Evidence map›Paper›PMID 42100512›Full record

ArticleFrontiers in public health2026

City- and county-level gaps in diabetes mortality improvement in the southeastern coastal region of China from 2005 to 2020, with provincial-level projections to 2030.

Xiuquan Lin, Chenglin Yang, Yuying Chen, Shaofen Huang, Yanrong Yin, Wenling Zhong, Weimin Ye

Abstract read
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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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

5 · Who and what money

Authors and funding

7 authors.

Xiuquan Lin *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Chenglin Yang *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Yuying Chen *School of Health Management, Fujian Medical University, Fuzhou, Fujian, China.
Shaofen HuangDepartment for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China.
Yanrong YinDepartment for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China.
Wenling ZhongDepartment for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China.
Weimin YeDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The growing health and economic burden of diabetes in China is a result of fast urbanization and lifestyle changes. This highlights the critical need for a comprehensive analysis of epidemiological patterns and forecasts in order to achieve the "Healthy China 2030" goals. Methods: Socio-Demographic Index (SDI) factors were used to evaluate data on diabetes mortality and YLL (2005-2020) for adults in 84 Fujian counties. Age-Period-Cohort (APC) models were used to examine the impacts of age, period, and birth cohort, while frontier analysis identified areas for health improvement. Bayesian APC (BAPC) modeling was used to forecast future mortality through 2030. Results: Standardized mortality and YLL rates indicated considerable promise for improvement in high SDI cities like Xiamen and Fuzhou. Age-period-cohort analysis reveals that diabetes mortality increased dramatically with age, reaching 464.79/100,000 for those 85 and older. The mortality risk decreased in subsequent generations after peaking in the 1947 birth cohort (RR = 1.51). Bayesian projections suggest the standardized mortality rate will rise to 17.14/100,000 by 2030, with a greater impact on females. Diabetes-related deaths are expected to rise by 58.88% (to 10,891). Conclusion: High-SDI areas like Xiamen and Fuzhou require optimized resource allocation to reduce mortality. Targeted interventions for older adults are required due to aging and cohort effects. As the 2030 burden intensifies, strengthening early screening and regional strategies is essential to mitigate future impacts.

Indexed as

Diabetes MellitusAdultAgedAged, 80 and overBayes TheoremChinaFemaleForecastingHumansMaleMiddle Agedcity- and county-level gapsdiabetesmortalitysocio-demographic indexyears of life lost

Identifiers

PMID42100512
PMCPMC13143594

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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 OpenQuestion graph.