.37). Mobility reduction and a lower positive rate were positively associated with a lower number of cases and incidence. In the spatiotemporal view, seven major districts were identified with a radial spreading pattern from one hard-hit district. Villages with a higher inflow degree centrality among people aged ≥ 60 years, having confirmed cases, specific land-use types, and with a higher aging index had higher infection risks than other villages. Early soft lockdown policy and detection of infected patients showed an effective strategy to control COVID-19 in Taiwan. [ABSTRACT FROM AUTHOR]

Both viruses and moods are transmitted through interpersonal contacts, but it has been extremely difficult to track each unique chain of contacts through which particular moods diffuse. By analyzing 56,060 contact records from 113 interlocking, yearlong diaries collected through a web-based platform in Taiwan, we traced mood states before and after each specific contact along a triplet of persons where B contacts C and subsequently contacts A. Multilevel analyses show that both positive and negative emotions are contagious, but the two paths diverge markedly in how the diffusion stops. Positive contact between C and B (which leads to improved mood for B) spreads to A through B's contact with A, making A feel better afterward, regardless of whether B's mood deteriorated between the two interactions. Negative contact between C and B (which leads to worsened mood for B) also spreads to A, making A feel worse after the contact with B. However, the spread of a negative mood discontinues if B's mood improved between the two contacts. The different patterns of diffusion suggest that a negative mood is harder to disperse, probably because people generally make efforts to keep their negative emotions from spreading to others. [ABSTRACT FROM AUTHOR]

Purpose: The metabolic syndrome was associated with bladder cancer in the previous studies. However, there have no large-scale cohort studies to elucidate the relationship between metabolic syndromes and urothelial carcinoma including urinary bladder urothelial carcinoma (UBUC) and upper tract urothelial carcinoma (UTUC). Methods: We analyze a population-based cohort study by using physical examination data and diagnosis of UC from the Taiwan Cancer Registry Database. Differences in demographic and clinical characteristics among UTUC and non-UTUC groups, UBUC and non-UBUC groups were compared. Odds ratios (ORs) for determining risk factors were estimated through the multiple logistic regression model. Results: A total of 557,063 records for 211,319 participants which consisted of 31 UTUC and 309 UBUC met the eligibility criteria in this study. Our results showed that female are more likely to develop UTUC than male. As opposed to UTUC, male are more likely to develop UBUC than female. It also showed that participants smoked or chewed betel quid daily are more likely to develop UBUC. Age and estimated glomerular filtration rate (eGFR) are significantly increased the risk of developing UTUC. The association between the eGFR and risk of UTUC is stronger (P < 0.001) for eGFR < 45 (vs. eGFR ≥ 75, OR = 6.795; 95% CI 2.901–15.917). Metabolic syndrome is related to higher risk of UBUC incidence [OR was 1.373 (95% CI 1.104–1.707)]. Conclusions: There was a significant relationship between the incidence of UBUC and metabolic syndrome. Renal function impairment presents higher risk in both UBUC and UTUC development. [ABSTRACT FROM AUTHOR]

In this study, we applied the Weather Research and Forecasting model to project 2050 urban and rural temperature. We applied a time-stratified analysis to compare it with mortality between 2001 and 2014 and between 2011 and 2014, to estimate the elevated risk of a 2050 heat event. We included change in daytime versus nighttime and urban versus rural temperatures as factors to project mortality, to evaluate the potential influence of climate change on mortality risk. Increases of 2.9 °C and 2.6 °C in maximum and minimum air temperature are projected in a 2050 heat event, with a day and a night that will have respective temperatures 9.8 °C and 4.9 °C higher than 2001–2014. Significantly higher mortality risk is forecasted in 2050 compared to 2001–2014 (IRR 1.721 [1.650, 1.796]) and 2011–2014 (IRR 1.622 [1.547, 1.701]) without consideration of temperature change. After consideration of changing temperature, change in maximum temperature in rural areas will induce the highest mortality risk during 2050, possibly due to rapid urbanization across the city, and with the second highest mortality risk induced by the change in minimum temperature in urbanized areas, possibly because local people in the city have been adapted to the maximum level of urban thermal stress during a summer day. Improvements to heat warning systems and sustainable planning protocols are urgently needed for climate change mitigation. [ABSTRACT FROM AUTHOR]

Previous studies have evaluated the effects of air pollution on various causes of death. However, there are few studies directly differentiating the regional effects of air pollution on specific causes of death from cardiovascular and respiratory mortality. It is important to target specific diseases and air pollutants for environmental management, in order to develop a sustainable environment. Therefore, a territory-wide register-based study was developed with cause-specific mortality data in Hong Kong between 2007 and 2014 (n = 257,090). Five groups of decedents were directly compared with the two control groups (cardiovascular and respiratory deaths), separately, based on binomial logistic regressions. Three air pollutants (NO2, PM2.5, black carbon (BC)) were selected to evaluate how spatial distribution of long-term air pollution exposure can induce the difference in mortality risks from two control groups. Based on the results, different air pollutants can contribute to different impact on mortality risk. PM2.5 and BC contributed to higher effects on mortality associated with mental and behavioral disorders than those linked to cardiovascular and respiratory diseases. BC can contribute to a higher mortality risk for mortality associated with diseases of the nervous and digestive systems than with respiratory diseases. There was also a stronger influence of neighborhood PM2.5 on mortality associated with diseases of the genitourinary system than with cardiovascular and respiratory diseases, and a stronger impact of neighborhood NO2 and BC on cancer-related mortality than on respiratory mortality. In conclusion, several air pollutants have greater impact on cause-specific mortality than cardiovascular and respiratory mortality. Therefore, it is necessary to have a comprehensive and systematic investigation to evaluate how different types of air pollutants influence various types of cause-specific mortality. In addition, specific health protocols, enhancement of urban design and improvement of environmental hygiene should be conducted for specific types of air pollution and deprived populations, rather than targeting solely the people with cardiorespiratory diseases. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Information on the associations of long-term exposure to fine particulate matter (with an aerodynamic diameter less than 2.5 μm; PM2.5) with the development of type 2 diabetes is scarce, especially for south-east Asia, where most countries are experiencing serious air pollution. This study aimed to investigate the long-term effects of exposure to ambient PM2.5 on the incidence of type 2 diabetes in a population of Taiwanese adults. Methods: A total of 147,908 participants without diabetes, at least 18 years of age, were recruited in a standard medical examination programme between 2001 and 2014. They were encouraged to take medical examinations periodically and underwent at least two measurements of fasting plasma glucose (FPG). Incident type 2 diabetes was identified as FPG ≥7 mmol/l or self-reported physician-diagnosed diabetes in the subsequent medical visits. The PM2.5 concentration at each participant's address was estimated using a satellite-based spatiotemporal model with a resolution of 1 × 1 km2. The 2 year average of PM2.5 concentrations (i.e. the year of and the year before the medical examination) was treated as an indicator of long-term exposure to ambient PM2.5 air pollution. We performed Cox regression models with time-dependent covariates to analyse the long-term effects of exposure to PM2.5 on the incidence of type 2 diabetes. A wide range of covariates were introduced in the models to control for potential effects, including age, sex, education, season, year, smoking status, alcohol drinking, physical activity, vegetable intake, fruit intake, occupational exposure, BMI, hypertension and dyslipidaemia (all were treated as time-dependent covariates except for sex). Results: Compared with the participants exposed to the first quartile of ambient PM2.5, participants exposed to the second, third and fourth quartiles of ambient PM2.5 had HRs of 1.28 (95% CI 1.18, 1.39), 1.27 (95% CI 1.17, 1.38) and 1.16 (95% CI 1.07, 1.26), respectively, for the incidence of type 2 diabetes. Participants who drank occasionally or regularly (more than once per week) or who had a lower BMI (<23 kg/m2) were more sensitive to the long-term effects of exposure to ambient PM2.5. Conclusions/interpretation: Long-term exposure to ambient PM2.5 appears to be associated with a higher risk of developing type 2 diabetes in this Asian population experiencing high levels of air pollution. [ABSTRACT FROM AUTHOR]

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011–2018 was twice that in 2004–2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29–2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO2 (r = 0.21) and O3 (r = 0.11). A 10 μg/m3 increase of NO2 and O3 was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02–1.10) and 1.04 (95% CI: 1.01–1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO2 and O3 may be associated with an increased risk of scarlet fever incidence, but direct causality is not established. The reason for a re-emergence of scarlet fever in China remains unclear. Here the authors show that the number of scarlet fever cases surged in 2011 peaking in 2018, this correlates with an increase in NO2 and O3 but does not necessarily imply causation. [ABSTRACT FROM AUTHOR]

To understand control interventions, the molecular epidemiology of acute hemorrhagic conjunctivitis (AHC) was investigated from 2004 to 2014.A total of 613,485 AHC cases (annualized cases 55,771) with two deaths were included. Our findings showed that AHC was reported in all provinces, predominantly in Southern and Eastern China. The incidence rates were highest in 2007 (5.65/100,000) and 2010 (21.78/100,000) respectively. A clear seasonal pattern was identified with a peak from August to October. AHC cases occurred in all age groups; however, five to 14 years was the predominant group [23.06%, 133, 510/578,909]. The median age was 24 years (one month~97 years). The median duration from onset to diagnosis was 1.5 days, and there was no difference between the <15, 15~60 and >60-year-old patients [p = 0.0653]. The phylogenetic analysis of 100 nonstructural proteins (3C) and 84 structural proteins (VP1) revealed that AHC outbreaks were caused by Coxsackievirus A24 variant. Genotypes G4-c5a, G4-c5b, and G4-c3 co-circulated with both temporal and geographical overlaps. In conclusion, despite the overall steady decline in the number of AHC cases since the peak in 2010, it still remains a serious public health problem in Southern and Eastern China that targets on the school aged children under 15 years old. [ABSTRACT FROM AUTHOR]