漏斗图
优势比
荟萃分析
出版偏见
置信区间
医学
空气污染
环境卫生
空气污染物
流行病学
子群分析
污染物
污染
人口学
内科学
化学
生物
社会学
有机化学
生态学
作者
Sai Li,Wei Wu,Gang Wang,Xinyi Zhang,Qian Guo,Beibei Wang,Suzhen Cao,Meilin Yan,Xiaochuan Pan,Tao Xue,Jicheng Gong,Xiaoli Duan
标识
DOI:10.1016/j.envres.2021.112472
摘要
Allergic rhinitis (AR) is one of the most common allergic diseases in the world, and usually persists throughout the activity. Epidemiological studies have shown a positive association between air pollution and allergic rhinitis. However, we could not find any meta-analysis of the risk of air pollutants (PM2.5, PM10, NO2, SO2, O3 and CO) on the prevalence of AR in people of all ages.Carry out a meta-analysis on the results of recent studies (up to 2020) to present valid information about exposure to air pollution and risk of prevalence of AR.We systematically searched three databases for studies up to December 17, 2020, including air pollution and AR. Random effect models were conducted to estimate the pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). Subgroup analysis, funnel plot, Egger's test, and the trim-and-fill method were also conducted.Thirty-five studies across 12 countries, including a total of 453,470 participants, were included. The OR per 10 μg/m3 increase of pollutants was 1.13 (1.04-1.22) for PM10 and 1.12 (1.05-1.20) for PM2.5. The OR per 10 μg/m3 increment of gaseous pollutants were 1.13 (1.07-1.20) for NO2, 1.13 (1.04-1.22) for SO2 and 1.07 (1.01-1.12) for O3. No significant association was observed between CO and AR. Children or adolescents are more sensitive to air pollution than adults. The effects of PM10 and SO2 were significantly stronger in Europe than Asia. The effects of air pollutants were more significant and higher in developing countries than in developed countries, except for PM10. A significant difference of subgroup test was found between developed and developing countries of NO2.This meta-analysis showed a positive association between air pollution and the prevalence of allergic rhinitis, and identified geographic area and economic level as the potential modifiers for the association.
科研通智能强力驱动
Strongly Powered by AbleSci AI