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Record W4390507009 · doi:10.1186/s41043-023-00479-y

Mother–child dyads of overnutrition and undernutrition in sub-Saharan Africa

2024· article· en· W4390507009 on OpenAlexaff
Joshua Okyere, Irene Esi Donkoh, Abdul‐Aziz Seidu, Bright Opoku Ahinkorah, Richard Gyan Aboagye, Sanni Yaya

Bibliographic record

VenueJournal of Health Population and Nutrition · 2024
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsOvernutritionUnderweightBreastfeedingMalnutritionMedicineOverweightOdds ratioPublic healthConfidence intervalEnvironmental healthOddsDemographyPediatricsLogistic regressionObesity

Abstract

fetched live from OpenAlex

BACKGROUND: Malnutrition remains one of the major public health concerns globally. To achieve the Sustainable Development Goal 2 which seeks to ensure that hunger and malnutrition are reduced by 2030, it is imperative to ascertain the factors influencing their occurrence. This study examined the prevalence and factors associated with mother-child dyads of overnutrition and undernutrition in sub-Saharan Africa. METHODS: Demographic and Health Survey data from 25 sub-Saharan African countries were used for the study. The sample was made up of 125,280 mother-child dyads. Descriptive analysis was performed to determine the prevalence of overweight or obese mother (OWOBM) with a stunted child (OWOBM-SC), OWOBM with an underweight child (OWOBM-UC), OWOBM with a wasted child (OWOBM-WC), and OWOBM with any form of child's undernutrition indicators (OWOBM-SUWC). Multilevel regression models were developed to examine the factors associated with these indicators. The results were presented using an adjusted odds ratio (AOR) with their respective 95% confidence interval (CI). RESULTS: Higher likelihood of OWOBM-SUWC was found among women aged 45-49 [AOR 2.20, 95% CI 1.70, 2.85], those with primary [AOR 1.32, 95% CI 1.21, 1.44] or secondary education [AOR 1.21, 95% CI 1.09, 1.35], and divorced women [AOR 1.32, 95% CI 1.02, 1.73]. However, lower odds of OWOBM-SUWC were observed among women who were working [AOR 0.82, 95% CI 0.76, 0.89] and those breastfeeding [AOR 0.75, 95% CI 0.70, 0.82]. The odds of OWOBM-SUWC was lower among females compared to male children [AOR 0.85, 95% CI 0.80, 0.90]. Compared to children aged <1 year, children of all other age groups were more likely to have OWOBM-SUWC. Other child characteristics significantly associated with OWOBM-SUWC were low birth weight [AOR 1.50, 95% CI 1.32, 1.71], having diarrhea [AOR 1.13, 95% CI 1.04, 1.24], and higher birth order [AOR 1.37, 95% CI 1.13, 1.66]. Children whose mothers used unimproved toilet facilities [AOR 0.90, 95% CI 0.83, 0.98], those who lived in rural areas [AOR 0.79, 95% CI 0.71, 0.87], and children from the Central [AOR 0.55, 95% CI 0.46, 0.65], Eastern [AOR 0.44, 95% CI 0.38, 0.52] and Western [AOR 0.76, 95% CI 0.65, 0.89] sub-Saharan Africa were less likely to have OWOBM-SUWC. CONCLUSION: Combination of child, maternal, and contextual factors could explain mother-child dyads of overnutrition and undernutrition in sub-Saharan Africa. Addressing this situation requires multidimensional policies and interventions that empower women through education and economic engagement. The observed sub-regional differences in policies and commitments related to addressing malnutrition suggest the need for comprehensive and coordinated efforts to implement and strengthen multisectoral comprehensive nutrition plans across sub-Saharan Africa. Sharing best practices and lessons learned can help improve the effectiveness and comprehensiveness of nutrition interventions and contribute to reducing the prevalence of malnutrition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.318
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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