The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates
Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver,...
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| Published in | Journal of exposure science & environmental epidemiology Vol. 21; no. 1; pp. 42 - 48 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.01.2011
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression
versus
monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. |
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| AbstractList | Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by similar to 16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2h spent at least 10km away, 0.66 for greater than or equal to 10h spent at least 40km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~ 16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Journal of Exposure Science and Environmental Epidemiology (2011) 21, 42-48; doi: 10.1038/jes.2010.14; published online 30 June 2010 Keywords: measurement error, bias, air pollution, space-time modeling. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~ 16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2 h spent at least 10 km away, 0.66 for ≥ 10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79-0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ~16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63-0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0-2h spent at least 10km away, 0.66 for ≥ 10h spent at least 40km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates. |
| Audience | Academic |
| Author | Setton, Eleanor Brauer, Michael Lundquist, Kathryn R Cloutier-Fisher, Denise Marshall, Julian D Hystad, Perry Keller, Peter |
| Author_xml | – sequence: 1 givenname: Eleanor surname: Setton fullname: Setton, Eleanor email: elsetton@uvic.ca organization: Geography Department, University of Victoria – sequence: 2 givenname: Julian D surname: Marshall fullname: Marshall, Julian D organization: Department of Civil Engineering, University of Minnesota – sequence: 3 givenname: Michael surname: Brauer fullname: Brauer, Michael organization: School of Environmental Health, University of British Columbia – sequence: 4 givenname: Kathryn R surname: Lundquist fullname: Lundquist, Kathryn R organization: Department of Civil Engineering, University of Minnesota – sequence: 5 givenname: Perry surname: Hystad fullname: Hystad, Perry organization: School of Population and Public Health, University of British Columbia – sequence: 6 givenname: Peter surname: Keller fullname: Keller, Peter organization: Geography Department, University of Victoria – sequence: 7 givenname: Denise surname: Cloutier-Fisher fullname: Cloutier-Fisher, Denise organization: Geography Department, University of Victoria |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/20588325$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Nature America, Inc. 2011 COPYRIGHT 2011 Nature Publishing Group Nature America, Inc. 2011. Copyright Nature Publishing Group Jan 2011 |
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| References_xml | – volume: 16 start-page: 471 issue: 5 year: 2006 ident: CR19 article-title: Estimating effects of ambient PM2.5 exposure on health using PM2.5 component measurements and regression calibration (vol 16, pg 30, 2006) publication-title: J Expo Sci Environ Epidemiol doi: 10.1038/sj.jes.7500524 – volume: 6 start-page: 157 issue: 2 year: 1995 end-page: 161 ident: CR21 article-title: When measurement errors correlate with truth — surprising effects of nondifferential misclassification publication-title: Epidemiology doi: 10.1097/00001648-199503000-00012 – volume: 132 start-page: 1176 issue: 6 year: 1990 end-page: 1184 ident: CR1 article-title: The effects of measurement errors on relative risk regressions publication-title: Am J Epidemiol doi: 10.1093/oxfordjournals.aje.a115761 – volume: 42 start-page: 7561 issue: 33 year: 2008 end-page: 7578 ident: CR6 article-title: A review of land-use regression models to assess spatial variation of outdoor air pollution 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start-page: 727 issue: 6 year: 2005 end-page: 736 ident: CR7 article-title: Spatial analysis of air pollution and mortality in Los Angeles publication-title: Epidemiology doi: 10.1097/01.ede.0000181630.15826.7d – volume: 39 start-page: 6444 issue: 34 year: 2005 end-page: 6462 ident: CR22 article-title: A review of intraurban variations in particulate air pollution: implications for epidemiological research publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2005.07.030 – volume: 19 start-page: 414 issue: 3 year: 2004 end-page: 434 ident: CR3 article-title: Statistical issues in studies of the long-term effects of air pollution: the Southern California Children's Health Study publication-title: Stat Sci doi: 10.1214/088342304000000413 – volume: 41 start-page: 2422 issue: 7 year: 2007 end-page: 2428 ident: CR5 article-title: Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter publication-title: 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BFjes201014_CR16 – volume: 19 start-page: 414 issue: 3 year: 2004 ident: BFjes201014_CR3 publication-title: Stat Sci doi: 10.1214/088342304000000413 – volume: 55 start-page: 651 issue: 10 year: 1998 ident: BFjes201014_CR2 publication-title: Occup Environ Med doi: 10.1136/oem.55.10.651 – volume: 40 start-page: 4381 issue: 23 year: 2006 ident: BFjes201014_CR10 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2006.03.034 – volume: 7 start-page: 39 issue: 1 year: 2008 ident: BFjes201014_CR17 publication-title: International Journal of Health Geographics doi: 10.1186/1476-072X-7-39 – volume: 6 start-page: 157 issue: 2 year: 1995 ident: BFjes201014_CR21 publication-title: Epidemiology doi: 10.1097/00001648-199503000-00012 – volume: 19 start-page: 409 issue: 3 year: 2008 ident: BFjes201014_CR20 publication-title: Epidemiology doi: 10.1097/EDE.0b013e3181673bab – volume: 19 start-page: 401 issue: 3 year: 2008 ident: BFjes201014_CR14 publication-title: Epidemiology doi: 10.1097/EDE.0b013e31816a1ce3 – volume: 356 start-page: 447 issue: 5 year: 2007 ident: BFjes201014_CR11 publication-title: N Engl J Med doi: 10.1056/NEJMoa054409 – volume: 35 start-page: 3405 issue: 20 year: 2001 ident: BFjes201014_CR8 publication-title: Atmos Environ doi: 10.1016/S1352-2310(01)00131-5 – volume: 42 start-page: 7561 issue: 33 year: 2008 ident: BFjes201014_CR6 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2008.05.057 – volume: 39 start-page: 6444 issue: 34 year: 2005 ident: BFjes201014_CR22 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2005.07.030 – volume: 41 start-page: 2422 issue: 7 year: 2007 ident: BFjes201014_CR5 publication-title: Environ Sci Technol doi: 10.1021/es0606780 – volume: 108 start-page: 419 issue: 5 year: 2000 ident: BFjes201014_CR23 publication-title: Environ Health Perspect doi: 10.1289/ehp.00108419 – volume: 16 start-page: 727 issue: 6 year: 2005 ident: BFjes201014_CR7 publication-title: Epidemiology doi: 10.1097/01.ede.0000181630.15826.7d – volume: 65 start-page: 579 issue: 9 year: 2008 ident: BFjes201014_CR13 publication-title: Occup Environ Med doi: 10.1136/oem.2007.035337 – volume: 5 start-page: 111 issue: 2 year: 1995 ident: BFjes201014_CR12 publication-title: J Expo Anal Environ Epidemiol – volume: 132 start-page: 1176 issue: 6 year: 1990 ident: BFjes201014_CR1 publication-title: Am J Epidemiol doi: 10.1093/oxfordjournals.aje.a115761 – volume: 113 start-page: 1447 issue: 10 year: 2005 ident: BFjes201014_CR4 publication-title: Environ Health Perspect doi: 10.1289/ehp.7673 – volume: 20 start-page: 775 issue: 8 year: 1986 ident: BFjes201014_CR15 publication-title: Environ Sci Technol doi: 10.1021/es00150a003 – volume: 42 start-page: 5499 issue: 21 year: 2008 ident: BFjes201014_CR9 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2008.02.005 – volume: 15 start-page: 366 issue: 4 year: 2005 ident: BFjes201014_CR18 publication-title: J Expo Anal Environ Epidemiol doi: 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| SubjectTerms | 692/700/478/174 704/172 Air Pollutants - analysis Air pollution Air Pollution - analysis Air Pollution - statistics & numerical data Bias British Columbia California City traffic Environmental aspects Environmental Exposure - analysis Environmental Exposure - statistics & numerical data Environmental health Environmental Monitoring - methods Epidemiology Estimates Exposure Health aspects Humans Interpolation Land use Medicine Medicine & Public Health Mobility Nitrogen dioxide Nitrogen Dioxide - analysis Residence Characteristics Residential location Spatial variations Time Factors Urban Health Vehicle Emissions - analysis |
| Title | The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates |
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