Heat-health warning systems: a comparison of the predictive capacity of different approaches to identifying dangerously hot days

Temporal dynamic effects of meteorological factors and air quality on the physical health of the older adults in Shenzhen, China

Introduction

Meteorological and environmental factors can affect people's lives and health, which is crucial among the older adults. However, it is currently unclear how they specifically affect the physical condition of older adults people.

Methods

We collected and analyzed the basic physical examination indicators of 41 older adults people for two consecutive years (2021 and 2022), and correlated them with meteorological and environmental factors. Partial correlation was also conducted to exclude unrelated factors as well.

Results

We found that among the physical examination indicators of the older adults for two consecutive years, five indicators (HB, WBC, HbAlc, CB, LDL-C) showed significant differences across the population, and they had significantly different dynamic correlation patterns with six meteorological (air pressure, temperature, humidity, precipitation, wind speed, and sunshine duration) and seven air quality factors (NO2, SO2, PM10, O3-1h, O3-8h, CO, PM2.5).

Discussion

Our study has discovered for the first time the dynamic correlation between indicators in normal basic physical examinations and meteorological factors and air quality indicators, which will provide guidance for the future development of policies that care for the healthy life of the older adults.

Indicators to support local public health to reduce the impacts of heat on health

Heat exposure presents a significant weather-related health risk in England and Wales, and is associated with acute impacts on mortality and adverse effects on a range of clinical conditions, as well as increased healthcare costs. Most heat-related health outcomes are preventable with health protection measures such as behavioural changes, individual cooling actions, and strategies implemented at the landscape level or related to improved urban infrastructure. We review current limitations in reporting systems and propose ten indicators to monitor changes in heat exposures, vulnerabilities, heat-health outcomes, and progress on adaptation actions. These indicators can primarily inform local area decision-making in managing risks across multiple sectors such as public health, adult and social care, housing, urban planning, and education. The indicators can be used alongside information on other vulnerabilities relevant for heat and health such as underlying morbidity or housing characteristics, to prioritise the most effective adaptation actions for those who need it the most.

Planning to Reduce the Health Impacts of Extreme Heat: A Content Analysis of Heat Action Plans in Local United States Jurisdictions

Objectives. To examine commonalities and gaps in the content of local US heat action plans (HAPs) designed to decrease the adverse health effects of extreme heat. Methods. We used content analysis to identify common strategies and gaps in extreme heat preparedness among written HAPs in the United States from jurisdictions that serve municipalities with more than 200 000 residents. We reviewed, coded, and analyzed plans to assess the prevalence of key components and strategies. Results. All 21 plans evaluated incorporated data on activation triggers, heat health messaging and risk communication, cooling centers, surveillance activities, and agency coordination, and 95% incorporated information on outreach to at-risk populations. Gaps existed in the specific applications of these broad strategies. Conclusions. Practice-based recommendations as well as future areas of research should focus on increasing targeted strategies for at-risk individuals and expanding the use of surveillance data outside of situational awareness. (Am J Public Health. 2023;113(5):559-567. https://doi.org/10.2105/AJPH.2022.307217).

Does the meteorological origin of heat waves influence their impact on health? A 6-year morbidity and mortality study in Madrid (Spain)

BACKGROUND

In Spain, two synoptic-scale conditions influence heat wave formation. The first involves advection of warm and dry air masses carrying dust of Saharan origin (North African Dust (NAF) = 1). The second entails anticyclonic stagnation with high insolation and stability (NAF) = 0). Some studies show that the meteorological origin of these heat waves may affect their impact on morbidity and mortality.

OBJECTIVE

To determine whether the impact of heat waves on health outcomes in Madrid (Spain) during 2013-2018 varied by synoptic-scale condition.

METHODOLOGY

Outcome data consist of daily mortality and daily hospital emergency admissions (morbidity) for natural, circulatory, and respiratory causes. Predictors include daily maximum and minimum temperatures and daily mean concentrations of NO₂, PM₁₀, PM_2.5, NO₂, and O₃. Analyses adjust for insolation, relative humidity, and wind speed. Generalized linear models were performed with Poisson link between the variables controlling for trend, seasonality, and auto-regression in the series. Relative Risks (RR) and Attributable Risks (AR) were determined. The RRs for mortality attributable to high temperatures were similar regardless of NAF status. For hospital admissions, however, the RRs for hot days with NAF = 0 are higher than for days with NAF = 1. We also found that atmospheric pollutants worsen morbidity and mortality, especially PM₁₀ concentrations when NAF = 1 and O₃ concentrations when NAF = 0.

RESULTS

The effect of heat waves on morbidity and mortality depends on the synoptic situation. The impact is greater under anticyclonic stagnation conditions than under Saharan dust advection. Further, the health impact of pollutants such as PM₁₀ and O₃ varies according to the synoptic situation.

CONCLUSIONS

Based on these findings, we strongly recommend prevention plans to include data on the meteorological situation originating the heat wave, on a synoptic-scale, as well as comprehensive preventive measures against the compounding effect of high temperatures and pollution.

Systematic identification of heat events associated with emergency admissions to enhance the heat-health action plan in a subtropical city: a data-driven approach

According to the United Nations Office for Disaster Risk Reduction (UNDRR), a heat-health action plan should address various impacts of hazards at different levels, including an early warning system to monitor risks and behaviour enhancement to increase disaster preparedness. It is necessary to comply with guidelines regarding heat duration/intensity. In this study, we developed a data-driven approach to rapidly and systematically estimate the impacts of various heat events on emergency admissions among the adult population (n = 7,086,966) in Hong Kong in order to enhance the heat-health action plan. Immediate, short-term, and long-term impacts determined by 1-day, 4-day, and 8-day windows were estimated to identify specific heat events suitable for early warnings. In addition, underestimated risk, determined by a continuous increase in heat risk after days without significant emergency admissions, was estimated to evaluate potential maladaptive behaviours among a specific subpopulation. Based on age- and gender-specific analyses, 1D, 1D1N, and 2D2N were observed to have a stronger immediate impact on emergency admissions. 1D1N and 2D2N also showed notable short-term and long-term impacts. Based on heat vulnerability factors (age and gender), 2D2N was a higher-priority extreme heat event for early warning measures than 1D1N. Furthermore, men aged 19 to 64 had the highest underestimated risk. Specifically, they had IRR values of 1.113 [1.087, 1.140], 1.061 [1.035, 1.087], and 1.069 [1.043, 1.095] during lag days 3-5 of 3D2N, respectively, possibly due to a lack of adaptive behaviour. By adopting our approach, the duration of heat events with significant health impacts can be identified in order to further enhance relevant heat stress information. This framework can be applied to other cities with a similar background for rapid assessment.

Impact of environmental factors on heat-associated mortalities in an urban desert region

The troubling trend of rising heat-associated mortalities in an urban desert region (Maricopa County, AZ, USA) has motivated us to explore the extent to which environmental factors may contribute to increased heat-health risks. Summertime data from 2010 to 2019 were used to construct a suite of models for daily heat-associated mortalities. The best-performing full model included the following predictors, ordered from strongest to weakest influence: daily average air temperature, average of previous 5 days daily average air temperature, year, day of year, average of previous 5 days daily average dew point temperature, average of previous 5 days daily average PM_2.5, and daily average PM₁₀. This full model exhibited a 5.39% reduction in mean absolute error in daily heat-associated mortalities as compared to the best-performing model that included only air temperature as an environmental predictor. The extent to which issued and modeled excessive heat warnings (from both the temperature only and full models) corresponded with heat-associated mortalities was also examined. Model hindcasts for 2020 and 2021 showed that the models were able to capture the high number of heat-associated mortalities in 2020, but greatly undercounted the highest yet observed number of heat-associated mortalities in 2021. Results from this study lend insights into environmental factors corresponding to an increased number of heat-associated mortalities and can be used for informing strategies towards reducing heat-health risks. However, as the best-performing model was unable to fully capture the observed number of heat-associated mortalities, continued scrutiny of both environmental and non-environmental factors affecting these observations is needed.

A self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature

Heat stroke that may cause acute central nervous system dysfunction, multiple organ dysfunction and even death has become a typical health problem in tropical developing countries. The primary goal of heat stroke treatment is to lower core body temperature, which necessitates physical or medical cooling in time. Here, we design a new self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature. This system can monitor body temperature in real time and transmit neural electrical stimulation signals into specific brain regions to lower the body temperature. The whole system can work without an external power supply and be powered by the body itself through the piezoelectric effect. The system comprises a temperature detecting unit, a power supply unit, a data processing module, and a brain stimulator. Demonstration of the system with stimulation electrodes implanted in the median preoptic nucleus brain region in mice reveals an evident decrease in body temperature (1.0 °C within 15 min). This self-powered strategy provides a new concept for future treatment of heat stroke and can extend the application of brain-machine-interface systems in medical care.

Evaluating the Sensitivity of Heat Wave Definitions among North Carolina Physiographic Regions

Exposure to extreme heat is a known risk factor that is associated with increased heat-related illness (HRI) outcomes. The relevance of heat wave definitions (HWDs) could change across health conditions and geographies due to the heterogenous climate profile. This study compared the sensitivity of 28 HWDs associated with HRI emergency department visits over five summer seasons (2011−2016), stratified by two physiographic regions (Coastal and Piedmont) in North Carolina. The HRI rate ratios associated with heat waves were estimated using the generalized linear regression framework assuming a negative binomial distribution. We compared the Akaike Information Criterion (AIC) values across the HWDs to identify an optimal HWD. In the Coastal region, HWDs based on daily maximum temperature with a threshold > 90th percentile for two or more consecutive days had the optimal model fit. In the Piedmont region, HWD based on the daily minimum temperature with a threshold value > 90th percentile for two or more consecutive days was optimal. The HWDs with optimal model performance included in this study captured moderate and frequent heat episodes compared to the National Weather Service (NWS) heat products. This study compared the HRI morbidity risk associated with epidemiologic-based HWDs and with NWS heat products. Our findings could be used for public health education and suggest recalibrating NWS heat products.

Internet searches and heat-related emergency department visits in the United States

Emerging research suggests that internet search patterns may provide timely, actionable insights into adverse health impacts from, and behavioral responses to, days of extreme heat, but few studies have evaluated this hypothesis, and none have done so across the United States. We used two-stage distributed lag nonlinear models to quantify the interrelationships between daily maximum ambient temperature, internet search activity as measured by Google Trends, and heat-related emergency department (ED) visits among adults with commercial health insurance in 30 US metropolitan areas during the warm seasons (May to September) from 2016 to 2019. Maximum daily temperature was positively associated with internet searches relevant to heat, and searches were in turn positively associated with heat-related ED visits. Moreover, models combining internet search activity and temperature had better predictive ability for heat-related ED visits compared to models with temperature alone. These results suggest that internet search patterns may be useful as a leading indicator of heat-related illness or stress.

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.

Evolving heat waves characteristics challenge heat warning systems and prevention plans

This paper analyses how recent trends in heat waves impact heat warning systems. We performed a retrospective analysis of the challenges faced by the French heat prevention plan since 2004. We described trends based on the environmental and health data collected each summer by the French heat warning system and prevention plan. Major evolutions of the system were tracked based on the evaluations organized each autumn with the stakeholders of the prevention plan. Excess deaths numbering 8000 were observed during heat waves between 2004 and 2019, 71% of these between 2015 and 2019. We observed major changes in the characteristics, frequency and the geographical spread of heat waves since 2015. Feedbacks led to several updates of the warning system such as the extension of the surveillance period. They also revealed that risk perception remained limited among the population and the stakeholders. The sharp increase in the number of heat warnings issued per year since 2015 challenges the acceptability of the heat warnings. Recent heat waves without historical equivalent interfere with the development of evidence-based prevention strategies. The growing public health impacts heat waves emphasize the urgent need to act to adapt the population, at different levels of intervention, from individual comportments to structural modifications. A specific attention should be given to increase the resources allocated to the evaluation and the management of heat-related risks, especially considering the needs to catch with the rapid rhythm of the changing climate.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure

BACKGROUND

Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health.

OBJECTIVES

This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California.

METHODS

SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status.

RESULTS

The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities.

CONCLUSION

Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

A heat-health watch and warning system with extended season and evolving thresholds

BACKGROUND

Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere.

METHODS

The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality.

RESULTS

We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season.

CONCLUSIONS

This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July-August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

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.

Effect of extreme hot and cold weather on cause-specific hospitalizations in Sweden: A time series analysis

Considering that several meteorological variables can contribute to weather vulnerability, the estimation of their synergetic effects on health is particularly useful. The spatial synoptic classification (SSC) has been used in biometeorological applications to estimate the effect of the entire suite of weather conditions on human morbidity and mortality. In this study, we assessed the relationships between extremely hot and dry (dry tropical plus, DT+) and hot and moist (moist tropical plus, MT+) weather types in summer and extremely cold and dry (dry polar plus, DP+) and cold and moist (moist polar, MP+) weather types in winter and cardiovascular and respiratory hospitalizations by age and sex. Time-series quasi-Poisson regression with distributed lags was used to assess the relationship between oppressive weather types and daily hospitalizations over 14 subsequent days in the extended summer (May to August) and 28 subsequent days during the extended winter (November to March) over 24 years in 4 Swedish locations from 1991 to 2014. In summer, exposure to hot weather types appeared to reduce cardiovascular hospitalizations while increased the risk of hospitalizations for respiratory diseases, mainly related to MT+. In winter, the effect of cold weather on both cause-specific hospitalizations was small; however, MP+ was related to a delayed increase in cardiovascular hospitalizations, whilst MP+ and DP + increased the risk of hospitalizations due to respiratory diseases. This study provides useful information for the staff of hospitals and elderly care centers who can help to implement protective measures for patients and residents. Also, our results could be helpful for vulnerable people who can adopt protective measures to reduce health risks.

Review of Biometeorology of Heatwaves and Warm Extremes in Europe

Numerous extreme heatwaves producing large impacts on human health, agriculture, water resources, energy demand, regional economies, and forest ecosystems occurred during the first twenty years of the 21st century. The present study strives to provide a systematic review of recent studies of warm biometeorological extremes in Europe. The main aim of this paper is to provide a methodical summary of the observed changes in warm extremes, duration, and variability in different parts of Europe. During the last decade, much attention has been paid to the negative impacts of heat and humidity on human health. Therefore, the human biometeorology is required to appraise the human thermal environment in a way that human thermoregulation is taken into account. In many European countries and regions, future heat exposure will indeed exceed critical levels, and a steep increase in biometeorological heatwaves and warm extremes are expected. The indices that take into account human energy balance along with weather conditions should be used to examine the impacts of extreme heatwaves on human health and should be used as a basis for the determination of acclimatization to high-heat-stress conditions. A detailed description of recent studies that have used biometeorological indices such as Physiological Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) for the estimation of warm extremes and their influence on human health is provided. Additionally, a short overview of the existence of the heat-health warning systems (HHWS), their conceptualization, and implementation across the European continent is considered, as well as the possibilities for further investigations and implementation of effective measures and programs that could reduce the adverse health impacts.

Hot and cold weather based on the spatial synoptic classification and cause-specific mortality in Sweden: a time-stratified case-crossover study

The spatial synoptic classification (SSC) is a holistic categorical assessment of the daily weather conditions at specific locations; it is a useful tool for assessing weather effects on health. In this study, we assessed (a) the effect of hot weather types and the duration of heat events on cardiovascular and respiratory mortality in summer and (b) the effect of cold weather types and the duration of cold events on cardiovascular and respiratory mortality in winter. A time-stratified case-crossover design combined with a distributed lag nonlinear model was carried out to investigate the association of weather types with cause-specific mortality in two southern (Skåne and Stockholm) and two northern (Jämtland and Västerbotten) locations in Sweden. During summer, in the southern locations, the Moist Tropical (MT) and Dry Tropical (DT) weather types increased cardiovascular and respiratory mortality at shorter lags; both hot weather types substantially increased respiratory mortality mainly in Skåne. The impact of heat events on mortality by cardiovascular and respiratory diseases was more important in the southern than in the northern locations at lag 0. The cumulative effect of MT, DT and heat events lagged over 14 days was particularly high for respiratory mortality in all locations except in Jämtland, though these did not show a clear effect on cardiovascular mortality. During winter, the dry polar and moist polar weather types and cold events showed a negligible effect on cardiovascular and respiratory mortality. This study provides valuable information about the relationship between hot oppressive weather types with cause-specific mortality; however, the cold weather types may not capture sufficiently effects on cause-specific mortality in this sub-Arctic region.

High ambient temperature in summer and risk of stroke or transient ischemic attack: A national study in Israel

OBJECTIVE

To examine whether high ambient temperature and diurnal temperature range during the summer are associated with risk of stroke/transient ischemic attack (TIA).

METHODS

A time-stratified case-crossover study design was conducted. The study sample comprised all individuals aged ≥50 years who had a stroke/TIA reported to the Israeli National Stroke Registry between 2014 and 2016 during the summer season. Daily temperature data were retrieved from the Israel Meteorological Service. Conditional logistic regression models were used with relative humidity and air pollution as covariates.

RESULTS

The sample included 15,123 individuals who had a stroke/TIA during the summer season (mean age 73 ± 12 years; 54% males). High ambient temperature was associated with stroke/TIA risk starting from the day before the stroke event, and increasing in strength over a six-day lag (OR = 1.10 95%CI 1.09-1.12). Moreover, a larger diurnal temperature range prior to stroke/TIA occurrence was associated with decreased stroke/TIA risk (OR = 0.96 95%CI 0.95-0.97 for a six-day lag).

CONCLUSIONS

High ambient temperature may be linked to increased risk of cerebrovascular events in subsequent days. However, relief from the heat during the night may attenuate this risk.

Assessing the use and understanding of the Portuguese heat-health warning system (ÍCARO)

BACKGROUND

Heatwaves can lead to increased mortality. In the Portuguese heat-health warning system (HHWS), ÍCARO, a daily report with heat-related mortality prediction is sent to heat-health action plan (HHAP) practitioners. HHAP practitioners assess risk and implement measures to prevent heatwave-related impact, but ÍCARO's use and understanding are unknown. We assessed ÍCARO's use and understanding by key HHAP practitioners.

METHODS

We conducted semi-structured interviews with national/regional HHAP practitioners. Interviews were recorded, transcribed and analysed using thematic content analysis. To maximize credibility a validation process was implemented through researcher triangulation; a sample of 30 segments was recorded by independent researchers.

RESULTS

We conducted six interviews with nine professionals (mean time 52 min) from five regions. We identified four categories: report's content and presentation, report's reception and communication, ÍCARO and risk assessment and other issues. Practitioners use ÍCARO and perceived it as relevant; they raised issues on its interpretation and felt these were not fully addressed, given researchers' use of statistical/epidemiological terms. We identified the need for improved communication and report's clarity.

CONCLUSIONS

Our study stresses the need for collaboration between experts within HHWS/HHAP. Despite ÍCARO's understanding being challenging, practitioners consider it a relevant tool. Researchers should use less statistical language and clarify ÍCARO's interpretation. Practitioners' needs should be considered when developing/revising tools.

Extreme heat episodes and risk of preterm birth in California, 2005-2013

BACKGROUND

Preterm birth is a leading cause of infant morbidity and mortality. Identifying potentially modifiable triggers toward the end of gestation, such as extreme heat, can improve understanding of the role of acute stress on early deliveries and inform warning systems. In this study we examined the association between extreme heat, variously defined during the last week of gestation, and risk of preterm birth among mothers in California.

METHODS

We created a population-based cohort comprised of 1,967,300 mothers who had live, singleton births in California, from May through September 2005-2013. Daily temperature data estimated at the maternal zip code of residence was used to create 12 definitions of extreme heat with varying relative temperatures (75th, 90th, 95th, and 98th percentiles) and durations (at least 2, 3, or 4 consecutive days). We estimated risk of preterm birth (<37 gestational weeks) in relation to exposure to extreme heat during the last week of gestation with multi-level Cox proportional hazard regression models, adjusting for maternal characteristics, sex of neonate, and seasonality. We also included randomly generated data, SAS code, and estimates for reproducibility purposes.

RESULTS

Approximately 7% of the cohort had a preterm birth. For all definitions of extreme heat, the risk of preterm birth was consistently higher among mothers who experienced an extreme heat episode during their last week of gestation. Hazard ratios ranged from 1.008 (95% CI: 0.997, 1.021) to 1.128 (95% CI: 1.052, 1.210), with increasing associations as the relative temperature and duration of extreme heat episode increased.

CONCLUSION

This study adds to the previous literature by considering multiple definitions of extreme heat and applying a time-to-event framework. Findings suggest that acute exposure to extreme heat during the last week of gestation may trigger an earlier delivery. Implementing heat warning systems targeted toward pregnant women may improve birth outcomes.

Using wearable sensors to assess how a heatwave affects individual heat exposure, perceptions, and adaption methods

Urban areas are typically warmer than nearby rural areas, especially during hot weather. This increases heat exposure, morbidity, and mortality rates of urban residents. Heat adaption methods can improve public safety during heat events, but the availability and usage of these resources vary based on socioeconomic and demographic characteristics, as well as personal perception of warmth. Heat events are often studied using city- and neighborhood-level meteorological and socioeconomic data, which do not reflect individual exposure or access to and use of heat adaption resources. We collected lifestyle surveys and individually experienced temperature and humidity data for 38 Knoxville, Tennessee, residents during a heatwave and a period of climatically normal summer conditions. Participants were less exposed to heat during the daytime than airport conditions suggest, indicating successful use of heat adaption methods, such as staying indoors. Some participants were warmer at night and during the non-heatwave period. Heat inequality is especially problematic at night, with older, less educated, and lower-income individuals being more exposed to heat. Even when exposed to dangerous heat levels, participants were less likely to take adaption actions to protect themselves from heat-health effects during the non-heatwave period and at night because they do not perceive themselves as being at risk or have the resources to do so. These findings signal the need for improved heat education, as future climate projections indicate an increase not only in heatwaves but also mean temperature and humidity during the warm season, and especially warmer temperatures at night.

Heat waves in South Korea: differences of heat wave characteristics by thermal indices

Heat wave warning systems and related research define heat waves using various indices and there exists no standard definition for a heat wave. Despite various weather forecast services for heat stress in South Korea, it is unclear how different thermal indices affect the designation of heat waves and health effect estimates. We aimed to analyze trends of heat wave characteristics and mortality associations using various criteria for the warm season (June-September) in 2011-5 for the most populated two cities in South Korea, Seoul and Busan. Hourly weather monitoring data and daily mortality data in each city were obtained. The following indices were estimated to define heat waves: air temperature, heat index (HI), humidex, apparent temperature (AT), effective temperature (ET), and wet-bulb globe temperature (WBGT). The thresholds of each index for heat wave definitions were obtained by statistical distribution (95th percentiles) and minimum mortality temperature (MMT). Thermal indices showed clustering of accumulation of excess heat above thresholds for northeast regions in the cities while air temperature showed it for central regions. Compared to 95th percentiles, the MMTs resulted 14 times longer heat wave days for thermal indices except for air temperature. When MMTs were used, nine times larger excess mortality from heat waves occurred for all indices compared to that from heat waves defined by the 95th percentiles. The thermal indices with the highest association between heat and mortality varied between the two cities: air temperature for Seoul and WBGT for Busan. Heat wave warnings should be based on a thorough comparison of how different heat wave criteria will affect the public health impact of heat wave warnings in terms of identifying a heat wave and degree of health impacts due to it.

The Role of Humidity in Associations of High Temperature with Mortality: A Multicountry, Multicity Study

BACKGROUND

There is strong experimental evidence that physiologic stress from high temperatures is greater if humidity is higher. However, heat indices developed to allow for this have not consistently predicted mortality better than dry-bulb temperature.

OBJECTIVES

We aimed to clarify the potential contribution of humidity an addition to temperature in predicting daily mortality in summer by using a large multicountry dataset.

METHODS

In 445 cities in 24 countries, we fit a time-series regression model for summer mortality with a distributed lag nonlinear model (DLNM) for temperature (up to lag 3) and supplemented this with a range of terms for relative humidity (RH) and its interaction with temperature. City-specific associations were summarized using meta-analytic techniques.

RESULTS

Adding a linear term for RH to the temperature term improved fit slightly, with an increase of 23% in RH (the 99th percentile anomaly) associated with a 1.1% [95% confidence interval (CI): 0.8, 1.3] decrease in mortality. Allowing curvature in the RH term or adding terms for interaction of RH with temperature did not improve the model fit. The humidity-related decreased risk was made up of a positive coefficient at lag 0 outweighed by negative coefficients at lags of 1-3 d. Key results were broadly robust to small model changes and replacing RH with absolute measures of humidity. Replacing temperature with apparent temperature, a metric combining humidity and temperature, reduced goodness of fit slightly.

DISCUSSION

The absence of a positive association of humidity with mortality in summer in this large multinational study is counter to expectations from physiologic studies, though consistent with previous epidemiologic studies finding little evidence for improved prediction by heat indices. The result that there was a small negative average association of humidity with mortality should be interpreted cautiously; the lag structure has unclear interpretation and suggests the need for future work to clarify. https://doi.org/10.1289/EHP5430.

Determining the correlation between outdoor heatstroke incidence and climate elements in Daegu metropolitan city

Background

Heatstroke is one of the most serious heat-related illnesses. However, establishing public policies to prevent heatstroke remains a challenge. This study aimed to investigate the most relevant climate elements and their warning criteria to prevent outdoor heatstroke (OHS).

Methods

We investigated heatstroke patients from five major hospitals in Daegu metropolitan city, Korea, from June 1 to August 31, 2011 to 2016. We also collected the corresponding regional climate data from Korea Meteorological Administration. We analyzed the relationship between the climate elements and OHS occurrence by logistic regression.

Results

Of 70 patients who had heatstroke, 45 (64.3%) experienced it while outdoors. Considering all climate elements, only mean heat index (MHI) was related with OHS occurrence (p=0.019). Therefore, the higher the MHI, the higher the risk for OHS (adjusted odds ratio, 1.824; 95% confidence interval, 1.102-3.017). The most suitable cutoff point for MHI by Youden's index was 30.0°C (sensitivity, 77.4%; specificity, 73.7%).

Conclusion

Among the climate elements, MHI was significantly associated with OHS occurrence. The optimal MHI cutoff point for OHS prevention was 30.0°C.

Tens of thousands additional deaths annually in cities of China between 1.5 °C and 2.0 °C warming

The increase in surface air temperature in China has been faster than the global rate, and more high temperature spells are expected to occur in future. Here we assess the annual heat-related mortality in densely populated cities of China at 1.5 °C and 2.0 °C global warming. For this, the urban population is projected under five SSPs, and 31 GCM runs as well as temperature-mortality relation curves are applied. The annual heat-related mortality is projected to increase from 32.1 per million inhabitants annually in 1986–2005 to 48.8–67.1 per million for the 1.5 °C warming and to 59.2–81.3 per million for the 2.0 °C warming, taking improved adaptation capacity into account. Without improved adaptation capacity, heat-related mortality will increase even stronger. If all 831 million urban inhabitants in China are considered, the additional warming from 1.5 °C to 2 °C will lead to more than 27.9 thousand additional heat-related deaths, annually.

Heatwaves are expected to increase under climate change, and so are the associated deaths. Here the authors determine the regional high temperature thresholds for 27 metropolises in China and analyze the changes to heat-related mortality, showing that the additional global-warming temperature increase of 0.5°C, from 1.5°C to 2.0°C, will lead to tens of thousands of additional deaths, annually.

Patterns of outdoor exposure to heat in three South Asian cities

Low socio-economic status has been widely recognized as a significant factor in enhancing a person's vulnerability to climate change including vulnerability to changes in temperature. Yet, little is known about exposure to heat within cities in developing countries, and even less about exposure within informal neighbourhoods in those countries. This paper presents an assessment of exposure to outdoor heat in the South Asian cities Delhi, Dhaka, and Faisalabad. The temporal evolution of exposure to heat is evaluated, as well as intra-urban differences, using meteorological measurements from mobile and stationary devices (April-September 2016). Exposure to heat is compared between low-income and other neighbourhoods in these cities. Results are expressed in terms of air temperature and in terms of the thermal indices Heat Index (HI), Wet Bulb Globe Temperature (WBGT) and Universal Thermal Climate Index (UTCI) at walking level. Conditions classified as dangerous to very dangerous, and likely to impede productivity, are observed almost every day of the measurement period during daytime, even when air temperature drops after the onset of the monsoon. It is recommended to cast heat warnings in terms of thermal indices instead of just temperature. Our results nuance the idea that people living in informal neighbourhoods are consistently more exposed to heat than people living in more prosperous neighbourhoods. During night-time, exposure does tend to be enhanced in densely-built informal neighbourhoods, but not if the low-income neighbourhoods are more open, or if they are embedded in green/blue areas.

Toward an Improved Air Pollution Warning System in Quebec

The nature of pollutants involved in smog episodes can vary significantly in various cities and contexts and will impact local populations differently due to actual exposure and pre-existing sensitivities for cardiovascular or respiratory diseases. While regulated standards and guidance remain important, it is relevant for cities to have local warning systems related to air pollution. The present paper proposes indicators and thresholds for an air pollution warning system in the metropolitan areas of Montreal and Quebec City (Canada). It takes into account past and current local health impacts to launch its public health warnings for short-term episodes. This warning system considers fine particulate matter (PM_2.5) as well as the combined oxidant capacity of ozone and nitrogen dioxide (O_x) as environmental exposures. The methodology used to determine indicators and thresholds consists in identifying extreme excess mortality episodes in the data and then choosing the indicators and thresholds to optimize the detection of these episodes. The thresholds found for the summer were 31 μg/m³ for PM_2.5 and 43 ppb for O_x in Montreal, and 32 μg/m³ and 23 ppb in Quebec City. In winter, thresholds found were 25 μg/m³ and 26 ppb in Montreal, and 33 μg/m³ and 21 ppb in Quebec City. These results are in line with different guidelines existing concerning air quality, but more adapted to the cities examined. In addition, a sensitivity analysis is conducted which suggests that O_x is more determinant than PM_2.5 in detecting excess mortality episodes.

Exploring the mechanisms of heat wave vulnerability at the urban scale based on the application of big data and artificial societies

Rapid urbanisation has altered the vulnerability of urban areas to heat wave disasters. There is an urgent need to identify the factors underlying the effect of heat waves on human health and the areas that are most vulnerable to heat waves. In this study, we plan to integrate indices associated with heat wave vulnerability based on meteorological observation data, remote sensing data and point of interest (POI) data; analyse the influence of urbanisation on the urban vulnerability environment; and explore the relationship between the vulnerability environment and heat-wave-related mortality. Finally, we attempt to map the spatial distribution of high heat-wave-related mortality risk based on the results of heat wave vulnerability study and artificial society. The results reveal that 1) there are differences in the influence of urbanisation on heat wave exposure, sensitivity and adaptability; 2) the exposure and sensitivity level effects on the lower limit of health impacts and the adaptability level effects on the upper limit of the health impact from heat wave in a given study area; and 3) areas vulnerable to the effects of heat waves are not confined to the city centre, which implies that residents living in suburban areas are also vulnerable to heat waves. Finally, this study not only explores the factors contributing to the impacts of heat waves but also describes the spatial distribution of the risk of disaster-associated mortality, thereby providing direct scientific guidance that can be used by cities to address heat wave disasters in the future.

Association between Weather Types based on the Spatial Synoptic Classification and All-Cause Mortality in Sweden, 1991⁻2014

Much is known about the adverse health impact of high and low temperatures. The Spatial Synoptic Classification is a useful tool for assessing weather effects on health because it considers the combined effect of meteorological factors rather than temperature only. The aim of this study was to assess the association between oppressive weather types and daily total mortality in Sweden. Time-series Poisson regression with distributed lags was used to assess the relationship between oppressive weather (Dry Polar, Dry Tropical, Moist Polar, and Moist Tropical) and daily deaths over 14 days in the extended summer (May to September), and 28 days during the extended winter (November to March), from 1991 to 2014. Days not classified as oppressive weather served as the reference category. We computed relative risks with 95% confidence intervals, adjusting for trends and seasonality. Results of the southern (Skåne and Stockholm) and northern (Jämtland and Västerbotten) locations were pooled using meta-analysis for regional-level estimates. Analyses were performed using the dlnm and mvmeta packages in R. During summer, in the South, the Moist Tropical and Dry Tropical weather types increased the mortality at lag 0 through lag 3 and lag 6, respectively. Moist Polar weather was associated with mortality at longer lags. In the North, Dry Tropical weather increased the mortality at shorter lags. During winter, in the South, Dry Polar and Moist Polar weather increased mortality from lag 6 to lag 10 and from lag 19 to lag 26, respectively. No effect of oppressive weather was found in the North. The effect of oppressive weather types in Sweden varies across seasons and regions. In the North, a small study sample reduces precision of estimates, while in the South, the effect of oppressive weather types is more evident in both seasons.

Estimating policy-relevant health effects of ambient heat exposures using spatially contiguous reanalysis data

BACKGROUND

Regional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes.

METHODS

We assessed the effect of multi-day lag exposure for maximum near-surface air temperature (T_max) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression.

RESULTS

We observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in T_max on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria.

CONCLUSION

Reanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.

The predictability of heat-related mortality in Prague, Czech Republic, during summer 2015-a comparison of selected thermal indices

We compared selected thermal indices in their ability to predict heat-related mortality in Prague, Czech Republic, during the extraordinary summer 2015. Relatively, novel thermal indices-Universal Thermal Climate Index and Excess Heat Factor (EHF)-were compared with more traditional ones (apparent temperature, simplified wet-bulb globe temperature (WBGT), and physiologically equivalent temperature). The relationships between thermal indices and all-cause relative mortality deviations from the baseline (excess mortality) were estimated by generalized additive models for the extended summer season (May-September) during 1994-2014. The resulting models were applied to predict excess mortality in 2015 based on observed meteorology, and the mortality estimates by different indices were compared. Although all predictors showed a clear association between thermal conditions and excess mortality, we found important variability in their performance. The EHF formula performed best in estimating the intensity of heat waves and magnitude of heat-impacts on excess mortality on the most extreme days. Afternoon WBGT, on the other hand, was most precise in the selection of heat-alert days during the extended summer season, mainly due to a relatively small number of "false alerts" compared to other predictors. Since the main purpose of heat warning systems is identification of days with an increased risk of heat-related death rather than prediction of exact magnitude of the excess mortality, WBGT seemed to be a slightly favorable predictor for such a system.

Comparison of health risks by heat wave definition: Applicability of wet-bulb globe temperature for heat wave criteria

Despite the active applications of thermal comfort indices for heat wave definitions, there is lack of evaluation for the impact of extended days of high temperature on health outcomes using many of the indices. This study compared the impact of heat waves on health outcomes among different heat wave definitions based on thermal comfort and air temperature. We compared heat waves in South Korea (cities and provinces) for the warm season for 2011-2014, using air temperature, heat index (HI), and web-bulb globe temperature (WBGT). Heat waves were defined as days with daily maximum values of each index at a specified threshold (literature-based, the 90th and 95th percentiles) or above. Distributed lag non-linear models and meta-analysis were used to estimate risk of mortality and hospitalization for all-causes, cardiovascular causes, respiratory causes and heat disorders during heat wave days compared to non-heat wave days. WBGT identified 1.15 times longer maximum heat wave duration for the study periods than air temperature when the thresholds were based on 90th and 95th percentiles. Over the study period, for heat waves defined by WBGT and HI, the Southwestern region showed the highest total number of heat wave days, whereas for air temperature the longest heat wave days were identified in the southeastern region. The highest and most significant impact of heat waves were found by WBGT for hospitalization from heat disorders (Relative risk = 2.959, 95% CI: 1.566-5.594). In sensitivity analyses using different structure of lags and temperature metrics (e.g., daily mean and minimum), the impacts of heat waves on most health outcomes substantially increased by using WBGT for heat wave definitions. As a result, WBGT and its thresholds can be used to relate heat waves and heat-related diseases to improve the prevention effectiveness of heat wave warnings and give informative health guidelines according to the range of WBGT thresholds.

Application of spatial synoptic classification in evaluating links between heat stress and cardiovascular mortality and morbidity in Prague, Czech Republic

Spatial synoptic classification (SSC) is here first employed in assessing heat-related mortality and morbidity in Central Europe. It is applied for examining links between weather patterns and cardiovascular (CVD) mortality and morbidity in an extended summer season (16 May-15 September) during 1994-2009. As in previous studies, two SSC air masses (AMs)-dry tropical (DT) and moist tropical (MT)-are associated with significant excess CVD mortality in Prague, while effects on CVD hospital admissions are small and insignificant. Excess mortality for ischaemic heart diseases is more strongly associated with DT, while MT has adverse effect especially on cerebrovascular mortality. Links between the oppressive AMs and excess mortality relate also to conditions on previous days, as DT and MT occur in typical sequences. The highest CVD mortality deviations are found 1 day after a hot spell's onset, when temperature as well as frequency of the oppressive AMs are highest. Following this peak is typically DT- to MT-like weather transition, characterized by decrease in temperature and increase in humidity. The transition between upward (DT) and downward (MT) phases is associated with the largest excess CVD mortality, and the change contributes to the increased and more lagged effects on cerebrovascular mortality. The study highlights the importance of critically evaluating SSC's applicability and benefits within warning systems relative to other synoptic and epidemiological approaches. Only a subset of days with the oppressive AMs is associated with excess mortality, and regression models accounting for possible meteorological and other factors explain little of the mortality variance.

The use of climate information to estimate future mortality from high ambient temperature: A systematic literature review

BACKGROUND AND OBJECTIVES

Heat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research.

METHODS

The electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis.

RESULTS

A total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised.

CONCLUSIONS

Most studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality.

Delineation of Spatial Variability in the Temperature-Mortality Relationship on Extremely Hot Days in Greater Vancouver, Canada

BACKGROUND

Climate change has increased the frequency and intensity of extremely hot weather. The health risks associated with extemely hot weather are not uniform across affected areas owing to variability in heat exposure and social vulnerability, but these differences are challenging to map with precision.

OBJECTIVES

We developed a spatially and temporally stratified case-crossover approach for delineation of areas with higher and lower risks of mortality on extremely hot days and applied this approach in greater Vancouver, Canada.

METHODS

Records of all deaths with an extremely hot day as a case day or a control day were extracted from an administrative vital statistics database spanning the years of 1998-2014. Three heat exposure and 11 social vulnerability variables were assigned at the residential location of each decedent. Conditional logistic regression was used to estimate the odds ratio for a 1°C increase in daily mean temperature at a fixed site with an interaction term for decedents living above and below different values of the spatial variables.

RESULTS

The heat exposure and social vulnerability variables with the strongest spatially stratified results were the apparent temperature and the labor nonparticipation rate, respectively. Areas at higher risk had values ≥ 34.4°C for the maximum apparent temperature and ≥ 60% of the population neither employed nor looking for work. These variables were combined in a composite index to quantify their interaction and to enhance visualization of high-risk areas.

CONCLUSIONS

Our methods provide a data-driven framework for spatial delineation of the temperature--mortality relationship by heat exposure and social vulnerability. The results can be used to map and target the most vulnerable areas for public health intervention. Citation: Ho HC, Knudby A, Walker BB, Henderson SB. 2017. Delineation of spatial variability in the temperature-mortality relationship on extremely hot days in greater Vancouver, Canada. Environ Health Perspect 125:66-75; http://dx.doi.org/10.1289/EHP224.

Impact of Extreme Heat Events on Emergency Department Visits in North Carolina (2007-2011)

Extreme heat is the leading cause of weather-related mortality in the U.S. Extreme heat also affects human health through heat stress and can exacerbate underlying medical conditions that lead to increased morbidity and mortality. In this study, data on emergency department (ED) visits for heat-related illness (HRI) and other selected diseases were analyzed during three heat events across North Carolina from 2007 to 2011. These heat events were identified based on the issuance and verification of heat products from local National Weather Service forecast offices (i.e. Heat Advisory, Heat Watch, and Excessive Heat Warning). The observed number of ED visits during these events were compared to the expected number of ED visits during several control periods to determine excess morbidity resulting from extreme heat. All recorded diagnoses were analyzed for each ED visit, thereby providing insight into the specific pathophysiological mechanisms and underlying health conditions associated with exposure to extreme heat. The most common form of HRI was heat exhaustion, while the percentage of visits with heat stroke was relatively low (<10%). The elderly (>65 years of age) were at greatest risk for HRI during the early summer heat event (8.9 visits per 100,000), while young and middle age adults (18-44 years of age) were at greatest risk during the mid-summer event (6.3 visits per 100,000). Many of these visits were likely due to work-related exposure. The most vulnerable demographic during the late summer heat event was adolescents (15-17 years of age), which may relate to the timing of organized sports. This demographic also exhibited the highest visit rate for HRI among all three heat events (10.5 visits per 100,000). Significant increases (p < 0.05) in visits with cardiovascular and cerebrovascular diseases were noted during the three heat events (3-8%). The greatest increases were found in visits with hypotension during the late summer event (23%) and sequelae during the early summer event (30%), while decreases were noted for visits with hemorrhagic stroke during the middle and late summer events (13-24%) and for visits with aneurysm during the early summer event (15%). Significant increases were also noted in visits with respiratory diseases (5-7%). The greatest increases in this category were found in visits with pneumonia and influenza (16%), bronchitis and emphysema (12%), and COPD (14%) during the early summer event. Significant increases in visits with nervous system disorders were also found during the early summer event (16%), while increases in visits with diabetes were noted during the mid-summer event (10%).

Susceptibility to Heat-Related Fluid and Electrolyte Imbalance Emergency Department Visits in Atlanta, Georgia, USA

Identification of populations susceptible to heat effects is critical for targeted prevention and more accurate risk assessment. Fluid and electrolyte imbalance (FEI) may provide an objective indicator of heat morbidity. Data on daily ambient temperature and FEI emergency department (ED) visits were collected in Atlanta, Georgia, USA during 1993-2012. Associations of warm-season same-day temperatures and FEI ED visits were estimated using Poisson generalized linear models. Analyses explored associations between FEI ED visits and various temperature metrics (maximum, minimum, average, and diurnal change in ambient temperature, apparent temperature, and heat index) modeled using linear, quadratic, and cubic terms to allow for non-linear associations. Effect modification by potential determinants of heat susceptibility (sex; race; comorbid congestive heart failure, kidney disease, and diabetes; and neighborhood poverty and education levels) was assessed via stratification. Higher warm-season ambient temperature was significantly associated with FEI ED visits, regardless of temperature metric used. Stratified analyses suggested heat-related risks for all populations, but particularly for males. This work highlights the utility of FEI as an indicator of heat morbidity, the health threat posed by warm-season temperatures, and the importance of considering susceptible populations in heat-health research.

A Statistical Framework to Evaluate Extreme Weather Definitions from A Health Perspective: A Demonstration Based on Extreme Heat Events

A statistical framework for evaluating definitions of extreme weather phenomena can help weather agencies and health departments identify the definition(s) most applicable for alerts nd other preparedness operations related to extreme weather episodes.

Temperature Observation Time and Type Influence Estimates of Heat-Related Mortality in Seven U.S. Cities

BACKGROUND

Extreme heat is a leading weather-related cause of mortality in the United States, but little guidance is available regarding how temperature variable selection impacts heat-mortality relationships.

OBJECTIVES

We examined how the strength of the relationship between daily heat-related mortality and temperature varies as a function of temperature observation time, lag, and calculation method.

METHODS

Long time series of daily mortality counts and hourly temperature for seven U.S. cities with different climates were examined using a generalized additive model. The temperature effect was modeled separately for each hour of the day (with up to 3-day lags) along with different methods of calculating daily maximum, minimum, and mean temperature. We estimated the temperature effect on mortality for each variable by comparing the 99th versus 85th temperature percentiles, as determined from the annual time series.

RESULTS

In three northern cities (Boston, MA; Philadelphia, PA; and Seattle, WA) that appeared to have the greatest sensitivity to heat, hourly estimates were consistent with a diurnal pattern in the heat-mortality response, with strongest associations for afternoon or maximum temperature at lag 0 (day of death) or afternoon and evening of lag 1 (day before death). In warmer, southern cities, stronger associations were found with morning temperatures, but overall the relationships were weaker. The strongest temperature-mortality relationships were associated with maximum temperature, although mean temperature results were comparable.

CONCLUSIONS

There were systematic and substantial differences in the association between temperature and mortality based on the time and type of temperature observation. Because the strongest hourly temperature-mortality relationships were not always found at times typically associated with daily maximum temperatures, temperature variables should be selected independently for each study location. In general, heat-mortality was more closely coupled to afternoon and maximum temperatures in most cities we examined, particularly those typically prone to heat-related mortality.

CITATION

Davis RE, Hondula DM, Patel AP. 2016. Temperature observation time and type influence estimates of heat-related mortality in seven U.S. cities. Environ Health Perspect 124:795-804; http://dx.doi.org/10.1289/ehp.1509946.

A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area

Apparent temperature is more closely related to mortality during extreme heat events than other temperature variables, yet spatial epidemiology studies typically use skin temperature (also known as land surface temperature) to quantify heat exposure because it is relatively easy to map from satellite data. An empirical approach to map apparent temperature at the neighborhood scale, which relies on publicly available weather station observations and spatial data layers combined in a random forest regression model, was demonstrated for greater Vancouver, Canada. Model errors were acceptable (cross-validated RMSE=2.04 °C) and the resulting map of apparent temperature, calibrated for a typical hot summer day, corresponded well with past temperature research in the area. A comparison with field measurements as well as similar maps of skin temperature and air temperature revealed that skin temperature was poorly correlated with both air temperature (R(2)=0.38) and apparent temperature (R(2)=0.39). While the latter two were more similar (R(2)=0.87), apparent temperature was predicted to exceed air temperature by more than 5 °C in several urban areas as well as around the confluence of the Pitt and Fraser rivers. We conclude that skin temperature is not a suitable proxy for human heat exposure, and that spatial epidemiology studies could benefit from mapping apparent temperature, using an approach similar to the one reported here, to better quantify differences in heat exposure that exist across an urban landscape.

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 (T_min) and daily maximum heat index (HI_max) 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 (T_min) and 7.3°C (HI_max) 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 (T_min) and 2.5°C (HI_max), respectively. LSM simulations assuming both land use and warming for 2040 led to summer average increases of up to 2.5°C (T_min) and 8.3°C (HI_max), 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.

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States

Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.

Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities

Epidemiological time series studies suggest daily temperature and humidity are associated with adverse health effects including increased mortality and hospital admissions. However, there is no consensus over which metric or lag best describes the relationships. We investigated which temperature and humidity model specification most adequately predicted mortality in three large European cities. Daily counts of all-cause mortality, minimum, maximum and mean temperature and relative humidity and apparent temperature (a composite measure of ambient and dew point temperature) were assembled for Athens, London, and Rome for 6 years between 1999 and 2005. City-specific Poisson regression models were fitted separately for warm (April-September) and cold (October-March) periods adjusting for seasonality, air pollution, and public holidays. We investigated goodness of model fit for each metric for delayed effects up to 13 days using three model fit criteria: sum of the partial autocorrelation function, AIC, and GCV. No uniformly best index for all cities and seasonal periods was observed. The effects of temperature were uniformly shown to be more prolonged during cold periods and the majority of models suggested separate temperature and humidity variables performed better than apparent temperature in predicting mortality. Our study suggests that the nature of the effects of temperature and humidity on mortality vary between cities for unknown reasons which require further investigation but may relate to city-specific population, socioeconomic, and environmental characteristics. This may have consequences on epidemiological studies and local temperature-related warning systems.

Heterogeneity in individually experienced temperatures (IETs) within an urban neighborhood: insights from a new approach to measuring heat exposure

Urban environmental health hazards, including exposure to extreme heat, have become increasingly important to understand in light of ongoing climate change and urbanization. In cities, neighborhoods are often considered a homogenous and appropriate unit with which to assess heat risk. This manuscript presents results from a pilot study examining the variability of individually experienced temperatures (IETs) within a single urban neighborhood. In July 2013, 23 research participants were recruited from the South End neighborhood of Boston and equipped with Thermochron iButtons that measured the air temperatures surrounding individuals as they went about their daily lives. IETs were measured during a heat wave period (July 17-20), which included 2 days with excessive heat warnings and 1 day with a heat advisory, as well as a reference period (July 20-23) in which temperatures were below seasonal averages. IETs were not homogeneous during the heat wave period; mean IETs were significantly different between participants (p < 0.001). The majority of participants recorded IETs significantly lower than outdoor ambient temperatures (OATs), and on average, the mean IET was 3.7 °C below the mean OAT. Compared with IETs during the reference period, IETs during the heat wave period were 1.0 °C higher. More than half of participants did not experience statistically different temperatures between the two test periods, despite the fact that the mean OAT was 6.5 °C higher during the heat wave period. The IET data collected for this sample and study period suggest that (1) heterogeneity in individual heat exposure exists within this neighborhood and that (2) outdoor temperatures misrepresent the mean experienced temperatures during a heat wave period. Individual differences in attributes (gender, race, socioeconomic status, etc.), behaviors (schedules, preferences, lifestyle, etc.), and access to resources are overlooked determinants of heat exposure and should be better integrated with group- and neighborhood-level characteristics. Understanding IETs for the population at large may lead to innovative advances in heat-health intervention and mitigation strategies.

Community trial on heat related-illness prevention behaviors and knowledge for the elderly

This study aims to explore whether broadcasting heat health warnings (HHWs), to every household and whether the additional home delivery of bottled water labeled with messages will be effective in improving the behaviors and knowledge of elderly people to prevent heat-related illness. A community trial on heat-related-illness-prevention behaviors and knowledge for people aged between 65 and 84 years was conducted in Nagasaki, Japan. Five hundred eight subjects were selected randomly from three groups: heat health warning (HHW), HHW and water delivery (HHW+W), and control groups. Baseline and follow-up questionnaires were conducted in June and September 2012, respectively. Of the 1524 selected subjects, the 1072 that completed both questionnaires were analyzed. The HHW+W group showed improvements in nighttime AC use (p = 0.047), water intake (p = 0.003), cooling body (p = 0.002) and reduced activities in heat (p = 0.047) compared with the control, while the HHW group improved hat or parasol use (p = 0.008). An additional effect of household water delivery was observed in water intake (p = 0.067) and cooling body (p = 0.095) behaviors. HHW and household bottled water delivery improved heat-related-illness-prevention behaviors. The results indicate that home water delivery in addition to a HHW may be needed to raise awareness of the elderly.

Spatial variation in hyperthermia emergency department visits among those with employer-based insurance in the United States - a case-crossover analysis

BACKGROUND

Predictions of intense heat waves across the United States will lead to localized health impacts, most of which are preventable. There is a need to better understand the spatial variation in the morbidity impacts associated with extreme heat across the country to prevent such adverse health outcomes.

METHODS

Hyperthermia-related emergency department (ED) visits were obtained from the Truven Health MarketScan(®) Research dataset for 2000-2010. Three measures of daily ambient heat were constructed using meteorological observations from the National Climatic Data Center (maximum temperature, heat index) and the Spatial Synoptic Classification. Using a time-stratified case crossover approach, odds ratio of hyperthermia-related ED visit were estimated for the three different heat measures. Random effects meta-analysis was used to combine the odds ratios for 94 Metropolitan Statistical Areas (MSA) to examine the spatial variation by eight latitude categories and nine U.S. climate regions.

RESULTS

Examination of lags for all three temperature measures showed that the odds ratio of ED visit was statistically significant and highest on the day of the ED visit. For heat waves lasting two or more days, additional statistically significant association was observed when heat index and synoptic classification was used as the temperature measure. These results were insensitive to the inclusion of air pollution measures. On average, the maximum temperature on the day of an ED visit was 93.4°F in 'South' and 81.9°F in the 'Northwest' climatic regions of United States. The meta-analysis showed higher odds ratios of hyperthermia ED visit in the central and the northern parts of the country compared to the south and southwest.

CONCLUSION

The results showed spatial variation in average temperature on days of ED visit and odds ratio for hyperthermia ED visits associated with extreme heat across United States. This suggests that heat response plans need to be customized for different regions and the potential role of hyperthermia ED visits in syndromic surveillance for extreme heat.

Children's health and vulnerability in outdoor microclimates: A comprehensive review

BACKGROUND

Children are routinely identified as a vulnerable population in environmental health risk assessments, experiencing adverse health outcomes due to exposure to a suite of atmospheric constituents.

OBJECTIVE

To provide a substantive overview of the research literature pertaining to biometeorological effects on children. Key information areas within urban environmental health research related to atmospheric variables (heat, air pollution, radiation) are assessed and integrated to better understand health outcomes and vulnerabilities in children. Critical avenues for improvement and understanding of children's health related to such biophysical parameters are also identified.

METHODS

This comprehensive review assesses past and current primary studies, organizational reports, educational books, and review articles. Emphasis is placed on the differential ambient exposures to temperature, air pollution, and radiation within urban microclimates commonly used by children (e.g., schoolyards, urban parks), and the resulting health impacts.

DISCUSSION

Exposure to heat, air pollution, and radiation are often enhanced in urban areas, specifically under the current design of the majority of outdoor child play places. Many heat indices, energy budget models, and health outcome studies fail to adequately parameterize children, yet those that do find enhanced vulnerability to ambient stressors, particularly heat and air pollution. Such environmental exposures relate strongly to behavior, activity, asthma, obesity, and overall child well-being. Current research indicates that a changing climate, growing urban population, and unsustainable design are projected to pose increasing complications.

CONCLUSIONS

Evidence-based research to link children's health, physiology, and behavior to atmospheric extremes is an important future research avenue, underscoring the fact that children are among the population groups disproportionately affected by ambient extremes. However, current methods and population-based models lack child-specific inputs and outputs, as well as designated thresholds for accurate predictions of child health impacts. More substantive evidence is needed for applicable child-specific policies and guidelines.

Influence of heat wave definitions to the added effect of heat waves on daily mortality in Nanjing, China

BACKGROUND

Few studies have explored the added effect of heat waves, especially in China. Moreover, no prior studies have assessed whether the choice of heat wave definitions affected this added effect. This study compared the associations between heat waves defined by different heat wave definitions (HWs) and cause-specific mortality in warm season in Nanjing, China.

METHODS

A distributed lag model was applied to evaluate the differences in daily mortality during heat-wave days (defined using 15 HWs) compared with non-heat-wave days in Nanjing, during 2007 to 2013. For different HWs, model fits were examined by the Akaike Information Criterion for quasi-Poisson and effects were compared by stratified analysis and bootstrapping. In addition, we explored the effect modifications by individual characteristics under different HWs.

RESULTS

Different HWs resulted in considerable differences in associations between heat waves and mortality. Heat waves defined as ≥4 consecutive days with daily average temperature >98th percentile had the best model fit and were associated with an increase of 24.6% (95% CI: 15.6%, 34.3%) total mortality, 46.9% (95% CI: 33.0%, 62.3%) cardiovascular mortality, 32.0% (95% CI: 8.5%, 60.5%) respiratory mortality, 51.3% (95% CI: 23.4%, 85.6%) stroke mortality, 63.4% (95% CI: 41.5%, 88.8%) ischemic heart disease mortality, and 47.6% (95% CI: 14.5%, 90.3%) chronic obstructive pulmonary disease mortality at lag day 2. Under different HWs, added effects of heat waves on mortality were higher for females versus males, the elderly versus young residents, and people with low education versus those with high education. Results were less sensitive to the inclusion of air pollutants.

CONCLUSIONS

Heat wave definition plays a critical role in the relationship between heat waves and mortality. Selecting an appropriate definition of heat waves is therefore important to design local heat warning systems and to reduce the burden of disease during heat waves.

Impact of extreme high temperature on mortality and regional level definition of heat wave: a multi-city study in China

BACKGROUND

Few multi-city studies have been conducted to explore the regional level definition of heat wave and examine the association between extreme high temperature and mortality in developing countries.

OBJECTIVES

The purpose of the present study was to investigate the impact of extreme high temperature on mortality and to explore the local definition of heat wave in five Chinese cities.

METHODS

We first used a distributed lag non-linear model to characterize the effects of daily mean temperature on non-accidental mortality. We then employed a generalized additive model to explore the city-specific definition of heat wave. Finally, we performed a comparative analysis to evaluate the effectiveness of the definition.

RESULTS

For each city, we found a positive non-linear association between extreme high temperature and mortality, with the highest effects appearing within 3 days of extreme heat event onset. Specifically, we defined individual heat waves of Beijing and Tianjin as being two or more consecutive days with daily mean temperatures exceeding 30.2 °C and 29.5 °C, respectively, and Nanjing, Shanghai and Changsha heat waves as ≥3 consecutive days with daily mean temperatures higher than 32.9 °C, 32.3 °C and 34.5 °C, respectively. Comparative analysis generally supported the definition.

CONCLUSIONS

We found extreme high temperatures were associated with increased mortality, after a short lag period, when temperatures exceeded obvious threshold levels. The city-specific definition of heat wave developed in our study may provide guidance for the establishment and implementation of early heat-health response systems for local government to deal with the projected negative health outcomes due to heat waves.

Assessing variability in the impacts of heat on health outcomes in New York City over time, season, and heat-wave duration

While the impacts of heat upon mortality and morbidity have been frequently studied, few studies have examined the relationship between heat, morbidity, and mortality across the same events. This research assesses the relationship between heat events and morbidity and mortality in New York City for the period 1991-2004. Heat events are defined based on oppressive weather types as determined by the Spatial Synoptic Classification. Morbidity data include hospitalizations for heat-related, respiratory, and cardiovascular causes; mortality data include these subsets as well as all-cause totals. Distributed-lag models assess the relationship between heat and health outcome for a cumulative 15-day period following exposure. To further refine analysis, subset analyses assess the differences between early- and late-season events, shorter and longer events, and earlier and later years. The strongest heat-health relationships occur with all-cause mortality, cardiovascular mortality, and heat-related hospital admissions. The impacts of heat are greater during longer heat events and during the middle of summer, when increased mortality is still statistically significant after accounting for mortality displacement. Early-season heat waves have increases in mortality that appear to be largely short-term displacement. The impacts of heat on mortality have decreased over time. Heat-related hospital admissions have increased during this time, especially during the earlier days of heat events. Given the trends observed, it suggests that a greater awareness of heat hazards may have led to increased short-term hospitalizations with a commensurate decrease in mortality.