Suchergebnisse

Abstract
Continued climate change is increasing the frequency, severity, and duration of populations' high temperature exposures. Indoor cooling is a key adaptation, especially in urban areas, where heat extremes are intensified—the urban heat island effect (UHI)—making residential air conditioning (AC) availability critical to protecting human health. In the United States, the differences in residential AC prevalence from one metropolitan area to another is well understood, but its intra-urban variation is poorly characterized, obscuring neighborhood-scale variability in populations' heat vulnerability and adaptive capacity. We address this gap by constructing empirically derived probabilities of residential AC for 45,995 census tracts across 115 metropolitan areas. Within cities, AC is unequally distributed, with census tracts in the urban "core" exhibiting systematically lower prevalence than their suburban counterparts. Moreover, this disparity correlates strongly with multiple indicators of social vulnerability and summer daytime surface UHI intensity, highlighting the challenges that vulnerable urban populations face in adapting to climate-change driven heat stress amplification.

RESEARCH OBJECTIVE: Nonpharmacological policies aimed at improving physical distancing, such as shelter in place orders or gathering bans are a central strategy used to mitigate disease spread, particularly in the early life‐course of a pandemic. We sought to assess the relationship between different types of early social distancing policies implemented across Europe, how they related to changes in population mobility and subsequent COVID‐19 case growth. STUDY DESIGN: Data on national social distancing policies were obtained from the Oxford COVID‐19 Government Response Tracker (OxCGRT). For each European country we defined index dates based on the implementation of the country's first social distancing policy. We then compared the value of each mobility metric in the week after the index date versus the 7‐day period extending from 9 to 2 days prior to the index date. We included a two‐day washout period given the volatility that typically precedes these orders. For mobility, each country was compared to itself over time. We employed a pre‐post comparison and two linear mixed‐effects models to first assess the relationship between policies and observed changes in mobility, and to assess the relationship between changes in mobility and rates of COVID‐19 infections in subsequent weeks. Aggregated and anonymized mobility data was obtained from Google. This data is similar to publicly accessible "Community Mobility Reports," with the addition of novel variables, such as relative change in the average number of hours individuals spent away from their primary place of residence. POPULATION STUDIED: Our analytic sample included 6075 country‐day observations across 27 European countries. PRINCIPAL FINDINGS: Spain saw the largest mobility decrease from the pre‐COVID baseline, as measured by the time spent away from residence, with a decline in aggregate mobility of nearly 70%. Sweden had the smallest decrease with approximately a 20% decline from baseline at the end of the study period (April 12th 2020). Policies ...

Abstract
High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5–18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8–46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7–37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3–16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

Background Social distancing have been widely used to mitigate community spread of SARS-CoV-2. We sought to quantify the impact of COVID-19 social distancing policies across 27 European counties in spring 2020 on population mobility and the subsequent trajectory of disease. Methods We obtained data on national social distancing policies from the Oxford COVID-19 Government Response Tracker and aggregated and anonymized mobility data from Google. We used a pre-post comparison and two linear mixed-effects models to first assess the relationship between implementation of national policies and observed changes in mobility, and then to assess the relationship between changes in mobility and rates of COVID-19 infections in subsequent weeks. Results Compared to a pre-COVID baseline, Spain saw the largest decrease in aggregate population mobility (~70%), as measured by the time spent away from residence, while Sweden saw the smallest decrease (~20%). The largest declines in mobility were associated with mandatory stay-at-home orders, followed by mandatory workplace closures, school closures, and non-mandatory workplace closures. While mandatory shelter-in-place orders were associated with 16.7% less mobility (95% CI: -23.7% to -9.7%), non-mandatory orders were only associated with an 8.4% decrease (95% CI: -14.9% to -1.8%). Large-gathering bans were associated with the smallest change in mobility compared with other policy types. Changes in mobility were in turn associated with changes in COVID-19 case growth. For example, a 10% decrease in time spent away from places of residence was associated with 11.8% (95% CI: 3.8%, 19.1%) fewer new COVID-19 cases. Discussion This comprehensive evaluation across Europe suggests that mandatory stay-at-home orders and workplace closures had the largest impacts on population mobility and subsequent COVID-19 cases at the onset of the pandemic. With a better understanding of policies' relative performance, countries can more effectively invest in, and target, early nonpharmacological interventions.