Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Environ Health. 2010 May 20;9:24. doi: 10.1186/1476-069X-9-24.
Diverse environmental exposures, studied separately, have been linked to health outcomes in adult asthma, but integrated multi-factorial effects have not been modeled. We sought to evaluate the contribution of combined social and physical environmental exposures to adult asthma lung function and disease severity.
Data on 176 subjects with asthma and/or rhinitis were collected via telephone interviews for sociodemographic factors and asthma severity (scored on a 0-28 point range). Dust, indoor air quality, antigen-specific IgE antibodies, and lung function (percent predicted FEV1) were assessed through home visits. Neighborhood socioeconomic status, proximity to traffic, land use, and ambient air quality data were linked to the individual-level data via residential geocoding. Multiple linear regression separately tested the explanatory power of five groups of environmental factors for the outcomes, percent predicted FEV1 and asthma severity. Final models retained all variables statistically associated (p < 0.20) with each of the two outcomes.
Mean FEV1 was 85.0 +/- 18.6%; mean asthma severity score was 6.9 +/- 5.6. Of 29 variables screened, 13 were retained in the final model of FEV1 (R2 = 0.30; p < 0.001) and 15 for severity (R2 = 0.16; p < 0.001), including factors from each of the five groups. Adding FEV1 as an independent variable to the severity model further increased its explanatory power (R2 = 0.25).
Multivariate models covering a range of individual and environmental factors explained nearly a third of FEV1 variability and, taking into account lung function, one quarter of variability in asthma severity. These data support an integrated approach to modeling adult asthma outcomes, including both the physical and the social environment.
单独研究的各种环境暴露因素与成人哮喘的健康结果有关，但尚未对综合多因素影响进行建模。我们旨在评估综合社会和物理环境暴露因素对成人哮喘肺功能和疾病严重程度的贡献。
通过电话访谈收集了 176 名哮喘和/或鼻炎患者的社会人口统计学因素和哮喘严重程度（0-28 分范围评分）的数据。通过家访评估了灰尘、室内空气质量、抗原特异性 IgE 抗体和肺功能（预计 FEV1 的百分比）。通过住宅地理编码将邻里社会经济地位、交通接近度、土地利用和环境空气质量数据与个体水平数据联系起来。多元线性回归分别测试了五组环境因素对预测 FEV1 和哮喘严重程度的解释力。最终模型保留了与两个结果中的每一个都具有统计学相关性（p<0.20）的所有变量。
平均 FEV1 为 85.0 +/- 18.6%；平均哮喘严重程度评分为 6.9 +/- 5.6。在筛选的 29 个变量中，有 13 个变量保留在 FEV1 的最终模型中（R2 = 0.30；p<0.001），15 个变量保留在严重程度模型中（R2 = 0.16；p<0.001），包括五个组中的因素。将 FEV1 作为独立变量添加到严重程度模型中，进一步增加了其解释能力（R2 = 0.25）。
涵盖一系列个体和环境因素的多变量模型解释了 FEV1 变异性的近三分之一，并且考虑到肺功能，解释了哮喘严重程度变异性的四分之一。这些数据支持采用综合方法对成人哮喘结果进行建模，包括物理和社会环境。
