Publications by authors named "Chih-Da Wu"

50 Publications

Emergency room visits for childhood atopic dermatitis are associated with floods?

Sci Total Environ 2021 Jun 28;773:145435. Epub 2021 Jan 28.

National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan; Department of Environmental and Occupational Medicine, College of Medicine, National Taiwan University (NTU) and NTU Hospital, Taipei, Taiwan; Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei 10055, Taiwan. Electronic address:

Floods are known to increase the level of allergens such as molds in the environments. Under climate change, the frequency of floods could be increased, which highlights the importance of understanding the impacts of floods on atopic diseases. However, there was a lack of studies. This study examines whether floods induce attacks of childhood atopic dermatitis (AD). A retrospective population-based study was conducted in Taiwan Island using Taiwan's National Health Insurance Research Database. Emergency room (ER) visits for AD were identified among children aged 0-12 years. Weekly data of flood occurrence, number of flood sites, temperature, and air pollution were obtained for each township of the identified cases. A time-stratified case-crossover design was used. The relationship between ER visits for AD and floods was assessed by conditional logistic regression, adjusting for weekly mean temperature, PM and NO. There were a total of 55,488 ER visits due to AD during the study period. Such visits increased when flood occurred, and then declined. The effects of floods at the week of flood remained robust, with OR of 1.14 (95% CI = 1.01-1.28) for flood occurrence and 1.31 (95% CI = 1.10-1.55) for the number of flood sites, after adjusting for covariates. Such effects were slightly higher in boys and children aged 1-12 years. This study demonstrated the impact of floods on flare-up of childhood AD, and the effect was most prominently at the week of flood. Healthcare workers should be alarmed for potential increase of AD flare ups after flood events.
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http://dx.doi.org/10.1016/j.scitotenv.2021.145435DOI Listing
June 2021

Residential green space structures are associated with a lower risk of bipolar disorder: A nationwide population-based study in Taiwan.

Environ Pollut 2021 Aug 15;283:115864. Epub 2020 Oct 15.

Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, 70101, Taiwan. Electronic address:

Although many researchers have identified the potential psychological benefits offered by greenness, the association between green space structures and mental disorders is not well understood. The purpose of this study was to identify associations between green space structures and the incidence of bipolar disorder. To this end, we investigated 1,907,776 individuals collected from Taiwan's National Health Insurance Research Database. After a follow-up investigation from 2005 to 2016, among those with no history of bipolar disorder, 20,548 individuals were further found to be diagnosed with bipolar disorder. A geographic information system and landscape index were used to quantify three indices of green space structures: mean patch area (area and edge), mean fractal dimension index (shape), and mean proximity index (proximity). Additionally, greenness indices, the normalized difference vegetation index, and the enhanced vegetation index were used to confirm the association between greenness and incidence of bipolar disorder. These five indices were used to represent the individual's exposure according to the township of the hospital that they most frequently visited with symptoms of the common cold. Spearman's correlation analysis was performed to select variables by considering their collinearity. Subsequently, the frailty model for each index was used to examine the specific associations between those respective indices and the incidence of bipolar disorder by adjusting for related risk factors, such as socioeconomic status, metabolic syndrome, and air pollution. A negative association was identified between the mean patch area and the mean proximity index, and the incidence of bipolar disorder. In contrast, a positive association was found between the mean fractal dimension index and the incidence of bipolar disorder. We observed similar results in sensitivity testing and subgroup analysis. Exposure to green spaces with a larger area, greater proximity, lower complexity, and greener area may reduce the risk of bipolar disorder.
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http://dx.doi.org/10.1016/j.envpol.2020.115864DOI Listing
August 2021

Effects of air pollution, land-use type, and maternal mental health on child development in the first two years of life in the Greater Taipei area.

Environ Res 2021 Jun 12;197:111168. Epub 2021 Apr 12.

School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan; Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan. Electronic address:

Introduction: Few studies have investigated the associations of child development with air pollution, land-use type, and maternal mental health simultaneously. Therefore, we evaluated the effect of exposure to air pollutants during several critical periods of life, with adjustment for land-use type and maternal mental status, on child development at 6, 12, and 24 months of age in the Greater Taipei area.

Methods: Participants were selected from an ongoing Taiwanese birth cohort study. We analyzed the data of the participants who had been recruited from January 2011 to April 2014. Self-administered standardized questionnaires were used to collect information on sociodemographic factors, infant development and health, maternal mental status, etc. Air pollution levels in pre- and postnatal periods were estimated using a spatial interpolation technique (ordinary kriging) at children's residential addresses. Land-use types around participants' homes were evaluated using buffer analysis. We used multiple logistic regression analysis to examine the relationships between child development delay and environmental factors.

Results: In total, 228, 361, and 441 families completed child development forms at 6, 12, and 24 months of age, respectively. Our results indicated that prenatal exposure to particulate matter with aerodynamic diameter ≤10 μm and O and postnatal exposure to NO were negatively associated with child development. Traffic-related land-use types, gas stations, and power generation areas around participants' homes were also adversely correlated with child development. Moreover, poor maternal mental health was associated with child development delay.

Conclusion: Prenatal exposure and postnatal exposure to air pollution were associated with development delay in children under 2 years of age, specifically those under 1 year of age, even after adjustment for land-use type and maternal mental status. Living environment is critical for the development of children under 2 years of age.
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http://dx.doi.org/10.1016/j.envres.2021.111168DOI Listing
June 2021

Alveolar epithelial inter-alpha-trypsin inhibitor heavy chain 4 deficiency associated with senescence-regulated apoptosis by air pollution.

Environ Pollut 2021 Jun 9;278:116863. Epub 2021 Mar 9.

School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan; Cell Physiology and Molecular Image Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address:

Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) is a type II acute-phase protein; however, the role of pulmonary ITIH4 after exposure to air pollution remains unclear. In this study, we investigated the role of ITIH4 in the lungs in response to air pollution. ITIH4 expression in bronchoalveolar lavage fluid (BAL) of 47 healthy human subjects and of Sprague-Dawley rats whole-body exposed to air pollution was determined, and the underlying antiapoptotic and matrix-stabilizing pathways in alveolar epithelial A549 cells induced by diesel exhaust particles (DEPs) as well as ITIH4-knockdown were investigated. We found that an interquartile range (IQR) increase in PM was associated with a decrease of 2.673 ng/mL in ITIH4, an increase of 1.104 pg/mL of 8-isoprostane, and an increase of 6.918 pg/mL of interleukin (IL)-6 in human BAL. In rats, increases in 8-isoprostane, IL-6, and p53 and a decrease in sirtuin-1 (Sirt1) in the lungs and decreases in ITIH4 in the BAL, lungs, and serum were observed after PM and gaseous exposure. ITIH4 levels in lung lysates were correlated with levels in BAL samples (r = 0.377, p < 0.01), whereas ITIH4 levels in BAL were correlated with IL-6 levels (r = -0.420, p < 0.01). ITIH4 expression was significantly reduced in alveolar epithelial A549 cells by DEP in a dose-dependent manner. A decrease in Sirt1 and increases in phosphorylated extracellular signal-regulated kinase (p-ERK) and caspase-3 were observed after DEP exposure and ITIH4-knockdown. In conclusion, air pollution decreased ITIH4 expression in the lungs, which was associated with alveolar epithelial cell senescence and apoptosis. ITIH4 could be a vital protein in regulating alveolar cell destruction and its inhibition after exposure to air pollution.
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http://dx.doi.org/10.1016/j.envpol.2021.116863DOI Listing
June 2021

Using a land use regression model with machine learning to estimate ground level PM.

Environ Pollut 2021 May 1;277:116846. Epub 2021 Mar 1.

National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan, Taiwan. Electronic address:

Ambient fine particulate matter (PM) 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 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. Daily average PM 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 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 exposures.
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http://dx.doi.org/10.1016/j.envpol.2021.116846DOI Listing
May 2021

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter.

Sci Rep 2021 Mar 1;11(1):4866. Epub 2021 Mar 1.

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, 02115, USA.

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM. Subgroup analyses were performed to determine whether LRIs and PM are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.
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http://dx.doi.org/10.1038/s41598-021-84435-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921673PMC
March 2021

Integrated analysis of source-specific risks for PM-bound metals in urban, suburban, rural, and industrial areas.

Environ Pollut 2021 Apr 4;275:116652. Epub 2021 Feb 4.

National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, 35053, Taiwan; Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung, 40402, Taiwan. Electronic address:

The levels and characteristics of atmospheric metals vary in time and location, can result in various health impacts, which increases the challenge of air quality management. We aimed to investigate PM-bound metals in multiple locations and propose a methodology for comparing metal elements across study regions and prioritizing source contributions through integrated health risk assessments. PM-bound metals were collected in the urban, suburban, rural, and industrial regions of Taiwan between 2016 and 2018. We incorporated the positive matrix factorization (PMF) with health risk assessments (considering estimates of the margin of exposure (MOE) and excess cancer risk (ECR)) to prioritize sources for control. We found that the concentrations of Fe, Zn, V, Cu, and Mn (industry-related metals) were higher at the industrial site (Kaohsiung) and Ba, Cr, Ni, Mo, and Co (traffic-related metals) were higher at the urban site (Taipei). The rural site (Hualian) had good air quality, with low PM and metal concentrations. Most metal concentrations were higher during the cold season for all study sites, except for the rural. Ambient concentrations of Mn, Cr, and Pb obtained from all study sites presents a higher health risk of concern. In Kaohsiung, south Taiwan, PM-bound metals from the iron ore and steel factory is suggested as the first target for control based on the calculated health risks (MOE < 1 and ECR > 10). Overall, we proposed an integrated strategy for initiating the source management prioritization of PM-bound metals, which can aid an effort for policymaking.
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http://dx.doi.org/10.1016/j.envpol.2021.116652DOI Listing
April 2021

Residential greenness and birth outcomes: Evaluating the mediation and interaction effects of particulate air pollution.

Ecotoxicol Environ Saf 2021 Mar 15;211:111915. Epub 2021 Jan 15.

Division of Allergy, Asthma, and Rheumatology, Department of Pediatrics, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan 33305, Taiwan, ROC. Electronic address:

Background: The few studies that examined the association between residential greenness and birth outcomes have produced inconsistent results, and the underlying mechanisms of these associations remain unclear.

Objectives: We examined the mediation and interaction effects of particulate matter (PM) air pollution on the relationship between greenness exposure during the first and third trimesters of pregnancy and birth outcomes, including preterm birth (PTB), term low birth weight (TLBW), small for gestational age (SGA), birth weight (BW), and head circumference (HC).

Methods: We conducted a retrospective cohort study on 16,184 singleton live births between 2010 and 2012 in Taiwan. Residential greenness was estimated based on the normalized difference vegetation index (NDVI), and the PM information during the first and third trimesters was estimated through hybrid kriging land use regression and ordinary kriging interpolation methods. Multiple regression analyses were performed to evaluate the associations between greenness exposure and birth outcomes. We estimated the mediating effects of PM associated with greenness exposure on birth outcomes through causal mediation analyses. We also examined the potential multiplicative and additive interactions between greenness exposure and PM and their effects on birth outcomes.

Results: The first trimester NDVI exposure was associated with reduced risks for PTB, TLBW, and SGA, which had an adjusted OR (aOR) of 0.93 (95% CI: 0.89-0.97), 0.91 (95% CI: 0.83-0.99), and 0.95 (95% CI: 0.91-1.00), respectively, per 0.1 unit increase in multi-pollutant models. The causal mediation analysis showed that PM mediated approximately 5-19% of the association between first and third trimester greenness and PTB and mediated approximately 15-37% of the association between greenness and SGA. We identified multiplicative interactions in log scale between first trimester PM and NDVI exposure for SGA (aOR = 0.92, p = 0.03) and HC (estimate = 1.47, p = 0.04).

Conclusions: This study revealed beneficial associations between residential greenness and birth outcomes, including PTB, TLBW, and SGA. The associations were partly mediated by a reduction in exposure to PM air pollution.

Summary: The beneficial effects of greenness on PTB and SGA are partly mediated by a reduction in exposure to PM air pollution.
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http://dx.doi.org/10.1016/j.ecoenv.2021.111915DOI Listing
March 2021

The Effect of Surrounding Greenness on Type 2 Diabetes Mellitus: A Nationwide Population-Based Cohort in Taiwan.

Int J Environ Res Public Health 2020 12 31;18(1). Epub 2020 Dec 31.

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.

This study determines whether surrounding greenness is associated with the incidence of type 2 diabetes Mellitus (T2DM) in Taiwan. A retrospective cohort study determines the relationship between surrounding greenness and the incidence of T2DM during the study period of 2001-2012 using data from the National Health Insurance Research Database. The satellite-derived normalized difference vegetation index (NDVI) from the global MODIS database in the NASA Earth Observing System is used to assess greenness. Cox proportional hazard models are used to determine the relationship between exposure to surrounding greenness and the incidence of T2DM, with adjustment for potential confounders. A total of 429,504 subjects, including 40,479 subjects who developed T2DM, were identified during the study period. There is an inverse relationship between exposure to surrounding greenness and the incidence of T2DM after adjustment for individual-level covariates, comorbidities, and the region-level covariates (adjusted HR = 0.81, 95% CI: 0.79-0.82). For the general population of Taiwan, greater exposure to surrounding greenness is associated with a lower incidence of T2DM.
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http://dx.doi.org/10.3390/ijerph18010267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795104PMC
December 2020

Comparison of Spatial Modelling Approaches on PM and NO Concentration Variations: A Case Study in Surabaya City, Indonesia.

Int J Environ Res Public Health 2020 11 29;17(23). Epub 2020 Nov 29.

Department of Geomatics, National Cheng Kung University, Tainan City 70101, Taiwan.

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM) and nitrogen dioxide (NO) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM variations and 46%, 47%, and 48% of NO variations, respectively. The GTWR model performed better (R = 0.51 for PM and 0.48 for NO) than the other two models (R = 0.49-0.50 for PM and 0.46-0.47 for NO), LUR and GWR. In the PM model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM and NO, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM and NO concentration variations within areas across Asia.
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http://dx.doi.org/10.3390/ijerph17238883DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730102PMC
November 2020

Residential greenness, activities of daily living, and instrumental activities of daily living: A longitudinal cohort study of older adults in China.

Environ Epidemiol 2019 Oct 14;3(5):e065. Epub 2019 Oct 14.

Environmental Research Center, Duke Kunshan University, Kunshan, China.

We aimed to explore whether higher levels of residential greenness were related to lower odds of disabilities in activities of daily living (ADL) and instrumental activities of daily living (IADL).

Methods: We included older adults 65 years of age or older from the Chinese Longitudinal Healthy Longevity Survey. Our exposure was Normalized Difference Vegetation Index in 500 m radius around residence. Our outcome was ADL and IADL. We used binary logistic regression and mixed-effects logistic regression to estimate the odds of ADL and IADL disabilities.

Results: A total of 36,803 and 32,316 participants were included for the analysis of ADL and IADL, with 71.6% free of ADL disability and 47.3% free of IADL disability. In the logistic regression model, compared with the participants living in the lowest quartile of residential greenness, those in the highest quartile had a 28% (odds ratio [OR] = 0.72; 95% confidence interval [CI] = 0.65, 0.79) lower odds of ADL disability and a 14% (OR = 0.86; 95% CI = 0.77, 0.95) lower odds of IADL disability. A similar association was found in the mixed-effects logistic regression models. During the follow-up period, 5,004 and 4,880 healthy participants developed ADL and IADL disabilities. Per 0.1-unit increase in baseline annual average Normalized Difference Vegetation Index (NDVI) was related to an OR of 0.95 of developing ADL disability (95% CI = 0.93, 0.98) and IADL disability (95% CI = 0.91, 0.98).

Conclusions: Our study suggests that increasing green space is associated with lower odds of ADL and IADL disabilities, which may reduce caregiver burden of long-term care for Chinese older adults.
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http://dx.doi.org/10.1097/EE9.0000000000000065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608893PMC
October 2019

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration.

Int J Environ Res Public Health 2020 09 23;17(19). Epub 2020 Sep 23.

Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan.

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial-temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms-random forest (RF) and extreme gradient boosting (XGBoost)-for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.
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http://dx.doi.org/10.3390/ijerph17196956DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579284PMC
September 2020

Associations of birth outcomes with air pollution and land use characteristics in the Greater Taipei Area.

Sci Total Environ 2021 Jan 11;750:141579. Epub 2020 Aug 11.

School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan; Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan. Electronic address:

Background: Understanding the effects of environmental factors on birth outcomes is crucial for public health because newborns' birth size affects their likelihood of childhood survival, risk of perinatal morbidity, and subsequent health and growth. Therefore, we investigated the associations of birth outcomes with prenatal air pollutant exposure and residential land use characteristics in the Greater Taipei Area.

Methods: Participants were selected from the Longitudinal Examination across Prenatal and Postpartum Health in Taiwan study, which is an ongoing prospective study launched in July 2011. Parental sociodemographic data and medical histories were collected using standardized questionnaires. Mean air pollutant levels during each trimester were estimated using the spatial interpolation technique (Ordinary Kriging). Land use types surrounding participants' homes were evaluated within a designated radius of their residential addresses. We used multiple regressions to examine relationships between birth outcomes (i.e., birth weight, height, and head circumference) and environmental factors after adjustment for parental characteristics.

Results: A total of 436 pregnant women-infant pairs were included. Birth weight was negatively associated with commercial land and greenhouse areas near the residence. Living near greenhouse areas negatively affected birth height, but higher greenness level within 100 m of the residence had a positive effect. Birth head circumference was only associated with sociodemographic factors in the multivariate model.

Conclusion: Land use types near the homes of pregnant women, but not exposure to air pollutants, were significantly associated with birth weight and height in the Greater Taipei Area. Increased greenness level was positively associated with birth height, and living near commercial or greenhouse areas had adverse effects on birth outcomes. Living in a healthy neighborhood is critical for the birth outcomes of infants and presumably their health in early childhood.
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http://dx.doi.org/10.1016/j.scitotenv.2020.141579DOI Listing
January 2021

Development of Hourly Indoor PM Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity.

Int J Environ Res Public Health 2020 08 14;17(16). Epub 2020 Aug 14.

Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 138 Sheng-Li Road, Tainan 70403, Taiwan.

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM) is a critical health risk factor. Therefore, measuring indoor PM concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM data is difficult and expensive. Therefore, several indoor PM concentration prediction models have been developed. However, these prediction models only assess the daily average PM concentrations in cold or temperate regions. The factors that influence PM concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM concentrations, the difference between indoor and outdoor CO levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM concentrations and suggested that outdoor PM concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM concentrations.
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http://dx.doi.org/10.3390/ijerph17165906DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460507PMC
August 2020

Outdoor air pollutants exposure associated with pulmonary function and EBC pH value in atopic asthmatic and non-asthmatic children.

J Asthma 2020 Sep 21:1-7. Epub 2020 Sep 21.

Department of Respiratory Therapy, College of Medicine, Chang Gung University, Taoyuan, Taiwan.

Objective: Air pollution is associated with the prevalence of respiratory diseases. This study aimed to evaluate the impacts of outdoor air pollutants and indoor 1 ( 1) exposure on levels of fractional exhaled nitric oxide (FeNO), exhaled breath condensate (EBC) pH, and pulmonary function in atopic children.

Methods: This study recruited 59 atopic mild-to-moderate asthmatic children and 23 atopic non-asthmatic children. Data on personal characteristics, FeNO, EBC pH, and pulmonary function were collected. Group 1 allergens of 1 were measured on the tops of mattresses and on bedroom floors in the children's homes, and outdoor air pollutant concentrations were estimated from air quality monitoring stations, using the ordinary kriging method.

Results: Exposure levels of outdoor air pollutants, except for particulate matter (PM), for the recruited children met outdoor air quality standards set by the Taiwan Environmental Protection Agency. The lag effect of outdoor PM exposure was negatively associated with the forced expiratory volume in one second (FEV) [(Lag 1: =-0.771,  = 0.028), and O (Lag 1-7: =-2.02,  = 0.04, Lag 1-28: =-3.213,  = 0.029)]. Median pulmonary function parameters differed significantly in forced vital capacity (FVC) ( = 0.004) and FEV ( = 0.024) values between atopic asthmatic and non-asthmatic children. No association was found between the FeNO/EBC pH level and exposure to 1 allergen and air pollutants in the recruited children.

Conclusions: Outdoor PM and O exposure was associated with reduction in FEV in atopic asthmatic and non-asthmatic children.
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http://dx.doi.org/10.1080/02770903.2020.1788075DOI Listing
September 2020

Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan.

Int J Environ Res Public Health 2020 06 23;17(12). Epub 2020 Jun 23.

Institute of Environmental and Occupational Health Sciences, National Yang-Ming University, Taipei 112, Taiwan.

Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862-0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817-0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632-0.824), and lung cancer (RR = 0.871; 95% CI = 0.735-1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.
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http://dx.doi.org/10.3390/ijerph17124525DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7344743PMC
June 2020

Association Between Traffic Count and Cardiovascular Mortality: A Prospective Cohort Study in Taiwan.

J Epidemiol 2021 May 25;31(5):343-349. Epub 2020 Nov 25.

Genomics Research Center, Academia Sinica.

Background: Exposure to traffic-related pollution is positively associated with cardiovascular diseases (CVD), but little is known about how different sources of traffic pollution (eg, gasoline-powered cars, diesel-engine vehicles) contribute to CVD. Therefore, we evaluated the association between exposure to different types of engine exhaust and CVD mortality.

Methods: We recruited 12,098 participants from REVEAL-HBV cohort in Taiwan. The CVD mortality in 2000-2014 was ascertained by the Taiwan Death Certificates. Traffic pollution sources (2005-2013) were based on information provided by the Directorate General of Highway in 2005. Exposure to PM was based on a land-use regression model. We applied Cox proportional hazard models to assess the association of traffic vehicle exposure and CVD mortality. A causal mediation analysis was applied to evaluate the mediation effect of PM on the relationship between traffic and CVD mortality.

Results: A total of 382 CVD mortalities were identified from 2000 to 2014. We found participants exposed to higher volumes of small car and truck exhausts had an increased CVD mortality. The adjusted hazard ratio (HR) was 1.10 for small cars (95% confidence interval [CI], 0.94-1.27; P-value = 0.23) and 1.24 for truck (95% CI, 1.03-1.51; P-value = 0.03) per one unit increment of the logarithm scale. The findings were still robust with further adjustment for different types of vehicles. A causal mediation analysis revealed PM had an over 60% mediation effect on traffic-CVD association.

Conclusions: Exposure to exhaust from trucks or gasoline-powered cars is positively associated with CVD mortality, and air pollution may play a role in this association.
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http://dx.doi.org/10.2188/jea.JE20200082DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021879PMC
May 2021

Effects of surrounding environment on incidence of end stage renal disease.

Sci Total Environ 2020 Jun 14;723:137915. Epub 2020 Mar 14.

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address:

The rising prevalence and incidence of end-stage renal disease (ESRD) have been noted around the world. However, no study has been conducted to examine the effect of surrounding environment on incidence of ESRD. This study assessed the associations of exposure to PM2.5 level and surrounding green spaces, separately, with incidence of ESRD in Taiwan. Demographic and clinical data used in this study was retrieved from the National Health Insurance Research Database from 2003 to 2012. PM2.5 data collected from the Environmental Protection Administration of Taiwan and a hybrid land-use regression model was used to approximate long-term exposure to PM2.5. Percentage of exposure to surrounding green spaces was used to determine individual exposure level. Cox proportional hazards models with a generalized estimating equation were applied to investigate the effect of surrounding environment on incidence of ESRD. The results showed significant positive association between exposure to PM2.5 level and incidence of ESRD; but inverse association between exposure to surrounding green spaces and incidence of ESRD (adjusted hazard ratio (AHR) = 1.08, 95% CI: 1.00-1.15 for exposure to PM2.5 level; AHR = 0.90, 95%CI: 0.84-0.98 for surrounding green spaces). Together, the findings from this study have added suggestive evidence on the adverse effect of exposure to PM2.5 level and the beneficial effect of exposure to surrounding green spaces on the incidence of ESRD in a general population in Taiwan.
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http://dx.doi.org/10.1016/j.scitotenv.2020.137915DOI Listing
June 2020

Effects of ambient PM and particle-bound metals on the healthy residents living near an electric arc furnace: A community- based study.

Sci Total Environ 2020 Aug 20;728:138799. Epub 2020 Apr 20.

Department of Public Health, China Medical University, Taichung, Taiwan. Electronic address:

Fine particulate matter (PM) emitted from electric arc furnaces (EAFs) poses health concerns. However, little research has been done on the impact of EAF on the health of community residents. This cross-sectional study conducted a PM exposure assessment and health examination of community residents living near an EAF. A total of 965 residents aged 40-90 years were recruited. The residents' exposure to PM was categorized according to the distance of their residence from the EAFs (<500, 500-1000, 1000-1500, 1500-2000, and > 2000 m). Average ambient PM concentrations were estimated using a hybrid kriging/land-use regression (LUR) model. In addition, we selected two air-sampling sites to monitor the 2-year levels of PM and particle-bound metals. A spot urine sample and blood samples were collected and ten heavy metal concentrations in the blood were analyzed. Inflammation- and oxidative stress-related biomarkers were measured. The associations between environmental factors and a biochemical examination were estimated using a generalized linear model. Active air sampling and hybrid kriging/LUR model simulation indicated increased levels of PM near the EAF. The metal concentrations in PM included Fe, Pb, Mn, Ni, As, Cu, Ni, Zn, and Al, which also significantly increased near the EAF. PM levels were significantly associated with an increased total cholesterol-high-density lipoprotein (TC/HDL) ratio. High levels of PM and malondialdehyde were associated with a 1.72-fold increased risk of TC/HDL ratio ≥ 4 (95% CI: 1.12-2.65) after adjusting for potential confounding factors. Blood Pb levels were significantly associated with increased systolic and diastolic blood pressure and decreased estimated glomerular filtration rate but negatively associated with distance from the EAF. The results show that people living near EAFs should pay more attention to adverse health problems, including atherogenic dyslipidemia, hypertension, and chronic kidney disease associated with exposure to PM and particle-bound metals.
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http://dx.doi.org/10.1016/j.scitotenv.2020.138799DOI Listing
August 2020

A hybrid kriging/land-use regression model with Asian culture-specific sources to assess NO spatial-temporal variations.

Environ Pollut 2020 Apr 27;259:113875. Epub 2019 Dec 27.

Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan. Electronic address:

Kriging interpolation and land use regression (LUR) have characterized the spatial variability of long-term nitrogen dioxide (NO), but there has been little research on combining these two methods to capture small-scale spatial variation. Furthermore, studies predicting NO exposure are almost exclusively based on traffic-related variables, which may not be transferable to Taiwan, a typical Asian country with diverse local emission sources, where densely distributed temples and restaurants may be important for NO levels. To advance the exposure estimates in Taiwan, a hybrid kriging/LUR model incorporates culture-specific sources as potential predictors. Based on 14-year NO observations from 73 monitoring stations across Taiwan, a set of interpolated NO values were generated through a leave-one-out ordinary kriging algorithm, and this was included as an explanatory variable in the stepwise LUR procedures. Kriging interpolated NO and culture-specific predictors were entered in the final models, which captured 90% and 87% of NO variation in annual and monthly resolution, respectively. Results from 10-fold cross-validation and external data verification demonstrate robust performance of the developed models. This study demonstrates the value of incorporating the kriging-interpolated estimates and culture-specific emission sources into the traditional LUR model structure for predicting NO, which can be particularly useful for Asian countries.
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http://dx.doi.org/10.1016/j.envpol.2019.113875DOI Listing
April 2020

PM Pollutant in Asia-A Comparison of Metropolis Cities in Indonesia and Taiwan.

Int J Environ Res Public Health 2019 12 5;16(24). Epub 2019 Dec 5.

Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya 60111, Indonesia.

Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM). In-situ observations of air pollutants from ground monitoring stations from 2016-2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25-5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM model, the essential predictors for DKI Jakarta were influenced by temperature, NDVI, humidity, and residential area, while those for the Taipei Metropolis region were influenced by PM, NO, SO, UV, rainfall, spring, main road, railroad, airport, proximity to airports, mining areas, and NDVI. The validation of the results of the estimated PM distribution use 10-cross validation with indicated values of 0.62 for DKI Jakarta and 0.84 for Taipei Metropolis. The results of cross-validation show the strength of the model.
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http://dx.doi.org/10.3390/ijerph16244924DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950409PMC
December 2019

The effects of fine and coarse particulate matter on lung function among the elderly.

Sci Rep 2019 10 15;9(1):14790. Epub 2019 Oct 15.

Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of medicine and NTU Hospital, Taipei City, Taiwan.

Impaired lung function is associated with morbidity and mortality in the elderly. However, there is a paucity of data regarding the long-term effects of particulate matter (PM) on lung function among the elderly. This study evaluated the exposure-response relationship between ambient PM and different lung function indices among the elderly in Taiwan. A cross-sectional survey of individuals aged ≥65 years was conducted in Taiwan from October 2015 to September 2016. Those who attended the annual health examination for the elderly in five hospitals of varying background PM concentrations were enrolled. The long-term (2015 annual mean concentration) exposure to air pollution was estimated by the Kriging method at the residence of each subject. The association between ambient PM exposure and lung function was evaluated by linear regression modeling, with adjustments for age, sex, height, weight, educational attainment, presence of asthma or chronic obstructive pulmonary disease, smoking status, season, and co-pollutants. There were 1241 subjects (mean age, 70.5 years). The mean residential PM and PM in 2015 was 26.02 and 18.01 μg/m, respectively. After adjustments for confounders and co-pollutants, the FVC decrease was best associated with fine particles (PM), whereas the FEV, FEF, FEF and FEF decreases were best associated with coarse particles (PM). An IQR (10 μg/m) increase in PM decreased FVC by 106.38 ml (4.47%), while an IQR (7.29 μg/m) increase in PM decreased FEV and FEF by 91.23 ml (4.85%) and 104.44 ml/s (5.58%), respectively. Among the Taiwanese elderly, long-term PM exposure mainly decreases the vital capacity of lung function. Moreover, PM has a stronger negative effect on the function of conductive airways than PM.
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http://dx.doi.org/10.1038/s41598-019-51307-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6794286PMC
October 2019

Association between residential greenness and cognitive function: analysis of the Chinese Longitudinal Healthy Longevity Survey.

BMJ Nutr Prev Health 2019 26;2(2):72-79. Epub 2019 Aug 26.

Environmental Research Center, Duke Kunshan University, Kunshan, China.

Introduction: Proximity to vegetated green space has been linked to better physical and mental health. However, the relationship between residential greenness and cognitive function and its decline among older adults is not clear in large cohort studies.

Methods: Our study used the 2000, 2002, 2005, 2008 and 2011 wave of the Chinese Longitudinal Healthy Longevity Survey. We calculated the Normalised Difference Vegetation Index (NDVI) using a 500 m radius around participants' residential addresses. Mini-Mental State Examination (MMSE) was applied to measure cognitive function. Our study included the cross-sectional analysis using the linear regression, and logistical regression, and also the longitudinal analysis using the linear mixed effects regression, and mixed effects logistic regression. Our study also conducted a sensitivity analysis using the survey-weighted regression. Additionally, our study participants were categorised into those living in areas of positive and negative changes in NDVI in relation to MMSE. All regression models were adjusted for a range of covariates.

Results: Among 38 327 participants at baseline, the mean MMSE score was 21. Annual average NDVI ranged from -0.11 to 0.76. In the cross-sectional analysis, each 0.1-unit increase in NDVI was associated with a 0.23-point increase in MMSE score (95% CI 0.16 to 0.29) in the linear regression, and an OR of 0.94 (95% CI 0.92 to 0.96) of having cognition impairment in the logistic regression. In the second analysis, looking at changes in NDVI and MMSE score, compared with the participants living in areas with an increase in NDVI, those living in areas with a decrease in greenness had an OR of 1.25 (95% CI 1.18 to 1.34) of a decrease in MMSE, and an OR of 0.90 (95% CI 0.84 to 0.96) of an increase in MMSE. In the longitudinal analysis, we found a significantly weak association (coefficient 0.069, 95% CI 0.0048 to 0.13) in the linear mixed effects regression, but not in the mixed effects logistic regression.

Conclusion: We found evidence of an association between higher residential greenness and better cognitive function among older adults. Our finding provides insight into neurodegeneration and has implications for preventing dementia and Alzheimer's disease in China.
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http://dx.doi.org/10.1136/bmjnph-2019-000030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664503PMC
August 2019

Effects of PM on Skeletal Muscle Mass and Body Fat Mass of the Elderly in Taipei, Taiwan.

Sci Rep 2019 08 1;9(1):11176. Epub 2019 Aug 1.

Department of Environmental and Occupational Medicine, National Taiwan University (NTU) College of medicine and NTU Hospital, Taipei, Taiwan.

Loss of skeletal muscle mass is common with aging and can cause morbidity and mortality in the elderly. The effects of particulate air pollution on skeletal muscle mass is not known. The study aims to assess the chronic effects of ambient fine particulates (PM) on the body composition of the elderly. From October 2015 to November 2016, a cross-sectional survey on 530 elderly (age > = 65 years) was conducted in the Taipei Basin, Taiwan. The body composition was measured by bioelectrical impedance analysis (InBody 120). One year exposure to air pollution was estimated using the Kriging method at the participant's residence. Multiple linear regression analysis, after adjustments for demographics and co-pollutants, was used to examine the effects of PM on body composition indices and force of handgrip. Changes in body composition for an interquartile (1.4 μm/m) increase in PM concentration included a 0.4 kg (95% confidence interval (CI): -0.31, -0.58; p < 0.0001) decrease in skeletal muscle mass (2.0%) and a 0.7 kg (95% CI: 0.47, 0.91; p < 0.0001) increase in body fat mass (3.6%). While PM reduced fat free mass in the upper extremities and trunk, but not in the lower extremities, it increased body fat mass in the three parts. There was no significant effect of PM on handgrip force. Higher physical activity (versus lower than median) was associated with less detrimental effect of PM on skeletal muscle mass and body fat mass (p values for interaction term: 0.009 and 0.013, respectively). Long-term PM exposure is associated with decreased skeletal muscle mass and increased body fat mass in the elderly, which can be ameliorated by physical activity.
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http://dx.doi.org/10.1038/s41598-019-47576-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6671961PMC
August 2019

Association Between Long-term Exposure to PM2.5 and Incidence of Type 2 Diabetes in Taiwan: A National Retrospective Cohort Study.

Epidemiology 2019 07;30 Suppl 1:S67-S75

From the Department of Environmental and Occupational Health, National Cheng Kung University, Tainan, Taiwan.

Background: Long-term exposure to fine particulate matter (PM with an aerodynamic diameter ≤2.5 µm; PM2.5) contributes to an elevated incidence of type 2 diabetes (T2D) in North America and Europe, but there is limited empirical evidence for Asian countries. This study determined the association between and the exposure-response relationship for PM2.5 and the incidence of T2D in Taiwan.

Methods: This retrospective cohort study was conducted for the years 2001-2012. Health information, including age, sex, health insurance premium, type of occupation, medication, and disease status, was retrieved from the Longitudinal Health Insurance Database 2000. Monitoring data for PM2.5 came from the Environmental Protection Administration of Taiwan, and Land-use Regression modeling was used to approximate participants' long-term exposure to PM2.5. Cox proportional hazards models with a generalized estimating equation to account for the correlation within the locations of the medical facilities were used to estimate the association between exposure to PM2.5 and the incidence of T2D, adjusting for the potential confounders. We also examined effect modification of sex, age, hyperlipidemia, and National Health Insurance premium for the association.

Results: Forty-eight thousand six hundred eleven new cases of diabetes were identified among 505,151 eligible participants, with the median follow-up of 12 years. Positive associations were identified between long-term exposure to PM2.5 exposure and the incidence of T2D. An increase of 10 μg/m PM2.5 was associated with an 11.0% increase in the risk of contracting diabetes (95% confidence interval = 8.0%, 13.0%). The results show that there is an almost linear relationship between exposure to PM2.5 and the incidence of T2D. Sex, age, hyperlipidemia, and National Health Insurance premium acted as effect modifiers of the association between diabetes incidence and levels of PM2.5 exposure in Taiwan.

Conclusions: In the population in Taiwan, long-term exposure to PM2.5 increases the risk of incidence of T2D by 11%. This effect is more pronounced in elderly male patients who exhibit hyperlipidemia and in individuals who have a lower insurance health insurance premium.
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http://dx.doi.org/10.1097/EDE.0000000000001035DOI Listing
July 2019

[Application of Geospatial Information Technologies in Assessing Changes in Regional Health Risks Related to Climate Change].

Authors:
Chih-Da Wu

Hu Li Za Zhi 2019 Jun;66(3):14-22

PhD, Assistant Professor, Department of Geomatics, National Cheng Kung University, Taiwan, ROC.

Changes in climate and global warming trends impact the ecological balance as well as human health. The recent development of geospatial information technologies such as Geographic Information Systems (GIS) and remote sensing provides useful tools to assess the impacts of climate changes on human health over large areas. This article aimed to conduct a literature review related to the application of geospatial information technologies in order to assess climate-change-related health risks in Taiwan, with primary outcomes of interest including physiological and mental health and infectious diseases. Three environmental factors, including temperature, precipitation, and air pollution, and their impacts on human health were considered. Comments were raised for future studies in Taiwan on this subject area. Among the hundred papers reviewed, 28 were related to the target topic, and air pollution and fine particle studies were the focus of most of these 28 papers. Studies related to extreme temperature indicted growing concern with this issue. However, limited research was found related to precipitation and environmental greenness. Therefore, future studies should pay greater attention to these two environmental issues. We hope that the findings of this literature review will encourage more researchers to investigate this subject.
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http://dx.doi.org/10.6224/JN.201906_66(3).03DOI Listing
June 2019

Association Between Surrounding Greenness and Schizophrenia: A Taiwanese Cohort Study.

Int J Environ Res Public Health 2019 04 19;16(8). Epub 2019 Apr 19.

Department of Environmental and Occupational Health, National Cheng Kung University, Tainan 70101, Taiwan.

This study aims to investigate the association between surrounding greenness and schizophrenia incidence in Taiwan. Data of 869,484 individuals without a history of schizophrenia were included from the Longitudinal Health Insurance Database from 2000 through 2010 for analysis. The diagnoses of schizophrenia were based on ICD-9 codes. Greenness exposure was assessed using the satellite-based normalized difference vegetation index, assuming individuals lived near the hospital they most often visited for common cold during the study period. Cox proportional hazards models were applied to assess the association between greenness exposure and schizophrenia incidence after adjustments were made for the potential confounders. A total of 5,069 schizophrenia cases were newly diagnosed during the study period. A negative significant (P < 0.05) association found using 2,000-m buffer distances (distance of a moderately paced 20-min walk) in the whole Taiwan island, cities, and metropolitan areas. The results of the stratified analysis based on sex and health insurance rate suggested surrounding greenness has approximately equal effects of reducing the risk of schizophrenia, regardless of sex or financial status. In conclusion, our findings suggest that more surrounding greenness may reduce the risk of schizophrenia.
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http://dx.doi.org/10.3390/ijerph16081415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6517986PMC
April 2019

Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration.

Int J Environ Res Public Health 2019 04 11;16(7). Epub 2019 Apr 11.

Department of Geomatics, National Cheng Kung University, Tainan 70101, Taiwan.

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O₃ concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency's (EPA) data of O₃ concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R² of 0.74 and a 10-fold cross-validated R² of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O₃ concentration variation in Asian areas.
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http://dx.doi.org/10.3390/ijerph16071300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480950PMC
April 2019

Residential greenness and mortality in oldest-old women and men in China: a longitudinal cohort study.

Lancet Planet Health 2019 01;3(1):e17-e25

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.

Background: Exposure to natural vegetation, or greenness, might affect health through several pathways, including increased physical activity and social engagement, improved mental health, and reductions in exposure to air pollution, extreme temperatures, and noise. Few studies of the effects of greenness have focused on Asia, and, to the best of our knowledge, no study has assessed the effect on vulnerable oldest-old populations. We assessed the association between residential greenness and mortality in an older cohort in China.

Methods: We used five waves (February, 2000-October, 2014) of the China Longitudinal Healthy Longevity Survey (CLHLS), a prospective cohort representative of the general older population in China. We assessed exposure to greenness through satellite-derived Normalised Difference Vegetation Index (NDVI) values in the 250 m and 1250 m radius around the residential address for each individual included in the study. We calculated contemporaneous NDVI values, cumulative NDVI values, and changes in NDVI from the start of the study over time. The health outcome of the study was all-cause mortality, excluding accidental deaths. Mortality rate ratios were estimated with Cox proportional hazards models, adjusted for age, sex, ethnicity, marital status, geographical region, childhood and adult socioeconomic status, social and leisure activity, smoking status, alcohol consumption, and physical activity.

Findings: Among 23 754 individuals (mean age at baseline 93 years [SD 7·5]) totaling 80 001 person-years, we observed 18 948 deaths during 14 years of follow-up, between June, 2000, and December, 2014. Individuals in the highest quartile of contemporaneous NDVI values had 27% lower mortality than those in the lowest quartile for the 250 m radius (hazard ratio [HR] 0·73, 95% CI 0·70-0·76), and 30% lower mortality for the 1250 m radius (0·70, 0·67-0·74). No clear association was observed for cumulative NDVI measurements and mortality. We did not detect an association between area-level changes in NDVI and mortality.

Interpretation: Our research suggests that proximity to more green space is associated with increased longevity, which has policy implications for the national blueprint of ecological civilisation and preparation for an ageing society in China.

Funding: Bill & Melinda Gates Foundation, US National Institute on Aging, US National Institute of Health, Natural Science Foundation of China, UN Population Fund, China Social Sciences Foundation, and Hong Kong Research Grants Council.
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http://dx.doi.org/10.1016/S2542-5196(18)30264-XDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6358124PMC
January 2019