论文标题
使用贝叶斯分层随机步行模型对意外怀孕和流产的全球估计
Global estimation of unintended pregnancy and abortion using a Bayesian hierarchical random walk model
论文作者
论文摘要
需要意外怀孕和堕胎估计，以告知和激励对全球健康计划和政策的投资。数据的可用性和可靠性的可变性对产生估计的挑战构成了挑战。我们开发了一种贝叶斯模型，该模型同时估计了195个国家和地区的意外怀孕和堕胎的发病率。我们的建模策略是由生育能力的近端决定因素告知的，（i）（i）由妇女人数（按婚姻和避孕药使用状态分组）及其各自的妊娠率所定义的意外怀孕的发生率，以及（ii）由小组特异性怀孕和成绩定义的堕胎率。分层随机步行模型用于估计国家群体特定的怀孕率和倾向的倾向。
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.
