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      The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012–2016 in Jinan, China

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          Abstract

          Background

          Few studies have investigated the associations between outdoor air pollution and outpatient visits for respiratory diseases (RDs) in general population.

          Methods

          We collected daily outpatient data of primary RDs from five hospitals in Jinan during January 2012 and December 2016, as well as daily measurements of air pollutants from the Jinan Environmental Monitoring Center and daily meteorological variables from the China Meteorological Data Sharing Service System. A generalized additive model (GAM) with quasi-Poisson regression was constructed to estimate the associations between daily average concentrations of outdoor air pollutants (PM 2.5,PM 10, SO 2, NO 2, CO and O 3) and daily outpatient visits of RDs after adjusting for long-time trends, seasonality, the “day of the week” effect, and weather conditions. Subgroup analysis stratified by gender, age group and the type of RDs was conducted.

          Results

          A total of 1,373,658 outpatient visits for RDs were identified. Increases of 10 μg/m 3 in PM 2.5, PM 10, NO 2, CO and O 3 were associated with0.168% (95% CI, 0.072–0.265%), 0.149% (95% CI, 0.082–0.215%), 0.527% (95% CI, 0.211–0.843%), 0.013% (95% CI, 0.003–0.023%), and 0.189% (95% CI, 0.032–0.347%) increases in daily outpatient visits for RDs, respectively. PM 2.5 and PM 10 showed instant and continuous effects, while NO 2, CO and O 3 showed delayed effects on outpatient visits for RDs. In stratification analysis, PM 2.5 and PM 10 were associated with acute RDs only.

          Conclusions

          Exposure to outdoor air pollutants including PM 2.5, PM 10, NO 2, CO and O 3 associated with increased risk of outpatient visits for RDs.

          Electronic supplementary material

          The online version of this article (10.1186/s12931-018-0958-x) contains supplementary material, which is available to authorized users.

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          Most cited references35

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          Gender differences in airway behaviour over the human life span.

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            Air pollution characteristics and their relation to meteorological conditions during 2014-2015 in major Chinese cities.

            In January 2013, the real-time hourly average concentrations of six pollutants (CO, NO2, O3, PM10, PM2.5 and SO2) based on data from air quality monitoring stations in major Chinese cities were released to the public. That report provided a good opportunity to publicise nationwide temporal and spatial pollution characteristics. Although several studies systematically investigated the temporal and spatial trends of pollutant concentrations, the relation between air pollution and multi-scale meteorological conditions and their spatial variations on a nationwide scale remain unclear. This study analysed the air pollution characteristics and their relation to multi-scale meteorological conditions during 2014-2015 in 31 provincial capital cities in China. The annual average concentrations of six pollutants for 31 provincial capital cities were 1.2 mg m-3, 42.4 μg m-3, 49.0 μg m-3, 109.8 μg m-3, 63.7 μg m-3, and 32.6 μg m-3 in 2014. The annual average concentrations decreased 5.3%, 4.9%, 11.4%, 12.0% and 21.5% for CO, NO2, PM10, PM2.5 and SO2, respectively, but increased 7.4% for O3 in 2015. The highest rate of a major pollutant over China was PM2.5 followed by PM10, O3, NO2, SO2 and CO. Meteorological conditions were the primary factor determining day-to-day variations in pollutant concentrations, explaining more than 70% of the variance of daily average pollutant concentrations over China. Meteorological conditions in 2015 were more adverse for pollutant dispersion than in 2014, indicating that the improvement in air quality was caused by emission controls.
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              On the use of generalized additive models in time-series studies of air pollution and health.

              F Dominici (2002)
              The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. When the data to which the GAM are being applied have two characteristics--1) the estimated regression coefficients are small and 2) there exist confounding factors that are modeled using at least two nonparametric smooth functions--the default settings in the gam function of the S-Plus software package (version 3.4) do not assure convergence of its iterative estimation procedure and can provide biased estimates of regression coefficients and standard errors. This phenomenon has occurred in time-series analyses of contemporary data on air pollution and mortality. To evaluate the impact of default implementation of the gam software on published analyses, the authors reanalyzed data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) using three different methods: 1) Poisson regression with parametric nonlinear adjustments for confounding factors; 2) GAM with default convergence parameters; and 3) GAM with more stringent convergence parameters than the default settings. The authors found that pooled NMMAPS estimates were very similar under the first and third methods but were biased upward under the second method.
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                Author and article information

                Contributors
                w533521@163.com
                1035312289@qq.com
                nam1106@163.com
                17865192155@163.com
                jyangela@163.com
                yunxialiu@163.com
                lihuaichen@163.com
                Journal
                Respir Res
                Respir. Res
                Respiratory Research
                BioMed Central (London )
                1465-9921
                1465-993X
                12 December 2018
                12 December 2018
                2018
                : 19
                : 246
                Affiliations
                [1 ]ISNI 0000 0004 1769 9639, GRID grid.460018.b, Department of Public Health, , Shandong Provincial Hospital affiliated to Shandong University, ; Jinan, 250021 Shandong China
                [2 ]ISNI 0000 0004 1769 9639, GRID grid.460018.b, Department of Respiratory Medicine, , Shandong Provincial Hospital affiliated to Shandong University, ; Jinan, 250021 Shandong China
                [3 ]ISNI 0000 0004 1761 1174, GRID grid.27255.37, Department of Respiratory Medicine, , School of Clinical Medicine, Shandong University, ; Jinan, 250012 Shandong China
                [4 ]GRID grid.454761.5, Department of Respiratory Medicine, , University of Jinan, ; Jinan, 250022 Shandong China
                [5 ]ISNI 0000 0004 1761 1174, GRID grid.27255.37, Department of Biostatistics, , School of Public Health, Shandong University, ; Jinan, 250012 Shandong China
                Article
                958
                10.1186/s12931-018-0958-x
                6292059
                30541548
                f54dd09f-8023-4a68-8053-4b53635f8804
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 August 2018
                : 30 November 2018
                Funding
                Funded by: Key Research & Development Program of Shandong Province
                Award ID: 2017GSF218052
                Funded by: Technology Development Plan Project of Jinan City
                Award ID: 201704100
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Respiratory medicine
                air pollution,outpatient visits,respiratory diseases,generalized additive model

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