A novel approach for inferring effects on pregnancy loss.

Identifying the determinants of pregnancy loss is a critical public health concern. However, pregnancy loss 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 non-generalizable results. We show mathematically and through simulations a novel approach that overcomes this measurement challenge to infer effects about pregnancy loss by utilizing more available data: the number of conceptions that led to live births-i.e., live-birth-identified conceptions (LBICs). We simulated ten years of conceptions, pregnancies, losses, and births under several confounding patterns, and two NO2-pregnancy loss relationships (no effect, mid-gestation effect). We fitted distributed lag models (DLMs) 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 two NO2-pregnancy loss relationships with appropriate coverage (>90% of confidence intervals 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, USA, we found that higher prenatal NO2 was associated with more pregnancy losses. Our proposed approach based on LBICs provides an alternative way to study causes of pregnancy loss.