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Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis
OBJECTIVE: This study aimed to assess the spatial distribution, individual and community-level factors associated with low birth weight in Ethiopia. METHOD: Secondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey data. A total of 2110 neonates were included in th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103935/ https://www.ncbi.nlm.nih.gov/pubmed/34036183 http://dx.doi.org/10.1136/bmjpo-2020-000968 |
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author | Liyew, Alemneh Mekuriaw Sisay, Malede Mequanent Muche, Achenef Asmamaw |
author_facet | Liyew, Alemneh Mekuriaw Sisay, Malede Mequanent Muche, Achenef Asmamaw |
author_sort | Liyew, Alemneh Mekuriaw |
collection | PubMed |
description | OBJECTIVE: This study aimed to assess the spatial distribution, individual and community-level factors associated with low birth weight in Ethiopia. METHOD: Secondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey data. A total of 2110 neonates were included in this study. Spatial autocorrelation analysis was conducted to assess the spatial clustering of LBW. Besides, the spatial scan statistics and ordinary kriging interpolation were done to detect the local level clusters and to assess predicted risk areas, respectively. Furthermore, a multilevel logistic regression model was fitted to determine individual and community-level factors associated with LBW. Finally, most likely clusters with log-likelihood ratio (LLR), relative risk and p value from spatial scan statistics and adjusted OR (AOR) with 95% CI for multilevel logistic regression model were reported. RESULTS: LBW was spatially clustered in Ethiopia. Primary (LLR=11.57; p=0.002) clusters were detected in the Amhara region. Neonates within this spatial window had a 2.66 times higher risk of being LBW babies as compared with those outside the window. Besides, secondary (LLR=11.4; p=0.003; LLR=10.14, p=0.0075) clusters were identified at southwest Oromia, north Oromia, south Afar and southeast Amhara regions. Neonates who were born from severely anaemic (AOR=1.40, 95% CI (1.03 to 2.15)), and uneducated (AOR=1.90, 95% CI (1.23 to 2.93)) mothers, those who were born before 37 weeks of gestation (AOR=5.97, 95% CI (3.26 to 10.95)) and women (AOR=1.41, 95% CI (1.05 to 1.89)), had significantly higher odds of being LBW babies. CONCLUSION: The high-risk areas of LBW were detected in Afar, Amhara and Oromia regions. Therefore, targeting the policy interventions in those hotspot areas and focusing on the improvement of maternal education, strengthening anaemia control programmes and elimination of modifiable causes of prematurity could be vital for reducing the LBW disparity in Ethiopia. |
format | Online Article Text |
id | pubmed-8103935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-81039352021-05-24 Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis Liyew, Alemneh Mekuriaw Sisay, Malede Mequanent Muche, Achenef Asmamaw BMJ Paediatr Open Epidemiology OBJECTIVE: This study aimed to assess the spatial distribution, individual and community-level factors associated with low birth weight in Ethiopia. METHOD: Secondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey data. A total of 2110 neonates were included in this study. Spatial autocorrelation analysis was conducted to assess the spatial clustering of LBW. Besides, the spatial scan statistics and ordinary kriging interpolation were done to detect the local level clusters and to assess predicted risk areas, respectively. Furthermore, a multilevel logistic regression model was fitted to determine individual and community-level factors associated with LBW. Finally, most likely clusters with log-likelihood ratio (LLR), relative risk and p value from spatial scan statistics and adjusted OR (AOR) with 95% CI for multilevel logistic regression model were reported. RESULTS: LBW was spatially clustered in Ethiopia. Primary (LLR=11.57; p=0.002) clusters were detected in the Amhara region. Neonates within this spatial window had a 2.66 times higher risk of being LBW babies as compared with those outside the window. Besides, secondary (LLR=11.4; p=0.003; LLR=10.14, p=0.0075) clusters were identified at southwest Oromia, north Oromia, south Afar and southeast Amhara regions. Neonates who were born from severely anaemic (AOR=1.40, 95% CI (1.03 to 2.15)), and uneducated (AOR=1.90, 95% CI (1.23 to 2.93)) mothers, those who were born before 37 weeks of gestation (AOR=5.97, 95% CI (3.26 to 10.95)) and women (AOR=1.41, 95% CI (1.05 to 1.89)), had significantly higher odds of being LBW babies. CONCLUSION: The high-risk areas of LBW were detected in Afar, Amhara and Oromia regions. Therefore, targeting the policy interventions in those hotspot areas and focusing on the improvement of maternal education, strengthening anaemia control programmes and elimination of modifiable causes of prematurity could be vital for reducing the LBW disparity in Ethiopia. BMJ Publishing Group 2021-05-05 /pmc/articles/PMC8103935/ /pubmed/34036183 http://dx.doi.org/10.1136/bmjpo-2020-000968 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Epidemiology Liyew, Alemneh Mekuriaw Sisay, Malede Mequanent Muche, Achenef Asmamaw Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title | Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title_full | Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title_fullStr | Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title_full_unstemmed | Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title_short | Spatial distribution and factors associated with low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016: spatial and multilevel analysis |
title_sort | spatial distribution and factors associated with low birth weight in ethiopia using data from ethiopian demographic and health survey 2016: spatial and multilevel analysis |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103935/ https://www.ncbi.nlm.nih.gov/pubmed/34036183 http://dx.doi.org/10.1136/bmjpo-2020-000968 |
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