Identifying High-Risk Areas for Type 2 Diabetes Mellitus Mortality in Guangdong, China: Spatiotemporal Clustering and Socioenvironmental Determinants.

Hai Ming LUO; Wen Biao HU; Yan Jun XU; Xue Yan ZHENG; Qun HE; Lu LYU; Rui Lin MENG; Xiao Jun XU; Fei ZOU

Return

Identifying High-Risk Areas for Type 2 Diabetes Mellitus Mortality in Guangdong, China: Spatiotemporal Clustering and Socioenvironmental Determinants.

Author: Hai Ming LUO ¹ ; Wen Biao HU ² ; Yan Jun XU ³ ; Xue Yan ZHENG ³ ; Qun HE ⁴ ; Lu LYU ³ ; Rui Lin MENG ³ ; Xiao Jun XU ³ ; Fei ZOU ⁵
Author Information

1. Department of Occupational Health and Occupational Medicine, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510020, Guangdong, China;Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, Guangdong, China.
2. School of Public Health and Social Work, Queensland University of Technology, Brisbane 4000, Queensland, Australia.
3. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, Guangdong, China.
4. Guangdong Provincial Institute of Public Health, Guangzhou 511430, Guangdong, China.
5. Department of Occupational Health and Occupational Medicine, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510020, Guangdong, China.
Publication Type:Journal Article
Keywords: Air pollutants; Economy; Mortality; Spatiotemporal clustering; Type 2 diabetes mellitus
MeSH: Diabetes Mellitus, Type 2/epidemiology*; China/epidemiology*; Humans; Risk Factors; Spatio-Temporal Analysis; Air Pollutants/analysis*; Socioeconomic Factors; Bayes Theorem; Female; Male; Middle Aged
From: Biomedical and Environmental Sciences 2025;38(5):585-597
CountryChina
Language:English
Abstract: OBJECTIVE:This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies.
METHODS:Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model.
RESULTS:A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide.
CONCLUSION:High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.