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10.3390/ijerph120505241 http://scihub22266oqcxt.onion/10.3390/ijerph120505241 C4454965!4454965!25993102     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2015 ; 12 (5): 5241-55 Nephropedia Template TP {{tp|p=25993102|t=2015. Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model |pdf=|usr=}}{{25993102}} gab.com Text Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=25993102 #FOAvip Individuals living in hot climates face health risks from hyperthermia due to excessive heat. Heat strain is influenced by weather exposure and by individual characteristics such as age, sex, body size, and occupation. To explore the population-level drivers of heat strain, we developed a simulation model that scales up individual risks of heat storage (estimated using Myrup and Morgan?s man model ?MANMO?) to a large population. Using Australian weather data, we identify high-risk weather conditions together with individual characteristics that increase the risk of heat stress under these conditions. The model identifies elevated risks in children and the elderly, with females aged 75 and older those most likely to experience heat strain. Risk of heat strain in males does not increase as rapidly with age, but is greatest on hot days with high solar radiation. Although cloudy days are less dangerous for the wider population, older women still have an elevated risk of heat strain on hot cloudy days or when indoors during high temperatures. Simulation models provide a valuable method for exploring population level risks of heat strain, and a tool for evaluating public health and other government policy interventions. Twit Text FOAVip Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=25993102 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25993102 #FOAvip English Wikipedia <ref>{{Cite pmid|25993102}}</ref> Estimating Risks of Heat Strain by Age and Sex: A Population-Level Simulation Model #MMPMID25993102 Glass K; Tait PW; Hanna EG; Dear K Int J Environ Res Public Health 2015[May]; 12 (5): 5241-55 PMID25993102show ga Individuals living in hot climates face health risks from hyperthermia due to excessive heat. Heat strain is influenced by weather exposure and by individual characteristics such as age, sex, body size, and occupation. To explore the population-level drivers of heat strain, we developed a simulation model that scales up individual risks of heat storage (estimated using Myrup and Morgan?s man model ?MANMO?) to a large population. Using Australian weather data, we identify high-risk weather conditions together with individual characteristics that increase the risk of heat stress under these conditions. The model identifies elevated risks in children and the elderly, with females aged 75 and older those most likely to experience heat strain. Risk of heat strain in males does not increase as rapidly with age, but is greatest on hot days with high solar radiation. Although cloudy days are less dangerous for the wider population, older women still have an elevated risk of heat strain on hot cloudy days or when indoors during high temperatures. Simulation models provide a valuable method for exploring population level risks of heat strain, and a tool for evaluating public health and other government policy interventions. äEstimating Risks of Heat Strain by Age and Sex: A Population-Level Sim(epmc).pdf DeepDyve Pubget Overpricing