Impact of measurement error on quantifying the importance of proximity to point sources of air pollution

Burstyn, Igor

doi:10.1038/jes.2008.67

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Another limitation is that the present work is based on the assumption that the distances from sources to monitoring stations were determined with negligible error. Previous work [25] showed that if the error was appreciable, then the shape of the association of interest can be distorted and power to detect associations diminished. Although this was shown under different parameterization of separation distance, a similar effect may be expected using our proposed model [26].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Dinu¹,

Chen²,

Burstyn³

2010

TOENVIRJ

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Abstract:The level and determinants of airborne concentrations were estimated by collecting air samples at 1206 fixed sites across a geographic area associated with primary oil and gas industry in the rural western Canada, in the provinces of Alberta, north-east British Columbia, and central and southern Sasketchewan from April 2001 to December 2002. Benzene concentrations integrated over one calendar month were determined using passive organic vapor monitors. Previous work applied linear mixed effects models to identify the determinants of airborne benzene concentrations, in particular the proximity to oil and gas facilities. We present results of a more flexible model using cubic splines to accommodate nonlinearities in the effects of determinants of airborne benzene concentrations, as well as time. Benzene concentrations exhibited monotonically increasing time trends for the months from July through December, and monotonically decreasing time trends corresponding to the months from December to July. We illustrated here how cubic splines can be used to identify complex relations between proximity to point sources of air pollution and observed extent of contamination, during the study period, and identified batteries as an important source of benzene emissions that was missed in previous analysis of the same data. These findings contribute to better understanding how positioning oil and gas facilities impacts air quality.

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Section: Discussionmentioning

confidence: 99%

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Dinu¹,

Chen²,

Burstyn³

2010

TOENVIRJ

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Aggregation of Exposure Level and Probability into a Single Metric in Job-Exposure Matrices Creates Bias

Burstyn

Lavoué

Tongeren

2012

The Annals of Occupational Hygiene

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Job-exposure matrices (JEMs) are often used in occupational epidemiological studies to provide an exposure estimate for a typical person in a 'job' during a particular time period. A JEM can produce exposure estimates on a variety of scales, such as (but not limited to) binary assessments of presence or absence of exposure, ordinal ranking of exposure level and frequency, and quantitative exposure estimates of exposure intensity and frequency. Specifically, one popular approach to construct a JEM, engendered in a Finnish job exposure matrix (FINJEM), provides a probability that a worker within an occupational group is exposed and an estimate of intensity of exposure among the exposed workers within this occupation. Often the product of the probability and intensity (aka level) is used to obtain the estimate of exposure for the epidemiological analyses. This procedure aggregates exposure across exposed and non-exposed individuals and the effect of this particular procedure on epidemiological analyses has never been studied. We developed a theoretical framework for understanding how these aggregate exposure estimates relate to true exposure (either unexposed or log-normally distributed for 'exposed'), assuming that there is no uncertainty about estimates of level and probability of exposure. Theoretical derivations show that multiplying occupation-specific exposure level and probability of non-zero exposure results in both systematic and differential measurement errors. Simulations demonstrated that under certain conditions bias in odds ratios in a cohort study away from the null are possible and that this bias is smaller when (a) arithmetic rather than geometric mean is used to assess exposure level and (b) exposure level and prevalence are positively correlated. We illustrate the potential impact of using the specified JEM in a simulation based on a case-control study of non-Hodgkin lymphoma and exposure to ionizing and non-ionizing radiation. Inflation of standard errors in the log-odds was observed as well as bias away from null for two out of three specific exposures/data structures. Overall, it is clear that influence of the phenomenon we studied on epidemiological results is complex and difficult to predict, being influenced a great deal by the structure of data. We recommend exploring the influence of JEMs that use the product of exposure level and probability in epidemiological analyses through simulations during planning of such studies to assess both the expected extent of the potential bias in risk estimates and impact on power. The SAS and R code required to implement such simulations are provided. All our calculations are either theoretical or based on simulated data.

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Impact of measurement error on quantifying the importance of proximity to point sources of air pollution

Cited by 2 publications

References 11 publications

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Aggregation of Exposure Level and Probability into a Single Metric in Job-Exposure Matrices Creates Bias

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