Evidence map›Paper›PMID 41581533›Full record

ArticleAmerican journal of preventive medicine2026

Sugar-Sweetened Beverage Warning Labels and Taxes: Simulated Impacts When Considering Implementation.

Natalie Riva Smith, Jennifer L Cruz, Anna H Grummon, Shu Wen Ng, Marissa G Hall, Leah Frerichs, Kristen Hassmiller Lich

Abstract read
In one paragraph

Article in American journal of preventive medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Natalie Riva SmithDepartment of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania. Electronic address: natsmith@pitt.edu.
Jennifer L CruzDepartment of Research, New York Academy of Medicine, New York, New York.
Anna H GrummonDepartment of Pediatrics, Stanford University School of Medicine, Palo Alto, California; Department of Health Policy, Stanford University School of Medicine, Stanford, California.
Shu Wen NgDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Marissa G HallCarolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Leah FrerichsDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Kristen Hassmiller LichDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Funding

Developing and evaluating a decision support tool to disseminate tobacco control research and inform policy implementationR00CA277135 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Natalie Riva Smith · 2025 to 2026
$492k
Developing and evaluating a decision support tool to disseminate tobacco control research and inform policy implementationK99CA277135 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI SMITH, NATALIE RIVA · 2023 to 2024
$254k
NCI NIH HHS K99 CA277135NCI NIH HHS R00 CA277135
6 · The paper itself

Abstract

introductionSugar-sweetened beverage warning label and excise tax policies hold promise for preventing Type 2 diabetes. Population-level impacts of sugar-sweetened beverage policies have been projected using simulation models, but the authors are unaware of any assessing the effect of warning labels on Type 2 diabetes or the effects of warning label and tax effects using the same modeling assumptions. Simulation models have also rarely considered how policy implementation factors such as design and sustainment affect policy outcomes.

methodsMicrosimulation model of Type 2 diabetes development in a closed cohort of U.S. adults aged 18-64 years over 10 years with 1,200 replications was performed. The model was developed using the National Health and Nutrition Examination Survey 2017-2018, U.S. Diabetes Surveillance System, and published literature. Policy implementation scenarios were modeled in 2025 for warning label (design: graphic or text warning label) and tax (sustainment: $0.02/fluid ounce excise tax sustained or repealed) policies, compared with the status quo.

resultsRelative to the status quo, a graphic warning label was estimated to avert 945,000 cases of Type 2 diabetes over 10 years (95% uncertainty interval=442,000; 1,820,000) compared with 480,000 (95% uncertainty interval=147,000; 1,140,000) cases averted under a text warning label. A $0.02/fluid ounce excise tax was estimated to avert 1,260,000 (95% uncertainty interval=646,000; 2,160,000) cases of Type 2 diabetes over 10 years. If repealed after 1 year, the policy would only avert 78,000 (95% uncertainty interval=0; 469,000) cases of Type 2 diabetes.

conclusionsSugar-sweetened beverage policies may be an approach to meaningfully decrease the number of individuals with Type 2 diabetes. Policy design and sustainment drive the magnitude of effects.

Indexed as

Diabetes Mellitus, Type 2Food LabelingSugar-Sweetened BeveragesTaxesAdolescentAdultFemaleHumansMaleMiddle AgedNutrition SurveysUnited StatesYoung Adult

Identifiers

PMID41581533
PMCPMC13093228

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.