Predictors of pregnancy loss among urban and rural women aged 15 to 49 years in Pakistan
Bibliographic record
Abstract
BACKGROUND: The burden of pregnancy loss remains high in low- and middle-income countries like Pakistan. The Every Newborn Action Plan (ENAP) aims to decrease the stillbirth rate to 12 per 1000 total births by 2030, in every country. Current estimates indicate that Pakistan is unlikely to achieve this ENAP target, as the stillbirth rate stands at 30.6 per 1000 total births. METHODS: This study used the 2019 Pakistan Maternal Mortality Survey to identify the community-level, sociodemographic, maternal, environmental, and health services factors that are associated with pregnancy loss. Due to characteristic differences in urban and rural communities, separate analyses were carried out for ever-married women of 15 to 49 years. Mixed effects negative binomial regression was used to analyze the urban (n = 5,887) and rural (n = 7,136) samples of women who reported at least one pregnancy. RESULTS: The separate analyses found the factors associated with pregnancy loss to vary between urban and rural areas. In urban areas, pregnancy loss was associated with maternal education, maternal age, current marital status, and sanitation facility type. In rural areas, pregnancy loss was associated with region of residence, wealth index, maternal age, current marital status, drinking water source, cooking fuel type, and sanitation facility type. CONCLUSION: This study carries significant implications for alleviating the burden of pregnancy loss in Pakistan, in line with ENAP objectives. The separate analyses provide a novel perspective regarding the factors influencing pregnancy loss in urban and rural areas, allowing for targeted interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".