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Revista de Salud Pública
Print version ISSN 0124-0064
Abstract
PARRA-SANCHEZ, Juan S.; OVIEDO-CARRASCAL, Ana I. and AMAYA-FERNANDEZ, Ferney O.. Data Analytics: incidence of air pollution on public health in Medellin, Colombia. Rev. salud pública [online]. 2020, vol.22, n.6, pp.609-617. Epub Sep 23, 2021. ISSN 0124-0064. https://doi.org/10.15446/rsap.v22n6.78985.
Objective
To analyze the impact of air pollution by PM2,5 particulate matter and its relationship with the number of attendances to health entities for respiratory diseases through data analytics.
Methods
Data from the Metropolitan Area of Medellín, Colombia, a city located in a densely populated and industrialized narrow valley and that has presented critical episodes of contamination in recent years, were analyzed. Three data sources were analyzed: meteorological data provided by SIATA (Early Warning System of Medellín and the Aburra Valley), PM2,5 particulate matter contamination data provided by SIATA, and RIPS reports (Individual Registers for the Provision of Health Services) provided by the health department.
Results
The relationship between the concentration of PM2,5 and medical care for the diagnoses of ARI, COPD and asthma was evidenced. In a critical episode of PM2,5 contamination, the following delays in medical care were found: between 0-2 days for IRA, 0-7 days for COPD, and 0-5 days for asthma.
Discussion
Correlation coefficients were found that show the association of the concentration of PM2,5 with the attendances for the diagnoses of ARI, COPD, and asthma. The highest correlation between the three morbidities was found for asthma. The meteorological variable with the highest correlation with the objective variable is air temperature in the case of COPD and asthma. In the case of IRA, the variable with the highest correlation is wind speed. On the other hand, the day of the week is a variable of great importance when carrying out a study of care for diseases.
Keywords : Analysis of data; data science; respiratory diseases; air pollution (source: MeSH, NLM).