Cargando…
A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models
BACKGROUND: Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and h...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123332/ https://www.ncbi.nlm.nih.gov/pubmed/27884187 http://dx.doi.org/10.1186/s12940-016-0186-0 |
_version_ | 1782469713022943232 |
---|---|
author | Dionisio, Kathie L. Chang, Howard H. Baxter, Lisa K. |
author_facet | Dionisio, Kathie L. Chang, Howard H. Baxter, Lisa K. |
author_sort | Dionisio, Kathie L. |
collection | PubMed |
description | BACKGROUND: Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. METHODS: ZIP-code level estimates of exposure for six pollutants (CO, NO(x), EC, PM(2.5), SO(4), O(3)) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. RESULTS: Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3–85% for population error, and 31–85% for total error. When CO, NO(x) or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. CONCLUSIONS: The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12940-016-0186-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5123332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51233322016-12-06 A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models Dionisio, Kathie L. Chang, Howard H. Baxter, Lisa K. Environ Health Research BACKGROUND: Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health. METHODS: ZIP-code level estimates of exposure for six pollutants (CO, NO(x), EC, PM(2.5), SO(4), O(3)) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs. RESULTS: Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3–85% for population error, and 31–85% for total error. When CO, NO(x) or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate. CONCLUSIONS: The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12940-016-0186-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-11-25 /pmc/articles/PMC5123332/ /pubmed/27884187 http://dx.doi.org/10.1186/s12940-016-0186-0 Text en © The Author(s). 2016 COPYRIGHT NOTICE. The article is a work of the United States Government; Title 17 U.S.C 105 provides that copyright protection is not available for any work of the United States government in the United States. Additionally, this is an open access article distributed under the terms of the Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0), which permits worldwide unrestricted use, distribution, and reproduction in any medium for any lawful purpose. |
spellingShingle | Research Dionisio, Kathie L. Chang, Howard H. Baxter, Lisa K. A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title | A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title_full | A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title_fullStr | A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title_full_unstemmed | A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title_short | A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
title_sort | simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123332/ https://www.ncbi.nlm.nih.gov/pubmed/27884187 http://dx.doi.org/10.1186/s12940-016-0186-0 |
work_keys_str_mv | AT dionisiokathiel asimulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels AT changhowardh asimulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels AT baxterlisak asimulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels AT dionisiokathiel simulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels AT changhowardh simulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels AT baxterlisak simulationstudytoquantifytheimpactsofexposuremeasurementerroronairpollutionhealthriskestimatesincopollutanttimeseriesmodels |