Risk of cardiovascular and respiratory diseases attributed to satellite-based PM 2.5 over 2017-2022 in Sanandaj, an area of Iran.

Autor: Rahmati S; Department of Epidemiology and Biostatistics, Faculty of Public Health, Kurdistan University of Medical Sciences, Sanandaj, Iran., Aboubakri O; Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran. asso.mhabad@gmail.com.; Health Metrics and Evaluation Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran. asso.mhabad@gmail.com., Maleki A; Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran., Rezaee R; Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran. rezaee.eng@gmail.com., Soleimani S; Student Research Committee, Department of Environmental Health Engineering, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran., Li G; Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China., Safari M; Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran., Ahmadiani N; Head of Forecasting Department, Iran Meteorological Organization, Kurdistan Meteorological Bureau, Sanandaj, Iran.
Jazyk: angličtina
Zdroj: International journal of biometeorology [Int J Biometeorol] 2024 Aug; Vol. 68 (8), pp. 1689-1698. Date of Electronic Publication: 2024 May 14.
DOI: 10.1007/s00484-024-02697-3
Abstrakt: The risk of cardiovascular and respiratory diseases attributed to satellite-based PM 2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM 2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM 2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM 2.5 by age and gender categories. The cumulative influence of PM 2.5 particles on respiratory illnesses was statistically significant at 13-16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM 2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.
(© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)
Databáze: MEDLINE
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