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Most of the epidemiological investigations looking at the health benefits of green spaces have measured the level of green areas by using only one approach, mainly the Normalized Difference Index - NDVI (a satellite-derived indicator). We hypothesized a difference in the association between health and green space depending on the metric used to measure green exposure. This study considers students' academic performance as a proxy of cognitive abilities (a health indicator). We estimated the relationship between green areas and students' academic performance in the Federal District (FD), Brazil, with three different greenness metrics: NDVI, distance to green spaces (m) - obtained from land use data, and quantity of green spaces (m2) - also from land use data. We assessed student-level academic performance data provided by the Department of the Education in the FD. The data includes students from the public schools in the FD for 256 schools (all the public schools in the FD) and 344,175 students (all the students enrolled in the public schools in the FD in 2017-2020).). For the first metric represented by the distance to green spaces, we estimated the straight-line distance between each school and the nearest green area. For NDVI and quantity of green spaces, we estimated the area of all green spaces within buffers of 500 m, 750 m, and 1 km around the schools. We applied a cross-sectional study design using mixed-effects regression models to analyze the association exposure to green areas around schools and student-level academic performance. Our results confirmed our hypothesis showing that the impact of green areas on students' performance varied significantly depending on the type of green metric. After adjustments for the covariates, we estimated that NDVI is positively associated with school-level academic performance, with an estimated coefficient of 0.91 (95%CI: 0.83; 0.99) for NDVI values at a school's centroid. Distance to green areas was negatively associated with academic performance [-2.09 × 10-5 (95CI: 3.91 × 10-5; -2.84 × 10-6]. The quantity of green areas was estimated with mixed results (direction of the association), depending on the buffer size. Results from this paper suggest that epidemiological investigations must consider the different effects of greenness measures when looking at the association between green space and academic performance. More studies on residual confounding from this association with a different study design are needed to promote public health by making schools healthier.

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Background: Air pollution exposure has been associated with critical neonatal morbidities, including low birth weight (LBW). However, little is known on short-term exposure to wildfire smoke and LBW. In this study, we estimated the association between birth weight following pregnancy and wildfire smoke exposure in more than 1.5 million newborns in Brazil (considered as a very fire-prone region worldwide). Methods: We applied a logistic regression model to estimate the percent variation in newborns with low birth weight when exposed to wildfire in different trimesters of the pregnancy. Findings: After adjusting the model with relevant covariates, we found that an increase of 100 wildfire records in Brazil was associated with an increase in low birth weight in the Midwest region [0.98% (95%CI:0.34; 1.63)] and in the South region [18.55% (95%CI:13.66; 23.65)] when the exposure occurred in the first trimester of pregnancy. Interpretation: Wildfires were associated with LBW and this should be of public health concern for policymakers. Funding: Brazilian Agencies National Council for Scientific and Technological Development (CNPq); Ministry of Science, Technology and Innovation in Brazil (MCTI); and Novo Nordisk Foundation Challenge Programme.

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The spread of the 2019 novel coronavirus (COVID-19) has challenged governments to develop public policies to reduce the load of the COVID-19 on health care systems, which is commonly referred to as "flattening the curve". This study aims to address this issue by proposing a spatial multicriteria approach to estimate the risk of the Brazilian health care system, by municipality, to exceed the health care capacity because of an influx of patients infected with the COVID-19. We estimated this risk for 5572 municipalities in Brazil using a combination of a multicriteria decision-making approach with spatial analysis to estimate the exceedance risk, and then, we examined the risk variation by designing 5 control intervention scenarios (3 scenarios representing reduction on social contacts, and 2 scenarios representing investment on health care system). For the baseline scenario using an average infection rate across Brazil, we estimated a mean Hospital Bed Capacity (HBC) value of -16.73, indicating that, on average, the Brazilian municipalities will have a deficit of approximately 17 beds. This deficit is projected to occur in 3338 municipalities with the north and northeast regions being at the greatest risk of exceeding health care capacity due to the COVID-19. The intervention scenarios indicate across all of Brazil that they could address the bed shortage, with an average of available beds between 23 and 32. However, when we consider the shortages at a municipal scale, bed exceedances still occur for at least 2119 municipalities in the most effective intervention scenario. Our findings are essential to identify priority areas, to compare populations, and to provide options for government agencies to act. This study can be used to provide support for the creation of effective health public policies for national, regional, and local intervention.

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Background and Purpose- Accurate prediction of acute ischemic stroke (AIS) caused by anterior large vessel occlusion (LVO) that is amendable to mechanical thrombectomy remains a challenge. We developed and validated a prediction model for anterior circulation LVO stroke using past medical history elements present on admission and neurological examination. Methods- We retrospectively reviewed AIS patients admitted between 2009 and 2017 to 3 hospitals within a large healthcare system in the United States. Patients with occlusions of the internal carotid artery or M1 or M2 segments of the middle cerebral artery were randomly split into 2/3 derivation and 1/3 validation cohorts for development of an anterior circulation LVO prediction model and score that was further curtailed for potential use in the prehospital setting. Results- A total of 1654 AIS were reviewed, including 248 (15%) with proximal anterior circulation LVO AIS. In the derivation cohort, National Institutes of Health Stroke Scale score at the time of cerebrovascular imaging, current smoking status, type 2 diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis was significantly associated with anterior circulation LVO stroke. The prehospital score was curtailed to National Institutes of Health Stroke Scale score, current smoking status, and type 2 diabetes mellitus. The areas under the curve for the prediction model, prehospital score, and National Institutes of Health Stroke Scale score alone were 0.796, 0.757, and 0.725 for the derivation cohort and 0.770, 0.689, and 0.665 for the validation cohort, respectively. The Youden index J was 0.46 for a score of >6 with 84.7% sensitivity and 62.0% specificity for the prediction model. Conclusions- Previously reported LVO stroke prediction scores focus solely on elements of the neurological examination. In addition to stroke severity, smoking, diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis were associated with anterior circulation LVO AIS. Although atherosclerotic stenosis may not be known until imaging is obtained, smoking and diabetes mellitus history can be readily obtained in the field and represent important elements of the prehospital score supplementing National Institutes of Health Stroke Scale score.

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Exposure to traffic emission is harmful to human health. Emission inventories are essential to public health policies aiming at protecting human health, especially in areas with incomplete or nonexistent air pollution monitoring networks. In Brazil, for example, only 1.7% of municipal districts have a monitoring network, and only a few studies have reported data on vehicle emission inventories. No studies have presented emission inventories by municipality. In this study, we predicted vehicular emissions for 5570 municipal districts in Brazil during the period 2001-2012. We used a top-down method to estimate emissions. Carbon dioxide (CO2) is the pollutant with the highest emissions, with approximately 190 million tons per year during the period 2001-2012). For the other traffic-related pollutants, we predicted annual emissions of 1.5 million tons for carbon monoxide (CO), 1.2 million tons of nitrogen oxides (NOx), 209,000 tons of nonmethane hydrocarbons (NMHC), 58,000 tons of particulate matter (PM), and 42,000 tons for methane (CH4). From 2001 to 2012, CO, NMHC, and PM emissions decreased by 41, 33, and 47%, respectively, whereas those CH4, NOx, and CO2 increased by 2, 4, and 84%, respectively. We estimated uncertainties in our study and found that NOx was the pollutant with the lowest percentage difference, 8%, and NMHC with the highest one, 30%. For CO, CH4, CO2, and PM, the values were 22, 14, 21, and 20%, respectively. Finally, we found that during 2001 and 2012 emissions increased in the Northwest and Northeast. In contrast, pollutant emissions, except for CO2, decreased in the Southeast, South, and part of Midwest. Our predictions can be critical to efforts developing cost-effective public policies tailored to individual municipal districts in Brazil. IMPLICATIONS: Emission inventories may be an alternative approach to provide data for air quality forecasting in areas where air quality data are not available. This approach can be an effective tool in developing spatially resolved emission inventories.

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Neighborhood characteristics affect an individual's quality of life. Although several studies have examined the relationship between neighborhood environments and human health, we are unaware of studies that have examined the distance-decay of this effect and then presented the risk results spatially. Our study is unique in that is explores the health effects in a less developed country compared to most studies that have focused on developed countries. The objective of our study is to quantify the distance-decay cardiorespiratory diseases risk related to 28 neighborhood aspects in the Federal District, Brazil and present this information spatially through risk maps of the region. Toward this end, we used a quantile regression model to estimate risk and GIS modeling techniques to create risk maps. Our analysis produced the following findings: i) a 2500 m increase in highway length was associated with a 46% increase in cardiorespiratory diseases; ii) 46,000 light vehicles in circulation (considering a buffer of ≤500 m from residences) was associated with 6 hospital admissions (95% CI: 2.6, 14.6) per cardiorespiratory diseases; iii) 74,000 m2 of commercial areas (buffer ≤1700 m) was associated with 12 hospital admissions (95% CI: 2.2, 20.8); iv) 1km2 increase in green areas intra urban was associated with less two hospital admissions, and; vi) those who live ≤500 m from the nearest point of wildfire are more likely to have cardiorespiratory diseases that those living >500 m. Our findings suggest that the approach used in this study can be an option to improve the public health policies.

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Many studies have suggested that socio-economic factors are strong modifiers of human vulnerability to air pollution effects. Most of these studies were performed in developed countries, specifically in the US and Europe. Only a few studies have been performed in developing countries, and analyzed small regions (city level) with no spatial disaggregation. The aim of this study was to assess the association between vehicle emissions and cardiorespiratory disease risk in Brazil and its modification by spatial clustering of socio-economic conditions. We used a quantile regression model to estimate the risk and a geostatistical approach (K means) to execute spatial cluster analysis. We performed the risk analysis in three stages. First, we analyzed the entire study area (primary analysis), and then we conducted a spatial cluster analysis based on various municipal-level socio-economic factors, followed by a sensitivity analysis. We studied 5444 municipalities in Brazil between 2008 and 2012. Our findings showed a significant association between cardiorespiratory disease risk and vehicular emissions. We found that a 15% increase in air pollution is associated with a 6% increase in hospital admissions rates. The results from the spatial cluster analysis revealed two groups of municipalities with distinct sets of socio-economic factors and risk levels of cardiorespiratory disease related to exposure to vehicular emissions. For example, for vehicle emissions of PM in 2008, we found a relative risk of 4.18 (95% CI: 3.66, 4.93) in the primary analysis; in Group 1, the risk was 0.98 (95% CI: 0.10, 2.05) while in Group 2, the risk was 5.56 (95% CI: 4.46, 6.25). The risk in Group 2 was 480% higher than the risk in Group 1, and 35% higher than the risk in the primary analysis. Group 1 had higher values (3rd quartile) for urbanization rate, highway density, and GDP; very high values (≥3rd quartile) for population density; median values for distance from the capital; and lower values (1st quartile) for rural population density. Group 2 had lower values (1st quartile) urbanization rate; median values for highway density, GDP, and population density; between median and third quartile values for distance from the capital; and higher values (3rd quartile) for rural population density. Our findings suggest that socio-economic factors are important modifiers of the human risk of cardiorespiratory disease due to exposure to vehicle emissions in Brazil. Our study provides support for creating effective public policies related to environmental health that are targeted to high-risk populations.

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