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Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China

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  • Zirong Ye

    (State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China
    Key Laboratory of Health Technology Assessment of Fujian Province University, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Li Xu

    (Department of Statistics, School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou 510006, Guangdong, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Zi Zhou

    (State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China
    Key Laboratory of Health Technology Assessment of Fujian Province University, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China)

  • Yafei Wu

    (State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China
    Key Laboratory of Health Technology Assessment of Fujian Province University, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China)

  • Ya Fang

    (State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China
    Key Laboratory of Health Technology Assessment of Fujian Province University, School of Public Health, Xiamen University, Xiang’an Nan Road, Xiang’an District, Xiamen 361102, Fujian, China)

Abstract

Most previous research on the disparities of hypertension risk has neither simultaneously explored the spatio-temporal disparities nor considered the spatial information contained in the samples, thus the estimated results may be unreliable. Our study was based on the China Health and Nutrition Survey (CHNS), including residents over 12 years old in seven provinces from 1991 to 2011. Bayesian B-spline was used in the extended shared component model (SCM) for fitting temporal-related variation to explore spatio-temporal distribution in the odds ratio (OR) of hypertension, reveal gender variation, and explore latent risk factors. Our results revealed that the prevalence of hypertension increased from 14.09% in 1991 to 32.37% in 2011, with men experiencing a more obvious change than women. From a spatial perspective, a standardized prevalence ratio (SPR) remaining at a high level was found in Henan and Shandong for both men and women. Meanwhile, before 1997, the temporal distribution of hypertension risk for both men and women remained low. After that, notably since 2004, the OR of hypertension in each province increased to a relatively high level, especially in Northern China. Notably, the OR of hypertension in Shandong and Jiangsu, which was over 1.2, continuously stood out after 2004 for males, while that in Shandong and Guangxi was relatively high for females. The findings suggested that obvious spatial–temporal patterns for hypertension exist in the regions under research and this pattern was quite different between men and women.

Suggested Citation

  • Zirong Ye & Li Xu & Zi Zhou & Yafei Wu & Ya Fang, 2018. "Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:1:p:55-:d:125087
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    References listed on IDEAS

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    1. Qingyun Du & Mingxiao Zhang & Yayan Li & Hui Luan & Shi Liang & Fu Ren, 2016. "Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies," IJERPH, MDPI, vol. 13(4), pages 1-14, April.
    2. Moraga, Paula & Lawson, Andrew B., 2012. "Gaussian component mixtures and CAR models in Bayesian disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1417-1433.
    3. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
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