Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries.

INTRODUCTION: Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). METHODS: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. RESULTS: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. CONCLUSION: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

How missing evidence-based medicine indicators can inform COVID-19 vaccine distribution policies: a scoping review and calculation of indicators from data in randomised controlled trials.

OBJECTIVE: Reports of efficacy, effectiveness and harms of COVID-19 vaccines have not used key indicators from evidence-based medicine (EBM) that can inform policies about vaccine distribution. This study aims to clarify EBM indicators that consider baseline risks when assessing vaccines' benefits versus harms: absolute risk reduction (ARR) and number needed to be vaccinated (NNV), versus absolute risk of the intervention (ARI) and number needed to harm (NNH). METHODS: We used a multimethod approach, including a scoping review of the literature; calculation of risk reductions and harms from data concerning five major vaccines; analysis of risk reductions in population subgroups with varying baseline risks; and comparisons with prior vaccines. FINDINGS: The scoping review showed few reports regarding ARR, NNV, ARI and NNH; comparisons of benefits versus harms using these EBM methods; or analyses of varying baseline risks. Calculated ARRs for symptomatic infection and hospitalisation were approximately 1% and 0.1%, respectively, as compared with relative risk reduction of 50%-95% and 58%-100%. NNV to prevent one symptomatic infection and one hospitalisation was in the range of 80-500 and 500-4000. Based on available data, ARI and NNH as measures of harm were difficult to calculate, and the balance between benefits and harms using EBM measures remained uncertain. The effectiveness of COVID-19 vaccines as measured by ARR and NNV was substantially higher in population subgroups with high versus low baseline risks. CONCLUSIONS: Priorities for vaccine distribution should target subpopulations with higher baseline risks. Similar analyses using ARR/NNV and ARI/NNH would strengthen evaluations of vaccines' benefits versus harms. An EBM perspective on vaccine distribution that emphasises baseline risks becomes especially important as the world's population continues to face major barriers to vaccine access-sometimes termed 'vaccine apartheid'.

Bring Your Own Location Data: Use of Google Smartphone Location History Data for Environmental Health Research.

BACKGROUND: Environmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data. OBJECTIVES: We aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research. METHODS: We included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution. RESULTS: Of 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment. DISCUSSION: We believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.

Measuring and modelling perceptions of the built environment for epidemiological research using crowd-sourcing and image-based deep learning models.

BACKGROUND: Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the difficulty in quantifying them objectively in large populations. OBJECTIVE: To measure and predict perceptions of the built environment from street-view images using crowd-sourced methods and deep learning models for application in epidemiologic studies. METHODS: We used the Amazon Mechanical-Turk crowdsourcing platform where participants compared two street-view images and quantified perceptions of nature quality, beauty, relaxation, and safety. We optimized street-view image sampling methods to improve the quality and resulting perception data specific to participants enrolled in the Washington State Twin Registry (WSTR) health study. We used a transfer learning approach to train deep learning models by leveraging existing image perception data from the PlacePulse 2.0 dataset, which includes 1.1 million image comparisons, and refining based on new WSTR perception data. Resulting models were applied to WSTR addresses to estimate exposures and evaluate associations with traditional built environment measures. RESULTS: We collected over 36,000 image comparisons and calculated perception measures for each image. Our final deep learning models explained 77.6% of nature quality, 68.1% of beauty, 72.0% of relaxation, and 64.7% of safety in pairwise image comparisons. Applying transfer learning with the new perception labels specific to the WSTR yielded an average improvement of 3.8% for model performance. Perception measures were weakly to moderately correlated with traditional built environment exposures for WSTR participant addresses; for example, nature quality and NDVI (r = 0.55), neighborhood area deprivation (r = -0.16), and walkability (r = -0.20), respectively. SIGNIFICANCE: We were able to measure and model perceptions of the built environment optimized for a specific health study. Future applications will examine associations between these exposure measures and mental health in the WSTR. IMPACT STATEMENT: Built environments influence health through complex pathways. Perceptions of nature quality, beauty, relaxation and safety may be particularly import for understanding these linkages, but few studies to-date have examined these perceptions objectively for large populations. For quantitative research, an exposure measure must be reproducible, accurate, and precise--here we work to develop such measures for perceptions of the urban environment. We created crowd-sourced and image-based deep learning methods that were able to measure and model these perceptions. Future applications will apply these models to examine associations with mental health in the Washington State Twin Registry.

Street-view greenspace exposure and objective sleep characteristics among children.

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007-2010 (mid-childhood, mean age 7.9 years) and 2012-2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep.

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets.

BACKGROUND: Combustion-related nitrogen dioxide (NO2) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO2 concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019. METHODS: We scaled an existing annual average NO2 concentration dataset for 2010-12 from a land use regression model (based on 5220 NO2 monitors in 58 countries and land use variables) to other years using NO2 column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO2-attributable paediatric asthma incidence. FINDINGS: We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO2 globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO2 in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO2 concentrations, paediatric population size, and asthma incidence rates to the change in NO2-attributable paediatric asthma incidence differed regionally. INTERPRETATION: Despite improvements in some regions, combustion-related NO2 pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children. FUNDING: Health Effects Institute, NASA.

Predicting Perceptions of the Built Environment using GIS, Satellite and Street View Image Approaches.

BACKGROUND: High quality built environments are important for human health and wellbeing. Numerous studies have characterized built environment physical features and environmental exposures, but few have examined urban perceptions at geographic scales needed for population-based research. The degree to which urban perceptions are associated with different environmental features, and traditional environmental exposures such as air pollution or urban green space, is largely unknown. OBJECTIVE: To determine built environment factors associated with safety, lively and beauty perceptions across 56 cities. METHODS: We examined perceptions collected in the open source Place Pulse 2.0 dataset, which assigned safety, lively and beauty scores to street view images based on crowd-sourced labelling. We derived built environment measures for the locations of these images (110,000 locations across 56 global cities) using GIS and remote sensing datasets as well as street view imagery features (e.g. trees, cars) using deep learning image segmentation. Linear regression models were developed using Lasso penalized variable selection to predict perceptions based on visible (street level images) and GIS/remote sensing built environment variables. RESULTS: Population density, impervious surface area, major roads, traffic air pollution, tree cover and Normalized Difference Vegetation Index (NDVI) showed statistically significant differences between high and low safety, lively, and beauty perception locations. Visible street level features explained approximately 18% of the variation in safety, lively, and beauty perceptions, compared to 3-10% explained by GIS/remote sensing. Large differences in prediction were seen when modelling between city (R2 67-81%) versus within city (R2 11-13%) perceptions. Important predictor variables included visible accessibility features (e.g. streetlights, benches) and roads for safety, visible plants and buildings for lively, and visible green space and NDVI for beauty. CONCLUSION: Substantial within and between city differences in built environment perceptions exist, which visible street level features and GIS/remote sensing variables only partly explain. This offers a new research avenue to expand built environment measurement methods to include perceptions in addition to physical features.

Associations of outdoor fine particulate air pollution and cardiovascular disease in 157 436 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study.

BACKGROUND: Most studies of long-term exposure to outdoor fine particulate matter (PM2·5) and cardiovascular disease are from high-income countries with relatively low PM2·5 concentrations. It is unclear whether risks are similar in low-income and middle-income countries (LMICs) and how outdoor PM2·5 contributes to the global burden of cardiovascular disease. In our analysis of the Prospective Urban and Rural Epidemiology (PURE) study, we aimed to investigate the association between long-term exposure to PM2·5 concentrations and cardiovascular disease in a large cohort of adults from 21 high-income, middle-income, and low-income countries. METHODS: In this multinational, prospective cohort study, we studied 157 436 adults aged 35-70 years who were enrolled in the PURE study in countries with ambient PM2·5 estimates, for whom follow-up data were available. Cox proportional hazard frailty models were used to estimate the associations between long-term mean community outdoor PM2·5 concentrations and cardiovascular disease events (fatal and non-fatal), cardiovascular disease mortality, and other non-accidental mortality. FINDINGS: Between Jan 1, 2003, and July 14, 2018, 157 436 adults from 747 communities in 21 high-income, middle-income, and low-income countries were enrolled and followed up, of whom 140 020 participants resided in LMICs. During a median follow-up period of 9·3 years (IQR 7·8-10·8; corresponding to 1·4 million person-years), we documented 9996 non-accidental deaths, of which 3219 were attributed to cardiovascular disease. 9152 (5·8%) of 157 436 participants had cardiovascular disease events (fatal and non-fatal incident cardiovascular disease), including 4083 myocardial infarctions and 4139 strokes. Mean 3-year PM2·5 at cohort baseline was 47·5 µg/m3 (range 6-140). In models adjusted for individual, household, and geographical factors, a 10 µg/m3 increase in PM2·5 was associated with increased risk for cardiovascular disease events (hazard ratio 1·05 [95% CI 1·03-1·07]), myocardial infarction (1·03 [1·00-1·05]), stroke (1·07 [1·04-1·10]), and cardiovascular disease mortality (1·03 [1·00-1·05]). Results were similar for LMICs and communities with high PM2·5 concentrations (>35 µg/m3). The population attributable fraction for PM2·5 in the PURE cohort was 13·9% (95% CI 8·8-18·6) for cardiovascular disease events, 8·4% (0·0-15·4) for myocardial infarction, 19·6% (13·0-25·8) for stroke, and 8·3% (0·0-15·2) for cardiovascular disease mortality. We identified no consistent associations between PM2·5 and risk for non-cardiovascular disease deaths. INTERPRETATION: Long-term outdoor PM2·5 concentrations were associated with increased risks of cardiovascular disease in adults aged 35-70 years. Air pollution is an important global risk factor for cardiovascular disease and a need exists to reduce air pollution concentrations, especially in LMICs, where air pollution levels are highest. FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).

Development of an environmental health tool linking chemical exposures, physical location and lung function.

BACKGROUND: A challenge in environmental health research is collecting robust data sets to facilitate comparisons between personal chemical exposures, the environment and health outcomes. To address this challenge, the Exposure, Location and lung Function (ELF) tool was designed in collaboration with communities that share environmental health concerns. These concerns centered on respiratory health and ambient air quality. The ELF collects exposure to polycyclic aromatic hydrocarbons (PAHs), given their association with diminished lung function. Here, we describe the ELF as a novel environmental health assessment tool. METHODS: The ELF tool collects chemical exposure for 62 PAHs using passive sampling silicone wristbands, geospatial location data and respiratory lung function measures using a paired hand-held spirometer. The ELF was tested by 10 individuals with mild to moderate asthma for 7 days. Participants wore a wristband each day to collect PAH exposure, carried a cell phone, and performed spirometry daily to collect respiratory health measures. Location data was gathered using the geospatial positioning system technology in an Android cell-phone. RESULTS: We detected and quantified 31 PAHs across the study population. PAH exposure data showed spatial and temporal sensitivity within and between participants. Location data was used with existing datasets such as the Toxics Release Inventory and the National Oceanic and Atmospheric Administration (NOAA) Hazard Mapping System. Respiratory health outcomes were validated using criteria from the American Thoracic Society with 94% of participant data meeting standards. Finally, the ELF was used with a high degree of compliance (> 90%) by community members. CONCLUSIONS: The ELF is a novel environmental health assessment tool that allows for personal data collection spanning chemical exposures, location and lung function measures as well as self-reported information.

Health Effects of Household Solid Fuel Use: Findings from 11 Countries within the Prospective Urban and Rural Epidemiology Study.

BACKGROUND: Household air pollution (HAP) from solid fuel use for cooking affects 2.5 billion individuals globally and may contribute substantially to disease burden. However, few prospective studies have assessed the impact of HAP on mortality and cardiorespiratory disease. OBJECTIVES: Our goal was to evaluate associations between HAP and mortality, cardiovascular disease (CVD), and respiratory disease in the prospective urban and rural epidemiology (PURE) study. METHODS: We studied 91,350 adults 35­70 y of age from 467 urban and rural communities in 11 countries (Bangladesh, Brazil, Chile, China, Colombia, India, Pakistan, Philippines, South Africa, Tanzania, and Zimbabwe). After a median follow-up period of 9.1 y, we recorded 6,595 deaths, 5,472 incident cases of CVD (CVD death or nonfatal myocardial infarction, stroke, or heart failure), and 2,436 incident cases of respiratory disease (respiratory death or nonfatal chronic obstructive pulmonary disease, pulmonary tuberculosis, pneumonia, or lung cancer). We used Cox proportional hazards models adjusted for individual, household, and community-level characteristics to compare events for individuals living in households that used solid fuels for cooking to those using electricity or gas. RESULTS: We found that 41.8% of participants lived in households using solid fuels as their primary cooking fuel. Compared with electricity or gas, solid fuel use was associated with fully adjusted hazard ratios of 1.12 (95% CI: 1.04, 1.21) for all-cause mortality, 1.08 (95% CI: 0.99, 1.17) for fatal or nonfatal CVD, 1.14 (95% CI: 1.00, 1.30) for fatal or nonfatal respiratory disease, and 1.12 (95% CI: 1.06, 1.19) for mortality from any cause or the first incidence of a nonfatal cardiorespiratory outcome. Associations persisted in extensive sensitivity analyses, but small differences were observed across study regions and across individual and household characteristics. DISCUSSION: Use of solid fuels for cooking is a risk factor for mortality and cardiorespiratory disease. Continued efforts to replace solid fuels with cleaner alternatives are needed to reduce premature mortality and morbidity in developing countries. https://doi.org/10.1289/EHP3915.

Health effects of household solid fuel use: findings from 11 countries within the prospective urban and rural epidemiology study

Approximately 2.5 billion individuals globally are exposed to household air pollution (HAP) from cooking with solid fuels such as coal, wood, dung, or crop residues (Smith et al. 2014). Concentrations of air pollutants, especially fine particulate matter [PM≤2:5 lminaerodynamicdiameterðPM2:5)], can be several orders of magnitude higher in homes cooking with solid fuels compared with those using clean fuels such as electricity or liquefied petroleum gas (LPG) (Clark et al. 2013; Shupler et al. 2018). PM2:5 in outdoor air has been linked to mortality, Address correspondence to Perry Hystad, School of Public Health and Human Sciences, Oregon State University, Milam Hall 10, 2520 SW Campus Way, Corvallis, OR 97331 USA. Telephone: (541) 737-4829. Email: Perry. hystad@oregonstate.edu SupplementalMaterialisavailableonline(https://doi.org/10.1289/EHP3915). The authors declared hey have no actual or potential competing financial interests. Received 16 May 2018; Revised 16 April 2019; Accepted 16 April 2019; Published 8 May 2019. Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact ehponline@niehs.nih.gov. Our staff will work with you to assess and meet your accessibility needs within 3 working days.is chemic heart disease (IHD), stroke, and respiratory diseases (Kim et al. 2015). Despite the large population exposed and the potential for adverse health effects, few prospective cohort studies have examined the health effects of HAP. Only four studies have examined HAP and mortality and reached contradictory conclusions (Alam et al. 2012; Kim et al. 2016; Mitter et al. 2016; Yu et al. 2018). Further, studies have not examined HAP and fatal as well as nonfatal cardiovascular disease (CVD) events. There is growing evidence of the adverse effects of HAP on respiratory diseases and lung cancer; however, most studies are cross sectional or case control in design, with relatively small sample sizes and limited geographic coverage (Gordon et al. 2014). To date, few prospective studies have examined HAP exposures and respiratory events in adults, and the existing studies have reported contradictory findings (Chanetal.2019; Ezzati and Kammen 2001; Mitter et al. 2016). Given the absence of direct epidemiological data, the Global Burden of Disease (GBD) study estimated the potential impact of HAP on health using exposure response relationships that pooled data from studies on outdoor air pollution, secondhand smoke, and active smoking (Burnett et al. 2014). These predictions indicated that 1.6 million deaths were attributable to HAP exposure in 2017, of which 39% were from IHD and stroke and 55% from respiratory outcomes [>90% from chronic obstructive pulmonary disease (COPD) and acute lower respiratory infections (ALRI)] (GBD 2017 Risk Factor Collaborators 2018). Given the lack of direct epidemiological evidence and this large predicted burden, there is an urgent need to directly characterize the health effects associated with HAP. Within the Prospective Urban and Rural Epidemiology (PURE) study, we conducted an analysis of 91,350 adults from 467 urban and rural communities in 11 low to middle-income countries (LMICs) where solid fuels are commonly used for cooking. We examined associations between cooking with solid fuels as a proxy indicator of HAP exposure and cause specific mortality, incident cases of CVD [ CVD death and incidence of nonfatal myocardial infarction (MI), stroke, and heart failure (HF)] and incident cases of respiratory disease [respiratory death, nonfatal COPD, pulmonary tuberculosis (TB), pneumonia, or lung cancer].We estimated associations between solid fuel use for cooking and these outcomes, controlling for extensive individual, household, and community covariates. (AU)

The discovery of quinoline-3-carboxamides as hematopoietic prostaglandin D synthase (H-PGDS) inhibitors.

With the goal of discovering more selective anti-inflammatory drugs, than COX inhibitors, to attenuate prostaglandin signaling, a fragment-based screen of hematopoietic prostaglandin D synthase was performed. The 76 crystallographic hits were sorted into similar groups, with the 3-cyano-quinoline 1a (FP IC50 = 220,000 nM, LE = 0.43) being a potent member of the 6,6-fused heterocyclic cluster. Employing SAR insights gained from structural comparisons of other H-PGDS fragment binding mode clusters, the initial hit 1a was converted into the 70-fold more potent quinoline 1d (IC50 = 3,100 nM, LE = 0.49). A systematic substitution of the amine moiety of 1d, utilizing structural information and array chemistry, with modifications to improve inhibitor stability, resulted in the identification of the 300-fold more active H-PGDS inhibitor tool compound 1bv (IC50 = 9.9 nM, LE = 0.42). This selective inhibitor exhibited good murine pharmacokinetics, dose-dependently attenuated PGD2 production in a mast cell degranulation assay and should be suitable to further explore H-PGDS biology.

Evaluating street view exposure measures of visible green space for health research.

Urban green space, or natural environments, are associated with multiple physical and mental health outcomes. Several proposed pathways of action for these benefits (e.g., stress reduction and attention restoration) require visual perception of green space; however, existing green space exposure measures commonly used in epidemiological studies do not capture street-scale exposures. We downloaded 254 Google Street View (GSV) panorama images from Portland, Oregon and calculated percent of green in each image, called Green View Index (GVI). For these locations we also calculated satellite-based normalized difference vegetation index (NDVI), % tree cover, % green space, % street tree buffering, distance to parks, and several neighborhood socio-economic variables. Correlations between the GVI and other green space measures were low (-0.02 to 0.50), suggesting GSV-based measures captured unique information about green space exposures. We further developed a GVI:NDVI ratio, which was associated with the amount of vertical green space in an image. The GVI and GVI:NDVI ratio were weakly related to neighborhood socioeconomic status and are therefore less susceptible to confounding in health studies compared to other green space measures. GSV measures captured unique characteristics of the green space environment and offer a new approach to examine green space and health associations in epidemiological research.

Residential green space and pathways to term birth weight in the Canadian Healthy Infant Longitudinal Development (CHILD) Study.

BACKGROUND: A growing number of studies observe associations between the amount of green space around a mother's home and positive birth outcomes; however, the robustness of this association and potential pathways of action remain unclear. OBJECTIVES: To examine associations between mother's residential green space and term birth weight within the Canadian Healthy Infant Longitudinal Development (CHILD) study and examine specific hypothesized pathways. METHODS: We examined 2510 births located in Vancouver, Edmonton, Winnipeg, and Toronto Canada. Green space was estimated around mother's residences during pregnancy using Landsat 30 m normalized difference vegetation index (NDVI). We examined hypothesized pathways of: (1) reduction of environmental exposure; (2) built environment features promoting physical activity; (3) psychosocial conditions; and (4) psychological influences. Linear regression was used to assess associations between green space and term birth weight adjusting first for a comprehensive set of confounding factors and then incrementally for pathway variables. RESULTS: Fully adjusted models showed non-statistically significant increases in term birth weight with increasing green space. For example, a 0.1 increase in NDVI within 500 m was associated with a 21.5 g (95% CI - 4.6, 47.7) increase in term birth weight. Associations varied by city and were most robust for high-density locations. For the two largest cities (Vancouver and Toronto), we observed an increase in birth weight of 41.2 g (95% CI 7.8, 74.6) for a 0.1 increase in NDVI within 500 m. We did not observe substantial reductions in the green space effect on birth weight when adjusting for pathway variables. CONCLUSION: Our results highlight the need to further characterize the interactions between green space, urban density and climate related factors as well as the pathways linking residential green space to birth outcomes.

Associations between multiple green space measures and birth weight across two US cities.

INTRODUCTION: Several measures of green space exposure have been used in epidemiological research, but their relevance to health, and representation of exposure pathways, remains unclear. Here we examine the relationships between multiple urban green space metrics and associations with term birth weight across two diverse US cities. METHODS: We used Vital Statistics data to create a birth cohort from 2005 to 2009 in the cities of Portland, Oregon (n = 90,265) and Austin, Texas (n = 88,807). These cities have similar green space levels but very different population and contextual characteristics. Green space metrics derived from mother's full residential address using multiple buffer distances (50-1000m) included: Landsat Normalized Difference Vegetation Index (NDVI), % tree cover, % green space, % street tree buffering, and access to parks (using US EPA EnviroAtlas Data). Correlation between green space metrics were assessed and mixed models were used to determine associations with term birth weight, controlling for a comprehensive set of individual and neighborhood factors. City-specific models were run to determine how contextual and population differences affected green space associations with birth weight. RESULTS: We observed moderate to high degrees of correlation between different green space metrics (except park access), with similar patterns between cities. Unadjusted associations demonstrated consistent protective effects of NDVI, % green space, % tree cover, and % street tree buffering for most buffer sizes on birth weight; however, in fully adjusted models most metrics were no longer statistically significant and no clear patterns remained. For example, in Austin the difference in birth weight for the highest versus lowest quartile of % green space within 50m was 38.3g (95% CI: 30.4, 46.1) in unadjusted and -1.5g (98% CI: -8.8, 6.3) in adjusted models compared to 55.7g (95%CI: 47.9, -63.6) and 12.9g (95% CI: 4.4, 21.4) in Portland. Maternal race, ethnicity and education had the largest impact on reducing green space and birth weight associations. However, consistent positive associations were observed for the high density areas of both cities using several green space metrics at small buffer distances. CONCLUSIONS: This study highlights the importance of understanding the individual and contextual factors that may confound and/or modify green space and birth weight associations.

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO2) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R2 within 2%) but not for Africa and Oceania (adjusted R2 within 11%) where NO2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO2 monitoring data or models.

Associations between residential greenness and birth outcomes across Texas.

BACKGROUND: The amount of greenness around mothers' residences has been associated with positive birth outcomes; however, findings are inconclusive. Here we examine residential greenness and birth outcomes in a population-based birth cohort in Texas, a state with large regional variation in greenness levels, several distinct cities, and a diverse population. METHODS: We used Vital Statistics data to create a birth cohort (n=3,026,603) in Texas from 2000 to 2009. Greenness exposure measures were estimated from full residential addresses across nine months of pregnancy, and each trimester specifically, using the mean of corresponding MODIS satellite 16-day normalized difference vegetation index (NDVI) surfaces at a 250m resolution, which have not been previously used. Logistic and linear mixed models were used to determine associations with preterm birth, small for gestational age (SGA) and term birth weight, controlling for individual and neighborhood factors. RESULTS: Unadjusted results demonstrated consistent protective effects of residential greenness on adverse birth outcomes for all of Texas and the four largest cities (Houston, San Antonio, Dallas, and Austin). However, in fully adjusted models these effects almost completely disappeared. For example, mothers with the highest (>0.52) compared to the lowest (<0.37) NDVI quartiles had a 24.4g (95% CI: 22.7, 26.1) increase in term birth weight in unadjusted models, which was attenuated to 1.9g (95% CI: 0.1, 3.7) in fully adjusted models. Maternal and paternal race, ethnicity and education had the largest impact on reducing associations. Trimester-specific greenness exposures showed similar results to nine-month average exposures. Some evidence was seen for protective effects of greenness for Hispanics, mothers with low education and mothers living in low income neighborhoods. CONCLUSIONS: In this large population-based study, across multiple urban areas in Texas and diverse populations, we did not observe consistent associations between residential greenness and birth outcomes.

Relationships between Changes in Urban Characteristics and Air Quality in East Asia from 2000 to 2010.

Characteristics of urban areas, such as density and compactness, are associated with local air pollution concentrations. The potential for altering air pollution through changing urban characteristics, however, is less certain, especially for expanding cities within the developing world. We examined changes in urban characteristics from 2000 to 2010 for 830 cities in East Asia to evaluate associations with changes in nitrogen dioxide (NO2) and fine particulate matter (PM2.5) air pollution. Urban areas were stratified by population size into small (100 000-250 000), medium, (250 000-1 000 000), and large (>1 000 000). Multivariate regression models including urban baseline characteristics, meteorological variables, and change in urban characteristics explained 37%, 49%, and 54% of the change in NO2 and 29%, 34%, and 37% of the change in PM2.5 for small, medium and large cities, respectively. Change in lights at night strongly predicted change in NO2 and PM2.5, while urban area expansion was strongly associated with NO2 but not PM2.5. Important differences between changes in urban characteristics and pollutant levels were observed by city size, especially NO2. Overall, changes in urban characteristics had a greater impact on NO2 and PM2.5 change than baseline characteristics, suggesting urban design and land use policies can have substantial impacts on local air pollution levels.

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.

There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.