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BACKGROUND: There are few studies on the health effects of long-term exposure to neighborhood greenness in a longitudinal setting, especially in Asian countries with high population densities. OBJECTIVES: This study investigates the association between long-term exposure to neighborhood greenness and hypertension among adults in Taiwan. METHODS: We selected 125,537 participants ([greater than or equal to] 18 years of age) without hypertension from Taiwan who had joined the standard medical examination program between 2001 and 2016. Neighborhood greenness was estimated using the normalized difference vegetation index (NDVI), derived from satellite images at a resolution of 250 [m.sup.2]. The 2-y average NDVI value within a 500-m circular buffer around participants' residences was calculated. A time-varying Cox regression model was used to investigate the association between neighborhood greenness and incident hypertension. Mediation analyses were performed to examine whether the association was explained by air pollution, leisure-time physical exercise, or body mass index (BMI). RESULTS: Compared with living in areas within the first quartile of neighborhood greenness, living in areas within the second, third, and fourth quartiles of neighborhood greenness was found to be associated with a lower risk of hypertension, with hazard ratios (HRs) and 95% confidence intervals (CIs) of 0.95 (95% CI: 0.91, 1.00), 0.95 (95% CI: 0.90, 0.99), and 0.93 (95% CI: 0.88, 0.97), respectively. Each 0.1-unit increase in the NDVI was associated with a 24% lower risk of developing hypertension (HR = 0.76; 95% CI: 0.66, 0.87), with this associations being stronger among males and those with higher education levels. This association was slightly mediated by BMI but not by air pollution or leisure-time physical exercise. DISCUSSION: Our findings suggest the protective effects of neighborhood greenness on hypertension development, especially in males and well-educated individuals. Our results reinforced the importance of neighborhood greenness for supporting health. https://doi.org/10.1289/EHP13071, Introduction Hypertension is one of the leading risk factors for cardiovascular diseases and premature deaths worldwide. It is estimated that high systolic blood pressure (SBP) contributed to 10.4 million deaths [...]

The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO 2) and ground-level concentrations is theoretically the NO 2 mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed a nested XGBoost machine learning model to convert VCDs of NO 2 into ground-level NO 2 concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework to estimate satellite-derived ground-level NO 2 concentrations. The inner machine learning model predicted the NMH from meteorological parameters, which were then input into the main XGBoost machine learning model to predict the ground-level NO 2 concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of ground-level NO 2 concentration estimates; i.e., the R2 values were improved from 0.73 to 0.93 in 10-fold cross-validation and from 0.88 to 0.99 in the fully trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO 2. Subsequently, the satellite-derived ground-level NO 2 data were analyzed across subregions with varying geographic locations and urbanization levels. Highly populated areas typically experienced peak NO 2 concentrations during the early morning rush hour, whereas areas categorized as lightly populated observed a slight increase in NO 2 levels 1 or 2 h later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO 2 from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate how short‐term changes (1‐, 3‐, and 5‐year) in obesity measures affect mortality and cardiovascular disease (CVD) risk. Methods: We analyzed longitudinal data from the MJ Health Centre (n = 43,304 for the 1‐year study; 24,295 for the 3‐year study; 16,138 for the 5‐year study) with median follow‐up periods of 15.8, 13.9, and 12.3 years, respectively. Associations of short‐term obesity indices changes with mortality and Framingham Risk Score changes were explored using time‐dependent coefficient Cox regression models, restricted cubic splines, and multivariable linear regression models. Results: All‐cause mortality was negatively associated with short‐term weight and BMI changes, with greater reductions causing poorer outcomes. Compared with stable groups, short‐term reduced weight and BMI were associated with greater risks of all‐cause mortality and CVD‐specific mortality (5‐year study only). Also, either 1‐ and 3‐year reduced or 3‐year increased waist circumference and waist to height ratio were related to higher all‐cause and CVD deaths than stable groups, respectively. Nonlinear relationships indicated lower cutoff values for short‐term changes in obesity indices in predicting all‐cause mortality. Decreased obesity indices significantly improved CVD profiles. Conclusions: Short‐term changes in obesity indices show complex mortality risks, urging personalized approaches beyond a simple weight loss focus. [ABSTRACT FROM AUTHOR]