Evidence map›Paper›PMID 33514573›Full record

ArticleBMJ open2021

Changing patterns of gender inequities in childhood mortalities during the Sustainable Development Goals era in Nigeria: findings from an artificial neural network analysis.

Daniel Adedayo Adeyinka, Pammla Margaret Petrucka, Elon Warnow Isaac, Nazeem Muhajarine

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Child Mortality and Nutritional Risks in Rural Chad: A Community-Based Cross-Sectional Study in Béré.International journal of environmental research and public health · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniel Adedayo AdeyinkaCommunity Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada daa929@usask.ca.ORCID 0000-0003-1855-6878
Pammla Margaret PetruckaCollege of Nursing, University of Saskatchewan, Regina, Saskatchewan, Canada.
Elon Warnow IsaacPaediatrics, College of Medical Sciences, Gombe State University, Gombe, Nigeria.
Nazeem MuhajarineCommunity Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn 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.

designGroup method of data handling-type artificial neural network (GMDH-type ANN) time series.

methodsThis 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.

resultsAt 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).

conclusionNigeria 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.

Indexed as

GoalsSustainable DevelopmentArtificial IntelligenceChildChild MortalityFemaleHumansInfantInfant MortalityInfant, NewbornMaleNeural Networks, ComputerNigeriachild protectioncommunity child healthepidemiologypreventive medicinepublic healthstatistics & research methods

Identifiers

PMID33514573
PMCPMC7849876

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.