RESUMEN
BACKGROUND: This study aimed to investigate the asthma profile among children/adolescents and the relationship of the prevalence of air pollution profiles using latent class analysis (LCA). OBJECTIVES: In this cross-sectional study, a case rural community was selected in an industrial area, and two rural control communities were selected in unexposed areas. METHODS: Hourly concentrations of PM10, SO2, NO2, and total volatile organic compounds were obtained from the records of a fixed air quality monitoring station, and the concentrations of benzene, toluene, xylenes styrene were measured during six campaigns. Asthma data was collected using the International Study of Asthma and Allergies in Childhood in 7-18 years old children/adolescents. The modeling was conducted using LCA. RESULTS: A higher amount of air pollution indices were observed in the case than both control communities. LCA divided the participants into three clusters; "healthy" (92.8%), "moderate" (2.8%), and "severe" (4.4%). A higher probability of severe asthma (6.8%) was observed in the case than control communities (2.6% and 1.8%). Additionally, after adjusting for possible confounders, the odds of asthma was lower in the control communities than the case in both moderate and sever classes (Odds Ratios in the range of 0.135-0.697). CONCLUSIONS: This study indicates asthma profiles of children/adolescents and the higher prevalence of severe class in the area, explaining the possible effect of air pollution.
Asunto(s)
Contaminación del Aire/estadística & datos numéricos , Asma/epidemiología , Adolescente , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Niño , Estudios Transversales , Humanos , Industrias , Análisis de Clases Latentes , Masculino , Material Particulado/análisis , Prevalencia , Población Rural , Compuestos Orgánicos Volátiles/análisisRESUMEN
BACKGROUND: The original step in reducing crash severity is recognition of the involved factors. The aim of this paper is to prioritize the factors affecting crashes severity. The current study was carried out in 2018 in Iran. METHODS: The present cross-sectional study focuses on factors affecting the crash severity. Due to the complicated nature of traffic accidents, Multi-Criteria Decision-Making methods can be considered as an effective approach. In this work, the factors affecting a crash severity were identified and then attained factors were scored by ten traffic safety experts. To prioritize and weigh these factors, the Analytic Network Process method and Super Decisions program were used. RESULTS: The results showed four main factors and 60 sub-factors in which the main factors in the order of priority were the safety (the most important sub-factor: speed over the upper limit), the other factors (the most important sub-factor: road user type), the health (the most important sub-factor: drowsiness), and the environment (the most important sub-factor: slipping the road). CONCLUSIONS: In order to control the crash severity, the presented factors in this study could help traffic safety experts to prioritize and perform controlling actions.