Evidence map›Paper›PMID 41419254›Full record

ArticleBMJ global health2025

Tracking progress towards Sustainable Development Goal 3.2 in Kenya using time series models.

Welcome Jabulani Dlamini, Sileshi Fanta Melesse, Henry Godwell Mwambi

Abstract read
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Article in BMJ global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Welcome Jabulani DlaminiFocus Area for Pure and Applied Analytics, North-West University, Private Bag X6001, Potchefstroom, North-West, South Africa Dlaminwel@gmail.com.ORCID 0000-0003-3046-3594
Sileshi Fanta MelesseSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa.
Henry Godwell MwambiSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSustainable Development Goal (SDG) 3.2, which aims to reduce under-five mortality rate (UFMR) below 25 deaths per 1000 live births by 2030, is still a crucial target for improved child survival in sub-Saharan Africa because UFMRs are still high and progress has stalled in recent years.

objectiveThis study aimed to model the possibility of reaching the SDG 3.2 target by 2030 and evaluate trends in under-five mortality in Kenya.

methodThree models: autoregressive integrated moving average (ARIMA), autoregressive fractionally integrated moving average (ARFIMA) and hybrid were fitted to annual national under-five mortality data from 1995 to 2022. Automated model selection showed ARIMA (0,2,1) as the best fitting model from information criteria, predictive accuracy and residual diagnostics. The model was tested with mean absolute error, root mean square error, mean absolute percentage error and tested against the 80/20 train-test split.

resultsKenya's UFMR has been slightly declining over the course of the study, but the ARIMA projection indicates that the rate of fall is slowing. By 2030, the UFMR is expected to be 27.8 deaths per 1000 live births (95% prediction interval (PI) 25.2 to 30.3), over the SDG 3.2 goal level (signifying an increase in predicted uncertainty). The upper bound of humanity's real 95% PI still far exceeds the aim, even as the lower bound has started to move closer. Kenya would require an accelerated annual decline in roughly 2.43 fatalities per 1000 starting in 2023 much higher than trends seen in the recent past to meet SDG 3.2.

conclusionKenya's UFMR has significantly decreased; however, the SDG 3.2 target might not be met by 2030 without more initiatives. To accelerate progress, it will be essential to improve mother and child health services, increase community-level interventions, address social injustices and employ more focused county-specific strategies. Using additional high-quality data and improved modelling tools could enhance child mortality monitoring and prediction in the future.

Indexed as

Child MortalityInfant MortalitySustainable DevelopmentChild, PreschoolGoalsHumansInfantInfant, NewbornKenyaModels, StatisticalChild healthGlobal HealthKenya

Identifiers

PMID41419254
PMCPMC12716503

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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.