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Cited by 4,685Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Management and ResearchGlobal Health and Epidemiology

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Open access
Ong, Kanyin Liane, Stafford, Lauryn K, McLaughlin, Susan A, Boyko, Edward J, Vollset, Stein Emil, Smith, Amanda E, Dalton, Bronte E, Duprey, Joe +787 more·2023·The Lancet
DOI 10.1016/s0140-6736(23)01301-6PMC PMC10364581
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Abstract

BACKGROUND: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. METHODS: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. FINDINGS: In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. INTERPRETATION: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. FUNDING: Bill & Melinda Gates Foundation.

AI snapshot

Written from the paper’s full text.

TL;DR

In 2021, an estimated 529 million people worldwide were living with diabetes, with a global age-standardised prevalence of 6·1%, and type 2 diabetes accounted for 96·0% of cases and 95·4% of diabetes DALYs. Projections indicate more than 1·31 billion people will have diabetes by 2050, with no countries or territories expected to see prevalence decrease.

Key findings

In 2021, there were 529 million (95% UI 500–564) people with diabetes globally, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5); type 2 diabetes made up 96·0% (95·1–96·8) of cases and 95·4% (94·9–95·9) of DALYs.

  • The highest age-standardised rates were in north Africa and the Middle East (9·3% [8·7–9·9]) and, regionally, Oceania (12·3% [11·5–13·0]); Qatar had the highest age-specific prevalence at 76·1% (73·1–79·5) in people aged 75–79 years.
  • High BMI was the leading risk factor, accounting for 52·2% (25·5–71·8) of global type 2 diabetes DALYs in 2021, and its contribution rose by 24·3% (18·5–30·4) between 1990 and 2021.
  • By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with global age-standardised total prevalence expected to reach 9·8% (9·4–10·2); 89 (43·6%) of 204 countries and territories are projected to exceed 10%, and no countries are expected to see a decrease.

Methods

The study produced estimates for 204 countries and territories across 25 age groups for males and females separately and combined, covering prevalence, mortality, YLLs, YLDs, and DALYs from 1990 to 2021, plus type-specific proportions, risk-attributable burden, and forecasts to 2050.

  • Mortality was modelled with CODEm using 25 666 location-years of vital registration and verbal autopsy data; non-fatal estimates used DisMod-MR 2.1 with 1527 location-years from 172 countries, and type 2 diabetes was computed by subtracting type 1 diabetes from total diabetes.
  • Risk-attributable type 2 diabetes burden was estimated for 16 risk factors using comparative risk assessment; forecasting used regression models with Socio-demographic Index for type 1 diabetes and high BMI for type 2 diabetes.

Limitations

Stated by the paper: self-reported diabetes not validated by blood glucose tests was excluded; estimates and forecasts do not reflect COVID-19 impacts in 2020–2021; ICD coding variation and reallocation of unspecified diabetes deaths relied on data from high-income and high-middle-income locations; single glucose tests may overestimate prevalence; blood collection method was missing for about half of data sources; gestational and monogenic diabetes were not modelled; all individuals younger than 15 years were assumed to have type 1 diabetes; and risk factor analyses were limited to modifiable risk–outcome pairs, with no cohort effects except tobacco.

  • Evident from the text: type 2 diabetes estimates were derived by subtracting type 1 diabetes from total diabetes because available data labelled as type 2 were not considered accurate, so type 2 estimates depend on the type 1 model; forecasts use only SDI for type 1 and high BMI for type 2 as predictors, not all known risk factors.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/s0140-6736(23)01301-6

Cites

90 of 90 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

20 of 41 cited sources checked · 9 supported · 0 flagged · accuracy not yet measured

  • Study design and conductcitation

    ICD coding practices documenting the underlying cause of death can vary.

    “Other limitations of our study include, first, that ICD coding practices documenting the underlying cause of death can vary.”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [84] Jougla, 1992, Death certificate coding practices related to diabetes in European countries—the ‘EURODIAB Subarea C’ Study, Int J Epidemiol source
    • cites [85] McEwen, 2006, Diabetes reporting as a cause of death: results from the Translating Research Into Action for Diabetes (TRIAD) study, Diabetes Care source
    • cites [86] Rampatige, 2014, Systematic review of statistics on causes of deaths in hospitals: strengthening the evidence for policy-makers, Bull World Health Organ source
  • Interpretationreasoning

    Differences between estimates produced by GBD and IDF or NCD-RisC are likely to be due to differences in methods, case inclusion criteria, and data sources used.

    “Differences between estimates produced by GBD and IDF or NCD-RisC are likely to be due to differences in methods, case inclusion criteria, and data sources used.”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [4] 2021

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [11] Zhou, 2016, Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants, Lancet source

      The Discussion (page not provided) reports that this paper’s diabetes estimates differed from IDF estimates in some countries and regions. The Methods describe its data sources and diabetes definitions, but our copy does not say that differences between GBD and IDF or NCD-RisC estimates are likely caused by differences in methods, case inclusion criteria, and data sources. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
  • Study design and conductcitation

    Under the GBD's comparative risk assessment framework, we do not currently capture risk factors that have been reported in the literature for type 1 diabetes.

    “Under the GBD's comparative risk assessment framework, we do not currently capture risk factors that have been reported in the literature for type 1 diabetes.93, 94”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [93] Pociot, 2016, Genetic risk factors for type 1 diabetes, Lancet source
    • cites [94] Rewers, 2016, Environmental risk factors for type 1 diabetes, Lancet source
  • Findingown data

    At the super-region level, the highest age-standardised total diabetes prevalence rate in 2021 was in north Africa and the Middle East, at 9·3% (95% UI 8·7-9·9).

    “At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9])”
    • Also stated in: § Total diabetes prevalence · plain
      “north Africa and the Middle East had an age-standardised total diabetes prevalence rate of 9·3% (8·7–9·9)”
    Quote in the paperNot a substantial claim
  • Findingown data

    In 2021, type 2 diabetes accounted for 96·0% (95% UI 95·1-96·8) of diabetes cases worldwide.

    “type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: All deaths due to diabetes in people younger than 15 years were from type 1 diabetes.
    • The authors state this as an assumption: There was no remission (no cure) in the type 1 diabetes model.
    • The authors state this as an assumption: All diabetes cases in people aged younger than 15 years were type 1 diabetes.
    • The authors state this as an assumption: All patients aged younger than 15 years with diabetes had sought hospital care due to their insulin dependence.
  • May combine two claims. 31
  • Classified as probably not a substantial claim. 44
  • The quote only partly states this claim. 1

  • May combine two claims.

    Premisecitation

    All risk factors modelled have been shown to be associated with type 2 diabetes, but high air temperature and low air temperature are the only risk factors associated with type 1 diabetes.

    “All risk factors have been shown to be associated with type 2 diabetes, but high air temperature and low air temperature are the only risk factors associated with type 1 diabetes.28”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [28] 2020, Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet source

      The Methods section (‘Determining the inclusion of risk–outcome pairs in GBD’; page unavailable) says high and low temperatures were evaluated against disease outcomes, but does not identify type 1 diabetes among them. Searches for type 1 diabetes, type 2 diabetes, and diabetes–temperature risk–outcome pairs found no passage establishing the claimed distinction or that all the relevant risk factors are associated with type 2 diabetes. (LLM, conf=0.72)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    According to IDF estimates, 537 million people worldwide had diabetes in 2021.

    “estimates by the International Diabetes Federation (IDF) indicating that 537 million people worldwide had diabetes in 2021”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [4] 2021

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [10] Sun, 2022, IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045, Diabetes Res Clin Pract source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    According to IDF estimates, diabetes resulted in health expenditures of US$966 billion globally in 2021, forecast to reach more than $1054 billion by 2045.

    “resulting in health expenditures of US$966 billion globally, forecast to reach more than $1054 billion by 2045.4, 10”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [4] 2021

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [10] Sun, 2022, IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045, Diabetes Res Clin Pract source

      The Results (Health expenditure) report global diabetes-related healthcare expenditure of USD 966 billion in 2021 and predict USD 1,054 billion for 2045. The source does not say the 2045 figure will be more than USD 1,054 billion; it gives that figure as the estimate. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    According to GBD 2019, diabetes was the eighth leading cause of death and disability combined in the world in 2019.

    “According to estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, diabetes was the eighth leading cause of death and disability combined in the world”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [6] 2020, Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet source

      The Results (Global trends; page unavailable) place diabetes among the ten leading causes of disability-adjusted life-years (DALYs) in both older age groups in 2019, and the Discussion describes it as a major contributor to disease burden. The passages in our copy do not establish that diabetes ranked eighth worldwide across all ages; searches for “eighth” and the global diabetes ranking did not find that rank. (LLM, conf=0.79)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The comparative risk assessment approach is predicated on a causal web of hierarchically organised, potentially overlapping health risks.

    “the comparative risk assessment approach, a framework used by GBD since 2002 that is predicated on a causal web of hierarchically organised, potentially overlapping health risks.29, 37”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [29] Murray, 1997, Global mortality, disability, and the contribution of risk factors: Global Burden of Disease Study, Lancet source
    • cites [37] Murray, 2003, Comparative quantification of health risks conceptual framework and methodological issues, Popul Health Metr source

      The Introduction (page unavailable) identifies comparative risk assessment as a GBD framework and says it considers multiple stages in a causal network, including interactions and joint effects of risk factors. The Risk Quantification Models section (page unavailable) describes layers of a causal web, supporting the claim about hierarchically organised, potentially overlapping risks. The paper identifies the framework with the GBD 2000 study but does not establish that GBD has used it since 2002. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In 2019, nearly 460 million people across every country and age group were living with diabetes.

    “with nearly 460 million people across every country and age group living with the disease in 2019.6”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [6] 2020, Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet source

      The Discussion (Main findings; page unavailable) describes rising diabetes prevalence globally, and the Results (Global trends; page unavailable) identify diabetes as a major contributor to disease burden in 2019. Neither passage gives a count of nearly 460 million people living with diabetes or establishes the claim for every country and age group. (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The 2016 NCD-RisC Study projected that the probability of meeting global targets to halt the rising diabetes prevalence by 2025 was lower than 1% for women and even lower for men.

    “The 2016 NCD Risk Factor Collaboration (NCD-RisC) Study projected that the probability of meeting global targets to halt the rising diabetes prevalence by 2025 was lower than 1% for women and even lower for men.11”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [11] Zhou, 2016, Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants, Lancet source

      The Results state that, if post-2000 trends continue, the worldwide probability of meeting the 2025 diabetes target is lower than 1% for men but is 1% for women. The claim gets the ordering right, but incorrectly says the probability for women is lower than 1%. (LLM, conf=0.94)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Type 1 diabetes often develops during childhood, while type 2 diabetes has a strong genetic component and a robust association with obesity and a sedentary lifestyle.

    “Type 1 diabetes often develops during childhood, while type 2 diabetes has a strong genetic component and a robust association with obesity and a sedentary lifestyle.10, 13”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [10] Sun, 2022, IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045, Diabetes Res Clin Pract source

      The Discussion > Limitations passage says type 1 diabetes incidence peaks in childhood and links type 2 diabetes to excess body weight and sedentary behaviour. It does not describe a strong genetic component of type 2 diabetes; its discussion of genetic factors concerns type 1 diabetes. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [13] Ahmad, 2022, Type 2 diabetes, Lancet source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Diabetes is a major risk factor for ischaemic heart disease and stroke.

    “Diabetes is also a major risk factor for ischaemic heart disease and stroke,12”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [12] 2010, Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies, Lancet source

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationcitation

    Efforts that succeed in halting the rise in diabetes could mitigate or delay associated health complications if implemented early.

    “Furthermore, efforts that succeed in halting the rise in diabetes could mitigate or delay associated health complications if implemented early.76, 77, 78, 79, 80”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites [76] Aggarwal, 2020, Diabetes mellitus association with coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis, J Diabetes source
    • cites [77] Feldman, 2020, COVID-19 and diabetes: a collision and collusion of two diseases, Diabetes source

      The Glycemic Control section reports better COVID-19 outcomes among people with diabetes whose blood glucose was well controlled. The Conclusions discuss improving care, but neither section addresses whether early efforts to halt the rise in diabetes could mitigate or delay its associated health complications; searches for diabetes prevention and early intervention found no such passage. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
    • cites [78] Kissela, 2005, Epidemiology of ischemic stroke in patients with diabetes: the greater Cincinnati/Northern Kentucky Stroke Study, Diabetes Care source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    The regional variation in sex differences in age-standardised diabetes prevalence rates revealed by our estimates is probably also related to variation in patterns in and the impact of obesity on type 2 diabetes, socioeconomic factors, and biological and hormonal differences.

    “The regional variation in sex differences in age-standardised diabetes prevalence rates revealed by our estimates is probably also related to variation in patterns in and the impact of obesity on type 2 diabetes, socioeconomic factors, and biological and hormonal differences.48”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [48] Kautzky-Willer, 2016, Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus, Endocr Rev source

  • May combine two claims.

    Premisecitation

    Various interventions and policies to address obesity have been developed and studied, but no programme to date has shown long-term, sustained, population-level reductions in obesity.

    “Various interventions and policies to address obesity have been developed and studied,53, 54, 55, 56 but no programme to date has shown long-term, sustained, population-level reductions in obesity.57”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [53] Maniadakis, 2013, A systematic review of the effectiveness of taxes on nonalcoholic beverages and high-in-fat foods as a means to prevent obesity trends, Clinicoecon Outcomes Res source

      The Discussion (page unavailable) says the evidence for pricing and tax policies reducing obesity rates is inconsistent. That supports a narrower concern about those policies, but the review does not establish that no obesity programme of any kind has achieved long-term, sustained, population-level reductions. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [54] Puzziferri, 2014, Long-term follow-up after bariatric surgery: a systematic review, JAMA source

      Citation 57 is attached to the claim that no programme has achieved sustained, population-level reductions in obesity. The Discussion and Abstract Conclusions address long-term follow-up and possible overestimation of outcomes after bariatric surgery, not population-level obesity trends or the effectiveness of programmes generally. Searches for sustained population-level reductions and obesity programmes found no passage addressing that claim; page numbers were unavailable in our copy. (LLM, conf=0.77)

      Experimental — accuracy not yet measured
    • cites [55] Sun, 2017, The effectiveness and cost of lifestyle interventions including nutrition education for diabetes prevention: a systematic review and meta-analysis, J Acad Nutr Diet source

  • May combine two claims.

    Premisecitation

    Although obesity is theoretically reversible and addressing it could provide the biggest opportunity to limit the advance of diabetes, current trends suggest that obesity rates are likely to continue to climb.

    “Although obesity is theoretically reversible28, 51, 52 and addressing it could provide the biggest opportunity to limit the advance of diabetes, current trends suggest that obesity rates are likely to continue to climb.45”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites [28] 2020, Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet source
    • cites [51] Afshin, 2017, Health effects of overweight and obesity in 195 countries, N Engl J Med source
    • cites [52] Teufel, 2021, Body-mass index and diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults, Lancet source

  • May combine two claims.

    Study design and conductcitation

    There is growing evidence of type 2 diabetes occurring in younger individuals in many parts of the world, but we do not have population-based studies that distinguish type 1 diabetes from type 2 diabetes among people younger than 15 years across time, age, and location.

    “Although there is growing evidence of type 2 diabetes occurring in younger individuals in many parts of the world,91, 92 we do not have population-based studies that distinguish type 1 diabetes from type 2 diabetes among people younger than 15 years across time, age, and location.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [91] Kao, 2016, Type 2 diabetes mellitus in children and adolescents, Aust Fam Physician

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [92] Nadeau, 2016, Youth-onset type 2 diabetes consensus report: current status, challenges, and priorities, Diabetes Care source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In 2010, there were 321 million (95% UI 304-341) people aged 18 years and older with diabetes.

    “and 321 million (304–341) people aged 18 years and older with diabetes in 2010”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In the 75-79 year age group, diabetes prevalence was lowest in central Europe, eastern Europe, and central Asia, at 19.8% (18.3-21.6).

    “and lowest in central Europe, eastern Europe, and central Asia, at 19·8% (18·3–21·6; figure 2).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Globally in 2021, age-standardised total diabetes prevalence was 6.5% (95% UI 6.2-7.0) in males and 5.8% (5.4-6.1) in females.

    “The global age-standardised total diabetes prevalence was higher in males than in females (6·5% [95% UI 6·2–7·0] vs 5·8% [5·4–6·1])”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    There was no difference in type-specific breakdown by sex.

    “There was no difference in type-specific breakdown by sex.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    By 2050, expected age-standardised total diabetes prevalence rates will be greater than 10% in two super-regions: 16·8% (95% UI 16·1-17·6) in north Africa and the Middle East and 11·3% (95% UI 10·8-11·9) in Latin America and Caribbean.

    “with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Annual remission in the total diabetes model could be no more than 1%.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In the 75-79 year age group, diabetes prevalence was highest in the north Africa and Middle East super-region, at 39.4% (36.3-42.3).

    “In this age group, prevalence was highest in the north Africa and Middle East super-region, at 39·4% (36·3–42·3)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In 2021, age-standardised diabetes prevalence rates were greater than 20% in the Marshall Islands (22·2%; 95% UI 20·7-23·9) and American Samoa (21·4%; 95% UI 19·9-22·7).

    “age-standardised prevalence rates were greater than 20% in the Marshall Islands, at 22·2% (20·7–23·9), and American Samoa, at 21·4% (19·9–22·7).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Diabetes prevalence rates are driven almost entirely by type 2 diabetes, which accounted for more than 96% of diabetes cases worldwide in 2021.

    “Because diabetes prevalence rates are driven almost entirely by type 2 diabetes, which accounted for more than 96% of diabetes cases worldwide in 2021, much of the following discussion will focus on type 2 diabetes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The age-standardised diabetes prevalence increased by more than 100% in 97 (47.5%) of 204 countries and territories and by more than 200% in three countries and one territory: Egypt (284.3%; 262.7-305.9), Greenland (263.6%; 236.8-296.3), Timor-Leste (225.3%; 206.7-243.7), and Seychelles (211.5%; 193.5-230.7).

    “The age-standardised diabetes prevalence increased by more than 100% in 97 (47·5%) of 204 countries and territories and by more than 200% in three countries and one territory: Egypt (284·3%; 262·7–305·9), Greenland (263·6%; 236·8–296·3), Timor-Leste (225·3%; 206·7–243·7), and Seychelles (211·5%; 193·5–230·7).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The age-standardised global prevalence of type 2 diabetes is projected to rise by 61.2% (95% UI 56.2-68.1), from 5.9% (5.5-6.3) in 2021 to 9.5% (9.0-9.9) in 2050, affecting more than 1.27 billion (1.19-1.35) people.

    “The age-standardised global prevalence of type 2 diabetes is projected to rise by 61·2% (95% UI 56·2–68·1), from 5·9% (5·5–6·3) in 2021 to 9·5% (9·0–9·9) in 2050, affecting more than 1·27 billion (1·19–1·35) people.”
    • Also stated in: § Discussion · plain
      “By 2050, we project a global increase in age-standardised type 2 diabetes prevalence of more than 60%, with increases of more than 70% in six regions: north Africa and the Middle East, east Asia, central sub-Saharan Africa, southern sub-Saharan Africa, central Latin America, and Australasia.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The global age-standardised diabetes DALY rate was 915.0 (782.6-1067.5) per 100,000.

    “The global age-standardised diabetes DALY rate was 915·0 (782·6–1067·5) per 100 000”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In 47 (23.0%) countries and territories, age-standardised DALY rates were greater than 2000 per 100 000.

    “In 47 (23·0%) countries and territories, age-standardised DALY rates were greater than 2000 per 100 000 (table).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Globally in 2021, there were 37.8 million (95% UI 35.4-40.2) total diabetes-related YLLs.

    “Globally, there were 37·8 million (95% UI 35·4–40·2) total diabetes-related YLLs”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Between 2021 and 2050, the global age-standardised total diabetes prevalence is expected to increase by 59.7% (95% UI 54.7-66.0), from 6.1% (5.8-6.5) to 9.8% (9.4-10.2), resulting in 1.31 billion (1.22-1.39) people living with diabetes in 2050, or an annualised rate of change of 3.31%.

    “Between 2021 and 2050, the global age-standardised total diabetes prevalence is expected to increase by 59·7% (95% UI 54·7–66·0), from 6·1% (5·8–6·5) to 9·8% (9·4–10·2), resulting in 1·31 billion (1·22–1·39) people living with diabetes in 2050, or an annualised rate of change of 3·31%.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Type 2 diabetes made up 96.6% (96.0-97.3) of diabetes YLDs.

    “96·6% (96·0–97·3) of YLDs”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The age-standardised DALY rate was more than 1000 per 100 000 in four GBD super-regions: Latin America and Caribbean, sub-Saharan Africa, north Africa and the Middle East, and south Asia.

    “The age-standardised DALY rate was more than 1000 per 100 000 in four GBD super-regions: Latin America and Caribbean, sub-Saharan Africa, north Africa and the Middle East, and south Asia.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The global age-standardised diabetes YLD rate was 477.6 (340.7-637.4) per 100,000.

    “and the YLD rate was 477·6 (340·7–637·4) per 100 000.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    At the regional level, the increase in type 2 diabetes DALYs attributable to high BMI between 1990 and 2021 was greater than 45% in south Asia (58.0%; 44.0 to 75.4), central sub-Saharan Africa (48.8%; 35.8 to 61.2), and east Asia (45.7%; 33.5 to 57.3).

    “At the regional level, the increase in type 2 diabetes DALYs attributable to high BMI between 1990 and 2021 was greater than 45% in south Asia (58·0%; 44·0 to 75·4), central sub-Saharan Africa (48·8%; 35·8 to 61·2), and east Asia (45·7%; 33·5 to 57·3).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Type 2 diabetes made up 94.0% (93.6-94.6) of diabetes YLLs.

    “Type 2 diabetes made up 94·0% (93·6–94·6) of diabetes YLLs”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Globally in 2021, there were 41.4 million (29.5-55.4) YLDs due to diabetes.

    “and 41·4 million (29·5–55·4) YLDs”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The global age-standardised diabetes YLL rate was 437.4 (409.2-464.1) per 100,000.

    “the YLL rate was 437·4 (409·2–464·1) per 100 000”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Of the projected increase in total diabetes prevalence between 2021 and 2050, 49.6% is driven by trends in obesity, and the remaining 50.4% is driven by demographic shifts.

    “Of this increase, 49·6% is driven by trends in obesity, and the remaining 50·4% is driven by demographic shifts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Diabetes was especially prevalent in people aged 65 years and older in every location, but in some locations prevalence rates were high even in younger adults, exceeding 10% among those aged 30-34 years in ten countries, all in Oceania.

    “Diabetes was especially prevalent in people aged 65 years and older in every location, but in some locations the prevalence rates were high even in younger adults, exceeding 10% among those aged 30–34 years in ten countries, all in Oceania.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Between 1990 and 2021, the global DALY count for diabetes increased by 189.8% (171.1-203.4).

    “Global | Global | 79 200 (67 800 to 92 500) | 189·8% (171·1 to 203·4) | 915·0 (782·6 to 1067·4) | 38·6% (29·7 to 45·3)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There were an estimated 529 million people living with diabetes in 2021, a number projected to more than double to about 1.31 billion by 2050.

    “There were an estimated 529 million people living with diabetes in 2021, a number we project will more than double to about 1·31 billion by 2050.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Globally in 2021, there were 79.2 million (67.8-92.5) DALYs due to diabetes.

    “yielding 79·2 million (67·8–92·5) DALYs due to diabetes in 2021”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Although there were increases in every super-region, the largest change in the proportion of type 2 diabetes DALYs attributable to high BMI occurred in south Asia, with an increase of 58.0% (44.0 to 75.4).

    “Although there were increases in every super-region, the largest change occurred in south Asia, with an increase of 58·0% (44·0 to 75·4).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In 13 of 18 countries and territories in Oceania, ten of 21 countries in north Africa and the Middle East, and one country in the Caribbean, the diabetes prevalence will be greater than 20% by 2050.

    “In 13 of 18 countries and territories in Oceania, ten of 21 countries in north Africa and the Middle East, and one country in the Caribbean, the diabetes prevalence will be greater than 20% by 2050.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Regionally, the age-standardised DALY rate varied from a high of 3577.0 (95% UI 3157.0-4120.5) per 100 000 in Oceania to a low of 511.8 (402.0-648.3) per 100 000 in western Europe.

    “Regionally, the age-standardised DALY rate varied from a high of 3577·0 (95% UI 3157·0–4120·5) per 100 000 in Oceania to a low of 511·8 (402·0–648·3) per 100 000 in western Europe.”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    Over 1990-2021, the proportion of DALYs due to high BMI increased in every country and territory, ranging from an increase of 77.2% (52.2 to 107.9) in Viet Nam to 1.3% (-1.5 to 4.1) in Czechia.

    “Over this period, the proportion of DALYS due to high BMI increased in every country and territory, ranging from an increase of 77·2% (52·2 to 107·9) in Viet Nam to 1·3% (–1·5 to 4·1) in Czechia (figure 3).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The age-standardised diabetes prevalence rate is projected to exceed 10% in 89 (43.6%) countries and territories and to surpass 20% in 24 (11.8%) countries and territories by 2050.

    “The age-standardised diabetes prevalence rate is projected to exceed 10% in 89 (43·6%) countries and territories and to surpass 20% in 24 (11·8%) countries and territories.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    At the country level, Fiji had the highest age-standardised DALY rate, at 7333.9 (6066.7-8776.7) per 100 000.

    “At the country level, Fiji had the highest age-standardised DALY rate, at 7333·9 (6066·7–8776·7) per 100 000.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Type 2 diabetes made up 95.4% (94.9-95.9) of diabetes DALYs.

    “and 95·4% (94·9–95·9) of DALYs.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The global age-standardised diabetes prevalence rate in 2021 was 6.1%, with highs of 9.3% in the north Africa and Middle East super-region and 12.3% in the Oceania region.

    “The global age-standardised diabetes prevalence rate in 2021 was 6·1%, with highs of 9·3% in the north Africa and Middle East super-region and 12·3% in the Oceania region.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given

    Diabetes prevalence continues to increase in every country and territory, age group, and in both males and females.

    “diabetes prevalence continues to increase in every country and territory, age group, and in both males and females.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The Lancet Commission on diabetes reported that 80% of diabetes cases occur in low-income and middle-income countries.

    “The Lancet Commission on diabetes published in 2020 highlights the unequal burden of the disease on people in low-income and middle-income countries (LMICs), reporting that 80% of diabetes cases occur in LMICs.2”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [2] Chan, 2021, The Lancet Commission on diabetes: using data to transform diabetes care and patient lives, Lancet source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Type 2 diabetes is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course.

    “Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    There were 1527 location-years of data from 172 countries (84·3% of the 204 countries and territories included in GBD) used in the diabetes modelling process.

    “There were 1527 location-years of data from 172 countries (84·3% of the 204 countries and territories included in GBD) used in the diabetes modelling process (appendix section 4.1.1).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    All evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors.

    “However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Type 1 and type 2 diabetes are the most common forms of diabetes and are diagnosed through well established criteria.

    “Type 1 and type 2 diabetes are the most common forms of the disease and are diagnosed through well established criteria.1, 2, 4”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [1]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [2] Chan, 2021, The Lancet Commission on diabetes: using data to transform diabetes care and patient lives, Lancet source
    • cites [4] 2021

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.

    “The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    There were 25 666 location-years of death data included in the fatal model.

    “There were 25 666 location-years of death data included in the fatal model (appendix section 3.1).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    All codes used for these analyses are publicly available online.

    “All codes used for these analyses are publicly available online.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    For this study, 27 193 data sources were identified to which the methodological and evidentiary framework provided by GBD was applied.

    “For this study, we identified 27 193 data sources to which we applied the methodological and evidentiary framework provided by GBD.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement.

    “This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement (appendix table S1).30”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Stevens, 2016, Guidelines for accurate and transparent health estimates reporting: the GATHER statement, PLoS Med

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    The lack of long-term, sustained, population-level reductions in obesity is probably because no strategy has attempted to deal with the multiple factors that potentially contribute to obesity.

    “This is probably because no strategy has attempted to deal with the multiple factors that potentially contribute to obesity.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Study design and conductreasoning

    The robust location-specific, age-specific, and sex-specific diabetes estimates produced by our analysis are largely due to our ability to leverage the rigorous evidentiary and methodological framework provided by the larger GBD enterprise.

    “The robust location-specific, age-specific, and sex-specific diabetes estimates produced by our analysis are largely due to our ability to leverage the rigorous evidentiary and methodological framework provided by the larger GBD enterprise.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    Few countries have health-care systems that are positioned to take a proactive approach or possess the infrastructure to prioritise early interventions.

    “Few countries have health-care systems, however, that are positioned to take a proactive approach or possess the infrastructure to prioritise early interventions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Despite methodological and reporting differences, our estimates of particularly high total diabetes prevalence rates in the north Africa and the Middle East super-region are supported by similar estimates reported in the 2021 IDF Atlas.

    “Despite methodological and reporting differences, our estimates of particularly high total diabetes prevalence rates in the north Africa and the Middle East super-region are supported by similar estimates reported in the 2021 IDF Atlas.4”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] 2021

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    Relying on a single glucose test could overestimate diabetes prevalence; however, the magnitude of error depends on the distribution of blood sugar concentrations as the reliability of a single glucose test increases with higher blood sugar values.

    “Relying on a single glucose test could overestimate diabetes prevalence; however, the magnitude of error depends on the distribution of blood sugar concentrations as the reliability of a single glucose test increases with higher blood sugar values.87”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [87] Selvin, 2007, Short-term variability in measures of glycemia and implications for the classification of diabetes, Arch Intern Med source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Due to limitations inherent in the available literature and the comparative risk factor framework, we were unable to account for cohort effects apart from those pertaining to tobacco exposure.

    “Finally, due to limitations inherent in the available literature and the comparative risk factor framework, we were unable to account for cohort effects apart from those pertaining to tobacco exposure.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Our analysis did not estimate gestational diabetes explicitly since this diagnosis is captured in another GBD disease category, "Other maternal disorders", nor did our study include rarer forms of diabetes such as monogenic diabetes, due to the paucity of relevant data.

    “Fifth, our analysis did not estimate gestational diabetes explicitly since this diagnosis is captured in another GBD disease category, “Other maternal disorders”, nor did our study include rarer forms of diabetes such as monogenic diabetes,1 due to the paucity of relevant data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Study design and conductnone given

    Although our forecasts do not incorporate all known risk factors, they capture the principal driver of type 2 diabetes, high BMI.

    “Although our forecasts do not incorporate all known risk factors, here we capture the principal driver of type 2 diabetes, high BMI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given

    GBD generates risk factor analyses for modifiable risk-outcome pairs; each risk factor is identified by a review of the literature, and the risk-outcome association is quantified through a rules-based assessment of existing evidence.

    “Seventh, GBD generates risk factor analyses for modifiable risk–outcome pairs. Each risk factor is identified by a review of the literature, and we regularly revisit the possibility of adding additional risk factors. The risk–outcome association is quantified through a rules-based assessment of existing evidence.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    No effective mitigation strategy is currently in place.

    “with no effective mitigation strategy currently in place”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Although clinical diagnostic criteria for diabetes require more than one abnormal glucose concentration in the absence of symptoms, very few population-based epidemiological studies require these diagnostic criteria.

    “Third, although clinical diagnostic criteria for diabetes require more than one abnormal glucose concentration in the absence of symptoms,1 very few population-based epidemiological studies require these diagnostic criteria.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

70 of 70

  • The authors state this as an assumption.

    All deaths due to diabetes in people younger than 15 years were from type 1 diabetes.

    “We assumed that all deaths due to diabetes in people younger than 15 years were from type 1 diabetes.”

  • The authors state this as an assumption.

    There was no remission (no cure) in the type 1 diabetes model.

    “We assumed there was no remission (ie, no cure).”

  • The authors state this as an assumption.

    Annual remission in the total diabetes model could be no more than 1%.

    “and assumed that annual remission could be no more than 1% (appendix sections 4.3.1 and 4.3.2).”

  • The authors state this as an assumption.

    All diabetes cases in people aged younger than 15 years were type 1 diabetes.

    “For data from people aged younger than 15 years, we assumed that all diabetes cases were type 1 diabetes”

  • The authors state this as an assumption.

    All patients aged younger than 15 years with diabetes had sought hospital care due to their insulin dependence.

    “and that all patients had sought hospital care due to their insulin dependence.”

  • The authors state this as an assumption.

    Reporting bias in self-reported diabetes data changes over time and across location.

    “we assumed that reporting bias would change over time and across location (eg, due to variability in diagnostic and screening efficacy), thus making bias adjustment challenging.”

  • The authors state this as an assumption.

    All individuals younger than 15 years have type 1 diabetes.

    “GBD currently assumes that all individuals younger than 15 years have type 1 diabetes.”

  • The authors state this as an assumption.

    SDI is a good proxy for better access to care leading to a lower case fatality rate, and to increases in type 1 diabetes prevalence.

    “However, we believe that SDI is a good proxy for better access to care leading to a lower case fatality rate, and to increases in type 1 diabetes prevalence.”

  • The paper relies on this without support.

    Type 1 and type 2 diabetes differ with respect to risk factor profiles and underlying pathophysiology.

    “Differences between type 1 and type 2 diabetes with respect to risk factor profiles and underlying pathophysiology”

  • The paper relies on this without support.

    Hyperglycaemia leads to similar complications for both type 1 and type 2 diabetes.

    “given that hyperglycaemia leads to similar complications for both types”

  • The paper relies on this without support.

    Type 2 diabetes can in some cases be prevented.

    “Although type 2 diabetes can in some cases be prevented”

  • The paper relies on this without support.

    Management of hyperglycaemia has the potential to improve outcomes.

    “management of hyperglycaemia has the potential to improve outcomes”

  • The paper relies on this without support.

    Type 2 diabetes continues to be a major public health problem due to high rates of obesity.

    “the disease continues to be a major public health problem due to high rates of obesity that place an increasing burden on individuals and health-care systems alike”
13 of 13

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Australasia has a lower DALY rate than the stated regional minimum

    Minor

    The text calls western Europe's rate of 511·8 per 100 000 the lowest among regions, but the table gives Australasia a lower rate of 469·2.

  • Global DALY-rate uncertainty interval differs between text and table

    Minor

    The upper bound of the same global age-standardised DALY-rate uncertainty interval is 1067·5 in the text but 1067·4 in the table.

2 of 2

Cites

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1 of the 91 references couldn’t be matched to a known paper.

Cited by

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Authors
Ong, K. L., Stafford, L. K., McLaughlin, S. A., Boyko, E. J., Vollset, S. E., Smith, A. E., Dalton, B. E., Duprey, J., Cruz, J. A., Hagins, H., Lindstedt, P. A., Aali, A., Abate, Y. H., Abate, M. D., Abbasian, M., Abbasi-Kangevari, Z., Abbasi-Kangevari, M., Abd ElHafeez, S., Abd-Rabu, R., Abdulah, D. M., Abdullah, A. Y. M., Abedi, V., Abidi, H., Aboagye, R. G., Abolhassani, H., Abu-Gharbieh, E., Abu-Zaid, A., Adane, T. D., Adane, D. E., Addo, I. Y., Adegboye, O. A., Adekanmbi, V., Adepoju, A. V., Adnani, Q. E. S., Afolabi, R. F., Agarwal, G., Aghdam, Z. B., Agudelo-Botero, M., Aguilera Arriagada, C. E., Agyemang-Duah, W., Ahinkorah, B. O., Ahmad, D., Ahmad, R., Ahmad, S., Ahmad, A., Ahmadi, A., Ahmadi, K., Ahmed, A., Ahmed, A., Ahmed, L. A., Ahmed, S. A., Ajami, M., Akinyemi, R. O., Al Hamad, H., Al Hasan, S. M., AL-Ahdal, T. M. A., Alalwan, T. A., Al-Aly, Z., AlBataineh, M. T., Alcalde-Rabanal, J. E., Alemi, S., Ali, H., Alinia, T., Aljunid, S. M., Almustanyir, S., Al-Raddadi, R. M., Alvis-Guzman, N., Amare, F., Ameyaw, E. K., Amiri, S., Amusa, G. A., Andrei, C. L., Anjana, R. M., Ansar, A., Ansari, G., Ansari-Moghaddam, A., Anyasodor, A. E., Arabloo, J., Aravkin, A. Y., Areda, D., Arifin, H., Arkew, M., Armocida, B., Ärnlöv, J., Artamonov, A. A., Arulappan, J., Aruleba, R. T., Arumugam, A., Aryan, Z., Asemu, M. T., Asghari-Jafarabadi, M., Askari, E., Asmelash, D., Astell-Burt, T., Athar, M., Athari, S. S., Atout, M. M. W., Avila-Burgos, L., Awaisu, A., Azadnajafabad, S., B, D. B., Babamohamadi, H., Badar, M., Badawi, A., Badiye, A. D., Baghcheghi, N., Bagheri, N., Bagherieh, S., Bah, S., Bahadory, S., Bai, R., Baig, A. A., Baltatu, O. C., Baradaran, H. R., Barchitta, M., Bardhan, M., Barengo, N. C., Bärnighausen, T. W., Barone, M. T. U., Barone-Adesi, F., Barrow, A., Bashiri, H., Basiru, A., Basu, S., Basu, S., Batiha, A.-M. M., Batra, K., Bayih, M. T., Bayileyegn, N. S., Behnoush, A. H., Bekele, A. B., Belete, M. A., Belgaumi, U. I., Belo, L., Bennett, D. A., Bensenor, I. M., Berhe, K., Berhie, A. Y., Bhaskar, S., Bhat, A. N., Bhatti, J. S., Bikbov, B., Bilal, F., Bintoro, B. S., Bitaraf, S., Bitra, V. R., Bjegovic-Mikanovic, V., Bodolica, V., Boloor, A., Brauer, M., Brazo-Sayavera, J., Brenner, H., Butt, Z. A., Calina, D., Campos, L. A., Campos-Nonato, I. R., Cao, Y., Cao, C., Car, J., Carvalho, M., Castañeda-Orjuela, C. A., Catalá-López, F., Cerin, E., Chadwick, J., Chandrasekar, E. K., Chanie, G. S., Charan, J., Chattu, V. K., Chauhan, K., Cheema, H. A., Chekol Abebe, E., Chen, S., Cherbuin, N., Chichagi, F., Chidambaram, S. B., Cho, W. C. S., Choudhari, S. G., Chowdhury, R., Chowdhury, E. K., Chu, D.-T., Chukwu, I. S., Chung, S.-C., Coberly, K., Columbus, A., Contreras, D., Cousin, E., Criqui, M. H., Cruz-Martins, N., Cuschieri, S., Dabo, B., Dadras, O., Dai, X., Damasceno, A. A. M., Dandona, R., Dandona, L., Das, S., Dascalu, A. M., Dash, N. R., Dashti, M., Dávila-Cervantes, C. A., De la Cruz-Góngora, V., Debele, G. R., Delpasand, K., Demisse, F. W., Demissie, G. D., Deng, X., Denova-Gutiérrez, E., Deo, S. V., Dervišević, E., Desai, H. D., Desale, A. T., Dessie, A. M., Desta, F., Dewan, S. M. R., Dey, S., Dhama, K., Dhimal, M., Diao, N., Diaz, D., Dinu, M., Diress, M., Djalalinia, S., Doan, L. P., Dongarwar, D., dos Santos Figueiredo, F. W., Duncan, B. B., Dutta, S., Dziedzic, A. M., Edinur, H. A., Ekholuenetale, M., Ekundayo, T. C., Elgendy, I. Y., Elhadi, M., El-Huneidi, W., Elmeligy, O. A. A., Elmonem, M. A., Endeshaw, D., Esayas, H. L., Eshetu, H. B., Etaee, F., Fadhil, I., Fagbamigbe, A. F., Fahim, A., Falahi, S., Faris, M. E. M., Farrokhpour, H., Farzadfar, F., Fatehizadeh, A., Fazli, G., Feng, X., Ferede, T. Y., Fischer, F., Flood, D., Forouhari, A., Foroumadi, R., Foroutan Koudehi, M., Gaidhane, A. M., Gaihre, S., Gaipov, A., Galali, Y., Ganesan, B., Garcia-Gordillo, M., Gautam, R. K., Gebrehiwot, M., Gebrekidan, K. G., Gebremeskel, T. G., Getacher, L., Ghadirian, F., Ghamari, S.-H., Ghasemi Nour, M., Ghassemi, F., Golechha, M., Goleij, P., Golinelli, D., Gopalani, S. V., Guadie, H. A., Guan, S.-Y., Gudayu, T. W., Guimarães, R. A., Guled, R. A., Gupta, R., Gupta, K., Gupta, V. B., Gupta, V. K., Gyawali, B., Haddadi, R., Hadi, N. R., Haile, T. G., Hajibeygi, R., Haj-Mirzaian, A., Halwani, R., Hamidi, S., Hankey, G. J., Hannan, M. A., Haque, S., Harandi, H., Harlianto, N. I., Hasan, S. M. M., Hasan, S. S., Hasani, H., Hassanipour, S., Hassen, M. B., Haubold, J., Hayat, K., Heidari, G., Heidari, M., Hessami, K., Hiraike, Y., Holla, R., Hossain, S., Hossain, M. S., Hosseini, M.-S., Hosseinzadeh, M., Hosseinzadeh, H., Huang, J., Huda, M. N., Hussain, S., Huynh, H.-H., Hwang, B.-F., Ibitoye, S. E., Ikeda, N., Ilic, I. M., Ilic, M. D., Inbaraj, L. R., Iqbal, A., Islam, S. M. S., Islam, R. M., Ismail, N. E., Iso, H., Isola, G., Itumalla, R., Iwagami, M., Iwu, C. C. D., Iyamu, I. O., Iyasu, A. N., Jacob, L., Jafarzadeh, A., Jahrami, H., Jain, R., Jaja, C., Jamalpoor, Z., Jamshidi, E., Janakiraman, B., Jayanna, K., Jayapal, S. K., Jayaram, S., Jayawardena, R., Jebai, R., Jeong, W., Jin, Y., Jokar, M., Jonas, J. B., Joseph, N., Joseph, A., Joshua, C. E., Joukar, F., Jozwiak, J. J., Kaambwa, B., Kabir, A., Kabthymer, R. H., Kadashetti, V., Kahe, F., Kalhor, R., Kandel, H., Karanth, S. D., Karaye, I. M., Karkhah, S., Katoto, P. D., Kaur, N., Kazemian, S., Kebede, S. A., Khader, Y. S., Khajuria, H., Khalaji, A., Khan, M. A., Khan, M., Khan, A., Khanal, S., Khatatbeh, M. M., Khater, A. M., Khateri, S., khorashadizadeh, F., Khubchandani, J., Kibret, B. G., Kim, M. S., Kimokoti, R. W., Kisa, A., Kivimäki, M., Kolahi, A.-A., Komaki, S., Kompani, F., Koohestani, H. R., Korzh, O., Kostev, K., Kothari, N., Koyanagi, A., Krishan, K., Krishnamoorthy, Y., Kuate Defo, B., Kuddus, M., Kuddus, M. A., Kumar, R., Kumar, H., Kundu, S., Kurniasari, M. D., Kuttikkattu, A., La Vecchia, C., Lallukka, T., Larijani, B., Larsson, A. O., Latief, K., Lawal, B. K., Le, T. T. T., Le, T. T. B., Lee, S. W. H., Lee, M., Lee, W.-C., Lee, P. H., Lee, S.-W., Lee, S. W., Legesse, S. M., Lenzi, J., Li, Y., Li, M.-C., Lim, S. S., Lim, L.-L., Liu, X., Liu, C., Lo, C.-H., Lopes, G., Lorkowski, S., Lozano, R., Lucchetti, G., Maghazachi, A. A., Mahasha, P. W., Mahjoub, S., Mahmoud, M. A., Mahmoudi, R., Mahmoudimanesh, M., Mai, A. T., Majeed, A., Majma Sanaye, P., Makris, K. C., Malhotra, K., Malik, A. A., Malik, I., Mallhi, T. H., Malta, D. C., Mamun, A. A., Mansouri, B., Marateb, H. R., Mardi, P., Martini, S., Martorell, M., Marzo, R. R., Masoudi, R., Masoudi, S., Mathews, E., Maugeri, A., Mazzaglia, G., Mekonnen, T., Meshkat, M., Mestrovic, T., Miao Jonasson, J., Miazgowski, T., Michalek, I. M., Minh, L. H. N., Mini, G., Miranda, J. J., Mirfakhraie, R., Mirrakhimov, E. M., Mirza-Aghazadeh-Attari, M., Misganaw, A., Misgina, K. H., Mishra, M., Moazen, B., Mohamed, N. S., Mohammadi, E., Mohammadi, M., Mohammadian-Hafshejani, A., Mohammadshahi, M., Mohseni, A., Mojiri-forushani, H., Mokdad, A. H., Momtazmanesh, S., Monasta, L., Moniruzzaman, M., Mons, U., Montazeri, F., Moodi Ghalibaf, A., Moradi, Y., Moradi, M., Moradi Sarabi, M., Morovatdar, N., Morrison, S. D., Morze, J., Mossialos, E., Mostafavi, E., Mueller, U. O., Mulita, F., Mulita, A., Murillo-Zamora, E., Musa, K. I., Mwita, J. C., Nagaraju, S. P., Naghavi, M., Nainu, F., Nair, T. S., Najmuldeen, H. H. R., Nangia, V., Nargus, S., Naser, A. Y., Nassereldine, H., Natto, Z. S., Nauman, J., Nayak, B. P., Ndejjo, R., Negash, H., Negoi, R. I., Nguyen, H. T. H., Nguyen, D. H., Nguyen, P. T., Nguyen, V. T., Nguyen, H. Q., Niazi, R. K., Nigatu, Y. T., Ningrum, D. N. A., Nizam, M. A., Nnyanzi, L. A., Noreen, M., Noubiap, J. J., Nzoputam, O. J., Nzoputam, C. I., Oancea, B., Odogwu, N. M., Odukoya, O. O., Ojha, V. A., Okati-Aliabad, H., Okekunle, A. P., Okonji, O. C., Okwute, P. G., Olufadewa, I. I., Onwujekwe, O. E., Ordak, M., Ortiz, A., Osuagwu, U. L., Oulhaj, A., Owolabi, M. O., Padron-Monedero, A., Padubidri, J. R., Palladino, R., Panagiotakos, D., Panda-Jonas, S., Pandey, A., Pandey, A., Pandi-Perumal, S. R., Pantea Stoian, A. M., Pardhan, S., Parekh, T., Parekh, U., Pasovic, M., Patel, J., Patel, J. R., Paudel, U., Pepito, V. C. F., Pereira, M., Perico, N., Perna, S., Petcu, I.-R., Petermann-Rocha, F. E., Podder, V., Postma, M. J., Pourali, G., Pourtaheri, N., Prates, E. J. S., Qadir, M. M. F., Qattea, I., Raee, P., Rafique, I., Rahimi, M., Rahimifard, M., Rahimi-Movaghar, V., Rahman, M. O., Rahman, M. A., Rahman, M. H. U., Rahman, M., Rahman, M. M., Rahmani, M., Rahmani, S., Rahmanian, V., Rahmawaty, S., Rahnavard, N., Rajbhandari, B., Ram, P., Ramazanu, S., Rana, J., Rancic, N., Ranjha, M. M. A. N., Rao, C. R., Rapaka, D., Rasali, D. P., Rashedi, S., Rashedi, V., Rashid, A. M., Rashidi, M.-M., Ratan, Z. A., Rawaf, S., Rawal, L., Redwan, E. M. M., Remuzzi, G., Rengasamy, K. R., Renzaho, A. M. N., Reyes, L. F., Rezaei, N., Rezaei, N., Rezaeian, M., Rezazadeh, H., Riahi, S. M., Rias, Y. A., Riaz, M., Ribeiro, D., Rodrigues, M., Rodriguez, J. A. B., Roever, L., Rohloff, P., Roshandel, G., Roustazadeh, A., Rwegerera, G. M., Saad, A. M. A., Saber-Ayad, M. M., Sabour, S., Sabzmakan, L., Saddik, B., Sadeghi, E., Saeed, U., Saeedi Moghaddam, S., Safi, S., Safi, S. Z., Saghazadeh, A., Saheb Sharif-Askari, N., Saheb Sharif-Askari, F., Sahebkar, A., Sahoo, S. S., Sahoo, H., Saif-Ur-Rahman, K., Sajid, M. R., Salahi, S., Salahi, S., Saleh, M. A., Salehi, M. A., Salomon, J. A., Sanabria, J., Sanjeev, R. K., Sanmarchi, F., Santric-Milicevic, M. M., Sarasmita, M. A., Sargazi, S., Sathian, B., Sathish, T., Sawhney, M., Schlaich, M. P., Schmidt, M. I., Schuermans, A., Seidu, A.-A., Senthil Kumar, N., Sepanlou, S. 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M., Tualeka, A. R., Tufa, D. G., Tyrovolas, S., Ullah, S., Upadhyay, E., Vahabi, S. M., Vaithinathan, A. G., Valizadeh, R., van Daalen, K. R., Vart, P., Varthya, S. B., Vasankari, T. J., Vaziri, S., Verma, M. V., Verras, G.-I., Vo, D. C., Wagaye, B., Waheed, Y., Wang, Z., Wang, Y., Wang, C., Wang, F., Wassie, G. T., Wei, M. Y. W., Weldemariam, A. H., Westerman, R., Wickramasinghe, N. D., Wu, Y., Wulandari, R. D., Xia, J., Xiao, H., Xu, S., Xu, X., Yada, D. Y., Yang, L., Yatsuya, H., Yesiltepe, M., Yi, S., Yohannis, H. K., Yonemoto, N., You, Y., Zaman, S. B., Zamora, N., Zare, I., Zarea, K., Zarrintan, A., Zastrozhin, M. S., Zeru, N. G., Zhang, Z.-J., Zhong, C., Zhou, J., Zielińska, M., Zikarg, Y. T., Zodpey, S., Zoladl, M., Zou, Z., Zumla, A., Zuniga, Y. M. H., Magliano, D. J., Murray, C. J. L., Hay, S. I., & Vos, T.
Affiliations
University of Washington (US)
ORCID
Venue
The Lancet
Publisher
Elsevier BV
Published
2023-07
Volume
402
Issue
10397
Pages
203-234
ISSN
  • 0099-5355 (ISSN-L)
  • 0140-6736
DOI
10.1016/s0140-6736(23)01301-6
PMC
PMC10364581
OpenAlex
W4381799391
Cited by
4,681
References
91
Citations by year
2026: 1296 · 2025: 1970 · 2024: 1249 · 2023: 162 · 2022: 2 · 2021: 1 · 2018: 1
FWCI
773.24
Type
journal article
Language
en
Update notices
Open access
Licence
Locations
  • The Lancet (publishedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Institutional Repository University of Antioquia (University of Antioquia) (publishedVersion, cc-by-nc-nd)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
  • Monash University Research Portal (Monash University) (acceptedVersion, cc-by)
  • London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • UEA Digital Repository (University of East Anglia) (acceptedVersion)
  • DSPACE System - Metalibrary (University of the Coast) (submittedVersion, cc-by)
  • ResearchOnline at James Cook University (James Cook University) (submittedVersion)
  • UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
  • UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
  • OAR@UM (University of Malta) (submittedVersion, other-oa)
  • Diva portal (Dalarna University Library) (submittedVersion, other-oa)
  • Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
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  • Spiral (Imperial College London) (submittedVersion, other-oa)
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Funders
  • Heart Foundation
  • European Commission
  • Fundação de Amparo à Pesquisa do Estado de Minas Gerais
  • National Health and Medical Research Council
  • Taibah University
  • International Medical University
  • Ministry of Education
  • McGill University
  • Universitätsklinikum Essen
  • National Institute of Biomedical Innovation
  • King Abdulaziz City for Science and Technology
  • Universidad de La Sabana
  • University of Aberdeen
  • Manipal Academy of Higher Education
  • Public Health Agency of Canada
  • Ministero della Salute (34/2017)
  • Academy of Scientific Research and Technology
  • University of Sydney
  • Medical Research Council
  • Ateneo de Manila University
  • Deutsche Forschungsgemeinschaft (FU 356/12-2)
  • Wellcome Trust (221854/Z/20/Z, R024227)
  • Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja (2011-2023)
  • Office of the Secretary of Transportation
  • China Medical University, Taiwan (CMU111-MF-55)
  • National Heart, Lung, and Blood Institute (CENTRO-04-3559-FSE-000162, K23HL157766)
  • Horizon 2020 Framework Programme (EDCTP-RIA2016E-1609)
  • Central University of Kerala
  • European and Developing Countries Clinical Trials Partnership
  • Belgian American Educational Foundation
  • JSS Academy of Higher Education and Research
  • Universiti Kebangsaan Malaysia
  • Chandigarh University
  • Korea Institute for Advancement of Technology
  • University Grants Commission
  • Marga und Walter Boll-Stiftung
  • Ministry of Higher Education, Malaysia (FRGS/1/2020/STG03/USM/03/5)
  • National Research Foundation of Korea (NRF-2021R1I1A4A01057428)
  • Ministry of Trade, Industry and Energy (P0017805)
  • National Natural Science Foundation of China (72204112)
  • National Institute on Aging (R01AG062553)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (304224/2022-7)
  • Instituto de Salud Carlos III (RD21/0005/0001)
  • National Science and Technology Council (NSTC 111-2410-H-003-100-SSS)
  • Bundesministerium für Bildung und Forschung (01EA1808A)
  • Research Council of Finland (350426)
  • Wellcome Trust DBT India Alliance (IA/CPHE/17/1/503345)
  • Applied Molecular Biosciences Unit (LA/P/0140/2020)
  • National Heart Foundation of Australia (102597)
  • Bill and Melinda Gates Foundation
  • Department of Biotechnology, Ministry of Science and Technology, India
  • King Abdulaziz University
  • Jazan University
  • Social Science Foundation of Jiangsu Province (21GLD008)
  • Fundação para a Ciência e a Tecnologia (UIDB/04084/2020, UIDP/04378/2020, UIDB/04378/2020)
  • Science and Technology Development Fund
  • Università ta' Malta
  • Australian Research Council (FT220100857)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Health Professions › Health Information Management › Global Health and Epidemiology
Keywords
diabetes mellitus, type 1 diabetes, type 2 diabetes, diabetes prevalence, diabetes mortality, disability-adjusted life years, years of life lost, years lived with disability, risk factors, body mass index, physical activity, tobacco use, alcohol consumption, dietary factors, sex differences, age differences, global burden of disease
MeSH terms
Global Burden of Disease, Aged, Bayes Theorem, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Humans, Life Expectancy, Male, Risk Factors, Global Health, Prevalence, Quality-Adjusted Life Years

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-16.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-16.

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Statistics and arithmetic are recomputed, not judged.

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