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Using GIS to Estimate Population at Risk Because of Residence Proximity to Asbestos Processing Facilities in Colombia.

Benjamin LysaniukMaría Fernanda Cely-GarcíaMargarita GiraldoJoan M LarrahondoLaura Marcela Serrano-CalderónJuan Carlos Guerrero-BernalLeonardo Briceno-AyalaEsteban Cruz RodriguezJuan Pablo Ramos-Bonilla
Published in: International journal of environmental research and public health (2021)
The recent enactment of the law banning asbestos in Colombia raises a significant number of challenges. The largest factories that have historically processed asbestos include five asbestos-cement facilities located in the cities of Sibaté (Cundinamarca), Cali (Valle del Cauca), and Barranquilla (Atlántico), and Manizales (Caldas), which has two, as well as a friction products facility in Bogotá D.C. An asbestos chrysotile mine has also operated in Colombia since 1980 in Campamento (Antioquia). In the framework of developing the National Asbestos Profile for Colombia, in this study, we estimated the population residing in the vicinity of asbestos processing plants or the mine and, therefore, potentially at risk of disease. Using a geographic information system, demographic data obtained from the last two general population censuses were processed to determine the number of people living within the concentric circles surrounding the asbestos facilities and the mine. In previous studies conducted in different countries of the world, an increased risk of asbestos-related diseases has been reported for people living at different distance bands from asbestos processing facilities. Based on these studies, circles of 500, 1000, 2000, 5000, and 10,000 m radii, centered on the asbestos processing facilities and the mine that operated in Colombia, were combined with the census data to estimate the number of people living within these radii. Large numbers of people were identified. It is estimated that in 2005, at the country level, 10,489 people lived within 500 m of an asbestos processing facility or mine. In 2018, and within a distance of 10,000 m, the number of people was 6,724,677. This information can aid public health surveillance strategies.
Keyphrases
  • public health
  • healthcare
  • machine learning
  • electronic health record
  • big data