An integrated model of environmental factors in adult asthma lung function and disease severity: a cross-sectional study

Environmental Health

Research

Hubert Chen¹  Laura Trupin¹  Patricia J Quinlan¹  Paul D Blanc²  Mark D Eisner²  John R Balmes³  Patricia P Katz⁴  Edward H Yelin⁵  Peter S Thorne⁶  S Katharine Hammond⁷  Fred Lurmann⁸ 

[1] Department of Medicine, University of California San Francisco, San Francisco, CA, USA;Department of Medicine, University of California San Francisco, San Francisco, CA, USA;Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA;Department of Medicine, University of California San Francisco, San Francisco, CA, USA;Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, CA, USA;Department of Medicine, University of California San Francisco, San Francisco, CA, USA;Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA, USA;Department of Medicine, University of California San Francisco, San Francisco, CA, USA;Sonoma Technology Incorporated, Petaluma, CA, USA;Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, IA, USA;Division of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, CA, USA;Sonoma Technology Incorporated, Petaluma, CA, USA;

关键词: Asthma;    Rhinitis;    Asthma Severity;    Dust Sample;    Physical Environmental Factor;   

DOI  :  10.1186/1476-069X-9-24

 received in 2010-01-02, accepted in 2010-05-20,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundDiverse 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.MethodsData 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.ResultsMean 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).ConclusionsMultivariate 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.

【 授权许可】

CC BY   
© Trupin et al; licensee BioMed Central Ltd. 2010