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Record W3092217254 · doi:10.1136/bmjgh-2020-003109

Childhood morbidity and its determinants: evidence from 31 countries in sub-Saharan Africa

2020· article· en· W3092217254 on OpenAlexaff
Sulaimon T. Adedokun, Sanni Yaya

Bibliographic record

VenueBMJ Global Health · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineSocioeconomic statusLogistic regressionPublic healthBirth orderPediatricsOddsDeveloping countryUnder-fiveDemographyOdds ratioEnvironmental healthChild mortalityPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Although under-five mortality reduced globally from 93 per 1000 live births in 1990 to 39 in 2018, sub-Saharan Africa witnessed an increase from 31% in 1990 to 54% in 2018. Morbidity has been reported to contribute largely to these deaths. This study examined the factors that are associated with childhood morbidity in sub-Saharan Africa. METHODS: Demographic and Health Surveys of 31 countries in sub-Saharan Africa were used in this study. The study involved 189 069 children who had or did not have fever, cough or diarrhoea in the 2 weeks preceding the surveys. Descriptive statistics and binary logistic regression were applied in the analysis. RESULTS: About 22% of the children suffered from fever, 23% suffered from cough and 16% suffered from diarrhoea. While the odds of experiencing fever increased by 37% and 18%, respectively, for children from poorest and poorer households, children of women aged 15-24 and 25-34 years are 47% and 23%, respectively, more likely to experience diarrhoea. The probability of suffering from morbidity increased for children who are 12-23 months, of higher order birth, small in size at birth and from households with non-improved toilet facility. CONCLUSIONS: This study has shown that childhood morbidity remains a major health challenge in sub-Saharan Africa with socioeconomic, maternal, child's and environmental factors playing significant roles. Efforts at addressing this problem should consider these factors.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.047
GPT teacher head0.360
Teacher spread0.313 · 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

Citations47
Published2020
Admission routes1
Has abstractyes

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