Why Is Infant Mortality Higher in Boys Than in Girls? A New Hypothesis Based on Preconception Environment and Evidence From a Large Sample of Twins
Bibliographic record
Abstract
Infant mortality is higher in boys than girls in most parts of the world. This has been explained by sex differences in genetic and biological makeup, with boys being biologically weaker and more susceptible to diseases and premature death. At the same time, recent studies have found that numerous preconception or prenatal environmental factors affect the probability of a baby being conceived male or female. I propose that these environmental factors also explain sex differences in mortality. I contribute a new methodology of distinguishing between child biology and preconception environment by comparing male-female differences in mortality across opposite-sex twins, same-sex twins, and all twins. Using a large sample of twins from sub-Saharan Africa, I find that both preconception environment and child biology increase the mortality of male infants, but the effect of biology is substantially smaller than the literature suggests. I also estimate the interacting effects of biology with some intrauterine and external environmental factors, including birth order within a twin pair, social status, and climate. I find that a twin is more likely to be male if he is the firstborn, born to an educated mother, or born in certain climatic conditions. Male firstborns are more likely to survive than female firstborns, but only during the neonatal period. Finally, mortality is not affected by the interactions between biology and climate or between biology and social status.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".