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Record W2144441880 · doi:10.1093/jeea/jvw002

The Cognitive Effects of Micronutrient Deficiency: Evidence from Salt Iodization in the United States

2017· article· en· W2144441880 on OpenAlexaboutno aff
James Feyrer, Dimitra Politi, David Weil

Bibliographic record

VenueJournal of the European Economic Association · 2017
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsIodised saltIodine deficiencyQuarter (Canadian coin)Environmental healthMicronutrientMedicinePopulationDemographyGeographyThyroidEndocrinology

Abstract

fetched live from OpenAlex

Iodine deficiency is the leading cause of preventable mental retardation in the world today. The condition, which was common in the developed world until the introduction of iodized salt in the 1920s, is connected to low iodine levels in the soil and water. We examine the impact of salt iodization on cognitive outcomes in the US by taking advantage of this natural geographic variation. Salt was iodized over a short period of time beginning in 1924. We use military data collected during WWI and WWII to compare outcomes of cohorts born before and after iodization in localities that were naturally poor and rich in iodine. We find that for the one quarter of the population most deficient in iodine this intervention raised IQ by approximately one standard deviation. Our results can explain roughly one decade's worth of the upward trend in IQ in the US (the Flynn Effect). We also document a large increase in thyroid-related deaths following the countrywide adoption of iodized salt, which affected mostly older individuals in localities with high prevalence of iodine deficiency.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations48
Published2017
Admission routes1
Has abstractyes

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