Application of SCM with bayesian B-spline to spatio-temporal analysis of hypertension in China

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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.

Figures

  • Figure 1. Cont.
  • Figure 1. Maps of standardized prevalence ratios (SPRs) for male and female in each period. Figure 1. Maps of standardized prevalence ratios (SPRs) for male and female in each period.
  • Table 1. The seven constructed models.
  • Table 2. Trends in prevalence of hypertension among the participants (%).
  • Table 3. Age distribution of the participants.
  • Figure 2. Prevalence of hypertension during eight waves of surveys from 1991 to 2011.
  • Table 4. Global cluster for prevalence of hypertension during eight waves of surveys.
  • Table 5. Result of the models including Dbar, pD, deviance information criterion (DIC).

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APA

Ye, Z., Xu, L., Zhou, Z., Wu, Y., & Fang, Y. (2018). Application of SCM with bayesian B-spline to spatio-temporal analysis of hypertension in China. International Journal of Environmental Research and Public Health, 15(1). https://doi.org/10.3390/ijerph15010055

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