Trends of inequalities in care seeking behavior for under-five children with suspected pneumonia in Ethiopia: evidence from Ethiopia demographic and health surveys (2005–2016)
Bibliographic record
Abstract
BACKGROUND: Pneumonia is a leading public health problem in under-five children worldwide and particularly in Africa. Unfortunately, progress in reducing pneumonia related mortality has been slow. The number of children with symptoms of pneumonia taken to health facilities for treatment is low in Ethiopia, and disparities among sub-groups regarding health seeking behavior for pneumonia have not been well explored in the region. This study assessed the trends of inequalities in care seeking behavior for children under five years of age with suspected pneumonia in Ethiopia. METHODS: Using cross-sectional data from the 2005, 2011 and 2016 Ethiopia Demographic and Health Surveys (DHS) and the World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT), this study investigated the inequalities in health seeking behavior for children with suspected pneumonia. Four measures of inequality were calculated: Difference, Ratio, Slope Index of Inequality and Relative Index of Inequality. Results were disaggregated by wealth, education, residence, and sex with computed 95% Uncertainty Intervals for each point estimate to determine significance. RESULTS: The percentage of under-five children with symptoms of pneumonia who were taken to a health facility was significantly lower for children in the poorest families, 15.48% (95% UI; 9.77, 23.64) as compared to children in the richest families, 61.72% (95% UI; 45.06, 76.02) in 2011. Substantial absolute (SII = 35.61; 95% UI: 25.31, 45.92) and relative (RII = 4.04%; 95% UI: 2.25, 5.84) economic inequalities were also observed. Both educational and geographic inequalities were observed; (RII = 2.07; 95% UI: 1.08, 3.06) and (D = 28.26; 95% UI: 7.14, 49.37), respectively. Economic inequality decreased from 2011 to 2016. There was no statistically significant difference between male and female under-five children with pneumonia symptoms taken to health facility, in all the studied years. CONCLUSIONS: Health care seeking behavior for children with pneumonia was lower among the poorest and non-educated families as well as children in rural regions. Policies and strategies need to target subpopulations lagging behind in seeking care for pneumonia treatment as it impedes achievement of key UN sustainable development goals (SDGs).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".