Detecting the Effects of Early-Life Exposures: Why Fecundity Matters

• Published in: Population research and policy review Vol. 38; no. 6; pp. 783 - 809
• Main Authors: Nobles, Jenna, Hamoudi, Amar
• Format: Journal Article
• Language: English
• Published: Dordrecht SPRINGER 01-12-2019; Springer Netherlands; Springer Nature B.V
• Subjects: Bias; Computer simulation; Demography; Environmental aspects; Environmental factors; Exposure; Fecundity; Fetuses; Health; Health care expenditures; Health problems; Health status; Infants; Inference; Innovations; Life course; Maternal & child health; Maternal and infant welfare; Maternal characteristics; Monte Carlo simulation; Mothers; ORIGINAL RESEARCH; Perinatal mortality; Population Economics; Pregnancy; Prenatal care; Prenatal experience; Probability; Scientific knowledge; Social Sciences; Sociology; Statistical analysis; Statistical significance; Survival; Survivor; Lifecourse health; Survival bias; Fetal origins; Fecundity; Mortality selection; survival bias; mortality selection; lifecourse health; fetal origins; fecundity
• ISSN: 0167-5923; 1573-7829
• DOI: 10.1007/s11113-019-09562-x

Prenatal exposures have meaningful effects on health across the life course. Innovations in causal inference have shed new light on these effects. Here, we motivate the importance of innovation in the characterization of fecundity, and prenatal selection in particular. We argue that such innovation is crucial for expanding knowledge of the fetal origins of later life health. Pregnancy loss is common, responsive to environmental factors, and closely related to maternal and fetal health outcomes. As a result, selection into live birth is driven by many of the same exposures that shape the health trajectories of survivors. Life course effects that are inferred without accounting for these dynamics may be significantly distorted by survival bias. We use a set of Monte Carlo simulations with realistic parameters to examine the implications of prenatal survival bias. We find that even in conservatively specified scenarios, true fetal origin effects can be underestimated by 50% or more. In contrast, effects of exposures that reduce the probability of prenatal survival but improve the health of survivors will be overestimated. The absolute magnitude of survival bias can even exceed small-effect sizes, resulting in inferences that beneficial exposures are harmful or vice versa. We also find reason for concern that moderately sized true effects, underestimated due to failure to account for selective survival, are missing from scientific knowledge because they do not clear statistical significance filters. This bias has potential real-world costs; policy decisions about interventions to improve maternal and infant health will be affected by underestimated program impact.