A process to design a system of prevention of occupational and work-related diseases caused by climate changes in the Chilean agrosilvopastoral sector
Palabras clave: climate change, disease, livestock, agriculture, forestry, work, scenarios
ResumenThe increase in temperature together with the reduction of rainfall in a large part of the national territory can generate unfamiliar and/or harmful environmental conditions, exposing workers to a new configuration of physical, chemical and biological agents; and thereby increasing labor morbidity. To coping with that problem, this article shows the process of designing a system for preventing diseases caused by climate change in the Chilean agrosilvopastoral sector during the 21st century. This process comprises these four sequential stages. 1) Characterization of the probable scenarios: based on secondary sources, the predicted climate states will be deployed with their respective probability of occurrence for at least five natural regions of the national territory, both insular and continental South American, and through a comparative geography it will predict the pattern of forestry, livestock and agriculture activity that best fits each new climate in each natural region. 2) Determination of causal relationships: through simulations with computational support whose inputs are experiences recorded in different parts of the world, it will identify the thresholds of variables of the scenario (climate - agrosilvopastoral activity) that generate the emergence of occupational diseases and/or increase the quantity, prevalence and/or severity of these. 3) Identification of alterations to scenarios: by means of dose-response heuristics, it will identify those variables of the scenario whose modification significantly reduces the labor morbidity, either in the severity of the disease, in the number of patients, in the appearance of new diseases and any other measurable harmful effect. 4) Assemblies of alterations: it will be assessed in monetary units both the cost of implementing each alteration and the avoided risk by reducing the occupational diseases through the alterations already identified for later, through a mathematical programming to achieve the optimal configuration of alterations.
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