EXPOSURE MISCLASSIFICATION BIAS IN ENVIRONMENTAL EPIDEMIOLOGY: WITHIN-HOUSEHOLD RESPONSE CONCORDANCE ON HOUSEHOLD CHARACTERISTICS IN THE 2008-2010 SURVEY OF THE HEALTH OF WISCONSIN (SHOW)

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Kristen Malecki ◽  
Justin Lo ◽  
Matt Walsh ◽  
Paul Peppard ◽  
F. Javier Nieto
Keyword(s):  
Environmental Epidemiology ◽  
Exposure Misclassification ◽  
Household Characteristics ◽  
Misclassification Bias

How polluted is your fat? What the study of adipose tissue can contribute to environmental epidemiology

2020 ◽  
Vol 74 (5) ◽  
pp. 401-407 ◽  
Author(s):  
Vicente Mustieles ◽  
Juan P Arrebola
Keyword(s):  
Adipose Tissue ◽  
Biological Effects ◽  
Environmental Epidemiology ◽  
Environmental Chemicals ◽  
Human Populations ◽  
Potential Contribution ◽  
Exposure Misclassification ◽  
Methodological Issues ◽  
Target Organs

The study of the potential contribution of low-dose exposure to environmental chemicals on the development of chronic conditions in human populations is often hampered by methodological issues, including exposure misclassification and the inability to assess biological effects in target organs. White adipose tissue (WAT) presents the unique feature of being both an advantageous matrix for assessing long-term exposure to mixtures of persistent organic pollutants and an interesting tissue to investigate early preclinical effects. Moreover, other lipophilic non-persistent chemicals and heavy metals have been recently quantified in fat, suggesting that human WAT contains chemical mixtures more complex than initially thought. However, WAT has been scarcely used in environmental epidemiology due to collection difficulties. In this essay we discuss the potential of using human WAT as a source of both exposure and effect biomarkers, with the aim of advancing the epidemiological research of obesity-related diseases, including metabolic syndrome and cancer. Overall, we discuss the implications of investigating WAT in a multidisciplinary framework combining toxicological and epidemiological knowledge in order to improve the inference of causal relationships in observational settings. We finalise by suggesting feasible designs and scenarios in which WAT samples may be reasonably collected.

scholarly journals Exposure misclassification bias in the estimation of vaccine effectiveness

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251622
Author(s):  
Ulrike Baum ◽  
Sangita Kulathinal ◽  
Kari Auranen
Keyword(s):  
Sensitivity And Specificity ◽  
Vaccination Coverage ◽  
Risk Ratio ◽  
Vaccine Effectiveness ◽  
Relative Reduction ◽  
Common Source ◽  
Exposure Misclassification ◽  
Misclassification Bias ◽  
Risk Of Disease ◽  
Differential Exposure

In epidemiology, a typical measure of interest is the risk of disease conditional upon exposure. A common source of bias in the estimation of risks and risk ratios is misclassification. Exposure misclassification affects the measurement of exposure, i.e. the variable one conditions on. This article explains how to assess biases under non-differential exposure misclassification when estimating vaccine effectiveness, i.e. the vaccine-induced relative reduction in the risk of disease. The problem can be described in terms of three binary variables: the unobserved true exposure status, the observed but potentially misclassified exposure status, and the observed true disease status. The bias due to exposure misclassification is quantified by the difference between the naïve estimand defined as one minus the risk ratio comparing individuals observed as vaccinated with individuals observed as unvaccinated, and the vaccine effectiveness defined as one minus the risk ratio comparing truly vaccinated with truly unvaccinated. The magnitude of the bias depends on five factors: the risks of disease in the truly vaccinated and the truly unvaccinated, the sensitivity and specificity of exposure measurement, and vaccination coverage. Non-differential exposure misclassification bias is always negative. In practice, if the sensitivity and specificity are known or estimable from external sources, the true risks and the vaccination coverage can be estimated from the observed data and, thus, the estimation of vaccine effectiveness based on the observed risks can be corrected for exposure misclassification. When analysing risks under misclassification, careful consideration of conditional probabilities is crucial.

scholarly journals Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

2020 ◽  
Vol 17 (16) ◽  
pp. 5845
Author(s):  
Ellen J. Kinnee ◽  
Sheila Tripathy ◽  
Leah Schinasi ◽  
Jessie L. C. Shmool ◽  
Perry E. Sheffield ◽  
...  
Keyword(s):  
New York ◽  
Spatial Patterns ◽  
Spatial Clustering ◽  
Health Effect ◽  
Environmental Epidemiology ◽  
Emergency Department Visits ◽  
Spatial Uncertainty ◽  
Street Segment ◽  
Exposure Misclassification ◽  
Minimal Impact

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km2), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

The impact of periodontitis exposure misclassification bias from partial‐mouth measurements on association with diabetes and cardiovascular disease

2020 ◽  
Vol 47 (12) ◽  
pp. 1457-1465
Author(s):  
Talal S. Alshihayb ◽  
Elizabeth A. Kaye ◽  
Yihong Zhao ◽  
Cataldo W. Leone ◽  
Brenda Heaton
Keyword(s):  
Cardiovascular Disease ◽  
Exposure Misclassification ◽  
Misclassification Bias ◽  
The Impact
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