A novel approach for inferring effects on pregnancy loss.

Identifying the determinants of pregnancy loss (PL) is a critical public health concern. However, PL is often not noticed, and even when it is, it is inconsistently recorded. Thus, past studies have been limited to medically identified losses or small, highly selected cohorts, which can lead to biased or nongeneralizable results. We show mathematically and through simulations a novel approach that overcomes this measurement challenge to infer effects about PL by using more available data: the number of conceptions that led to live births (ie, live-birth-identified conceptions [LBICs]). We simulated 10 years of conceptions, pregnancies, losses, and births under several confounding patterns, and 2 nitrogen dioxide (NO2)-PL relationships (no effect, mid-gestation effect). We fitted distributed lag models adjusted for season, year, and temperature, and assessed model performance through bias and coverage. Our simulations showed that our models, across all scenarios, identified the 2 NO2-PL relationships with appropriate coverage (>90% of CIs captured the true effect) and low bias (never exceeded ±2%). In an applied example using NO2-a traffic emissions tracer-and live-birth data from a large tertiary-care hospital in Massachusetts, we found that higher prenatal NO2 exposure was associated with more PLs. Our proposed approach based on LBICs provides an alternative way to study causes of PL.