Atmospheric particulate matter and hospital admission due to lower respiratory tract infection: a

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1 Med J Chin PLA, Vol. 41, No. 2, February 1, (PM 10 /PM 2.5 ) [ ] Pearson logistic Pearson PM 2.5 PM 10 SO 2 NO 2 CO O 3 PM 2.5 5d PM g/m 3 OR (95%CI) 1.010(1.005~1.015) 1.006(1.003~1.009) PM 2.5 5d PM g/m 3 1.0%(0.5%~1.5%) 0.6%(0.3%~0.9%) PM PM 2.5 <60 <60 PM (PM 10 /PM 2.5 ) [ ](PM 10 ) (PM 2.5 ) [ ] R517.6 [ ] A [ ] (2016) [DOI] /j.issn Atmospheric particulate matter and hospital admission due to lower respiratory tract infection: a BAI Zi-na, DUAN Zheng * Second Department of Respiratory, Second Hospital of Hebei Medical University, Shijiazhuang , China * Corresponding author, duanzheng1971@126.com This work was supported by the Hebei Provincial Science and Technology Plan Project (No D) [Abstract] Objective To explore the association between atmospheric particulate matter (PM 10 /PM 2.5 ) levels and hospital admissions due to lower respiratory tract infection in Shijiazhuang. Methods Data of air pollution, meteorologic data, and the data of patients admitted to hospital due to lower respiratory tract infection were retrospectively analyzed. Pearson's correlation coefficients were calculated to analyze correlations between atmospheric particulate matter and meteorologic factors. Data of hospital admission due to lower respiratory tract infection and of atmospheric air pollution levels in Shijiazhuang were obtained, a bidirectional case-crossover design was used to investigate the association between hospital admissions due to lower respiratory tract infection and levels of atmospheric particles. Stratified analyses of exposure based on age, gender, complications and season were performed to evaluate the effect. Results Pearson's correlation analysis showed positive correlations among PM 2.5,PM 10,SO 2, NO 2 and CO. The concentration of all these five pollutants were negatively correlated with O 3 and daily mean temperature, while a positive correlation was found between concentrations of the 5 pollutants and daily average temperature and O 3. In single-pollutant model, every 10 g/m 3 increase in PM 2.5 and PM 10 at lag5 brought the corresponding OR values (95%CI) up to 1.010( ) and 1.006( ) respectively. In the multi-pollutant models, the observed effects of PM 2.5 remained significant. Stratified analysis based on gender, age, season and comorbidities showed that the effect of PM 2.5 exposure on lower respiratory tract infection [ ]( D) [ ] [ ] () [ ] duanzheng1971@126.com

2 admissions was stronger in males, persons younger than 60 years of age and persons without comorbidities, and even more stronger in cold season. The effect of PM 10 exposure on lower respiratory tract infection admissions was stronger in females, persons older than 60 years of age and persons with comorbidities, and even more stronger in cold season. Conclusion Our study demonstrates that higher levels of atmospheric particulate matter (PM 10 /PM 2.5 ) may increase the risk of hospital admissions due to lower respiratory tract infection. [Key words] lower respiratory tract infection; inhalable particulate matter (PM 10 ); fine particulate matter (PM 2.5 ); casecrossover design; hospital admission (PM m ) (PM ) (PM 2.5 )(PM 0.1 ) PM m PM 2.5 [1-3] (lower respiratory tract infection LRTI) [4] [5] [6-8] PM 2.5 PM 2.5 PM ( ) 70%(499/717) ( J22) ( J18) ( J44) ( J47) (ICD-10) (6.94 ) 8 (10.39 ) 1695 (54.22%) 1431 (45.78%) (59.15%) < (40.85%) 2168 (69.35%) 958 (30.65%) (10 ~ 3 ) 1525 (48.78%) (4~9 ) 1601(51.22% 1) (www. hebqx.com) ( 6 PM 2.5 PM 10 SO 2 NO 2 CO O PM 2.5 PM Pearson PM 2.5 PM 10 SO 2 NO 2 CO O Number of patients admitted Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. Admission month of Fig. 1 Daily average hospitalizations of patients with lower respiratory tract infection each month

3 Med J Chin PLA, Vol. 41, No. 2, February 1, Tab.1 Statistic description of daily environmental data and temperature Pollutant concentration Temperature ( ) PM 2.5 ( g/m 3 ) PM 10 ( g/m 3 ) NO 2 ( g/m 3 ) SO 2 ( g/m 3 ) O 3 ( g/m 3 ) CO (mg/m 3 ) P P P x±s 14.33± ± ± ± ± ± ± PM 2.5 PM 10 SO 2 NO 2 CO O 3 [9] (lag 0 ) 1~5d(lag 1-5 ) (OR ) PM 2.5 PM 10 SO 2 NO 2 CO O 3 SO 2 +NO 2 SO 2 +CO SO 2 +O 3 NO 2 +CO NO 2 +O 3 CO+O 3 SO 2 +NO 2 +CO SO 2 +NO 2 +O 3 SO 2 +CO+O 3 NO 2 +CO+O 3 SO 2 +NO 2 +CO+O ( ) (<60 60 ) () [ (4 ~9 ) (10 ~ 3 )] 1.41 : 2 Pearson PM 2.5 PM 10 SO 2 NO 2 CO O (day of the week) [10] 1 logistic SPSS 17.0 (OR ) 10 g/m 3 OR 95% (95%CI) P< Pearson PM 2.5 PM 10 SO 2 NO 2 CO O 3 O 3 (P<0.01 2) 2Pearson Tab. 2 Pearson correlation between environmental pollutants and daily average temperature Pollutant T PM 2.5 PM 10 NO 2 SO 2 CO PM (1) PM (1) (1) NO (1) (1) (1) SO (1) (1) (1) (1) CO (1) (1) (1) (1) (1) O (1) (1) (1) (1) (1) (1) (1)P< PM 2.5 PM 10 (P<0.05) PM 2.5 ORPM g/m 3 5d PM 10 OR5d PM g/m 3 PM 2.5 PM 10 5d ( 3) NO 2 NO 2 +O 3 O 3 NO 2 +SO 2 +O 3 NO 2 +SO 2 SO 2 SO 2 +O 3 CO+NO 2 +SO 2 CO+NO 2 +SO 2 +O 3 PM 2.5 ( P 0.05) NO 2 CO+NO 2 NO 2 +O 3 CO O 3 NO 2 +SO 2 CO+NO 2 +SO 2 CO+NO 2 +O 3 NO 2 +SO 2 +O 3 CO+NO 2 +SO 2 +O 3 PM 10 (P<0.05) ( 2)

4 (OR 95%CI) Tab.3 Correlation of environmental pollutant concentrations to hospital admission due to lower respiratory tract infection at different lag days (OR, 95%CI) Pollutant Lag0 Lag1 Lag2 Lag3 Lag4 Lag5 PM ( ) (1) 1.008( ) (1) 1.008( ) (1) 1.008( ) (1) 1.006( ) (1) 1.008( ) (1) PM ( ) 1.004( ) (1) 1.005( ) (1) 1.005( ) (1) 1.004( ) (1) 1.006( ) (1) CO 1.083( ) (1) 1.063( ) (1) 1.063( ) (1) 1.061( ) (1) 1.049( ) (1) 1.061( ) (1) SO ( ) 1.001( ) 1.001( ) 1.001( ) 1.001( ) 1.002( ) (1) NO ( ) (1) 1.003( ) (1) 1.003( ) (1) 1.002( ) (1) 1.001( ) 1.002( ) (1) O ( ) 0.999( ) 1.001( ) (1) 1.000( ) 1.000( ) 1.000( ) Calculated for an interquartile range increases of PM 2.5 (10 g/m 3 ), PM 10 (10 g/m 3 ), SO 2 (1 g/m 3 ), NO 2 (1 g/m 3 ), CO(1mg/m 3 ), and O 3 (1 g/m 3 ); (1)P<0.05 Odd ratio (OR) for increase of every 10 g/m 3 in concentration of PM 2.5 Odd ratio (OR) for increase of every 10 g/m 3 in concentration of PM PM 2.5 PM 2.5 +CO PM 2.5 +NO 2 PM 2.5 +SO 2 PM 2.5 +O 3 PM 2.5 +CO+NO 2 PM 2.5 +CO+SO 2 PM 2.5 +CO+O 3 PM 2.5 +NO 2 +SO 2 PM 2.5 +NO 2 +O 3 PM 2.5 +SO 2 +O 3 PM 2.5 +CO+NO 2 +SO 2 PM 2.5 +CO+NO 2 +O 3 PM 2.5 +CO+SO 2 +O 3 PM 2.5 +NO 2 +SO 2 +O 3 PM 2.5 +CO+NO 2 +SO 2 +O 3 PM 2.5 PM 2.5 +CO PM 2.5 +NO 2 PM 2.5 +SO 2 PM 2.5 +O 3 PM 2.5 +CO+NO 2 PM 2.5 +CO+SO 2 PM 2.5 +CO+O 3 PM 2.5 +NO 2 +SO 2 PM 2.5 +NO 2 +O 3 PM 2.5 +SO 2 +O 3 PM 2.5 +CO+NO 2 +SO 2 PM 2.5 +CO+NO 2 +O 3 PM 2.5 +CO+SO 2 +O 3 PM 2.5 +NO 2 +SO 2 +O 3 PM 2.5 +CO+NO 2 +SO 2 +O 3 2 Fig. 2 Correlation of pollutant concentrations to hospital admission due to lower respiratory tract infection in the multi-pollutant models <60 PM 2.5 OR (P 0.05)60 PM 10 OR (P ) PM 2.5 PM PM 2.5 PM 10 PM g/m 3 OR= %

5 Med J Chin PLA, Vol. 41, No. 2, February 1, (OR 95%CI) Tab. 4 Stratification analyses of the effects of atmospheric pollutant concentration on the admitted patients with lower respiratory tract infection in the multiple-pollutant models (OR, 95% CI) Covariate PM 2.5 PM 10 OR 95%CI OR 95%CI Sex Male (1) (1) Female (1) (1) Age(year) (1) (1) (1) Complications With (1) (1) Without (1) Season Cold season (1) (1) Warm season (1)P<0.05; Warm season is defined as April to September, while cold season defined as October to March of the next year PM g/m 3 OR= % PM 2.5 PM g/m 3 2.2% [6] 1.6% [8] 5 PM g/m 3 6.5% [7] PM 10 PM g/m 3 (COPD) 5% [11] PM g/m 3 COPD 2.5% 1.95% [12] COPD [7,13-16] PM 2.5 5d PM 10 Santus [15] PM 2.5 5d OR PM 10 3d OR Negrisoli [16] 4d PM 10 California 2d COPD [17] [18] [19] [20-23] PM 2.5 <60 PM Zanobetti [24] PM 10 <75 PM 10 COPD >75 PM 10 COPD1 Santus [15] PM 2.5 PM 10 Ko [25] COPD COPD Medina-Ramon Burnett Anderson [26-28] PM 10 [29] PM [30-31]

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