The Development of an Empirical Model for Estimation of the Sensitivity to Heat Stress in the Outdoor Workers at Risk
Background: Workers who work in hot environments may be at risk for heat stress. Exposure to heat can result in occupational illnesses, including heat stroke, heat cramps, and heat exhaustion. The risk of exposure to heat depends on individual, environmental, and occupational risk factors. Individual risk factors may decrease the individual’s tolerance to heat stress. Sensitivity as an intrinsic factor may predispose a person to heat stress. Aim: This study was aimed to determine the criteria for sensitivity parameter, specify their weights using the fuzzy Delphi-analytical hierarchy, and finally providing a model to estimate sensitivity. The significant of the study is presenting a model to estimate the sensitivity to heat stress. Materials and Methods: The expert’s opinions were used to extract the criteria in Delphi method. After determining the weight of each criterion, Fuzzy analytic hierarchy Process (FAHP), by mathematical principles matrix and triangular fuzzy numbers, was applied for the prioritization of criteria. Results: According to experts’ viewpoints and considering some exclusion, 10 of 36 criteria were selected. Among 10 selected criteria, age had the highest percentage of responses (90% (27/30)) and its relative weight was 0.063. After age, the highest percentages of response were assigned to the factors of preexisting disease (66.6% (20/30)), body mass index (56.6% (17/30)), work experience (53.3% (16/30)), and clothing (40% (16/30)), respectively. Other effective criteria on sensitivity were metabolic rate, daily water consumption, smoking habits, drugs that interfere with the thermoregulatory processes, and exposure to other harmful agents. Conclusions: Eventually, based on the criteria, a model for estimation of the workers’ sensitivity to heat stress was presented for the first time, by which the sensitivity is estimated in percent.
Keywords: Heat stress, Sensitivity, Personal factors, Fuzzy AHP