Geographic and demographic variation in worry about extreme heat and COVID-19 risk in summer 2020.

Extreme heat is a major health hazard that is exacerbated by ongoing human-caused climate change. However, how populations perceive the risks of heat in the context of other hazards like COVID-19, and how perceptions vary geographically, are not well understood. Here we present spatially explicit estimates of worry among the U.S. public about the risks of heat and COVID-19 during the summer of 2020, using nationally representative survey data and a multilevel regression and poststratification (MRP) model. Worry about extreme heat and COVID-19 varies both across states and across demographic groups, in ways that reflect disparities in the impact of each risk. Black or African American and Hispanic or Latino populations, who face greater health impacts from both COVID-19 and extreme heat due to institutional and societal inequalities, also tend to be much more worried about both risks than white, non-Hispanic populations. Worry about heat and COVID-19 were correlated at the individual and population level, and patterns tended to be related to underlying external factors associated with the risk environment. In the face of a changing climate there is an urgent need to address disparities in heat risk and develop responses that ensure the most at-risk populations are protected.

Climate change and physical activity: ambient temperature and urban trail use in Texas.

Individuals in the USA are insufficiently active, increasing their chronic disease risk. Extreme temperatures may reduce physical activity due to thermal discomfort. Cooler climate studies have suggested climate change may have a net positive effect on physical activity, yet research gaps remain for warmer climates and within-day physical activity patterns. We determined the association between ambient temperatures (contemporary and projected) and urban trail use in a humid subtropical climate. At a trail in Austin, TX, five electronic counters recorded hourly pedestrian and cyclist counts in 2019. Weather data were acquired from World Weather Online. Generalized additive models estimated the association between temperature and trail counts. We then combined the estimated exposure-response relation with weather projections from climate models for intermediate (RCP4.5) and high (RCP8.5) emissions scenarios by NASA NEX-GDDP. From summer to autumn to spring to winter, hourly trail counts shifted from bimodal (mid-morning and early-evening peaks) to one mid-day peak. Pedestrians were more likely to use the trail between 7 and 27 °C (45-81°F) with peak use at 17 °C (63°F) and cyclists between 15 and 33 °C (59-91°F) with peak use at 27 °C (81°F) than at temperature extremes. A net decrease in trail use was estimated by 2041-2060 (RCP4.5: pedestrians = - 4.5%, cyclists = - 1.1%; RCP8.5: pedestrians = - 6.6%, cyclists = - 1.6%) and 2081-2100 (RCP4.5: pedestrians = - 7.5%, cyclists = - 1.9%; RCP8.5: pedestrians = - 16%, cyclists = - 4.5%). Results suggest climate change may reduce trail use. We recommend interventions for thermal comfort at settings for physical activity.

A Case-Crossover Analysis of Indoor Heat Exposure on Mortality and Hospitalizations among the Elderly in Houston, Texas.

BACKGROUND: Despite the substantial role indoor exposure has played in heat wave-related mortality, few epidemiological studies have examined the health effects of exposure to indoor heat. As a result, knowledge gaps regarding indoor heat-health thresholds, vulnerability, and adaptive capacity persist. OBJECTIVE: We evaluated the role of indoor heat exposure on mortality and morbidity among the elderly (≥65 years of age) in Houston, Texas. METHODS: Mortality and emergency hospital admission data were obtained through the Texas Department of State Health Services. Summer indoor heat exposure was modeled at the U.S. Census block group (CBG) level using building energy models, outdoor weather data, and building characteristic data. Indoor heat-health associations were examined using time-stratified case-crossover models, controlling for temporal trends and meteorology, and matching on CBG of residence, year, month, and weekday of the adverse health event. Separate models were fitted for three indoor exposure metrics, for individual lag days 0-6, and for 3-d moving averages (lag 0-2). Effect measure modification was explored via stratification on individual- and area-level vulnerability factors. RESULTS: We estimated positive associations between short-term changes in indoor heat exposure and cause-specific mortality and morbidity [e.g., circulatory deaths, odds ratio per 5°C increase=1.16 (95% CI: 1.03, 1.30)]. Associations were generally positive for earlier lag periods and weaker across later lag periods. Stratified analyses suggest stronger associations between indoor heat and emergency hospital admissions among African Americans compared with Whites. DISCUSSION: Findings suggest excess mortality among certain elderly populations in Houston who are likely exposed to high indoor heat. We developed a novel methodology to estimate indoor heat exposure that can be adapted to other U.S. LOCATIONS: In locations with high air conditioning prevalence, simplified modeling approaches may adequately account for indoor heat exposure in vulnerable neighborhoods. Accounting for indoor heat exposure may improve the estimation of the total impact of heat on health. https://doi.org/10.1289/EHP6340.

Intersecting vulnerabilities: climatic and demographic contributions to future population exposure to Aedes-borne viruses in the United States.

Understanding how climate change and demographic factors may shape future population exposure to viruses such as Zika, dengue, or chikungunya, transmitted by Aedes mosquitoes is essential to improving public health preparedness. In this study, we combine projections of cumulative monthly Aedes-borne virus transmission risk with spatially explicit population projections for vulnerable demographic groups to explore future county-level population exposure across the conterminous United States. We employ a scenario matrix-combinations of climate scenarios (Representative Concentration Pathways) and socioeconomic scenarios (Shared Socioeconomic Pathways)-to assess the full range of uncertainty in emissions, socioeconomic development, and demographic change. Human exposure is projected to increase under most scenarios, up to + 177% at the national scale in 2080 under SSP5*RCP8.5 relative to a historical baseline. Projected exposure changes are predominantly driven by population changes in vulnerable demographic groups, although climate change is also important, particularly in the western region where future exposure would be about 30% lower under RCP2.6 compared to RCP8.5. The results emphasize the crucial role that socioeconomic and demographic change play in shaping future population vulnerability and exposure to Aedes-borne virus transmission risk in the United States, and underline the importance of including socioeconomic scenarios in projections of climate-related vector-borne disease impacts.

Characterizing the role of socioeconomic pathways in shaping future urban heat-related challenges.

Urban dwellers worldwide are increasingly affected by more frequent and intense extreme temperature events, ongoing urbanization, and changes in socioeconomic conditions. Decades of research have shown that vulnerability is a crucial determinant of heat-related risk and mortality in cities, yet assessments of future urban heat-related challenges have largely overlooked the contribution of changes in socioeconomic conditions to future heat-related risk and mortality. The scenario framework for climate change research, made up of socioeconomic scenarios (Shared Socioeconomic Pathways - SSPs) combined with climate scenarios (Representative Concentration Pathways - RCPs), facilitates the integration of socioeconomic scenarios into climate risks assessments. In this study, we used Greater Houston (Texas) as a case study to implement the scenario framework at the intra-urban scale. Integrating locally extended SSPs along with a range of sectoral modelling approaches, we combined projections of urban extreme heat - which account for SSP-specific urban heat islands - with projections of future population and vulnerability. We then produced estimates of future heat-related risk and mortality for 2041-2060 (2050s) summers at Census tract level, for multiple combinations of climate and socioeconomic scenarios. Using a scenario matrix, we showed that the projected ~15,738-24,521 future summer excess mortalities compared to 1991-2010 are essentially driven by population growth and changes in vulnerability, with changes in climatic conditions alone being of little influence. We outline methods to apply the new scenario framework at intra-urban scale and to better characterize the contribution of socioeconomic pathways to future urban climate risks. This socio-climatic approach provides comprehensive estimates of future climate risks in urban areas, which are essential for adaptation planning under climatic and socioeconomic uncertainty.

Urban heat and air pollution: A framework for integrating population vulnerability and indoor exposure in health risk analyses.

Urban growth and climate change will exacerbate extreme heat events and air pollution, posing considerable health challenges to urban populations. Although epidemiological studies have shown associations between health outcomes and exposures to ambient air pollution and extreme heat, the degree to which indoor exposures and social and behavioral factors may confound or modify these observed effects remains underexplored. To address this knowledge gap, we explore the linkages between vulnerability science and epidemiological conceptualizations of risk to propose a conceptual and analytical framework for characterizing current and future health risks to air pollution and extreme heat, indoors and outdoors. Our framework offers guidance for research on climatic variability, population vulnerability, the built environment, and health effects by illustrating how health data, spatially resolved ambient data, estimates of indoor conditions, and household-level vulnerability data can be integrated into an epidemiological model. We also describe an approach for characterizing population adaptive capacity and indoor exposure for use in population-based epidemiological models. Our framework and methods represent novel resources for the evaluation of health risks from extreme heat and air pollution, both indoors and outdoors.

Application of geostatistical approaches to predict the spatio-temporal distribution of summer ozone in Houston, Texas.

Mitigation of adverse effects of air pollution requires understanding underlying exposures, such as ambient ozone concentrations. Geostatistical approaches were employed to analyze temporal trends and estimate spatial patterns of summertime ozone concentrations for Houston, Texas, based on hourly ozone observations obtained from the Texas Commission on Environmental Quality. We systematically assess the accuracy of several spatial interpolation methods, comparing inverse distance weighting, simple kriging, ordinary kriging, and universal kriging methods utilizing the hourly ozone observations and meteorological measurements from monitoring sites. Model uncertainty was assessed by leave-one-out cross-validation. Kriging methods performed better, showing greater consistency in the generated surfaces, fewer interpolation errors, and lower biases. Universal kriging did not significantly improve the interpolation results compared to ordinary kriging, and thus ordinary kriging was determined to be the optimal method, striking a balance between accuracy and simplicity. The resulting spatial patterns indicate that the more industrialized areas east and northeast of Houston exhibit the highest summertime ozone concentrations. Estimated daily maximum 8 h ozone concentration fields generated will be used to inform research on population health risks from exposure to surface ozone in Houston.

Assessment of heat exposure in cities: Combining the dynamics of temperature and population.

Urban populations are typically subject to higher outdoor heat exposure than nearby rural areas due to the urban heat island (UHI) effect. Excessive Heat Events (EHEs) further amplify heat stress imposed on city dwellers. Heat exposure largely depends on the spatial and temporal distribution of temperature and population, however, few studies considered their concurrent variations. To better characterize exposure to heat in the context of long-term urban climatology and during excessive heat events, this study focuses on the dynamics of ambient temperature and population and proposes an open-data-based approach for spatiotemporal analysis of urban exposure to heat by using air temperature estimated from satellite observations and commute-adjusted diurnal population calculated primarily on the Census Transportation Planning Products. We use the metropolitan area of Chicago, U.S.A. as a case study to analyze the urban heat pattern changes during EHEs and their influence on population heat exposure diurnally. The intra-urban spatiotemporal analysis reveals that the population's exposure to heat changes fast as the nighttime temperature increases and the EHEs increase the spatial exposure impact due to the ubiquitous higher nocturnal temperature over the Chicago metropolitan area. "Hotspots" associated with a higher temperature and greater number of urban residents are identified in the heat exposure map. Meanwhile, the spatial extent of high ambient exposure areas varies diurnally. Our study contributes to a better understanding of the dynamic heat exposure patterns in urban areas. The approaches presented in this article can be used for informing heat mitigation as well as emergency response strategies at specific times and locations.

On the Seasonal Occurrence and Abundance of the Zika Virus Vector Mosquito Aedes Aegypti in the Contiguous United States.

INTRODUCTION: An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti. METHODS: We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact.     RESULTS: Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti.     DISCUSSION: Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

Potential Impacts of Future Warming and Land Use Changes on Intra-Urban Heat Exposure in Houston, Texas.

Extreme heat events in the United States are projected to become more frequent and intense as a result of climate change. We investigated the individual and combined effects of land use and warming on the spatial and temporal distribution of daily minimum temperature (Tmin) and daily maximum heat index (HImax) during summer in Houston, Texas. Present-day (2010) and near-future (2040) parcel-level land use scenarios were embedded within 1-km resolution land surface model (LSM) simulations. For each land use scenario, LSM simulations were conducted for climatic scenarios representative of both the present-day and near-future periods. LSM simulations assuming present-day climate but 2040 land use patterns led to spatially heterogeneous temperature changes characterized by warmer conditions over most areas, with summer average increases of up to 1.5°C (Tmin) and 7.3°C (HImax) in some newly developed suburban areas compared to simulations using 2010 land use patterns. LSM simulations assuming present-day land use but a 1°C temperature increase above the urban canopy (consistent with warming projections for 2040) yielded more spatially homogeneous metropolitan-wide average increases of about 1°C (Tmin) and 2.5°C (HImax), respectively. LSM simulations assuming both land use and warming for 2040 led to summer average increases of up to 2.5°C (Tmin) and 8.3°C (HImax), with the largest increases in areas projected to be converted to residential, industrial and mixed-use types. Our results suggest that urbanization and climate change may significantly increase the average number of summer days that exceed current threshold temperatures for initiating a heat advisory for metropolitan Houston, potentially increasing population exposure to extreme heat.

Characterizing urban vulnerability to heat stress using a spatially varying coefficient model.

Identifying and characterizing urban vulnerability to heat is a key step in designing intervention strategies to combat negative consequences of extreme heat on human health. This study combines excess non-accidental mortality counts, numerical weather simulations, US Census and parcel data into an assessment of vulnerability to heat in Houston, Texas. Specifically, a hierarchical model with spatially varying coefficients is used to account for differences in vulnerability among census block groups. Socio-economic and demographic variables from census and parcel data are selected via a forward selection algorithm where at each step the remaining variables are orthogonalized with respect to the chosen variables to account for collinearity. Daily minimum temperatures and composite heat indices (e.g. discomfort index) provide a better model fit than other ambient temperature measurements (e.g. maximum temperature, relative humidity). Positive interactions between elderly populations and heat exposure were found suggesting these populations are more responsive to increases in heat.

Intra-urban societal vulnerability to extreme heat: the role of heat exposure and the built environment, socioeconomics, and neighborhood stability.

Extreme heat is an important weather hazard associated with excess mortality and morbidity. We determine the relative importance of heat exposure and the built environment, socioeconomic vulnerability, and neighborhood stability for heat mortality (Philadelphia, PA, USA) or heat distress (Phoenix, AZ, USA), using an ecologic study design. We use spatial Generalized Linear and Mixed Models to account for non-independence (spatial autocorrelation) between neighboring census block groups. Failing to account for spatial autocorrelation can provide misleading statistical results. Phoenix neighborhoods with more heat exposure, Black, Hispanic, linguistically and socially isolated residents, and vacant households made more heat distress calls. Philadelphia heat mortality neighborhoods were more likely to have low housing values and a higher proportion of Black residents. Our methodology can identify important risk factors and geographic areas to target interventions.