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Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.

Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.

期刊: Geospatial health 日期: 2026-07-23 PMID: 42489680 DOI: 10.4081/gh.2026.1498 浏览: 11
作者: Wang J, Zhang E, Yang K
J, W., E, Z., & K, Y. (2026). Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.. Geospatial health. https://doi.org/10.4081/gh.2026.1498
J W, E Z, K Y. Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.. Geospatial health. 2026; doi: 10.4081/gh.2026.1498
J W, E Z, K Y. Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.[J]. Geospatial health. 2026. DOI: 10.4081/gh.2026.1498.
@article{j2026,
  author = {Wang J and Zhang E and Yang K},
  title = {Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.},
  journal = {Geospatial health},
  year = {2026},
  doi = {10.4081/gh.2026.1498},
  note = {PMID: 42489680},
}
TY  - JOUR
AU  - Wang J
AU  - Zhang E
AU  - Yang K
TI  - Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors.
T2  - Geospatial health
PY  - 2026
DO  - 10.4081/gh.2026.1498
AN  - PMID:42489680
ER  - 

摘要

Hypertension poses a major health challenge for the rural elderly in China, but evidence on its spatial patterns and key influencing factors is still limited. This study examined the spatial distribution of hypertension prevalence among rural older adults assessing its associations with dietary, behavioural, socioeconomic and environmental variables. Pearson correlation and maximum entropy (MaxEnt) were used to analyze correlations and identify spatial risk levels. The results showed that hypertension prevalence was negatively correlated with grain intake (r=-0.600, p=0.002) and poultry consumption (r=-0.504,p=0.010) and alcohol use showed the highest contribution among behavioural variables (39.2%) in the MaxEnt model. Climate zones were also associated with prevalence (r=-0.260,p=0.010), with higher risk in temperate and some subtropical regions. The MaxEnt model showed good discriminatory ability (area under the curve (AUC)=0.871) in identifying hypertension high-risk areas mainly in northern and north-eastern rural China. This study provides spatial epidemiological evidence on hypertension among the rural elderly, suggesting that dietary patterns, alcohol consumption and climate conditions may be associated with spatial risk variation. These findings support the use of MaxEnt as an exploratory tool for chronic disease spatial analysis and may provide spatial reference for public health strategies and resource allocation.

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