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 the...

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Main Authors: Daniel Adedayo Adeyinka, Pammla Margaret Petrucka, Elon Warnow Isaac, Nazeem Muhajarine
Format: Article
Language:English
Published: BMJ Publishing Group 2021-01-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/11/1/e040302.full
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spelling doaj-0bb712cb1ffd4d839226f83b16c7d8a82021-02-20T12:32:05ZengBMJ Publishing GroupBMJ Open2044-60552021-01-0111110.1136/bmjopen-2020-040302Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysisDaniel Adedayo Adeyinka0Pammla Margaret Petrucka1Elon Warnow Isaac2Nazeem Muhajarine3Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaCollege of Nursing, University of Saskatchewan, Regina, Saskatchewan, CanadaPaediatrics, College of Medical Sciences, Gombe State University, Gombe, NigeriaCommunity Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, CanadaObjectives 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.https://bmjopen.bmj.com/content/11/1/e040302.full
collection DOAJ
language English
format Article
sources DOAJ
author Daniel Adedayo Adeyinka
Pammla Margaret Petrucka
Elon Warnow Isaac
Nazeem Muhajarine
spellingShingle Daniel Adedayo Adeyinka
Pammla Margaret Petrucka
Elon Warnow Isaac
Nazeem Muhajarine
Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis
BMJ Open
author_facet Daniel Adedayo Adeyinka
Pammla Margaret Petrucka
Elon Warnow Isaac
Nazeem Muhajarine
author_sort Daniel Adedayo Adeyinka
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_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_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_sort changing patterns of gender inequities in childhood mortalities during the sustainable development goals era in nigeria: findings from an artificial neural network analysis
publisher BMJ Publishing Group
series BMJ Open
issn 2044-6055
publishDate 2021-01-01
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.
url https://bmjopen.bmj.com/content/11/1/e040302.full
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