Trends in regional inequalities in childhood anemia in Ethiopia: evidence from the 2005–2016 Ethiopian Demographic and Health Surveys
Bibliographic record
Abstract
Abstract Introduction Globally, 269 million children aged 6–59 months were anemic in 2019. Of these, 103 million anemic children were from Africa. Childhood anemia is still a serious public health concern in SSA countries, including Ethiopia. In Ethiopia, the prevalence of childhood anemia largely varies by geographic administration regions. This study is aimed to examine trends in regional inequalities in childhood anemia in Ethiopia over the period 2005–2016. Method This cross-sectional study was based on a pooled total sample of 17,766 children aged 6–59 months drawn from three rounds of the Ethiopian Demography and Health Surveys (2005–2016). We employed multilevel binary logistic regression analysis to identify the determinants of childhood anemia among children aged 6–59 months. We also used Theil and multivariate decomposition analyses to examine the levels and trends in relative regional inequalities in childhood anemia. Result A combination of individual-, household- and community-level factors were significantly (p < 001) associated with childhood anemia. From the pooled data, the highest childhood anemia was observed in Somali (78.68%) followed by Afar region (72.76%) while the lowest childhood anemia was in Amhara (41.01%), Addis Ababa (42.64%) and SNNPR (44%) between 2005 and 2016. The total relative inequality declined from 0.620 in 2005 to 0.548 in 2016. Overall, one-third of change in regional inequalities in childhood anemia was due to the differential resulted from the difference in observable characteristics of the subjects. Conclusion Overall progress made in Ethiopia was very slow with only a 13.14% reduction in the relative regional inequalities in childhood anemia over 11 years. The present study underscores addressing the existing disparities in socioeconomic status, maternal anemia and maternal employment status between emerging and non-emerging regions to reduce regional inequality in childhood anemia.
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 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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".