Does reducing infant mortality depend on preventing low birthweight? An analysis of temporal trends in the Americas
Bibliographic record
Abstract
Low birthweight (LBW) is highly associated with death during infancy, and countries with the highest LBW rates also have the highest infant mortality rates. We compared temporal trends in LBW with both overall and birthweight-specific infant mortality in United States, Canada, Argentina, Chile, and Uruguay over two time periods, using cohort and cross-sectional analysis of national population-based vital statistics for 1985-89 and 1995-98. Infant mortality diminished substantially (RR = 0.60-0.80 for the later vs. earlier periods) and to a similar degree in all birthweight categories in all five study countries, despite an increase in LBW in the US and Uruguay, minimal changes in Canada and Argentina, and a decrease in Chile. The strength of the (positive) association between LBW and overall infant mortality diminished over the two time periods (from r(s) = +0.80 to +0.25 and RR per SD increase in LBW rate from 2.13 [2.09, 2.17] to 1.76 [1.74, 1.79]). The proportion of infant deaths occurring among LBW infants was negatively correlated with overall infant mortality in both time periods (r(s) = -0.30 and -0.60, RR = 0.68 [0.67, 0.68] and 0.47 [0.46, 0.47]). Developed and less developed countries in the Americas have succeeded in reducing infant mortality in all birthweight groups despite inconsistent changes in LBW rates, and none has achieved this success primarily by reducing LBW. Although our results are not necessarily generalisable to the least developed countries in South Asia and sub-Saharan Africa, it is likely that all countries can substantially reduce their infant mortality rates by improving the care of infants at normal and low birthweights.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".