ArticleDiabetes care2015
Geographical variation in diabetes prevalence and detection in china: multilevel spatial analysis of 98,058 adults.
Article in Diabetes care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05333822 (Effects of Bashan,a Vegetable Plant Based Compound Drink , on Glucose in Type 2 Diabetes Patients and Its Related Mechanisms), which is not on this map. Cited by 73 papers, 5 of them syntheses that pooled 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.
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.
Effects of Bashan,a Vegetable Plant Based Compound Drink , on Glucose in Type 2 Diabetes Patients and Its Related Mechanisms
Who cites it
73 citing papers in PubMed, 5 syntheses or guidelines pooled it, 132 citations in OpenAlex.
- Prevalence of Diabetic Nephropathy among Patients with Type 2 Diabetes Mellitus in China: A Meta-Analysis of Observational Studies.Journal of diabetes research · 2020Pooled it
- Geographic and area-level socioeconomic variation in cardiometabolic risk factor distribution: a systematic review of the literature.International journal of health geographics · 2019Pooled it
- Built environmental characteristics and diabetes: a systematic review and meta-analysis.BMC medicine · 2018Pooled it
- Socioeconomic status and prevalence of type 2 diabetes in mainland China, Hong Kong and Taiwan: a systematic review.Journal of global health · 2017Pooled it
- Lifestyle interventions in preventing new type 2 diabetes in Asian populations.Internal and emergency medicine · 2016Pooled it
- Associations between El Niño-Southern Oscillation (ENSO) and tuberculosis incidence in China: a nationwide study.One health (Amsterdam, Netherlands) · 2026Article
- Geographical differences in the prevalence of diabetic kidney disease in middle-aged and elderly patients in China: an analysis based on the Bayesian conditional autoregressive model.BMC nephrology · 2026Article
- The evolving burden of heart failure in China: a 34-year subnational analysis of trends and causes from the Global Burden of Disease Study 2023.Military Medical Research · 2025Article
- The effect of the urban built environment on the prevalence rate of chronic diseases in the community neighborhood level: a case study in Qingdao, China.BMC public health · 2025Article
- The national and provincial prevalence and non-fatal burdens of diabetes in China from 2005 to 2023 with projections of prevalence to 2050.Military Medical Research · 2025Article
- Associations of nutrient intake, dietary behaviours, and patterns with metabolic profiles and obesity measures: findings from a cross-sectional study in a southern Chinese population.Frontiers in nutrition · 2025Article
- National burden and risk factors of diabetes mellitus in China from 1990 to 2021: Results from the Global Burden of Disease study 2021.Journal of diabetes · 2024Article
- Geographical variation in dementia prevalence across China: a geospatial analysis.The Lancet regional health. Western Pacific · 2024Article
- National and Provincial-Level Prevalence and Risk Factors of Carotid Atherosclerosis in Chinese Adults.JAMA network open · 2024Article
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- Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030.The Lancet regional health. Western Pacific · 2023Article
- Diabetic Foot Complications: A Retrospective Cohort Study.International journal of environmental research and public health · 2022Article
- Incidence and risk factors of diabetes mellitus in the Chinese population: a dynamic cohort study.BMJ open · 2022Article
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13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 4 countries.
Funding
Abstract
objectiveTo investigate the geographic variation in diabetes prevalence and detection in China. RESEARCH DESIGN AND
methodsSelf-report and biomedical data were collected from 98,058 adults aged ≥18 years (90.5% response) from 162 areas spanning mainland China. Diabetes status was assessed using American Diabetes Association criteria. Among those with diabetes, detection was defined by prior diagnosis. Choropleth maps were used to visually assess geographical variation in each outcome at the provincial level. The odds of each outcome were assessed using multilevel logistic regression, with adjustment for person- and area-level characteristics.
resultsGeographic visualization at the provincial level indicated widespread variation in diabetes prevalence and detection across China. Regional prevalence adjusted for age, sex, and urban/rural socioeconomic circumstances (SECs) ranged from 8.3% (95% CI 7.2%, 9.7%) in the northeast to 12.7% (11.1%, 14.6%) in the north. A clear negative gradient in diabetes prevalence was observed from 13.1% (12.0%, 14.4%) in the urban high-SEC to 8.7% (7.8%, 9.6%) in rural low-SEC counties/districts. Adjusting for health literacy and other person-level characteristics only partially attenuated these geographic variations. Only one-third of participants living with diabetes had been previously diagnosed, but this also varied substantively by geography. Regional detection adjusted for age, sex, and urban/rural SEC, for example, spanned from 40.4% (34.9%, 46.3%) in the north to 15.6% (11.7%, 20.5%) in the southwest. Compared with detection of 40.8% (37.3%, 44.4%) in urban high-SEC counties, detection was poorest among rural low-SEC counties at just 20.5% (17.7%, 23.7%). Person-level characteristics did not fully account for these geographic variations in diabetes detection.
conclusionsStrategies for addressing diabetes risk and improving detection require geographical targeting.
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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.