Hand, foot, and mouth disease (HFMD) has threatened East Asia for more than three decades and has become an important public health issue owing to its severe sequelae and mortality among children. The lack of effective treatment and vaccine for HFMD highlights the urgent need for efficiently integrated early warning surveillance systems in the region. In this study, we try to integrate the available surveillance and weather data in East Asia to elucidate possible spatiotemporal correlations and weather conditions among different areas from low to high latitude. The general additive model (GAM) was applied to understand the association between HFMD and latitude, as well as meteorological factors for islands in East Asia, namely, Japan, Taiwan, Hong Kong, and Singapore, from 2012 to 2014. The results revealed that latitude was the most important explanatory factor associated with the timing and amplitude of HFMD epidemics (P < 0.0001). Meteorological factors including higher dew point, lower visibility, and lower wind speed were significantly associated with the rise of epidemics (P < 0.01). In summary, weather conditions and geographic location could play some role in affecting HFMD epidemics. Regional integrated surveillance of HFMD in East Asia is needed for mitigating the disease risk.

Gastric cancer is one of the most common and aggressive malignancies. Both bacterial virulence factors and host chronic inflammation are thought to promote gastric cancer development. In this study, we investigated the potential involvement of follicular helper T cells in gastric cancer. Functions of follicular helper T subsets were examined in Helicobacter pylori-infected gastric cancer patients and H. pylori-infected but asymptomatic individuals. We found that the follicular helper T cells in gastric cancer individuals were skewed toward the Th1 and Th17 subsets compared to those in H. pylori-infected but asymptomatic individuals. In a naive B cell-follicular helper T cell coculture, the Th1-follicular helper T cells by themselves were ineffective at stimulating a robust antibody response, unlike the Th2-follicular helper T and Th17-follicular helper T cells. However, Th1-follicular helper T cells significantly promoted the immunoglobulin G response in collaboration with other follicular helper T subsets, through the secretion of interferon gamma. We also found that Th1-follicular helper T cells suppressed the development of interleukin-10 regulatory B cells, a cell type previously thought to protect H. pylori-infected individuals from tissue damage. In addition, the frequency of Th1-follicular helper T cells in gastric cancer patients was negatively correlated with the disease-free survival of gastric cancer patients after tumor resection. These results suggested that dysregulation of follicular helper T subsets in gastric cancer patients, characterized by increased Th1-follicular helper T cells, contributed to inflammation and tumor development.

Assessing access to healthcare for an entire healthcare system involves accounting for demand, supply, and geographic variation. In order to capture the interaction between healthcare services and populations, various measures of healthcare access have been utilized, including the popular two-step floating catchment area (2SFCA) method. However, despite the many advantages of 2SFCA, the problems, such as inappropriate assumption of healthcare demand and failure to capture cascading effects across the system have not been satisfactorily addressed. In this paper, a statistical model for evaluating flows of individuals was added to the 2SFCA method (hereafter we refer to it as F2SFCA) in order to overcome limitations associated with its current restriction. The proposed F2SFCA model can incorporate both spatial and nonspatial dimensions and thus synthesizes them into one framework. Moreover, the proposed F2SFCA model can be easily adapted to measure access for different types of individuals, over different service provider types, or with capacity constraints in a healthcare system. We implemented the proposed model in a case study assessing access to healthcare for the elderly in Taipei City, Taiwan, and compared the weaknesses and strengths to the 2SFCA method and its variations.

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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.

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BackgroundEpidemics of hand, foot and mouth disease (HFMD) among children in East Asia have been a serious annual public health problem. Previous studies in China and island-type territories in East Asia showed that the onset of HFMD epidemics evolved with increased latitude. Based on the natural characteristics of the epidemics, we developed regression models for issuing aberration alerts and predictions.MethodsHFMD sentinel surveillance data from 2008 to 2014 in Japan are used in this study, covering 365 weeks and 47 prefectures between 24 and 46° of north latitude. Average HFMD cases per sentinel are standardized as Z rates. We fit weekly Z rate differences between prefectures located in the south and north of a designated prefecture with linear regression models to detect the surging trend of the epidemic for the prefecture. We propose a rule for issuing an aberration alert determined by the strength of the upward trend of south–north Z rate differences in the previous few weeks. In addition to the warning, we predict a Z rate for the next week with a 95 % confidence interval.ResultsWe selected Tokyo and Kyoto for evaluating the proposed approach to aberration detection. Overall, the peaks of epidemics in Tokyo mostly occurred in weeks 28–31, later than in Kyoto, where the disease peaked in weeks 26–31. Positive south–north Z rate differences in both prefectures were clearly observed ahead of the HFMD epidemic cycles. Aberrations in the major epidemics of 2011 and 2013 were successfully detected weeks earlier. The prediction also provided accurate estimates of the epidemic’s trends.ConclusionsWe have used only the latitude, one geographical feature affecting the spatiotemporal distribution of HFMD, to develop rules for early aberration detection and prediction. We have also demonstrated that the proposed rules performed well using real data in terms of accuracy and timeliness. Although our approach may provide helpful information for controlling epidemics and minimizing the impact of diseases, the performance could be further improved by including other influential meteorological factors in the proposed latitude-based approach, which is worth further investigation.

Background According to a WHO report, nearly 15% of adults aged 60 and over suffer from a mental disorder, constituting 6.6% of the total disability for this age group. Taipei City faces rapid transformation towards an aging society, with the proportion of elderly in the total population rising from 12% in 2008 to 16% in 2016. The aim of this study is to identify the prevalence of mental disorders among the elderly in Taipei City and to elucidate risk factors contributing to mental disorders. Methods The elderly health examination database was obtained from the Department of Health, Taipei City government, from 2005 to 2012. A total of 86,061 people underwent publicly funded health examinations, with 348,067 visits. Each year, there are around 43,000 elderly persons in Taipei City using this service. We used a mental health questionnaire including five questions to estimated relative risks among potential risk factors with the generalized estimating equations (GEE) model to measure the mental health status of the elderly. Mood disorders were measured with the Brief Symptom Rating Scale (BSRS-5) questionnaire. Age, education level, gender, marital status, living alone, drinking milk, eating vegetables and fruits, long-term medication, smoking status, frequency of alcohol consumption, frequency of physical activity, BMI, and number of chronic diseases were included as covariates. Results The results show that being male (odds ratio (OR) 0.57; 95% CI = 0.56, 0.59), higher education (OR 0.88; 95% CI = 0.82, 0.95), no long-term medication (OR 0.57; 95% CI = 0.56, 0.58), and exercising three or more times per week (OR 0.94; 95% CI = 0.91, 0.98) were all positively correlated with better emotional status. However, being divorced (OR = 1.22, 95% CI = 1.09, 1.36), not drinking milk (OR = 1.12, 95% CI = 1.09, 1.14), not eating enough vegetables and fruits every day (OR = 1.78, 95% CI = 1.73, 1.83), daily smoking (OR = 1.15, 95% CI = 1.01, 1.32), and having more chronic diseases (OR = 1.02, 95% CI = 1.01, 1.03) were all correlated with poor mental status among the elderly. Conclusions The findings of this research can both estimate the prevalence of mood disorders at the community level, and identify risk factors of mood disorders at the personal level.