Qian Zhengmin, He Qingci, Lin Hung-Mo, Kong Lingli, Zhou Dunjin, Liang Shengwen, Zhu Zhichao, Liao Duanping, Liu Wenshan, Bentley Christy M, Dan Jijun, Wang Beiwei, Yang Niannian, Xu Shuangqing, Gong Jie, Wei Hongming, Sun Huilin, Qin Zudian
Pennsylvania State College of Medicine, Hershey, Pennsylvania, USA.
Res Rep Health Eff Inst. 2010 Nov(154):91-217.
Fewer studies have been published on the association between daily mortality and ambient air pollution in Asia than in the United States and Europe. This study was undertaken in Wuhan, China, to investigate the acute effects of air pollution on mortality with an emphasis on particulate matter (PM*). There were three primary aims: (1) to examine the associations of daily mortality due to all natural causes and daily cause-specific mortality (cardiovascular [CVD], stroke, cardiac [CARD], respiratory [RD], cardiopulmonary [CP], and non-cardiopulmonary [non-CP] causes) with daily mean concentrations (microg/m3) of PM with an aerodynamic diameter--10 pm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), or ozone (O3); (2) to investigate the effect modification of extremely high temperature on the association between air pollution and daily mortality due to all natural causes and daily cause-specific mortality; and (3) to assess the uncertainty of effect estimates caused by the change in International Classification of Disease (ICD) coding of mortality data from Revision 9 (ICD-9) to Revision 10 (ICD-10) code. Wuhan is called an "oven city" in China because of its extremely hot summers (the average daily temperature in July is 37.2 degrees C and maximum daily temperature often exceeds 40 degrees C). Approximately 4.5 million residents live in the core city area of 201 km2, where air pollution levels are higher and ranges are wider than the levels in most cities studied in the published literature. We obtained daily mean levels of PM10, SO2, and NO2 concentrations from five fixed-site air monitoring stations operated by the Wuhan Environmental Monitoring Center (WEMC). O3 data were obtained from two stations, and 8-hour averages, from 10:00 to 18:00, were used. Daily mortality data were obtained from the Wuhan Centres for Disease Prevention and Control (WCDC) during the study period of July 1, 2000, to June 30, 2004. To achieve the first aim, we used a regression of the logarithm of daily counts of mortality due to all natural causes and cause-specific mortality on the daily mean concentrations of the four pollutants while controlling for weather, temporal factors, and other important covariates with generalized additive models (GAMs). We derived pollutant effect estimations for 0-day, 1-day, 2-day, 3-day, and 4-day lagged exposure levels, and the averages of 0-day and 1-day lags (lag 0-1 day) and of 0-day, 1-day, 2-day, and 3-day lags (lag 0-3 days) before the event of death. In addition, we used individual-level data (e.g., age and sex) to classify subgroups in stratified analyses. Furthermore, we explored the nonlinear shapes ("thresholds") of the exposure-response relations. To achieve the second aim, we tested the hypothesis that extremely high temperature modifies the associations between air pollution and daily mortality. We developed three corresponding weather indicators: "extremely hot," "extremely cold," and "normal temperatures." The estimates were obtained from the models for the main effects and for the pollutant-temperature interaction for each pollutant and each cause of mortality. To achieve the third aim, we conducted an additional analysis. We examined the concordance rates and kappa statistics between the ICD-9-coded mortality data and the ICD-10-coded mortality data for the year 2002. We also compared the magnitudes of the estimated effects resulting from the use of the two types of ICD-coded mortality data. In general, the largest pollutant effects were observed at lag 0-1 day. Therefore, for this report, we focused on the results obtained from the lag 0-1 models. We observed consistent associations between PM10 and mortality: every 10-microg/m3 increase in PM10 daily concentration at lag 0-1 day produced a statistically significant association with an increase in mortality due to all natural causes (0.43%; 95% confidence interval [CI], 0.24 to 0.62), CVD (0.57%; 95% CI, 0.31 to 0.84), stroke (0.57%; 95% CI, 0.25 to 0.88), CARD (0.49%; 95% CI, 0.04 to 0.94), RD (0.87%; 95% CI, 0.34 to 1.41), CP (0.52%; 95% CI, 0.27 to 0.77), and non-CP (0.30%; 95% CI, 0.05 to 0.54). In general, these effects were stronger in females than in males and were also stronger among the elderly (> or = 65 years) than among the young. The results of sensitivity testing over the range of exposures from 24.8 to 477.8 microg/m3 also suggest the appropriateness of assuming a linear relation between daily mortality and PM10. Among the gaseous pollutants, we also observed statistically significant associations of mortality with NO, and SO2, and that the estimated effects of these two pollutants were stronger than the PM10 effects. The patterns of NO2 and SO2 associations were similar to those of PM10 in terms of sex, age, and linearity. O3 was not associated with mortality. In the analysis of the effect modification of extremely high temperature on the association between air pollution and daily mortality, only the interaction of PM10 with temperature was statistically significant. Specifically, the interaction terms were statistically significant for mortality due to all natural (P = 0.014), CVD (P = 0.007), and CP (P = 0.014) causes. Across the three temperature groups, the strongest PM10 effects occurred mainly on days with extremely high temperatures for mortality due to all natural (2.20%; 95% CI, 0.74 to 3.68), CVD (3.28%; 95% CI, 1.24 to 5.37), and CP (3.02%; 95% CI, 1.03 to 5.04) causes. The weakest effects occurred at normal temperature days, with the effects on days with low temperatures in the middle. To assess the uncertainty of the effect estimates caused by the change from ICD-9-coded mortality data to ICD-10-coded mortality data, we compared the two sets of data and found high concordance rates (> 99.3%) and kappa statistics close to 1.0 (> 0.98). All effect estimates showed very little change. All statistically significant levels of the estimated effects remained unchanged. In conclusion, the findings for the aims from the current study are consistent with those in most previous studies of air pollution and mortality. The small differences between mortality effects for deaths coded using ICD-9 and ICD-10 show that the change in coding had a minimal impact on our study. Few published papers have reported synergistic effects of extremely high temperatures and air pollution on mortality, and further studies are needed. Establishing causal links between heat, PM10, and mortality will require further toxicologic and cohort studies.
与美国和欧洲相比,关于亚洲每日死亡率与环境空气污染之间关联的研究较少。本研究在中国武汉开展,旨在调查空气污染对死亡率的急性影响,重点关注颗粒物(PM*)。本研究有三个主要目标:(1)研究因各种自然原因导致的每日死亡率以及特定病因每日死亡率(心血管病[CVD]、中风、心脏病[CARD]、呼吸系统疾病[RD]、心肺疾病[CP]和非心肺疾病[non-CP]病因)与空气动力学直径≤10微米的颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)或臭氧(O3)的日平均浓度(微克/立方米)之间的关联;(2)研究极端高温对空气污染与因各种自然原因导致的每日死亡率以及特定病因每日死亡率之间关联的效应修正;(3)评估死亡率数据从国际疾病分类第9版(ICD-9)编码变更为第10版(ICD-10)编码所导致的效应估计值的不确定性。武汉因其酷热的夏季(7月平均日气温为37.2摄氏度,日最高气温经常超过40摄氏度)在中国被称为“火炉城”。约450万居民生活在面积为201平方公里的核心城区,这里的空气污染水平高于已发表文献中研究的大多数城市,且范围更广。我们从武汉市环境监测中心(WEMC)运营的五个固定站点空气监测站获取了PM10、SO2和NO2的日平均浓度水平。臭氧(O3)数据来自两个站点,并采用了10:00至18:00的8小时平均值。在2000年7月1日至2004年6月30日的研究期间,每日死亡率数据来自武汉市疾病预防控制中心(WCDC)。为实现第一个目标,我们使用广义相加模型(GAMs),在控制天气、时间因素和其他重要协变量的同时,对因各种自然原因导致的每日死亡率以及特定病因每日死亡率的对数与四种污染物的日平均浓度进行回归分析。我们得出了0天、1天、2天、3天和4天滞后暴露水平的污染物效应估计值,以及死亡事件发生前0天和1天滞后(滞后0 - 1天)以及0天、1天、2天和3天滞后(滞后0 - 3天)的平均值。此外,我们在分层分析中使用个体层面的数据(如年龄和性别)对亚组进行分类。此外,我们还探索了暴露 - 反应关系的非线性形状(“阈值”)。为实现第二个目标,我们检验了极端高温会修正空气污染与每日死亡率之间关联的假设。我们开发了三个相应的天气指标:“极热”、“极冷”和“正常温度”。估计值来自于每种污染物和每种死亡原因的主效应模型以及污染物 - 温度相互作用模型。为实现第三个目标,我们进行了额外的分析。我们检查了2002年ICD - 9编码的死亡率数据和ICD - 10编码的死亡率数据之间的一致性率和kappa统计量。我们还比较了使用两种类型的ICD编码死亡率数据得出得估计效应的大小。一般来说,在滞后0 - 1天观察到最大的污染物效应。因此,在本报告中,我们重点关注滞后0 - 1模型得出的结果。我们观察到PM10与死亡率之间存在一致的关联:在滞后0 - 1天,PM10日浓度每增加10微克/立方米,与因各种自然原因导致的死亡率增加(0.43%;95%置信区间[CI],0.24至0.62)、CVD(0.57%;95% CI,0.31至0.84)、中风(0.57%;95% CI,0.25至0.88)、CARD(0.49%;95% CI,0.04至0.94)、RD(0.87%;95% CI,0.34至1.41)、CP(0.52%;95% CI,0.27至0.77)和非CP(0.30%;95% CI,0.05至0.5)之间存在统计学上的显著关联。一般来说,这些效应在女性中比在男性中更强,在老年人(≥65岁)中比在年轻人中也更强。在24.8至477.8微克/立方米的暴露范围内进行的敏感性测试结果也表明,假设每日死亡率与PM10之间存在线性关系是合适的。在气态污染物中,我们还观察到死亡率与NO和SO2之间存在统计学上的显著关联,并且这两种污染物的估计效应比PM10的效应更强。NO2和SO2关联的模式在性别、年龄和线性方面与PM10相似。O3与死亡率无关。在分析极端高温对空气污染与每日死亡率之间关联的效应修正时,只有PM10与温度的相互作用具有统计学意义。具体而言,对于因各种自然原因(P = 0.014)、CVD(P = 0.007)和CP(P = 0.014)导致的死亡率,相互作用项具有统计学意义。在三个温度组中,PM10的最强效应主要发生在因各种自然原因(2.20%;95% CI,0.74至3.68)、CVD(3.28%;95% CI,1.24至5.37)和CP(3.02%;95% CI,1.03至5.04)导致的死亡率的极热天气日。最弱的效应发生在正常温度日,低温日的效应居中。为评估从ICD - 9编码的死亡率数据变更为ICD - 10编码的死亡率数据所导致的效应估计值的不确定性,我们比较了两组数据,发现一致性率很高(> 99.3%),kappa统计量接近1.0(> 0.98)。所有效应估计值变化很小。所有估计效应的统计学显著水平均保持不变。总之,本研究目标的结果与以往大多数空气污染与死亡率研究的结果一致。使用ICD - 9和ICD - 10编码的死亡人数的死亡率效应之间的微小差异表明编码变更对我们的研究影响极小。很少有已发表的论文报道极端高温和空气污染对死亡率的协同效应,因此需要进一步研究。确定高温、PM10与死亡率之间的因果关系需要进一步的毒理学和队列研究。