Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

Alexeeff, Stacey; Carroll, Raymond J.; Coull, Brent A.

doi:10.1093/biostatistics/kxv048

Cited by 41 publications

(29 citation statements)

References 30 publications

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“…In an analysis of a Massachusetts birth cohort from 2008, Alexeeff et al found that a 1 μg/m 3 difference in ambient PM 2.5 exposure during the third trimester was associated with 3.5 g lower birth weight. 7 This difference increased in magnitude to 4.9 g lower birth weight when they applied their spatial simulation extrapolation measurement error correction method. Unlike our approach, they corrected exposures estimated from averages of independent monthly spatial exposure models instead of correcting predictions from a spatiotemporal model.…”

Section: Discussionmentioning

confidence: 99%

“…If the exposure surface is assumed to be random with fixed monitor and cohort locations, such as in the classical formulation of kriging problems, 12 then a parametric bootstrap, or a similar approximate procedure, can be used to correctly remove bias and adjust standard errors. 2 A second approach to measurement error correction under this paradigm is the spatial simulation extrapolation method developed by Alexeeff et al , 7 which introduces additional measurement error into simulated datasets and then calculates a back-transformed value that corresponds to no measurement error.…”

Section: Introductionmentioning

confidence: 99%

“…13–16 In birth outcome studies, estimates of time-varying exposures are needed for specific pregnancy windows using reported gestational age and birth date. Other than the work of Alexeeff et al ., 7 who applied spatial simulation extrapolation to spatial exposure models to correct for measurement error in a Massachusetts birth cohort, published studies of birth weight and PM 2.5 have not accounted for exposure measurement error. Here, we extend bootstrap-based measurement error correction methods to spatiotemporal exposures at the trimester scale and compare the results obtained from applying different correction approaches.…”

Section: Introductionmentioning

confidence: 99%

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Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

et al. 2017

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Background Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, i.e., monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. Methods We analyzed the association between fine particulate matter (PM2.5) and birth weight in the U.S. state of Georgia using records with estimated date of conception during 2002–2005 (n=403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n=180,440). We accounted for additional measurement error via a non-parametric bootstrap. Results Third-trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (−2.4g per 1 μg/m3 difference in exposure; 95% Confidence Interval [CI]: −3.9, −0.8) and bootstrap-corrected (−2.5g, 95% CI: −4.2, −0.8) analyses. Results for the unrestricted analysis were attenuated (−0.66g, 95% CI: −1.7, 0.35). Conclusions This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

et al. 2017

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“…80 To date, studies have not used regression calibration methods to correct for non-differential EDC exposure misclassification, but studies of air pollutants have successfully used these methods to study a variety of health effects while accounting for exposure measurement error. 208 …”

Section: Challenges To Making Stronger Inferences About Edcs and Chilmentioning

confidence: 99%

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

2016

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Endocrine disrupting chemicals (EDCs) may increase the risk of childhood diseases by disrupting hormonally mediated processes critical for growth and development during gestation, infancy, or childhood. The fetus, infant, and child may have enhanced sensitivity to environmental stressors like EDCs due to rapid development and greater exposure to some EDCs that results from their developmentally appropriate behavior, anatomy, and physiology. This review summarizes epidemiological studies examining the relations of early-life exposure to bisphenol A (BPA), phthalates, triclosan, and perfluoroalkyl substance (PFAS) with childhood neurobehavioral disorders and obesity. The available epidemiological evidence suggests that prenatal exposure to several of these ubiquitous EDCs is associated with adverse neurobehavior (BPA and phthalates) and excess adiposity or increased risk of obesity/overweight (PFAS). Quantifying the effects of EDC mixtures, improving EDC exposure assessment, reducing bias from confounding, identifying periods of heightened vulnerability, and elucidating the presence and nature of sexually dimorphic EDC effects would result in stronger inferences from epidemiological studies. Ultimately, better estimates of the causal effects of EDC exposures on child health could help identify susceptible sub-populations and lead to public health interventions to reduce these exposures.

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“…More recently, Huque et al demonstrated that the expected amount of bias depends on the correlation between exposure and the random error from the regression model, and proposed parametric (64) and semiparametric (65) approaches to address this bias. Other recent work has addressed such spatial misalignment between measurements of exposures, covariates, and health outcomes using a variety of methods, including corrections in two-stage exposure models (66), SIMEX (67), and likelihood-based approaches (68). …”

Section: Accounting For Measurement Errormentioning

confidence: 99%

Measurement Error and Environmental Epidemiology: a Policy Perspective

Edwards

Keil

2017

Curr Envir Health Rpt

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Purpose of review Measurement error threatens public health by producing bias in estimates of the population impact of environmental exposures. Quantitative methods to account for measurement bias can improve public health decision making. Recent findings We summarize traditional and emerging methods to improve inference under a standard perspective, in which the investigator estimates an exposure response function, and a policy perspective, in which the investigator directly estimates population impact of a proposed intervention. Summary Under a policy perspective, the analysis must be sensitive to errors in measurement of factors that modify the effect of exposure on outcome, must consider whether policies operate on the true or measured exposures, and may increasingly need to account for potentially dependent measurement error of two or more exposures affected by the same policy or intervention. Incorporating approaches to account for measurement error into such a policy perspective will increase the impact of environmental epidemiology.

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Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures

Cited by 41 publications

References 30 publications

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

Early-life exposure to EDCs: role in childhood obesity and neurodevelopment

Measurement Error and Environmental Epidemiology: a Policy Perspective

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