Investigating Health Inequality Using Trend, Decomposition and Spatial Analyses: A Study of Maternal Health Service Use in Nepal
Bibliographic record
Abstract
Objectives: (a) To quantify the level and changes in socioeconomic inequality in the utilization of antenatal care (ANC), institutional delivery (ID) and postnatal care (PNC) in Nepal over a 20-year period; (b) identify key drivers of inequality using decomposition analysis; and (c) identify geographical clusters with low service utilization to inform policy. Methods: Data from the most recent five waves of the Demographic Health Survey were used. All outcomes were defined as binary variables: ANC (=1 if ≥4 visits), ID (=1 if place of delivery was a public or private healthcare facility), and PNC (=1 if ≥1 visits). Indices of inequality were computed at national and provincial-level. Inequality was decomposed into explanatory components using Fairile decomposition. Spatial maps identified clusters of low service utilization. Results: During 1996–2016, socioeconomic inequality in ANC and ID reduced by 10 and 23 percentage points, respectively. For PND, the gap remained unchanged at 40 percentage points. Parity, maternal education, and travel time to health facility were the key drivers of inequality. Clusters of low utilization were displayed on spatial maps, alongside deprivation and travel time to health facility. Conclusion: Inequalities in the utilization of ANC, ID and PNC are significant and persistent. Interventions targeting maternal education and distance to health facilities can significantly reduce the gap.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".