Trends in inequalities in childhood stunting in Ethiopia from 2000 to 2016: a cross sectional study
Bibliographic record
Abstract
BACKGROUND: The decrease in the magnitude of stunting over the past 20 years has been slow in Ethiopia. To date, in Ethiopia, the trends in and extent of inequality in stunting have not been investigated using methods suitably developed for disparity studies. This paper investigated both the extent and overtime dynamics of stunting inequality in Ethiopia over the last 17 years. METHODS: Using the World Health Organization's Health Equity Assessment Toolkit software, data from the Ethiopia Demographic and Health surveys (EDHS) were analyzed between 2000 and 2016. The inequality analysis consisted of disaggregated rates of stunting using five equity stratifiers (economic status, education, residence, region and sex) and four summary measures (Difference, Population Attributable risk, Ratio and Absolute Concentration Index). A 95% uncertainty interval was constructed around point estimates to measure statistical significance. RESULTS: The study showed that both absolute and relative inequalities in stunting exist in all the studied years in Ethiopia. The inequality disfavors children of mothers who are poor, uneducated and living in rural areas and specific regions such as Amhara. The pro-rich (R = 1.2; 1.1, 1.3 in 2000 to R = 1.7; 1.4, 2 in 2016) and pro-educated (R = 1.6; 95%UI = 1.3, 1.9 in 2000 and R = 2.3; 95%UI = 1.5, 3 in 2011) inequalities slightly increased with time. Male children bear a disproportionately higher burden of stunting, and the disparity increased between the first and the last time points (PAR = -1.5 95%UI = -2.5, -0.6 in 2000 and PAR = -2.9 95%UI = -3.9, -1.9) based on complex measures but remained constant with simple measures (R = 1; 95%UI = 0.9, 1.1 in 2000 and R = 1.1 95%UI = 1, 1.2 in 2016). Similarly, both the sub-national regional and residence-related stunting disparities generally widened over time according to some of the inequality measures. CONCLUSIONS: Stunting appeared to be highly prevalent among certain sub-groups (i.e. poor, uneducated and living in rural regions). The subpopulations experiencing excessively high stunting prevalence should be the focus of policy makers' attention as they work to achieve the WHO 40% reduction in stunting target by 2025 and the UN Agenda 2030 for Sustainable Development Goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".