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Background: Physical activity (PA) increases a person's inhalation of air pollutants due to greater ventilation, possibly leading to larger adverse health effects. This study aims to investigate the combined effects of long-term exposure to fine particulate matter (PM2.5) and habitual PA on lung function in adults.Methods: This was a longitudinal cohort study that included 278,065 Taiwan residents with an age of 20 years old or above who joined a standard medical screening programme between 2001 and 2014. Each participant received at least one medical examination (including spirometric, blood, and urinary tests and a standard self-administered questionnaire survey) during the study period. We estimated the 2-year average PM2.5 concentrations at each participant's address using a new physical model based on observational data. Information on the participants' PA was collected using the standard self-administrated questionnaire. Generalised linear mixed models were used to investigate the combined effects of PM2.5 and PA on pulmonary function. We also performed stratified analyses by different levels of PM2.5 exposure and habitual PA.Results: Each 10 MET-h increase in PA was associated with a higher level of 0.20%, 0.16%, and 0.19% in forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and maximum mid-expiratory flow (MMEF), respectively, after adjusting for PM2.5 exposure and a wide range of covariates including age, sex education, body mass index, lifestyles, and health conditions. Each 10 μg/m3 increase in PM2.5 was associated with a lower FVC, FEV1, and MMEF (2.43%, 2.78% and 3.10%, respectively). Negative interactions were observed, and PM2.5 exposure was associated with a greater reduction in lung function among the participants with higher PA levels.Conclusions: We found significant negative interaction effects between long-term exposure to PM2.5 and habitual PA, suggesting that the increased intake of PM2.5 due to PA may attenuate the benefits of habitual PA on lung function. However, the PA benefits generally remained stable at different stratum of PM2.5 in the stratified analyses, and habitual PA may still be recommended to people residing in relatively polluted regions. [ABSTRACT FROM AUTHOR]

Background: Epidemics 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.Methods: HFMD 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.Results: We 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.Conclusions: We 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. [ABSTRACT FROM AUTHOR]

Background: Outbreaks of low and high pathogenic avian influenza (LPAI, HPAI) H5N2 in chickens have occurred in Taiwan since 2003 and 2012, respectively. Fully understanding the different awareness, attitudes and protective behaviors adopted by workers in live-poultry markets (LPMWs) and local community residents (CRs) to face the challenges of LPAI and HPAI is very important to minimize viral adaptations to human populations.Methods: A structural questionnaire containing information on respondents' occupation, personal risk awareness, attitudes toward different policies, and preventative measures was administered. The two-stage survey (before and after HPAI H5N2 outbreaks) was conducted from 2007 to 2012, including: (1) 430 LPMWs and 418 CRs at LPMs from different geographical areas of Taiwan after the government announced outbreaks of LPAI H5N2 during 2007-2009, and (2) 73 LPMWs and 152 CRs at two LPMs in central Taiwan after the HPAI H5N2 outbreaks in 2012. The chi-squared test and logistic regression were applied for univariate and multivariate analyses, respectively.Results: Before HPAI-H5N2 outbreaks, higher educated respondents demonstrated greater risk awareness and concerns regarding AI. However, LPM-workers protected themselves less from AI viruses (AIVs) and had lower acceptance of human or avian influenza vaccines. Most importantly, the participants who opposed (versus agreed with) the policy on banning live-poultry slaughtering at LPMs reported lower awareness of government prevention and control policies [Odds Ratio (OR): 0.76, 95 % Confidence Interval (CI): 0.56-1.01] or practiced preventive measures (OR: 0.42, 95 % CI: 0.25-0.70). After HPAI-H5N2 outbreaks, the risk awareness about AI in central Taiwan significantly increased [LPAI to HPAI LPMWs: 34.6 to 65.6 %, p < 0.05; CRs: 44.0 to 76.5 %, p < 0.05] and LPMWs' belief in the effectiveness of vaccination to prevent human or avian influenza virus infection strikingly decreased (92.3 to 68.5 %, p < 0.05).Conclusions: Risk awareness depends on high or low pathogenicity of AIVs, working in LPMs, levels of education, age, and proximity to the sites of severe AI outbreaks. Regardless of novel LPAI or HPAI virus reassortants that pose public health risks, prompt and clear risk communication focusing on both correct information about AIVs and the most appropriate preventive measures are important for effective prevention of human infection. [ABSTRACT FROM AUTHOR]

Background: Severe epidemics of enterovirus have occurred frequently in Malaysia, Singapore, Taiwan, Cambodia, and China, involving cases of pulmonary edema, hemorrhage and encephalitis, and an effective vaccine has not been available. The specific aim of this study was to understand the epidemiological characteristics of mild and severe enterovirus cases through integrated surveillance data.Methods: All enterovirus cases in Taiwan over almost ten years from three main databases, including national notifiable diseases surveillance, sentinel physician surveillance and laboratory surveillance programs from July 1, 1999 to December 31, 2008 were analyzed. The Pearson's correlation coefficient was applied for measuring the consistency of the trends in the cases between different surveillance systems. Cross correlation analysis in a time series model was applied for examining the capability to predict severe enterovirus infections. Poisson temporal, spatial and space-time scan statistics were used for identifying the most likely clusters of severe enterovirus outbreaks. The directional distribution method with two standard deviations of ellipse was applied to measure the size and the movement of the epidemic.Results: The secular trend showed that the number of severe EV cases peaked in 2008, and the number of mild EV cases was significantly correlated with that of severe ones occurring in the same week [r = 0.553, p < 0.01]. These severe EV cases showed significantly higher association with the weekly positive isolation rates of EV-71 than the mild cases [severe: 0.498, p < 0.01 vs. mild: 0.278, p < 0.01]. In a time series model, the increase of mild EV cases was the significant predictor for the occurrence of severe EV cases. The directional distribution showed that both the mild and severe EV cases spread extensively during the peak. Before the detected spatio-temporal clusters in June 2008, the mild cases had begun to rise since May 2008, and the outbreak spread from south to north.Conclusions: Local public health professionals can monitor the temporal and spatial trends plus spatio-temporal clusters and isolation rate of EV-71 in mild and severe EV cases in a community when virus transmission is high, to provide early warning signals and to prevent subsequent severe epidemics. [ABSTRACT FROM AUTHOR]

Background: Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city., Methods: In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city., Results: The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation., Conclusion: Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Background: Cases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level., Methods: A total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space-time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC's surveillance statistics., Results: Among the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map., Conclusions: We designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.

Background: The occurrence of out-of-hospital cardiac arrest (OHCA) is a critical life-threatening event which frequently warrants early defibrillation with an automated external defibrillator (AED). The optimization of allocating a limited number of AEDs in various types of communities is challenging. We aimed to propose a two-stage modeling framework including spatial accessibility evaluation and priority ranking to identify the highest gaps between demand and supply for allocating AEDs., Methods: In this study, a total of 6135 OHCA patients were defined as demand, and the existing 476 publicly available AEDs locations and 51 emergency medical service (EMS) stations were defined as supply. To identify the demand for AEDs, Bayesian spatial analysis with the integrated nested Laplace approximation (INLA) method is applied to estimate the composite spatial risks from multiple factors. The population density, proportion of elderly people, and land use classifications are identified as risk factors. Then, the multi-criterion two-step floating catchment area (MC2SFCA) method is used to measure spatial accessibility of AEDs between the spatial risks and the supply of AEDs. Priority ranking is utilized for prioritizing deployment of AEDs among communities because of limited resources., Results: Among 6135 OHCA patients, 56.85 % were older than 65 years old, and 79.04 % were in a residential area. The spatial distribution of OHCA incidents was found to be concentrated in the metropolitan area of Kaohsiung City, Taiwan. According to the posterior mean estimated by INLA, the spatial effects including population density and proportion of elderly people, and land use classifications are positively associated with the OHCA incidence. Utilizing the MC2SFCA for spatial accessibility, we found that supply of AEDs is less than demand in most areas, especially in rural areas. Under limited resources, we identify priority places for deploying AEDs based on transportation time to the nearest hospital and population size of the communities., Conclusion: The proposed method will be beneficial for optimizing resource allocation while considering multiple local risks. The optimized deployment of AEDs can broaden EMS coverage and minimize the problems of the disparity in urban areas and the deficiency in rural areas.

Background: Epidemics 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., Methods: HFMD 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., Results: We 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., Conclusions: We 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: Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI., Methods: The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM., Results: The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods., Conclusions: The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.

Background: Instead of traditional statistical models for large spatial areas and weekly or monthly temporal units, what public health workers urgently need is a timely risk prediction method for small areas. This risk prediction would provide information for early warning, target surveillance and intervention., Methods: Daily dengue cases in the 457 urban villages of Kaohsiung City, Taiwan from 2009 to 2012 were used for model development and evaluation. There were in total 2,997 confirmed dengue cases during this period. A logistic regression model was fitted to the daily incidents occurring in the villages for the past 30 days. The fitted model was then used to predict the incidence probabilities of dengue outbreak for the villages the next day. A percentile of the 457*30 fitted incidence probabilities was chosen to determine a cut-point for issuing the alerts. The covariates included three different levels of spatial effect, and with four lag time periods. The population density and the meteorological conditions were also included for the prediction., Results: The performance of the prediction models was evaluated on 122 consecutive days from September 1 to December 31, 2012. With the 80th percentile threshold, the median sensitivity was 83% and the median false positive rate was 23%. We found that most of the coefficients of the predictors of having cases at the same village in the previous 14 days were positive and significant for the 122 daily updated models. The estimated coefficients of population density were significant during the peak of the epidemic in 2012., Conclusions: The proposed method can provide near real-time dengue risk prediction for a small area. This can serve as a useful decision making tool for front-line public health workers to control dengue epidemics. The precision of the spatial and temporal units can be easily adjusted to different settings for different cities.

Background: The purpose of this paper is to integrate a multi-objective integer programming formulation and geographic information system (GIS) into dynamically planning the insecticide spraying area for preventing the transmission of dengue fever., Methods: The optimal spraying area to combat dengue infections is calculated by the multi-objective integer programming model using the dengue epidemic in 2007 in Tainan City of southern Taiwan and is compared with the areas actually sprayed by the local health department. The dynamic epidemic indicators (i.e. frequency, intensity and duration) that identify major temporal characteristics of the dynamic process of an epidemic are all incorporated into the model., Results: The results indicate that the model can design the spraying area effectively when the trade-off between the coverage of dengue epidemics risk and area compactness is considered., Conclusions: The model provides an alternative way to obtain a cost-effective spraying area in controlling future dengue epidemics. The proposed model in this study will be beneficial for strategically allocating dengue control resources.

Background: Smoking is known to aggravate tuberculosis (TB), but such information has been ignored in clinical practice, as it was not thought to be relevant. The aim of this study is to assess the benefits of smoking cessation on TB mortality reduction., Methods: The study attempts to quantify smokers' risks on subsequent TB mortality and the change in such risks after smokers quit smoking. In this prospective cohort study, the TB mortality risks of smokers, never smokers and former smokers were compared, by using the Cox proportional model to estimate the hazard ratio (HR) of TB.The cohort, consisting of 486,341 adults, participated in standard medical screening programs since 1994, including 5,036 with self-reported TB history. Of 15,268 deaths identified as of 2007, 77 were coded as TB., Results: Smokers with self-reported TB history (1.2%) had very high TB mortality (HR = 44.02). Among those without self-reported TB history, smoking increased TB mortality by nine-fold (HR = 8.56), but when they quit smoking, the risk was reduced by more than half (65%), to a level not different from those who had never smoked. The overwhelming majority of TB deaths (83%) occurred among those without self-reported TB history. Given the high smoking prevalence and the high HR, smoking accounted for more than one-third (37.7%) of TB mortality in Taiwan. Smokers reported less TB history but died more from TB than those who had never smoked., Conclusions: Smokers had very high TB mortality, as much as nine times those who had never smoked, but once they quit, the risk reduced substantially and was similar to those who never smoked. Smoking cessation has benefits to the smokers far beyond reducing TB risk, but successful tobacco control could favorably impact the TB mortality rate and reduce this public health burden, which has long haunted the Taiwanese population. Smoking cessation could reduce nearly one-third of tuberculosis deaths.

Background: Buffer analyses have shown that air pollution is associated with an increased incidence of asthma, but little is known about how air pollutants affect health outside a defined buffer. The aim of this study was to better understand how air pollutants affect asthma patient visits in a metropolitan area. The study used an integrated spatial and temporal approach that included the Kriging method and the Generalized Additive Model (GAM)., Results: We analyzed daily outpatient and emergency visit data from the Taiwan Bureau of National Health Insurance and air pollution data from the Taiwan Environmental Protection Administration during 2000-2002. In general, children (aged 0-15 years) had the highest number of total asthma visits. Seasonal changes of PM10, NO2, O3 and SO2 were evident. However, SO2 showed a positive correlation with the dew point (r = 0.17, p < 0.01) and temperature (r = 0.22, p < 0.01). Among the four pollutants studied, the elevation of NO2 concentration had the highest impact on asthma outpatient visits on the day that a 10% increase of concentration caused the asthma outpatient visit rate to increase by 0.30% (95% CI: 0.16%~0.45%) in the four pollutant model. For emergency visits, the elevation of PM10 concentration, which occurred two days before the visits, had the most significant influence on this type of patient visit with an increase of 0.14% (95% CI: 0.01%~0.28%) in the four pollutants model. The impact on the emergency visit rate was non-significant two days following exposure to the other three air pollutants., Conclusion: This preliminary study demonstrates the feasibility of an integrated spatial and temporal approach to assess the impact of air pollution on asthma patient visits. The results of this study provide a better understanding of the correlation of air pollution with asthma patient visits and demonstrate that NO2 and PM10 might have a positive impact on outpatient and emergency settings respectively. Future research is required to validate robust spatiotemporal patterns and trends.