Exposure measurement error in time-series studies of air pollution: concepts and consequences
Open accessAbstract
Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.
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The resulting measurement error may have implications for interpreting the effect of particles.
“The resulting measurement error may have implications for interpreting the effect of particles (Zeger et al. 2000), although a study suggested that this measurement error would generally tend to bias estimates downward (Samet et al. 2000).”
From Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China (2007) · cites this paper as Zeger et al. 2000
Failure to measure indoor sources will not introduce further bias in the estimated effects of ambient particles when indoor fine particle concentrations are independent of ambient levels.
“When this is true, failure to measure indoor sources will not introduce further bias in the estimated effects of ambient particles (Zeger et al. 2000).”
From Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China (2007) · cites this paper as Zeger et al. 2000
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- Authors
- Zeger, S. L., Thomas, D., Dominici, F., Samet, J. M., Schwartz, J., Dockery, D., & Cohen, A.
- Affiliations
- Johns Hopkins University (US)
- Venue
- Environmental Health Perspectives
- Publisher
- American Chemical Society (ACS)
- Published
- 2000-05-01
- Volume
- 108
- Issue
- 5
- Pages
- 419-426
- ISSN
- 0091-6765 (ISSN-L)
- 1552-9924
- OpenAlex
- W1976888543
- Cited by
- 1,156
- References
- 36
- Citations by year
- 2026: 37 · 2025: 47 · 2024: 33 · 2023: 54 · 2022: 67 · 2021: 70 · 2020: 83 · 2019: 73 · 2018: 63 · 2017: 52 · 2016: 45 · 2015: 62 · 2014: 62 · 2013: 52 · 2012: 51
- FWCI
- 25.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/licenses/by/4.0/ (vor, 6451 d)
- Locations
- Environmental Health Perspectives (publishedVersion, public-domain)
- Environmental Health Perspectives (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Environmental Protection Agency
- Johns Hopkins University
- National Institute of Environmental Health Sciences (5P30 ESO 3819-12)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- exposure measurement error, air pollution, time-series studies, Berkson error, log-linear regression, particulate matter, PM10, mortality, exposure misclassification, exposure assessment
- MeSH terms
- Air Pollution, Environmental Exposure, Environmental Health, Epidemiologic Methods, Humans, Mortality, Regression Analysis, Time Factors, Bias, Risk Assessment
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