Evidence map›Paper›PMID 42290889›Full record

ArticleMilitary Medical Research2026

The national and provincial disease and indirect economic burden of high alcohol use in China from 1990 to 2023 with forecasting to 2050.

Yang Yang, Da-Chuang Zhou, Zhen-Ping Zhao, Ke-Jia Zhou, Di Zhang, Yu-Ting Xia, Peng Yin, Mai-Geng Zhou, Wen-Xi Tang

Abstract read
In one paragraph

Article in Military Medical Research, 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
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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

9 authors.

Yang YangNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Da-Chuang ZhouCenter for Pharmacoeconomics and Outcomes Research & Department of Public Affairs Management, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China.
Zhen-Ping ZhaoNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Ke-Jia ZhouCenter for Pharmacoeconomics and Outcomes Research & Department of Public Affairs Management, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China.
Di ZhangCenter for Pharmacoeconomics and Outcomes Research & Department of Public Affairs Management, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China.
Yu-Ting XiaCenter for Pharmacoeconomics and Outcomes Research & Department of Public Affairs Management, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China.
Peng YinNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Mai-Geng ZhouNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Wen-Xi TangCenter for Pharmacoeconomics and Outcomes Research & Department of Public Affairs Management, School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 211198, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: High alcohol use, defined as consumption exceeding the theoretical minimum risk exposure level, poses a major public health challenge in China, yet comprehensive long-term estimates combining disease burden and indirect economic burdens are limited. This study quantified national and subnational trends in disease and indirect economic burdens attributable to high alcohol use in China from 1990 to 2023 and forecasted the high alcohol use-related indirect economic burden to 2050. Methods: Disease burden data were obtained from the Global Burden of Disease Study 2023. Economic parameters were sourced from the China Statistical Yearbook 2024, the Penn World Table, and the Seventh National Population Census. The indirect economic burden was estimated using a cost-of-illness model. The estimated annual percentage change (EAPC) was calculated to assess trends (1990-2023) in age-standardized mortality and DALY rates, as well as productivity losses due to premature death, morbidity, and total indirect economic burden. Spatial distributions of all-cause burden and the top six leading diseases (ranked by DALYs) were compared between 1990 and 2023. Sex and age-specific patterns were analyzed using absolute DALY numbers and indirect economic burden values. Finally, the Bayesian age-period-cohort model was used to forecast productivity losses due to premature death and morbidity, along with the total indirect economic burden to 2050. Results: National age-standardized mortality rate attributable to high alcohol use declined from 27.68/100,000 [95% uncertainty interval (UI) 13.17-48.69] in 1990 to 10.58/100,000 (95% UI 4.60-19.26) in 2023 (EAPC=-3.02%, 95% UI -4.15 to -1.85; Conclusions: From 1990 to 2023, age-standardized mortality and DALY rates attributable to high alcohol use declined substantially in China, and the indirect economic burden as a share of GDP decreased modestly, despite marked growth in absolute productivity loss. Subnational heterogeneity persisted among different regions. Forecasts of the indirect economic burden indicate a nadir around 2039, followed by a subsequent increase with uncertainty.

Indexed as

Alcohol DrinkingCost of IllnessAdultAgedBayes TheoremChinaFemaleForecastingGlobal Burden of DiseaseHumansMaleMiddle AgedChinaDisease burdenForecastingHigh alcohol useIndirect economic burdenSpatiotemporal analysis

Identifiers

PMID42290889
PMCPMC13264366

What Socratic holds

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LicenceCC BY
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Registered trials

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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.