Maternal Chronic Ill Health Negatively Affects Child Survival in a Poor Rural Population of Pakistan
Bibliographic record
Abstract
Pakistan ranks fourth globally in terms of absolute numbers of under-5 deaths. Although several determinants of child deaths have been identified, the possibility of an association between mother's health and under-5 deaths has not been assessed in Pakistan. We compared data on 106 deceased children 0-59 months old with those on 3718 live children, using a cross-sectional survey of 2276 households among 99 randomly selected villages in Thatta, a rural district of Pakistan. We examined the association between self-reported maternal health status and under-5 deaths, using the SUDAAN statistical package to account for cluster sampling technique. Three models for logistic regression analysis were Model-1: demographic factors, Model-2: household socio-economic factors and Model-3: demographic and household socio-economic factors. Mothers of deceased children were 60% more likely to report chronic illnesses than mothers of live children after controlling for child's age, mother's age and type of house (final Model-3 analysis) (adjusted odds ratio [aOR; 95% confidence interval]: 1.6 [1.01, 2.5]). The association of self-reported maternal ill health with under-5 deaths in Thatta suggests the role of maternal health in child survival. Child survival strategies should include screening and treating mothers for common chronic illnesses. This is particularly important in a setting where only a quarter of chronically ill mothers seek care outside the home.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".