Evidence map›Paper›PMID 41845442›Full record

ArticleBMC medicine2026

Cause-specific years of life lost attributable to non-optimal body mass index by county, sex, race, and ethnicity in the USA, 2000-2019: a systematic analysis of health disparities.

GBD US Health Disparities Collaborators, Farah Mouhanna, Ethan Kahn, Chris A Schmidt, Theresa A McHugh, Mathew M Baumann, Yekaterina O Kelly, Wichada La Motte-Kerr, Rebecca M Cogen, Xiaochen Dai and 15 more

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Article in BMC 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

25 authors.

GBD US Health Disparities Collaborators
Farah Mouhanna *Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Ethan Kahn *Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Chris A SchmidtInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Theresa A McHughInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Mathew M BaumannInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Yekaterina O KellyInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Wichada La Motte-KerrInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Rebecca M CogenInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Xiaochen DaiInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Emmanuela GakidouInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
César Montalvo-ClavijoInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Zhuochen LiInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Michael CeloneInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Nicole DeCleeneInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Kosuke TamuraDivision of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Kelvin ChoiDivision of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Juliana Teruel CamargoEpidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Amanda S HinermanEpidemiology and Community Health Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Christian S AlvarezDivision of Intramural Research, National Institute On Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
George A MensahCenter for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Eliseo J Pérez-StableUniversity of California, San Francisco, San Francisco, CA, USA.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Ali H MokdadInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Laura Dwyer-LindgrenInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA. ladwyer@uw.edu.

Funding

NIH HHS 75N94019C00016NIMHD NIH HHS 75N94023C00004
6 · The paper itself

Abstract

backgroundOver 334,000 deaths in 2021 in the USA were attributed to non-optimal body mass index ([BMI] greater than 20 to 21 kg/m

methodsUsing survey data from the Behavioral Risk Factor Surveillance System (BRFSS), Gallup Daily, and National Health and Nutrition Examination Survey (NHANES), we estimated obesity prevalence annually, stratified by county, age, sex, and five mutually exclusive racial and/or ethnic populations (AIAN, Asian or Pacific Islander [Asian], Black, Latino or Hispanic [Latino], and White). We calculated population attributable fractions (PAFs) and estimated YLLs attributable to non-optimal BMI for 27 causes of death (focusing on ischemic heart disease [IHD], colorectal cancer, and diabetes) using cause-specific YLL estimates from a previous analysis.

resultsAge-standardized obesity prevalence increased by 12.3 percentage points (95% uncertainty interval 11.9-12.8) to 40.2% (40.0-40.6) in the USA from 2000 to 2019 and was highest in the Black population, followed by the AIAN, Latino, White, and Asian populations. In 2019, the Black population had the highest rates of IHD and colorectal cancer YLLs attributable to non-optimal BMI, followed by the AIAN, White, Latino, and Asian populations. The AIAN population had the highest attributable YLL rate for diabetes in 2019, followed by the Black, Latino, White, and Asian populations. All racial and/or ethnic populations had statistically significant reductions in IHD and diabetes YLL rates attributable to non-optimal BMI from 2000 to 2019, with declines in total YLL rates for these causes more than offsetting increases in obesity prevalence and PAFs. Relative disparities among counties were two to four times as large for attributable YLL rates as for obesity prevalence.

conclusionsRacial and/or ethnic disparities in obesity prevalence are substantial, but disparities in YLLs attributable to non-optimal BMI are larger because they are compounded by disparities in YLL rates.

Indexed as

Body Mass IndexEthnicityHealth Status DisparitiesLife ExpectancyObesityRacial GroupsAdultAgedCause of DeathFemaleHumansMaleMiddle AgedNutrition SurveysPrevalenceUnited StatesAttributable years of life lostBody mass indexBRFSSHealth disparitiesObesityRace and ethnicitySmall-area estimationUS counties

Identifiers

PMID41845442
PMCPMC13112685

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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 Socratic graph.