论文标题
估计使用贝叶斯分层惩罚样条模型提供的公共和私营部门提供的现代避孕药具的比例
Estimating the proportion of modern contraceptives supplied by the public and private sectors using a Bayesian hierarchical penalized spline model
论文作者
论文摘要
量化国家内公共/私营部门的避孕方法供应对于有效和可持续的计划生育(FP)至关重要。在许多低收入和中等收入国家(LMIC)中,测量避孕供应来源通常依赖人口健康调查(DHS)。但是,其中许多国家大约每3 - 5年进行一次DHS,并且在2015/16年度之外没有最近的数据。我们估计一组相关的避孕供应共享结果(公共/私营部门提供的现代避孕方法的比例)的目的是利用数据集中存在的潜在属性来产生年度,特定于国家/地区的估计和不确定性的预测。我们提出了一个具有多元正常样条系数的贝叶斯,分层惩罚的模型,以捕获交叉方法相关性。我们的方法提供了一种直观的方式来共享各个国家和次要人士的信息,对避孕供应份额的变化进行了建模,随着时间的推移,避孕供应份额的变化,考虑到调查观察性错误,并产生概率估计和预测,这些概率和预测因避孕供应份额的过去变化而告知,避孕供应份额的变化以及不同方法之间的速率相关性。这些结果将为评估FP程序有效性提供有价值的信息。据我们所知,这是估计这些数量的第一个模型。
Quantifying the public/private sector supply of contraceptive methods within countries is vital for effective and sustainable family planning (FP) delivery. In many low and middle-income countries (LMIC), measuring the contraceptive supply source often relies on Demographic Health Surveys (DHS). However, many of these countries carry out the DHS approximately every 3-5 years and do not have recent data beyond 2015/16. Our objective in estimating the set of related contraceptive supply-share outcomes (proportion of modern contraceptive methods supplied by the public/private sectors) is to take advantage of latent attributes present in dataset to produce annual, country-specific estimates and projections with uncertainty. We propose a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients to capture cross-method correlations. Our approach offers an intuitive way to share information across countries and sub-continents, model the changes in the contraceptive supply share over time, account for survey observational errors and produce probabilistic estimates and projections that are informed by past changes in the contraceptive supply share as well as correlations between rates of change across different methods. These results will provide valuable information for evaluating FP program effectiveness. To the best of our knowledge, it is the first model of its kind to estimate these quantities.