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Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data

INTRODUCTION: Gestational weight gain (GWG) has important implications for maternal and child health and is an ideal modifiable factor for preconceptional and antenatal care. However, the average levels of GWG across all low-income and middle-income countries of the world have not been characterised...

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Autores principales: Wang, Dongqing, Wang, Molin, Darling, Anne Marie, Perumal, Nandita, Liu, Enju, Danaei, Goodarz, Fawzi, Wafaie W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661366/
https://www.ncbi.nlm.nih.gov/pubmed/33177038
http://dx.doi.org/10.1136/bmjgh-2020-003423
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author Wang, Dongqing
Wang, Molin
Darling, Anne Marie
Perumal, Nandita
Liu, Enju
Danaei, Goodarz
Fawzi, Wafaie W
author_facet Wang, Dongqing
Wang, Molin
Darling, Anne Marie
Perumal, Nandita
Liu, Enju
Danaei, Goodarz
Fawzi, Wafaie W
author_sort Wang, Dongqing
collection PubMed
description INTRODUCTION: Gestational weight gain (GWG) has important implications for maternal and child health and is an ideal modifiable factor for preconceptional and antenatal care. However, the average levels of GWG across all low-income and middle-income countries of the world have not been characterised using nationally representative data. METHODS: GWG estimates across time were computed using data from the Demographic and Health Surveys Program. A hierarchical model was developed to estimate the mean total GWG in the year 2015 for all countries to facilitate cross-country comparison. Year and country-level covariates were used as predictors, and variable selection was guided by the model fit. The final model included year (restricted cubic splines), geographical super-region (as defined by the Global Burden of Disease Study), mean adult female body mass index, gross domestic product per capita and total fertility rate. Uncertainty ranges (URs) were generated using non-parametric bootstrapping and a multiple imputation approach. Estimates were also computed for each super-region and region. RESULTS: Latin America and Caribbean (11.80 kg (95% UR: 6.18, 17.41)) and Central Europe, Eastern Europe and Central Asia (11.19 kg (95% UR: 6.16, 16.21)) were the super-regions with the highest GWG estimates in 2015. Sub-Saharan Africa (6.64 kg (95% UR: 3.39, 9.88)) and North Africa and Middle East (6.80 kg (95% UR: 3.17, 10.43)) were the super-regions with the lowest estimates in 2015. With the exception of Latin America and Caribbean, all super-regions were below the minimum GWG recommendation for normal-weight women, with Sub-Saharan Africa and North Africa and Middle East estimated to meet less than 60% of the minimum recommendation. CONCLUSION: The levels of GWG are inadequate in most low-income and middle-income countries and regions. Longitudinal monitoring systems and population-based interventions are crucial to combat inadequate GWG in low-income and middle-income countries.
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spelling pubmed-76613662020-11-20 Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data Wang, Dongqing Wang, Molin Darling, Anne Marie Perumal, Nandita Liu, Enju Danaei, Goodarz Fawzi, Wafaie W BMJ Glob Health Original Research INTRODUCTION: Gestational weight gain (GWG) has important implications for maternal and child health and is an ideal modifiable factor for preconceptional and antenatal care. However, the average levels of GWG across all low-income and middle-income countries of the world have not been characterised using nationally representative data. METHODS: GWG estimates across time were computed using data from the Demographic and Health Surveys Program. A hierarchical model was developed to estimate the mean total GWG in the year 2015 for all countries to facilitate cross-country comparison. Year and country-level covariates were used as predictors, and variable selection was guided by the model fit. The final model included year (restricted cubic splines), geographical super-region (as defined by the Global Burden of Disease Study), mean adult female body mass index, gross domestic product per capita and total fertility rate. Uncertainty ranges (URs) were generated using non-parametric bootstrapping and a multiple imputation approach. Estimates were also computed for each super-region and region. RESULTS: Latin America and Caribbean (11.80 kg (95% UR: 6.18, 17.41)) and Central Europe, Eastern Europe and Central Asia (11.19 kg (95% UR: 6.16, 16.21)) were the super-regions with the highest GWG estimates in 2015. Sub-Saharan Africa (6.64 kg (95% UR: 3.39, 9.88)) and North Africa and Middle East (6.80 kg (95% UR: 3.17, 10.43)) were the super-regions with the lowest estimates in 2015. With the exception of Latin America and Caribbean, all super-regions were below the minimum GWG recommendation for normal-weight women, with Sub-Saharan Africa and North Africa and Middle East estimated to meet less than 60% of the minimum recommendation. CONCLUSION: The levels of GWG are inadequate in most low-income and middle-income countries and regions. Longitudinal monitoring systems and population-based interventions are crucial to combat inadequate GWG in low-income and middle-income countries. BMJ Publishing Group 2020-11-11 /pmc/articles/PMC7661366/ /pubmed/33177038 http://dx.doi.org/10.1136/bmjgh-2020-003423 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Wang, Dongqing
Wang, Molin
Darling, Anne Marie
Perumal, Nandita
Liu, Enju
Danaei, Goodarz
Fawzi, Wafaie W
Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title_full Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title_fullStr Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title_full_unstemmed Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title_short Gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
title_sort gestational weight gain in low-income and middle-income countries: a modelling analysis using nationally representative data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661366/
https://www.ncbi.nlm.nih.gov/pubmed/33177038
http://dx.doi.org/10.1136/bmjgh-2020-003423
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