Information on the association between long-term exposure to PM2.5 and gastrointestinal cancer mortality is scarce. This study investigated the association between long-term exposure to PM2.5 and deaths from gastrointestinal cancer and its subtypes in adults in Taiwan. A total of 385,650 Taiwanese adults (≥18 years old) jointed a standard medical examination program between 2001 and 2014 and were followed up until 2016. Their vital data were obtained from the National Death Registry maintained by the Ministry of Health and Welfare in Taiwan. We estimated the ambient PM2.5 concentration at individual's address utilising a satellite-based spatiotemporal model at a resolution of 1 km2. Cox proportional hazard regression model was used to investigate the associations between ambient PM2.5 and deaths from gastrointestinal, stomach, colorectal and liver cancers. We found that each 10 µg/m3 increase in PM2.5 was associated with an increased hazard risk (HR) of 1.09 (95% confidence interval (CI): 1.03-1.16) and 1.13 (95%CI: 1.02-1.24) in deaths from gastrointestinal and liver cancers, respectively. The association between PM2.5 and death from colorectal cancer was marginally statistically significant [HR: 1.13 (95%CI: 1.00-1.26)]. We did not find significant associations between PM2.5 and mortality from stomach cancer. Long-term exposure to ambient PM2.5 was associated with an increased risk of deaths from gastrointestinal cancers, liver cancer and also potentially colorectal cancer. Air pollution control strategies are necessary to reduce the burden of gastrointestinal cancer.

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.

This study aimed to assess the mortality risks for human infection with high (HPAI) and low (LPAI) pathogenicity avian influenza viruses. The HPAI case fatality rate (CFR) was far higher than the LPAI CFR [66.0% (293/444) vs. 68.75% (11/16) vs. 40.4% (265/656) vs. 0.0% (0/18) in the cases with H5N1, H5N6, H7N9, and H9N2 viruses, respectively; p < 0.001]. Similarly, the CFR of the index cases was greater than the secondary cases with H5N1 [100% (43/43) vs. 43.3% (42/97), p < 0.001]. Old age [22.5 vs. 17 years for H5N1, p = 0.018; 61 vs. 49 years for H7H9, p < 0.001], concurrent diseases [18.8% (15/80) vs. 8.33% (9/108) for H5N1, p = 0.046; 58.6% (156/266) vs. 34.8% (135/388) for H7H9, p < 0.001], delayed confirmation [13 vs. 6 days for H5N1, p < 0.001; 10 vs. 8 days for H7N9, p = 0.011] in the fatalities and survivors, were risk factors for deaths. With regard to the H5N1 clusters, exposure to poultry [67.4% (29/43) vs. 45.2% (19/42), p = 0.039] was the higher risk for the primary than the secondary deaths. In conclusion, old age, comorbidities, delayed confirmation, along with poultry exposure are the major risks contributing to fatal outcomes in human HPAI and LPAI infections.

Green space and air pollution have been recognized as vital health determinants. There is a paucity of studies examining the interplay between green space, fine particulate matter (PM_2.5), and the incidence of specific cancers. We aimed to explore the contributions of green space and ambient PM_2.5 to the risk of specific cancers in terms of the most common cancers based on incidence or mortality rate in Taiwan and to ascertain the interaction between green space and PM_2.5 and their role in cancer risk. This retrospective longitudinal cohort study included 407,415 participants. Data were obtained from the 2000-2015 Mei Jau Health Examination Database linked to the Taiwan Cancer Registry and Causes of Death datasets. All participants were aged ≥20 years and had no history of cancer. The environmental exposure were the normalized difference vegetation index (NDVI) and the 2-year average PM_2.5 at baseline. Multivariate adjusted hazard ratios (HRs) were calculated using Cox proportional hazards models. We adjusted for covariates including demographics, anthropometrics, comorbidities, health behaviors, biochemical data, and environmental factors. During a median follow-up of 10.37 years, 11,576 cancer cases were reported. PM_2.5 exposure increased the risk of all cancers (HR: 1.11, [95% CI: 1.06-1.15]), stomach cancer (HR: 1.27, [1.02-1.58]), endocrine gland cancer (HR: 2.13, [1.39-3.26]), breast cancer (HR: 1.12, [1.03-1.22]), and lung cancer (HR: 1.12, [1.01-1.24]). An increase in NDVI reduced the risk of prostate cancer (HR: 0.93, [0.88-0.99]) and lung cancer (HR: 0.95, [0.91-0.99]). NDVI influenced the incidence of prostate and all cancers by reducing PM_2.5 concentrations. Long-term PM_2.5 exposure is associated with an increased risk of some types of cancers. In contrast, an increase in environmental green space exposure is associated with lowering of the risk of prostate and lung cancer.

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.

In the era of big data, ubiquitous Flickr geotagged photos have opened a considerable opportunity for discovering valuable geographic information. Point of interest (POI) and region of interest (ROI) are significant reference data that are widely used in geospatial applications. This study aims to develop an efficient method for POI/ROI discovery from Flickr. Attractive footprints in photos with a local maximum that is beneficial for distinguishing clusters are first exploited. Pattern discovery is combined with a novel algorithm, the spatial overlap (SO) algorithm, and the naming and merging method is conducted for attractive footprint clustering. POI and ROI, which are derived from the peak value and range of clusters, indicate the most popular location and range for appreciating attractions. The discovered ROIs have a particular spatial overlap available which means the satisfied region of ROIs can be shared for appreciating attractions. The developed method is demonstrated in two study areas in Taiwan: Tainan and Taipei, which are the oldest and densest cities, respectively. Results show that the discovered POI/ROIs nearly match the official data in Tainan, whereas more commercial POI/ROIs are discovered in Taipei by the algorithm than official data. Meanwhile, our method can address the clustering issue in a dense area.