Infant and young child feeding practices in Ethiopia: analysis of socioeconomic disparities based on nationally representative data
Bibliographic record
Abstract
BACKGROUND: Undernutrition among children is a priority area of public health concern in Ethiopia. The purpose of this study was to examine disparities in Infant and Young Child Feeding (IYCF) practices among children 6-23 months. METHOD: Data were drawn from the 2016 Ethiopian Demographic and Health Surveys (EDHS). A total of 3240 children aged 6-23 months were used for the present analysis. The outcome variable was IYCF practice score (ranging 0-7) which was constructed based on the linear and combined effects of four sets of variables: breastfeeding, avoidance of bottle feeding, diet diversity score and minimum feeding frequency. IYCF practice score was further recoded into three categories. Proportional odds regression was used to assess the determinants of IYCF category. RESULTS: The proportional odds regression analysis showed that IYCF scores significantly decreased by 5% (Adjusted Odds Ratio (AOR) = 0.95; 95% CI: 0.93-0.97) for every unit increase in the child's age. Households with fathers of primary and secondary and above level education were 1.37 times (95% CI: 1.14-1.66) and 1.67 times (95% CI: 1.26-2.23) more likely to be in the high IYCF category than in the poor IYCF category. The likelihood of being in the high IYCF practice category decreased for non-working mothers by 30% (AOR = 0.70; 95% CI: 0.59-0.83) compared to those working in gainful employment. The chance of being in the high IYCF practice category decreased by 29% for households with no access to radio (AOR = 0.71; 95% CI: 0.59-0.85). Those with medium and rich/richer wealth category were 1.54 times (95% CI: 1.22-1.94) and 1.40 times (95% CI: 1.11-1.75) more likely to belong to high IYCF practice category than being in poor IYCF category. For every unit increase in health service utilization, the chance of falling in higher IYCF category increases by 1.15 times (95% CI: 1.08-1.23). The chance of falling in higher IYCF practice category decreases for rural residents by 37% (AOR = 0.63; 95% CI: 0.47-0.84) compared to those residing in urban areas. CONCLUSION: For a child, the first two years is the time span during which linear faltering of growth is most prevalent and the period when the process of becoming stunted is almost complete. This study recommends improving access to women for gainful employment, provision of economic support to poor rural women, education and promotion of nutrition messages using most accessible media and boosting the positive role of fathers in child feeding practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".