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Background: The thermal environment can directly affect workers' occupational health and safety, and act as a contributing factor to injury or illness. However, the literature addressing risks posed by varying temperatures on work-related injuries and illnesses is limited., Objectives: To examine the occupational injury and illness risk profiles for hot and cold conditions., Methods: Daily numbers of workers' compensation claims in Adelaide, South Australia from 2003 to 2013 (n = 224,631) were sourced together with daily weather data. The impacts of maximum daily temperature on the risk of work-related injuries and illnesses was assessed using a time-stratified case-crossover study design combined with a distributed lag non-linear model., Results: The minimum number of workers' compensation claims occurred when the maximum daily temperature was 25 °C. Compared with this optimal temperature, extremely hot temperatures (99th percentile) were associated with an increase in overall claims (RR: 1.30, 95%CI: 1.18-1.44) whereas a non-significant increase was observed with extremely cold temperatures (1st percentile, RR: 1.10 (95%CI: 0.99-1.21). Heat exposure had an acute effect on workers' injuries whereas cold conditions resulted in delayed effects. Moderate temperatures were associated with a greater injury burden than extreme temperatures., Conclusion: Days of very high temperatures were associated with the greatest risks of occupational injuries; whereas moderate temperatures, which occur more commonly, have the greatest burden. These findings suggest that the broader range of thermal conditions should be considered in workplace injury and illness prevention strategies., (Copyright © 2018 Elsevier Inc. All rights reserved.)

This study aims to (1) investigate the associations between climatic factors and dengue; and (2) identify the susceptible subgroups. De-identified daily dengue cases in Guangzhou for 2005-2014 were obtained from the Chinese Center for Disease Control and Prevention. Weather data were downloaded from the China Meteorological Data Sharing Service System. Distributed lag non-linear models (DLNM) were used to graphically demonstrate the three-dimensional temperature-dengue association. Generalised estimating equation models (GEE) with piecewise linear spline functions were used to quantify the temperature-dengue associations. Threshold values were estimated using a broken-stick model. Middle-aged and older people, people undertaking household duties, retirees, and those unemployed were at high risk of dengue. Reversed U-shaped non-linear associations were found between ambient temperature, relative humidity, extreme wind velocity, and dengue. The optimal maximum temperature (T max ) range for dengue transmission in Guangzhou was 21.6-32.9°C, and 11.2-23.7°C for minimum temperature (T min ). A 1°C increase of T max and T min within these ranges was associated with 11.9% and 9.9% increase in dengue at lag0, respectively. Although lag effects of temperature were observed for up to 141 days for T max and 150 days for T min , the maximum lag effects were observed at 32 days and 39 days respectively. Average relative humidity was negatively associated with dengue when it exceeded 78.9%. Maximum wind velocity (>10.7m/s) inhibited dengue transmission. Climatic factors had significant impacts on dengue in Guangzhou. Lag effects of temperature on dengue lasted the local whole epidemic season. To reduce the likely increasing dengue burden, more efforts are needed to strengthen the capacity building of public health systems., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Dengue fever is an important climate-sensitive mosquito-borne viral disease that poses a risk to half the world's population. The disease is a major public health issue in China where in 2014 a major outbreak occurred in Guangdong Province. This study aims to gauge health professionals' perceptions about the capacity of infectious disease control and prevention to meet the challenge of dengue fever in the face of climate change in Guangdong Province, China., Methods: A cross-sectional questionnaire survey was administered among staff in the Centers for Disease Control and Prevention (CDCs) in Guangdong Province. Data analysis was undertaken using descriptive methods and logistic regression., Results: In total, 260 questionnaires were completed. Most participants (80.7%) thought climate change would have a negative effect on population health, and 98.4% of participants reported dengue fever had emerged or re-emerged in China in recent years. Additionally, 74.9% of them indicated that the capability of the CDCs to detect infectious disease outbreak/epidemic at an early stage was excellent; 86.3% indicated laboratories could provide diagnostic support rapidly; and 83.1% believed levels of current staff would be adequate in the event of a major outbreak. Logistic regression analysis showed higher levels of CDCs were perceived to have better capacity for infectious disease control and prevention. Only 26.8% of participants thought they had a good understanding of climate change, and most (85.4%) thought they needed more information about the health impacts of climate change. Most surveyed staff suggested the following strategies to curb the public health impact of infectious diseases in relation to climate change: primary prevention measures, strengthening the monitoring of infectious diseases, the ability to actively forecast disease outbreaks by early warning systems, and more funding for public health education programs., Conclusion: Vigilant disease and vector surveillance, preventive practice and health promotion programs will likely be significant in addressing the threat of dengue fever in the future. Further efforts are needed to strengthen the awareness of climate change among health professionals, and to promote relevant actions to minimize the health burden of infectious diseases in a changing climate. Results will be critical for policy makers facing the current and future challenges associated with infectious disease prevention and control in China., (Copyright © 2016 Elsevier Inc. All rights reserved.)