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[Display omitted] • Greenspace coverage and greenness are negatively associated with suicide. • Association between landscape indices for greenspace and suicide risk is revealed. • Mean patch area and proximity index are negatively associated with suicide. • Area-weighted mean fractal dimension index is positively associated with suicide. Previous studies have demonstrated the preventive effects of greenness and greenspace using three classes of exposure metrics (greenspace coverage, greenness, and landscape indices for greenspace) on suicide. Nevertheless, most studies were individually employed for each metric using a single-phase method, representing the short-term exposure. We hierarchically investigated the association between three classes of exposure metrics and suicide using long-term data from 2000 to 2018 in Taiwan. Normalized difference vegetation index (NDVI) was used to represent the greenness exposure, while mean patch area (area and edge), area-weighted mean fractal dimension index (shape), and proximity index (proximity) were opted to represent landscape indices for greenspace. Using the generalized additive mixed model, we revealed that the relative risk (95% confidence interval) of greenspace coverage, NDVI, mean patch area, area-weighted mean fractal dimension index, and proximity index was 0.998 (0.997, 0.999), 0.860 (0.811, 0.912), 0.999 (0.999, 0.999), 1.485 (1.056, 2.088), and 0.999 (0.999, 0.999), respectively. This indicates that long-term exposure to both greenspace coverage and greenness is associated with a reduced suicide ratio. Similarly, long-term exposure to greenspace that is greater in size, simpler in shape, and closer in proximity is associated with a decreased suicide risk. For formulating future afforestation policies, the layout and connectivity of greenspaces should be considered besides increasing green areas. This study not only demonstrated the significance of long-term exposure to greenspace for reducing the suicide ratio, but also illustrated a hierarchical approach to examine the effects of greenspace. [ABSTRACT FROM AUTHOR]

Highlights • Decreased systolic blood pressure and heart rate are observed with time. • Increased vigor and decreased negative emotions are observed in forest settings. • Increased fatigue and decreased self-esteem were reported in urban settings. • Greater benefits were found when immersing in forest settings. Abstract Previous studies used pictures or movies to investigate the impact of virtual nature environments on physiological and psychological health, providing inferior immersive experiences. The latest virtual reality (VR), launched in 2016, allows users to be fully immersed in simulated surroundings. However, the effects of the simulated environments created by the latest VR technology on health were not yet known. This study employed both cross-over and pretest-posttest design to examine the influence of forest and urban VR environments on restoration (N = 30). Both physiological and psychological responses were collected. The results show that participants' systolic blood pressure and heart rate decreased with time, regardless of environmental differences. About psychological responses, an increased level of fatigue and a decreased level of self-esteem were reported in simulated urban environments. In contrast, an increased level of vigor and a decreased level of negative emotions (i.e., confusion, fatigue, anger-hostility, tension, and depression) were observed in simulated forest environments. In sum, greater benefits were found when immersing in forest settings. The latest VR technology can serve as an alternative way to access nature environments for restoration. [ABSTRACT FROM AUTHOR]

This study investigated sociological factors that may influence women’s utilization of and adherence to oral contraceptive pills. This was a retrospective cross-sectional study using the 2010–2012 Medical Expenditure Panel Survey. Female adults aged 18–50 years were included. Logistic regression was performed to discern women’s decisions to use oral contraceptive pills or not. Ordinary least squares and Poisson regressions were conducted to examine the number of oral contraceptive pills received, refill frequency, and annual out-of-pocket expenditure on oral contraceptive pills. Covariates were based on the Andersen model of health care utilization. Among the study sample (weighted n = 207,007,531), 14.8% were oral contraceptive pill users. Factors positively related to oral contraceptive pill use included non-Hispanic white ethnicity, younger age, not currently married, having private insurance, residing in the Midwest, higher education level, and higher annual family income. Being non-Hispanic white and having a higher education level were positively related to oral contraceptive pill adherence. Our findings therefore demonstrate disparities in oral contraceptive pill utilization and adherence, especially according to women’s race/ethnicity and educational level. This study serves as a baseline assessment for the impact of the Affordable Care Act on oral contraceptive pill utilization and adherence for future studies. [ABSTRACT FROM AUTHOR]

There have been many studies associating various aspects of greenspaces with physical health. Very few of these investigations are available for developing countries such as Indonesia. Our study focused on evaluating the association between greenspace and the incidence rate of non-communicable diseases (NCDs) in terms of ischemic heart disease (IHD), diabetes mellitus (DM), rheumatoid arthritis (RA), and chronic kidney disease (CKD). Greenspace was presented by satellite-derived normalized difference vegetation index (NDVI) and forest-related green cover datasets to define exposures to the resolution of 250-m. The Institute for Health Metrics and Evaluation provided age and gender incident data of NCDs at the province level. A generalized additive mixed model coupled with sensitivity test was used to evaluate the exposure-outcome association. Stratified analyses were also employed. After adjusting for covariates, there was a significant negative association for incidence of NCDs and greenspace. We found that an interquartile unit increase of NDVI, and a percentage of forest were closely related to a decrease in the risk of NCDs by 0.3–9.4% and 0.6–6.2%, respectively. Stratified by exposure level, a greater effect of greenspace on reducing NCDs risk occurred in high exposure areas. Considering the socioeconomic factors, greenspace could influence on reducing NCD risks in high urbanization, low-high poverty, and low-high literacy areas. An increment unit of greenspace was associated with a decreased risk of NCDs. This study underscores important health benefits associated with exposures to nature supporting efforts to preserve greenspaces in Indonesia. • A study assessing the relationship between greenspace and physical health diseases. • High levels of greenspace were associated with 0.3%−9.4% lower NCD rates. • Proximity to greenspace may encourage physical work, which in turn reduce NCD risks. • Greenspace may serve as free healthcare in supporting quality of life in Indonesia. • The link of greenspace and NCD remained after adjusting for socioeconomic factors. [ABSTRACT FROM AUTHOR]

For indoor air modelling, difficulties in collecting indoor parameters including life activity patterns and building characteristics are dilemmas when conducting a large-area study. Land-use/land cover information which is easier to obtain could represent as surrogates of emission sources for assessing indoor air quality. Moreover, low-cost sensors and machine learning provide a better way to enhance model accuracy. This study proposed an alternative estimation approach to assess daily PM 2.5 concentration for indoor environments of schools in a large area by integrating low-cost sensors, land-use/land cover predictors, and machine learning-based modelling approaches. Indoor PM 2.5 data was collected from 145 indoor AirBox sensors in Kaohsiung and Pingtung Counties of Taiwan. Geospatial predictors were extracted from the circular buffers surrounding each AirBox sensor. Spearman correlation analysis and stepwise variable selection procedures were performed to select variables for land-use regression (LUR) and integrated with XGBoost, Random Forest (RF), and LGBM machine learning models. The results revealed that outdoor PM 2.5 and distance to the nearest thermal power plant were the main determinants of indoor estimation variations, when there were no indoor sources. When incorporating machine learning, the R2 increased from 0.59 for LUR to 0.85 for LUR-XGBoost while the RMSE decreased from 8.63 to 5.27 μg/m3, which performed better than both LUR-RF and LUR-LGBM. This study demonstrates the value of the proposed alternative approach by incorporating data from a low-cost sensor with LUR model and machine learning algorithm in estimating the spatiotemporal variability of indoor PM 2.5 for a large area. [Display omitted] • An alternative approach was proposed to estimate indoor PM 2.5 for school campuses. • Low-cost sensor and land-use/land cover were incorporated using machine learning. • Important land-use/land cover variables affecting indoor PM 2.5 were identified. • The R2 of indoor LUR-XGBoost model was 0.85 with RMSE 5.27 μg/m3. [ABSTRACT FROM AUTHOR]

• • Indonesia's first study to examine the link between greenness and depressive disorder. • • A 15-year database was employed in a series of analyses. • • Many types of green spaces exhibited a negative association with depressive disorder. • • Green space is estimated to reduce the risk of depressive disorders by >5%. • • In high-urbanization areas, green spaces reduced risk of depressive disorders by >10%. Previous studies have confirmed the beneficial effects of green space exposure on depressive disorders, but the majority of them were conducted in developed countries. Given the difference in utilization of green spaces in countries with agriculturally heavy economies such as Indonesia, the effects of green space remain unknown. This study aimed to investigate the association between green space exposure and depressive disorders, focusing on Indonesia. Depressive disorders data provided by the Institute for Health Metrics and Evaluation was used. Exposure to the green space was based on the normalized difference vegetation index (MODIS-NDVI) and green land-cover data provided by the Geospatial Information Agency of Indonesia. A generalized additive mixed model was conducted, followed by a sensitivity test and positive-negative control analysis, to evaluate the association. Stratified analysis was employed to assess the effects of green space on depressive disorders in areas with different levels of urbanization. Exposure to green space significantly reduced the risk of depressive disorders by up to 5% per interquartile unit increment of NDVI. Moreover, there was a 12.4% reduced risk of depressive disorders coinciding with an 8.4% increase in the proportion of forest and total vegetation. In high-urbanization areas, the results of sensitivity analysis illustrated that green spaces reduced the risk of depressive disorders by 10%. Exposure to green spaces was confirmed to be associated with a reduced risk of depressive disorders in Indonesia. Findings of this ecological study could serve as references for urban planning, considering the health benefits of green spaces in developing countries. [ABSTRACT FROM AUTHOR]

Ambient fine particulate matter (PM 2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM 2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM 2.5. Daily average PM 2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM 2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM 2.5 exposures. [Display omitted] • Estimating long-term daily PM 2.5 concentration with machine learning models. • Land-use patterns were included in machine learning models by using land-use regression. • Explanatory power of daily PM 2.5 concentration was increased from 0.58 to 0.94. • XGboost outperformed random forest and deep neural network algorithms. [ABSTRACT FROM AUTHOR]

• Participants expressed lower fatigue and depression after viewing nature scenes. • Virtual nature scenes rated higher in perceived restorativeness. • Virtual natural environments were described as appeasing and relaxing. • VR natural settings can be a restorative experience for middle-aged and elderly group. Previous studies have demonstrated health benefits result from exposure to natural environments. Virtual reality (VR) may offer an alternative to actual outdoor immersion by generating a simulated health-promoting environment. Given that health issues are more prevalent in older adults, this study investigated the restorative effects of virtual natural settings on middle-aged and elderly adults. A cross-over pretest-posttest design was used to measure changes in participants' mood levels, physiological and psychological responses, and attentional measures of cognitive functioning (N = 34). Semi-structured interviews after the VR experiences were conducted to evaluate participants' experiences. Physiological responses to VR did not differ between virtual natural and urban settings. In contrast, participants expressed more positive feelings and lower levels of fatigue and depression after viewing virtual nature settings than after viewing virtual urban settings. Virtual nature settings were also rated as more restorative than virtual urban settings. Further, participants described virtual natural settings as appeasing and relaxing, so much so that they were motivated to travel outdoors to experience the settings shown in VR. Our findings provide additional evidence that viewing simulated natural settings in VR can be beneficial for this population. Perhaps the most promising finding is that VR may motivate older adults to experience nature outdoors, thus promoting synergistic benefits first during virtual exposure and then during actual exposure. [ABSTRACT FROM AUTHOR]

• Linkage between residential green space and Asian children with AR. • A 9-year nationwide cohort database in Taiwan was conducted as a representative. • Green spaces significantly increased the risk of AR by up to 8.4%. • Sensitivity tests confirmed the robustness of the model estimates. The prevalence of allergic rhinitis (AR) continues to increase, and the greatest increases were observed mostly in non-Western regions, particularly Asian and Pacific regions; however, only a few studies have been conducted in Asia. Using Taiwan as an example of a typical Asian country, this study aimed to evaluate the association between green areas and the frequency of clinical visits for AR among children in Asia. We used the Longitudinal Health Insurance Database to identify subjects diagnosed with AR at four years old in 2003, who were then followed up with until 2011. Greenness exposure was assessed using the MODIS Normalized Difference Vegetation Index (NDVI). The Generalized Additive Mixed Model (GAMM) was applied to examine the association between green areas and the rate of AR after controlling for confounders. The major results indicate a positive correlation, as green areas significantly increased the risk of AR by up to 8.4%. Further, sensitivity test analyses confirmed the robustness of the model estimates, even after adjusting for confounders. Stratified analysis results also display areas with high humidity and low temperatures increasing the risk of AR by up to 14% and 27%, respectively. In contrast, areas with low humidity and high temperatures decreased the risk of AR by up to 4% and 22%, respectively. Moreover, the positive relationship between greenness and AR among children was consistent across different sub-populations. Greenness exposure contributes to an increased risk of allergic rhinitis (AR). This finding could serve as a reference for designing green landscapes, especially in residential areas. [ABSTRACT FROM AUTHOR]