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Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis
OBJECTIVES: In line with the child survival and gender equality targets of Sustainable Development Goals (SDG) 3 and 5, we aimed to: (1) estimate the age and sex-specific mortality trends in child-related SDG indicators (ie, neonatal mortality rate (NMR) and under-five mortality rate (U5MR)) over 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/PMC7849876/ https://www.ncbi.nlm.nih.gov/pubmed/33514573 http://dx.doi.org/10.1136/bmjopen-2020-040302 |
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author | Adeyinka, Daniel Adedayo Petrucka, Pammla Margaret Isaac, Elon Warnow Muhajarine, Nazeem |
author_facet | Adeyinka, Daniel Adedayo Petrucka, Pammla Margaret Isaac, Elon Warnow Muhajarine, Nazeem |
author_sort | Adeyinka, Daniel Adedayo |
collection | PubMed |
description | OBJECTIVES: In line with the child survival and gender equality targets of Sustainable Development Goals (SDG) 3 and 5, we aimed to: (1) estimate the age and sex-specific mortality trends in child-related SDG indicators (ie, neonatal mortality rate (NMR) and under-five mortality rate (U5MR)) over the 1960s–2017 period, and (2) estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. DESIGN: Group method of data handling-type artificial neural network (GMDH-type ANN) time series. METHODS: This study used an artificial intelligence time series (GMDH-type ANN) to forecast age-specific childhood mortality rates (neonatal and under-five) and sex-specific U5MR from 2018 to 2030. The data sets were the yearly historical mortality rates between 1960s and 2017, obtained from the World Bank website. Two scenarios of mortality trajectories were simulated: (1) status quo scenarios—assuming the current trend continues; and (2) acceleration scenarios—consistent with the SDG targets. RESULTS: At the projected rates of decline of 2.0% for NMR and 1.2% for U5MR, Nigeria will not achieve the child survival SDG targets by 2030. Unexpectedly, U5MR will begin to increase by 2028. To put Nigeria back on track, annual reduction rates of 7.8% for NMR and 10.7% for U5MR are required. Also, female U5MR is decreasing more slowly than male U5MR. At the end of SDG era, female deaths will be higher than male deaths (80.9 vs 62.6 deaths per 1000 live births). CONCLUSION: Nigeria is not likely to achieve SDG targets for child survival and gender equities because female disadvantages will worsen. A plausible reason for the projected increase in female mortality is societal discrimination and victimisation faced by female child. Stakeholders in Nigeria need to adequately plan for child health to achieve SDG targets by 2030. Addressing gender inequities in childhood mortality in Nigeria would require gender-sensitive policies and community mobilisation against gender-based discrimination towards female child. |
format | Online Article Text |
id | pubmed-7849876 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-78498762021-02-02 Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis Adeyinka, Daniel Adedayo Petrucka, Pammla Margaret Isaac, Elon Warnow Muhajarine, Nazeem BMJ Open Public Health OBJECTIVES: In line with the child survival and gender equality targets of Sustainable Development Goals (SDG) 3 and 5, we aimed to: (1) estimate the age and sex-specific mortality trends in child-related SDG indicators (ie, neonatal mortality rate (NMR) and under-five mortality rate (U5MR)) over the 1960s–2017 period, and (2) estimate the expected annual reduction rates needed to achieve the SDG-3 targets by projecting rates from 2018 to 2030. DESIGN: Group method of data handling-type artificial neural network (GMDH-type ANN) time series. METHODS: This study used an artificial intelligence time series (GMDH-type ANN) to forecast age-specific childhood mortality rates (neonatal and under-five) and sex-specific U5MR from 2018 to 2030. The data sets were the yearly historical mortality rates between 1960s and 2017, obtained from the World Bank website. Two scenarios of mortality trajectories were simulated: (1) status quo scenarios—assuming the current trend continues; and (2) acceleration scenarios—consistent with the SDG targets. RESULTS: At the projected rates of decline of 2.0% for NMR and 1.2% for U5MR, Nigeria will not achieve the child survival SDG targets by 2030. Unexpectedly, U5MR will begin to increase by 2028. To put Nigeria back on track, annual reduction rates of 7.8% for NMR and 10.7% for U5MR are required. Also, female U5MR is decreasing more slowly than male U5MR. At the end of SDG era, female deaths will be higher than male deaths (80.9 vs 62.6 deaths per 1000 live births). CONCLUSION: Nigeria is not likely to achieve SDG targets for child survival and gender equities because female disadvantages will worsen. A plausible reason for the projected increase in female mortality is societal discrimination and victimisation faced by female child. Stakeholders in Nigeria need to adequately plan for child health to achieve SDG targets by 2030. Addressing gender inequities in childhood mortality in Nigeria would require gender-sensitive policies and community mobilisation against gender-based discrimination towards female child. BMJ Publishing Group 2021-01-29 /pmc/articles/PMC7849876/ /pubmed/33514573 http://dx.doi.org/10.1136/bmjopen-2020-040302 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. http://creativecommons.org/licenses/by-nc/4.0/ http://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/. |
spellingShingle | Public Health Adeyinka, Daniel Adedayo Petrucka, Pammla Margaret Isaac, Elon Warnow Muhajarine, Nazeem Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title | Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title_full | Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title_fullStr | Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title_full_unstemmed | Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title_short | Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis |
title_sort | changing patterns of gender inequities in childhood mortalities during the sustainable development goals era in nigeria: findings from an artificial neural network analysis |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849876/ https://www.ncbi.nlm.nih.gov/pubmed/33514573 http://dx.doi.org/10.1136/bmjopen-2020-040302 |
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