美国癌症协会研究微粒空气污染与死亡率之间的联系的扩展跟踪和空间分析。

Daniel Krewski, Michael Jerrett, Richard T Burnett, Renjun Ma, Edward Hughes, Yuanli Shi, Michelle C Turner, C Arden Pope, George Thurston, Eugenia E Calle, Michael J Thun, Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D K Moore, K Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, Barbara Tempalski
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In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). 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引用次数: 0

摘要

我们对美国癌症协会(ACS)癌症预防研究II (CPS-II)队列进行了扩展的随访和空间分析,以进一步研究美国大城市长期暴露于颗粒空气污染与死亡率之间的关系。目前的研究试图澄清我们早期由hei赞助的对原始ACS研究数据的再分析(粒子流行病学再分析项目)所产生的突出的科学问题。具体来说,我们研究了(1)社区和邻里水平的生态协变量如何混淆和修改空气污染与死亡率的关联;(2)如何在随机效应Cox模型中考虑空间自相关和多层次数据(如个体和邻域);(3)利用土地利用回归改进城市内(或城市内)尺度的空气污染暴露测量可能如何影响洛杉矶和纽约城市地区健康影响的大小和显著性;(4)暴露时间窗可能对空气污染与死亡率的关系最为关键。18年的随访(从最初研究的7年延长[Pope et al. 1995])包括截至2000年12月31日具有多种死因代码的CPS-II队列(约120万参与者)的重要状态数据以及来自大都市地区空气污染监测点的最新暴露数据。在全国分析中,生态协变量数据(如受教育程度、住房特征和收入水平)的影响;1980年美国人口普查数据;在邮政编码区(ZCA)尺度、大都市统计区(MSA)尺度以及每个ZCA值与MSA值(DIFF)之间的差异上,对空气污染与死亡率的关联进行了检验。与之前没有直接包括ZCA尺度的生态协变量的分析相反,当生态协变量包括在所有尺度时,风险估计值增加。生态协变量对缺血性心脏病(IHD)死亡率的影响最大,这也是与暴露于PM2.5(空气动力学直径2.5微米或更小的颗粒)、硫酸盐(SO4(2-))和二氧化硫(SO2)最密切相关的健康结局,也是唯一与暴露于二氧化氮(NO2)显著相关的结局。当生态协变量同时包括MSA和DIFF水平时,与PM2.5暴露(1999-2000年平均浓度)相关的IHD死亡率风险比(HR)增加了7.5%,与SO4(2-)暴露(1990年平均浓度)相关的风险比(HR)增加了12.8%。对pm2.5 -死亡率关联产生最大混淆影响的两个协变量是接受过12年级教育的人口比例和家庭收入中位数。此外,在全国范围内的分析中,通过扩展随机效应Cox模型(见报告末尾的统计术语表)探索了CPS-II数据中的复杂空间模式,该模型能够聚类到两个地理水平的数据。使用该模型往往会增加暴露于空气污染的人力资源估计值,也会增加估计值的不确定性。生态协变量的加入降低了MSA和ZCA尺度上结果的方差;基于同时包含MSA和DIFF数据水平的模型,残差减少幅度最大,这表明将生态协变量划分为MSA之间和MSA内值更完整地捕获了空气污染、生态协变量和死亡率之间关系的变异来源。对纽约市和洛杉矶地区进行了城市内部分析。在洛杉矶空间分析的结果中,我们发现了洛杉矶地区的高暴露对比,结果表明,空气污染导致的死亡风险几乎是之前分析报告的3倍。这表明,与PM2.5暴露的城市内梯度相关的慢性健康影响,可能比先前报道的MSA内zca之间的关联更大。然而,在纽约市的空间分析中,我们发现纽约地区zca之间的暴露差异很小,所有原因的死亡率、心肺疾病(CPD)和肺癌都没有升高。PM2.5暴露与IHD呈正相关,这提供了与具有高度生物学合理性的死亡原因存在特定关联的证据。当分析控制(1)44个个体水平协变量(来自1982年ACS入组问卷;参见第22页的44个人水平协变量侧栏)和(2)使用随机效应Cox模型的空间聚类。 当包括ZCA量表的失业率时,效果略低。为了研究是否存在一个关键的暴露时间窗是导致与环境空气污染相关的死亡率增加的主要原因,我们为一组有居住史的ACS CPS-II参与者构建了颗粒和气体空气污染物(PM2.5和SO2)的个体时间依赖暴露谱。我们考虑的三个暴露时间窗的相关性是用死亡率的相对危险度(HR)和赤池信息标准(AIC)来衡量的,后者衡量模型与数据的拟合优度。对于PM2.5,没有一个暴露时间窗表现出最大的HR;也没有任何明确的模式表明人力资源从最近的窗口向更遥远的窗口转移,反之亦然。三个曝光时间窗间的AIC值差异也较小。与二氧化硫暴露相关的死亡率hr在最近的时间窗(1至5年)最高,尽管这些hr均未显著升高。确定关键暴露时间窗仍然是一个挑战,需要进一步研究其他相关数据集。这项研究为制定具有成本效益的空气质量管理政策和战略提供了额外的支持。本文报告的流行病学结果与其他基于人群的研究结果一致,这些研究都有力地支持了长期暴露于PM2.5会增加普通人群死亡率的假设。未来的研究将使用扩展的Cox-Poisson随机效应方法、先进的地质统计建模技术和更新的暴露评估技术,将提供更多的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

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