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

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“the fact that something is difficult to measure is no reason not to try to think clearly about it”

-James Wood (1994) on fetal loss

Abstract

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.

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Notes

  1. This observation echoes a recent review (Bruckner and Catalano 2018), which notes that only 13 studies describe “selection in utero” in 20 years of studies published in top epidemiology journals. See Liew et al. (2015) and Raz et al. (2018) for exceptions.

  2. Analysis of IVF samples suggest that as many as 75% of failed implantations are embryos with cytogenetic abnormality, consistent with descriptions of the endometrium as a filter that works to prevent the advancement of pregnancies that are unlikely to become live births (Larsen et al. 2013).

  3. The simulations are implemented in Stata and R. Code available on the first author’s GitHub page.

  4. Note that Bozzoli et al. (2009) allow this fetal origin effect to diminish over age. Palloni and Beltrán-Sánchez (2017), by contrast, allow it to amplify over age. For parsimony, we allow it to be stable through the lifecourse.

  5. Estimated with the 2010 U.S. natality files from the National Vital Statistics System (NVSS n/d).

  6. The 2020 proposal, for example, includes a 15% cut to WIC funding from FY2019 appropriations. https://www.whitehouse.gov/wp-content/uploads/2019/03/budget-fy2020.pdf.

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Acknowledgements

The authors are grateful for support from the Center for Demography & Ecology (National Institute of Child Health and Human Development: P2C HD047873) and the WARF at UW-Madison. They thank Kenneth Wachter, Nathan Jones, Alberto Palloni, Florencia Torche, Dalton Conley, Marianne Bitler, Russell Dimond, Yiyue Huangfu, Moheb Zidan, Abby Fox, and Naomi Clear. Errors are the authors’.

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    Jenna Nobles & Amar Hamoudi

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Nobles, J., Hamoudi, A. Detecting the Effects of Early-Life Exposures: Why Fecundity Matters. Popul Res Policy Rev 38, 783–809 (2019). https://doi.org/10.1007/s11113-019-09562-x

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