The impact of heat islands on mortality in Paris during the August 2003 heat wave

High-resolution modeling and projection of heat-related mortality in Germany under climate change

BACKGROUND

Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels.

METHOD

Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate change scenarios.

RESULTS

We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014-2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7-14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions.

CONCLUSIONS

Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning.

Understanding excess mortality in 2022: The dual impact of COVID-19 and heatwaves on the Italian elderly population

In this study, we employ a comprehensive approach to model the concurrent effects of the COVID-19 epidemic and heatwaves on all-cause excess mortality. Our investigation uncovers distinct peaks in excess mortality, notably among individuals aged 80 years and older, revealing a strong positive correlation with excess temperatures (ET) during the summer of 2022 in Italy. Furthermore, we identify a notable role played by COVID-19 hospitalizations, exhibiting regional disparities, particularly during the winter months. Leveraging functional data regression, we offer robust and coherent insights into the excess mortality trends observed in Italy throughout 2022.

Is home where the heat is? comparing residence-based with mobility-based measures of heat exposure in San Diego, California

BACKGROUND

Heat can vary spatially within an urban area. Individual-level heat exposure may thus depend on an individual's day-to-day travel patterns (also called mobility patterns or activity space), yet heat exposure is commonly measured based on place of residence.

OBJECTIVE

In this study, we compared measures assessing exposure to two heat indicators using place of residence with those defined considering participants' day-to-day mobility patterns.

METHODS

Participants (n = 599; aged 35-80 years old [mean =59 years]) from San Diego County, California wore a GPS device to measure their day-to-day travel over 14-day intervals between 2014-10-17 and 2017-10-06. We measured exposure to two heat indicators (land-surface temperature [LST] and air temperature) using an approach considering their mobility patterns and an approach considering only their place of residence. We compared participant mean and maximum exposure values from each method for each indicator.

RESULTS

The overall mobility-based mean LST exposure (34.7 °C) was almost equivalent to the corresponding residence-based mean (34.8 °C; mean difference in means = -0.09 °C). Similarly, the mean difference between the overall mobility-based mean air temperature exposure (19.2 °C) and the corresponding residence-based mean (19.2 °C) was negligible (-0.02 °C). Meaningful differences emerged, however, when comparing maximums, particularly for LST. The mean mobility-based maximum LST was 40.3 °C compared with a mean residence-based maximum of 35.8 °C, a difference of 4.51 °C. The difference in maximums was considerably smaller for air temperature (mean = 0.40 °C; SD = 1.41 °C) but nevertheless greater than the corresponding difference in means.

IMPACT

As the climate warms, assessment of heat exposure both at and away from home is important for understanding its health impacts. We compared two approaches to estimate exposure to two heat measures (land surface temperature and air temperature). The first approach only considered exposure at home, and the second considered day-to-day travel. Considering the average exposure estimated by each approach, the results were almost identical. Considering the maximum exposure experienced (specific definition in text), the differences between the two approaches were more considerable, especially for land surface temperature.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Analyzing effect modifiers of the temperature-mortality relationship in the Paris region to identify social and environmental levers for more effective adaptation to heat

Adaptation to heat is a major challenge for the Paris region (France). Based on fine-scale data for the 1,287 municipalities of the region over 2000-2017, we analyzed (time-serie design) the temperature-mortality relationship by territories (urban, suburban, rural), age (15-64 and ≥ 65) and sex, and explored how it was modified by vegetation and socio-economic indicators. Heat was associated with an increased mortality risk for all territories, age groups, sex, and mortality causes. Women aged 65 and over residing in the most deprived municipalities had a relative risk (RR) of deaths at 29.4 °C (compared to 16.6 °C) of 4.2 [3.8:4.5], while the RR was 3.4 [3.2:3.7] for women living in less deprived municipalities. Actions to reduce such sex and social inequities should be central in heat adaptation policy.

Climate Change and Congenital Anomalies: A Population-Based Study of the Effect of Prolonged Extreme Heat Exposure on the Risk of Neural Tube Defects in France

BACKGROUND

Exposure to long-lasting extreme ambient temperatures in the periconceptional or early pregnancy period might increase the risk of neural tube defects (NTDs). We tested whether prolonged severe heat exposure as experienced during the 2003 extreme heatwave in France, affected the risk of NTDs.

METHODS

We retrieved NTD cases spanning from January 1994 to December 2018 from the Paris Registry of Congenital Malformations. The 2003 heatwave was characterized by the long duration and high intensity of nine consecutive days with temperatures ≥35°C. We classified monthly conceptions occurring in August 2003 as "exposed" to prolonged extreme heat around conception (i.e., periconceptional period). We assessed whether the risk of NTDs among cohorts exposed to the prolonged severe heatwave of 2003 in the periconceptional period differed from expected values using Poisson/negative binomial regression.

FINDINGS

We identified 1272 NTD cases from January 1994 to December 2018, yielding a monthly mean count of 4.24. Ten NTD cases occurred among births conceived in August 2003. The risk of NTD was increased in the cohort with periconceptional exposure to the August 2003 heatwave (relative risk = 2.14, 95% confidence interval: 1.46 to 3.13), compared to non-exposed cohorts. Sensitivity analyses excluding July and September months or restricting to summer months yielded consistent findings.

INTERPRETATION

Evidence from the "natural experiment" of an extreme climate event suggests an elevated risk of NTDs following exposure to prolonged extreme heat during the periconceptional period.

Using geographic effect measure modification to examine socioeconomic-related surface temperature disparities in New York City

BACKGROUND

Lower socioeconomic (SES) communities are more likely to be situated in urban heat islands and have higher heat exposures than their higher SES counterparts, and this inequality is expected to intensify due to climate change.

OBJECTIVES

To examine the relationship between surface temperatures and SES in New York City (NYC) by employing a novel analytical approach. Through incorporating modifiable features, this study aims to identify potential locations where mitigation interventions can be implemented to reduce heat disparities associated with SES.

METHODS

Using the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data surface temperatures (measured on 8/12/2016), and the NYC Land Cover Dataset at the census tract level (2098 tracts), this study examines the association between two components of tract-level SES (percentage of individuals living below the poverty line and the percentage of individuals without a high school degree) and summer day surface temperature in NYC. First, we examine this association with an unrestricted NYC linear regression, examining the city-wide association between the two SES facets and summer surface temperature, with additional models adjusting for altitude, shoreline, and nature-cover. Then, we assess geographic effect measure modification by employing the same models to three supplemental regression model strategies (borough-restricted and community district-restricted linear regressions, and geographically weighted regression (GWR)) that examined associations within smaller intra-city areas.

RESULTS

All regression strategies identified areas where lower neighborhood SES composition is associated with higher summer day surface temperatures. The unrestricted NYC regressions revealed widespread disparities, while the borough-restricted and community district-restricted regressions identified specific political boundaries within which these disparities existed. The GWR, addressing spatial autocorrelation, identified significant socioeconomic heat disparities in locations such as northwest Bronx, central Brooklyn, and uptown Manhattan. These findings underscore the need for targeted policies and community interventions, including equitable urban planning and cooling strategies, to mitigate heat exposure in vulnerable neighborhoods.

IMPACT STATEMENT

This study redefines previous research on urban socioeconomic disparities in heat exposure by investigating both modifiable (nature cover) and non-modifiable (altitude and shoreline) built environment factors affecting local temperatures at the census tract level in New York City. Through a novel analytical approach, the research aims to highlight intervention opportunities to mitigate heat disparities related to socioeconomic status. By examining the association between surface temperatures and socioeconomic status, as well as investigating different geographic and governmental scales, this study offers actionable insights for policymakers and community members to address heat exposure inequalities effectively across different administrative boundaries. The objective is to pinpoint potential sites for reducing socioeconomic heat exposure disparities at various geographic and political levels.

Climate change effects in older people's health: A scoping review

BACKGROUND

Climate change has serious consequences for the morbidity and mortality of older adults.

OBJECTIVE

To identify the effects of climate change on older people's health.

METHODS

A scoping review was conducted following the Joanna Briggs Institute guidelines and the PRISMA-ScR checklist. Quantitative research and reports from organizations describing the effects of climate change on older people were selected.

RESULTS

Sixty-three full-text documents were selected. Heat and air pollution were the two factors that had the most negative effects on cardiovascular and respiratory morbidity and mortality in older people. Mental health and cognitive function were also affected.

CONCLUSIONS

Climate change affects several health problems in older individuals, especially high temperatures and air pollution. Nursing professionals must have the necessary skills to respond to the climate risks in older adults. More instruments are required to determine nursing competencies on climate change and the health of this population group.

PATIENT OF PUBLIC CONTRIBUTION

No patient or public contribution.

Indoor overheating: A review of vulnerabilities, causes, and strategies to prevent adverse human health outcomes during extreme heat events

The likelihood of exposure to overheated indoor environments is increasing as climate change is exacerbating the frequency and severity of hot weather and extreme heat events (EHE). Consequently, vulnerable populations will face serious health risks from indoor overheating. While the relationship between EHE and human health has been assessed in relation to outdoor temperature, indoor temperature patterns can vary markedly from those measured outside. This is because the built environment and building characteristics can act as an important modifier of indoor temperatures. In this narrative review, we examine the physiological and behavioral determinants that influence a person's susceptibility to indoor overheating. Further, we explore how the built environment, neighborhood-level factors, and building characteristics can impact exposure to excess heat and we overview how strategies to mitigate building overheating can help reduce heat-related mortality in heat-vulnerable occupants. Finally, we discuss the effectiveness of commonly recommended personal cooling strategies that aim to mitigate dangerous increases in physiological strain during exposure to high indoor temperatures during hot weather or an EHE. As global temperatures continue to rise, the need for a research agenda specifically directed at reducing the likelihood and impact of indoor overheating on human health is paramount. This includes conducting EHE simulation studies to support the development of consensus-based heat mitigation solutions and public health messaging that provides equitable protection to heat-vulnerable people exposed to high indoor temperatures.

Examining racial and ethnic heat exposure disparities in New York City (NYC) across different spatial and political scales through geographic effect measure modification

Structural racism in the United States has resulted in neighborhoods with higher proportions of non-Hispanic Black (Black) or Hispanic/Latine residents having more features that intensify, and less that cool, the local-heat environment. This study identifies areas of New York City (NYC) where racial/ethnic heat exposure disparities are concentrated. We analyzed data from the 2013-2017 American Community Survey, U.S Landsat-8 Analysis Ready Data on summer surface temperatures, and NYC Land Cover Dataset at the census tract-level (n = 2098). Four cross-sectional regression modeling strategies were used to estimate the overall City-wide association, and associations across smaller intra-city areas, between tract-level percent of Black and percent Hispanic/Latine residents and summer day surface temperature, adjusting for altitude, shoreline, and nature-cover: overall NYC linear, borough-specific linear, Community District-specific linear, and geographically weighted regression models. All three linear regressions identified associations between neighborhood racial and ethnic composition and summer day surface temperatures. The geographically weighted regression models, which address the issue of spatial autocorrelation, identified specific locations (such as northwest Bronx, central Brooklyn, and uptown Manhattan) within which racial and ethnic disparities for heat exposures are concentrated. Through examining the overall effects and geographic effect measure modification across spatial scales, the results of this study identify specific geographic areas for intervention to mitigate heat exposure disparities experienced by Black and Hispanic/Latine NYC residents.

Heatwaves in Peninsular Malaysia: a spatiotemporal analysis

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950-2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.

Spatial contrasts and temporal changes in fine-scale heat exposure and vulnerability in the Paris region

Heat is identified as a key climate risk in Europe. Vulnerability to heat can be aggravated by enhanced exposure (e.g., urban heat island), individual susceptibility (e.g., age, income), and adaptive capacity (e.g., home ownership, presence of vegetation). We investigated the spatial and temporal patterns of the environmental and social drivers of vulnerability to heat in the Paris region, France, over the 2000-2020 period, and their association with mortality (restricted to 2000-2017). Daily temperatures were modeled for the 5265 IRIS of the Paris region for 2000-2020. Annual land use and socioeconomic data were collected for each IRIS. They were used to identify a priori five classes of heat-vulnerable areas based on a cluster analysis. The temperature-mortality relationship was investigated using a time-series approach stratified by clusters of vulnerability. The Paris region exhibited a strong urban heat island effect, with a marked shift in temperature distributions after 2015. The clustering suggested that the most heat-vulnerable IRIS in the Paris region have a high or very high exposure to temperature in a highly urbanized environment with little vegetation, but are not systematically associated with social deprivation. A similar J-shape temperature-mortality relationship was observed in the five clusters. Between 2000 and 2017, around 8000 deaths were attributable to heat, 5600 of which were observed in the most vulnerable clusters. Vulnerability assessments based on geographical indicators are key tools for urban planners and decision-makers. They complement the knowledge about individual risk factors but should be further evaluated through interdisciplinary collaborations.

Premature Deaths Due To Heat Exposure: The Potential Effects of Neighborhood-Level Versus City-Level Acclimatization Within US Cities

For the population of a given US city, the risk of premature death associated with heat exposure increases as temperatures rise, but risks in hotter cities are generally lower than in cooler cities at equivalent temperatures due to factors such as acclimatization. Those living in especially hot neighborhoods within cities might therefore suffer much more than average if such adaptation is only at the city-wide level, whereas they might not experience greatly increased risk if adjustment is at the neighborhood level. To compare these possibilities, we use high spatial resolution temperature data to evaluated heat-related deaths assuming either adjustment at the city-wide or at the neighborhood scale in 10 large US cities. On average, we find that if inhabitants are adjusted to their local conditions, a neighborhood that was 10°C hotter than a cooler one would experience only about 1.0-1.5 excess heat deaths per year per 100,000 persons. By contrast, if inhabitants are acclimatized to city-wide temperatures, the hotter neighborhood would experience about 15 excess deaths per year per 100,000 persons. Using idealized analyses, we demonstrate that current city-wide epidemiological data do not differentiate between these differing adjustments. Given the very large effects of assumptions about neighborhood-level acclimatization found here, as well as the fact that current literature is conflicting on the spatial scale of acclimatization, more neighborhood-level epidemiological data are urgently needed to determine the health impacts of variations in heat exposure within urban areas, better constrain projected changes, and inform mitigation efforts.

Evaluation of fused multisource data of air temperature based on dropsonde and satellite observation

Continuous vertical air temperature (AT) from in-situ observation is of crucial importance for understanding the atmospheric environment, but the satellite data that have complete spatial coverage lacked vertical in-situ observation data, and the vertical dropsonde data from in-situ observations only were single-point observations. Therefore, this article introduced machine learning algorithms for fusing in-situ observation and multi-satellite data to achieve spatial continuity of vertical data on a large scale. Specially, random forest (RF), support vector regression (SVR), artificial neural network (ANN) and recurrent neural network (RNN) were employed to capture the non-linear relationships between the variables and estimated AT. The pre-training process and fine-tuning process ensured the prediction of AT spatiotemporal distribution. The four models were implemented for three-dimensional AT estimating across China. Additionally, we used the radiosonde observation data to evaluate the accuracy of estimated AT data under conventional weather and typhoon conditions. Our results revealed that the RF model performed the best with the R of 0.9992, the MAE of 0.70 °C, and the RMSE of 1.04 °C at the national scale, followed by the SVR and ANN models. The RNN model exhibited promising results under typhoon conditions, which will be valuable insights for further research on the applicability of machine learning models under different weather conditions. Besides, having a larger sample size does not necessarily result in reduced errors. For instance, the MAE value for SVR in the pressure height range of 100-200 hPa was larger than that in the pressure height range of 300-400 hPa, but the former sample size was 16,324, which was 7433 higher than the latter.

Economic valuation of temperature-related mortality attributed to urban heat islands in European cities

As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€ - 314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability.

Anticipating older populations' health risk exacerbated by compound disasters based on mortality caused by heart diseases and strokes

The health of older populations in the Southeastern U.S. receives threats from recurrent tropical cyclones and extreme heat, which may exacerbate the mortality caused by heart diseases and strokes. Such threats can escalate when these extremes form compound disasters, which may be more frequent under climate change. However, a paucity of empirical evidence exists concerning the health threats of compound disasters, and anticipations regarding the health risks of older populations under future compound disaster scenarios are lacking. Focusing on Florida, which has 67 counties and the second-largest proportion of older populations among U.S. states, we calibrate Poisson regression models to explore older populations' mortality caused by heart diseases and strokes under single and compound disasters. The models are utilized to estimate the mortality across future disaster scenarios, the changing climate, and the growing population. We identify that under multiple hurricanes or heat, current-month hurricanes or heat can affect mortality more heavily than previous-month hurricanes or heat. Under future scenarios, co-occurring hurricanes and extreme heat can exacerbate the mortality more severely than other disaster scenarios. The same types of compound disasters can coincide with an average of 20.5% higher mortality under RCP8.5-SSP5 than under RCP4.5-SSP2. We assess older populations' future health risks, alerting health agencies to enhance preparedness for future "worst-case" scenarios of compound disasters and proactively adapt to climate change.

Greenness and excess deaths from heat in 323 Latin American cities: Do associations vary according to climate zone or green space configuration?

Green vegetation may protect against heat-related death by improving thermal comfort. Few studies have investigated associations of green vegetation with heat-related mortality in Latin America or whether associations are modified by the spatial configuration of green vegetation. We used data from 323 Latin American cities and meta-regression models to estimate associations between city-level greenness, quantified using population-weighted normalized difference vegetation index values and modeled as three-level categorical terms, and excess deaths from heat (heat excess death fractions [heat EDFs]). Models were adjusted for city-level fine particulate matter concentration (PM2.5), social environment, and country group. In addition to estimating overall associations, we derived estimates of association stratified by green space clustering by including an interaction term between a green space clustering measure (dichotomized at the median of the distribution) and the three-level greenness variable. We stratified analyses by climate zone (arid vs. temperate and tropical combined). Among the 79 arid climate zone cities, those with moderate and high greenness levels had modestly lower heat EDFs compared to cities with the lowest greenness, although protective associations were more substantial in cities with moderate versus high greenness levels and confidence intervals (CI) crossed the null (Beta: -0.41, 95% CI: -1.06, 0.25; Beta -0.23, 95% CI: -0.95, 0.49, respectively). In 244 non-arid climate zone cities, associations were approximately null. We did not observe evidence of effect modification by green space clustering. Our results suggest that greenness may offer modest protection against heat-related mortality in arid climate zone Latin American cities.

Does choice of outdoor heat metric affect heat-related epidemiologic analyses in the US Medicare population?

Outdoor air temperature is associated with increased morbidity and mortality. Other thermal indices theoretically confer greater physiological relevance by incorporating additional meteorological variables. However, the optimal metric for predicting excess deaths or hospitalizations owing to extreme heat among US Medicare beneficiaries remains unknown.

Methods

We calculated daily maximum, minimum, and mean outdoor air temperature (T), heat index (HI), wet-bulb globe temperature (WBGT), and Universal Thermal Climate Index (UTCI) for populous US counties and linked estimates with daily all-cause mortality and heat-related hospitalizations among Medicare beneficiaries (2006-2016). We fit distributed-lag nonlinear models for each metric and compared relative risks (RRs) at the 99th percentile.

Results

Across all heat metrics, extreme heat was statistically significantly associated with elevated risks of morbidity and mortality. Associations were more pronounced for maximum daily values versus the corresponding minimum for the same metric. The starkest example was between HImax (RR = 1.14; 95% confidence interval [CI] = 1.12, 1.15) and HImin (RR = 1.10; 95% CI = 1.09, 1.11) for hospitalizations. When comparing RRs across heat metrics, we found no statistically significant differences within the minimum and maximum heat values (i.e., no significant differences between Tmax/HImax/WBGTmax/UTCImax or between Tmin/HImin/WBGTmin/UTCImin). We found similar relationships across the National Climate Assessment regions.

Conclusion

Among Medicare beneficiaries in populous US counties, daily maximum and mean values of outdoor heat are associated with greater RRs of heat-related morbidity and all-cause mortality versus minimum values of the same metric. The choice of heat metric (e.g., temperature versus HI) does not appear to substantively affect risk calculations in this population.

Elevated heat indices resulting from hurricane-related defoliation: a case study

Defoliation caused by strong tropical cyclones can modify the partitioning of incident solar radiation between the sensible, latent, and substrate heat fluxes. While previous work has shown hurricane defoliation to warm near-surface air temperature along its track, this study more directly contextualizes the warming to human heat stress and exposure via the heat index (HI). For this case study, the spatial extent and temporal persistence of defoliation produced by Hurricane Laura (2020) in southwestern Louisiana was characterized using the normalized difference vegetation index (NDVI). The defoliated land surface was then assimilated into the Weather Research and Forecasting (WRF) model version 4.2 and compared to a control, normal-foliage simulation for the 30 days following landfall. Over southwest Louisiana, the largest HI increase occurred at 0600 UTC (1:00 AM LT) with an average increase of + 0.25 °C, and the exposure time to HI ≥ 30 °C increased by 8.1% after accounting for the defoliated landscape. Meanwhile, Cameron, Louisiana, the site of Laura's landfall where defoliation was most severe, cumulatively experienced an extra 33 h of HI values exceeding 26 °C, while mean HI increased by 1.2 °C at 0300 UTC. Additional WRF experiments were performed with altered "landfall" years of 2017 and 2018 to determine the sensitivity of defoliation-driven HI changes to the ambient synoptic conditions. While synoptic conditions modulated the magnitude of increase, HIs nonetheless experienced statistically significant increases in both hypothetical "landfall" years. Such findings are valuable for emergency managers and community health officials because overnight minimum temperatures are a strong indicator of heat mortality.

Urban heat island effect-related mortality under extreme heat and non-extreme heat scenarios: A 2010-2019 case study in Hong Kong

The urban heat island (UHI) effect exacerbates the adverse impact of heat on human health. However, while the UHI effect is further intensified during extreme heat events, prior studies have rarely mapped the UHI effect during extreme heat events to assess its direct temperature impact on mortality. This study examined the UHI effect during extreme heat and non-extreme heat scenarios and compared their temperature-mortality associations in Hong Kong from 2010 to 2019. Four urban heat island degree hour (UHIdh) scenarios were mapped onto Hong Kong's tertiary planning units and classified into three levels (Low, Moderate, and High). We assessed the association between temperature and non-external mortality of populations living in each UHIdh level for the extreme heat/non-extreme heat scenarios during the 2010-2019 hot seasons. Our results showed substantial differences between the temperature-mortality associations in the three levels under the UHIdh extreme heat scenario (UHIdh_EH). While there was no evidence of increased mortality in Low UHIdh_EH areas, the mortality risk in Moderate and High UHIdh_EH areas were significantly increased during periods of hot temperature, with the High UHIdh_EH areas displaying almost double the risk (RR: 1.08, 95%CI: 1.03, 1.14 vs. RR: 1.05, 95 % CI: 1.01, 1.09). However, other non-extreme heat UHI scenarios did not demonstrate as prominent of a difference. When stratified by age, the heat effects were found in Moderate and High UHIdh_EH among the elderly aged 75 and above. Our study found a difference in the temperature-mortality associations based on UHI intensity and potential heat vulnerability of populations during extreme heat events. Preventive measures should be taken to mitigate heat especially in urban areas with high UHI intensity during extreme heat events, with particular attention and support for those prone to heat vulnerability, such as the elderly and poorer populations.

Interaction effects of night-time temperature and PM2.5 on preterm birth in Huai River Basin, China

Nocturnal temperature is observed increasing with global warming. However, evidence on night-time non-optimal temperature on the risk of preterm birth (PTB) is limited, and the potential interactions with air pollution on PTB has not been well clarified. We therefore conducted a population-based retrospective cohort study to evaluate the effect of night-time temperature extremes on the risk of PTB and its interaction with air pollution. Records of 196,780 singleton births from 4 counties in Huai River Basin (2013-2018) were obtained. Gridded data on night-time temperature were collected from a high-quality Chinese Air Quality Reanalysis dataset. We used a multivariate logistic regression to evaluate the effects of night-time heat and cold exposure on the risk of PTB as well as its subtypes. Potential interactions between night-time temperature extremes and fine particulate matter < 2.5 µm (PM_2.5) were examined using the relative excess risk due to interaction (RERI). We found that the risk of PTB was positively associated with third trimester night-time extremely heat and cold exposure, with adjusted OR of 1.898 (95 %CI: 1.655-2.177) and 2.044 (95 %CI: 1.786-2.339). Similar effects were observed for PTB subtypes, moderately PTB (mPTB) and very PTB (vPTB). Synergistic effects (RERI greater than 0) of each trimester night-time temperature extremes exposure and PM_2.5 on PTB were observed. We identified consistent positive interactions between night-time temperature extremes and PM_2.5 on mPTB. No significant interaction of night-time temperature extremes and PM_2.5 on vPTB was found. In conclusion, this large retrospective cohort study found that third trimester night-time heat and cold exposure significantly increased the risk of PTB and its subtypes. There is a synergistic effect between night-time temperature extremes and high PM_2.5 levels on PTB and mPTB. In the context of climate warming, our results add new evidence to the current understanding of night-time non-optimal temperature exposure on PTB.

Development and Evaluation of Options for Action to Progress on the SDG 6 Targets in Austria

The Agenda 2030 of the United Nations stipulates an ambitious set of 17 Sustainable Development Goals (SDGs). They were globally agreed upon and demand coherent, context-specific implementation at the national level. To address the complexity of challenges therein, the Agenda is designed to be integrated, indivisible, and universal. The numerous multifaceted interactions in-between the SDGs and with corresponding measures pose a complex challenge for decision-makers implementing them worldwide that requires support for a comprehensive discourse in the science-society-policy arena. Research on the interactions between the SDGs has been flourishing and can help to understand where policy options might be most successfully located. A catalytic effect on several other goals is, e.g., often attributed to SDG 6 on water and sanitation. However, beyond the where to locate policy options, it is similarly important to understand how potential policy options would affect the SDGs and their targets. We developed eleven options and 85 measures as context-specific pathways to advance the SDG 6 Targets in Austria. As a country in the Global North and with a generally far-established water and sanitation infrastructure and management, this responds to the Agenda's demand for universal applicability and can serve as an example to illustrate potential challenges beyond basic infrastructure provision and management. The proposed options cover resources-oriented sanitation, blue-green-brown infrastructure, efficient use and integrated management of water resources, maintenance and restoration of ecological functions of inland waters, reduction of diffuse discharge of nutrients and problematic substances as well as trace substances, water, sanitation and hygiene in public spaces, groundwater protection, development cooperation as well as co-design and co-creation. Their effects on the SDG 6 Targets are evaluated using a 7-point-scale. The evaluation method is simple and practicable, and fosters discourse on the entire water cycle amongst the expert group applying the method. The evaluated effects on the targets are found to be unanimously positive or neutral, but trade-offs might arise when including other SDGs in the assessment, making an expansion of the evaluation necessary for coherent implementation. The results can be used as a baseline to support follow-up discussions with stakeholders and decision-makers.

The influence of heat and cold waves on mortality in Russian subarctic cities with varying climates

Publications on ambient temperature-related mortality among Arctic or subarctic populations are extremely rare. While circumpolar areas cover large portions of several European countries, Canada, and the USA, the population of these territories is relatively small, and the data needed for statistical analysis of the health impacts of extreme temperature events are frequently insufficient. This study utilizes standard time series regression techniques to estimate relative increases in cause- and age-specific daily mortality rates during heat waves and cold spells in four Russian cities with a subarctic climate. The statistical significance of the obtained effect estimates tends to be greater in the continental climate than in the marine climate. A small meta-analysis was built around the obtained site-specific health effects. The effects were homogeneous and calculated for the selected weather-dependent health outcomes. The relative risks of mortality due to ischemic heart disease, all diseases of the circulatory system, and all non-accidental causes during cold spells in the age group ≥ 65 years were 1.20 (95% CI: 1.11-1.29), 1.14 (1.08-1.20), and 1.12 (1.07-1.17), respectively. Cold spells were more harmful to the health of the residents of Murmansk, Archangelsk, and Magadan than heat waves, and only in Yakutsk, heat waves were more dangerous. The results of this study can help the public health authorities develop specific measures for the prevention of excess deaths during cold spells and heat waves in the exposed subarctic populations.

Early delivery following chronic and acute ambient temperature exposure: a comprehensive survival approach

Background

Ambient temperature, particularly heat, is increasingly acknowledged as a trigger for preterm delivery but study designs have been limited and results mixed. We aimed to comprehensively evaluate the association between ambient temperature throughout pregnancy and preterm delivery.

Methods

We estimated daily temperature throughout pregnancy using a cutting-edge spatiotemporal model for 5347 live singleton births from three prospective cohorts in France, 2002–2018. We performed Cox regression (survival analysis) with distributed lags to evaluate time-varying associations with preterm birth simultaneously controlling for exposure during the first 26 weeks and last 30 days of pregnancy. We examined weekly mean, daytime, night-time and variability of temperature, and heatwaves accounting for adaptation to location and season.

Results

Preterm birth risk was higher following cold (5th vs 50th percentile of mean temperature) 7–9 weeks after conception [relative risk (RR): 1.3, 95% CI: 1.0–1.6 for 2°C vs 11.6°C] and 10–4 days before delivery (RR: 1.6, 95% CI: 1.1–2.1 for 1.2°C vs 12.1°C). Night-time heat (95th vs 50th percentile of minimum temperature; 15.7°C vs 7.4°C) increased risk when exposure occurred within 5 weeks of conception (RR: 2.0, 95% CI: 1.05–3.8) or 20–26 weeks after conception (RR: 2.9, 95% CI: 1.2–6.8). Overall and daytime heat (high mean and maximum temperature) showed consistent effects. We found no clear associations with temperature variability or heatwave indicators, suggesting they may be less relevant for preterm birth.

Conclusions

In a temperate climate, night-time heat and chronic and acute cold exposures were associated with increased risk of preterm birth. These results suggest night-time heat as a relevant indicator. In the context of rising temperatures and more frequent weather hazards, these results should inform public health policies to reduce the growing burden of preterm births.

Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.

Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

Global population is experiencing more frequent, longer, and more severe heat waves due to global warming and urbanization. Episodic heat waves increase mortality and morbidity rates and demands for water and energy. Urban managers typically assess heat wave risk based on heat wave hazard, population exposure, and vulnerability, with a general assumption of spatial uniformity of heat wave hazard. We present a novel analysis that demonstrates an approach to determine the spatial distribution of a set of heat wave properties and hazard. The analysis is based on the Livneh dataset at a 1/16-degree resolution from 1950 to 2009 in Maricopa County, Arizona, USA. We then focused on neighborhoods with the most frequent, severe, earlier, and extended periods of heat wave occurrences. On average, the first heat wave occurs 40 days earlier in the eastern part of the county; the northeast part of this region experiences 12 days further extreme hot days and 30 days longer heat wave season than other regions of the area. Then, we applied a multi-criteria decision-making (MCDM) tool (TOPSIS) to evaluate the total hazard posed by heat wave components. We found that the northern and central parts of the metropolitan area are subject to the greatest heat wave hazard and that individual heat wave hazard components did not necessarily indicate heat hazard. This approach is intended to support local government planning for heat wave adaptation and mitigation strategies, where cooling centers, heat emergency water distribution networks, and electrical energy delivery can be targeted based on current and projected local heat wave characteristics.

Aging Hearts in a Hotter, More Turbulent World: The Impacts of Climate Change on the Cardiovascular Health of Older Adults

PURPOSE OF REVIEW

Climate change has manifested itself in multiple environmental hazards to human health. Older adults and those living with cardiovascular diseases are particularly susceptible to poor outcomes due to unique social, economic, and physiologic vulnerabilities. This review aims to summarize those vulnerabilities and the resultant impacts of climate-mediated disasters on the heart health of the aging population.

RECENT FINDINGS

Analyses incorporating a wide variety of environmental data sources have identified increases in cardiovascular risk factors, hospitalizations, and mortality from intensified air pollution, wildfires, heat waves, extreme weather events, rising sea levels, and pandemic disease. Older adults, especially those of low socioeconomic status or belonging to ethnic minority groups, bear a disproportionate health burden from these hazards. The worldwide trends responsible for global warming continue to worsen climate change-mediated natural disasters. As such, additional investigation will be necessary to develop personal and policy-level interventions to protect the cardiovascular wellbeing of our aging population.

Indoor Temperatures in the 2018 Heat Wave in Quebec, Canada: Exploratory Study Using Ecobee Smart Thermostats

Background

Climate change, driven by human activity, is rapidly changing our environment and posing an increased risk to human health. Local governments must adapt their cities and prepare for increased periods of extreme heat and ensure that marginalized populations do not suffer detrimental health outcomes. Heat warnings traditionally rely on outdoor temperature data which may not reflect indoor temperatures experienced by individuals. Smart thermostats could be a novel and highly scalable data source for heat wave monitoring.

Objective

The objective of this study was to explore whether smart thermostats can be used to measure indoor temperature during a heat wave and identify houses experiencing indoor temperatures above 26°C.

Methods

We used secondary data—indoor temperature data recorded by ecobee smart thermostats during the Quebec heat waves of 2018 that claimed 66 lives, outdoor temperature data from Environment Canada weather stations, and indoor temperature data from 768 Quebec households. We performed descriptive statistical analyses to compare indoor temperatures differences between air conditioned and non–air conditioned houses in Montreal, Gatineau, and surrounding areas from June 1 to August 31, 2018.

Results

There were significant differences in indoor temperature between houses with and without air conditioning on both heat wave and non–heat wave days (P<.001). Households without air conditioning consistently recorded daily temperatures above common indoor temperature standards. High indoor temperatures persisted for an average of 4 hours per day in non–air conditioned houses.

Conclusions

Our findings were consistent with current literature on building warming and heat retention during heat waves, which contribute to increased risk of heat-related illnesses. Indoor temperatures can be captured continuously using smart thermostats across a large population. When integrated with local heat health action plans, these data could be used to strengthen existing heat alert response systems and enhance emergency medical service responses.

Association of Extreme Heat With All-Cause Mortality in the Contiguous US, 2008-2017

IMPORTANCE

The number of extreme heat events is increasing because of climate change. Previous studies showing an association between extreme heat and higher mortality rates generally have been limited to urban areas, and whether there is heterogeneity across different populations is not well studied; understanding whether this association varies across different communities, particularly minoritized racial and ethnic groups, may allow for more targeted mitigation efforts.

OBJECTIVE

To the assess the association between extreme heat and all-cause mortality rates in the US.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study involved a longitudinal analysis of the association between the number of extreme heat days in summer months from 2008 to 2017 (obtained from the Centers for Disease Control and Prevention's Environmental Public Health Tracking Program) and county-level all-cause mortality rates (obtained from the National Center for Health Statistics), using a linear fixed-effects model across all counties in the contiguous US among adults aged 20 years and older. Data analysis was performed from September 2021 to March 2022.

EXPOSURES

The number of extreme heat days per month. Extreme heat was identified if the maximum heat index was greater than or equal to 90 °F (32.2 °C) and in the 99th percentile of the maximum heat index in the baseline period (1979 to 2007).

MAIN OUTCOMES AND MEASURES

County-level, age-adjusted, all-cause mortality rates.

RESULTS

There were 219 495 240 adults aged 20 years and older residing in the contiguous US in 2008, of whom 113 294 043 (51.6%) were female and 38 542 838 (17.6%) were older than 65 years. From 2008 to 2017, the median (IQR) number of extreme heat days during summer months in all 3108 counties in the contiguous US was 89 (61-122) days. After accounting for time-invariant confounding, secular time trends, and time-varying environmental and economic measures, each additional extreme heat day in a month was associated with 0.07 additional death per 100 000 adults (95% CI, 0.03-0.10 death per 100 000 adults; P = .001). In subgroup analyses, greater increases in mortality rates were found for older vs younger adults (0.19 death per 100 000 individuals; 95% CI, 0.04-0.34 death per 100 000 individuals), male vs female adults (0.12 death per 100 000 individuals; 95% CI, 0.05-0.18 death per 100 000 individuals), and non-Hispanic Black vs non-Hispanic White adults (0.11 death per 100 000 individuals; 95% CI, 0.02-0.20 death per 100 000 individuals).

CONCLUSIONS AND RELEVANCE

These findings suggest that from 2008 to 2017, extreme heat was associated with higher all-cause mortality in the contiguous US, with a greater increase noted among older adults, men, and non-Hispanic Black individuals. Without mitigation, the projected increase in extreme heat due to climate change may widen health disparities between groups.

Warmer summer nocturnal surface air temperatures and cardiovascular disease death risk: a population-based study

BACKGROUND

In recent summers, some populous mid-latitude to high-latitude regions have experienced greater heat intensity, more at night than by day. Such warming has been associated with increased cause-specific adult mortality. Sex-specific and age-specific associations between summer nocturnal surface air temperatures (SAT) and cardiovascular disease (CVD) deaths have yet to be established.

METHODS

A monthly time series analysis (June-July, 2001-2015) was performed on sex-specific CVD deaths in England and Wales of adults aged 60-64 and 65-69 years. Using negative binomial regression with autocorrelative residuals, associations between summer (June-July) nocturnal SAT anomalies (primary exposure) and CVD death rates (outcome) were computed, controlling for key covariates. To explore external validity, similar associations with respect to CVD death in King County, Washington, USA, also were calculated, but only for men aged 60-64 and 65-69 years. Results are reported as incidence rate ratios.

RESULTS

From 2001 to 2015, within these specific cohorts, 39 912 CVD deaths (68.9% men) were recorded in England and Wales and 488 deaths in King County. In England and Wales, after controlling for covariates, a 1°C rise in anomalous summer nocturnal SAT associated significantly with a 3.1% (95% CI 0.3% to 5.9%) increased risk of CVD mortality among men aged 60-64, but not older men or either women age groups. In King County, after controlling for covariates, a 1°C rise associated significantly with a 4.8% (95% CI 1.7% to 8.1%) increased risk of CVD mortality among those <65 years but not older men.

CONCLUSION

In two mid-latitude regions, warmer summer nights are accompanied by an increased risk of death from CVD among men aged 60-64 years.

Associations Between Historical Redlining and Present-Day Heat Vulnerability Housing and Land Cover Characteristics in Philadelphia, PA

Historical, institutional racism within the housing market may have impacted present-day disparities in heat vulnerability. We quantified associations between historically redlined areas with present-day property and housing characteristics that may enhance heat vulnerability in Philadelphia, PA. We used color-coded Home Owners Loan Corporation (HOLC) maps and tax assessment data to randomly select 100 present-day (2018-2019) residential properties in each HOLC grade area (A = Best; B, C, and D = Most hazardous; N = 400 total). We conducted virtual inventories of the properties using aerial and streetview imagery for land cover and housing characteristics (dark roof color, flat roof shape, low or no mature tree canopy, no recently planted street trees) that may enhance heat vulnerability. We used modified Poisson regression models to estimate associations of HOLC grades with the property characteristics, unadjusted and adjusted for historical and contemporary measures of the neighborhood sociodemographic environment. Compared to grade A areas, higher proportions of properties in grade B, C, and D areas had dark roofs, low/no mature tree canopy, and no street trees. Adjusting for historical sociodemographics attenuated associations, with only associations with low or no tree canopy remaining elevated. Adjusting for present-day concentrated racial and socioeconomic deprivation did not substantially impact overall findings. In Philadelphia, PA, HOLC maps serve as spatial representations of present-day housing and land cover heat vulnerability characteristics. Further analyses incorporating longitudinal data on urban redevelopment, reinvestment, and neighborhood change are needed to more fully represent complex relationships among historical racism, residential segregation, and heat vulnerability.

Exposure to Abnormally Hot Temperature and the Demand for Commercial Health Insurance

Using the China Health and Retirement Longitudinal Study, this paper studies the impact of abnormal hot temperature on residents' demand for commercial health insurance. The results show that for every 1°F rise in abnormal temperature, the probability of people buying commercial health insurance increased by 6%. Furthermore, the abnormal hot temperature has a more significant impact on the commercial health insurance demand of women, residents in the South and residents in the East. Channel analysis shows that abnormal hot temperature affects the demand for commercial health insurance through two channels: increasing residents' concern about climate risk and affecting health. This paper provides evidence for actively promoting sustainable development and improving the construction of medical security system.

Social disparities in neighborhood heat in the Northeast United States

Upward trends in ground-level warming are expected to intensify, affecting the health of human populations. Specific to the United States, the Northeast (NE) region is one of the most vulnerable to these warming trends. Previous research has found social disparities in the distribution of heat, while recent studies have examined associations between metropolitan racial/ethnic segregation and heat exposures. We advance upon previous research by including a novel measure of neighborhood-level racial/ethnic diversity in our examination of social inequalities in heat for NE neighborhoods (census tracts). We paired data derived from the United States Geological Survey on mean land surface temperature (LST) for the summer months of 2013-2017 with sociodemographic data from the American Community Survey (5-year estimates, 2013-2017). We use multivariable generalized estimating equations (GEEs) that adjust for geographic clustering. Findings reveal heat exposure disparities across NE neighborhoods. Neighborhoods with higher proportions of racial/ethnic minorities, people of lower socioeconomic status, households without access to an automobile, and greater diversity experience higher temperatures. Diversity was more strongly related to increased heat in neighborhoods with lower Latinx and lower Black composition suggesting that neighborhood homogeneity confers a differentially greater cooling effect based on higher White composition. The social groups that carry the unequal thermal burdens are also those who are most vulnerable. Interventions to reduce heat risks in the NE should therefore prioritize reducing the burden on historically disadvantaged communities.

Evaluating urban greening scenarios for urban heat mitigation: a spatially explicit approach

Urban green infrastructure, especially trees, are widely regarded as one of the most effective ways to reduce urban temperatures in heatwaves and alleviate the adverse impacts of extreme heat events on human health and well-being. Nevertheless, urban planners and decision-makers are still lacking methods and tools to spatially evaluate the cooling effects of urban green spaces and exploit them to assess greening strategies at the urban agglomeration scale. This article introduces a novel spatially explicit approach to simulate urban greening scenarios by increasing the tree canopy cover in the existing urban fabric and evaluating their heat mitigation potential. The latter is achieved by applying the InVEST urban cooling model to the synthetic land use/land cover maps generated for the greening scenarios. A case study in the urban agglomeration of Lausanne, Switzerland, illustrates the development of tree canopy scenarios following distinct spatial distribution strategies. The spatial pattern of the tree canopy strongly influences the human exposure to the highest temperatures, and small increases in the abundance of tree canopy cover with the appropriate spatial configuration can have major impacts on human health and well-being. The proposed approach supports urban planning and the design of nature-based solutions to enhance climate resilience.

Urban heat: an increasing threat to global health

Shilu Tong and colleagues describe the health consequences of extreme urban heat and the priorities for action and research to mitigate the harms

Prioritizing Street Tree Planting Locations to Increase Benefits for All Citizens: Experience From Joliette, Canada

As the climate continues to warm and the world becomes more urbanized, our reliance on trees and the benefits they provide is rapidly increasing. Many cities worldwide are planting trees to offset rising temperatures, trap pollutants, and enhance environmental and human health and well-being. To maximize the benefits of planting trees and avoid further increasing social inequities, a city needs to prioritize where to establish trees by first identifying those areas of greatest need. This work aims to demonstrate a spatially explicit approach for cities to determine these priority locations to achieve the greatest returns on specific benefits. Criteria for prioritization were developed in tandem with the City of Joliette, Canada, and based on nine indicators: surface temperature, tree density, vegetation cover, resilience, tree size and age, presence of species at risk, land use type, socioeconomic deprivation, and potential for active transportation. The City’s preferences were taken into account when assigning different weights to each indicator. The resulting tree planting priority maps can be used to target street tree plantings to locations where trees are needed most. This approach can be readily applied to other cities as these criteria can be adjusted to accommodate specific tree canopy goals and planning constraints. As cities are looking to expand tree canopy, we hope this work will assist in sustaining and growing their urban forest, enabling it to be more resilient and to keep providing multiple and sustained benefits where they are needed the most.

Future Climate Change Impact on Urban Heat Island in Two Mediterranean Cities Based on High-Resolution Regional Climate Simulations

The Mediterranean is recognized among the most responsive regions to climate change, with annual temperatures projected to increase by 1–5 °C until 2100. Large cities may experience an additional stress discomfort due to the Urban Heat Island (UHI) effect. In the present study, the WRF-ARW numerical weather prediction model was used to investigate the climate change impact on UHI for two Mediterranean cities, Rome and Thessaloniki. For this purpose, three 5-year time-slice simulations were conducted (2006–2010, 2046–2050, 2096–2100) under the Representative Concentration Pathway (RCP) 8.5 emission scenario, with a spatial resolution of 2 km. In order to comprehensively investigate the urban microclimate, we analyze future simulation data across sections crossing urban/non-urban areas, and after grouping them into three classes depending on the location of the grid cells. The urban areas of both cities present increased average minimum temperature (Tmin) in winter/summer compared to other rural areas, with an UHI of ~+1.5–3 °C on average at night/early morning. Considering UHI under future climate change, we found no significant variations (~±0.2 °C). Finally, we found that the numbers of days with Tmin ≥ 20 °C will mostly increase in urban coastal areas until 2100, while the largest increase of minimum Discomfort Index (DImin) is expected in urban low-ground areas.

The association of compound hot extreme with mortality risk and vulnerability assessment at fine-spatial scale

The frequency and intensity of compound hot extremes will be likely to increase in the context of global warming. Epidemiological studies have demonstrated the adverse effect of simple hot extreme events on mortality, but little is known about the effects of compound hot extremes on mortality. Daily meteorological, demographic, and mortality data during 2011-2017 were collected from 160 streets in Guangzhou City, China. We used distributed lag non-linear model (DLNM) to analyze the associations of different hot extremes with mortality risk in each street. Street-specific associations were then combined using a meta-analysis approach. To assess the spatial distribution of vulnerability to compound hot extremes, vulnerable characteristics at street level were selected using random forest model, and then we calculated and mapped spatial vulnerability index (SVI) at each street in Guangzhou. At street level, compared with normal day, compound hot extreme significantly increased mortality risk (relative risk(RR)=1.43, 95%CI:1.28-1.59) with higher risk for female (RR=1.54 [1.35-1.76]) and the elderly(RR for aged 65-74=1.41 [1.14-1.74]; RR for ≥75years=1.63 [1.45-1.84]) than male (RR=1.32 [1.15-1.52]) and population <65 years (RR=1.01 [0.83-1.22]). Areas with high vulnerability were in the urban center and the edge of suburban. High proportion of population over 64 years old in urban center, and high proportions of outdoor workers and population with illiteracy in suburban areas were the determinants of spatial vulnerability. We found that compound hot extreme significantly increased mortality risk at street level, which is modified by socio-economic and demographic factors. Our findings help allocate resources targeting vulnerable areas at fine-spatial scale.°.

Effects of Hot Nights on Mortality in Southern Europe

BACKGROUND

There is strong evidence concerning the impact of heat stress on mortality, particularly from high temperatures. However, few studies to our knowledge emphasize the importance of hot nights, which may prevent necessary nocturnal rest.

OBJECTIVES

In this study, we use hot-night duration and excess to predict daily cause-specific mortality in summer, using multiple cities across Southern Europe.

METHODS

We fitted time series regression models to summer cause-specific mortality, including natural, respiratory, and cardiovascular causes, in 11 cities across four countries. We included a distributed lag nonlinear model with lags up to 7 days for hot night duration and excess adjusted by daily mean temperature. We summarized city-specific associations as overall-cumulative exposure-response curves at the country level using meta-analysis.

RESULTS

We found positive but generally nonlinear associations between relative risk (RR) of cause-specific mortality and duration and excess of hot nights. RR of duration associated with nonaccidental mortality in Portugal was 1.29 (95% confidence interval [CI] = 1.07, 1.54); other associations were imprecise, but we also found positive city-specific estimates for Rome and Madrid. Risk of hot-night excess ranged from 1.12 (95% CI = 1.05, 1.20) for France to 1.37 (95% CI = 1.26, 1.48) for Portugal. Risk estimates for excess were consistently higher than for duration.

CONCLUSIONS

This study provides new evidence that, over a wider range of locations, hot night indices are strongly associated with cause-specific deaths. Modeling the impact of thermal characteristics during summer nights on mortality could improve decisionmaking for preventive public health strategies.

Greening is a promising but likely insufficient adaptation strategy to limit the health impacts of extreme heat

BACKGROUND

Adapting the urban environment to heat is a public health priority in the context of climate change. Cities are now considering interventions on specific urban characteristics known to contribute to the urban heat island (UHI) such as vegetation and imperviousness.

OBJECTIVES

To explore how these urban characteristics influence the temperature-mortality relationship in the Paris region.

METHODS

We modeled the temperature-mortality relationship for the 1300 municipalities of the region from 1990 to 2015, while including an interaction with indicators that summarize the municipalities' main urban characteristics. Four indicators were tested: lack of green spaces, lack of trees, proportion of impervious surface, and overexposed population to a potential night UHI.

RESULTS

The shape of the temperature-mortality relationship was similar across all municipalities, but with a higher slope at the highest temperatures in municipalities with less green spaces, less trees, and more impervious soil. For instance, in Paris and its close suburbs, the relative risk associated with a temperature in the 99th percentile of the temperature distribution (compared to the 50th percentile) was 2.17 [IC95% 1.98:2.38] in municipalities with 40% of their surface covered by trees compared to 2.57 [IC 95% 2.47:2.68] in municipalities with only 3% of their surface covered by trees.

DISCUSSION

A lack of vegetation and a high degree of imperviousness were associated with a higher risk of heat-related mortality in the Paris region. Therefore, we can assume that interventions targeting these characteristics could reduce the health impacts of extreme heat. Such interventions should be coupled with other initiatives such as protecting the most vulnerable and promoting appropriate behaviors.

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products

BACKGROUND

Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error.

METHODS

We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution.

RESULTS

Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center.

CONCLUSION

Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

The effect of the heatwave on the morbidity and mortality of diabetes patients; a meta-analysis for the era of the climate crisis

INTRODUCTION

From the perspective of public health, the climate crisis is also causing many health problems worldwide. In contrast with the cardiovascular, respiratory, and urinary system, the adverse effects of heatwaves on the endocrine system, particularly in people with diabetes mellitus (DM), are not well established to date. In this study, the author investigated the morbidity and mortality changes of DM patients during heatwave periods, using the meta-analysis method.

METHODS

The author searched MEDLINE, EMBASE, and the Cochrane Library until March 12, 2020. The quality of each included study was assessed using the National Institutes of Health (NIH) Quality Assessment tools. The meta-analysis was conducted using the studies with a relative risk (RR) estimate and odds ratio (OR) estimate. The subgroup analysis and the meta-ANOVA analysis were conducted using various covariates, including lag days considered.

RESULTS

Only 36 articles were included in the meta-analysis. The pooled RR of mortality and of morbidity for diabetics under the heatwave were 1.18 (95% CI 1.13-1.25) and 1.10 (95% CI 1.06-1.14). For mortality studies, whether or not the lag days considered were 10 days or more was only a significant covariate for the meta-ANOVA analysis (Q = 3.17, p = 0.075). For morbidity studies, the definition of the heatwave (Q = 65.94, p < 0.0001), whether or not the maximum temperature was 40 °C or more (Q = 4.78, p = 0.0288), and the type of morbidity (Q = 60.23, p < 0.0001) were significant covariates for the analysis.

DISCUSSION

The mortality and morbidity risks of diabetes patients under the heatwave were mildly increased by about 18 percent for mortality and 10 percent for overall morbidity. The mortality risk of diabetics can increase more when lag days of 10 days or more are considered than when lag days of less than 10 days are considered. These valuable findings can be used in developing public health strategies to cope with heatwaves in the current era of aggravating global warming and climate crisis.

Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City

Heat stress provokes thermal discomfort to people living in semiarid and arid climates. This study evaluates thermal discomfort levels, building design concepts, and some heat mitigation strategies in low-income neighborhoods of Faisalabad, Pakistan. The outdoor and indoor weather data are collected from April to August 2016 using a weather station installed ad hoc in urban settings, and the 52 houses of the five low-income participating communities living in congested and less environment-friendly areas of Faisalabad. The discomfort index values, related to the building design concepts, including (i) house orientation to sunlight and (ii) house ventilation, are calculated from outdoor and indoor dry-bulb and wet-bulb temperatures. Our results show that although June was the hottest month of summer 2016, based on the monthly mean temperature of the Faisalabad region, the month of May produced the highest discomfort levels, which were higher in houses exposed to sunlight and without ventilation. The study also identifies some popular heat mitigation strategies adopted by the five participating low-income communities during various heat-related health complaints. The strategies are gender-biased and have medical, cultural/customary backgrounds. For example, about 52% of the males and 28% of the females drank more water during dehydration, diarrhea, and eye infection. Over 11% and 19% of the males and females, respectively, moved to cooler places during fever. About 43% of the males and 51% of the females took water showers and rested to combat flu (runny nose), headache, and nosebleed. The people did not know how to cure muscular fatigue, skin allergy (from a type of Milia), and mild temperature. Planting trees in an area and developing open parks with greenery and thick canopy trees can be beneficial for neighborhoods resembling those evaluated in this study.

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.

Impacts of Building Features on the Cooling Effect of Vegetation in Community-Based MicroClimate: Recognition, Measurement and Simulation from a Case Study of Beijing

Due to the accumulation of heat, the urban environment and human health are threatened. Land surface cover has effects on the thermal environment; nevertheless, the effects of land surface features and spatial patterns remain poorly known in a community-based microclimate. This study quantified and verified the impacts of normalized difference vegetation index (NDVI) on land surface temperature (LST) (K, the slope of the trend line of a linear regression between NDVI and LST) in different building density by using building outline and Landsat 8 satellite imagery. Comparing the cooling effect and distribution of vegetation showed that the vegetative cover had a cooling effect on LST, characterized by synchronous change, and building density had a significant impact on the cooling effect of vegetation. Through identification and simulation, it was found that the key factor is the wind speed between the buildings because, in different building densities, the wind speed was different, and studies had shown that when the building density was between 0.35 and 0.50, the wind speed between buildings was higher, resulting in a better cooling effect of vegetation. This conclusion has important reference significance for urban planning and mitigating the impact of the thermal environment on human health.

Heat related mortality in the two largest Belgian urban areas: A time series analysis

BACKGROUND

Summer temperatures are expected to increase and heat waves will occur more frequently, be longer, and be more intense as a result of global warming. A growing body of evidence indicates that increasing temperature and heatwaves are associated with excess mortality and therefore global heating may become a major public health threat. However, the heat-mortality relationship has been shown to be location-specific and differences could largely be explained by the most frequent temperature. So far, in Belgium there is little known regarding the heat-mortality relationship in the different urban areas.

OBJECTIVES

The objective of this study is to assess the heat-mortality relationship in the two largest urban areas in Belgium, i.e. Antwerp and Brussels for the warm seasons from 2002 until 2011 taking into account the effect of air pollution.

METHODS

The threshold in temperature above which mortality increases was determined using segmented regressions for both urban areas. The relationship between daily temperature and mortality above the threshold was investigated using a generalized estimated equation with Poisson distribution to finally determine the percentage of deaths attributable to the effect of heat.

RESULTS

Although only 50 km apart, the heat-mortality curves for the two urban areas are different. More specifically, an increase in mortality occurs above a maximum temperature of 25.2 °C in Antwerp and 22.8 °C in Brussels. We estimated that above these thresholds, there is an increase in mortality of 4.9% per 1 °C in Antwerp and of 3.1% in Brussels. During the study period, 1.5% of the deaths in Antwerp and 3.5% of the deaths in Brussels can be attributed to the effect of heat. The thresholds differed considerably from the most frequent temperature, particularly in Antwerp. Adjustment for air pollution attenuated the effect of temperature on mortality and this attenuation was more pronounced when adjusting for ambient ozone.

CONCLUSION

Our results show a significant effect of temperature on mortality above a city-specific threshold, both in Antwerp and in Brussels. These findings are important given the ongoing global warming. Recurrent, intense and longer episodes of high temperature and expected changes in air pollutant levels will have an important impact on health in urban areas.

Earth Observation Data Supporting Non-Communicable Disease Research: A Review

A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common—environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries’ economy due to healthcare costs and missing work force. Additionally, they affect the individual’s immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.

Geospatial context of social and environmental factors associated with health risk during temperature extremes: Review and discussion

This study reviews forty-six publications between 2008 and 2017 dealing with socio-environmental impacts on adverse health effects of temperature extremes, in a geospatial context. The review showed that most studies focus on extremely hot weather but lack analysis of how spatial heterogeneity across a region can influence cold mortality/morbidity. There are limitations regarding the use of temperature datasets for spatial analyses. Only a few studies have applied air temperature datasets with high spatial resolution to health studies, but none of these studies have used anthropogenic heat as a factor for analysis of health risk. In addition, the elderly is generally recognized as a vulnerable group in most studies, but the interaction between old age and temperature risk varies by location. Other socio-demographic factors such as low income, low education and accessibility to community shelters may also need to be considered in the future. There are only a few studies which investigate the interaction between temperature and air pollution in a geospatial context, despite the fact that this is a known interaction that can influence health risk under extreme weather. In conclusions, although investigation of temperature effects on health risk is already at the "mature stage", studies of socio-environmental influences on human health under extreme weather in a geospatial context is still being investigated. A comprehensive assessment is required to analyse how the spatial aspects of the geophysical and social environments can influence human health under extreme weather, in order to develop a better community plan and health protocols for disaster preparedness.