Original Articles
Vol. 21 No. 2 (2026)
Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Published: 23 July 2026
0
Views
0
Downloads
Authors
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.
Downloads
Download data is not yet available.
Citations
Ansari M, Soriano-Paños D, Ghoshal G, White AD, 2022. Inferring spatial source of disease outbreaks using maximum entropy. Phys Rev E 106:014306.
Aune D, Keum N, Giovannucci E, Fadnes LT, Boffetta P, Greenwood DC, Tonstad S, Vatten LJ, Riboli E, Norat T, 2016. Whole grain consumption and risk of cardiovascular disease, cancer, and all cause and cause specific mortality: systematic review and dose-response meta-analysis of prospective studies. BMJ 353:i2716.
Cartierre N, Coulon N, Demerval R, 2011. Analyse confirmatoire de la version courte de la Center for Epidemiological Studies of Depression Scale (CES-D10) chez les adolescents. L’Encéphale 37:273-277.
Cecchini M, Filippini T, Whelton PK, Iamandii I, Di Federico S, Boriani G, Vinceti M, 2024. Alcohol Intake and Risk of Hypertension: A Systematic Review and Dose-Response Meta-Analysis of Nonexperimental Cohort Studies. Hypertension 81:1701-15.
Cheng H, Gu Y, Ma X, Tang H, Liu X, 2022. Urban–rural disparities in hypertension prevalence, awareness, treatment, and control among Chinese middle-aged and older adults from 2011 to 2015: a repeated cross-sectional study. BMC Cardiovasc Disord 22:319.
Chinese Society of Cardiology, Chinese Medical Association; Hypertension Committee of Cross-Straits Medicine Exchange Association; Cardiovascular Disease Prevention and Rehabilitation Committee, Chinese Association of Rehabilitation Medicine, 2024. Clinical practice guideline for the management of hypertension in China. Chin Med J 137:2907-2952.
Duan H, Cao Q, Wang L, Gu X, Ashrafi K, 2024. Exploring the relationships between 3D urban landscape patterns and PM2.5 pollution using the multiscale geographic weighted regression model. Prog Phys Geogr 48:368-88.
ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, Collins BS, Hilliard ME, Isaacs D, Johnson EL, Kahan S, Khunti K, Leon J, Lyons SK, Perry ML, Prahalad P, Pratley RE, Seley JJ, Stanton RC, Gabbay RA, on behalf of the American Diabetes Association, 2023. 1. Improving Care and Promoting Health in Populations: Standards of Care in Diabetes-2023. Diabetes Care 46:S10-S18.
Fan L, Liu L, Zhao Y, Mo Y, Li J, Cai L, 2023. Trends in the prevalence and economic burden of hypertension and its socioeconomic disparities in rural southwestern China: two repeated cross-sectional studies. BMJ Open 13:e076694.
Fu R, Huang Z, Lin Y, Tang X, Zheng Z, Hu Z, 2024. Temporal-spatial distribution characteristics and associated socioeconomic factors of visiting frequency for rural patients with hypertension in Fujian Province, Southeast China. BMC Public Health 24:656.
He Z, Jia Y, Li J, Cao J, Liu F, Li H, Chen J, Hu D, Shen C, Zhao Y, Liu X, Yu L, Huang J, Lu X, Gu D, Chen S, 2023. Fruit and vegetable intake and the risk of arterial hypertension in China: A prospective cohort study. Chronic Dis Transl Med 9:309-19.
Huang K, Yang X, Liang F, Liu F, Li J, Xiao Q, Chen J, Liu X, Cao J, Shen C, Yu L, Lu F, Wu X, Zhao L, Wu X, Li Y, Hu D, Huang J, Liu Y, Lu X, Gu D, 2019. Long-Term Exposure to Fine Particulate Matter and Hypertension Incidence in China: The China-PAR Cohort Study. Hypertension 73:1195-1201.
Jaynes ET, 1957a. Information Theory and Statistical Mechanics. Phys Rev 106:620-630.
Jaynes ET, 1957b. Information Theory and Statistical Mechanics. II. Phys Rev 108:171-190.
Jiang J, Chen G, Li B, Li N, Liu F, Lu Y, Guo Y, Li S, Chen L, Xiang H. 2021. Associations of residential greenness with hypertension and blood pressure in a Chinese rural population: a cross-sectional study. Environ Sci Pollut Res 28:51693-51701.
Kolak E, Radić J, Vučković M, Bučan Nenadić D, Begović M, Radić M, 2022. Nutritional and Hydration Status and Adherence to Dietary Recommendations in Dalmatian Dialysis Patients. Nutrients 14:3553.
Kowal P, Dowd JE, 2001. Definition of an older person. Proposed working definition of an older person in Africa for the MDS Project. World Health Organization 10:5188-9286.
Li Q, Ren H, Zheng L, Cao W, Zhang A, Zhuang D, Lu L, Jiang H, 2017. Ecological niche modeling identifies fine-scale areas at high risk of dengue fever in the Pearl River delta, China. Int J Environ Res Public Health 14:619.
Liu B, Gao X, Ma J, Jiao Z, Xiao J, Wang H, 2018. Influence of host and environmental factors on the distribution of the Japanese encephalitis vector culex tritaeniorhynchus in China. Int J Environ Res Public Health 15:1848.
Liu X, Lai H, Mi B, Qi X, Gan W, Du H. 2020. Associations of Coarse Grain Intake with Undiagnosed Hypertension among Chinese Adults: Results from the China Kadoorie Biobank. Nutrients 12:3814.
Llanaj E, Dejanovic GM, Valido E, Bano A, Gamba M, Kastrati L, Minder B, Stojic S, Voortman T, Marques-Vidal P, Stoyanov J, Metzger B, Glisic M, Kern H, Muka T., 2022. Effect of oat supplementation interventions on cardiovascular disease risk markers: a systematic review and meta-analysis of randomized controlled trials. Eur J Nutr 61:1749-1778.
Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, Cheng X, Mu L, Zhang H, Liu J, Su M, Zhao H, Spatz ES, Spertus JA, Masoudi FA, Krumholz HM, Jiang L, 2017. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet 390:2549-58.
Madsen H, Sen A, Aune D, 2023. Fruit and vegetable consumption and the risk of hypertension: a systematic review and meta-analysis of prospective studies. Eur J Nutr 62:1941-1955.
Mills KT, Stefanescu A, He J, 2020. The global epidemiology of hypertension. Nat Rev Nephrol 16:223-237.
Nan X, Lu H, Wu J, Xue M, Qian Y, Wang W, Wang X. 2021. The interactive association between sodium intake, alcohol consumption and hypertension among elderly in northern China: a cross-sectional study. BMC Geriatr 21:135.
Niraj KC, Singh A, Shukla DP, 2023. Effect of the Normalized Difference Vegetation Index (NDVI) on GIS-enabled bivariate and multivariate statistical models for landslide susceptibility mapping. J Indian Soc Remote Sens 51:1739-1756.
Ranzani OT, Kalra A, Di Girolamo C, Curto A, Valerio F, Halonen JI, Basagaña X, Tonne C. 2022. Urban-rural differences in hypertension prevalence in low-income and middle-income countries, 1990–2020: A systematic review and meta-analysis. PLOS Med 19:e1004079.
Roerecke M, Tobe SW, Kaczorowski J, Bacon SL, Vafaei A, Hasan OSM, Krishnan RJ, Raifu AO, Rehm J, 2018. Sex‐specific associations between alcohol consumption and incidence of hypertension: a systematic review and meta‐analysis of cohort studies. J Am Heart Assoc 7:e008202.
Rossios K, Antza C, Kachtsidis V, Kotsis V, 2023. The modern environment: the new secondary cause of hypertension? Medicina (Kaunas) 59:2095.
Schutte AE, SrinivasapuraVenkateshmurthy N, Mohan S, Prabhakaran D, 2021. Hypertension in Low- and Middle-Income Countries. Circ Res 128:808-826.
Tark JY, Li R, Yu B, Wood AC, Padhye NS, de Oliveira Otto MC, 2025. Dietary Intake of Protein by Food Source and Incident Hypertension Among Diverse US Adults: The MESA Study. J Am Heart Assoc 14:e037813.
Tsutsumida N, Rodríguez-Veiga P, Harris P, Balzter H, Comber A, 2019. Investigating spatial error structures in continuous raster data. Int J Appl Earth ObsGeoinf 74:259-268.
Wang J-F, Zhang T-L, Fu B-J, 2016. A measure of spatial stratified heterogeneity. Ecol Indic 67:250-256.
Wang J, Sun W, Wells GA, Li Z, Li T, Wu J, Zhang Y, Liu Y, Li L, Yu Y, Liu Y, Qi C, Lu Y, Liu N, Yan Y, Liu L, Hui G, Liu B. 2018. Differences in prevalence of hypertension and associated risk factors in urban and rural residents of the northeastern region of the People’s Republic of China: A cross-sectional study. PLOS ONE 13:e0195340.
Wang L, Zhou B, Zhao Z, Yang L, Zhang M, Jiang Y, Li Y, Zhou M, Wang L, Huang Z, Zhang X, Zhao L, Yu D, Li C, Ezzati M, Chen Z, Wu J, Ding G, Li X, 2021. Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004–18. Lancet 398:53-63.
Wang Y, Yao Y, Chen Y, Zhou J, Wu Y, Fu C, Wang N, Liu T, Xu K, 2022. Association between Drinking Patterns and Incident Hypertension in Southwest China. Int J Environ Res Public Health 19:3801.
Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, Shao L, Tian Y, Dong Y, Zheng C, Wang J, Zhu M, Weintraub WS, Gao R; China Hypertension Survey Investigators, 2018. Status of hypertension in China: results from the China hypertension survey, 2012–2015. Circulation 137:2344-56.
Wei J, Miao Y, Zhang J, Wu J, Shen Z, Bai J, Zhu D, Ren R, Li X, Zhen M, Zhang J, Cui J, Guo D, Dong W, Tarimo CS, Zhao Q, Liu R, Hu J, Li M, 2025. Spatial heterogeneity of blood pressure control and its influencing factors in elderly patients with essential hypertension: A small-scale spatial analysis. Health Place 92:103428.
Xing L, Jing L, Tian Y, Lin M, Du Z, Yan H, Ren G, Dong Y, Sun Q, Liu S, 2019. Urban–Rural disparities in status of hypertension in northeast China: a population-based study, 2017–2019. CLEP Volume 11:801-20.
Xu X, 2017. China population spatial distribution kilometer grid dataset. Resource and Environment Science and Data Center of Chinese Academy of Sciences
Xu X, 2023. China’s Township Administrative Division Boundary Data. Resource and Environment Science and Data Center of Chinese Academy of Sciences
Yan X, Li J, Wu J, Lin S, Wang Z, Pei L, Zheng C, Wang X, Cao X, Hu Z, Tian Y, 2024. Association between short-term daily temperature variability and blood pressure in the Chinese population: From the China hypertension survey. Environ Int 184:108463.
Yang Z, Ren ZD, Wang J, Dong W, 2025. Based on the MaxEnt model the analysis of influencing factors and simulation of potential risk areas of human infection with avian influenza A (H7N9) in China. Front. Cell. Infect. Microbiol 14:1496991.
Zhang M, Shi Y, Zhou B, Huang Z, Zhao Z, Li C, Zhang X, Han G, Peng K, Li X, Wang Y, Ezzati M, Wang L, Li Y. 2023. Prevalence, awareness, treatment, and control of hypertension in China, 2004-18: findings from six rounds of a national survey. BMJ e071952.
Zhang Y, Tang J, Ren G, Zhao K, Wang X, 2021. Global potential distribution prediction of Xanthium italicum based on Maxent model. Sci Rep 11:16545.
Zhang Y, Zhang D, 2018. Red meat, poultry, and egg consumption with the risk of hypertension: a meta-analysis of prospective cohort studies. J Hum Hypertens 32:507-517.
CRediT authorship contribution
Jie Wang designed the study, conducted the data analysis, and drafted the initial manuscript. Enwei Zhang supervised the study, contributed to the interpretation of results, and critically revised the manuscript for important intellectual content. Kun Yang contributed to data collection, provided methodological support, and assisted in revising the manuscript. All authors reviewed and approved the final version of the manuscript.
Data Availability Statement
The individual-level data analyzed in this study are publicly available from the China Health and Retirement Longitudinal Study (CHARLS) at http://charls.pku.edu.cn. Socio-environmental raster datasets were obtained from publicly accessible sources, including the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (http://www.resdc.cn) and global remote sensing platforms.
How to Cite
Spatial risk of hypertension in rural China: modelling the effect of dietary, behavioural and climate factors. (2026). Geospatial Health, 21(2). https://doi.org/10.4081/gh.2026.1498
Copyright (c) 2026 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
