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Record W4311502878 · doi:10.2196/34809

The Global, Regional, and National Burden and Trends of NAFLD in 204 Countries and Territories: An Analysis From Global Burden of Disease 2019

2022· article· en· W4311502878 on OpenAlexvenueno aff
Huilong Chen, Yuan Zhan, Sheng Cheng, Yuhao Zhou, Liyuan Chen, Zhilin Zeng

Bibliographic record

VenueJMIR Public Health and Surveillance · 2022
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicinePopulationIncidence (geometry)Disease burdenDemographyEpidemiologyConfoundingEnvironmental healthNonalcoholic fatty liver diseaseYears of potential life lostDiseaseSocioeconomic statusMortality rateFatty liverLife expectancyInternal medicine

Abstract

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BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) poses a substantial socioeconomic burden and is becoming the fastest growing driver of chronic liver disease, potentially accompanied by a poor prognosis. OBJECTIVE: We aim to elucidate the global and regional epidemiologic changes in NAFLD during the past 30 years and explore the interconnected diseases. METHODS: Data on NAFLD incidence, prevalence, death, and disability-adjusted life-years (DALYs) were extracted from the Global Burden of Disease Study 2019. The age-standardized incident rate (ASIR), age-standardized prevalent rate (ASPR), age-standardized death rate (ASDR), and age-standardized DALYs were calculated to eliminate the confounding effects of age when comparing the epidemiologic changes between different geographical regions. In addition, we also investigated the correlation between the NAFLD burden and the sociodemographic index (SDI). Finally, the associations of the 3 common comorbidities with NAFLD were determined. RESULTS: Globally, the incidence and prevalence of NAFLD both increased drastically during the past 3 decades (incidence: from 88,180 in 1990 to 172,330 in 2019, prevalence: from 561,370,000 in 1990 to 1,235,700,000 in 2019), mainly affecting young adults who were aged from 15 to 49 years. The ASIR increased slightly from 1.94 per 100,000 population in 1990 to 2.08 per 100,000 population in 2019, while ASPR increased from 12,070 per 100,000 population in 1990 to 15,020 per 100,000 population in 2019. In addition, the number of deaths and DALYs attributable to NAFLD increased significantly as well from 93,760 in 1990 to 168,970 in 2019 and from 2,711,270 in 1990 to 4,417,280 in 2019, respectively. However, the ASDR and age-standardized DALYs presented decreasing trends with values of estimated annual percentage change equaling to -0.67 and -0.82, respectively (ASDR: from 2.39 per 100,000 population in 1990 to 2.09 per 100,000 population in 2019; age-standardized DALYs: from 63.28 per 100,000 population in 1990 to 53.33 per 100,000 population in 2019). Thereinto, the burden of death and DALYs dominated the patients with NAFLD who are older than 50 years. Moreover, SDI appeared to have obvious negative associations with ASPR, ASDR, and age-standardized DALYs among 21 regions and 204 countries, although there is no marked association with ASIR. Finally, we found that the incidence and prevalence of NAFLD were positively related to those of diabetes mellitus type 2, stroke, and ischemic heart disease. CONCLUSIONS: NAFLD is leading to increasingly serious health challenges worldwide. The morbidity presented a clear shift toward the young populations, while the heavier burden of death and DALYs in NAFLD was observed in the aged populations and in regions with relatively low SDI. Comprehensive acquisition of the epidemiologic pattern for NAFLD and the identification of high-risk comorbidities may help policy makers and clinical physicians develop cost-effective prevention and control strategies, especially in countries with a high NAFLD burden.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.319
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations54
Published2022
Admission routes1
Has abstractyes

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