Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model.


Journal

BMC nutrition
ISSN: 2055-0928
Titre abrégé: BMC Nutr
Pays: England
ID NLM: 101672434

Informations de publication

Date de publication:
07 Jan 2022
Historique:
received: 02 04 2021
accepted: 22 12 2021
entrez: 8 1 2022
pubmed: 9 1 2022
medline: 9 1 2022
Statut: epublish

Résumé

The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model. We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05. The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14-1.39), wasting (OR = 1.35, 95% BCI (1.15-1.57), maternal anemia (OR = 1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62-0.85). Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.

Sections du résumé

BACKGROUND BACKGROUND
The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model.
METHODS METHODS
We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05.
RESULT RESULTS
The 9267 children in our study were between 6 and 59 months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR = 1.26, 95% BCI (1.14-1.39), wasting (OR = 1.35, 95% BCI (1.15-1.57), maternal anemia (OR = 1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR = 1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR = 0.73, 95% BCI (0.62-0.85).
CONCLUSION CONCLUSIONS
Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.

Identifiants

pubmed: 34996515
doi: 10.1186/s40795-021-00495-3
pii: 10.1186/s40795-021-00495-3
pmc: PMC8740428
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2

Informations de copyright

© 2022. The Author(s).

Références

Am J Epidemiol. 2001 Jun 15;153(12):1222-6
pubmed: 11415958
Public Health Nutr. 1999 Sep;2(3):243-52
pubmed: 10512558
PLoS One. 2014 Dec 01;9(12):e114059
pubmed: 25438147
Asian Pac J Trop Med. 2012 Oct;5(10):803-9
pubmed: 23043920
World Rev Nutr Diet. 2016;115:36-45
pubmed: 27197830
PLoS One. 2018 May 18;13(5):e0197171
pubmed: 29775472
Malar J. 2006 Nov 03;5:99
pubmed: 17083720
PLoS One. 2013 Apr 23;8(4):e62883
pubmed: 23626861
Malar J. 2013 Dec 01;12:435
pubmed: 24289142
Hippokratia. 2008;12(4):240-50
pubmed: 19158969
Afr Health Sci. 2020 Dec;20(4):2007-2021
pubmed: 34394267
Adolesc Health Med Ther. 2015 Dec 15;6:189-96
pubmed: 26719736
Curationis. 2014 Sep 23;37(1):1160
pubmed: 25685988
Lancet. 2012 Mar 31;379(9822):1225-33
pubmed: 22464386
Nutrients. 2016 Nov 02;8(11):
pubmed: 27827838
BMC Hematol. 2014 Aug 18;14(1):13
pubmed: 25170422
Pediatrics. 2010 Jul;126(1):e140-9
pubmed: 20547647
Arch Dis Child Fetal Neonatal Ed. 2002 May;86(3):F182-7
pubmed: 11978749
PLoS One. 2019 Jul 5;14(7):e0218961
pubmed: 31276472
PLoS Negl Trop Dis. 2017 Oct 9;11(10):e0005948
pubmed: 28991894
Sci Rep. 2019 Nov 12;9(1):16540
pubmed: 31719548
Int Health. 2014 Mar;6(1):35-45
pubmed: 24486460
J Health Popul Nutr. 2010 Aug;28(4):359-68
pubmed: 20824979
BMC Infect Dis. 2016 Oct 28;16(1):613
pubmed: 27793110
Nutrients. 2019 Jun 28;11(7):
pubmed: 31261779
Trans R Soc Trop Med Hyg. 1999 May-Jun;93(3):240-6
pubmed: 10492749
PLoS Med. 2011 Jun;8(6):e1000438
pubmed: 21687688
BMC Public Health. 2018 May 22;18(1):650
pubmed: 29788935
Matern Child Nutr. 2018 Nov;14 Suppl 4:e12478
pubmed: 28857410
PLoS One. 2019 Jul 3;14(7):e0219170
pubmed: 31269082
Nutr J. 2019 Feb 21;18(1):10
pubmed: 30791904
Ital J Pediatr. 2018 Jul 11;44(1):79
pubmed: 29996879
Malar J. 2012 Jan 06;11:8
pubmed: 22225997
Eur J Clin Nutr. 2014 Feb;68(2):253-8
pubmed: 24300911
BMJ Open. 2019 Apr 4;9(4):e027276
pubmed: 30948614
Anemia. 2011;2011:260380
pubmed: 21738863
Pathog Glob Health. 2013 Mar;107(2):58-65
pubmed: 23683331
Indian Pediatr. 2003 Oct;40(10):985-90
pubmed: 14581738
Am J Trop Med Hyg. 2007 Dec;77(6 Suppl):88-98
pubmed: 18165479
Pediatrics. 2008 Mar;121(3):e673-7
pubmed: 18310187
Arch Pediatr. 2015 Nov;22(11):1188-97
pubmed: 26433575
BMC Public Health. 2019 Feb 20;19(1):215
pubmed: 30786883
Hematol Oncol Clin North Am. 2016 Apr;30(2):247-308
pubmed: 27040955
Bull World Health Organ. 2011 Jun 1;89(6):459-68
pubmed: 21673862
Geospat Health. 2021 Oct 28;16(2):
pubmed: 34726035
PLoS One. 2010 May 21;5(5):e10775
pubmed: 20505829
Nutrients. 2016 Sep 30;8(10):
pubmed: 27706021
Am J Clin Nutr. 2001 Dec;74(6):776-82
pubmed: 11722959
Am J Clin Nutr. 1982 Feb;35(2):229-35
pubmed: 6461244
J Nutr. 2000 Jul;130(7):1724-33
pubmed: 10867043
J Pediatr (Rio J). 2015 Sep-Oct;91(5):471-7
pubmed: 26070864
Acta Trop. 2017 Sep;173:1-10
pubmed: 28522274
Am J Trop Med Hyg. 2005 Jan;72(1):47-59
pubmed: 15728867
JAMA Netw Open. 2018 Sep 7;1(5):e182899
pubmed: 30646183
Trop Med Int Health. 2006 Apr;11(4):490-503
pubmed: 16553932
J Infect Dis. 2006 Jul 1;194(1):108-14
pubmed: 16741889
Ecohealth. 2012 Jun;9(2):122-31
pubmed: 22160444
BMJ Open. 2018 May 14;8(5):e019654
pubmed: 29764873
PLoS One. 2015 Oct 21;10(10):e0140840
pubmed: 26488490

Auteurs

Bilal Shikur Endris (BS)

School of Public Health, Department of Nutrition and Dietetics, Addis Ababa University, Addis Ababa, Ethiopia. bilalshikur10@gmail.com.

Geert-Jan Dinant (GJ)

School CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands.

Seifu H Gebreyesus (SH)

School of Public Health, Department of Nutrition and Dietetics, Addis Ababa University, Addis Ababa, Ethiopia.

Mark Spigt (M)

School CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands.
General Practice Research Unit, Department of Community Medicine, The Arctic University of Tromsø, Tromsø, Norway.

Classifications MeSH