Back-extrapolation of estimates of exposure from current land-use regression models |
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| Authors: | Hong Chen Mark S. Goldberg Dan L. Crouse Richard T. Burnett Michael Jerrett Paul J. Villeneuve Amanda J. Wheeler France Labrèche Nancy A. Ross |
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| Affiliation: | 1. Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland;2. University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland;3. Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands;4. School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK;5. Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany;6. Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark;7. Interface Demography – Department of Sociology, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Brussel, Belgium;8. Unit Health & Environment — Sciensano, Rue Juliette Wytsmanstraat 14, 1050, Brussels, Belgium;9. Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada;10. Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark;11. Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece;12. Department Population Health Sciences, Department of Analytical, Environmental and Forensic Sciences, School of Population Health & Environmental Sciences, King''s College Strand, London WC2R 2LS, UK;13. National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, Netherlands;14. Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, United States of America;15. Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway;p. Department of Epidemiology, Lazio Region Health Service/ASL, Roma 1, Via Cristoforo Colombo, 112 – 00147 Rome, Italy;q. Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden;r. Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany;s. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands |
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| Abstract: | ![]()
Land use regression has been used in epidemiologic studies to estimate long-term exposure to air pollution within cities. The models are often developed toward the end of the study using recent air pollution data. Given that there may be spatially-dependent temporal trends in urban air pollution and that there is interest for epidemiologists in assessing period-specific exposures, especially early-life exposure, methods are required to extrapolate these models back in time. We present herein three new methods to back-extrapolate land use regression models. During three two-week periods in 2005–2006, we monitored nitrogen dioxide (NO2) at about 130 locations in Montreal, Quebec, and then developed a land-use regression (LUR) model. Our three extrapolation methods entailed multiplying the predicted concentrations of NO2 by the ratio of past estimates of concentrations from fixed-site monitors, such that they reflected the change in the spatial structure of NO2 from measurements at fixed-site monitors. The specific methods depended on the availability of land use and traffic-related data, and we back-extrapolated the LUR model to 10 and 20 years into the past. We then applied these estimates to residential information from subjects enrolled in a case–control study of postmenopausal breast cancer that was conducted in 1996.Observed and predicted concentrations of NO2 in Montreal decreased and were correlated in time. The estimated concentrations using the three extrapolation methods had similar distributions, except that one method yielded slightly lower values. The spatial distributions varied slightly between methods. In the analysis of the breast cancer study, the odds ratios were insensitive to the method but varied with time: for a 5 ppb increase in NO2 using the 2006 LUR the odds ratio (OR) was about 1.4 and the ORs in predicted past concentrations of NO2 varied (OR～1.2 for 1985 and OR～1.3–1.5 for 1996). Thus, the ORs per unit exposure increased with time as the range and variance of the spatial distributions decreased, and this is due partly to the regression coefficient being approximately inversely proportional to the variance of exposure. Changing spatial variability complicates interpretation and this may have important implications for the management of risk. Further studies are needed to estimate the accuracy of the different methods. |
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