Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Environ Health. 2011 Apr 23;10:34. doi: 10.1186/1476-069X-10-34.
The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases.
This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression.
Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model.
There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study.
哮喘与交通相关污染物之间的关系受到了广泛关注。使用个体水平的暴露测量方法，如居住地或接近排放源的位置，可以避免生态偏见。
本研究关注的是密歇根州底特律市的儿科医疗补助计划（Medicaid）人群，这是一个与哮喘相关事件风险较高的人群。采用基于人群的匹配病例对照分析方法，研究居住地与主要道路（包括高速公路）的接近程度与急性哮喘结果之间的关联。哮喘病例被定义为在研究期间（2004-06 年）至少有一次哮喘就诊的所有儿童，包括住院和急诊就诊。根据非呼吸道相关疾病，在医疗补助计划的其余人群中随机选择个体匹配对照。我们使用条件逻辑回归，将距离作为分类和连续变量，并使用多项式样条检查距离的非线性关系。然后，通过使用多分类条件逻辑回归考虑多种哮喘状态（基于急性哮喘结果的频率），扩展条件逻辑回归模型。
哮喘事件与接近主要道路有关，使用条件逻辑回归，距离每增加 1 公里，哮喘事件的比值比为 0.97（95%CI：0.94，0.99），这意味着随着居住地与主要道路之间的距离增加，哮喘事件的可能性越低。使用多分类条件逻辑回归也发现了类似的关系和效应大小。另一种可能的暴露测量方法，即代表道路污染物大气扩散的简化响应面模型，在该暴露模型下并不相关。
基于这项基于人群的匹配病例对照研究的结果，有相当有力的证据表明，靠近主要道路会增加哮喘的风险。
