Early-Life Environmental Exposures and Height, Hypertension, and Cardiovascular Risk Factors Among Older Adults in India
Bibliographic record
Abstract
Environmental exposures like rainfall and temperature influence infectious disease exposure and nutrition, two key early-life conditions linked to later-life health. However, few tests of whether early-life environmental exposures impact adult health have been performed, particularly in developing countries. This study examines the effects of experiencing rainfall and temperature shocks during gestation and up through the first four years after birth on measured height, hypertension, and other cardiovascular risk factors using data on adults aged 50 and above (N = 1,036) from the 2007-2008 World Health Organization Study on Global Ageing and Adult Health (SAGE) and district-level meteorological data from India. Results from multivariate logistic regressions show that negative rainfall shocks during gestation and positive rainfall shocks during the postbirth period increase the risk of having adult hypertension and CVD risk factors. Exposure to negative rainfall shocks and positive temperature shocks in the postbirth period increases the likelihood of falling within the lowest height decile. Prenatal shocks may influence nutrition in utero, while postnatal shocks may increase exposure to infectious diseases and malnutrition. The results suggest that gestation and the first two years after birth are critical periods when rainfall and temperature shocks take on increased importance for adult health.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".