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Record W4406641398 · doi:10.1097/phh.0000000000002108

Associations Between Sugar-Sweetened Beverage Taxes and Weight Outcomes Among US Adolescents

2025· article· en· W4406641398 on OpenAlexaff
Dinghe Cui, Christopher F. Baum, Summer Sherburne Hawkins

Bibliographic record

VenueJournal of Public Health Management and Practice · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsOverweightFluid ounce (US)Body mass indexObesitySugarEnvironmental healthConsumption (sociology)Adolescent ObesityPercentage pointAdded sugarFood scienceDemographyMedicineChemistryEconomicsEndocrinology

Abstract

fetched live from OpenAlex

Given the recent implementation and preemption of sugar-sweetened beverage taxes across the United States, we aimed to evaluate the associations between sugar-sweetened beverage (SSB) taxes and adolescent weight-related outcomes using data on 364,540 adolescents drawn from 1999 to 2021 district Youth Risk Behavior Surveys. We used difference-in-differences models to assess the associations and the potential mediating roles of SSBs, milk, and 100% fruit juice consumption. We found that a one cent per ounce increase in SSB taxes was associated with a 0.26 lower body mass index ( P < .01), and a 2.19 ( P < .01) and 1.68 ( P < .01) percentage point decrease in the probability of being affected by overweight and obesity, respectively. SSB consumption had a mediating role, as tax increases were associated with a 2.45 ( P < .01) percentage point decrease in adolescents' probability of drinking any SSB. Milk and 100% fruit juices likely also played a role, as we found changes in their consumption in response to tax increases.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.115
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.358
Teacher spread0.313 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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