| Objective:Particulate matter(PM), one of the main pollutant of the city air, is a mixture of solid or liquid particles in the air. Its size, shape and components are closely associated with the people’s health. Particulate matter is Usually divided into three categories according to its aerodynamic diameter: coarse PM(PM with an average aerodynamic diameter between 2.5 and 10 micrometers),PM2.5(PM with an average aerodynamic diameter less than 2.5 micrometers), and ultra-fine PM(PM with an average aerodynamic diameter less than or equal to0. 1micrometers).Chronic obstructive pulmonary disease(COPD) has become a major global public health problem because of the huge number of patients, the high mortality and the heavy economic burden. In our country, COPD is also an important chronic respiratory disease that threaten the people’s health. Many epidemiological studies and toxicology researches in the foreign countries indicate that air pollutant has a short or long impact on the mortality and morbidity of respiratory diseases. Our country began to monitor and publish the data of the air pollutants including PM2.5, PM10 and PM2.5-10. Thus, the research on the impact of PM on hospital admissions for AECOPD is less well documented. Because particles show considerable heterogeneity over space and season, the effect of particles is likely to vary. So, it is very important to detect associations between particulate matter and hospital admissions for AECOPD in China. Shi Jiazhuang is one of the most polluted cities in China, so it’s representative to select Shi Jiazhuang as the objective of the study.This study was undertaken to examine the association between levels of ambient particulate matter(PM10/PM2.5) and hospital admissions for AECOPD among people residing in Shi Jiazhuang,2013, using case-crossover design.Methods:1 This study was carried out in the nine hospitals in Shi Jiazhuang, China. Patients were included in the study carried out between January 1st, 2013 and December 31 st, 2013 if they were AECOPD. The records for AECOPD admissions were collected by excel, including sex, age, residential address, season, onset date, admission date, complications and so on.2 Data on air pollutants were provided by Shi Jiazhuang Environmental Monitoring Center. Seven pollutants were monitored including PM2.5, PM10, PM2.5-10, NO2, SO2, O3 and CO. For each day, hourly air pollution data were obtained, the 24 h average levels of these pollutants were computed. Daily information on mean temperature was provided by the center of meteorological agency in Shi Jiazhuang city.3 We used a 1:2 matched case-crossover design. We set the one week before and after onset of the disease as the control period, and the same day as the case period. We set the weeks as the intervals to control the day of the week. We use the lag models to observe the effect of air pollutant on the lag0 and lag1-5 on AECOPD admissions of citizens in Shi Jiazhuang. We confirm the best lag period according to the OR value. The associations between hospital admissions for AECOPD and levels of pollutants were estimated using the odds ratio(OR) and their 95% confidence intervals(CI) which were produced using conditional logistic regression with weights equal to the number of hospital admissions on that day. Both single-pollutant models and multi-pollutant models were fitted with a different combination of pollutants to assess the stability of the effect of PM. Stratified analyses of exposure based on the average exposure at lag 0 to lag 5 based on age, gender, season and underlying disease was undertaken to evaluate effect modification. All statistical analyses were performed using the SPSS13.0 package, All statistical tests were two-sided. Values of P<0.05 were considered statistically significant.Results:1 During the study period, 810 AECOPD admissions were recorded. The number of average AECOPD admissions is most in March, and least in July(Figure 4). The majority were men(56.54%) and older(71.11%). 81.98 % of the AECOPD admissions had complications. And 55.80% AECOPD admissions happened in the cold season.(Table 1, Figure3)2 According to the data of air pollutants, the distribution characteristics of air pollutants is shown in Table2 and Figure2 and PM2.5and PM10 were the main pollutant. The mean annual concentration average level of PM2.5,PM10,SO2 and NO2 were significantly higher compared with current WHO criterion and Chinese national secondary standard. The data indicated that the four pollutants were out of limit including PM2.5, PM10, NO2 and SO2, especially PM2.5 and PM10.3 The effect and hysteresis effect of PM concentration on AECOPD admissions in the single-pollutant model: The single-pollutant model showed that AECOPD admissions were positively associated with an increase of concentrations of PM10 and PM2.5-10 by 10μg/m3. And the impact of concentrations of PM10 on lag5 and PM2.5-10 on lag0 and lag5 on AECOPD admissions is statistically significant(P<0.05). AECOPD admissions were positively associated with an increase of concentrations of CO by 1mg/m3. And the impact of concentrations of CO on lag0 and on lag5 on AECOPD admissions is statistically significant(P<0.05). AECOPD admissions were positively associated with an increase of concentrations of SO2 by 1ug/m3. And the impact of concentrations of SO2 on lag0 and on lag5 on AECOPD admissions is statistically significant(P<0.05). AECOPD admissions were positively associated with an increase of concentrations of PM2.5, NO2, O3, which was not statistically significant. The concentration of PM10, PM2.5-10 and CO on lag5 has the higher OR value. According to the effect on health, PM10,PM2.5-10,CO,SO2 were taken into the multiple-pollutant models to analysis the effect of PM on AECOPD admissions.4 For multiple-pollutant models, the effect of a PM10 remained significant after CO, SO2, CO+SO2 were included in the model. The effect of a PM2.5-10 remained significant after O3 and NO2 was included in the model, which was not statistically significant.5 We also considered effect modified by age, sex, season, and underlying disease. Increase in PM10 and PM2.5-10 was associated with increase in hospital admissions for AECOPD, and a larger association was seen in male, younger and admissions with underlying disease. And the effect was not significantly on season. Males were more susceptible to PM, so were those with underlying disease. The result was statistically significant.Conclusion:There was positive association between atmosphere particulate matter(PM2.5-10/PM10)and hospital admissions for AECOPD in Shi Jiazhuang. The effect of concentration of PM2.5-10 on lag5 on AECOPD admissions has the higher OR value, which was statistically significant. AECOPD admission rate increases by 1.8% with the concentration of PM2.5-10 increases by 10μg/m3. The effect of concentration of PM10 on lag5 on AECOPD admissions has the higher OR value, which was statistically significant. AECOPD admission rate increases by 0.8% with the concentration of PM10 increases by 10μg/m3. While the effect of concentration of PM2.5 on AECOPD admissions was not statistically significant. |
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