Hypertension is a major disease of burden worldwide. Previous studies have indicated that air pollution might be a risk factor for hypertension, but the results were controversial. To fill this gap, we performed a meta-analysis of epidemiological studies to investigate the associations of short-term and long-term exposure to ambient air pollutants with hypertension. We searched all of the studies published before September 1, 2015, on the associations of ozone (O ₃ ), carbon monoxide (CO), nitrogen oxide (NO ₂ and NO _X ), sulfur dioxide (SO ₂ ), and particulate matter (PM ₁₀ and PM _2.5 ) with hypertension in the English electronic databases. A pooled odds ratio (OR) for hypertension in association with each 10 μg/m ³ increase in air pollutant was calculated by a random-effects model (for studies with significant heterogeneity) or a fixed-effect model (for studies without significant heterogeneity). A total of 17 studies examining the effects of short-term (n=6) and long-term exposure (n=11) to air pollutants were identified. Short-term exposure to SO ₂ (OR=1.046, 95% confidence interval [CI]: 1.012–1.081), PM _2.5 (OR=1.069, 95% CI: 1.003–1.141), and PM ₁₀ (OR=1.024, 95% CI: 1.016–1.032) were significantly associated with hypertension. Long-term exposure (a 10 μg/m ³ increase) to NO ₂ (OR=1.034, 95% CI: 1.005–1.063) and PM ₁₀ (OR=1.054, 95% CI: 1.036–1.072) had significant associations with hypertension. Exposure to other ambient air pollutants (short-term exposure to NO ₂ , O ₃ , and CO and long-term exposure to NO _x , PM _2.5 , and SO ₂ ) also had positive relationships with hypertension, but lacked statistical significance. Our results suggest that short-term or long-term exposure to some air pollutants may increase the risk of hypertension.

In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics. Dengue epidemics have posed a great burden expanding of disease, with areas expanding and incidence increasing in China recently. It has remained challenging to develop a robust and accurate forecast model and enhance predictability of dengue incidence. Several state-of-the-art machine learning algorithms, including the support vector regression algorithm, step-down linear regression model, gradient boosted regression tree algorithm, negative binomial regression model, least absolute shrinkage and selection operator linear regression model and generalized additive model, were compared and evaluated to forecast dengue incidence in this study. The SVR model, based on selection by a cross-validation technique, was superior to other models assessed using weekly dengue surveillance data, Baidu search query data and meteorological data during 2011–2014 in Guangdong province. The high accuracy and robustness of the proposed SVR model to predict the occurrence of an outbreak was also validated using data from other provinces, including Yunnan, Guangxi, Hunan, Fujian and Zhejiang, spanning southern China. To the best of our knowledge, this is the first attempt to thoroughly evaluate different algorithms for dengue incidence prediction. Our identification of the optimal model will help to precisely track dengue dynamics in the country.

Long-term exposure to ambient fine particulate pollution (PM _2.5 ) has been associated with cardiovascular diseases. Hypertension, a major risk factor for cardiovascular diseases, has also been hypothesized to be linked to PM _2.5 . However, epidemiological evidence has been mixed. We examined long-term association between ambient PM _2.5 and hypertension and blood pressure. We interviewed 12 665 participants aged 50 years and older and measured their blood pressures. Annual average PM _2.5 concentrations were estimated for each community using satellite data. We applied 2-level logistic regression models to examine the associations and estimated hypertension burden attributable to ambient PM _2.5 . For each 10 μg/m ³ increase in ambient PM _2.5 , the adjusted odds ratio of hypertension was 1.14 (95% confidence interval, 1.07–1.22). Stratified analyses found that overweight and obesity could enhance the association, and consumption of fruit was associated with lower risk. We further estimated that 11.75% (95% confidence interval, 5.82%–18.53%) of the hypertension cases (corresponding to 914, 95% confidence interval, 453–1442 cases) could be attributable to ambient PM _2.5 in the study population. Findings suggest that long-term exposure to ambient PM _2.5 might be an important risk factor of hypertension and is responsible for significant hypertension burden in adults in China. A higher consumption of fruit may mitigate, whereas overweight and obesity could enhance this effect.