Time trends in socio-economic and geographic-based inequalities in childhood wasting in Guinea over 2 decades: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Today, an estimated 7.3% (50 million) of all children <5 y of age suffer from wasting, with more burden in African countries including Guinea. Investigating inequalities in childhood wasting is essential for designing efficient programs and interventions, but no related evidence exists in Guinea. This study aimed to examine the trends in the prevalence of childhood wasting and the extent of sex, socio-economic and geographic-based disparities in Guinea. METHODS: Data from the 1999, 2005 and 2012 Guinea Demographic and Health Surveys and the 2016 Guinea Multiple Indicator Cluster Survey, with a total of 16 137 children <5 y of age were included for analysis. For inequality analysis, we used the 2019 updated World Health Organization Health Equity Assessment Toolkit (HEAT) software. Inequality was measured using four summary measures (difference [D], population attributable risk [PAR], ratio [R] and population attributable fraction [PAF]) for five equity stratifiers (economic status, education, place of residence, sex and subnational region). We computed 95% confidence intervals (CIs) around the points estimates to measure statistical significance. RESULTS: The findings revealed a pro-rich (R=1.68 [95% CI 1.11 to 2.24]), pro-urban (PAR=-1.04 [95% CI -1.90 to -0.18]) and subnational region (D=8.11 [95% CI 4.85 to 11.36]) inequalities in childhood wasting across all surveys. Except in 2005, education-based disparities (PAF=-18.2 [95% CI -36.10 to -0.26]) were observed across all survey years, but not sex-based disparities. An approximately constant inequality pattern was seen across all dimensions. CONCLUSIONS: This study showed inequalities in childhood wasting in Guinea with a disproportionately higher risk of wasting among children from disadvantaged subpopulations/mothers, including uneducated, poorest/poor, rural residents and regions. Policies that target disadvantaged populations need to be considered in order to ensure social protection, access to a wholesome diet and universal and quality health services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".