Women’s Preferences for Maternal and Neonatal Morbidity and Mortality in Childbirth

Abstract

Purpose. To measure utility values that describe women's willingness to tradeoff maternal morbidity for fetal benefit among pregnant and nonpregnant women of reproductive age. Methods. We recruited English-speaking women aged 18 to 45 years in clinical and community-based settings. Eight health states were studied: 4 maternal (healthy, stroke, hysterectomy, death) and 4 neonatal (healthy, severe cerebral palsy [CP], severe mental retardation [MR], death). Utilities were assessed on a subset of 9 pairs of mom/baby delivery outcomes. Participants ranked the 9 pairs of outcomes in order of preference, then standard gamble methods were used to calculate utilities. Numeracy skills were assessed. Results. Utilities were obtained from 477 participants (recruitment rate = 94%). Twenty-one percent were pregnant, 63% were parents, and 54% were African American. Utilities did not differ significantly between pregnant and nonpregnant women or based on numeracy score. The highest (nonhealthy) values were assigned to baby healthy/mom hysterectomy (0.999), baby healthy/mom stroke (0.946), and baby CP/mom healthy (0.940). The lowest values were assigned to baby death/mom hysterectomy (0.203), baby MR/mom death (0.150), and baby death/mom stroke (0.087). Nonwhite participants assigned a significantly higher value to baby MR/mom death (P = 0.01), baby MR/mom stroke (P = 0.02), baby MR/mom healthy (P < 0.01), and baby MR/mom hysterectomy (P = 0.02) than white participants. Conclusion. When asked to value pairs of maternal/fetal outcomes that required a tradeoff of morbidity and mortality, women tended to assign the highest utility to combinations that resulted in a "healthy baby." They assigned the lowest values to combinations that resulted in a baby's death or MR. Our findings highlight the importance of 1) assessing individual preferences and goals of care for pregnancy outcomes and 2) measuring utilities among reproductive-aged women when modeling obstetric decision making.