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Record W2296910123 · doi:10.1002/ajhb.22848

The influence of birth season on mortality in the United States

2016· article· en· W2296910123 on OpenAlexaboutno aff
Kitae Sohn

Bibliographic record

VenueAmerican Journal of Human Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)DemographyHumSeason of birthNational Health Interview SurveyMedicineLive birthBirth recordsPopulationEnvironmental healthPregnancyGeographyBirth weightBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: Birth season is related to a variety of later outcomes. Among them, mortality is of great interest because it represents lifetime health outcomes. We examined the relationship between birth season and mortality in the US. METHODS: We merged the US National Health Interview Survey (NHIS) and NHIS public-use linked mortality files and analyzed 17,082 men and 19,075 women who were followed for 20 years from 1986 to 2006. We used the Cox proportional hazards model to relate birth quarter to mortality, controlling for birth year fixed effects. RESULTS: After controlling for years of schooling and birth year fixed effects, we found that, relative to men born in the first quarter, men born in the fourth quarter were 11% less likely to die. For women, the benefit was the largest for women born in the third quarter who were 14% less likely to die than women born in the first quarter. In the relationship between birth season and mortality, cardiovascular diseases played a noticeable role for men and malignant neoplasms for women. CONCLUSIONS: These results were consistent with those for some developed countries, but not entirely with those for contemporary developing countries and developed countries of the past. Simple mechanisms based on the perinatal environment cannot account for the inconsistent results. We suggest that family background may play some, but not an exhaustive, role in the relationship between birth season and mortality. Am. J. Hum. Biol. 28:662-670, 2016. © 2016 Wiley Periodicals, Inc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.351
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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