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在R语言中为单一目标人群估计计划生育指标。

: Estimating family planning indicators in R for a single population of interest.

作者信息

Guranich Gregory, Cahill Niamh, Alkema Leontine

机构信息

Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, USA.

Department of Mathematics and Statistics, Maynooth University, Kildare, Ireland.

出版信息

Gates Open Res. 2021 Feb 24;5:24. doi: 10.12688/gatesopenres.13211.1. eCollection 2021.

Abstract

The global Family Planning Estimation model (FPEM) combines a Bayesian hierarchical model with country-specific time trends to yield estimates of contraceptive prevalence and unmet need for family planning for countries worldwide. In this paper, we introduce the R package that carries out the estimation of family planning indicators for a single population, for example, for a single country or smaller area. In this implementation of FPEM, all non-population-specific parameters are fixed at outcomes obtained in a prior global FPEM run. The development of this model was motivated by the demand for computational efficiency, without loss of model accuracy, when estimates and projections from FPEM were needed only for a single country. We present use cases to produce estimates for a single population of women by union status or all women based on package-provided data bases and user-specified data. We also explain how to aggregate estimates across multiple populations. The R package forms the basis of the Track20 Family Planning Estimation Tool to monitor trends in family planning indicators for the FP2020 initiative. is available from: https://github.com/AlkemaLab/fpemlocal.

摘要

全球计划生育估计模型(FPEM)将贝叶斯层次模型与特定国家的时间趋势相结合,以得出全球各国的避孕普及率和计划生育未满足需求的估计值。在本文中,我们介绍了一个R包,该包可对单一人群(例如,单个国家或较小区域)的计划生育指标进行估计。在FPEM的这种实现方式中,所有非特定人群的参数都固定为先前全球FPEM运行所获得的结果。开发此模型的动机是,当仅需要为单个国家进行FPEM估计和预测时,在不损失模型准确性的前提下,提高计算效率。我们展示了使用案例,根据包提供的数据库和用户指定的数据,按婚姻状况为单一女性人群或所有女性生成估计值。我们还解释了如何汇总多个群体的估计值。该R包构成了Track20计划生育估计工具的基础,用于监测FP2020倡议的计划生育指标趋势。可从以下网址获取:https://github.com/AlkemaLab/fpemlocal。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dce2/8008158/8175e8ccfb56/gatesopenres-5-14429-g0000.jpg

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