Environmental hazards, social inequality, and fetal loss: Implications of live-birth bias for estimation of disparities in birth outcomes

Abstract

Restricting to live births can induce bias in studies of pregnancy and developmental outcomes, but whether this live-birth bias results in underestimating disparities is unknown. Bias may arise from collider stratification due to an unmeasured common cause of fetal loss and the outcome of interest, or depletion of susceptibles, where exposure differentially causes fetal loss among those with underlying susceptibility.

Methods: We conducted a simulation study to examine the magnitude of live-birth bias in a population parameterized to resemble one year of conceptions in California (N = 625,000). We simulated exposure to a non-time-varying environmental hazard, risk of spontaneous abortion, and time to live birth using 1000 Monte Carlo simulations. Our outcome of interest was preterm birth. We included a social vulnerability factor to represent social disadvantage, and estimated overall risk differences for exposure and preterm birth using linear probability models and stratified by the social vulnerability factor. We calculated how often confidence intervals included the true point estimate (CI coverage probabilities) to illustrate whether effect estimates differed qualitatively from the truth.

Results: Depletion of susceptibles resulted in a larger magnitude of bias compared with collider stratification, with larger bias among the socially vulnerable group. Coverage probabilities were not adversely affected by bias due to collider stratification. Depletion of susceptibles reduced coverage, especially among the socially vulnerable (coverage among socially vulnerable = 46%, coverage among nonsocially vulnerable = 91% in the most extreme scenario).

Conclusions: In simulations, hazardous environmental exposures induced live-birth bias and the bias was larger for socially vulnerable women.

Keywords: Environmental hazard; fetal loss; health disparities; live-birth bias; miscarriage; perinatal health; preterm birth; selection bias; spontaneous abortion.

Figure 1.

Directed acyclic graph depicting selection with no live-birth bias for a target population of live births: the exposure of interest A is a cause of preterm birth Y and fetal loss C. Conditioning on C does not affect the estimation of causal effect of A on Y because C is not a collider.

Figure 2.

Directed acyclic graph depicting live birth bias: the exposure of interest A is a cause of preterm birth Y and fetal loss C. Fetal loss and preterm birth also share an unmeasured common cause U. A social vulnerability factor W is a common cause of A and Y. By conditioning on C, as we do when using only live births in analyses, we create a backdoor path from the exposure to preterm birth.

Figure 3.

Effect of exposure on the risk of a pregnancy not resulting in term birth among all conceptions or all live births when there is no unmeasured common cause of fetal loss and preterm birth.

Figure 4.

Effect of exposure on the risk of preterm birth among people with and without social vulnerability in the counterfactual and truncated data when there is collider stratification bias. The counterfactual data captures the risk of preterm birth if there were no spontaneous abortion, while the truncated data limits to live births.

Figure 5.

Bias in effect estimates among people with and without social vulnerability due to collider stratification.

Figure 6.

Coverage probabilities among people with and without social vulnerability when there is collider stratification bias. The horizontal dotted line is at the 95% coverage level.

Figure 7.

Effect of exposure on the risk of preterm birth among people with and without social vulnerability in the counterfactual and truncated data when there is depletion of susceptibles. The counterfactual data captures the risk of preterm birth if there were no spontaneous abortion, while the truncated data limits to live births.

Figure 8.

Bias in effect estimates among people with and without social vulnerability due to depletion of susceptibles.

Figure 9.

Coverage probabilities among people with and without social vulnerability when there is bias due to depletion of susceptibles. The horizontal dotted line is at the 95% coverage level.