Heat-Related Mortality in a Warming World: Implications and Ways Forward

By Caroline Blanck

A Senior Honors Thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science with Honors in Environmental Science at Brown University

Thesis Advisor: Gregory Wellenius Collaborator: Kate Weinberger Second Reader: J. Timmons Roberts

April 2019

Abstract

Background: Heat has been, and will continue to be, a major environmental hazard, public health concern, and local and national economic and security challenge. Extreme high temperatures lasting multiple consecutive days (heat waves) and high ambient temperatures may cause heat-related mortality. However, it is not well known how the risk of heat-related health effects varies by age, gender, or race. Objectives: This thesis examines the influences of age, gender, and race on a person’s risk of heat-related mortality, using data from the Brown University School of Public Health and the National Center for Health Statistics. Methods: The time period for this study is identified as 1987 to 2006. Using data from the National Center for Health Statistics, we acquired individual-level data on all deaths (excluding those from external causes) in counties in the contiguous United States with a population greater than 100,000 residents. We chose to focus on the twenty counties with the greatest number of deaths. This study analyzed deaths due to heat, aggregated by age, gender, and race. Results: The results from this thesis indicate that, although the demographic groups with the highest relative risk for heat-related mortality varied by county, overall the elderly, females, and Black people are at increased risk of heat-related mortality. Furthermore, populations in cooler climates will be more greatly affected by heat-related mortality than those in warmer climates. Conclusions: The effects of heat-related mortality in the United States are localized, and thus highlight the need for local policies and initiatives that aim to diminish heat-related mortality.

Acknowledgements

To my mother, Wendy, my father, Peter, and my siblings, Jason, Daniel, Albert, Elise and Harry, words cannot begin to express the appreciation and gratitude I feel for your ceaseless love, support, encouragement, and unsolicited advice. Thank you for instilling in me a love of learning and a drive to repair the world. I love you.

I would like to express deepest thanks to Gregory Wellenius, my thesis advisor, Kate Weinberger, my collaborator, and Timmons Roberts, my academic advisor, along with the countless other faculty I have encountered at Brown University. It was under your tutelage and guidance which I discovered my passion. Your mentorship and intuition have been invaluable.

To the people who have shaped my experience at Brown University, the friends who have become family, I thank you. I have learned from you everyday over the past four years and it is because of each of you that this thesis is complete. Without your inspiration and insight, friendship and fortitude, this thesis would just be a collection of words and this university just a piece of land.

Table of Contents

Introduction 4

PART ONE: LITERATURE REVIEW 7 Climate change and heat 8 Heat-related morbidity and mortality 8 Heat-related mortality over time 10 Urban Heat Island Effect 11 Determinants of Heat-Related Mortality Vulnerability 13 Heat-related mortality and age 14 Heat-related mortality and gender 17 Heat-related mortality and race 20 Heat-related mortality and access to air-conditioning 22 Economic Costs of Heat-Related Mortality 23

PART TWO: QUANTITATIVE ANALYSIS 26 Introduction 27 Methods 28 Results 31 Heat-related mortality overall 33 Heat-related mortality and age 38 Heat-related mortality and gender 42 Heat-related mortality and race 47 Discussion 52 Heat-related mortality and age 53 Heat-related mortality and gender 54 Heat-related mortality and race 54 Socioeconomic conditions 55 Limitations 56

PART THREE: POLICY IMPLICATIONS 57 United States Climate Projections 58 United States Demographic Projections 59 Heat-related Mortality Projections 60 Mitigation and Adaptation Measures 62 Heat response plans (HRPs) 63 Case Study: Milwaukee, Wisconsin 66 Case Study: Maricopa County, Arizona 67 Vulnerability Mapping 69 Case Study: New York State 71 Case Study: Philadelphia, PA 72 Stakeholder Participation 73 Communication and Social Marketing 74 Case Study: Chicago, IL 75 Reducing the Urban Heat Island Effect 77 Efficient air conditioning 78

Conclusion 80

References 82

Appendix 89

Introduction

Heat and hot weather is a crucial determinant of human health and mortality. Heat is a grave peril for the human health and society, which can lead to morbidity, mortality and economic damages. Thousands of excess deaths occur annually resulting from heat stroke and the aggravation of chronic respiratory and cardiovascular conditions (Kovats & Hajat, 2008;

Basu & Samet, 2002). Estimates suggest that, on average, there were approximately 618 deaths directly attributed to heat each year in the United States between 1999 and 2010 (CDC, 2012), killing more people, on average, than any other extreme weather event (Office of Climate,

Water, and Weather Services, 2013; Berko et al., 2014).

Not only is heat detrimental to health, but also to the economy. According to the Fourth

National Climate Assessment produced by the United States Global Change Research Program

(USGCRP), annual damages associated with heat-related deaths may cost the United States economy up to $140 billion in 2090 under current climate projections (Ebi et al., 2018). Thus, due to the health and economic effects of extreme heat, it is crucial that this issue is studied to create efficient and equitable solutions that will save the most lives, and in turn the most money and equity, possible.

There is much consensus on the effects of heat on environmental systems, such as flora, fauna, ecological and hydraulic systems, and on the human body (IPCC, 2007), yet there is little consensus on what exactly constitutes heat. Hot is a relative term depending on geographic location and heat waves vary in intensity and duration (Kilbourne, 1997). Consequently, there is difficulty in estimating the actual number of heat-related deaths annually. Currently, there is a

lack of a standardized criteria for attributing death due to heat and death from other causes that may have been exacerbated by heat, such as cardiovascular disease or respiratory disease, which often are not included in estimations (Berko et al., 2014).

Heat has been, and will continue to be, a major environmental hazard, public health concern, and local and national economic and security challenge. Extreme high temperatures lasting multiple consecutive days (heat waves) and high ambient temperatures may cause heat-related mortality. While human populations may acclimate to their local climate conditions, in physiological, behavioral, and cultural manners, there are still absolute limits of heat exposure that humans may withstand. Humans may adapt to changing climates and environments using air-conditioning. However, the amount of heat an individual may be able to tolerate and withstand may vary with physical and socioeconomic factors (Kovats & Hajat, 2008).

While heat waves and high temperatures may not be preventable unless actions to mitigate climate change are taken, heat-related mortality may be preventable depending on public health measures taken (Kilbourne et al., 1982). To best prevent heat-related mortality, the public health and environmentalist communities must understand who is most vulnerable to heat and how compounding factors work to increase or decrease these vulnerabilities (Clarke, 1972;

Ellis, 1972; Schuman, 1966; Jones et al., 1982; Martinez et al., 1989; Rogot et al., 1992).

Heat has vastly different consequences for different demographics within the United

States. Elderly populations, women, and racial/ethnic minorities may have heightened vulnerability not only to the health effects of heat (CDC, 2012), but also to its economic impacts

(Schmeltz et al., 2016). To equitably and effectively adapt to the increased frequency and

severity of heat due to climate change, it is imperative that different the impacts of heat on different populations are acknowledged, studied, and understood.

This thesis examines the influences of age, gender, and race on a person’s risk of heat-related mortality, using data from the Brown University School of Public Health and the

National Center for Health Statistics. It focuses on the twenty counties across the United States with the highest absolute number of deaths due to all-cause mortality, which include Los

Angeles County, CA, Cook County, IL, Wayne County, MI, Kings County, NY, Maricopa

County, AZ, San Diego County, CA, Harris County, TX, Philadelphia County, PA, Queens

County, NY, Orange County, CA, Allegheny County, PA, Cuyahoga County, OH, Broward

County, FL, New York County, NY, Dallas County, TX, Pinellas County, FL, Palm Beach

County, FL, Middlesex County, MA, Nassau County, NY and Bronx County, NY, and investigates the differing levels of heat-related mortality among the aforementioned groups within those sites. This study reviews twenty years of continuous data. It is unique in that it is among the only studies of its size that analyzes heat-related mortality broken down by age, gender, and race over a continuous twenty year period.

As global temperatures rise, the impacts of heat-related mortality may become more profound and widespread. This study aims to shed light on factors that increase an individual’s vulnerability for heat related mortality. It then offers policy recommendations to help address these vulnerabilities. It is crucial that the knowledge generated in this thesis is used to further resilience to climate change induced heat.

PART ONE:

LITERATURE REVIEW

Climate change and heat

There is unequivocal evidence that the Earth’s global average temperatures are warming.

Climate change poses myriad threats to the natural environment and humans. It significantly impacts the environment, which in turn affects human health, human and societal development, and human prosperity. Climate change is predicted to increase the frequency, severity, and length of heat spells and heat waves throughout the planet (IPCC, 2007).

While climate change will inevitably increase the frequency and severity of heat waves in the future, heat waves and their subsequent public health consequences already are an issue of major concern and are currently causing mortality, as defined by death, and morbidity, as defined by the rate of disease in a population. However, the effects of heat waves are not felt equally by all (IPCC, 2007). The objective of this thesis is to determine those segments of the population within the United States that are most vulnerable to heat waves and subsequent heat-related mortality currently, and who may be most affected in the future.

Heat-related morbidity and mortality

Heat may have a variety of effects on the human body. Prolonged exposure to high environmental temperatures may directly cause certain morbidities and mortality, such as heat stroke, heat exhaustion, heat syncope, heat cramps, or death. Exposure to heat and humidity compromises other bodily functions and existing conditions, such as cardiovascular compensatory mechanisms, respiratory mechanisms, and chronic diseases (Becker & Stewart,

2011), contributing to morbidity or mortality. Up to a heat threshold, healthy, human adults may be equipped with heat regulatory systems. The absolute core temperature that a body may reach

before cellular damage occurs is 104°F (40°C), leading to organ failure and death (Becker &

Stewart, 2011). Studies have characterized over three hundred thermal indices that have been used to set standards for limits to heat exposure in light of associated health effects (Quayle &

Doehring, 1981; Parsons, 2014).

Thermal homeostasis, the process by which the human body attempts to maintain a constant core temperature, is not a function of temperature alone. From an environmental perspective, there are four meteorological factors that influence the physical process of maintaining thermal homeostasis: (l) air temperature, (2) humidity, (3) air motion (wind speed), and (4) solar radiant heat energy (Kilbourne, 1997). From a biological perspective, thermal homeostasis involves four mechanisms: (1) heat gain from metabolism, (2) heat loss from evaporation, (3) heat gain or loss from conduction and convection, and (4) gain or loss of radiant heat energy (Kilbourne, 1997).

Through perspiration and vasodilation, the human body may increase radiant, convective, and evaporative heat loss to cope with heat (Kilbourne, 1992). Low ambient temperatures allow metabolically generated heat to be lost more easily from the body to the air via conduction and convection. It is more difficult for convection heat loss to occur as temperature rises.

Convective heat loss is not possible when air temperatures are above body temperature because bodies may gain heat from the air. The cooling effect of the evaporation of perspiration and secretion is impeded when humidity is high, which may lead to increased heat stress. Convective heat transfer and the evaporation of sweat may be aided by increased air motion and wind speed.

Radiant heat may be the heat that one feels from direct sunlight, which adds to heat stress, independent of other variables (Kilbourne, 1997).

Heat-related mortality over time

Heat-related mortality is influenced by not only the weather conditions at the time, but also previous weather conditions. A discrepancy occurs between the onset of a heat wave and the occurrence of negative effects on public health. When anomalously high temperatures occur on several days in succession, a noticeable increase in mortality occurs. Whereas, heat waves lasting one week produce relatively fewer additional deaths at the end of the week than in the initial few days. The most lethal conditions occur during heat waves when the heat is sustained. For example, heat waves that have little nighttime cooling are more lethal than those that have more substantial nighttime cooling (Ellis, 1975; Lyster, 1976; Oechsli & Buechley, 1970).

As heat waves, or increased temperatures, continue, humans develop the ability to acclimatize, as is demonstrated during the summer, in which heat waves have been documented to be less lethal in September than in June and July (Wyndham et al., 1976; Bronner et al., 1976).

For example, of the 7,233 heat-related deaths documented by the CDC between 1999 to 2009, approximately 94% (n=6,821) occurred during May to September. Approximately 39%

(n=2,825) of deaths occurred in July and approximately 27% (n=1,925) occurred in August

(CDC, 2013). During prolonged heat waves, there is an initial spike in mortality rates, but then the mortality rate usually returns to the baseline rate, despite continued elevated temperatures

(Kilbourne, 1997; Marmor, 1975).

Urban Heat Island Effect

Image Source: World Meteorological Organization. (2019). Urban development - Megacities. https://public.wmo.int/en/our-mandate/focus-areas/urban-development-megacities.

The health effects of heat and the vulnerabilities of urban populations to heat-related mortality will be augmented by the evolution of current climate and urbanization trends.

Currently, more than half of the world’s population resides in urban areas, an increase of approximately 30% in the last 50 years (UN, 2011). The result of this urbanization has been the alteration of natural vegetation to vast tracts of cement and engineered infrastructure, which increases the thermal-storage capacity of urban areas, resulting in the Urban Heat Island (UHI) effect. The UHI effect causes increased thermal-storage, which causes urban environments to be significantly hotter than adjacent rural regions (Luber & McGeehin, 2008).

Several facets of the UHI effect may have public health consequences. Physical conditions of the UHI effect may affect heat-related mortality. Increased thermal mass stemming from concrete surfaces may be coupled with low ventilation due to tall buildings and point-source heat emissions from vehicles, air-conditioners and other emitters. This further

amplifies temperatures, prompting the need for more air-conditioning, creating a positive-feedback loop (Arnfield, 2003). The UHI effect has been found to add 2 to 10 degrees

Fahrenheit to urban areas during daytime (EPA, 2005; Vose et al., 2004). At night, the UHI effect may radiate heat absorbed during the daytime, raising the nighttime minimum temperatures, leading to excess mortality (EPA, 2006).

Furthermore, sociodemographic facets of the UHI effect may affect heat-related mortality. Access to transportation, medical care, and cooling centers may affect heat-related mortality. Housing type and neighborhood land use may also affect heat-related mortality (Luber

& McGeehin, 2008). Lower socioeconomic and ethnic minority groups have been found to be more likely to live in warmer neighborhoods and have greater exposure to heat stress due to higher rates of residence in areas of high settlement density, characterized by apartment buildings, and sparse vegetation. These groups have been found to lack the necessary social and material resources to deal with exposure to heat (Harlan et al., 2006). Studies indicate that high-risk populations living in urban areas that have not been affected by heat waves for several years are most at risk of heat-related excess mortality (Clarke, 1972; Ellis, 1972; Schuman, 1966;

Jones et al., 1982; Martinez et al., 1989; Rogot et al., 1992).

Determinants of Heat-Related Mortality Vulnerability

Image Source: Kovats, R. S., & Hajat, S. (2008). Heat stress and public health: a critical review. Annu. Rev. Public Health, 29, 41-55.

There is heterogeneity across studies examining heat-related mortality. The relationship between temperature and mortality vary across different geographic locations and populations identified as subgroups vulnerable to heat-related mortality differ depending on the study location and study population (Basu, 2009). There are many factors that work individually, and in conjunction, to influence an individual's vulnerability to heat-related mortality. Vulnerability not only may be a product of physical factors, but also it may be related to a myriad of cultural, social, environmental, political and economic conditions (Cardona et al., 2012).

Risk factors that increase an individual’s vulnerability to heat may be categorized as intrinsic (age, gender, race) and extrinsic (behaviors, location, socioeconomic status) (Kovats &

Hajat, 2008). While the analysis conducted in this thesis may not directly analyze the socioeconomic status of individuals due to a lack of access to records, socioeconomic status may be an indicator of the level of an individual's vulnerability to heat. In addition, gender and race may be proxy indicators for socioeconomic status and subsequently health outcomes. The

present analysis examines the effects of age, gender, and race on an individual’s vulnerability to heat-related illness.

Heat-related mortality and age

Age is one of the most significant factors that may contribute to an individual’s vulnerability to heat. Extremes of age, which include children under the age of four and elderly people, have increased vulnerability to heat-related mortality. Regardless of gender or race, elderly individuals are more susceptible to heat-related morbidity and mortality than younger individuals (McGeehin & Mirabelli, 2001; Semenza et al., 1999; CDC, 2017), in which sharp increases in vulnerability occur after age 65 and continue increasing in vulnerability as individuals age (CDC, 2017; McGeehin & Mirabelli, 2001). Studies document the percent increase in mortality risk for elderly people due to heat (Anderson & Bell, 2009; Fouillet et al.,

2006; Whitman et al., 1997 Conti et al., 2005 Huynen et al., 2001; Stafoggia et al., 2006;

D'Ippoliti et al., 2010; McGeehin & Mirabelli, 2001; CDC, 2017). Age-related vulnerability to heat occurs for a plethora of reasons, including biological and circumstantial reasons (Anderson & Bell, 2009). Circumstantially, individuals of extreme ages, which include children and the elderly, may have limited abilities or may not be able to care for themselves. Children under the age of four and the elderly may rely on other people to provide water, food, shelter, transportation and other necessities. If an individual of an extreme age does not have a caretaker to provide for them, they may be susceptible to heat (Knochel &

Reed, 1994; WMO & WHO, 2015).

Biologically, extreme ages occur outside the range of time for optimal bodily function.

Children under the age of four have yet to develop fully and are more susceptible to environmental perils such as heat. Conversely, elderly people experience alteration and deterioration of bodily functions. Changes in thermoregulatory responses occur as an individual ages. Relative risk may begin to increase as age surpasses 50 years (Flynn et al., 2005; Grundy,

2006; Thomas & Soliman, 2002). As individuals age, they have reduced sweating rate, skin blood flow, and cardiovascular function (Kenney & Munce, 2003; Kenny et al., 2010). Elderly individuals may experience physiological changes in renal function and water and electrolyte homeostasis, which increase the risk of renal failure, presenting as a vulnerability to heat. Renal function controls potassium levels and subsequently cardiac rhythm, thus changes in renal function may be detrimental to health (Flynn, 2005).

Additionally, reduced water intake and dehydration is common in elderly individuals, but during heat waves and high ambient temperatures it may cause hypernatremia, augmenting the risk of coronary and cerebral thrombosis, and impaired function of the central nervous system

(Kenny et al., 2010). It also may decrease plasma volume and venous return, in turn decreasing cardiac output (Knochel & Reed, 1994).

Figure 1. Age-Specific Crude Death Rates for Heat Related Deaths by Race, Gender, and Age Groups for U.S. residents - United States, 1999-2010 (n=6,850).

Image Source: Center for Disease Control and Prevention (CDC). (2017). Picture of America: Heat-related illness fact sheet. https://www.cdc.gov/pictureofamerica/pdfs/Picture_of_America_Heat-Related_Illness.pdf.

Data from the Center for Disease Control presents information on heat-related death that aggregates the effects of race, gender, and age. Figure 1 presents that crude rates of heat-related mortality increase with age, excluding children under four-years old, with substantial increases in crude death rates occurring around age 55 and thereafter, complementing the aforementioned studies. Figure 1 indicates that males of races other than Black or White have the highest crude rates for heat-related death, followed by Black males. Females of races other than Black or

White and Black females have the next highest crude rates for heat-related death. Lastly, White females and males have the lowest crude death rates, with females having lower rates than males

(CDC, 2017).

Heat-related mortality and gender

Increased temperatures due to climate change will have pronounced effects on already vulnerable populations who have historically been marginalized by society. According to the

World Bank, “Women appear more vulnerable in the face of natural disasters, with the impacts strongly linked to poverty… more women than men die from natural hazards [globally]” (World

Bank, 2011). Impoverished women may be those most vulnerable to climate change. Structural barriers, such as systemic misogyny, and quotidian challenges, such as patriarchal societal norms, impede women’s ability to mitigate and adapt to climate change. To combat the effects of climate change, it is imperative that women not only have equal treatment within society, but also that they have equitable and just access to resources and opportunities. Thus, it is crucial that gender is configured into climate change and heat policies.

Environmental, social, and economic factors and inequalities work separately and in conjunction to influence the vulnerability of women in the face of climate change. The intersectionality of women’s identities and the unequal treatment women face exacerbate and reinforce vulnerabilities. According to Naila Kabeer, “‘Vertical inequalities’ rank individuals/households by their place in the income/wealth hierarchy, in contrast to ‘horizontal inequalities’, which refer to inequalities between socially defined groups that often cut across income groups” (Kabeer, 2015). Identifying as a woman results in a “horizontal inequality,”

which transcends socially defined groups but intensifies “vertical inequalities,” such as poverty or class.

In 2015, in the United States, on average women had an annual income of $40,742 and men had an annual income of $51,212, despite equal levels of education (United States Census

Bureau, 2015). Women often are not in an economic position to cope with extreme heat events or even rising ambient temperatures. This is because women may either have fewer economic resources to cope with extreme heat due to economic disadvantages, or women may be reliant on a partner and may have less economic independence to cope with extreme heat in an equitable and just manner.

European studies indicate women are at an increased risk of heat-related mortality than are men (Havenith, 2005; Burse, 1979). According to the United Nations Population Fund, increased health issues associated with unsustainably developed urban areas, such as caused by the ‘urban heat island’ (“UHI”), will be exacerbated in women. The UHI is exacerbated in women because exposure to anomalously hot temperatures adversely affects birth outcomes, including causing changes to the length of gestation, birth weights, stillbirths, and neonatal stress

(UNFPA, 2007; Kuehn & McCormick, 2017). One study found that during prolonged heat exposure, women’s heart rates elevate on average 10-17 beats/min higher than men’s, which may lead to or exacerbate other medical issues (Avellini et al., 1980).

Studies conducted in Australia, Europe, and Mexico City in the 2000s found that women and individuals over the age of 65 had a high risk of mortality on days with higher apparent temperatures, the temperature felt by humans as a combination of the effects of air temperature,

higher relative humidity, and higher wind speed (Bell et al., 2008; Ishigami et al., 2008;

Vaneckova et al., 2008; Hajat et al., 2007; Stafoggia et al., 2006).

Data from the Center for Disease Control presents conflicting information to the aforementioned studies. The CDC obtained records for cases specifically coded as death due to heat, whereas other studies used different metrics to estimate heat-related mortality, which may account for the difference in results. Between 1999-2009 approximately 7,233 people died in the

United States directly attributed to exposure to excessive natural heat, averaging 658 people per year. Of those who died due to heat-related mortality, approximately 72% of deaths (n=5,201) were directly attributed to exposure to excessive heat and approximately 28% of deaths

(n=2,032) were attributed to heat as a contributing factor. Approximately 69% of those who died were male (n=4,955) and approximately 36% were over the age of 65 (n=2,621) (CDC, 2013).

However, only approximately 49 percent of the United States population is male and 15.6 percent of the population is over the age of 65 (US Census Bureau, 2018). Figure 2 below indicates that every year from 1999 to 2010 more males succumbed to heat-related mortality than females in the United States (CDC, 2012).

Figure 2. Deaths attributed to exposure to natural heat, as the underlying and contributing causes of death.

Image Source: Center for Disease Control and Prevention (CDC). (2012). QuickStats: Number of heat-related deaths by sex - National Vital Statistics System, United States, 1999-2010. MMWR. Morbidity and mortality weekly report. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6136a6.htm.

Heat-related mortality and race

In the United States, African-Americans have higher rates of death, disease, and disability than do Whites, a pattern documented since prior to the Civil War (National Center for

Health Statistics, 1994). Current projections estimate that racial/ethnic minorities will be disproportionately affected by the effects of climate change on human health (Gamble et al.,

2016; Rudolph & Gould, 2015; Schmeltz et al., 2016; Knowlton, 2008). Furthermore, poor and racial/ethnic minority populations are often segregated to residential areas that have higher vulnerability to heat-related health risks, such as urban heat islands, areas that are densely populated and have reduced vegetative cover (Jesdale et al., 2013).

Social and behavioral scientists attribute racial variations in health to racial differences in socioeconomic circumstances due to the significant relationship between race/ethnicity and systems of inequality (Williams et al., 1997). In 2015, the median income of non-Hispanic White households in the United States was $62,950. The median income for African-American

households was $36,898. Approximately 87% of African-Americans had a high school degree or higher and 22.5% had a Bachelor’s degree or higher, whereas 93% of White people had a high school degree or higher and 36.2% had a Bachelor’s degree or higher (United States Census

Bureau, 2015). Socioeconomic and educational status are major determinants of an individual’s health status.

The weathering hypothesis, theorized by Geronimus (1992), proposes that the health of

African-American women may depreciate as their age increases and significant deterioration of health may begin in early adulthood, exhibiting the physical consequences of cumulative socioeconomic disadvantage (Geronimus, 1992). Geronimus (1992) supports her hypothesis by demonstrating different health outcomes between women with the same education level or same socioeconomic status. Weathering is attributed to consistent toxic stress, in which consistent amounts of stress may cause an actual deterioration of the brain or impede development. Toxic stress may be due to systemic racism, leading to premature deterioration of the body.

Geronimus (2006) followed up her study in 2006 and found that, controlling for education and income, African-American women had the highest allostatic load scores, a measurement of stress-associated body chemicals and their effects on the body’s systems, as compared to White women and White and African-American men. Geronimus concludes that this is due to the compounding burdens of racial and gender discrimination that

African-American women face (Geronimus et al., 2006).

Racial/ethnic minorities have a higher risk of heat-related mortality and hospitalizations due to extreme heat events as compared to White people (Schmeltz et al., 2016; Gronlund, 2014; Hansen et al., 2013; Knowlton, 2008; O’Neill et al., 2005; Schwartz, 2005). In a study of nine

Californian counties in May-September 1999-2003, using case-crossover methodology, Basu and

Ostro (2008) found increased risk of heat-related mortality for individuals that were Black and individuals that were elderly, but found no increased risk for women over men (Basu and Ostro,

2008).

Multiple other studies conducted in the United States in the early 2000s find Black individuals are at an increased risk of experiencing heat-related mortality (O’Neill et al., 2005;

O’Neill et al., 2003). Furthermore, Hansen et al. (2013) found that there are many factors that contribute to increased heat-susceptibility of minority ethnic groups in the United States, including economic and social disparities, housing conditions, language barriers, and occupational exposures and hazards (Hansen et al., 2013).

Heat-related mortality and access to air-conditioning

Access to air-conditioning and cooling centers largely is determined by an individual’s socioeconomic status and is a major determinant of an individual’s ability to cope with heat.

Those with air-conditioning are more resilient to heat-related mortality than those without air-conditioning (Curriero et al., 2002). Widespread proliferation of air-conditioning in homes and residential buildings began in the late 1950s to early 1960s (Barreca et al., 2012; Department of Energy, 2015). Currently, approximately 90 percent of homes in the United States have air-conditioning and, as of 2016, there were approximately 374 million air-conditioning units in the United States (IEA, 2018).

Since the advent of air-conditioning, the mortality effect of extremely hot days (days exceeding 90 degrees Fahrenheit) declined by approximately 80% between 1900-1959 and

1960-2004, with almost all of the decline occurring after 1960. Between 1960-2004 approximately six hundred heat-related mortalities occurred annually in the United States. If the relationship between temperature and mortality had remained at pre-1960 rates and population change was accounted for, approximately 3,600 heat-related mortalities would have occurred annually between 1960-2004 (Barreca et al., 2012). In one study in 1992 (n=72,740), Rogot et al. found the death rate for individuals with central air-conditioning was 42% lower than for individuals without central air-conditioning, controlling for confounding variables (Rogot et al.,

1992).

Economic Costs of Heat-Related Mortality

To assess the broader impacts of extreme heat in a warming world, it is crucial to examine the economic costs associated with heat-related morbidity and mortality. For example, it is estimated that one heat wave in California in 2006 cost approximately $179 million due to hospitalizations, emergency department visits, and outpatient visits and $5.1 billion dollars in premature death based on the Value of a Statistical Life (Knowlton et al., 2011). In a decade-long, nationwide survey, Schmeltz et al. found that the median cost of hospitalization for

73,180 patients who experienced heat-related illness was approximately $8,965 (Schmeltz et al.,

2016). Economic costs associated with extreme heat are expected to rise as extreme heat events increase in frequency and severity, and mortality and hospitalizations rise due to heat increase

(Schmeltz et al., 2016).

Individuals living in poverty and racial/ethnic minorities are projected to be disproportionately affected by climate change, and may be affected disproportionately by these

projected costs (Gamble et al., 2016; Rudolph and Gould, 2015; Schmeltz et al., 2016). In a study conducted from 2001-2010, using the Nationwide Inpatient Sample, Schmeltz et al., found that the mean costs of hospitalization for heat-related illness were higher in racial/ethnic minorities (Blacks, Hispanics, and Asian/Pacific Islanders) as compared to their White counterparts. Within this subset, those living in lower zip-code income areas and those without insurance paid higher costs for hospitalizations for heat-related illnesses. Discrepancies in the cost of heat-related illness among racial/ethnic minorities and White individuals stem from inequalities, because of race/ethnicity and low socioeconomic status, in health and healthcare (LaVeist et al., 2011; Schmeltz et al., 2016).

Moreover, Schmeltz et al., found that although women have lower rates of hospitalization due to heat-related illness, the economic costs associated with the hospitalization of women due to heat-related illness are significantly higher than that of men. Additionally, Schmeltz et al. found that, although individuals aged 40–64 years old had higher rates of heat-related illness hospitalizations, individuals 65 years or older had significantly higher costs for hospitalizations for heat-related illness (Schmeltz et al., 2016). Between 2004 and 2005, the average temperature of the United States was 1.5 degrees

Fahrenheit higher than historical means. During this time, hyperthermia-related inpatient and outpatient hospitalization rates doubled among Medicare enrollees. The total cost of hyperthermia-related inpatient and outpatient hospitalizations in Medicare enrollees increased from $11 to $25 million in this time period (Noe et al., 2012). As temperatures rise, hospitalization rates due to heat-related illness will rise among the elderly, contributing to

economic strains on currently precarious healthcare programs (Altman & Frist, 2015; Schmeltz et al., 2016).

According to the Fourth Annual Climate Assessment, between the present and the year

2090, “Annual damages associated with the additional extreme temperature-related deaths in

2090 were projected to be $140 billion (in 2015 dollars) under a higher scenario (RCP8.5) and

$60 billion under a lower scenario (RCP4.5)” (Ebi et al., 2018).

PART TWO:

QUANTITATIVE ANALYSIS

Introduction

There have been many studies that seek to analyze the relationship between heat and mortality through physiological and socioeconomic perspectives. Studies analyze specific heat waves and their effects on mortality. Other studies examine total heat-related mortality, not accounting for specific socioeconomic factors or differentiating based upon demographic characteristics.

Overarching themes that exist in scientific literature demonstrate the enhanced vulnerability of individuals aged 65 and older to heat-related mortality as opposed to individuals aged below 65 (Anderson & Bell, 2009; Fouillet et al., 2006; Whitman et al., 1997 Conti et al.,

2005 Huynen et al., 2001; Stafoggia et al., 2006; D'Ippoliti et al., 2010; McGeehin & Mirabelli, 2001; CDC, 2017). There exists mixed evidence as to whether Black or White people are more vulnerable to heat-related mortality. Some studies suggest that Black people have a higher risk of heat-related mortality (Schmeltz et al., 2016; Gronlund, 2014; Hansen et al., 2013; Knowlton, 2008; O’Neill et al., 2005; Schwartz, 2005; Jesdale et al., 2013). A contrasting study suggests that White people have higher rates of heat-related mortality (CDC, 2017). There also exists mixed evidence as to whether females or males are more vulnerable to heat-related mortality. Some studies have found increased vulnerability of females to heat-related mortality (Bell et al., 2008;

Ishigami et al., 2008; Vaneckova et al., 2008; Hajat et al., 2007; Stafoggia et al., 2006). Other studies have found increased vulnerability to heat-related mortality for men (CDC, 2012; CDC,

2013; CDC, 2017). Socioeconomic factors play a large role in determining an individual's

vulnerability to heat (Williams et al., 1997; Hansen et al., 2013) and different demographic groups have increased vulnerability to heat-related mortality in different communities.

There exists a gap in the literature that reviews national heat-related mortality by age, gender, and race because most studies focus on a specific extreme heat event. The present study is unique in that it uses twenty years of continuous data to analyze how heat-related mortality affects individuals differently depending on their age, gender, and race. It examines national averages of the twenty countries in the United States with the highest mortality rates and examines each county individually. This study is analyzing the largest dataset to date and will be able to substantiate or challenge claims made by previous researchers. This dataset aims to clarify discrepancies among existing literature.

Methods

The time period for this study is identified as 1987 to 2006, twenty years of continuous data, which are the most recent decades for which mortality statistics are publicly available.

Using data from the National Center for Health Statistics, we acquired individual-level data on all deaths (excluding those from external causes) in counties in the contiguous United States with a population greater than 100,000 residents. We constructed time series of the daily number of deaths occurring in each of the 297 counties for which we had continuous mortality data for the study period of 1987 to 2006. We chose to focus on the twenty counties in the United States with the greatest number of all-cause deaths over the course of our study period. Daily deaths were then aggregated by age, gender, and race.

Using the Parameter-elevation Relationships on Independent Slopes Model (PRISM)

(PRISM Climate Group OSU, 2016; Daly et al., 2008; Daly et al., 2015) gridded estimates of daily ambient temperature at a spatial resolution of 4 by 4 km for the continental U.S. were collected. These estimates were verified by measurements of mean daily temperatures obtained from first order weather stations (Spangler et al., 2018). These estimates were used to construct a population-weighted value of mean temperature for each day in each county (Spangler,

Weinberger, and Wellenius, 2018). Using the year 2000 U.S. Census, we acquired estimates of the total population of each county (US Census Bureau, 2016).

This analysis was conducted in accordance with other large, multi-county analyses of the association between mean daily temperature and mortality (Gasparrini et al., 2015; Weinberger et al., 2019) to calculate mortality due to extreme heat in each county.

To begin, the association between mean daily temperature and daily mortality counts in all of the twenty counties were modeled. A distributed lag non-linear model (Gasparrini et al.,

2010) with a quasi-Poisson distribution to estimate the cumulative association between county-specific mean daily temperature over 21 days of lag and mortality was used in each county. We used a quadratic B-spline with three internal knots at the 10th, 75th, and 90th percentiles of the county-specific temperature distribution to model the temperature variable. We used a natural cubic B-spline with three internal knots at equally spaced values on the log scale

(i.e., at values of approximately 1, 3, and 8) to model the lag function. To control for seasonal and long-term time trends, we used a natural cubic spline with 8 degrees of freedom per year, and terms for day of week and federal holidays. The number of degrees of freedom per year were varied to adjust for seasonal and long-term trends in sensitivity analyses.

Next, the cumulative association between temperature and mortality estimated from each county was used to create a multivariate meta-analytic model, with predictors, which included the mean and range of county-specific temperatures over the entire 20-year study period. The best linear unbiased prediction (BLUP) of temperature-mortality association for each county was obtained using this model. The spline of the temperature at the county-specific minimum mortality temperature was centered.

Using the method proposed by Gasparrini and Leone (2014), the annual number of deaths attributed to heat in each county was calculated using the BLUP. “Heat” was defined as all temperatures greater than the county-specific temperature of minimum mortality, and extreme

th heat constituted temperatures greater than the 97.5 percentile of the county-specific temperature distribution.

The attributable number and its empirical 95% confidence interval (eCI) are presented for each county and in the aggregate across the twenty counties. The annual attributable number per million people in each county is used to allow for comparisons among counties with different population sizes.

All analyses were done in R version 3.3.3 (R Core Team, 2017) using the packages

‘dlnm’ (Gasparrini, 2011) and ‘mvmeta’ (Gasparrini, 2012). We present the attributable number and its 95% empirical confidence interval (eCI) aggregated across all twenty counties.

Results

The results of this study present the total number of deaths in the twenty counties in the

United States with the largest total number of deaths during the study years, which occurred between 1987 and 2006, from among the 297 counties for which we have continuous mortality data with a 95% empirical confidence interval. Findings are presented in the body of the text for the twenty counties in the aggregate and highlighted for Los Angeles County, CA, Cook County,

IL, Wayne County, MI, Kings County, NY, and Maricopa County, AZ. The complete results from all twenty counties are presented in the Appendix to this thesis.

Curves plotting the relative risk of mortality at varying temperature percentiles are presented. In this study, the relative risk, also known as the risk ratio, (RR), is the ratio of the risk or rate of death at a given temperature as compared to the risk or rate at a different or reference temperature. The relative risk presented in this study is the increased or decreased probability that individuals of a certain demographic group (i.e. age, gender, race), in the aggregate, will experience heat-related mortality, as compared to their same demographic group at optimal temperatures. At optimal temperatures, the relative risk mortality is a value of one. A relative risk greater than one indicates that the risk of death at a given high temperature is greater than the risk at the optimal temperature.

Figure 3 illustrates curves of the meta-analytic association between temperature and mortality across the twenty U.S. counties, and in Los Angeles, Cook, Wayne, Kings, and

Maricopa Counties, in 1987-2006, presenting the overall relative risk of heat-related mortality.

Meta-analytic association between temperature and mortality by age across the twenty U.S. counties and in Los Angeles, Cook, Wayne, Kings, and Maricopa Counties in 1987-2006 are

presented in Figure 4. Meta-analytic association between temperature and mortality by gender across the twenty U.S. counties and in Los Angeles, Cook, Wayne, Kings, and Maricopa

Counties in 1987-2006 are presented in Figure 5. Meta-analytic association between temperature and mortality by race across the twenty U.S. counties and in Los Angeles, Cook, Wayne, Kings, and Maricopa Counties in 1987-2006 are presented in Figure 6.

These curves coincide with the definition of heat used to determine the fraction of death attributable to heat in the following tables. The tables that compliment the figures are divided by age, gender, and race, comparing age groups, females and males, and Whites and Blacks. The total number of death from all causes of mortality is presented in the first column of each table.

The fraction of the total number of deaths that each demographic group contributes to the total number of deaths is represented parentheses in the same column.

The second column presents the fraction of deaths attributable to heat. The fraction of deaths attributable to heat is the fraction of deaths attributed to heat on days that are above the heat threshold. Heat threshold is defined as all temperature greater than the county-specific minimum mortality temperature. The fraction of deaths attributable to extreme heat are presented in the third column of each table. Extreme heat is defined as all temperature greater than the

th 97.5 percentile of the county-specific temperature distribution. The absolute number of deaths attributed to heat are presented in last column of each table.

Heat-related mortality overall

Figure 3. Meta-analytic association across the 20 US counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties overall 1987-2006.

Figure 3, presented above, shows the average relative risk of mortality at temperatures greater than the county-specific minimum mortality temperature, which will be referred to as

“heat-related mortality,” averaged across the twenty counties examined and highlighted in five specific counties: Los Angeles County, CA, Cook County, IL, Wayne County, MI, Kings

County, NY, and Maricopa County, AZ.

The first curve, the “Meta-analytic association across 20 U.S. counties - overall;

1987-2006,” presents the cumulative curve for the relative risk of mortality at each temperature percentile. The shaded lines indicated the confidence intervals for the estimated relative risk of mortality at each temperature. The relative risk for mortality is 1 at the optimal temperature threshold, also known as the minimum temperature for heat-related mortality to occur. This occurs at approximately the 80th temperature percentile. At the 0th temperature percentile the relative risk for mortality is approximately 1.2, indicating a 20 percent increased risk for cold-related mortality. The relative risk steadily declines until the 80th percentile, when it reaches 1, and then increased to 1.5 at the 100th percentile, indicating a 50 percent increased risk for heat-related mortality.

The results indicate that in each county there is a different threshold in which heat becomes lethal. In counties that have warmer climates, the residents are acclimatized to warmer weather and heat does not become lethal until higher temperatures. Furthermore, heat is less lethal in warmer climates that are acclimatized to heat and more lethal in cooler climates that are not acclimatized to heat. For example, in Kings County, NY heat becomes lethal around 71 degrees Fahrenheit, and relative risk for heat-related mortality reaches approximately 1.7.

Whereas in in Maricopa County, AZ heat does not become lethal until around 94 degrees

Fahrenheit and the relative risk for heat-related mortality reaches approximately 1.4. This is also highlighted in Cook County, IL where minimum temperature for heat-related mortality occurs at approximately 80 degrees Fahrenheit and presents the highest relative risk for heat-related mortality, reaching 2.5 at approximately 90 degrees Fahrenheit, which is significantly higher than the average of the twenty counties.

It is interesting to note that in Los Angeles County, CA, the minimum temperature for heat-related mortality to occur is approximately 65 degrees Fahrenheit, despite having a warmer climate. However, the relative risk for heat-related mortality in Los Angeles County, CA remains essentially at one until approximately 80 degrees Fahrenheit, at which point it rises to approximately 1.5 at around 90 degrees Fahrenheit. Thus, despite having a minimum temperature for heat-related mortality of 65 degrees Fahrenheit, increased risk of heat-related deaths may not actually occur until around 80 degrees Fahrenheit, which coincides with the other counties located in warm climates.

Table 1. Death and heat across 20 U.S. counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties overall 1987-2006.

County Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat 1987-2006 in 20 US heat counties

Los Angeles, CA 1,125,678 0.7 0.2 7880

Cook, IL 854,591 0.5 0.4 4273

Wayne, MI 379,383 0.4 0.3 1518

Kings, NY 375,980 1.7 0.6 6392

Maricopa, AZ 362,167 0.2 0.2 724

All 20 counties 7,172,260 0.7 0.3 50206

In total, there were approximately 7,172,260 deaths due to all-cause mortality in the twenty counties examined in this study during the study period (Table 1). The average fraction of death attributed to heat in the twenty counties over the twenty year study period was 0.7 percent.

Thus, approximately 50,206 (95% eCI: 35,861, 64,550) deaths were attributed to heat over the twenty year period, averaging at about 2,510 deaths in these twenty counties in United States attributable to heat each year. The full results for each of the twenty counties are shown in the

Appendix.

The total fraction of deaths attributed to heat in the United States remains under two percent in each of the twenty counties, though this was not the case when examining individual demographic groups, detailed below. Only four of the twenty counties had total fractions of death attributable to heat greater than one percent. Palm Beach County, FL had the highest fraction of death attributed to heat at 1.9 percent, followed by New York County, NY and Bronx

County, NY at 1.8 percent, followed by Kings County, NY at 1.7 percent. The fraction of death attributed to extreme heat remained below one percent across the twenty counties. The fraction of death attributable to extreme heat was much smaller across each county than fraction of death attributable to heat.

Although heat may not constitute a large portion of deaths in these twenty counties, the absolute number of deaths attributable to heat was nevertheless significant. Los Angeles County,

CA had the highest absolute number of heat-related mortality in this study at 7,880 deaths.

However, the fraction of death attributable to heat was only 0.7 percent. While Los Angeles

County, CA had the highest number of deaths attributed to heat, it also had the highest absolute number of deaths overall, illustrating how a smaller fraction of death due to heat may yield a

significant issue. New York County, NY, Philadelphia County, PA, Cook County, IL, Palm

Beach County, FL all had approximately 4,000 deaths due to heat throughout the study period, as shown in Table 1.1 in the Appendix. However, the fraction of death attributable to heat ranged in each county.

Table 1 compliments Figure 3 in illustrating the smaller relative risk of heat-related mortality for individuals in warmer climates. Maricopa County, AZ had the lowest fraction of death attributable to heat and the lowest absolute number of heat-related deaths of the five counties highlighted. Conversely, Kings County, NY had the highest fraction of death attributable to heat and the second highest absolute number of heat-related deaths of the five counties highlighted.

Heat-related mortality and age

Figure 4. Meta-analytic association across the 20 US counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by age 1987-2006.

Figure 4 presents the relative risk of death at each temperature for age groups below 65 and 65-74, 75-84, and 85 plus. The first curve presents the average relative risk for heat-related mortality of the twenty counties in this study. The blue lines indicates the relative risk of heat-related mortality for individuals 0-64 years old and the red line indicates the relative risk for heat-related mortality for individuals older than 65. The shaded area represents the confidence intervals for the estimated relative risk of mortality at each temperature. Across the twenty counties, above the optimal temperature threshold, which occurred at the 80th percentile for temperature, individuals aged 65 and above and individuals aged 0-64 years old have the same relative risk for heat-related mortality. Once the temperature exceeds the optimal threshold, the relative risk for heat-related death spikes. At the 80th temperature percentile, the optimal temperature threshold, the relative risk for heat-related death is one. At the 100th percentile for temperature, the relative risk for heat-related death is approximately 1.5. The relative risks of both age groups remains equivalent in this portion of the curve.

Below the optimal temperature threshold, however, individuals aged 65 and above have higher relative risk for heat-related mortality than individuals aged 0-64 years old. At the 0th temperature percentile, the relative risk for mortality for individuals aged 65 and older is approximately 1.35, and this risk steadily declines from 1.35 at the 0th percentile to one at the

80th percentile. For individuals aged 0-64, at the 0th temperature percentile, the relative risk for mortality was slightly below one, then it rose to approximately 1.15 at the 5th temperature percentile and declined back to one at the 80th temperature percentile.

Similar to Figure 3, Figure 4 also shows that the minimum temperature for heat-related mortality varies by county. Above the optimal temperature threshold, the 75-84 age groups in

Cook County, IL Wayne County, MI and Bronx County, NY and the 85 plus age group in Cook

County, IL had the highest relative risk of deaths at approximately 3.5 and the 65-74 age groups in King County, NY and San Diego County, CA had the second highest relative risk of mortality at approximately 3.4 (Figure 4.1).

Individuals older than 65 had much lower relative risk of heat-related mortality in warmer climates, Los Angeles County, CA and Maricopa County, AZ, than in cooler climates,

Kings County, NY, Cook County, IL, and Wayne County, MI.

Table 2. Death and heat across 20 U.S. counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by age 1987-2006.

Total number of deaths Fraction of deaths attributable Fraction of deaths attributable Number of deaths attributable occurring between 1987-2006 to heat to extreme heat to heat

in 20 US counties 0-64 65-74 75-84 85+ 65+ 0-64 65-74 75-84 85+ 65+ 0-64 65-74 75-84 85+ 65+ Count 0-64 years years years years years years years years years years years years years years years 65-74 75-84 85+ 65+ y years [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] [%] years years years years Los 301649 219893 315066 288886 823845 Angele 0.1 0.2 0.6 1.2 0.5 0.1 0.2 0.2 0.2 0.2 302 440 1890 3467 4119 (26.8) (19.5) (28.0) (25.7) (73.2) s, CA Cook, 229924 178115 242503 203935 624553 0.4 0.4 0.6 0.6 0.5 0.4 0.4 0.5 0.5 0.5 920 712 1455 1224 3123 IL (26.9) (20.8) (28.4) (23.9) (73.1) Wayne 111665 83466 105960 78237 267663 1.6 0.3 0.8 0.9 0.5 0.2 0.3 0.6 0.2 0.4 1787 250 848 704 1338 , MI (29.4) (22.0) (27.9) (20.6) (70.6) Kings, 119248 72687 98059 85979 256725 1.5 1.3 1.5 2.5 1.8 0.4 0.7 0.6 0.7 0.7 1789 945 1471 2149 4621 NY (31.7) (19.3) (26.1) (22.9) (68.3) Marico 83703 73457 111920 93006 278383 0.3 0.2 0.3 0 0.2 0.2 0.2 0.3 0 0.2 251 147 336 0 557 pa, AZ (23.1) (20.3) (30.9) (25.7) (76.9) All 20 183543 142385 206563 184629 533579 countie 0.8 1.2 0.7 0.9 0.6 0.3 0.3 0.3 0.3 0.3 14684 17086 14459 16617 32015 8 5 9 8 2 s

The majority of deaths across the twenty counties in this study occurred in individuals over the age of 65. During the course of the study, across the twenty counties, 1,835,438 individuals under the age of 65 died and 5,335,792 individuals over the age of 65 died. Across the five counties highlighted, the percent of the population above 65 years old that died ranged from 68.3 percent to 76.9 percent. The percent of the population that died over the age of 65 was

divided fairly equally among the five counties highlighted. Across all twenty counties averaged, the 75-84 years age group had the highest percent of deaths in the study sample of any age group, followed by the 85 plus age group, followed by the 65-74 years age group.

Interestingly, across all twenty counties averaged, the fraction of death attributable to heat was higher among the 0-64 years age group, at 0.8 percent, than among the 65 plus age group, at 0.6 percent. Overall, the 65-74 years age group had the highest fraction of deaths attributable to heat at 1.2 percent. Of the five counties highlighted, the highest fraction of death attributable to heat was among the 85 plus age group in Kings County, NY at 2.5 percent.

Across the twenty counties, both groups of people aged 0-64 years old and 65 years and older had a fraction of death attributable to extreme heat of 0.3 percent. Across the five counties highlighted, and among the twenty counties overall, the fraction of death attributable to extreme heat remained under one percent (Table 2.1).

Despite a higher fraction of death attributable to heat among the 0-64 age group, the absolute number of deaths attributable to heat across the twenty counties was lower in this age group as compared to the 65 plus age group. Over the course of this study, across the twenty counties, there were approximately 14,684 deaths due to heat of individuals aged 0-64, whereas there were approximately 32,015 deaths due to heat of individuals aged 65 and older. Among the five counties highlighted, the highest number of deaths attributable to heat was 4,621 in the 65 plus age group in Kings County, NY. This same age group in Kings County, NY had the second highest fraction of death attributable to heat at 1.8 percent.

Heat-related mortality and gender

Figure 5. Meta-analytic association across the 20 US counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by gender 1987-2006.

In Figure 5, the blue line indicates the relative risk of heat-related mortality for males at each temperature above the county-specific minimum mortality temperature and the red line indicates the relative risk of heat-related mortality for females at each temperature above the county-specific minimum mortality temperature. The shaded lines indicate the confidence intervals for the estimated relative risk of mortality at each temperature. In each county, the temperature at which a female’s relative risk of death equals one and a male’s relative risk of death equals one are equivalent.

Across the twenty counties averaged, the relative risk for heat-related mortality for males and females were close to equivalent, both above and below the optimal temperature threshold, at the 80th temperature interval. At the 0th temperature interval males had a relative risk of mortality slightly lower than 1.2 and females had a relative risk of mortality that was approximately 1.25. The two curves merged around the 1st temperature percentile and both decreased to a relative risk of mortality of 1 at the 80th temperature interval. Above the 80th temperature percentile, the relative risk of mortality for both males and females increase in tandem and at the 100th temperature percentile the relative risk of mortality for females was approximately 1.4 and the relative risk of mortality for males was approximately 1.5. Overall, females and males had a similar relative risk for heat-related mortality.

Of the twenty counties examined in this study (Figure 5.1), approximately 11 counties,

Los Angeles County, CA, Cook County, IL, Wayne County, MI, Kings County, NY, Harris

County, TX, Queens County, NY, Broward County, FL, New York County, NY, Dallas County,

TX, Middlesex County, MA, and Bronx County, NY, presented equal relative risk of mortality for males and females at each temperature.

Of the twenty counties examined in this study, approximately 7 counties, Maricopa

County, AZ, San Diego County, CA, Philadelphia County, PA, Orange County, CA, Cuyahoga

County, OH, Pinellas County, FL, and Palm Beach County, FL, presented higher relative risk of death at each temperature above the county-specific minimum mortality temperature for males than females. The smallest discrepancy was in Pinellas County, FL, in which males had a higher relative risk of about 0.2. The largest discrepancy was in San Diego County, CA, in which males had a higher relative risk of about 3.

Table 3. Death and heat across 20 U.S. counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by gender 1987-2006.

Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat 1987-2006 in 20 US heat counties

County Male [%] Female [%] Male [%] Female [%] Male [%] Female [%] Male Female

Los 553840 571838 0.1 0.7 0.1 0.2 554 4003 Angeles, (49.2) (50.8) CA

Cook, IL 414603 439988 0.5 0.5 0.4 0.5 2073 2200 (48.5) (51.5)

Wayne, MI 186942 192441 0.3 0.5 0.2 0.3 561 962 (49.3) (50.7)

Kings, NY 181900 194080 1.3 2.1 0.5 0.7 2365 4076 (48.4) (51.6)

Maricopa, 182975 179192 0.3 0.1 0.3 0.1 549 179 AZ (50.5) (49.5)

All 20 3494436 3677824 0.5 0.9 0.3 0.3 17472 33100 counties

Table 3 shows that the gender distribution of the total number of deaths was essentially even across the twenty counties and in the five counties highlighted. Fifteen of the twenty counties had higher rates of mortality occur in females than males; this discrepancy ranged from

1.4% to 9.6% (Table 3.1). In the five counties with higher rates of mortality in males than females, the discrepancy ranges from 0.6% to 2.1% (Table 3.1). This indicates that females overall have a slightly higher rates of all-cause mortality than males. The range of discrepancy in the rate of all-cause mortality of males and females demonstrates that each county has unique characteristics that cause one gender to have increased rates of mortality, however these characteristics are not explained in the data from this study.

On average, over the course of the study period in the twenty counties, 0.5 percent of deaths in males were attributed to heat and 0.9 percent of deaths in females were attributed to heat. Approximately 3,494,436 males and 3,677,824 females died in the twenty counties over the course of the study period, thus approximately 17,472 males died due to heat in the twenty counties during the study period, whereas approximately 33,100 females died due to heat in the twenty counties during the study period. In eleven of the twenty counties, the fraction of death attributable to heat was higher in females than in males. In one county the fraction of death attributable to heat was equal between females and males. In eight counties the fraction of death attributable to heat was higher in males than females (Table 3.1).

Of the five counties highlighted, Los Angeles County, CA presented the largest discrepancy between males and females in the absolute number of heat-related deaths. Over the course of the study, approximately 554 males in Los Angeles County, CA died due to heat, whereas approximately 4,003 females died due to heat. The discrepancy in the fraction of death

attributable to heat in Los Angeles County, CA was approximately 0.6 percent among males and females, with males at 0.1 percent and females at 0.7 percent. The largest discrepancy in the fraction of death attributed to heat among the five counties highlighted occurred in Kings

County, NY with males at 1.3 and females at 2.1. However, this discrepancy in the number of deaths attributable to heat was not the highest among the five counties highlighted.

Between males and females, the difference between the fraction of death attributable to extreme heat ranged from 0 to 0.3 percent, with females having higher rates in ten counties, males having higher rates in seven counties, and males and females having equal rates of the fraction of death attributable to extreme heat in three counties (Table 3.1). Overall, males and females had the same fraction of death attributable to extreme heat across the twenty counties at

0.3 percent. The discrepancy in the fraction of death attributable to extreme heat among males and females in the five counties highlighted was small, ranging from 0 to 0.2 percent.

Heat-related mortality and race

Figure 6. Meta-analytic association across the 20 US counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by race 1987-2006.

Across the twenty counties, on average, above the optimal temperature threshold, which occurred at approximately the 80th temperature percentile, Black people had a higher relative risk of heat-related mortality than White people. The relative risk for Black and White people began to diverge around the 95th temperature percentile. Between the 80th and 100th percentiles, both the relative risk for Black and White people increased steadily, however, the relative risk for White people increased from 1 to 1.4, whereas the relative risk for Black people increased from 1 to 1.8. Below the optimal temperature threshold, Black and White people had approximately equal relative risks of heat-related mortality. Both White and Black people had approximately a relative risk of 1.2 at 0th percentile, which decreased steadily to a relative risk of 1 at the 80th percentile.

Each county examined presents vastly different relative risk of heat-related mortality curves for race. The two counties with the highest relative risk of heat-related mortality curves were Los Angeles County, CA and Cook County, IL, with a relative risk for heat-related mortality for Black people of approximately 3.5 for Cook County and 3.25 for Los Angeles

County. In Cook County the relative risk for heat-related mortality for White people was approximately 2.5, but in Los Angeles the relative risk for heat-related mortality for White people was only 1.3. These counties not only presented the highest relative risk of heat-related mortality for Black people, but also the largest discrepancies in relative risk for White people and

Black people. In Los Angeles County, CA the discrepancy of relative risk for heat-related mortality between Black and White people was approximately 2 and in and Cook County, IL this discrepancy was approximately 1.

In Maricopa County, AZ, the relative risk of heat-related mortality was slightly higher for

Black people than White people, at 2.1, compared to 1.4. The relative risks of heat-related mortality in Kings County, NY and in Wayne County, MI were approximately equal between

Black people and White people.

Table 4. Death and heat across 20 U.S. counties and breakout by Los Angeles, Cook, Wayne, Kings, and Maricopa Counties by race 1987-2006.

Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat 1987-2006 in 20 US heat counties

County White [%] Black [%] White [%] Black [%] White [%] Black [%] White Black

Los 888781 155583 0.6 0.3 0.2 0.3 5333 467 Angeles, (79.0) (13.8) CA

Cook, IL 607081 235879 0.4 0.7 0.3 0.6 2428 1651 (71.0) (27.6)

Wayne, MI 222947 154207 0.4 0.4 0.3 0.2 892 617 (58.8) (40.6)

Kings, NY 249029 119107 1.9 1.4 0.6 0.6 4732 1667 (66.2) (31.7)

Maricopa, 344989 10936 (3.0) 0.2 4.5 0.2 0.9 690 492 AZ (95.3)

All 20 5642535 1338253 0.6 1.3 0.2 0.5 33855 17397 counties

Across the twenty counties, over the course of this study, approximately 5,642,535 White individuals died, whereas only 1,338,253 Black individuals died. Across the twenty counties, the fraction of death attributable to heat for White people was 0.6 percent, whereas the fraction of death attributable to heat for Black people was 1.3 percent. Throughout the twenty counties in

the United States during this study, approximately 33,855 White individuals died due to heat and

17,397 Black individuals died due to heat. Despite more than four times as many total deaths,

White people only had twice as many more heat-related deaths than Black people, demonstrating the heightened risk of Black people to heat-related death.

In five of the twenty counties, the fraction of death attributable to heat was higher in

White people than Black people. In fourteen of the twenty counties, the fraction of death attributable to heat was higher in Black people than White people. In one county the fraction of death attributable to heat was equal between White and Black people. In each county, the majority of mortality was experienced by White individuals (Table 4.1).

In San Diego County, CA, the fraction of death attributed to heat for Black people was

12.5 percent (Table 4.1). This is the largest fraction of death attributed to heat of any demographic group in this study. During the time period of this study, in San Diego 14,954

Black individuals died. Thus, this model estimates that approximately 1,869 Black individuals died due to heat in San Diego in the twenty year period. Although the fraction of death attributable to heat in San Diego was substantially smaller for White people, at 0.2 percent, approximate 355,112 White individuals died in San Diego during this study period, thus attributing approximately 6,231 deaths of White individuals due to heat. Despite higher percentages of death due to heat for Black people in San Diego, the absolute value of number of deaths due to heat was significantly higher for White people than Black people (Table 4.1).

The second highest fraction of death attributed to heat was also among Black people in

Pinellas County, FL at seven percent. However, this number is only approximately 848 deaths

due to heat of Black individuals. The fraction of death attributable to heat for White people was 0 percent in Pinellas County, FL (Table 4.1).

Of the five counties highlighted, Maricopa County, AZ presented the largest discrepancy in the fraction of deaths attributable to heat, with White people at 0.2 percent and Black people at

4.5 percent. However, despite having a substantially higher fraction of death due to heat, the absolute number of deaths attributable to heat of Black individuals was smaller than that of

White individuals (690 vs. 492).

Between White and Black people, the differences of the fraction of death attributable to extreme heat ranged from 0 to 0.7 percent, with Black people having higher rates in eleven counties, White people having higher rates in six counties, and Black and White people having equal rates of the fraction of death attributable to extreme heat in three counties. Overall, the fraction of death attributed to extreme heat was 0.2 percent for White people and 0.5 percent for

Black people (Table 4).

Discussion

The data in this study that model the relative risk of heat-related mortality, fraction of death due to heat, fraction of death due to extreme heat, and the number of deaths due to heat are descriptive. The data illustrate national and local trends of heat-related mortality over the course of the twenty year period. However, the data are not causative, providing reasoning as to why one demographic group may have higher rates of heat-related mortality.

In this study of twenty United States counties, we estimate that, on average, there were approximately 50,206 (95% eCI: 35,861, 64,550) deaths attributable to heat over the course of the twenty year study, averaging 2,510 heat-related deaths each year. This number is substantially larger than estimates from the CDC, which obtained estimates through death records for cases specifically coded as death due to heat, claiming that approximately 658 people die each year in the United States due to heat (CDC, 2013).

The results presented varying information about which demographic groups (age, gender, race) had the highest relative risk of heat-related mortality, the highest fraction of death attributable to heat, the highest fraction of death attributable to extreme heat, and the highest absolute number of deaths attributable to heat.

Cook County, IL had the highest relative risk of heat-related mortality among all of the counties across age, gender, and race. Age group 74-85, females, and Black people all had a relative risk of heat-related mortality of 3.5 in Cook County, IL. The fraction of death attributable to heat in Cook County, IL was 0.5 percent. These results support evidence that suggests that heat waves are more lethal in cooler climates, where the residents are not acclimatized to heat. Furthermore, this evidence supports existing literature that vulnerable

groups in society (the elderly, females, and racially marginalized groups) are at heightened risk for heat-related mortality (CDC, 2012).

Heat-related mortality and age

The data from this study are consistent with overarching themes that exist in current literature that indicate age is the largest determinant of relative risk of heat-related mortality, regardless of gender and race (Anderson & Bell, 2009; Fouillet et al., 2006; Whitman et al.,

1997; Conti et al., 2005; Huynen et al., 2001; Stafoggia et al., 2006; D'Ippoliti et al., 2010; McGeehin & Mirabelli, 2001; CDC, 2017). This is demonstrated across the twenty counties, with the majority of deaths attributable to heat (32,015 out of 50,206) occurring in individuals aged 65 and older (Table 2), while only approximately 15.6 percent of the population is over the age of 65 (US Census Bureau, 2018).

However, it is interesting to note that the fraction of death attributable to heat for age group 0-64 was 0.8 percent, whereas the fraction of death attributable to heat for age group 65 plus was 0.6 percent. Despite having a higher fraction of death attributable to heat, age group

0-64 had a overall lower total number of all-cause deaths, and thus had a lower overall number of deaths attributed to heat than age group 65 plus.

Among the twenty counties overall, the largest fraction of death attributable to heat occurred in age group 65-74 was 1.2 percent. This age group also had the highest number of deaths due to heat, at 17,086. Increased attention should be given to this age group when creating heat mitigation strategies.

Heat-related mortality and gender

The results from this study indicate that females have both a higher attributable fraction of death due to heat than males (0.9 percent vs. 0.5 percent) and a higher absolute number of heat-related mortality than men (33,100 vs. 17,472) (Table 3). The results show that overall females have a similar relative risk of heat-related mortality to males (Figure 5).

The present study supports existing literature that claims that females have higher rates of heat-related mortality than males (Table 3) (Bell et al., 2008; Ishigami et al., 2008; Vaneckova et al., 2008; Hajat et al., 2007; Stafoggia et al., 2006). However, the results of higher relative risk of heat-related mortality for females was not consistent across all counties. In some counties, males had a higher relative risk of heat-related mortality, aligning with CDC data (CDC, 2013). The results from this study indicate that local demographic and socioeconomic trends are key determinants of which groups are most vulnerable to heat-related mortality.

Heat-related mortality and race

The results from this study may shed light on the discrepancy in existing literature over whether Black or White people are more vulnerable to heat-related mortality. This study affirms studies that indicate Black people have higher rates of heat-related mortality than Whites (Schmeltz et al., 2016; Gronlund, 2014; Hansen et al., 2013; Knowlton, 2008; O’Neill et al., 2005; Schwartz, 2005; Jesdale et al., 2013), but in the United States there are lower overall numbers of heat related-mortality of Black individuals than Whites (CDC, 2013; CDC, 2017).

This study supports the claims that ethnic minority groups have higher rates of exposure to heat stress (Harlan et al., 2006) as evidenced by the higher fraction of death attributable to

heat. This study shows that, despite higher percentages of heat-related mortality in Black people, the absolute number of deaths due to heat is higher in White people than in Black people. Thus,

Black people may have a higher relative risk of heat-related mortality, but overall lower absolute numbers of heat-related mortality.

Socioeconomic conditions

This study obtained mixed results regarding gender and race as having the highest relative risk of heat-related mortality, in both absolute and comparative terms. Thus, the data show that the determinants of who is most vulnerable to heat-related mortality in each county may be largely determined by local demographics and local conditions. The data support claims by previous authors that socioeconomic status, which is often proxied by age, gender, and race, is a crucial determinant of an individual's relative risk for heat-related mortality (Kovats & Hajat,

2008).

For example, in Los Angeles County, 13.8 percent of those who died were Black, however, only 9 percent of the population is Black (US Census Bureau, 2010). Black people have higher percentages of the total mortality than their percentage of the population.

Furthermore, 73.2 percent of those who died were older than 65, but only 13.2 percent of the county was older than 65 (US Census Bureau, 2010). The elderly and Black people are marginalized groups and thus have disproportionate death rates.

Limitations

The data from this study present the demographic distributions of the people who died due to heat in each county. Limitations of this study may include the diagnostic criteria for heat-related mortality. The diagnosis of heat-related mortality in this study is based upon a defining “heat” as all temperatures greater than the county-specific temperature of minimum

th mortality, and “extreme heat” constituted temperatures greater than the 97.5 percentile of the county-specific temperature distribution. This definition of heat may deviate from other studies.

Another limitation of this data may be that the data do not weight these percentages based upon the demographic distributions of each county. Thus, there may be a higher number of death among one racial group (i.e. Whites) because a county has a higher percentage of that race in its population. To overcome this, our data presented the fraction of death attributable to heat, which demonstrated the impact of heat within demographic groups. This study demonstrated that, although the fraction of death attributable to heat may be low in some counties, the absolute number of deaths is still significant and policies must be created to mitigate these deaths.

PART THREE:

POLICY IMPLICATIONS

Heat will continue to be a major public health and economic threat to the United States as the climate continues to warm. Yet, extreme heat and rising temperatures do not leave a stark reminder of their ruin, a trail of destruction. Signs of a heat wave dissipate as soon as the hot weather does. Thus, despite widespread consensus on the impacts of heat, public recognition and action remain sparse.

As the present results indicate, each community in the United States will feel the impacts of heat in unique manners and the risk of demographic groups varies by community. Changes in climate and demographic shifts within the United States necessitate novel responses to heat.

Local policy solutions that take into account specific, current and future, community demographics are needed. This section will review local policy measures that have been put in place throughout the United States to combat heat-related mortality and morbidity and offer pathways forward.

United States Climate Projections

Historically, heat waves and extreme heat events have been substantial public health concerns in the United States, causing more deaths than all other weather events combined

(Office of Climate, Water, and Weather Services, 2013; Berko et al., 2014). Within the twenty-first century, climate change is projected to intensify the frequency and severity of heat waves in the United States in areas where heat waves already occur (IPCC, 2007). There is evidence suggesting that this increased frequency may have already begun. Between 1949 and

1995, a twenty percent overall increase in the frequency of heat waves occurred in the eastern and western United States (Gaffen & Ross, 1998; Luber & McGeehin, 2008).

It is projected that cities in the Northeastern and Midwestern United States will experience the largest number of heat-related illnesses and mortality due to rising summer temperatures (McGeehin & Mirabelli, 2001). It also is projected that the number of heat waves in

Chicago will rise 25 percent over the period of 2080 to 2099 (Meehl & Tebaldi, 2004). Los

Angeles will experience an increase in the total annual number of heat wave days from 12 to

44-95 over the 2070 to 2099 time period (Hayhoe et al., 2004; Field & Mortsch, 2007). Some estimates suggest that excess annual summer deaths in the United States will rise from 1,840 to

1,981–4,100 by 2020, and by 2050 up to 3,190–4,748 excess deaths will occur each summer

(Kalkstein & Greene, 1997).

United States Demographic Projections

This thesis presents significant information about the vulnerability of elderly populations, genders, and racial minorities in the face of heat. Current information regarding vulnerability to extreme heat, coupled with future demographic projections, may inform targeted heat mitigation interventions and initiatives. It is projected that there will be more than 400 million people in the

United States by the year 2058, with the population growing by approximately 1.8 million people each year between 2017 and 2060. Elderly populations and racial minorities will experience the largest demographic transitions in the coming decades (US Census Bureau, 2017).

Elderly populations have significantly increased in the past century and will continue to increase, augmenting the absolute number of people at risk of heat-related mortality. Over the course of the past century, from 1900 to 2000, the population of people older than 65 grew from

3 million to almost 35 million, and this population is projected to increase to 90 million by 2060.

In 1990, the percent of the population older than 65 was 4.1%, whereas in 2000 the percent of the population older than 65 was 12.4%. In 2060 it is projected that people older than 65 will account for over 20% of the population (Wilmoth & Longino, 2006; Himes, 2001). In the coming decades, elderly women will continue to outnumber elderly men, but the gap will narrow

(US Census Bureau, 2017).

Non-Hispanic Whites are projected to remain the single largest ethnic or racial group for the next 40 years. However, by 2045, it is projected tha Non-Hispanic Whites will no longer make up the majority of the United States. By 2030, it is projected that immigration will overtake natural increase as the primary driver of population growth in the United States. It is projected that the percent of the population that is Black will remain constant, at approximately 13-15 percent, but the percent of the population that is Hispanic will rise from 25 percent in 2016 to 32 percent in 2060 (US Census Bureau, 2017). Changing demographics must be factored into policy and initiatives to effectively and equitable mitigate the effects of rising temperatures.

Heat-related Mortality Projections

Figure 7. Projected Change in Annual Extreme Temperature Mortality.

Image source: United States Global Change Research Program. (2018). Fourth National Climate Assessment: Chapter 14: Human Health. 430 R 17 001. U.S. Global Change Research Program, Washington, DC. - - -

According to the Fourth National Climate Assessment, it is projected that with regards to annual net mortality due to extremely hot and cold days in 49 U.S. cities for 2080-2099 as compared to 1989-2000, there will be an additional 9,300 deaths each year under a higher scenario of climate change (RCP8.5) and 3,900 deaths each year under a lower scenario of climate change (RCP4.5) (USGCRP, 2018). If adaptation measure are taken comparable to those available to Dallas residents today (such as the availability of air-conditioning or physiological adaptation), reduction in mortality may be up to half (Ebi et al., 2018; USGCRP, 2018). The areas of the map with large increases in heat-related mortality coincide with our results, of counties with the highest rates of heat-related mortality, and thus should receive critical attention from local and national policy makers.

Mitigation and Adaptation Measures

Recent literature (2012-2015), on the health impacts of observed and projected increased summer temperatures, suggests that studies based upon projected temperature increases present substantial increases in heat-related mortality and morbidity. Whereas, observational studies, based upon historical climate and health records, indicate a decline in the overall negative health impacts of rising temperatures. The divergence in the literature stems from the extent and effectiveness of mitigation and adaptation measures and how said measures are quantified

(Hondula et al., 2015).

Diminishment of the effects of heat may take the form of local and national initiatives and policies that aim to reduce the impacts of heat before it occurs. Adaptation may take the form of physiological adaptation (or acclimatization), behavioral, infrastructure, and technological adaptation. The ability of a group or an individual to adapt to climate change and rising temperatures may be based upon location, and physical, social, and economic vulnerabilities.

Currently, there is limited quantitative literature regarding the role of adaptation measure in reducing the health impacts of rising temperatures. While some public health studies may use adaptation measures as an explanation for changes in mortality over time, most studies do not directly quantify and analyze adaptation measures in response to heat (Deschenes, 2014).

Adaptation measures will be instrumental in reducing heat-related mortality as the climate continues to warm.

The United States is home to different climates that experience extreme heat in disparate manners. Understanding the differing impacts of heat in a few major metropolitan areas in each

region may shed light on how other cities may grapple with rising temperatures. The following initiatives and case studies will examine local and state adaptation measures and policies to combat extreme heat and reduce public health externalities associated with extreme heat. This analysis will offer pathways forward.

Heat response plans (HRPs)

Effective mitigation of, and adaptation to, heat-related illness and mortality requires that every community in the United States develop a community-specific Heat Response Plan (HRP).

The case studies detailed below illustrate how a few major metropolitan areas throughout the

United States have begun to tackle extreme heat and rising temperatures. However, many municipalities around the country still lack concrete heat response plans or initiatives.

In a study conducted in the summer of 2002, heat-response plans of 18 cities with a history of heat-related mortality were reviewed. Approximately six of the 18 cities lacked any written heat-response plan, including any heat-specific actions incorporated into all-hazard response plans. Ten cities provided explicit heat-response plans (Bernard & McGeehin, 2004).

Preventative public health measures and municipal planning are crucial to preventing heat-related mortality. To begin with, it is essential that a lead government agency and participating organizations are identified as the logistical leaders of implementing heat-response plans. Centralization of leadership is essential to effective implementation of both preventative and reactive public health measures to mitigate heat-related illness and mortality. Cooperation and coordination among multiple city departments, including, but not limited to, public health departments, meteorological departments, and safety departments, and local non-governmental

organizations, is necessary for implementation of heat-response plans. Plans must detail roles and responsibilities of each organization (Bernard & McGeehin, 2004; Luber & McGeehin, 2008). Next, it is necessary to have a uniform and standardized warning system in response to specific weather conditions. Standardized protocol for the activation and deactivation of heat-response plans must be put in place. Each community has differing temperature thresholds associated with excess mortality from heat and thus require unique response measures based upon community-specific metrics (Kalkstein, 2000; Bernard & McGeehin, 2004; Luber &

McGeehin, 2008). Threshold temperature, heat index (incorporating heat and humidity), synoptic air mass method, extremes in diurnal highs and lows, and deviations from local norms should be used to determine the initiation of heat-response plans (Robinson, 2001; Kalkstein & Greene, 1997; Bernard & McGeehin, 2004; Luber & McGeehin, 2008). Initiation of heat-response plans should commence when temperature thresholds are forecasted, not solely when temperature thresholds are reached.

Once a standardized warning system is put in place, standardized communication and public education measures are essential. Coordination between local agencies is key to effective risk communication. Communication and public outreach should be initiated once extreme heat conditions are forecasted and should remain prevalent throughout the entire extreme heat event.

Communication and public education should include information about both community-wide initiatives, such as cooling centers, and individual-level health tips, such as proper hydration and rest (Luber & McGeehin, 2008).

Heat-response plans must cater to vulnerable populations and take into consideration community-specific parameters. Heat-response plans must take into account the location and accessibility of vulnerable populations. Plans should have specific initiatives detailed for differing vulnerable groups that attend to their specific needs. It is crucial that vulnerable populations are identified and consulted in order to create the most effective initiatives for that specific vulnerable population (Luber & McGeehin, 2008). It is essential that qualitative and quantitative data is collected regarding the implementation of heat-response plans for evaluation and revision of said plans. Following extreme heat events, participating agencies and organization should review response activities and should receive feedback from the public. Plans should be revised annually prior to the onset of warm temperatures to confirm response protocol and participation of organizations and personnel (Bernard & McGeehin, 2004; CDC, 1995; Kizer, 2000; Luber & McGeehin, 2008).

Case Study: Milwaukee, Wisconsin

A case study in Milwaukee, Wisconsin illuminates the crucial role public health preparedness and response play in the reduction of heat-related mortality by examining different responses to the 1995 and 1999 heat waves. In 1995, 91 people died in Milwaukee County due to heat, whereas in 1999, only 11 people died due to heat. In 1999, the amount of heat-related deaths and emergency medical service (EMS) runs made were 49% lower than in 1995. In both years, the majority of deaths occurred in people over 65, there were slightly more males that died than females (55% and 57%, respectively), and the majority of those who died were White, though rates per 100,000 deaths were similar among Whites and Blacks. In 1999, there were much lower proportions of deaths occurring in people who lived in low income neighborhoods as compared to 1995 (27% and 55%, respectively) (Weisskopf et al., 2002).

The reduction in deaths of residents from low income neighborhoods suggest that public health measures taken to address vulnerable populations were effective. Following the 1995 heat wave in Milwaukee, Milwaukee’s plan for extreme heat conditions was refined to incorporate new knowledge. The first changes were to assign multijurisdictional leadership for and within the Milwaukee Health Department, and to assign specific roles for over 20 agencies. Springtime preparation, communications tests, and public/professional education efforts were incorporated into the plan. The plan was indexed to local National Weather Service advisory criteria. Stepped responses appropriate to early forecasts were created. The city of Milwaukee and partner agencies sent out mass media alerts via fax and email. The plan detailed and promoted cooling measures other than air-conditioning and created a 24-hour hotline and active Internet-assisted heat-related injury surveillance during heat warning advisories. Due to the UHI effect, elevated nighttime temperatures may be more detrimental to health than daytime temperatures. The 24-hour hotline established received peak calls in the evening and helped address nighttime heat concerns (Weisskopf et al., 2002).

Furthermore, a last major difference between the responses to the 1995 and 1999 heat wave was the issuing of heat advisories. Prior to the 1995 heat wave, there was no heat advisory. Prior to the 1999 heat wave, there were two heat advisories earlier in the month, though the heat indices of the two prior warnings did not reach the highest advisory levels. The two prior heat advisories psychologically prepared residents of Milwaukee and acted as practice runs, contributing to decreased mortality rates (Weisskopf et al., 2002).

Case Study: Maricopa County, Arizona

Maricopa County, Arizona is one of the largest metropolitan areas in the Southwest United States, encompassing Glendale, Mesa, Phoenix, Scottsdale, and other cities, with approximately 4 million residents. Maricopa County experiences temperatures above 110 degrees fahrenheit approximately 26 days per year (Arguez et al., 2012).

Maricopa County, Arizona is among the top twenty counties in the United States for all-cause mortality from 1987-2006 at 362,167 deaths, yet it has a small fraction of deaths attributable to heat at 0.2 percent and relatively few heat-related deaths over the course of the twenty year period, at approximately 724 deaths (Table 1). Maricopa County has a very low relative risk for heat-related mortality, ranging from 1 to 1.4 above optimal temperature threshold (Figure 3).

According to estimates from the Maricopa County Department of Public Health (MCDPH), approximately an average of 87 heat-related deaths and more than 1000 heat-related illnesses occurred annually between 2010 and 2014 (MCDPH, 2015). In 2005, there was an extreme heat event in which 35 people died due to heat over the course of nine days in Maricopa County, Arizona. Following this incident, the City of Phoenix and the Maricopa County Association of Governments (MAG) founded the Heat Relief Network (HRN). The HRN combats heat-related mortality and morbidity through providing cooling centers, disseminating water, and various other measures (MCDPH, 2017). Cooling centers are dispersed throughout the city, often located within community, senior, or religious centers, which provide various services for at least 1,500 individuals each day. The cooling centers serve vulnerable populations, such as the unemployed or homeless (Berisha et al., 2017).

The decline in risk of heat-related illness and mortality in Maricopa County, Arizona may be attributed to public health interventions. In a study conducted during the summer of 2014, 53 cooling centers in Maricopa were evaluated to gauge their characteristics that contribute to or hinder effectiveness during extreme heat events. Data was collected in this study through direct observation of daily operations and through manager and visitor surveys (Berisha et al., 2017).

Of the 53 cooling centers, 48 were operational on weekdays, 20 were operational on Saturdays, and only 11 were operational on Sundays. Only three of the 53 cooling centers in Maricopa County were open to the public 24 hours everyday. Most cooling centers were operational during normal business hours, meaning many people were left at risk to elevated nighttime temperatures and weekends. Providing additional hours for the cooling centers would further reduce the amount of heat-related illness and death within the community (Berisha et al., 2017).

Approximately 1,500-2,000 individuals used the cooling centers each day for heat-relief and other services. The cooling centers provided free water and restrooms. Depending on the

primary function of the cooling center, other services were provided, including, but not limited to, vending machines, food distribution, electrical outlets, wireless Internet access, indoor recreation areas, books, magazines, religious services, and educational services (Berisha et al., 2017).

The majority of the facility managers interviewed for this study said that there was no additional cost to running their facilities as cooling centers. Some managers indicated that costs went up slightly, but due to donations and assistance from the Heat Relief Network, these additional costs did not present a barrier to operation of cooling centers. Limited operating hours, staffing, and budgets were cited as impediments to operation. The managers stated, that with increased resources, they believed it would be beneficial to extend the operating season to include March and April. Furthermore, they would like to provide housing, restrooms, and heat-protection items (i.e. fans, misters, hats, sunscreen) (Berisha et al., 2017).

Ninety percent of the facilities were easily accessible, according to field evaluation teams, and were reached by personal vehicle, walking, or public transportation. Approximately one-quarter of visitors used public transport to reach cooling centers. The general public was alerted of the cooling centers through various forms of communication, including, but not limited to, government and community organization website messages, e-mails, social media posts, regional newspaper ads, religious and community newsletters, posters, public service announcements, and pamphlets within utility bills. Of the 53 cooling centers, only 17 had visible signs outside indicating that the facility was a cooling center that provided refuge and water. There was very little communication between the facility managers and the HRN, approximately only one-third of the managers had communication with the HRN (Berisha et al., 2017).

Many adjustments may be made in order to enhance the effectiveness and equity of the cooling centers. First, improved designation of centers is necessary. Every cooling center should have clearly marked signs in front of the entrances, detailing that the cooling center is a HRN participant site, and the facility hours and services, in both English and Spanish. Second, it is crucial that information on the location of cooling centers is more widely distributed to underserved and marginalized communities. Mass text messages, such as those used for AMBER Alerts, and flyers should be dispersed throughout communities describing the location and hours of cooling centers. Third, due to the high rates of public transportation use to reach cooling centers, cooling centers should collaborate with public transit networks to provide advertisements for said cooling centers and to offer direct routes to said cooling centers. Lastly, the cooling centers should increase public health education about extreme heat and how to increase resilience in the face of rising temperatures (Berisha et al., 2017).

In 2016, Maricopa County, Arizona again experienced anomalously high temperatures and excess heat-related deaths reported by the county health department. Maricopa County experienced 155 heat-related deaths in 2016, approximately 82 percent higher than the standardized prior ten year average (MCDPH, 2017).

Using a time series regression model for the excess heat and heat-related death estimates for 2016, Putnam et al. measured the association between the excess heat and excess deaths to determine the extent to which the heat caused the deaths. They found that the excess heat-related deaths in Maricopa County in 2016 were not related to excess heat. In fact, the model produced average or below average heat-related death rates as compared to historical averages. The relationship between experienced temperatures and deaths should have resulted in approximately 80 deaths in 2016, according to modeled estimates (95% C.I.), however, in reality 155 individuals experienced heat-related mortality. Thus, the authors concluded that factors other than excess heat were responsible for excess heat-related deaths in 2016, such as a substantial increase in the unsheltered homeless population. It is crucial to take into account effect modifiers such as timing and duration, air quality, social attributes, health outcomes observed in previous seasons, and mortality displacement. The authors conclude that exceptionally high temperatures and excess deaths need not be correlated, instead, the strength and effectiveness of public health measures may be a stronger indicator of heat-related mortality outcomes (Putnam et al., 2018).

Vulnerability Mapping

Extreme heat events disproportionately affect poor and minority populations within urban communities. Census data and satellite remote sensing instruments may provide spatial information regarding the residential location of vulnerable populations and surface temperatures, and the UHI effect in those locations (Medina-Ramon et al., 2006).

High-resolution remote sensing technologies can map vegetation, land use, and thermal profiles

(EPA, 2006; Harlan et al., 2006; Sawaya et al., 2003; Patz et al., 2005). Using GIS, this information can be combined with vulnerability indices including, but not limited to, demographic profiles, household income, housing stock, air-conditioning accessibility, and transportation accessibility (Vescovi et al., 2005; Wilhelmi et al., 2004; Luber & McGeehin,

2008). Vulnerability mapping allows local heat response plans to be tailored to the specific needs of communities.

Few studies have begun to examine environmental hazards and social vulnerability hazards and combine these vulnerabilities into heat vulnerability maps (Vescovi et al., 2005;

Reid et al., 2009). In a 2009 study, ten vulnerability variables were used to map vulnerability to heat within communities across the United States - percent of population below the poverty line, percent of population with less than a high school diploma, percent of population of a race other than white, percent of population living alone, percent of population ≥ 65 years of age, percent of population ≥ 65 years of age living alone, percent of census tract area not covered in vegetation, percent of population ever diagnosed with diabetes, percent of households without central AC, and percent of households without any AC (Reid et al., 2009). Four of the ten variables, -- 1. social/environmental vulnerability (combined education/poverty/race/green space), 2. social isolation, 3. air conditioning prevalence, and 4. proportion elderly/diabetes -- explained more than 75 percent of the total variance in vulnerability variables (Reid et al., 2009). This method of mapping vulnerabilities may serve as a template to map local and regional vulnerable populations. These location specific maps may be used to inform local policies and initiatives.

Case Study: New York State

Heat-related morbidity and mortality will rise in New York State as extreme heat events increase in frequency, severity, and duration. However, the effects of extreme heat are not felt uniformly across the state due to environmental and socio-demographic variables (IPCC, 2007). In order to combat heat-related morbidity and mortality, the New York State Department of Health created the Heat Vulnerability Index (HVI), based upon 13 Census tract level environmental and socio-demographic heat-related vulnerability factors identified from prior studies. Four categories that represent differing aspects of heat vulnerability were derived from the 13 environmental and socio-demographic heat-related vulnerability factors. These four categories were 1) language vulnerability; 2) socio-economic vulnerability; 3) environmental and urban vulnerability; and 4) elderly isolation and elderly vulnerability (Nayak et al., 2018).

Implementation of the Heat Vulnerability Index found that spatial variability in heat vulnerability existed across the state. The HVI indicated that metropolitan areas were most vulnerable to heat. Language barriers and socioeconomic disadvantages contributed the most to heat vulnerability. Complimenting data found that areas with higher rates of heat stress coincided with areas with the highest HVI score (Nayak et al., 2018).

The purpose of the HVI is to assist communities and counties in appropriating adaptation resources based upon the unique characteristics of vulnerable populations in that community and to inform long-term heat-mitigation planning efforts in the community based upon these unique characteristics. The HVI may assist local communities is setting up cooling centers for those who do not have access to air conditioning, provide transportation to and from cooling centers, effectively communicate risks with vulnerable populations, and arrange home visits for those in high risk groups who may be immobile, such as the elderly or disabled. The New York State Department of Health also created Heat Vulnerability Index maps in order to display the HVI for each census tract, which was obtained using the 2006-2010 US Census Bureau American Community Survey (ACS) and 2011 National Land Cover Database (NLCD) (New York State Department of Health, 2018; Nayak et al., 2018).

The New York Department of Health Heat Vulnerability Index may serve as a platform for other states, and countries, to replicate in order to identify those who are most vulnerable to heat-related morbidity and mortality. Once vulnerable populations are identified, mitigation efforts may be customized to meet the needs of those populations (Nayak et al., 2018).

Case Study: Philadelphia, PA

Philadelphia is among the growing number of cities across the United States using social and environmental mapping techniques to determine vulnerability to heat. A local Advisory Group, consisting of experts and local decision makers from academia, non-profit organizations, government, and the private sector, spearheaded mapping. The Advisory Group defined vulnerability to extreme heat events as a combination of exposure, sensitivity, and adaptive capacity, using the following equation: exposure + sensitivity - adaptive capacity = vulnerability (Weber et al., 2015).

The project used existing heat-related datasets and products from the National Oceanic and Atmospheric Administration (NOAA), National Weather Services (NWS), and the U.S. Census Bureau. Ground and satellite based data on urban and suburban temperatures and vegetation in Philadelphia over a historical period of 10-30 years helped identify temporal and geospatial trends in heat exposure. MODIS Land Surface Temperature (LST) data and urban and non-urban temperature monitor data identified localized trends of increasing urban extreme heat events. A Philadelphia-specific set of indicators was developed to map exposure, social sensitivity, and vulnerability of urban populations. The heat exposure indicator was merged with data regarding high social sensitivity to produce a vulnerability indicator. Identifying spatial variability of exposure to heat and variable sensitivity and adaptive capacity allows policy makers and city managers to create targeted interventions for vulnerable populations (Weber et al., 2015).

The use of existing heat-related datasets and products allows the mapping conducted in Philadelphia to be generalizable to other cities. Mapping techniques may be used on the neighborhood scale to achieve the most targeted interventions possible. The Philadelphia Department of Public Health and City Planning Commission planned to use the mapping results to update District Plans regarding zoning, public facilities, and infrastructure investments (Weber et al., 2015).

The results of this project were communicated via a report using images, geographic information system (GIS)-compatible files, and Google Earth files and were disseminated to the Advisory Group. A sample .html coded tool was developed to share the resulting maps. Using graphic information to publicize results allows decision makers, and the public, to visualize local sensitivity. Depicting separate aspects of vulnerability allows decision makers to understand what contributes to vulnerability and create targeted mitigation and adaptation strategies. Decision makers may focus on long-term responses to extreme heat, such as increasing vegetation in urban areas, or short-term responses, such as providing social services for vulnerable populations (Weber et al., 2015).

Stakeholder Participation

The knowledge, beliefs, perception, and attitude of the public greatly impact the success of extreme heat mitigation and adaptation plans. Experiences and culture, understanding of the dangers of extreme heat, and approval of an agency can each shape individuals’ support for and compliance with extreme heat management decisions and policies (NOAA, 2015).

Successful resiliency measures to climate change and rising temperatures requires active participation in the development and implementation of resilience practices. To develop effective and equitable extreme heat protocols, it is crucial that all relevant parties participate in the planning process. Local governments, local agencies and nongovernmental organizations, and vulnerable populations must participate in heat response planning. It is essential that existing power structures are addressed to distribute leadership in the heat planning process and all voices are heard throughout the process (Aldunce et al., 2016).

Vulnerable populations should be incorporated into the heat response planning process through surveys, interviews, public meetings, focus groups, hotlines, and internet. At-risk populations are familiar with local social and political landscapes and can incorporate this knowledge into heat response plans. Through assisting in developing heat-response plans, at-risk populations have greater awareness of procedures, leading to successful implementation of plans in the future. Furthermore, different stakeholders may learn from each other and gain a greater understanding of the issue (NOAA, 2015).

Stakeholder participation may also be used following extreme heat events to evaluate response measures. Surveys and focus groups may be used to determine the effectiveness of heat response plans and adjust for future extreme heat events.

Communication and Social Marketing

Communication and social marketing are imperative to efficient and effective heat-response initiatives. Communication may be used to inform, influence, or motivate individuals or the public, while social marketing may be used to develop and distribute products and services to influence behavior (Maibach et al., 2008). A recent study conducted in four major metropolitan areas indicated that while almost 90 percent of respondents were aware of heat warnings, knowledge of what to do in the face of extreme heat was extremely low, and only about half of respondents changed their behavior (Sheridan, 2007).

Not only is it vital to be communicative about what is currently happening with regard to heat and health, but also it is necessary to be communicative about what the future holds and how climate change may affect this future. Recent literature suggests that, despite public outreach and communication regarding the perils of extreme heat, many individuals in vulnerable populations remain unaware, unwilling, or unable to perform appropriate and effective preventative action (Sheridan, 2007). Another study found communication campaigns promoting household disaster preparedness have produced mix results (Mileti et al., 2002).

Successful communication campaigns use simple clear messages, repeated often, by a myriad of trusted sources. Clear messages specify those individuals or groups in society that are at risk, how severe the risks are, and measures that may be taken so diminish risks.

Communication campaigns should be distributed through interpersonal and media channels, in print and electronically, and should be accessible to illiterate individuals or individuals with sensory disabilities. Those trusted sources delivering the communication campaign may include scientists, doctors, community leaders, journalists, etc. (Maibach et al., 2008).

Communication and social marketing efforts must be tailored to targeted audiences. Just as there is no “one size fits all” method for heat response initiatives, there is no “one size fits all” method for heat response communication. To optimize success, campaigns must be customized to address a targeted audiences’ experiences, culture, and behavioral patterns (Maibach, 1993;

Slater, 1995). Targeted audiences may include socioeconomic groups, demographic groups, neighborhoods, school districts, etc.

Case Study: Chicago, IL

A case study in Chicago, Illinois highlights the critical role communication and social marketing, or the lack thereof, play in the death toll of heat waves. In July of 1995, Chicago experienced the most deadly heat wave in recent American history. Estimates suggest that approximately 750 people died due to heat over the course of a single week in Cook County, IL. This perilous heat wave was not necessarily a natural disaster, but a disaster that was the consequence of human error, poor public planning, and inefficient communication (Klinenberg, 2015).

The first front-page article published about the heat wave in the Chicago Tribune, titled, “If you can stand the heat, you must be out-of-towner,” was published the day after temperatures tied all-time record highs, and began with the phrase, “Stop your whining” (Le & Kates, 1995). The Chicago Tribune was not the only media outlet that undervalued the gravity of the situation. Many weather channels treated the heat wave as a local, brief weather phenomenon, rather than the catastrophic event that it would become. Some even made a mockery of the situation, running segments featuring competing meteorologists to decide who was “hotter,” or more attractive (Klinenberg, 2015).

Throughout the heat wave, a consensus was never reached on the severity of the situation. Despite proclamations from the city’s Chief Medical Examiner, the media remained skeptical of government claims, believing that the government had overdramatized the heat wave. Five days into the heat wave, one prominent journalist, Mike Royko, published a piece titled, “Killer Heat Wave or Media Event?” (Royko, 1995; Klinenberg, 2015). The media’s treatment of the situation led to inadequate public response, ultimately culminating in hundreds of unnecessary deaths.

The reaction of the media to the heat wave was in part due to the statements released from the then Mayor Daley’s office. Mayor Daley denied the severity of the situation and blamed deaths on the victims and their families and neighbors. Thus, a media battle ensued between

the Mayor and the Chief Medical Examiner, in which conflicting information was released to the public. Mayor Daley insisted that all appropriate public health measures were being taken by the city, but that it was the responsibility of individuals to take cautionary measures. The public health crisis caused by the heat wave was exacerbated by the diminishment of its severity by the Mayor and the media. Heat-response plans were not put in place until it was too late. The morgue overflowed and hospitals exceeded capacity (Hertz, 2015; Klinenberg, 2015).

The majority of those who died during Chicago’s 1995 Heat Wave were poor, elderly, and socially isolated. Black people, single men, and people living in high-crime neighborhoods were significantly overrepresented in the final death toll. The most significant factor that influenced an individual’s chance of death during this heat wave was their neighborhood of residence (Klinenberg, 2015).

For example, two adjacent neighborhoods on Chicago’s West Side, North Lawndale and South Lawndale, with different racial compositions, experienced dramatically different rates of heat-related mortality. North Lawndale, an almost exclusively impoverished black community, had among the highest death rates among neighborhoods throughout the city. South Lawndale, a predominantly Mexican immigrant community, home to a prosperous commercial district and a firm network of civil organizations, had one of the lowest death rates among neighborhoods throughout the city. While this evidence might suggest that race and income may be the most significant factors that influence an individual’s chance of death, three predominantly black communities on Chicago’s South Side and low-income Latino neighborhoods experienced among the lowest death rates throughout the city, suggesting that neighborhood of residence was more influential than race or income (Klinenberg, 2015).

Evidence from the 1995 Chicago Heat Wave suggests that the existence of robust neighborhood social life is crucial to preventing heat-related mortality. Community businesses, active civil organizations, and crime-free streets contribute to the ability of communities to care for its residents and support one another (Klinenberg, 2015). Strong social networks enable communication of the risks of heat and how to cope with it. Furthermore, social life within communities facilitates assistance among members, especially those who are disabled, elderly, or live alone.

Reducing the Urban Heat Island Effect

The Urban Heat Island (UHI) effect is a major contributor to heat-related mortality in the

United States. As our results indicate, the majority of death occurred in major metropolitan areas.

In addition to public health initiatives and heat response plans, the consequences of extreme heat may be mitigated by reducing the UHI effect. Reducing the UHI effect may reduce heat-related illness and mortality both directly, through lower temperatures, and indirectly, through reduction of energy demand, greenhouse gas emissions, and air-pollution (Luber & McGeehin, 2008). Due to the unique design of each city, it is necessary that city adaptation plans are location specific, but certain urban design plans may be beneficial in reducing heat in any location. To effectively reduce the UHI effect, it is essential that low-albedo surfaces, surfaces that have low reflexivity and absorb the majority of incoming solar radiation in the form of heat, are replaced with high-albedo surfaces, which reflect the majority of incoming solar radiation and trap less heat (Luber & McGeehin, 2008; EPA, 2005). Cities should replace low-albedo surfaces with high albedo surfaces during routine maintenance of roads and buildings (Luber &

McGeehin, 2008; Akbari et al., 2001).

Vegetative cover provides higher albedo surfaces than concrete or building materials.

Increased vegetation throughout urban areas lessens the UHI effect by providing shade and cooling via evaporation. Vegetation may protect against erosion and provide natural air filtration.

Vegetation may consist of trees, grass, or general green spaces. Green roofs, roofs with vegetation and gardens, provide a method of increasing urban green space without redistributing land (EPA, 2005).

Furthermore, surfaces may be converted from low-albedo surfaces to high-albedo surfaces through the installation of cooling roofs and cooling pavements. Cooling roofs and cooling pavement employ highly reflective materials and coatings to reflect sunlight and remain significantly cooler than traditional buildings and pavements (EPA, 2005).

Efficient air conditioning

While air-conditioning has proven to be beneficial for health in many ways, it also has proven to be detrimental to the climate through other mechanisms, which, in turn, may be detrimental to health. Air-conditioning presents negative impacts from the absolute number of units, electricity it uses, and greenhouse gases it produces and emits (Schlossberg, 2016). Energy use from and pollution caused by air-conditioning often causes and exacerbates respiratory and cardiovascular morbidity and mortality.

Furthermore, air-conditioning is predicted to become one of the largest drivers of global electricity demand. It is projected that by 2050 the absolute number of air-conditioning units in use in the United States will rise from current levels of 374 million to 542 million units (IEA,

2018).

As society progresses, it is imperative that the harmful climate and health byproducts of air-conditioning are addressed. To mitigate the impact of air-conditioning on the climate and health, the quantity, efficiency, and emissions of air-conditioners must be reconfigured.

Tightening minimum energy performance standards (MEPS) for air-conditioning equipment are crucial for improving global air-conditioning efficiency and decreasing air-conditioning demand and pollution. According to the IEA, a policy scenario described as the Efficient Cooling

Scenario may reduce global cooling energy demand by 45 percent and double the average air-conditioning efficiency by 2050, as compared to continuing with business as usual practices

(IEA, 2018).

Employing stricter performance standards for air-conditioning will reduce the total global energy demand by diminishing the need for new power plants and energy generation, and the need for power plants to meet peak energy demand. In turn, these steps will assuage climate change. More efficient air-conditioning units as outlined by the Efficient Cooling Scenario would reduce carbon dioxide emissions from space cooling by up to half (IEA, 2018). A reduction in energy usage and carbon dioxide emissions would not only be beneficial to the atmosphere, but also to health. Reduced carbon dioxide emissions would reduce rates of heat-related morbidity and mortality significantly (Bernstein et al., 2004).

Conclusion

This thesis provides evidence that may enhance the predicted effects of climate change on heat-related mortality. The findings suggest the need for more nuanced predictions of health consequences of climate change, taking into consideration individual vulnerabilities, such as age, gender, and race. Furthermore, this thesis has helped to shed light on considerations that need to be made to construct effective adaptation policies. Demographics, age, gender, social determinants of health, socioeconomic and educational status, physical location within the country and city, access to preventative measures, and the response and acclimatization measures of vulnerable populations must be configured into heat-related adaptation initiatives and policies.

The effects of climate change will be felt everywhere throughout the country and the planet. Extreme heat events will increase in frequency and severity and have grave impacts on human health. However, extreme heat will manifest itself differently in disparate regions and demographic groups. Already vulnerable and marginalized communities will face increased risks in the face of climate change and heat. Our findings highlight the distinct effects of heat on local communities throughout the United States.

The results from this thesis indicate that, although the demographic groups with the highest relative risk for heat-related mortality varied by county, overall the elderly, females, and

Black people are at increased risk of heat-related mortality. Furthermore, populations in cooler climates will be more greatly affected by heat-related mortality than those in warmer climates.

The present findings highlight the extent to which the effects of heat-related mortality in the United States are localized, and thus highlight the need for local policies and initiatives that

aim to diminish heat-related mortality. It is crucial that local, grassroots measures are taken to address climate change and heat and their adverse effects on human health.

Studies suggest that projections of heat-related mortality may be more greatly impacted by adaptation measures than future temperature scenarios (Gosling et al., 2017). Examination of adaptation measures of different demographic groups may shed light the risk of each group to heat-related mortality. As the United States continues to warm, it is imperative that the interactions among the physical, social, and economic determinants of heat-related mortality are understood and optimized to create the most efficient and equitable heat adaptation policies and initiatives.

The present data are limited in explaining why certain demographic groups are more vulnerable to heat-related mortality. Further research into the subject would provide information regarding exposure circumstances contributing to risk. Future studies should consider the impacts of housing quality, technology, local topography, urban design and behavioural factors on heat-related mortality in order to further assess vulnerabilities and risks of populations.

Heat-related mortality, a single facet of the impacts of climate change, has been, and will continue to be, an issue of significant implications for health, economics, and social justice as the world continues to warm. This thesis demonstrates the unequal implications of climate change and heat-related mortality by age, gender, and race. Fighting this injustice requires equitable solutions to climate change induced heat-related mortality.

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Appendix

Figure 3.1. Meta-analytic association across each of the 20 US counties overall 1987-2006.

Figure 3.1. Meta-analytic association across each of the 20 US counties overall 1987-2006. (Cont.)

Figure 4.1. Meta-analytic association across each of the 20 US counties by age 1987-2006.

Figure 4.1. Meta-analytic association across each of the 20 US counties by age 1987-2006. (Cont.)

Figure 5.1. Meta-analytic association across each of the 20 US counties by gender 1987-2006.

Figure 5.1. Meta-analytic association across each of the 20 US counties by gender 1987-2006. (Cont.)

Figure 6.1. Meta-analytic association across each of the 20 US counties by race 1987-2006.

Figure 6.1. Meta-analytic association across each of the 20 US counties by race 1987-2006. (Cont.)

Table 1.1. Death and heat across 20 U.S. counties overall 1987-2006.

Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat County 1987-2006 in 20 US heat counties

Los Angeles, CA 1125678 0.7 0.2 7880

Cook, IL 854591 0.5 0.4 4273

Wayne, MI 379383 0.4 0.3 1518

Kings, NY 375980 1.7 0.6 6392

Maricopa, AZ 362167 0.2 0.2 724

San Diego, CA 341963 0.2 0.2 684

Harris, TX 339203 0.4 0.1 1357

Philadelphia, PA 333971 1.3 0.5 4342

Queens, NY 325198 0.5 0.3 1626

Orange, CA 295674 0.4 0.1 1183

Allegheny, PA 288301 0.4 0.3 1153

Cuyahoga, OH 286237 0.5 0.3 1431

Broward, FL 284264 0.1 0 284

New York, NY 248499 1.8 0.6 4473

Dallas, TX 239678 0.4 0.1 959

Pinellas, FL 232176 0 0 0

Palm Beach, FL 218076 1.9 0.3 4143

Middlesex, MA 216051 0.2 0.1 432

Nassau, NY 212841 0.8 0.3 1703

Bronx, NY 212329 1.8 0.7 3822

All 20 counties 7172260 0.7 0.3 50206

Table 3.1. Death and heat across 20 U.S. counties by gender 1987-2006.

Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat 1987-2006 in 20 US heat counties

County Male [%] Female [%] Male [%] Female [%] Male [%] Female [%] Male Female

Los 553840 571838 Angeles, 0.1 0.7 0.1 0.2 554 4003 (49.2) (50.8) CA

414603 439988 Cook, IL 0.5 0.5 0.4 0.5 2073 2200 (48.5) (51.5)

186942 192441 Wayne, MI 0.3 0.5 0.2 0.3 561 962 (49.3) (50.7)

181900 194080 Kings, NY 1.3 2.1 0.5 0.7 2365 4076 (48.4) (51.6)

Maricopa, 182975 179192 0.3 0.1 0.3 0.1 549 179 AZ (50.5) (49.5)

San Diego, 168553 173410 0.3 0.2 0.3 0.1 506 347 CA (49.3) (50.7)

172038 167165 Harris, TX 0.7 0.4 0.1 0.1 1204 669 (50.7) (49.3)

Philadelphia 160081 173890 1.3 1.4 0.6 0.4 2081 2434 , PA (47.9) (52.1)

154831 170367 Queens, NY 0.3 0.8 0.2 0.4 464 1363 (47.6) (52.4)

140549 155125 Orange, CA 0.1 0.6 0.1 0.1 141 931 (47.5) (52.5)

Allegheny, 134446 153855 0.2 0.6 0.1 0.4 269 923 PA (46.6) (53.4)

Cuyahoga, 134925 151312 0.7 0.4 0.5 0.2 944 605 OH (47.1) (52.9)

Broward, 143068 141196 0.2 0.1 0 0.1 286 141 FL (50.3) (49.7)

New York, 124898 123601 2.2 1.6 0.7 0.5 2748 1978 NY (50.3) (49.7)

117199 122479 Dallas, TX 0.1 0.6 0.1 0.2 117 735 (48.9) (51.1)

100

113611 118565 Pinellas, FL 0.1 0 0.1 0 114 0 (48.9) (51.1)

Palm Beach, 111022 107054 0.5 6.3 0.4 0.3 555 6744 FL (50.9) (49.1)

Middlesex, 97595 118456 0 0.4 0 0.1 0 474 MA (45.2) (54.8)

100006 112835 Nassau, NY 1.2 0.5 0.3 0.3 1200 564 (47.0) (53.0)

101354 110975 Bronx, NY 1.5 2 0.6 0.7 1520 2220 (47.7) (52.3)

All 20 3494436 3677824 0.5 0.9 0.3 0.3 17472 33100 counties

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Table 4.1. Death and heat across 20 U.S. counties by race 1987-2006.

Total number of deaths Fraction of deaths Fraction of deaths Number of deaths occurring between attributable to heat attributable to extreme attributable to heat 1987-2006 in 20 US heat counties

County White [%] Black [%] White [%] Black [%] White [%] Black [%] White Black

Los 888781 155583 Angeles, 0.6 0.3 0.2 0.3 5333 467 (79.0) (13.8) CA

607081 235879 Cook, IL 0.4 0.7 0.3 0.6 2428 1651 (71.0) (27.6)

222947 154207 Wayne, MI 0.4 0.4 0.3 0.2 892 617 (58.8) (40.6)

249029 119107 Kings, NY 1.9 1.4 0.6 0.6 4732 1667 (66.2) (31.7)

Maricopa, 344989 10936 (3.0) 0.2 4.5 0.2 0.9 690 492 AZ (95.3)

San Diego, 311552 14954 (4.4) 0.2 12.5 0.2 0.5 623 1869 CA (91.1)

249299 83490 Harris, TX 0 1.2 0 0.3 0 1002 (73.5) (24.6)

Philadelphia 197968 132986 1.1 1.7 0.3 0.8 2178 2261 , PA (59.3) (39.8)

248269 61462 Queens, NY 0.6 0.8 0.3 0.5 1490 492 (76.3) (18.9)

274024 Orange, CA 3127 (1.1) 0.6 0 0.1 0 1644 0 (92.7)

Allegheny, 256479 31192 0.3 1.9 0.2 0.6 769 593 PA (89.0) (10.8)

Cuyahoga, 217910 67468 0.4 0.7 0.3 0.5 872 472 OH (76.1) (23.6)

Broward, 253799 29349 0.1 1.2 0 0 254 352 FL (89.3) (10.3)

New York, 168466 69515 1.7 2.2 0.6 0.5 2864 1529 NY (67.8) (28.0)

Dallas, TX 185071 51767 0.3 3.1 0.2 0.1 555 1605

102

(77.2) (21.6)

219339 Pinellas, FL 12117 (5.2) 0 7 0 0.4 0 848 (94.5)

Palm Beach, 198829 18802 (8.6) 2.8 0.7 0.3 0.3 5567 132 FL (91.2)

Middlesex, 209688 4235 (2.0) 0.3 0 0.1 0 629 0 MA (97.1)

194576 Nassau, NY 16097 (7.6) 0.7 3.1 0.3 0.2 1362 499 (91.4)

144439 65980 Bronx, NY 1.7 2 0.6 0.8 2455 1320 (68.0) (31.1)

All 20 5642535 1338253 0.6 1.3 0.2 0.5 33855 17397 counties

103