Brunekreef Bert, Beelen Rob, Hoek Gerard, Schouten Leo, Bausch-Goldbohm Sandra, Fischer Paul, Armstrong Ben, Hughes Edward, Jerrett Michael, van den Brandt Piet
Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
Res Rep Health Eff Inst. 2009 Mar(139):5-71; discussion 73-89.
Evidence is increasing that long-term exposure to ambient air pollution is associated with deaths from cardiopulmonary diseases. In a 2002 pilot study, we reported clear indications that traffic-related air pollution, especially at the local scale, was related to cardiopulmonary mortality in a randomly selected subcohort of 5000 older adults participating in the ongoing Netherlands Cohort Study (NLCS) on diet and cancer. In the current study, referred to as NLCS-AIR, our objective was to obtain more precise estimates of the effects of traffic-related air pollution by analyzing associations with cause-specific mortality, as well as lung cancer incidence, in the full cohort of approximately 120,000 subjects. Cohort members were 55 to 69 years of age at enrollment in 1986. Follow-up was from 1987 through 1996 for mortality (17,674 deaths) and from late 1986 through 1997 for lung cancer incidence (2234 cases). Information about potential confounding variables and effect modifiers was available from the questionnaire that subjects completed at enrollment and from publicly available data (including neighborhood-scale information such as income distributions). The NLCS was designed for a case-cohort approach, which makes use of all the cases in the full cohort, while data for the random subcohort are used to estimate person-time experience in the study. Full information on confounders was available for the subjects in the random subcohort and for the emerging cases of mortality and lung cancer incidence during the follow-up period, and in NLCS-AIR we used the case-cohort approach to examine the relation between exposure to air pollution and cause-specific mortality and lung cancer. We also specified a standard Cox proportional hazards model within the full cohort, for which information on potential confounding variables was much more limited. Exposure to air pollution was estimated for the subjects' home addresses at baseline in 1986. Concentrations were estimated for black smoke (a simple marker for soot) and nitrogen dioxide (NO2) as indicators of traffic-related air pollution, as well as nitric oxide (NO), sulfur dioxide (SO2), and particulate matter with aerodynamic diameter < or = 2.5 microm (PM2.5), as estimated from measurements of particulate matter with aerodynamic diameter < or = 10 microm (PM10). Overall long-term exposure concentrations were considered to be a function of air pollution contributions at regional, urban, and local scales. We used interpolation from data obtained routinely at regional stations of the National Air Quality Monitoring Network (NAQMN) to estimate the regional component of exposure at the home address. Average pollutant concentrations were estimated from NAQMN measurements for the period 1976 through 1996. Land-use regression methods were used to estimate the urban exposure component. For the local exposure component, geographic information systems (GISs) were used to generate indicators of traffic exposure that included traffic intensity on and distance to nearby roads. A major effort was made to collect traffic intensity data from individual municipalities. The exposure variables were refined considerably from those used in the pilot study, but we also analyzed the data for the full cohort in the current study using the exposure indicators of the pilot study. We analyzed the data in models with the estimated overall pollutant concentration as a single variable and with the background concentration (the sum of regional and urban components) and the local exposure estimate from traffic indicators as separate variables. In the full-cohort analyses adjusted for the limited set of confounders, estimated overall exposure concentrations of black smoke, NO2, NO, and PM2.5 were associated with mortality. For a 10-microg/m3 increase in the black smoke concentration, the relative risk (RR) (95% confidence interval [CI]) was 1.05 (1.00-1.11) for natural-cause (nonaccidental) mortality, 1.04 (0.95-1.13) for cardiovascular mortality, 1.22 (0.99-1.50) for respiratory mortality, 1.03 (0.88-1.20) for lung cancer mortality, and 1.04 (0.97-1.12) for noncardiopulmonary, non-lung cancer mortality. Results were similar for NO2, NO, and PM2.5. For a 10-microg/m3 increase in PM2.5 concentration, the RR for natural-cause mortality was 1.06 (95% CI, 0.97-1.16), the same as in the results of the American Cancer Society Study reported by Pope and colleagues in 2002. The highest relative risks were found for respiratory mortality, though confidence intervals were wider for this less-frequent cause of death. No associations with mortality were found for SO2. Some of the associations between the traffic indicator variables used to assess traffic intensity near the home and mortality reached statistical significance in the full cohort. For an increase in traffic intensity of 10,000 motor vehicles in 24 hours (motor vehicles/day) on the road nearest a subject's residence, the RR was 1.03 (95% CI, 1.00-1.08) for natural-cause mortality, 1.05 (0.99-1.12) for cardiovascular mortality, 1.10 (0.95-1.26) for respiratory mortality, 1.07 (0.96-1.19) for lung cancer mortality, and 1.00 (0.94-1.06) for noncardiopulmonary, non-lung cancer mortality. Results were similar for traffic intensity in a 100-m buffer around the subject's residence and living near a major road (a road with more than 10,000 motor vehicles/day). Distance in meters to the nearest major road and traffic intensity on the nearest major road were not associated with any of the mortality outcomes. We did not find an association between cardiopulmonary mortality and living near a major road as defined using the methods of the pilot study. In the case-cohort analyses adjusted for all potential confounders, we found no associations between background air pollution and mortality. The associations between traffic intensity and mortality were weaker than in the full cohort, and confidence intervals were wider, consistent with the smaller number of subjects. The lower relative risks of mortality associated with traffic variables in the case-cohort study population could be related to the particular subcohort that was randomly selected from the full cohort, as the risks estimated with the actual subcohort were well below the average estimates obtained for 100 new case-cohort analyses with 100 alternative subcohorts of 5000 subjects each that we randomly selected from the full cohort. Differences in adjusted relative risks between the full-cohort and the case-cohort analyses could be explained by random error introduced by sampling from the full cohort and by a selection effect resulting from the relatively large number of missing data for variables in the extensive confounder model used in the case-cohort analyses. More complete control for confounding probably did not contribute much to the lower relative risks in the case-cohort analyses, especially for the traffic variables, as results were similar when the limited confounder model for the full cohort was used in analyses of the subjects in the case-cohort study population. In additional analyses using black smoke concentrations as the exposure variables, we found that the association between overall black smoke and cardiopulmonary mortality was somewhat stronger for case-cohort subjects who did not change residence during follow-up, and in the full cohort, there was a tendency for relative risks to be higher for subjects living in the three major cities included in the study. Adjustment for estimated exposure to traffic noise did not affect the associations of background black smoke and traffic intensity with cardiovascular mortality. There was some indication of an association between traffic noise and cardiovascular mortality only for the 1.6% of the subjects in the full cohort who were exposed to traffic noise in the highest category of > 65 A-weighted decibels (dB(A); decibels with the sound pressure scale adjusted to conform with the frequency response of the human ear). Examination of sex, smoking status, educational level, and vegetable and fruit intake as possible effect modifiers showed that for overall black smoke concentrations, associations with mortality tended to be stronger in case-cohort subjects with lower levels of education and those with low fruit intake, but differences between strata were not statistically significant. For lung cancer incidence, we found essentially no relation to exposure to NO2, black smoke, PM2.5, SO2, or several traffic indicators. Associations of overall air pollution concentrations and traffic indicator variables with lung cancer incidence were, however, found in subjects who had never smoked, with an RR of 1.47 (95% CI, 1.01-2.16) for a 10-microg/m3 increase in overall black smoke concentration. In the current study, the mortality risks associated with both background air pollution and traffic exposure variables were much smaller than the estimate previously reported in the pilot study for risk of cardiopulmonary mortality associated with living near a major road (RR, 1.95; 95% CI, 1.09-3.51). The differences are most likely due to the extension of the follow-up period in the current study and to random error in the pilot study related to sampling from the full cohort. Though relative risks were generally small in the current study, long-term average concentrations of black smoke, NO2, and PM2.5 were related to mortality, and associations of black smoke and NO2 exposure with natural-cause and respiratory mortality were statistically significant. Traffic intensity near the home was also related to natural-cause mortality. The highest relative risks associated with background air pollution and traffic variables were for respiratory mortality, though the number of deaths was smaller than for the other mortality categories. (ABSTRACT TRUNCATED)
越来越多的证据表明,长期暴露于环境空气污染与心肺疾病死亡有关。在2002年的一项初步研究中,我们报告了明确的迹象,即与交通相关的空气污染,尤其是在局部范围内,与参与正在进行的荷兰饮食与癌症队列研究(NLCS)的5000名老年人的随机子队列中的心肺死亡率有关。在当前这项被称为NLCS-AIR的研究中,我们的目标是通过分析与特定病因死亡率以及肺癌发病率的关联,在约120,000名受试者的整个队列中更精确地估计与交通相关的空气污染的影响。队列成员在1986年入组时年龄为55至69岁。对死亡率(17,674例死亡)的随访时间为1987年至1996年,对肺癌发病率(2234例病例)的随访时间为1986年末至1997年。关于潜在混杂变量和效应修饰因素的信息可从受试者在入组时填写的问卷以及公开可用数据(包括邻里层面信息,如收入分布)中获取。NLCS设计采用病例队列方法,该方法利用整个队列中的所有病例,而随机子队列的数据用于估计研究中的人时经历。随机子队列中的受试者以及随访期间死亡率和肺癌发病率的新发病例可获取关于混杂因素的完整信息,在NLCS-AIR中,我们使用病例队列方法来研究空气污染暴露与特定病因死亡率和肺癌之间的关系。我们还在整个队列中指定了一个标准的Cox比例风险模型,对于该模型,关于潜在混杂变量的信息要有限得多。在1986年基线时,对受试者的家庭住址进行了空气污染暴露估计。对黑烟(烟尘的一个简单标志物)和二氧化氮(NO₂)的浓度进行了估计,作为与交通相关的空气污染指标,同时还对一氧化氮(NO)、二氧化硫(SO₂)以及空气动力学直径≤2.5微米的颗粒物(PM₂.₅)进行了估计,这些是根据空气动力学直径≤10微米的颗粒物(PM₁₀)的测量值估算得出的。总体长期暴露浓度被认为是区域、城市和局部尺度空气污染贡献的函数。我们利用从国家空气质量监测网络(NAQMN)区域站点定期获取的数据进行插值,以估计家庭住址处的区域暴露成分。根据1976年至1996年期间NAQMN的测量值估算平均污染物浓度。使用土地利用回归方法来估计城市暴露成分。对于局部暴露成分,利用地理信息系统(GIS)生成交通暴露指标,包括附近道路的交通强度和距离。我们做出了很大努力从各个城市收集交通强度数据。与初步研究中使用的暴露变量相比,当前研究中的暴露变量有了很大改进,但我们也使用初步研究的暴露指标对整个队列的数据进行了分析。我们在模型中分析数据,将估计的总体污染物浓度作为单个变量,以及将背景浓度(区域和城市成分之和)和来自交通指标的局部暴露估计值作为单独变量。在针对有限的混杂因素集进行调整的整个队列分析中,估计的黑烟、NO₂、NO和PM₂.₅的总体暴露浓度与死亡率相关。对于黑烟浓度每增加10微克/立方米,自然原因(非意外)死亡率的相对风险(RR)(95%置信区间[CI])为1.05(1.00 - 1.11),心血管死亡率为1.04(0.95 - 1.13),呼吸死亡率为1.22(0.99 - 1.50),肺癌死亡率为1.03(0.88 - 1.20),非心肺、非肺癌死亡率为1.04(0.97 - 1.12)。NO₂、NO和PM₂.₅的结果类似。对于PM₂.₅浓度每增加10微克/立方米,自然原因死亡率的RR为1.06(95% CI,0.97 - 1.16),与2002年Pope及其同事报告的美国癌症协会研究结果相同。呼吸死亡率的相对风险最高,不过对于这种较少见的死亡原因,置信区间更宽。未发现SO₂与死亡率有关。用于评估家庭附近交通强度的交通指标变量与死亡率之间的一些关联在整个队列中达到了统计学显著性。对于受试者居住地址最近道路上24小时内机动车流量增加10,000辆(机动车/天),自然原因死亡率的RR为1.03(95% CI,1.00 - 1.08),心血管死亡率为1.05(0.99 - 1.12),呼吸死亡率为1.10(0.95 - 1.26),肺癌死亡率为1.07(0.96 - 1.19),非心肺、非肺癌死亡率为1.00(0.94 - 1.06)。受试者居住地址周围100米缓冲区内的交通强度以及居住在主要道路(每天机动车流量超过10,000辆的道路)附近的情况结果类似。到最近主要道路的距离(米)以及最近主要道路上的交通强度与任何死亡率结局均无关联。我们未发现按照初步研究方法定义的居住在主要道路附近与心肺死亡率之间存在关联。在针对所有潜在混杂因素进行调整的病例队列分析中,我们未发现背景空气污染与死亡率之间存在关联。交通强度与死亡率之间的关联比在整个队列中要弱,置信区间更宽,这与受试者数量较少一致。病例队列研究人群中与交通变量相关的死亡率相对风险较低可能与从整个队列中随机选择的特定子队列有关,因为用实际子队列估计的风险远低于我们从整个队列中随机选择的100个包含5000名受试者的替代子队列进行的100次新病例队列分析所获得的平均估计值。整个队列分析与病例队列分析之间调整后相对风险的差异可以通过从整个队列抽样引入的随机误差以及病例队列分析中广泛混杂因素模型中变量存在相对大量缺失数据所导致的选择效应来解释。对混杂因素更全面的控制可能对病例队列分析中较低的相对风险贡献不大,尤其是对于交通变量,因为当在病例队列研究人群的分析中使用整个队列的有限混杂因素模型时,结果类似。在使用黑烟浓度作为暴露变量的额外分析中,我们发现对于随访期间未搬家的病例队列受试者,总体黑烟与心肺死亡率之间的关联更强一些,并且在整个队列中,居住在研究中纳入的三个主要城市的受试者的相对风险有升高的趋势。对估计的交通噪声暴露进行调整并未影响背景黑烟和交通强度与心血管死亡率之间的关联。仅在整个队列中1.6%暴露于交通噪声最高类别(> 65 A加权分贝(dB(A));经声压级调整以符合人耳频率响应的分贝)的受试者中,有一些迹象表明交通噪声与心血管死亡率之间存在关联。对性别、吸烟状况、教育水平以及蔬菜和水果摄入量作为可能的效应修饰因素进行检查发现,对于总体黑烟浓度,教育水平较低和水果摄入量低的病例队列受试者与死亡率之间的关联往往更强,但各层之间的差异无统计学显著性。对于肺癌发病率,我们发现其与NO₂、黑烟、PM₂.₅、SO₂或几个交通指标的暴露基本无关。然而,在从不吸烟的受试者中发现了总体空气污染浓度和交通指标变量与肺癌发病率之间的关联,对于总体黑烟浓度每增加10微克/立方米,RR为1.47(95% CI,1.01 - 2.16)。在当前研究中,与背景空气污染和交通暴露变量相关的死亡风险远小于初步研究中先前报告的居住在主要道路附近与心肺死亡风险的估计值(RR,1.95;95% CI,1.09 - 3.51)。差异很可能是由于当前研究随访期的延长以及初步研究中与从整个队列抽样相关的随机误差。尽管在当前研究中相对风险总体较小,但黑烟、NO₂和PM₂.₅的长期平均浓度与死亡率相关,并且黑烟和NO₂暴露与自然原因和呼吸死亡率之间的关联具有统计学显著性。家庭附近的交通强度也与自然原因死亡率相关。与背景空气污染和交通变量相关的最高相对风险是呼吸死亡率,不过死亡人数比其他死亡率类别要少。