The effect of non-differential exposure misclassification on estimates of the attributable and prevented fraction

1988 ◽  
Vol 7 (10) ◽  
pp. 1073-1085 ◽  
Author(s):  
Chung-Cheng Hsieh ◽  
Stephen D. Walter
Keyword(s):  
Exposure Misclassification ◽  
Differential Exposure

scholarly journals Occupational epidemiologist's quest to tame measurement error in exposure

2019 ◽  
Author(s):  
Igor Burstyn
Keyword(s):  
Measurement Error ◽  
False Positive ◽  
False Negative ◽  
Epidemiologic Study ◽  
Occupational Epidemiology ◽  
Exposure Misclassification ◽  
Exposure Limits ◽  
Workplace Exposure ◽  
Negative Results ◽  
Differential Exposure

I aimed to assess current practices and opportunities for addressing the problem of errors in exposure in occupational epidemiology. Occupational epidemiologists appreciate that errors in exposure are a concern, but almost none correct for these errors, although there are currently no theoretical and practical barriers for this inertia. The most serious barrier to change is a faulty belief that a well-conducted epidemiologic study suffers only non-differential exposure misclassification and that its sole impact is to attenuate risk gradients, causing a false negative. On the contrary, differential exposure misclassification is the most defensible model in occupational epidemiology, and errors in exposure increase chance of both false positive and negative results. Resistance to mathematical adjustment (correction) for errors in exposure is equivalent to denying the value of more valid exposure estimates and undermines the discipline’s relevance to protection of workers by informing workplace exposure limits.

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 How did Sweden Fail the Pandemic?

2021 ◽  
pp. 002073142199484
Author(s):  
Finn Diderichsen
Keyword(s):  
Elderly Care ◽  
The Elderly ◽  
Nordic Countries ◽  
The Other ◽  
Differential Survival ◽  
Adequate Care ◽  
Differential Exposure ◽  
The Government ◽  
Differential Immunity ◽  
Inequalities In Mortality

Sweden has since the start of the pandemic a COVID-19 mortality rate that is 4 to 10 times higher than in the other Nordic countries. Also, measured as age-standardized all-cause excess mortality in the first half of 2020 compared to previous years Sweden failed in comparison with the other Nordic countries, but only among the elderly. Sweden has large socioeconomic and ethnic inequalities in COVID-19 mortality. Geographical, ethnic, and socioeconomic inequalities in mortality can be due to differential exposure to the virus, differential immunity, and differential survival. Most of the country differences are due to differential exposure, but the socioeconomic disparities are mainly driven by differential survival due to an unequal burden of comorbidity. Sweden suffered from an unfortunate timing of tourists returning from virus hotspots in the Alps and Sweden's government response came later and was much more limited than elsewhere. The government had an explicit priority to protect the elderly in nursing and care homes but failed to do so. The staff in elderly care are less qualified and have harder working conditions in Sweden, and they lacked adequate care for the clients. Sweden has in recent years diverged from the Scandinavian welfare model by strong commercialization of primary care and elderly care.

scholarly journals Trimester effects of source-specific PM10 on birth weight outcomes in the Avon Longitudinal Study of Parents and Children (ALSPAC)

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yingxin Chen ◽  
Susan Hodgson ◽  
John Gulliver ◽  
Raquel Granell ◽  
A. John Henderson ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Birth Weight ◽  
Regression Models ◽  
Linear Regression Models ◽  
Exposure Misclassification ◽  
Third Trimester ◽  
Residential History ◽  
The Third ◽  
Parents And Children ◽  
Avon Longitudinal Study

Abstract Background Evidence suggests that exposure to particulate matter with aerodynamic diameter less than 10 μm (PM10) is associated with reduced birth weight, but information is limited on the sources of PM10 and exposure misclassification from assigning exposures to place of residence at birth. Methods Trimester and source-specific PM10 exposures (PM10 from road source, local non-road source, and total source) in pregnancy were estimated using dispersion models and a full maternal residential history for 12,020 births from the Avon longitudinal study of parents and children (ALSPAC) cohort in 1990–1992 in the Bristol area. Information on birth outcomes were obtained from birth records. Maternal sociodemographic and lifestyle factors were obtained from questionnaires. We used linear regression models for continuous outcomes (birth weight, head circumference (HC), and birth length (BL) and logistic regression models for binary outcomes (preterm birth (PTB), term low birth weight (TLBW) and small for gestational age (SGA)). Sensitivity analysis was performed using multiple imputation for missing covariate data. Results After adjustment, interquartile range increases in source specific PM10 from traffic were associated with 17 to 18% increased odds of TLBW in all pregnancy periods. We also found odds of TLBW increased by 40% (OR: 1.40, 95%CI: 1.12, 1.75) and odds of SGA increased by 18% (OR: 1.18, 95%CI: 1.05, 1.32) per IQR (6.54 μg/m3) increase of total PM10 exposure in the third trimester. Conclusion This study adds to evidence that maternal PM10 exposures affect birth weight, with particular concern in relation to exposures to PM10 from road transport sources; results for total PM10 suggest greatest effect in the third trimester. Effect size estimates relate to exposures in the 1990s and are higher than those for recent studies – this may relate to reduced exposure misclassification through use of full residential history information, changes in air pollution toxicity over time and/or residual confounding.

Retracted:MHC diversity and differential exposure to pathogens in kestrels (Aves:Falconidae)

2010 ◽  
Vol 19 (4) ◽  
pp. 691-705 ◽  
Author(s):  
MIGUEL ALCAIDE ◽  
JESÚS A. LEMUS ◽  
GUILLERMO BLANCO ◽  
JOSÉ L. TELLA ◽  
DAVID SERRANO ◽  
...  
Keyword(s):  
Differential Exposure

Is differential exposure to college linked to the development of critical thinking?

1996 ◽  
Vol 37 (2) ◽  
pp. 159-174 ◽  
Author(s):  
Ernest T. Pascarella ◽  
Louise Bohr ◽  
Amaury Nora ◽  
Patrick T. Terenzini
Keyword(s):  
Critical Thinking ◽  
Differential Exposure
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