Self-Managed Abortion: Addressing Methodological Challenges in Measuring Safety, Incidence, and Effectiveness

Barriers to safe, effective, and affordable methods of abortion include restrictive laws, unwilling or untrained providers, long wait times, high costs, lack of services in the public sector, and abortion stigma. These barriers prevent individuals from exercising their fundamental human right to choose if and when to have a child, and threaten bodily and reproductive autonomy. Self-managed abortion with medication—defined as ending one’s own pregnancy outside of a formal healthcare setting using misoprostol alone or in combination with mifepristone—can be safe and effective; however, there is a lack of research on its incidence, safety, and effectiveness, and there are considerable methodological challenges in its assessment.This dissertation aims to address key methodological challenges in the measurement of self-managed abortion via the following approaches: development of a conceptual framework for measuring abortion complications; assessment of a novel application of respondent-driven sampling (RDS) to study the incidence of abortion; and estimation of the effectiveness of self-managed medication abortion in probabilistic bias-adjusted models. Chapter 1 describes the development of a framework for measuring complications and outcomes from medication abortion based on self-report, informed by thematic analysis of in-depth interviews from people with various abortion experiences in Argentina, Indonesia, Mexico, Nepal, Nigeria, and Sierra Leone. Findings from this analysis demonstrated that individuals describe and quantify their experiences with bleeding and cramping in varied ways, and highlights the need for a person-centered framework that emphasizes the individual’s preferences around medical care seeking. Chapter 2 is a methodological assessment of an RDS study on the incidence of abortion in Soweto, South Africa, and explores the implications of potential violations of RDS assumptions on incidence estimation. In this study, several key assumptions of RDS were not met, yielding potentially biased estimates of abortion incidence. Chapter 3 utilizes data from a prospective observational study assessing the effectiveness of self-managed medication abortion among callers to accompaniment groups and safe abortion hotlines in Argentina and Nigeria, and demonstrates a Monte Carlo Sensitivity Analysis approach to adjusting for misclassification and selection bias. After adjusting for potential misclassification, selection bias, and enrollment of ineligible participants, self-managed medication abortion remains highly effective, conditional on our assumptions around the chosen bias parameters.Findings from this dissertation will contribute to the development of a self-report questionnaire to measure complications and outcomes from medication abortion; highlights the pitfalls of respondent-driven sampling and offers potential remedies; and can be used to inform future analyses of the effectiveness of self-managed medication abortion based on observational data