Effects of Particulate Matter on the Incidence of Respiratory Diseases in the Pisan Longitudinal Study

Salvatore Fasola, Sara Maio, Sandra Baldacci, Stefania La Grutta, Giuliana Ferrante, Francesco Forastiere, Massimo Stafoggia, Claudio Gariazzo, Giovanni Viegi and on behalf of the BEEP Collaborative Group
Additional contact information
Salvatore Fasola: Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy
Sara Maio: Institute of Clinical Physiology, National Research Council, 56126 Pisa, Italy
Sandra Baldacci: Institute of Clinical Physiology, National Research Council, 56126 Pisa, Italy
Stefania La Grutta: Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy
Giuliana Ferrante: Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialities, University of Palermo, 90127 Palermo, Italy
Francesco Forastiere: Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy
Massimo Stafoggia: Department of Epidemiology, Lazio Region Health Service—ASL Roma 1, 00147 Rome, Italy
Claudio Gariazzo: Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers’ Compensation Authority (INAIL), Monte Porzio Catone, 00144 Rome, Italy
Giovanni Viegi: Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy
on behalf of the BEEP Collaborative Group: Membership of the BEEP Collaborative Group is provided in the Acknowledgments.

IJERPH, 2020, vol. 17, issue 7, 1-13

Abstract: The current study aimed at assessing the effects of exposure to Particulate Matter (PM) on the incidence of respiratory diseases in a sub-sample of participants in the longitudinal analytical epidemiological study in Pisa, Italy. Three hundred and five subjects living at the same address from 1991 to 2011 were included. Individual risk factors recorded during the 1991 survey were considered, and new cases of respiratory diseases were ascertained until 2011. Average PM 10 and PM 2.5 exposures (µg/m 3 , year 2011) were estimated at the residential address (1-km 2 resolution) through a random forest machine learning approach, using a combination of satellite data and land use variables. Multivariable logistic regression with Firth’s correction was applied. The median (25th–75th percentile) exposure levels were 30.1 µg/m 3 (29.9–30.7 µg/m 3 ) for PM 10 and 19.3 µg/m 3 (18.9–19.4 µg/m 3 ) for PM 2.5 . Incidences of rhinitis and chronic phlegm were associated with increasing PM 2.5 : OR = 2.25 (95% CI: 1.07, 4.98) per unit increase (p.u.i.) and OR = 4.17 (1.12, 18.71) p.u.i., respectively. Incidence of chronic obstructive pulmonary disease was associated with PM 10 : OR = 2.96 (1.50, 7.15) p.u.i. These results provide new insights into the long-term respiratory health effects of PM air pollution.

Keywords: air pollution; particulate matter; long-term exposure; random forest; questionnaire; respiratory symptoms/diseases; incidence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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