ABSTRACT
Organophosphorus flame retardants (OPFRs) pollution and its impacts on human health are of global concern. The review briefly reviewed the current state-of-knowledge on exposure assessment and epidemiological evidence of OPFRs-related health effects. Specifically, this paper provided an overview and comparison of the levels of respiratory and gastrointestinal exposure to OPFRs and their body burden in different populations worldwide; summarized potential adverse effects of long-term low-level OPFRs exposure on children's neurodevelopment, adults' reproductive system, and thyroid function. Available epidemiological studies have revealed that the OPFRs exposure level of Chinese population is low, and rice consumption may be a potential source of exposure to OPFRs; OPFRs such as tris (1-chloro-2-propyl) phosphate (TCIPP) and tris (2-chloroethyl) phosphate (TCEP) have both neurotoxicity and reproductive toxicity, and possibly affect the thyroid function in adults and increase the risk of wheezing and eczema in children. Finally, the future research focus on population exposure and health effects of OPFRs was prospected.
ABSTRACT
Objective@#To know the incidence of heat stroke and explore it's prediction model in Pudong New Area of Shanghai. @*Methods@#An epidemiological investigation was conducted on heat stroke cases in Pudong New Area of Shanghai from 2013 to 2017. Daily temperature data during this period were collected to explore it’s influence. @*Results@#246 heat stroke cases were reported and investigated, 70.3% (173/246) of them were male. 170 cases are severe heat stroke, accounting for 69.1%. 28 patients died, accounting for 11.4% of all cases of heat stroke, and 16.5% (28/170) of severe heat stroke cases. Thermoplegia (56.5%, 96/170) was the most popular type among severe heat stroke cases. Heat prostration, heat cramps and mixed type account for 17.1% (29/170) , 12.4% (21/170) and 14.0% (24/170) respectively. Scatter plot and linear regression demonstrated that there was a significant linear relation between number of high temperature days and number of heat stroke cases (P<0.01) . And the prediction model is: Predictive number of annual heat stroke cases=β×Number of annual high temperature days+Intercept. Leave-one-out cross validation result shows that the predictive number of annual heat stroke cases from 2013 to 2017 were 85.7%, 90.9%, 83.3%, 91.9 and 84.3% respectively. @*Conclusion@#There was a significant linear relation between number of high temperature days and number of heat stroke cases in Pudong New Area. The related work arrangement for heat stroke prevention could be well planed according to the prediction model.