- Our Research
My research focused on association analysis between weather variations, ambient air quality and risk of infectious diseases, such as diarrhea, infectious gastrointestinal disease, enterovirus infection/complicated illness, eye disease/conjunctivitis, skin disease and influenza. In addition, assessments on temperature indices and mortality and morbidity risks of Taiwanese population in association with prolonged extreme temperatures event (heatwave and cold spell) were also the focus of my research. I tried to transform my research findings into practical applications and interface with governmental priorities. I expanded the temperature-health risk association study from using historical observations to real-time, seasonal and long-term predicting weather/climate data, to make practical applications of climate services in the field of public health. For years 2017-2020, my research team systematically evaluated the area-sex-age stratified disease-specific (more than 100 diseases categories) health risk associated with ambient environment using 23 million population-based vital statistics and national health insurance claims. Risks of hourly ambulance services associated with total fine particulate matter and it constituents were also evaluated. In the next few years, I will cooperate with investigators from Sweden and the United States, and partners from the Asia Pacific Region (India, China, Vietnam, Nepal, Bangladesh, and Indonesia; referred to as the focus area) to establish a multinational consortium of scientists that will perform a comparative analysis of diarrheal disease risk associated with extreme weather events. Our consortium will develop a transferable solution — seasonal to sub-seasonal (S2S) early warnings for diarrheal disease — that will be implemented across the focus area to reduce extreme weather-related diarrheal disease burdens and improve community resilience to climate change.
Wang YC, Sung FC, Chen YJ, Cheng CP, Lin YK(2020) Effects of extreme temperatures, fine particles and ozone on hourly ambulance dispatches, Science of the Total Environment (in press).
Lin YK, Sung FC, Honda Y, Chen YJ, Wang YC* (2020) Comparative Assessments of Mortality from and Morbidity of Circulatory Diseases in Association with Extreme Temperatures, Science of the Total Environment, 723, 138012.
Lin YK, A. T. Maharani, Chang FZ, Wang YC* (2019) Mortality and Morbidity Associated With Ambient Temperatures in Taiwan. Science of the Total Environment, 651, 210-217.
Wang YC#, Lin YK#, Chen YJ, Hung SC, Zafirah Y, Sung FC* (2020) Ambulance Services Associated with Extreme Temperatures and Fine Particles in a Subtropical Island. Scientific Reports, 10, 2855. Available at: https://doi.org/10.1038/s41598-020-59294-8.
Wang YC, Chen YC, Ko CY, Guo YL, Sung FC* (2018) Pre-existing comorbidity modify emergency room visit for out-of-hospital cardiac arrest in association with ambient environments. PLoS One, Available at: https://doi.org/10.1371/journal.pone.0204593.
Impacts Assessments and Adaptive Decisions for Health Risk Associated with Climate Change in Taiwan Geographical variation, urbanization, socio-economic conditions, demographics, and the installation of air conditioning systems all affect the temperature (especially extreme heat) – health risk association. This 3-year study aimed, used the 23 million population-based National Health Insurance and vital statistics data obtaining from Department of Health and Welfare from 2000 to 2014, to systematically evaluate the associations between daily mortality from and morbidity of climate sensitive diseases and temperature for the entire population in each county and city of Taiwan, and to assess the modifying effects of socioeconomic factors on the risk associated with extreme temperature. In Taiwan, New Taipei City, Taoyuan City and Miaoli County were the most affected area by extreme low temperature, while Taichung City, Yunlin County, Nantou County, Chiayi County and Kaohsiung City were the most affected area by extreme high temperatures. Areas with less number of employed people had higher risks of all-cause emergency room visits in association with low temperature, and areas with more employed people had higher risks of emergency room visits of circulatory diseases associated with high temperature. In areas with higher proportion of elders, heat-related risks of all-cause mortality and morbidity were increased. In gender analysis, male all-cause mortality was significantly associated with high temperature in the Northern Taiwan and female all-cause mortality was significantly associated with high temperature in the Southern Taiwan. In counties and cities where mortality and emergency room visits significantly associated with extreme low temperature for the elderly population, risk has no gender difference. This study suggested emergency room visits of circulatory diseases for population aged 65 years and above, significantly associated with extreme low temperature, can be used as a target group for future health policy performance assessment.
Meteorological Information Application Service - Solomon Islands Dengue-Like Illness Early Warning System This study aimed to analyze the correlations between dengue-like illness (DLI) cases of the WHO Pacific Syndromic Surveillance System Weekly Bulletin and the risk of local DLI incidence from 2014 to 2018, as well as the meteorological data from eight manual weather stations (Auki, Handerson, Honiara, Kirakira, Lata, Munda, Taro, and Tingoa) across the major islands of the Solomon Islands. The temporal risk association between monthly DLI and monthly weather variations for whole country was adopted to build a web-based DLI warning system, namely SoSAFE, on the website of the Taiwan Central Weather Bureau (http://220.127.116.11/sosafe/products_display/product?menu_index=8). The system provides 2 early warning information. First type is the monthly warning signals by red, yellow and green in next season that use the moving average of monthly average temperature and rainfall to predict the risk of DLI in Solomon Islands. Second type is the 3-month forecasting number of dengue-like illness. The forecasting data will be analyzed using the average temperature, average rainfall and monthly population index by the Autoregressive Integrated Moving Average model.