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Record W4306154883 · doi:10.1371/journal.pone.0275495

Prevalence and predictors of oral rehydration therapy, zinc, and other treatments for diarrhoea among children under-five in sub-Saharan Africa

2022· article· en· W4306154883 on OpenAlexaff
Bright Opoku Ahinkorah, Richard Gyan Aboagye, Abdul‐Aziz Seidu, James Boadu Frimpong, Abdul Cadri, Agani Afaya, John Elvis Hagan, Sanni Yaya

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of OttawaGlobal Affairs CanadaMcGill University
Fundersnot available
KeywordsMedicineConfidence intervalOdds ratioLogistic regressionDiarrheaPediatricsCross-sectional studyOddsDemographyDeveloping countryStatistical significanceEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the evidence-based effectiveness of diarrhoea treatment in preventing diarrhoea-related child mortality, the accessibility and utilization of diarrhoea treatments remain low in sub-Saharan Africa, even though these treatments are available. Therefore, this study aimed to assess the prevalence and predictors of diarrhoea treatment among under-five children in sub-Saharan Africa. METHODS: This study involved cross-sectional analyses of secondary data from the most recent Demographic and Health Surveys of 30 countries in sub-Saharan Africa. Percentages with their respective 95% confidence intervals (CI) were used to summarise the prevalence of diarrhoea treatment. A multivariable multilevel binary logistic regression analysis was employed to examine the predictors of diarrhoea treatment among children under five years in sub-Saharan Africa. The regression results were presented using adjusted odds ratio with their accompanying 95% confidence intervals. Statistical significance was set at p<0.05. Stata software version 16.0 was used for the analyses. RESULTS: The overall prevalence of diarrhoea treatment among under-five children in sub-Saharan Africa was 49.07% (95% CI = 44.50-53.64). The prevalence of diarrhoea treatment ranged from 23.93% (95% CI = 20.92-26.94) in Zimbabwe to 66.32% (95% CI = 61.67-70.97) in Liberia. Children aged 1 to 4 years, those whose mothers had at least primary education, those whose mothers had postnatal care visits, those whose mothers believed that permission to go and get medical help for self was a big problem, and those whose mothers' partners had at least primary education were more likely to undergo diarrhoea treatment as compared to their counterparts. The odds of diarrhoea treatment increased with increasing wealth index with the highest odds among those in the richest quintile. Also, the odds of diarrhoea treatment was higher in the Central, Eastern, and Western geographical subregions compared to those in the Southern geographical subregion. However, children whose mothers were cohabiting, those whose mothers were exposed to watching television, and those living in female-headed households were less likely to undergo diarrhoea treatment. CONCLUSION: The study found that the prevalence of diarrhoea treatment among children in sub-Saharan Africa was relatively low and varied across countries. The sub-regional estimates of diarrhoea treatment and identified associated factors can support country-specific needs assessments targeted at improving policy makers' understanding of within-country disparities in diarrhoea treatment. Planned interventions (e.g., provision of quality and affordable supply of oral rehydration salts and zinc) should seek to scale up diarrhoea treatment uptake among under-five children in sub-Saharan Africa with much focus on the factors identified in this study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.242
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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