We recently utilized a suite of environmental fate and transport models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations, and also assessed the association of those concentrations with preeclampsia for participants in the C8 Health Project (a cross-sectional study of over 69,000 people who were environmentally exposed to PFOA near a major U.S. fluoropolymer production facility located in West Virginia). However, the exposure estimates from this integrated model relied on default values for key independent exposure parameters including water ingestion rates, the serum PFOA half-life, and the volume of distribution for PFOA. The aim of the present study is to assess the impact of inter-individual variability and epistemic uncertainty in these parameters on the exposure estimates and subsequently, the epidemiological association between PFOA exposure and preeclampsia. We used Monte Carlo simulation to propagate inter-individual variability/epistemic uncertainty in the exposure assessment and reanalyzed the epidemiological association. Inter-individual variability in these parameters mildly impacted the serum PFOA concentration predictions (the lowest mean rank correlation between the estimated serum concentrations in our study and the original predicted serum concentrations was 0.95) and there was a negligible impact on the epidemiological association with preeclampsia (no change in the mean adjusted odds ratio (AOR) and the contribution of exposure uncertainty to the total uncertainty including sampling variability was 7%). However, when epistemic uncertainty was added along with the inter-individual variability, serum PFOA concentration predictions and their association with preeclampsia were moderately impacted (the mean AOR of preeclampsia occurrence was reduced from 1.12 to 1.09, and the contribution of exposure uncertainty to the total uncertainty was increased up to 33%). In conclusion, our study shows that the change of the rank exposure among the study participants due to variability and epistemic uncertainty in the independent exposure parameters was large enough to cause a 25% bias towards the null. This suggests that the true AOR of the association between PFOA and preeclampsia in this population might be higher than the originally reported AOR and has more uncertainty than indicated by the originally reported confidence interval.

Many epidemiology studies have investigated associations of perfluorooctanoate (PFOA) exposures with a variety of adverse health outcomes for participants in the C8 Health Project. The exposure concentrations (i.e., air and groundwater) used in these studies were determined primarily based on participant's residential locations. However, for residential addresses that could not be geocoded to the street level, the exposure concentrations were assigned based on population-weighted ZIP code centroid, which may result in exposure mischaracterization. The aim of this current study is to evaluate the potential impact of mischaracterized exposure concentrations due to geocoding uncertainty on the predicted serum PFOA concentrations and the epidemiological association between PFOA exposure and preeclampsia. For both workplace addresses and incompletely geocoded residential addresses, we used Monte Carlo (MC) simulation to assign alternate geographic locations within the reported ZIP code (instead of population-weighted ZIP code centroids) and the corresponding exposure concentrations. We found that mischaracterization of residential exposure due to population-weighted ZIP code centroid assignment had no significant impact on the serum PFOA concentration predictions and the epidemiological association of PFOA exposure with preeclampsia. In contrast, the uncertainty in workplace exposure moderately impacted the rank exposure among the participants. We observed a 41% increase in the average adjusted odds ratio of preeclampsia occurrence that may be due to differing proportions of cases (64.3%) and controls (54.5%) with workplace address geocodes during pregnancy. This finding suggests that differential exposure mischaracterization can be reduced by obtaining accurate exposure information such as street addresses and tap water consumption, for both workplaces and residences. The analysis we present is one approach for estimating the potential impacts of positional errors in a geocoding-based exposure assessment on exposure estimates and epidemiological study results.

Background

Uncertainty in exposure estimates from models can result in exposure measurement error and can potentially affect the validity of epidemiological studies. We recently used a suite of environmental models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations and assess the association with preeclampsia from 1990 through 2006 for the C8 Health Project participants.

Objectives

The aims of the current study are to evaluate impact of uncertainty in estimated PFOA drinking-water concentrations on estimated serum concentrations and their reported epidemiological association with preeclampsia.

Methods

For each individual public water district, we used Monte Carlo simulations to vary the year-by-year PFOA drinking-water concentration by randomly sampling from lognormal distributions for random error in the yearly public water district PFOA concentrations, systematic error specific to each water district, and global systematic error in the release assessment (using the estimated concentrations from the original fate and transport model as medians and a range of 2-, 5-, and 10-fold uncertainty).

Results

Uncertainty in PFOA water concentrations could cause major changes in estimated serum PFOA concentrations among participants. However, there is relatively little impact on the resulting epidemiological association in our simulations. The contribution of exposure uncertainty to the total uncertainty (including regression parameter variance) ranged from 5% to 31%, and bias was negligible.

Conclusions

We found that correlated exposure uncertainty can substantially change estimated PFOA serum concentrations, but results in only minor impacts on the epidemiological association between PFOA and preeclampsia.

Citation

Avanasi R, Shin HM, Vieira VM, Savitz DA, Bartell SM. 2016. Impact of exposure uncertainty on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Health Perspect 124:126-132; http://dx.doi.org/10.1289/ehp.1409044.

Environmental epidemiology has proven critical to study various associations between environmental exposures and adverse human health effects. However, there is a perception that it often does not sufficiently inform quantitative risk assessment. To help address this concern, in 2017, the Health and Environmental Sciences Institute initiated a project engaging the epidemiology, exposure science, and risk assessment communities with tripartite representation from government agencies, industry, and academia, in a dialogue on the use of environmental epidemiology for quantitative risk assessment and public health decision making. As part of this project, four meetings attended by experts in epidemiology, exposure science, toxicology, statistics, and risk assessment, as well as one additional meeting engaging funding agencies, were organized to explore incentives and barriers to realizing the full potential of epidemiological data in quantitative risk assessment. A set of questions was shared with workshop participants prior to the meetings, and two case studies were used to support the discussion. Five key ideas emerged from these meetings as areas of desired improvement to ensure that human data can more consistently become an integral part of quantitative risk assessment: 1) reducing confirmation and publication bias, 2) increasing communication with funding agencies to raise awareness of research needs, 3) developing alternative funding channels targeted to support quantitative risk assessment, 4) making data available for reuse and analysis, and 5) developing cross-disciplinary and cross-sectoral interactions, collaborations, and training. We explored and integrated these themes into a roadmap illustrating the need for a multi-stakeholder effort to ensure that epidemiological data can fully contribute to the quantitative evaluation of human health risks, and to build confidence in a reliable decision-making process that leverages the totality of scientific evidence.