Background: Human exposure to parabens and other antimicrobial chemicals is continual and pervasive. The hormone-disrupting properties of these environmental chemicals may adversely affect human reproduction. Objective: We aimed to prospectively assess couples’ urinary concentrations of antimicrobial chemicals in the context of fecundity, measured as time to pregnancy (TTP). Methods: In a prospective cohort of 501 couples, we examined preconception urinary chemical concentrations of parabens, triclosan and triclorcarban in relation to TTP; chemical concentrations were modeled both continuously and in quartiles. Cox’s proportional odds models for discrete survival time were used to estimate fecundability odds ratios (FORs) and 95% confidence intervals (CIs) adjusting for a priori–defined confounders. In light of TTP being a couple-dependent outcome, both partner and couple-based exposure models were analyzed. In all models, FOR estimates < 1.0 denote diminished fecundity (longer TTP). Results: Overall, 347 (69%) couples became pregnant. The highest quartile of female urinary methyl paraben (MP) concentrations relative to the lowest reflected a 34% reduction in fecundity (aFOR = 0.66; 95% CI: 0.45, 0.97) and remained so when accounting for couples’ concentrations (aFOR = 0.63; 95% CI: 0.41, 0.96). Similar associations were observed between ethyl paraben (EP) and couple fecundity for both partner and couple-based models (p-trend = 0.02 and p-trend = 0.05, respectively). No associations were observed with couple fecundity when chemicals were modeled continuously. Conclusions: Higher quartiles of preconception urinary concentrations of MP and EP among female partners were associated with reduced couple fecundity in partner-specific and couple-based exposure models.
Background: In occupational studies, which are commonly used for risk assessment for environmental settings, estimated exposure–response relationships often attenuate at high exposures. Relative risk (RR) models with transformed (e.g., log- or square root–transformed) exposures can provide a good fit to such data, but resulting exposure–response curves that are supralinear in the low-dose region may overestimate low-dose risks. Conversely, a model of untransformed (linear) exposure may underestimate risks attributable to exposures in the low-dose region.
Methods: We examined several models, seeking simple parametric models that fit attenuating exposure–response data well. We have illustrated the use of both log-linear and linear RR models using cohort study data on breast cancer and exposure to ethylene oxide.
Results: Linear RR models fit the data better than do corresponding log-linear models. Among linear RR models, linear (untransformed), log-transformed, square root–transformed, linear-exponential, and two-piece linear exposure models all fit the data reasonably well. However, the slopes of the predicted exposure–response relations were very different in the low-exposure range, which resulted in different estimates of the exposure concentration associated with a 1% lifetime excess risk (0.0400, 0.00005, 0.0016, 0.0113, and 0.0100 ppm, respectively). The linear (in exposure) model underestimated the categorical exposure–response in the low-dose region, whereas log-transformed and square root–transformed exposure models overestimated it.
Conclusion: Although a number of models may fit attenuating data well, models that assume linear or nearly linear exposure–response relations in the low-dose region of interest may be preferred by risk assessors, because they do not depend on the choice of a point of departure for linear low-dose extrapolation and are relatively easy to interpret.