Fertility is a key predictor of the double burden of malnutrition among women of child-bearing age in sub-Saharan Africa.
Journal
Journal of global health
ISSN: 2047-2986
Titre abrégé: J Glob Health
Pays: Scotland
ID NLM: 101578780
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
entrez:
28
10
2020
pubmed:
29
10
2020
medline:
3
8
2021
Statut:
ppublish
Résumé
The ongoing nutrition transition in sub-Saharan Africa (SSA) is exhibiting spatial heterogeneity and temporal variability leading to different forms of malnutrition burden across SSA, with some regions exhibiting the double burden of malnutrition. This study aimed to develop a predictive understanding of the malnutrition burden among women of child-bearing age. Data from 34 SSA countries were acquired from the Demographic and Health Survey, World Bank, and Swiss Federal Institute of Technology. The SSA countries were classified into malnutrition classes based on their national prevalence of underweight, overweight, and obesity using a 10% threshold. Next, random forest analysis was used to examine the association between country-level demographic variables and the national prevalence of underweight, overweight and obesity. Finally, random forest analysis and multinomial logistic regression models were utilized to investigate the association between individual-level social and demographic variables and Body Mass Index (BMI) categories of underweight, normal weight, and combined overweight and obesity. Four malnutrition classes were identified: Class A had 5 countries with ≥10% of the women underweight; Class B had 11 countries with ≥10% each of underweight and overweight; Class C1 had 7 countries with ≥10% overweight; and Class C2 had 11 countries with ≥10% obesity. At the country-level, fertility rate predicted underweight, overweight and obesity prevalence, but economic indicators were also important, including the gross domestic product per capita - a measure of economic opportunity that predicted both overweight and obesity prevalence, and the GINI coefficient - a measure of economic inequality that predicted both underweight and overweight prevalence. At the individual-level, parity was a risk factor for underweight in underweight burdened countries and a risk factor for overweight/obesity in overweight/obesity burdened countries, whereas age and wealth were protective factors for underweight but risk factors for overweight/obesity. Beyond the effect of economic indicators, this study revealed the important role of fertility rate and parity, which may represent risk factors for both underweight and combined overweight and obesity among women of child-bearing age. Health professionals should consider combining reproductive health services with nutritional programs when addressing the challenge of malnutrition in SSA.
Sections du résumé
BACKGROUND
BACKGROUND
The ongoing nutrition transition in sub-Saharan Africa (SSA) is exhibiting spatial heterogeneity and temporal variability leading to different forms of malnutrition burden across SSA, with some regions exhibiting the double burden of malnutrition. This study aimed to develop a predictive understanding of the malnutrition burden among women of child-bearing age.
METHODS
METHODS
Data from 34 SSA countries were acquired from the Demographic and Health Survey, World Bank, and Swiss Federal Institute of Technology. The SSA countries were classified into malnutrition classes based on their national prevalence of underweight, overweight, and obesity using a 10% threshold. Next, random forest analysis was used to examine the association between country-level demographic variables and the national prevalence of underweight, overweight and obesity. Finally, random forest analysis and multinomial logistic regression models were utilized to investigate the association between individual-level social and demographic variables and Body Mass Index (BMI) categories of underweight, normal weight, and combined overweight and obesity.
RESULTS
RESULTS
Four malnutrition classes were identified: Class A had 5 countries with ≥10% of the women underweight; Class B had 11 countries with ≥10% each of underweight and overweight; Class C1 had 7 countries with ≥10% overweight; and Class C2 had 11 countries with ≥10% obesity. At the country-level, fertility rate predicted underweight, overweight and obesity prevalence, but economic indicators were also important, including the gross domestic product per capita - a measure of economic opportunity that predicted both overweight and obesity prevalence, and the GINI coefficient - a measure of economic inequality that predicted both underweight and overweight prevalence. At the individual-level, parity was a risk factor for underweight in underweight burdened countries and a risk factor for overweight/obesity in overweight/obesity burdened countries, whereas age and wealth were protective factors for underweight but risk factors for overweight/obesity.
CONCLUSIONS
CONCLUSIONS
Beyond the effect of economic indicators, this study revealed the important role of fertility rate and parity, which may represent risk factors for both underweight and combined overweight and obesity among women of child-bearing age. Health professionals should consider combining reproductive health services with nutritional programs when addressing the challenge of malnutrition in SSA.
Identifiants
pubmed: 33110582
doi: 10.7189/jogh.10.020423
pii: jogh-10-020423
pmc: PMC7568927
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
020423Informations de copyright
Copyright © 2020 by the Journal of Global Health. All rights reserved.
Déclaration de conflit d'intérêts
Competing interest: The authors completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available upon request from the corresponding author), and declare no conflicts of interest.
Références
Am J Clin Nutr. 2005 Mar;81(3):714-21
pubmed: 15755843
BMC Public Health. 2015 Jul 16;15:670
pubmed: 26178521
Obes Res Clin Pract. 2015 Jan-Feb;9(1):75-86
pubmed: 24925607
Int J Obes (Lond). 2007 May;31(5):805-12
pubmed: 17060925
PLoS One. 2014 Jun 11;9(6):e99327
pubmed: 24919199
BMC Public Health. 2014 Nov 01;14:1126
pubmed: 25361603
Econ Hum Biol. 2012 Mar;10(2):147-53
pubmed: 22305524
Am J Hypertens. 2013 Mar;26(3):382-91
pubmed: 23382489
Ageing Res Rev. 2012 Jul;11(3):361-73
pubmed: 22440321
Scand J Public Health. 2018 Jul;46(5):557-564
pubmed: 29082809
Int J Epidemiol. 2012 Dec;41(6):1602-13
pubmed: 23148108
SSM Popul Health. 2015 Nov 18;1:16-25
pubmed: 29349117
Nutr Rev. 2012 Jan;70(1):3-21
pubmed: 22221213
Lancet. 2013 Aug 3;382(9890):427-451
pubmed: 23746772
Public Health Nutr. 2002 Feb;5(1A):93-103
pubmed: 12027297
World Health Organ Tech Rep Ser. 1995;854:1-452
pubmed: 8594834
J Epidemiol Community Health. 2007 Sep;61(9):802-9
pubmed: 17699536
Obstet Gynecol. 2010 May;115(5):982-8
pubmed: 20410772
Milbank Q. 2005;83(4):731-57
pubmed: 16279965
BMC Bioinformatics. 2008 Jul 11;9:307
pubmed: 18620558
BMC Public Health. 2011 Oct 13;11:801
pubmed: 21995618
BMC Obes. 2017 Jan 19;4:5
pubmed: 28127440
PLoS One. 2014 Jun 30;9(6):e101103
pubmed: 24979753
BMJ Open. 2019 Jul 3;9(7):e029545
pubmed: 31272983
Psychol Methods. 2009 Dec;14(4):323-48
pubmed: 19968396
PLoS One. 2015 Jun 22;10(6):e0129943
pubmed: 26098561
Proc Nutr Soc. 2008 Feb;67(1):105-8
pubmed: 18234138
Lancet. 2017 Dec 16;390(10113):2627-2642
pubmed: 29029897
Sociol Health Illn. 2008 Apr;30(3):445-62
pubmed: 18373507
Lancet. 2020 Jan 4;395(10217):65-74
pubmed: 31852602
Am J Clin Nutr. 2006 Sep;84(3):633-40
pubmed: 16960179
Public Health Nutr. 2013 Apr;16(4):573-81
pubmed: 22583613