Objective: To contribute to decision analysis by estimating utility, defined as an individual’s valuation of specific health states, for different pregnancy contexts.
Study design: Cross-sectional analysis of data from pregnant women recruited at pregnancy testing clinics during June 2014-June 2015. Utility was measured using the visual analog scale (VAS), PROMIS GSF-derived utility, standard gamble (SG), and time-trade-off (TTO) approaches. Six dimensions of pregnancy context were assessed including: intention, desirability, planning, timing, wantedness, and happiness. Multivariable regression modeling was used to examine the associations between pregnancy context and utility while controlling for women’s sociodemographic and health characteristics.
Results: Among 123 participants with diverse characteristics, aged 27±6 years, with mean gestation of 7.5±3 weeks, few reported optimal pregnancy contexts. Mean utility of the pregnancy state varied across contexts, whether measured with VAS (0.28-0.91), PROMIS GSF-derived utility (0.66-0.75), SG (0.985-1.00) or TTO (0.9990-0.99999). The VAS-derived mean utility score for unintended pregnancy was 0.68 (95% CI 0.59, 0.77). Multivariable regression analysis demonstrated significant disutility of unintended pregnancy, as well as all other unfavorable pregnancy contexts, when measured by VAS. In contrast, PROMIS GSF-derived utility only detected a significant reduction in utility among ambivalent compared to wanted pregnancy, while SG and TTO did not show meaningful differences in utility across pregnancy contexts.
Conclusions: Unintended pregnancy is associated with significant patient-reported disutility, as is pregnancy occurring in other unfavorable contexts. VAS-based measurements provide the most nuanced measures of the utility for pregnancy in varying contexts.
Implications: Decision analyses, including assessments of the cost-effectiveness of pregnancy related interventions, should incorporate measures of the utility of pregnancy in various contexts.
Author: Lundsberg, L. S., Xu, X., Schwarz, E. B., & Gariepy, A. M. (2017).