Equity trends for the UHC service coverage sub-index for reproductive, maternal, newborn and child health in Pakistan: evidence from demographic health surveys
Bibliographic record
Abstract
BACKGROUND: Pakistan, the world's sixth most populous country and the second largest in South Asia, is facing challenges related to reproductive, maternal, newborn and child health (RMNCH) that are exacerbated by various inequities. RMNCH coverage indicators such as antenatal care (ANC) and deliveries at health facilities have been improving over time, and the maternal mortality ratio (MMR) is gradually declining but not at the desired rates. Analysing and documenting inequities with reference to key characteristics are useful to unmask the disparities and to amicably implement targeted equity-oriented interventions. METHODS: Pakistan Demographic Health Survey (PDHS) based UHC service coverage tracer indicators were derived for the RMNCH domain at the national and subnational levels for the two rounds of the PDHS in 2012 and 2017. These derivations were subgrouped into wealth quintiles, place of residence, education and mothers' age. Dumbbell charts were created to show the trends and quintile-specific coverage. The UHC service coverage sub-index for RMNCH was constructed to measure the absolute and relative parity indices, such as high to low absolute difference and high to low ratios, to quantify health inequities. The population attributable risk was computed to determine the overall population health improvement that is possible if all regions have the same level of health services as the reference point (national level) across the equity domains. RESULTS: The results indicate an overall improvement in coverage across all indicators over time, but with a higher concentration of data points towards higher coverage among the wealthiest groups, although the poorest quintile continues to have low coverage in all regions. The UHC service coverage sub-index on RMNCH shows that Pakistan has improved from 45 to 63 overall, while Punjab improved from 50 to 59 and Sindh from 43 to 55. The highest improvement is evident in Khyber Pakhtunkhwa (KP) province, which has increased from 31 in 2012 to 51 in 2017. All regions made slow progress in narrowing the gap between the poorest and wealthiest groups, with particularly noteworthy improvements in KP and Sindh, as indicated by the parity ratio. The RMNCH service coverage sub-index gap was the greatest among women aged 15-19 years, those who belonged to the poorest wealth quintile, had no education, and resided in rural areas. CONCLUSIONS: Analysing existing data sources from an equity lens supports evidence-based policies, programs and practices with a focus on disadvantaged subgroups.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".