Climate change affects human health, however, there have been no large-scale, systematic efforts to quantify the heat-related human health impacts that have already occurred due to climate change. Here we use empirical data from 732 locations in 43 countries to estimate the mortality burdens associated with the additional heat exposure that has resulted from recent human-5 induced warming, during the period 1991-2018. Across all study countries, we find that 37.0% (range 20.5-76.3%) of heat-related deaths can be attributed to anthropogenic climate change, and that increased mortality is evident on every continent. Burdens varied geographically, but were on the order of dozens to hundreds of deaths per year in many locations. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to minimize the public 10 health impacts of climate change.

Extreme weather and climate events, such as heat waves, cyclones, and floods, are an expression of climate variability. These events and events influenced by climate change, such as wildfires, continue to cause significant human morbidity and mortality and adversely affect mental health and well-being. Although adverse health impacts from extreme events declined over the past few decades, climate change and more people moving into harm's way could alter this trend. Long-term changes to Earth's energy balance are increasing the frequency and intensity of many extreme events and the probability of compound events, with trends projected to accelerate under certain greenhouse gas emissions scenarios. While most of these events cannot be completely avoided, many of the health risks could be prevented through building climate-resilient health systems with improved risk reduction, preparation, response, and recovery. Conducting vulnerability and adaptation assessments and developing health system adaptation plans can identify priority actions to effectively reduce risks, such as disaster risk management and more resilient infrastructure. The risks are urgent, so action is needed now. Expected final online publication date for the Annual Review of Public Health, Volume 42 is April 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

BackgroundHigh temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors.MethodsWe compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression.ResultsCitywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age.ConclusionsWithin the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

There is near consensus in the scientific community that humans will experience higher future temperatures due to the ongoing accumulation of greenhouse gases in the atmosphere. The human response to this climatic change, particularly if accompanied by a surge in extreme heat events, is a key topic being addressed by scientists across many disciplines. In this article, we review recent (2012-2015) research on human health impacts of observed and projected increases in summer temperature. We find that studies based on projected changes in climate indicate substantial increases in heat-related mortality and morbidity in the future, while observational studies based on historical climate and health records show a decrease in negative impacts during recent warming. The discrepancy between the two groups of studies generally involves how well and how quickly humans can adapt to changes in climate via physiological, behavioral, infrastructural, and/or technological adaptation, and how such adaptation is quantified.

Background:Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios.Objectives:This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments.Methods:We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty.Results:The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty.Conclusions:Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634

BackgroundExtreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events.ObjectivesThis quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season.MethodsUsing Poisson regression models that controlled for temporal confounders, we assessed daily temperature–health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different “trigger points” at which heat-health intervention measures might be activated.ResultsWe found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22°C) across multiple health events and diagnoses.ConclusionConsideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades.CitationPetitti DB, Hondula DM, Yang S, Harlan SL, Chowell G. 2016. Multiple trigger points for quantifying heat-health impacts: new evidence from a hot climate. Environ Health Perspect 124:176–183; http://dx.doi.org/10.1289/ehp.1409119

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.

Mortality rates increase immediately after periods of high air temperature. In the days and weeks after heat events, time series may exhibit mortality displacement-periods of lower than expected mortality. We examined all-cause mortality and meteorological data from 1980 to 2009 in the cities of Atlanta, Georgia; Boston, Massachusetts; Minneapolis-St. Paul, Minnesota; Philadelphia, Pennsylvania; Phoenix, Arizona; Seattle, Washington; and St. Louis, Missouri. We modeled baseline mortality using a generalized additive model. Heat waves were defined as periods of 3 or more consecutive days in which the apparent temperature exceeded a variable percentile. For each heat wave, we calculated the sum of excess and deficit mortality. Mortality displacement, which is the ratio of grand sum deficit to grand sum excess mortality, decreased as a function of event strength in all cities. Displacement was close to 1.00 for the weakest events. At the highest temperatures, displacement varied from 0.35 (95% confidence interval: 0.21, 0.55) to 0.75 (95% confidence interval: 0.54, 0.97). We found strong evidence of acclimatization across cities. Without consideration of displacement effects, the net impacts of heat-wave mortality are likely to be significant overestimations. A statistically significant positive relationship between the onset temperature of nondisplaced heat mortality and mean warm-season temperature (R(2) = 0.78, P < 0.01) suggests that heat mortality thresholds may be predictable across cities.