The Excess Mortality Among Twins in the Dominican Republic and Haiti Through the Components of Age Under Five: A Comparative Study of Trends and Associated Factors
Bibliographic record
Abstract
Abstract Despite the decline in mortality rates among children in developing countries, disparities persist between countries, particularly between twins and singletons. This study employed data from nine Demographic and Health Surveys in the Dominican Republic and Haiti to estimate and compare mortality rates for twins and singletons in categories of the under-5 age group (neonatal, postneonatal, and child mortality) and examine the factors associated with excess mortality among twins. From 1996 to 2013, the under-5 mortality rate (U5MR) for singletons in the Dominican Republic declined from 56‰ (95% CI [47, 64) to 30‰ (22–39) and from 108‰ (53–164) to 53‰ (16–89) among twins. In Haiti, between 1994 and 2016, the U5MR declined from 121‰ (109–133) to 77‰ (68–80) for singletons and from 432‰ (327–538) to 204‰ (149–260) among twins. The adjusted risk of neonatal death for twins is 1.4 (1.0–1.9) times higher than for singletons in the Dominican Republic, compared to a risk of 4.3 (3.5–5.3) times higher in Haiti. In the post-neonatal period, the mortality risk for twins in the Dominican Republic was 1.8 (1.0–3.1) times higher than that for singletons, 2.9 (2.3–3.8) in Haiti. The risk of death for twins was not significantly different from that for singletons in both the Dominican Republic and Haiti at ages 1–4 years. Low birth weight, lack of breastfeeding, absence of, or inadequate, antenatal care, noncesarean section birth, and high birth order were associated with excess mortality among twins in both countries.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".