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

Association of long-term exposure to PM2.5 with hypertension and diabetes among the middle-aged and elderly people in Chinese mainland: a spatial study

Background

Epidemiological evidence has shown an association between long-term exposure to fine particulate matter (PM_2.5) and hypertension and diabetes, but few studies have considered the spatial properties of the samples. This study aimed to investigate the long-term effect of PM_2.5 exposure on hypertension and diabetes among middle-aged and elderly people in China based on a spatial study.

Methods

We conducted a national cross-sectional study of the most recently launched wave 4 2018 data of the China Health and Retirement Longitudinal Study (CHARLS) to calculate the prevalence of hypertension and diabetes. The exposure data of annual average PM_2.5 concentrations were estimated combined with satellite observations, chemical transport modeling, and ground-based monitoring. A shared component model (SCM) was used to explore the association of PM_2.5 with hypertension and diabetes, in which these two diseases borrowed information on spatial variations from each other. Then, we evaluated the effect variations in PM_2.5 in different periods and smoking status on changes in outcomes.

Results

The prevalence of hypertension and diabetes was 44.27% and 18.44%, respectively, among 19,529 participants. The annual average PM_2.5 concentration in 31 provinces ranged from 4.4 μg/m³ to 51.3 μg/m³ with an average of 27.86 μg/m³ in 2018. Spatial auto-correlations of the prevalence of hypertension and diabetes and PM_2.5 concentrations were seen (Moran’s I = 0.336, p = 0.01; Moran’s I = 0.288, p = 0.03; Moran’s I = 0.490, p = 0.01). An interquartile range (IQR: 16.2 μg/m³) increase in PM_2.5 concentrations was significantly associated with a higher prevalence of hypertension and diabetes with odds ratios (ORs) of 1.070 [95% credible interval (95% CrI): 1.034, 1.108] and 1.149 (95% CrI: 1.100, 1.200), respectively. Notably, the effect of PM_2.5 on both hypertension and diabetes was relatively stronger among non-smokers than smokers.

Conclusion

Our nationwide study demonstrated that long-term exposure to PM_2.5 might increase the risk of hypertension and diabetes, and could provide guidance to public policymakers to prevent and control hypertension and diabetes according to the spatial distribution patterns of the above effects in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12984-6.

Spatial analysis of the effects of PM2.5 on hypertension among the middle-aged and elderly people in China

Hypertension is currently one of the most common chronic diseases with high global prevalence associated with a huge social and economic burden. In recent years, air pollution has become a focus of research, especially the effects of PM2.5 on hypertension. However, few studies have considered the spatial properties of the sample; thus, the results might be unreliable. Based on the China Health and Retirement Longitudinal Study (CHARLS) and the Environmental Status Bulletin for each province in China, we used the extended shared component model (SCM) to fit the spatial variation of hypertension risk and to reveal the impact of PM2.5 on hypertension in males and females. Our results revealed that the crude prevalence of hypertension for the whole population in China was 32.74% in 2015, with the prevalence in men experiencing slightly higher than that in women (32.92% vs. 32.58%). We found that the distribution of hypertension prevalence exhibited obvious spatial aggregation for the whole population in China (Moran's I = 0.39, P = 0.001), with similar results in both men (Moran's I = 0.18, P = 0.027) and women (Moran's I = 0.52, P = 0.001). Furthermore, the smoothed results obtained using the SCM indicated that some eastern and central provinces had relatively higher hypertension risk, while the risk in southeastern provinces was much lower. The risk was also relatively lower in most western provinces, except for some northwestern regions. Notably, our results showed that PM2.5 was a risk factor for hypertension, and the impact of PM2.5 on women was slightly greater than that on men, with odds ratios (OR) of 1.063 (1.041, 1.086) and 1.048 (1.025, 1.071), respectively. Our findings suggest the existence of distinct spatial differences in the prevalence of hypertension and small sex-related differences in the risk of hypertension in China.

Association between the proportions of carbohydrate and fat intake and hypertension risk: findings from the China Health and Nutrition Survey

OBJECTIVE

The aim of this study was to expound the dietary effects of different proportions of carbohydrate and fat on hypertension in the Chinese population.

METHODS

We used data derived from the China Health and Nutrition Survey (CHNS) from 1991 to 2011. In total, 10 459 Chinese participants aged over 12 years were included in the final analysis. A multivariable Cox regression was used to calculate the hazard ratio and 95% confidence interval (95% CI) of hypertension in each group, and the medium proportion of carbohydrate and fat (MPCF) diet intake group was used as the reference.

RESULTS

Compared with the participants who consumed an MPCF diet, the individuals who consumed a high-carbohydrate and low-fat (HCLF) diet had a higher risk of developing hypertension (hazard ratio: 1.295, 95% CI: 1.167-1.436), especially the individuals who were young (hazard ratio: 1.422, 95% CI: 1.106-1.828), were living in rural areas (hazard ratio: 1.373, 95% CI: 1.206-1.565) and consumed alcohol (hazard ratio: 1.363, 95% CI: 1.153-1.611). In addition, a low-carbohydrate and high-fat (LCHF) diet was not associated with hypertension (hazard ratio: 0.861, 95% CI: 0.694-1.068). Moreover, these associations were observed at the majority energy intake level.

CONCLUSION

An HCLF diet was significantly associated with an increased risk of hypertension.

Spatial-Temporal Modelling of Disease Risk Accounting for PM2.5 Exposure in the Province of Pavia: An Area of the Po Valley

Spatio-temporal Bayesian disease mapping is the branch of spatial epidemiology interested in providing valuable risk estimates in certain geographical regions using administrative areas as statistical units. The aim of the present paper is to describe spatio-temporal distribution of cardiovascular mortality in the Province of Pavia in 2010 through 2015 and assess its association with environmental pollution exposure. To produce reliable risk estimates, eight different models (hierarchical log-linear model) have been assessed: temporal parametric trend components were included together with some random effects that allowed the accounting of spatial structure of the region. The Bayesian approach allowed the borrowing information effect, including simpler model results in the more complex setting. To compare these models, Watanabe–Akaike Information Criteria (WAIC) and Leave One Out Information Criteria (LOOIC) were applied. In the modelling phase, the relationship between the disease risk and pollutants exposure (PM2.5) accounting for the urbanisation level of each geographical unit showed a strong significant effect of the pollutant exposure (OR = 1.075 and posterior probability, or PP, >0.999, equivalent to p < 0.001). A high-risk cluster of Cardiovascular mortality in the Lomellina subareas in the studied window was identified.