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Fpemlocal: Estimating family planning indicators in R for a single population of interest.
Guranich, Gregory; Cahill, Niamh; Alkema, Leontine.
Afiliação
  • Guranich G; Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, USA.
  • Cahill N; Department of Mathematics and Statistics, Maynooth University, Kildare, Ireland.
  • Alkema L; Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, USA.
Gates Open Res ; 5: 24, 2021.
Article em En | MEDLINE | ID: mdl-33842844
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 fpemlocal 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. Fpemlocal is available from https//github.com/AlkemaLab/fpemlocal.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: Gates Open Res Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: Gates Open Res Ano de publicação: 2021 Tipo de documento: Article