Dataset Information

Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

ABSTRACT: The process of creating a cohort or cohort substudy may induce misleading exposure-health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk.We aimed to demonstrate how such biases of the exposure-health effect association arise and how one may mitigate them.We used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).Analyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively.Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls.Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure-health effect estimates from selection in cohort studies: are environmental studies at particular risk? Environ Health Perspect 123:1113-1122; http://dx.doi.org/10.1289/ehp.1408888.

SUBMITTER: Weisskopf MG

PROVIDER: S-EPMC4629739 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Weisskopf Marc G MG Sparrow David D Hu Howard H Power Melinda C MC

Environmental health perspectives 20150508 11

<h4>Background</h4>The process of creating a cohort or cohort substudy may induce misleading exposure-health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk.<h4>Objectives</h4>We aimed to demonstrate how such biases of the exposure-health effect association arise and how one may mitigate them.<h4>Methods</h4>We used directed acyclic graphs and the exampl ...[more]

PMID: 25956004

Similar Datasets

Project description:Attrition is a potential source of bias in cohort studies. Although attrition may be inevitable in cohort studies of older people, there is little empirical evidence as to whether bias due to such attrition is also inevitable. Anonymised primary care data, routinely collected in clinical practice and independent of any cohort research study, represents an ideal unselected comparison dataset with which to compare primary care data from consenting responders to a cohort study. Our objective was to use this method as a novel means to assess if (i) responders at follow-up stages in a cohort study remain representative of responders at baseline and (ii) attrition biases estimates of longitudinal associations. We compared primary care consultation morbidities and prescription prevalences among circa 32,000 patients aged 50+ who contribute to an anonymised general practice database (Consultations in Primary Care Archive (CiPCA)) with those from patients aged 50+ in the North Staffordshire Osteoarthritis Project (NorStOP) cohort, United Kingdom (2002-2008; n=16,159). 8,197 (51%) persons responded to the NorStOP baseline survey and consented to medical record review. 5,121 and 3,311 responded at 3- and 6-year follow-ups. Differences in consulting prevalence of non-musculoskeletal morbidities between NorStOP responders and CiPCA comparison population did not increase over the two follow-up points except for ischaemic heart disease. Differences observed at baseline for osteoarthritis-related consultations were generally unchanged at the two follow-ups (standardised prevalence ratios for osteoarthritis (1.09-1.13) and joint pain (1.12-1.23)). Age and gender adjusted associations between baseline consultation for chronic morbidity and future new osteoarthritis and related consultations were similar in CiPCA (adjusted Hazard Ratio: 1.40; 95% Confidence Interval: 1.34,1.47) and NorStOP 6-year responders (1.32; 1.15,1.51). There was little evidence that responders at follow-ups represented any further selection bias to that present at baseline. Attrition in cohort studies of older people does not inevitably indicate bias.

Project description:People consistently act in ways that harm the environment, even when believing their actions are environmentally friendly. A case in point is a biased judgment termed the negative footprint illusion, which arises when people believe that the addition of "eco-friendly" items (e.g., environmentally certified houses) to conventional items (e.g., standard houses), reduces the total carbon footprint of the whole item-set, whereas the carbon footprint is, in fact, increased because eco-friendly items still contribute to the overall carbon footprint. Previous research suggests this illusion is the manifestation of an "averaging-bias." We present two studies that explore whether people's susceptibility to the negative footprint illusion is associated with individual differences in: (i) environment-specific reasoning dispositions measured in terms of compensatory green beliefs and environmental concerns; or (ii) general analytic reasoning dispositions measured in terms of actively open-minded thinking, avoidance of impulsivity and reflective reasoning (indexed using the Cognitive Reflection Test; CRT). A negative footprint illusion was demonstrated when participants rated the carbon footprint of conventional buildings combined with eco-friendly buildings (Study 1 and 2) and conventional cars combined with eco-friendly cars (Study 2). However, the illusion was not identified in participants' ratings of the carbon footprint of apples (Study 1 and 2). In Studies 1 and 2, environment-specific dispositions were found to be unrelated to the negative footprint illusion. Regarding reflective thinking dispositions, reduced susceptibility to the negative footprint illusion was only associated with actively open-minded thinking measured on a 7-item scale (Study 1) and 17-item scale (Study 2). Our findings provide partial support for the existence of a negative footprint illusion and reveal a role of individual variation in reflective reasoning dispositions in accounting for a limited element of differential susceptibility to this illusion.

Project description:Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

Project description:BackgroundTransnational immigration has increased since the 1950s. In countries such as the United States, immigrants now account for >15% of the population. Although differences in health between immigrants and nonimmigrants are well documented, it is unclear how environmental exposures contribute to these disparities.ObjectivesWe summarized current knowledge comparing immigrants' and nonimmigrants' exposure to and health effects of environmental exposures.MethodsWe conducted a title and abstract review on articles identified through PubMed and selected those that assessed environmental exposures or health effects separately for immigrants and nonimmigrants. After a full text review, we extracted the main findings from eligible studies and categorized each article as exposure-focused, health-focused, or both. We also noted each study's exposure of interest, study location, exposure and statistical methods, immigrant and comparison groups, and the intersecting socioeconomic characteristics controlled for.ResultsWe conducted a title and abstract review on 3,705 articles, a full text review on 84, and extracted findings from 50 studies. There were 43 studies that investigated exposure (e.g., metals, organic compounds, fine particulate matter, hazardous air pollutants) disparities, but only 12 studies that assessed health disparities (e.g., mortality, select morbidities). Multiple studies reported higher exposures in immigrants compared with nonimmigrants. Among immigrants, studies sometimes observed exposure disparities by country of origin and time since immigration. Of the 50 studies, 43 were conducted in North America.DiscussionThe environmental health of immigrants remains an understudied area, especially outside of North America. Although most identified studies explored potential exposure disparities, few investigated subsequent differences in health effects. Future research should investigate environmental health disparities of immigrants, especially outside North America. Additional research gaps include the role of immigrants' country of origin and time since immigration, as well as the combined effects of immigrant status with intersecting socioeconomic characteristics, such as race/ethnicity, income, and education attainment. https://doi.org/10.1289/EHP9855.

Dataset Information

Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Publications

Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets