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Background:

Extensive exposure to per- and polyfluoroalkyl substances (PFAS) have been observed in many countries. Current deterministic frameworks for risk assessment lack the ability to predict the likelihood of effects and to assess uncertainty. When exposure exceeds tolerable intake levels, these shortcomings hamper risk management and communication.

Objective:

The integrated probabilistic risk assessment (IPRA) combines dose-response and exposure data to estimate the likelihood of adverse effects. We evaluated the usefulness of the IPRA for risk characterization related to decreased levels of total triiodothyronine (${\mathrm{T}}_3$) in humans following a real case of high exposure to PFAS via drinking water.

Methods:

PFAS exposure was defined as serum levels from residents of a contaminated area in Ronneby, Sweden. Median levels were $270\text{\hspace{0.17em}ng}/\mathrm{mL}$ [perfluorooctane sulfonic acid (PFOS)] and $229\text{\hspace{0.17em}ng}/\mathrm{mL}$ [perfluorohexane sulfonic acid (PFHxS)] for individuals who resided in Ronneby 1 y before the exposure termination. This data was integrated with data from a subchronic toxicity study in monkeys exposed daily to PFOS. Benchmark dose modeling was employed to describe separate dose–effect relationship for males and females, and extrapolation factor distributions were used to estimate the corresponding human benchmark dose. The critical effect level was defined as a 10% decrease in total ${\mathrm{T}}_3$.

Results:

The median probability of critical exposure, following a combined exposure to PFOS and PFHxS, was estimated to be [2.1% (90% CI: $0.4\%–13.1\%$)]. Gender-based analysis showed that this risk was almost entirely distributed among women, namely [3.9% (90% CI: $0.8\%–21.6\%$)].

Discussion:

The IPRA was compared with the traditional deterministic Margin of Exposure (MoE) approach. We conclude that probabilistic risk characterization represents an important step forward in the ability to adequately analyze group-specific health risks. Moreover, quantifying the sources of uncertainty is desirable, as it improves the awareness among stakeholders and will guide future efforts to improve accuracy. https://doi.org/10.1289/EHP6654