  Unintended pregnancy and abortion estimates are needed to inform and motivate
investment in global health programmes and policies. Variability in the
availability and reliability of data poses challenges for producing estimates.
We developed a Bayesian model that simultaneously estimates incidence of
unintended pregnancy and abortion for 195 countries and territories. Our
modelling strategy was informed by the proximate determinants of fertility with
(i) incidence of unintended pregnancy defined by the number of women (grouped
by marital and contraceptive use status) and their respective pregnancy rates,
and (ii) abortion incidence defined by group-specific pregnancies and
propensities to have an abortion. Hierarchical random walk models are used to
estimate country-group-period-specific pregnancy rates and propensities to
abort.
