Estimates of pregnancies, abortions and pregnancy intentions can help assess how effectively women and couples are able to fulfill their childbearing aspirations. Abortion incidence estimates are also a necessary foundation for research on the safety of abortions performed and the consequences of unsafe abortion. However, estimating the distribution of pregnancies by intention and outcome is challenging. Data requirements include information on the proportion of births that are intended and on the incidence of abortion. Countries may lack data on one or both of these variables, for some or all time periods in question. Additionally, the availability and reliability of data may vary non-randomly. For these reasons, a standard regression-based approach to estimation may produce questionable results. To address these issues, we propose a theoretically grounded Bayesian model which jointly estimates unplanned birth and abortion rates as functions of the numbers of women by marital status, contraceptive need and use.
Presented in Session 203. Demographic Estimation for Monitoring and Decision-making in Sparse-Data Settings