Women who have undergone abortion in the city of Rio de Janeiro, Brazil: application of a Bayesian hierarchical model

Abstract: Estimates of number of women who have undergone induced abortion in jurisdictions with restrictive abortion laws are still scarce in the scientific literature, and the disparate estimates from currently used methods call for the application of innovative estimation techniques such as new indirect methods. This need is especially acute in more densely populated areas, such as Brazil’s state capitals, given the magnitude of unsafe abortions and the resulting risks and harms. The article aims to estimate the number of women who had induced abortions in the city of Rio de Janeiro in 2011, based on a Bayesian hierarchical model. The model was applied to data from a household survey that supported the use of the network scale-up method in the city of Rio de Janeiro, a Bayesian hierarchical model using indirect information based on the contact networks of randomly selected participants from the general population. Among the 1,758,145 women 15-49 years of age living in the city of Rio de Janeiro, 13,025 women (95%CrI: 10,635; 15,748) had induced abortions in 2011, resulting in a mean cumulative incidence of 7.41 (95%CrI: 6.05; 8.96) for every 1,000 women 15-49 years of age. The model’s self-validation process identified patterns of underestimation in stigmatized subpopulations with low social visibility, such as women who have undergone induced abortion. Induced abortion is a common practice among women in the city of Rio de Janeiro. New indirect estimation methods can contribute to more precise measurement of this event, considering the context of illegality, and thereby contribute to appropriate health policies.

摘要：在科学文献中，针对限制性堕胎法律辖区内接受人工流产（induced abortion）的女性人数的估算仍较为匮乏，且当前主流估算方法所得结果差异显著，亟需引入创新估算技术（如新型间接估算方法）。鉴于不安全流产的高发态势及其引发的健康风险与危害，这一需求在巴西各州首府等人口稠密地区尤为迫切。本研究旨在基于贝叶斯分层模型（Bayesian hierarchical model），估算2011年里约热内卢市接受人工流产的女性人数。研究将该模型应用于里约热内卢市一项支持网络规模放大法（network scale-up method）应用的家庭调查所获数据；该模型为基于普通人群随机选取受访者的社交网络间接信息构建的贝叶斯分层模型。在里约热内卢市居住的1758145名15~49岁女性中，2011年共有13025名接受了人工流产（95%可信区间CrI：10635~15748），对应15~49岁女性每千人的平均累积发病率为7.41（95%CrI：6.05~8.96）。模型的自验证过程发现，在社会可见度较低的受污名化亚群（如接受过人工流产的女性）中存在估算偏低的模式。人工流产在里约热内卢市女性群体中较为普遍。考虑到堕胎行为的非法性语境，新型间接估算方法可助力对该事件的精准测量，进而为制定合理的卫生政策提供支撑。