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12-2018
The Heat Is On! Perspectives and Practices Regarding Extreme Heat Risk
Emily D. Esplin Utah State University
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This Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. THE HEAT IS ON! PERSPECTIVES AND PRACTICES
REGARDING EXTREME HEAT RISK
by
Emily D. Esplin
A thesis submitted in partial fulfillment of the requirements for the degree
of
MASTER OF SCIENCE
in
Geography
Approved:
______Peter D. Howe, Ph.D. S.-Y. Simon Wang, Ph.D. Major Professor Committee Member
______Courtney Flint, Ph.D. Laurens H. Smith, Ph.D. Committee Member Interim Vice President of Research and Interim Dean of the School of Graduate Studies
UTAH STATE UNIVERSITY Logan, UT
2018 ii
Copyright © Emily D. Esplin 2018
All Rights Reserved
iii ABSTRACT
The Heat Is on! Perspectives and Practices
Regarding Extreme Heat Risk
by
Emily D. Esplin, Master of Science
Utah State University, 2018
Major Professor: Dr. Peter D. Howe Department: Environment and Society
Extreme heat events are the deadliest natural hazard in the US even though heat- related illnesses are largely preventable phenomena. As heat waves intensify due to climate change and more people become exposed due to population growth in the most heat-prone regions, it becomes increasingly important to understand what motivates protective behaviors against heat risks. This includes understanding what heat risk messaging effectively encourages people to implement heat-adaptive practices. Although several studies have examined adaptive capacity and risk communication for many natural hazards, few have examined what influences protective behaviors in extreme heat situations. Building on literature addressing adaptive capacity, experience, and risk communication, this thesis is composed of two studies that ask what influences heat protective behaviors and what is the best way to communicate heat risks to motivate protective actions? Using data from a nationally representative survey, the first study analyzes the spatial, demographic, and experiential influences on heat protective iv behaviors of US populations. In the second study, heat risk communication practices were analyzed using mental model interviews of practitioners responsible for communicating heat risks in the state of Utah. Survey results indicate that some demographic factors were important predictors for certain protective behaviors during a heat wave. Previous experience with heat-related health symptoms strongly predicted all reported heat protective behaviors. Interview results demonstrate that while heat risk communication products in Utah were somewhat unfamiliar dependent on geography and profession, personal experience with extreme heat influenced heat risk decisions and messaging practices of many practitioners. Furthermore, public forecasters have experienced an institutional shift to better measure and communicate the dangers of dry heat in the Intermountain West. These studies support the positive influence of negative impacts on future protective actions and provide insight for professionals seeking to improve heat risk awareness and communication practices.
(162 pages)
v PUBLIC ABSTRACT
The Heat Is on! Perspectives and Practices
Regarding Extreme Heat Risk
Emily D. Esplin
Remembering negative experiences with extreme heat may promote future protective actions and provide insight to improve heat risk awareness and communication practices.
This two-part thesis found 1) that experiencing heat-related health symptoms predicted what Americans would do to protect themselves and others during subsequent heat waves; and 2) that Utah professionals regard heat-related experience as an important factor in how they responded to extreme heat events.
In the first study, a US national survey showed that personal experience with heat- related health symptoms was related to the tendency to say that one engaged in different protective behaviors, while other factors like risk perception and temperature were less related to self-reported behaviors. Sociodemographic factors such as age, race, and gender were related to Americans’ reported efforts to check on other people during a heat wave—with African-Americans, women, and older adults being more likely to do so— but did not have much relationship with how people personally protect themselves.
The second study found that heat experience was an important factor in how public officials and media broadcasters manage extreme heat situations. Interviews of professionals in Utah revealed that experience with heat impacts influenced public forecasters, practitioners, and media members alike in their heat risk decisions and messaging practices even though official heat risk communication products in Utah were vi somewhat unfamiliar. This study also found that public forecasters recently changed how they measure extreme heat to better communicate the dangers of dry heat in the
Intermountain West. This change will likely cause more official heat alerts to be issued in this region. vii ACKNOWLEDGMENTS
I would like to thank my committee members, Peter Howe, Courtney Flint, and
Simon Wang for their insight, encouragement, and feedback. I am especially grateful for my major professor, Dr. Peter Howe, who provided regular support, timely feedback, and wise advice as my research evolved and came to fruition. His patience, respect, and trust in my abilities to learn, grow, and persevere have been paramount to my success. I am grateful for my co-authors, Anthony Leiserowitz and Jennifer Marlon, for showing interest in my work and providing support in preparing my first article for journal submission, and for the collaboration and camaraderie of my lab partner, Yajie Li, for her willingness and diligence as the second coder for my inter-rater reliability analysis.
Financial support was provided in part by two National Science Foundation grants, including a fellowship in the Climate Adaptation Science (CAS) program. Nancy Huntly and CAS gave me the opportunity to apply my research to a practitioner setting in a meaningful way. National Weather Service (NWS) partners, Randy Graham and Brian
McInerney, provided awareness of the new heat risk initiative and direct contact information for potential participants, making this study more applicable to the current practices of heat risk warning and communication systems in Utah and beyond.
Lastly, I am deeply grateful for the support of my family and friends throughout the entire process. I would not have succeeded without their patience, love, and listening ears. This is especially true for my parents, DaLon and Lisa Esplin, whose encouragement and belief in me never end. Gratitude for them, for my grandparents’ viii hospitality, and for my siblings is immeasurable. ‘Thank you’ does little justice for the support my entire family has rendered me.
Emily D. Esplin
ix CONTENTS
Page
ABSTRACT ...... iii
PUBLIC ABSTRACT ...... v
ACKNOWLEDGMENTS ...... vii
LIST OF TABLES ...... x
LIST OF FIGURES ...... xi
CHAPTER
1. INTRODUCTION ...... 1
2. CAN YOU TAKE THE HEAT? HEAT-INDUCED HEALTH SYMPTOMS INFLUENCE PROTECTIVE BEHAVIORS ...... 4
3. IT’S A DRY HEAT: SHIFTING PROFESSIONAL PERSPECTIVES ON EXTREME HEAT RISK IN UTAH ...... 45
4. CONCLUSIONS ...... 79
REFERENCES ...... 83
APPENDICES ...... 101
A. SAMPLE DISTRIBUTION MAP ...... 102 B. RANDOM EFFECTS ODDS RATIOS ...... 103 C. PREDICTED PROBABILITES OF INTERACTION TERM ...... 110 D. ALTERNATIVE DICHOTOMIZATION ...... 112 E. ALTERNATIVE EXPOSURE VARIABLES ...... 115 F. EXPERT MODEL (ORIGINAL) ...... 117 G. STEPS TO A MENTAL MODELS APPROACH ...... 118 H. INTERVIEW PROTOCOL ...... 119 I. INTERVIEW CODEBOOK ...... 135 J. RANKING CODEBOOKS ...... 143 K. PERMISSION LETTERS...... 148
x LIST OF TABLES
Table Page
1 Descriptive statistics of variables used as fixed effects ...... 33
2 Descriptive statistics of the protective behaviors analyzed ...... 34
3 Descriptive statistics for the individual levels of the random effects used in the multi-level models ...... 35
4 Coefficients for fixed effects and number of levels for random effects used in each model ...... 38
5 Results for Checking on Others model ...... 40
6 Demographics of interview sample ...... 71
7 Code mention totals by code group and interviewee type ...... 72
8 Average rankings for extreme heat risks ...... 73
9 Average rankings for heat risk reduction practices ...... 75
10 Alternative dichotomization of dependent variables results ...... 112
11 Checking on others alternative dichotomization results ...... 114
12 Alternative exposure variables model results ...... 115
13 Finalized interview codebook ...... 135
14 Ranking codebook for questions 15-16 ...... 143
15 Ranking codebook for questions 23-24 ...... 146
xi LIST OF FIGURES
Figure Page
1 Conceptual model used to build a multi-level logistic regression model to investigate heat protective behaviors in the United States ...... 41
2 Coefficients of the negative health effects of heat and risk perception for all measured protective behaviors ...... 42
3 Odds ratios for the fixed effects of checking on others during a heat wave ...... 43
4 Marginal effects of checking on others during a heat wave ...... 44
5 Extreme Heat Risk & Warning System Model created in this study ...... 76
6 Top ranked most effective practices by professional group ...... 78
7 Survey sample distribution ...... 102
8 Odds ratios for states ...... 104
9 Odds ratios for regions ...... 105
10 Odds ratios for metro vs. non-metro areas ...... 105
11 Odds ratios for age groups ...... 106
12 Odds ratios for education levels ...... 106
13 Odds ratios for income levels...... 107
14 Odds ratios for ethnic/racial groups ...... 107
15 Odds ratios for gender ...... 108
16 Odds ratios for political ideology ...... 108
17 Odds ratios for the interaction term ...... 109
18 Predicated probabilities between Political Ideology and Education for the interaction term including Political Ideology, Gender, and Education ...... 110
19 Predicated probabilities between Political Ideology and Gender for the interaction term including Political Ideology, Gender, and Education ...... 111 xii
20 Original expert model ...... 117
21 Steps to a mental models approach to risk communication ...... 118
CHAPTER 1
INTRODUCTION
Extreme heat events, or heat waves, are increasing in frequency, duration, and severity (White-Newsome et al. 2011; Akompab et al. 2013; Romero-Lankao et al.
2014b; Sampson et al. 2013; Vose et al. 2017). This trend will continue under most climate change scenarios (Romero-Lankao et al. 2014b; Vose et al. 2017; Sampson et al.
2013; Mora et al. 2017a). Extreme heat as a natural hazard is unique in that it affects individual’s bodies differently even when exposed to the same external conditions. This is because several factors contribute to one’s susceptibility to hot temperatures (Wilhelmi and Hayden 2010; Hayden et al. 2011; Kuras et al. 2017, 2015). Hence, when a heat wave moves through an area, the path of its damage is not obvious or clear as with most other hazards, like a flood or tornado. Damage created by extreme heat is largely experienced by individuals and systems without causing visible, structural failure. While the effects of heat waves are difficult to visually capture, they continue to be the deadliest weather-related hazard in the United States (Bernard and McGeehin 2004; Kalkstein and
Sheridan 2007; Borden and Cutter 2008; NWS 2016). More people are being exposed to extreme temperatures as populations continue to grow the most in heat-prone locations
(Sampson et al. 2013; Anderson and Bell 2009, 2011; Harlan et al. 2006a; Sarofim et al.
2016; Jones et al. 2015). Utah in particular is experiencing high rates of population growth in urban areas with growing exposure to extreme heat (US Census Bureau 2016a;
Davidson 2018; Jones et al. 2015).
Heat risk perception in the US varies widely but correlates with areas that experience heat waves regularly and amongst populations who may experience more exposure due to 2 lack of amenities to mitigate the heat (Howe et al. 2018). If previous experience with heat waves influences one’s risk perception, it may also influence actions to protect oneself or others from the heat. The experience-behavior hypothesis suggests this is possible especially when experience and behavior are defined flexibly and mediating variables are incorporated to more fully understand how experience influences behavior (Jackson
1981; Lindell and Perry 2012; Mishra and Suar 2007; Mishra et al. 2009; Norris et al.
1999; Siegrist and Gutscher 2008; Zaalberg et al. 2009; Wachinger et al. 2013). How extreme heat experience influences future behavior may be better understood by measuring how heat affects one’s health negatively. Experiencing heat-health symptoms is a subjective outcome of heat exposure that could explain the variation of how people respond in future heat events. Dillon et al. (2014, 2011) and Sharma and Patt (2012) emphasize that exposure or experience best predicts future protective behavior if it measures how a hazard negatively impacts someone. Heat-health symptoms are a negative impact of heat exposure.
Understanding what influences protective behavior against extreme heat risk and how to encourage those behaviors is essential to successfully prepare for heat waves and avoid unnecessary injury and loss of life. Thus far, few studies have investigated what influences heat-protective behaviors and even fewer have examined what communication strategies best promote these behaviors (Hawkins et al. 2016). Little is understood about what heat risk messaging is most effective in reaching vulnerable populations and encouraging adaptive practices (World Health Organization 2009; Hawkins et al. 2016).
This thesis explores these gaps in the literature through two separate investigations that address these overarching questions:
3 1) What influences heat protective behaviors in the US?
2) Does experience with heat-health symptoms influence protective
behavior?
3) What risk communication practices promote adaptive capacity for
extreme heat events?
The broad idea of understanding how people engage in heat protective actions ties these questions together. To address these questions two studies with different methods were used. Chapter 2 (Study 1) investigates the idea of using subjective experience of heat- health symptoms as a measure of protective behavior as well as what other factors influence heat protective behaviors in the US. Chapter 3 (Study 2) supports the first chapter’s findings through qualitative interviews that attempt to identify risk communication practices that promote adaptive capacity in extreme heat events in Utah.
4 CHAPTER 2
CAN YOU TAKE THE HEAT? HEAT-INDUCED HEALTH SYMPTOMS
INFLUENCE PROTECTIVE BEHAVIORS1
1. Abstract
The risks associated with extreme heat are increasing as heat waves become more frequent and severe across larger areas. As people begin to experience heat waves more often and in more places, how will individuals respond? Measuring experience with heat simply as exposure to extreme temperatures may not fully capture how people subjectively experience those temperatures or their varied impacts on human health.
These impacts may also influence an individual’s response to heat and motivate risk- reduction behaviors. If subjectively experiencing negative health effects from extreme heat promotes protective actions, these effects could be used alongside temperature exposure to more accurately measure extreme heat experience and inform risk prevention and communication strategies according to local community needs. Using a multi-level regression model, this study analyzes geo-referenced national survey data to assess whether Americans’ exposure to extreme heat and experience with its health effects are associated with self-reported protective behaviors. Subjective experience with heat- related health symptoms strongly predicted all reported protective behaviors while measured heat exposure had a much weaker influence. Risk perception was strongly associated with some behaviors. This study focuses particularly on the practice of checking on family, friends, and neighbors during a heat wave, which can be carried out
1Co-authors: Jennifer R. Marlon, Anthony Leiserowitz, and Peter D. Howe
5 by many people regardless of resources. For this behavior, age, race/ethnicity, gender, and income, along with subjective experience and risk perception, were important predictors. Results suggest that the subjective experience of extreme heat influences health-related behavioral responses and should therefore be considered when designing or improving local heat protection plans.
2. Introduction
Heat waves are increasing in frequency, intensity and duration across the United
States (US) (White-Newsome et al. 2011; Vose et al. 2017). This trend is expected to continue due to climate change (Akompab et al. 2013; Vose et al. 2017), and populations are growing in areas most exposed to extreme heat (Jones et al. 2015). Heat waves are a serious environmental health hazard, but no universal definition or metric has emerged in the literature to classify these events (Smith et al. 2013). Instead, heat waves are often defined by absolute thresholds or relative to local climate conditions (Hawkins et al.
2016). The health effects of heat exposure vary across and within populations due to individual factors that cannot be captured by arbitrary thresholds or cutoffs (Kuras et al.
2017). Incorporating health outcomes into how heat experience is measured may inform research on the complex relationship between hazard experience and future behavior
(Wachinger et al. 2013; Weinstein 1989). For the case of heat hazards, characterizing the subjectivity of heat-related health impacts can improve our understanding of how heat is experienced (Demuth et al. 2016; Palm and Hodgson 1992; Scolobig et al. 2012; Wei et al. 2013; Weinstein 1989). The purpose of this study is to understand how individual
6 factors, including previous subjective experience with heat-related health symptoms, influence Americans’ protective behaviors. We ask the following research questions:
1a) How does previous subjective experience with heat-related health
symptoms influence protective behaviors?
1b) Is there a positive relationship between heat risk perception and
protective behaviors?
2) How do these protective behaviors vary across space and among
demographic groups in the United States?
We address these questions by using nationally representative georeferenced survey data from 2015 on self-reported heat-related health symptoms, risk perceptions, and protective behaviors to predict five heat-related protective behaviors with a multi-level logistic regression model. Long-term average temperatures, anomalies, and a heat wave percentile threshold (Anderson and Bell 2011; Smith et al. 2013), as well as other geographic characteristics were also tested as predictors in the model. From this study, practitioners seeking to reduce heat-related deaths can gain insight into what factors, including experience, influence individuals to be more or less likely to implement protective behaviors during extreme heat. Results could inform heat risk communication and prevention efforts to build resilience in vulnerable areas as more heat events occur.
3. Background
Current research indicates that heat waves in the United States are occurring more often, becoming more intense, and lasting longer (Akompab et al. 2013; Vose et al. 2017;
Sampson et al. 2013; White-Newsome et al. 2011). The US may be particularly
7 vulnerable to this trend because population growth is occurring in the places most exposed to extreme heat (Jones et al. 2015). Although there is no universally accepted definition of a heat wave, it is commonly understood that these events characterize unseasonably warm or exceptionally high temperatures for an extended period and can cause negative health symptoms resulting in serious illness and death (Basu and Samet
2002; Bernard and McGeehin 2004; Harlan et al. 2014; Robinson 2001; Sampson et al.
2013; Smith et al. 2013; Whitman et al. 1997; Sarofim et al. 2016).
While heat-related mortality rates can be projected based on increased exposure under various climate scenarios (Sarofim et al. 2016; Mora et al. 2017a), these rates depend largely on the adaptability of a population. Observational studies show that mortality rates are decreasing due to adaptation (Sheridan and Allen 2018; Hondula et al.
2015) but a recent study by Guo et al (2018) found that heat-related mortality rates in the
United States will increase even when accounting for adaptation measures. Heat leads to death in diverse ways that everyone can be susceptible to, even the young and healthy
(Mora et al. 2017b). Extreme heat events are considered the deadliest weather-related and natural hazard in the US (Kalkstein and Sheridan 2007; Borden and Cutter 2008).
Conditions for lethal heat events are expected to increase by at least 48% worldwide by the year 2100 (Mora et al. 2017a). Clearly, there is a need to understand what promotes and impedes people from taking protective action during extremely hot weather to prevent unnecessary loss of life (CDC 2018; EPA 2006).
a. Contributing factors to heat risk
Several risk factors contribute to illness and death from extreme heat, including
8 individual as well as contextual and environmental factors. Sociodemographic influences include age, gender, ethnicity, and socioeconomic status (Anderson and Bell 2009;
Harlan et al. 2014; Klinenberg 2015; Harlan et al. 2006b; Jenerette et al. 2011).
Klinenberg (2015) found that social isolation and lack of community cohesion make certain individuals and groups more vulnerable to heat stress regardless of other demographic characteristics. Other factors such as acclimatization, poor cardiovascular health, poor respiratory health, and chronic illness contribute to the onset of heat-related health symptoms in the human body (Alberini et al. 2011; Browning et al. 2006; Hajat and Kosatky 2010; Hajat et al. 2010; Klinenberg 2015). Some studies also show that more people suffer heat-related health symptoms and death during the first heat wave of the warm season even if it is less severe than subsequent heat events (Anderson and Bell
2009, 2011; Liss et al. 2017). Highly developed areas with little vegetation create urban heat islands that prevent people’s ability to cool down sufficiently at night as the heat continues to radiate from buildings and impervious surfaces (Clarke 1972; Harlan et al.
2014). Regardless of the context, individualized health factors and protective responses greatly determine whether someone experiences negative health effects from heat
(Alberini et al. 2011; Bernard and McGeehin 2004; Hajat et al. 2010; Khare et al. 2015;
Klinenberg 2015).
Despite the seriousness of this hazard, the social implications of heat waves are relatively understudied in hazards literature although heat has received more attention in public health research. Scholars emphasize that how one perceives risk influences a person’s vulnerability (Jonsson and Lundgren 2015; Slovic 1987; Zografos et al. 2016;
Wilhelmi and Hayden 2010; Grothmann and Patt 2005), but few studies have explored
9 heat wave risk perceptions in the US (Kalkstein and Sheridan 2007; Sampson et al. 2013;
Semenza et al. 2008; Sheridan 2007). Few, if any, studies explicitly explore the impact that experience with heat-related health symptoms may have on protective behaviors in future heat events in the US. Physical exposure to a hazard influences one’s risk (Basu and Samet 2002; Zografos et al. 2016), even one’s perception of that risk (Demski et al.
2017; Howe et al. 2013; Kalkstein and Sheridan 2007), and, depending on the hazard, may or may not influence future response (Dillon et al. 2014, 2011; Lindell and Perry
2000; Silver and Andrey 2013; Sorenson 2000; Zografos et al. 2016; Norris et al. 1999).
However, differences in the relationship between personal experience and behavior has not received substantial attention; in other words, different people may respond differently to the same heat exposure.
b. Evolution of the experience – behavior hypothesis
Although many studies have concluded that prior experience either does not have a significant influence on protective behavior or that its influence is mixed (Demuth et al.
2016; Palm and Hodgson 1992; Scolobig et al. 2012; Wei et al. 2013; Weinstein 1989), scholars have approached the measurement of these variables differently with varying results (Becker et al. 2017; Demuth et al. 2016; Lindell and Perry 2012; Mishra and
Mazumdar 2015; Mishra and Suar 2007; Mulilis et al. 2003; Norris et al. 1999; Siegrist and Gutscher 2006, 2008; Stumpf et al. 2017; Zaalberg et al. 2009). Weinstein (1989) noted several contradictory findings for various hazards, partly attributable to diverse methodological and measurement issues that may explain conflicting results, which has also been found in subsequent studies (Mishra and Mazumdar 2015; Sharma and Patt
10 2012; Zaalberg et al. 2009). For example, experience and protective behaviors are often operationalized as dichotomous variables, when in reality several types and ranges of experience and behavior may exist and can manifest in various ways (Demuth et al. 2016;
Mishra and Mazumdar 2015; Mishra and Suar 2007; Sharma and Patt 2012; Zaalberg et al. 2009). Limiting experience or behavior to one measurement can restrict our ability to understand the nature and complexity of the relationship (Becker et al. 2017; Demuth
2015; Demuth et al. 2016; Lindell and Hwang 2008; Sharma and Patt 2012; Zaalberg et al. 2009). Some argue that the question should not be whether experience influences behavior but instead how it may influence behavior (Demuth et al. 2016; Zaalberg et al.
2009).
Dillon et al. (2014, 2011) explain the contextual importance of prior experience by defining the effect of ‘near miss’ events on future preparedness. Their findings and others
(Sharma and Patt 2012) show that prior experience is not predictive of protective action unless it is evaluated in terms of its negative impacts on that person. The same concepts can be applied to contextual experiences of heat. Unless heat experience is evaluated in the context of negative health impacts, prior experience of extreme temperature exposure alone may not be an effective indicator of protective action.
The question of how experience influences protective actions can be partly understood by focusing on mediators between experience and behavior (Wachinger et al.
2013). For example, risk perception has been found to influence the relationship between prior experience and adaptive behaviors (Becker et al. 2017; Demuth 2015; Demuth et al.
2012; Jackson 1981; Lindell and Perry 2012; Mishra and Suar 2007; Mishra et al. 2009;
Norris et al. 1999; Siegrist and Gutscher 2008; Zaalberg et al. 2009; Wachinger et al.
11 2013). Risk perception can mediate prior experience and protective behavior through a
“risk perception paradox” which is created when either 1) the benefits of taking the risk are perceived to outweigh the likelihood and extent of the costs, 2) personal responsibility to prevent losses has been shifted to another party, or 3) there is a lack of resources to implement the protective actions (Wachinger et al. 2013). In such cases, the relationship between risk perception and protective behaviors is controversial, unclear, and cannot be assumed to be highly positively correlated. When variables such as risk perception are controlled, hazard experience can have substantial (Becker et al. 2017; Wei et al. 2013), lasting, and pervasive effects on behavior (Norris et al., 1999; Demuth et al., 2016). As the specific relationship between heat risk perception and heat-health behaviors is not yet established in the literature, this study controls for risk perception as a first step in analyzing how its influence may affect the heat-health symptoms experience.
c. Broadening the heat experience definition
Heat stress can be inferred from ambient temperature, Heat Index, or other related metrics like Wet Bulb Globe Temperature (WBGT). Although these metrics measure some level of exposure, they do not explain how any given individual’s body will respond to heat or their own subjective experience of the phenomenon (Anderson and
Bell 2009; Bell et al. 2008). Several components create one’s heat experience (Kuras et al. 2017, 2015). Just as experience is varied and multi-faceted for other hazards, it is likewise complex for heat because of its direct impact on personal health. Few heat risk studies have attempted to define heat experience by including measures of subjective heat-health impacts alongside temperature exposure. One exception is a study by Mishra
12 and Suar (2007), that measured heat wave severity with questions related to personal and secondary experience with heat-health consequences, which directly influenced how participants prepare for future heat.
Although heat-related illness and death are preventable (CDC 2018; EPA 2006), people are often unable to quickly identify the onset of heat stroke or heat exhaustion symptoms before serious illness ensues (Harlan et al. 2014; Mishra and Suar 2007). As a result, extreme heat is often considered a “silent killer” (Klinenberg 2015; Mishra and
Suar 2007; Poumadère et al. 2005). Research on thermal comfort can provide techniques to mitigate heat exposure to avoid unnecessary loss of life and enhance urban planning
(Chen and Ng 2012). Experts are investigating ways to measure heat stress in humans more accurately (Kuras et al. 2017; Lee et al. 2013, 2016) but such methods are not yet being used in the hazards and risk communication fields.
This study explores the influence of subjective experiences with heat-health effects in a model that also incorporates traditional predictors of behavior including risk perception and temperature exposure. If previous experience with negative health effects of heat increases one’s protective actions, heat risk prevention plans and campaigns may be able to use the unique aspects of experience to communicate heat risk more effectively, mobilize adaptive practices, and ultimately improve current extreme heat event guidance (CDC 2018). Designing messages that elicit memories of past events, for example, or that help people connect with the visceral health experiences of others, may increase the effectiveness of messages, warnings and advisories.
13 d. Differentiating protective behaviors
Protective behaviors can be viewed or categorized in a variety of ways whether egocentric, prosocial, or purely altruistic (Piliavin and Charng 1990; Haski-Leventhal
2009; Piliavin, Jane Allyn 2001). In disaster situations, the stress caused by the event promotes many people to act on behalf of others’ welfare and enhance social cohesion in their communities while at the same time other people express anti-social behaviors more frequently (e.g., crime) (Lemieux 2014). Furthermore, people are more willing to express concern and act on behalf of others when they know the person and when they think no else will help (Lemieux 2014). This literature suggests that responses to extreme heat may manifest differently according to the altruistic nature of different populations.
Populations may also respond differently for heat hazards because of their “silent” nature.
For example, if people believe the threats of extreme heat will manifest before officials respond, will they act on others’ behalf more readily? Our study examines four behaviors that are focused on preserving personal health during a heat wave and one behavior that focuses on preserving the well-being of others.
e. Spatial variation
While previous research establishes who may be more physiologically and socioeconomically vulnerable to extreme heat, little research explains how spatial factors contribute to people’s decisions to adapt to the hazard. Although localized studies have measured protective behaviors through surveys, interviews, or experiments (Akompab et al. 2013; Alberini et al. 2011; Kalkstein and Sheridan 2007; Khare et al. 2015; Kim et al.
2014; Lefevre et al. 2015; Romero-Lankao et al. 2014a; Sheridan 2007; White-Newsome
14 et al. 2011), we are not aware of a study that has assessed what influences adaptive or protective behaviors on a national level for the US.
It is important to understand spatial variation in heat response behaviors in order to provide context for creating population and location-specific preparedness initiatives.
Heat exposure varies widely across the US, and urban heat islands also create localized extremes that exacerbate heat exposure in densely populated areas, especially in areas with little vegetation cover (Clarke 1972; Harlan et al. 2006b). This varied exposure creates different levels of acclimatization among populations according to local norms and makes experiences of extreme heat a subjective threshold that may be partially explained geographically. Protective behaviors in response to these thresholds may also be spatially dependent. Understanding the factors that influence protective behaviors at different geographic scales will help practitioners create effective heat wave response programs both locally and regionally (Browning et al. 2006; Klinenberg 2015; Lee et al.
2015).
4. Methods
We used survey and temperature data from 2015 to investigate the aforementioned questions. 2015 was the second warmest year on record for the contiguous United States
(NOAA 2015), and every state had an annual temperature warmer than average including four states experiencing their warmest year on record. June 2015 was the second warmest
June recorded, particularly for the West and Southeast where several western cities set new all-time June temperature records. The South, Northwest, and Northeast were
15 warmer than average in July and several locations in the Northwest and Northeast were record warm in August.
a. Dependent variables
This study is based on georeferenced data from the Climate Change in the American
Mind project, a series of nationally representative surveys conducted regularly by the
Yale Program on Climate Change Communication and the George Mason Center for
Climate Change Communication. Adults 18 and older were sampled from Sept. 30, 2015 to Oct. 19, 2015 online via the GfK Knowledge Panel (n = 1330), which uses probabilistic, address-based sampling (see Appendix A for sample distribution). The survey had an average margin of error of ± 3% at 95% confidence (Leiserowitz et al.
2015). GfK anonymized the locations of participants through a random jittering process within 150 m of their household address.
This survey measured five heat protective behaviors with the following question and a 4-point scale for each item (Never, Rarely, Occasionally, Often):
“When your local area experiences a heat wave, how often do you do the following?” (Use fans at home; Stay indoors; Use air conditioning at home; Check in on family, friends, or neighbors; Leave home and go to a cooler place)
Responses were dichotomized into two groups: ‘Never’ and ‘Rarely’ as one group and
‘Occasionally’ and ‘Often’ as the other. Between 153 and 156 participants who declined to respond to any of these five items were excluded from the model. An alternative dichotomization was also analyzed by grouping ‘Never’ responses alone, and ‘Rarely’ responses with the other response options (see Appendix D for alternative results).
16 b. Predictor variables
1) HEALTH EXPERIENCE AND RISK PERCEPTION
The survey measured the negative effects of heat-related health symptoms with the following items:
“How often have you experienced the following effects of heat waves during the past year?” (Decreased productivity at work; Personal discomfort; Heat-related illness (such as heat exhaustion or heat stroke))
Each item was measured with a 4-point scale (Never, Rarely, Occasionally and Often).
Cronbach’s α indicated that the sum of these three items into a scale was reliable (α =
.746) (DeVellis 2016). The values for these three questions were summed and divided by the maximum outcome to create a negative health effects score, which was used as a fixed effect in the model.
Heat wave risk perception was measured in the survey using a slider bar from 0 to
100 with the following items:
“A heat wave is a period of unusually and uncomfortably hot weather. If a heat wave were to occur in your local area, how much, if at all, do you think it would harm the following?” (Your health; The health of others in your community)
The slider bar included a descriptive scale (Would cause no harm at all, A little harm,
Moderate harm, A great deal of harm, Would cause extreme harm). Cronbach’s α indicated a combination of these two items into a scale was reliable (α = .902) (DeVellis
2016). The values for these two questions were summed and divided by the maximum outcome to create a risk perception score used as a fixed effect in the model.
17 2) SOCIO-DEMOGRAPHIC CHARACTERISTICS AND SPATIAL SCALES
Demographic characteristics collected from the survey were used as random effects according to the conceptual model in Figure 1. Income levels were binned to reflect fairly equal numbers of respondents at each level. To control for behaviors that may be related to having access to air conditioning, a variable indicating access to air conditioning
(“AnyAC”) was included as a random effect by dichotomizing between those who reported having central air or a window A/C unit and those who have neither. Any
“refused” responses to either type of A/C were coded as having no AC access overall (n =
24). Self-reported political ideology was consolidated into three groups: liberal, conservative, and moderate, and included as a random effect. Including political ideology in this model will test if the climate beliefs and perceptions of local temperature found to be associated with political orientation also manifest in protective behaviors for this hazard (Howe and Leiserowitz 2013; Howe et al. 2013; McCright et al. 2014). Random effects for county, state, and census division were also included. To account for possible variation between urban and rural residents, the 2013 Rural-Urban Continuum Codes from the United State Department of Agriculture (USDA) at the county level were used to create another predictor variable. This coding scheme differentiates urban counties by the population size of their metro area and rural counties by the degree of urbanization and adjacency to a metro area. The nine metro codes were dichotomized into two groups:
‘Metro’ and ‘Non-metro’ consistent with the USDA classification scheme.
18 3) CLIMATIC INDICATORS OF EXPOSURE
Climatic and temperature exposure were not measured directly in these survey data; therefore, exposure variables based on the locations of survey respondents were created from existing climate data sources. Most heat waves occur from May to September in the northern hemisphere and this time frame is often called the warm season (Smith et al.
2013). Monthly mean temperature data for May through September 2015, and 30-year averages for these months were downloaded at 800m spatial resolution from the PRISM
Climate Group (Oregon State University 2017) and then extracted to the county level.
Mean temperature data is appropriate for this context because mean temperatures are highly correlated with maximum and minimum temperatures and extreme heat events are created in part by high daytime temperatures combined with high nightly lows (Smith et al. 2013). The mean values of mean daily temperature for each county’s warm season were calculated for the five months of the 2015 warm season and the 30-yr average for the same five-month period. The 2015 averages were then subtracted from the 30-yr averages to create temperature anomaly values for the warm season immediately prior to survey administration. These two values, the 30-year average of mean temperature for the
2015 warm season and the 2015 mean temperature anomaly for the warm season, were used as separate climate-related exposure variables at the county level. Using both variables captured relative differences in baseline climatology and seasonal deviations from normal temperature for each location. The county-level 2015 temperature anomaly and 30-year warm season average for each respondent were added to the model as the
‘exposure’ predictor variables and used as fixed effects alongside the negative health
19 effects and risk perception scores. We also investigated alternative heat wave exposure variables derived from the Daymet dataset (Thornton et al. 2018). These variables represented the number days the mean temperature exceeded the 90th, 95th, or 99th percentile (based on the 30-year climatology) for two consecutive days by census tract and averaged per county. These variables are based on previous definitions of heat waves
(Anderson and Bell 2011, 2009; Smith et al. 2013). Further explanation of the alternative exposure variables and results are explained in Appendix E.
c) Analytical approach
The five protective behaviors above were analyzed separately as dependent variables through a multi-level logistic regression model in R using the ‘lme4’ (Bates et al. 2015),
‘arm’ (Gelman and Su 2016), and ‘sjPlot’ packages (Lüdecke 2017). Models were built iteratively by adding one random effect at a time. An ANOVA was conducted after each addition and only predictors that improved model fit (α = 0.10) were retained.
Interactions between significant demographic variables were tested and included or excluded in the same way. This process was conducted for each dependent variable; hence, the demographic random effects differ for each protective behavior model.
Random effects that measured spatial variation (region, state, county, and metro vs. non- metro) were kept in all models. Fixed effects were added to the model after the random effects. To control for measured exposure, the fixed effects of warm season 30-year average and 2015 warm season anomaly at the county level were kept in the model regardless of effect size and improvement of model fit.
20 5. Results
The negative health effects score had a mean of 0.53 (σ = 0.19) and risk perception score had a mean of 0.39 (σ = 0.24), both on a 0-1 scale (Table 1). Most participants reported using air conditioning at home often and never going to a cooler place during a heat wave (Table 2). Responses for checking on family, friends, and neighbors are spread somewhat evenly across all response options. Respondents are representatively distributed across the nine census divisions (region), political ideology, gender, and several levels of age and income (Table 3). The distribution of respondents across the metro vs. non-metro counties, race/ethnicity groups, and presence/absence of air conditioning categories was more uneven. Most attributes are representative of the spatial and demographic distribution of the US population. As compared to the 2015 census
American Community Survey, ‘White, Non-Hispanics’, adults with a bachelor’s degree or higher, 45 to 59 year olds, and adults 60 years and older are overrepresented in the sample by 8.5%, 7.0%, 8.7% and 14.5% respectively. ‘Other, Non-Hispanics’ and households that make less than $25,000 annual income are underrepresented by 7.0% and
6.6% respectively.
Results from the multi-level logistic regression predicting behavioral responses to extreme heat show that the temperature variables (long-term warm-season mean and
2015 warm-season anomaly) had a small and nonsignificant effect across all protective behaviors while experience with heat-health symptoms had a large positive association with all behaviors (Table 4). Alternative models using percentile thresholds for extreme heat had similar results (see Appendix E). The effect of risk perception varied depending on the behavior measured. Risk perception and negative health effects had low
21 intercorrelation for all models (between r = 0.336 and r = 0.399). When considering their associated confidence intervals, negative health effects was a more consistent predictor than risk perception for all behaviors (Fig. 2). Overall, experience with heat-health symptoms was a much stronger predictor than risk perception for all protective behaviors except ‘Checking on Others’ and ‘Using AC at home’ where the effect sizes of risk perception were comparably large.
Spatial variables had little influence in most models. Controlling for other variables in the model, households in the South Atlantic census division used AC at home 6 percentage points more than the national average and California households 8 percentage points less than the national average. Californians were 15 percentage points less likely than the national average to check on others. People in the Pacific census division were
16 percentage points more likely to go to a cooler place than people in the South Atlantic and 14 percentage points more likely than the national average. Non-metro residents were
10 percentage points less likely to stay indoors than metro residents. However, most spatial random effects were not significantly different from the national average.
Risk perception and negative health effects were strong predictors of checking on family, friends, and neighbors during a heat wave (Fig. 3). By contrast, the physical exposure variables (long-term warm-season average temperature and 2015 temperature anomaly) had a negligible influence. The marginal effects of these predictors indicate that
80% of adults with the highest risk perception score would be predicted to report checking on others during a heat wave (Fig. 4). By contrast, adults with the lowest risk perception score have a 33% probability of reporting that they would check on others.
Holding risk perception constant, the likelihood that adults with the most prior experience
22 with heat-related health symptoms will check on others is 71% while the likelihood for those with the least experience is 35%.
Demographic random effects exhibited the most variation in predicting checking on others during a heat wave (Table 5). Age was the strongest individual predictor while education had essentially no influence. Adults 45 years and older were 19 (60+ years old) to 20 (45-59 year olds) percentage points more likely to check on family, friends, and neighbors than younger adults (18 to 29 year olds). Other significant predictors include being female (11 percentage points more than males), Black (11 percentage points more than Whites and 12 percentage points more than Hispanics), moderate political ideology
(7 percentage points more than the national average) and having income less than
$25,000 (5 percentage points more than the average). Even though education did not significantly predict this behavior, an interaction between education, gender, and political ideology had considerable influence on checking on others, with greater variance (σ2 =
0.14) than all other demographic variables except age. Overall, female moderates with less than a high school education were 24 percentage points more likely than the average
American to check on others with the highest probability of all random effects in the study (P = .77, β = .57, se = .30). Male conservatives with a high school diploma were 10 percentage points more likely than the national average (β = .55, se = .26), and male moderates with some college education were 8 percentage points more likely than the average (β = .39, se = .25). Odds ratios and predicted probabilities for random effects of this model are found in Appendices B and C. Models that alternately dichotomized behavioral responses as “never” vs. all other responses showed similar results for the
23 main hypothesized predictors, with somewhat smaller demographic effects across all behaviors (see Appendix D).
6. Discussion
Existing methods of measuring heat experience may not fully capture how health impacts from exposure to extreme temperatures can influence protective behavior.
Measurements of subjective experience with extreme heat that include personal health- related impacts have a strong positive relationship with self-reported protective behavior.
On average, people in the US reported taking more protective actions against extreme heat when they had had experience with the negative health effects of heat, such as feelings of discomfort or heat exhaustion. This result could relate to observed decreasing trends in US heat mortality rates (Hondula et al. 2015; Gasparrini et al. 2015; Bobb et al.
2014) as people experience and adapt to heat over time. Assuming there is a causal relationship between experience and behavior, incorporating references to prior experience with heat-health symptoms into risk communication strategies may improve awareness of heat risk and adaptation practices. For example, messaging that triggers memories of people’s past negative experiences with heat or, for those who have not had such experiences, that stimulates connecting vicariously with others’ negative health experience could promote adaptive practices and motivate people to make heat protection plans. By thinking first about past experiences and results, people may be more likely to evaluate their resources and needs more accurately for future events. Such imaginative exercises could be a key step in plans to help municipalities be more prepared for future heat events.
24 This study indicates that heat risk perception’s relationship with adaptive practices varies across behaviors. Risk perception predicted the chances that people would check on others more than prior experience with negative health symptoms, but this relationship did not hold for other protective behaviors. Assuming this is a causal relationship, high perception of heat risk may encourage people to think about others and act altruistically, but not motivate individuals as much to protect themselves personally against heat by using fans, staying indoors, or going to a cooler place. In contrast, prior experience with heat-health symptoms consistently predicted altruistic and personal protective actions.
This supports the importance of measuring direct, negative impacts of a hazard (Dillon et al. 2014, 2011; Sharma and Patt 2012). While risk perception is an important indicator of vulnerability (Jonsson and Lundgren 2015; Slovic 1987; Zografos et al. 2016), prior experience with heat-related health symptoms is a related and possibly more consistent predictor of behavior and should be considered part of how heat experience is measured in future work.
For future risk communication studies, harnessing the predictive influence of prior heat-health experience on protective behavior into an effective risk communication tool has the potential to reduce vulnerability and increase resilience among populations that may not otherwise have the immediate resources to reduce their risk through other means.
For example, creating messaging about the signs of and treatment for heat stroke that triggers memory of negative experiences with heat-health symptoms may help people take precautionary steps to protect themselves and those around them. This work calls for exploration of heat-health experience as a risk communication tool.
25 The personal protective behaviors measured in this study were not heavily influenced by socio-demographic characteristics, a result that contrasts with other research regarding heat risk (Wilhelmi and Hayden 2010) and has rarely been found in other hazard literature (Silver and Andrey 2013). Although indicators like age, gender, race or ethnicity, income, and education are good predictors for risk perception and vulnerability, reported heat protective behaviors span these groups regardless of their risk. Many of these behaviors are accessible to most of the population across different demographic characteristics, which supports the notion that heat morbidity and mortality are preventable when people have both the right information and access to resources at the right time. Although there are financial constraints to accessing air-conditioning, other effective behaviors examined here are generally accessible and low-cost.
Even so, in the model for going to a cooler place, income is not the only constraining variable for this behavior; age and ethnicity also play a role. This is not surprising because age can impede mobility and low-income households may not be able to afford transportation to a cooler place or feel safe going out in their neighborhoods (Klinenberg,
2015). Overall, it appears that when people had access to AC and the income to afford this amenity, they used it instead of going to a cooler place regardless of cultural boundaries, but when people did not have air conditioning or could not afford its use, some demographic influences differentiated who seeks out a cooler location and who does not. Staying indoors is another protective behavior that is accessible to the majority of the population, with the exception of those who work outside or are required to engage in other activities outside. In this study, older adults and men tended to stay indoors less during a heat wave than younger adults and females. This model supports previous
26 research stating that men have lower heat risk perceptions (Kalkstein and Sheridan 2007;
Klinenberg 2015) and that older adults may not consider themselves to be part of a vulnerable population; they may not see themselves to be at risk in part because they may not consider themselves to be elderly (Wolf et al. 2010a).
This research contributes to the heat risk research literature by distinguishing what predicts specific self-reported protective behaviors. In particular, we identified a unique difference between altruistic and personal behaviors. Checking on others was the only altruistic protective behavior measured and although this is something most adults can do, this behavior was influenced more heavily by sociodemographic factors than any other. Adults 45 years and older tended to check on family, friends, and others more than
18 to 29-year-old adults. The opposite effect applies to the relationships between age and personal protective behaviors, with 18 to 29-year-olds tending to personally protect themselves and older adults (45 and older) less so. This is consistent with studies that found older adults manifest more prosocial behaviors (Haski-Leventhal 2009) and implies that older adults may be more concerned about others’ than their own health while younger adults act to protect themselves from the heat, but are less likely to transfer this concern to help those around them. This knowledge can help practitioners emphasize certain aspects of heat risk messaging and planning for different groups. Interactions with older adults can emphasize the need to take care of one’s health so they are able to help others effectively and outreach with younger adults can encourage them to be more aware of vulnerable people around them and what they can do to help.
Other demographic predictors including gender and race/ethnicity had some association with altruistic self-reported behaviors. On average, men tend to check on
27 others during a heat wave less than women, and Black or African American respondents tended to check on others more than White respondents. Previous research has found that men perceive lower risk from heat (Kalkstein and Sheridan 2007; Klinenberg 2015;
Harlan et al. 2014), which may lead them to be less aware of the threat to others and therefore act less altruistically. Community heat protection plans may maximize their efforts by both incorporating women more directly into their strategies to check on neighbors and encouraging men to be more active in checking on others and to be aware of their own risk. African-Americans and older adults could also be recruited for neighborhood outreach initiatives. Contrary to previous studies regarding the resilience of
Hispanic communities to extreme heat events (Kalkstein and Sheridan 2007; Klinenberg
2015), this study found that Hispanic respondents did not check on others more than
White respondents. Although the observed cohesive nature of Hispanic communities may be present in many locations, more research on the adaptive capacity of these communities is needed as Hispanics are one of the ethnic groups most exposed to heat based on their geographic distribution in the US. These results may also indicate the importance of group influence and collective norms in determining altruistic actions
(Haski-Leventhal 2009).
Although education significantly improved the model fit for checking on others, its influence was negligible altogether. A person’s education level may not necessarily be indicative of their knowledge of what causes heat vulnerability and how to avoid and treat it, nor their ability to implement this knowledge. Regardless of understanding these principles, several other factors influence or impede one’s ability to implement protective
28 action and these barriers must be overcome in order to foster preparedness and response
(Jenerette et al. 2011; Harlan et al. 2006b; White-Newsome et al. 2014).
Only one interaction term predicted the altruistic behavior of checking on others.
Although education had negligible influence on its own, relationships emerge when education was coupled with political ideology and gender, both with moderate to large effect sizes. For most groups, as education increased, the likelihood of checking on others decreased. The only groups that responded differently were female moderates with less than a high school diploma, male conservatives with a high school diploma, and male moderates with some college education. Such interactions may explain the specific groups responsible for the marginal effect of political ideology, and add an additional dimension to the finding that men check on others less than women in general. Clearly this interaction is complex, but indicates that these exceptions to the individual predictors are large associations that should be investigated and possibly considered when drawing conclusions about altruistic behaviors for certain groups. This finding calls for further inquiry to understand what implications the combination of these influences may have for risk communication and emergency management officials seeking to maximize strategies and efforts to build heat resilient communities.
Although the spatial variables did not predict protective behaviors, including them did help control for possible biases introduced by spatial clustering. The absence of significant spatial effects may explain the subjectivity of heat experience. Although experience with heat-health symptoms improved the ability to measure heat behavior, these symptoms manifest on an individual level and may be dependent on other factors not measured in the study. Chronic health conditions and health status influence when
29 heat-related health symptoms occur (Anderson and Bell 2009, 2011; Sampson et al.
2013). Different acclimatization levels can alter resilience to heat for people who travel from a cooler climate to a warmer one even though they have good health status and do not have chronic health conditions. Localized acclimatization may explain why there is little spatial variation for these protective behaviors. Extreme heat occurs in all regions of the US but the threshold of what is considered extreme is dependent on climate and different personal thresholds of heat tolerance. People feel the effects of ‘extreme’ heat differently and depending on the climate they are accustomed to.
To summarize, although the altruistic action of checking on family, friends, and neighbors can be performed by most people with little or no monetary cost like some of the other behaviors analyzed in this study, societal and cultural norms may influence whether Americans choose to do so. It is possible there are social barriers that impede or encourage people to reach out to others at risk to heat stress. These barriers can depend on neighborhood culture or social norms of any given cultural or generational group as well as broad expectations of American society in general (Klinenberg 2015; Colten and
Sumpter 2009; Poumadère et al. 2005; Wolf et al. 2010b; Lemieux 2014). As noted by
Klinenberg (2015), the ‘silent’ nature of heat waves can delay official government response; potentially vulnerable neighborhoods may go unnoticed for some time. It is possible these more altruistic groups act on behalf of others more readily during heat events because they are from neighborhoods where they think no one else will respond in time (Lemieux 2014). Further research on this particular behavior as well as other altruistic behaviors in the context of heat may better inform the nature of altruistic actions
30 that are unique to this specific hazard and what that means for practitioners striving to better mitigate heat risk in their communities.
7. Limitations
This study has several limitations, including the possible bias introduced by the nature of self-reported survey data. Participants may have reported inaccurate measures of their experience with heat-health symptoms, heat risk perceptions, and protective behaviors due to poor memory recall or desire to appear more or less experienced with symptoms, aware of the risk, or active in protecting themselves or others. Coupling survey results with an experimental design that measures the actual occurrence of heat- health symptoms and protective actions would be a useful next step in future research.
The spatial and temporal scale used in this analysis may be too coarse to see high- resolution variation of participant exposure to heat. Although the climatological variables used to measure exposure were georeferenced to each respondent’s county, there may be short-term weather and fine scale effects within the summer season on reported behavior that may not be captured by the temperature variables used here. The scale limitation is also related to the survey sample; since the survey was nationally representative, more people were sampled from densely populated areas than from low density areas. The possible influence of air conditioning on the measured behaviors would also be better understood with more information about which participants cannot afford its use and those who do not have access to AC (who represented only 10% of our sample). Lastly, only five heat-protective behaviors and three heat-health effects were analyzed.
Additional important behaviors and health effects could be examined in future work.
31 8. Conclusions
Life and property are threatened when human behaviors are insufficient to protect against extreme heat. The heat risk research community acknowledges heat-health symptoms as a major impact of extreme heat events (Kuras et al. 2017, 2015), yet few studies use this direct effect to enhance heat experience measurements attempting to predict behavior and preparedness (Mishra and Suar 2007). This study addresses this gap by examining subjective experience with the negative effects of heat on one’s health. We found that experience with heat-health symptoms strongly influenced self-reported protective behaviors while traditional measures of heat exposure had little influence. This finding supports the heat risk research community’s call to measure exposure on an individual level (Kuras et al 2017). Risk perceptions had an important, but smaller influence on behaviors than did previous experience. At least 60% of participants had previously experienced some heat-related health symptoms. As time passes, it is likely that more people will accumulate this experience as heat wave frequency increases.
Therefore, this experience should be incorporated regularly into heat experience measurements alongside temperature exposure in order to provide more accurate insight on what motivates people to protect themselves during extreme heat. Risk communication and risk planning professionals can use these findings to better promote heat protective behaviors for different US populations, improve local heat protection plans, and thereby more effectively prevent unnecessary suffering and loss of life due to heat exposure.
32 9. Acknowledgments
Funding for this research was partially supported by the National Science Foundation
Decision Risk and Management Sciences Program, grant SES-1459872 “Collaborative
Research: Multi-Scale Modeling of Public Perceptions of Heat Wave Risk.”
33 10. Tables
TABLE 1. Descriptive statistics of variables used as fixed effects. The mean for the climatological average for the warm season of 2015 in the United States was 21.58 °C. Participants chose between Never, Rarely, Occasionally, and Often for each negative health effect item included in the negative health effects score. Participants used a slider bar between 0 and 100 with a descriptive scale (Would cause no harm at all, A little harm, Moderate harm, A great deal of harm, Would cause extreme harm) to respond to the risk perception score item.
Descriptive statistics for selected variables
Statistic N Mean St. Dev. Negative health effects score 1,180 0.53 0.19 Risk perception score 1,180 0.39 0.24 Warm Season 30yr (1985-2015) Average (°C) 1,180 21.58 3.53 2015 Warm Season Anomaly (°C) 1,180 0.66 0.47
34 TABLE 2. Descriptive statistics of the protective behaviors analyzed. N is indicated for each response option with the corresponding percentage of participants who responded to that question in parentheses. We acknowledge that the limitations to the benefits of fan use under certain conditions may influence the results for this particular behavior.
Frequency of Dependent Variable Responses
Never Rarely Occasionally Often NA N
103 110 288 675 4 Use Fans 1180 (8.73 %) (9.32 %) (24.41 %) (57.20 %) (0.34 %)
36 125 373 643 3 Stay Indoors 1180 (3.05 %) (10.59 %) (31.61 %) (54.49 %) (0.25 %)
85 48 172 871 4 Use AC at Home 1180 (7.20 %) (4.07 %) (14.58 %) (73.81 %) (0.34 %)
Check on 251 311 415 197 6 Family, Friends, 1180 (21.27 %) (26.36 %) (35.17 %) (16.69 %) (0.51 %) & Neighbors
Go to a Cooler 588 300 202 86 4 1180 Place (49.83 %) (25.42 %) (17.12 %) (7.29 %) (0.34 %)
35
TABLE 3. Descriptive statistics for the individual levels of the random effects used in the multi-level models.
36 Frequency of Independent Variables
N %
Region
New England 68 5.76
Mid-Atlantic 160 13.56
Eat-North Central 171 14.49
West-North Central 97 8.22
South Atlantic 219 18.56
East-South Central 65 5.51
West-South Central 110 9.32
Mountain 91 7.71
Pacific 199 16.86
Rural v. Urban Metro 1017 86.19
Non-Metro 163 13.81
Age 18-29 174 14.75
30-44 248 21.02
45-59 340 28.81
60+ 418 35.42
Any AC at home No AC 118 10.00
Yes AC 1062 90
Education Less than high school 93 7.88
37 High school 322 27.29
Some college 352 29.83
Bachelor’s degree or higher 413 35.00
Ethnicity/Race White, Non-Hispanic 860 72.88
Black, Non-Hispanic 110 9.32
Other, Non-Hispanic 45 3.81
Hispanic 130 11.02
2+ Races, Non-Hispanic 35 2.97
Gender Male 541 45.85
Female 639 54.15
Income Less than $25,000 183 15.51
$25,000 to $39,999 172 14.58
$40,000 to $59,999 199 16.86
$60,000 to $84,999 203 17.20
$85,000 to $124,999 239 20.25
$125,000 or more 184 15.59
Political Ideology Refused 15 1.27
Liberal 313 26.53
Moderate 471 39.92
Conservative 381 32.29 TOTAL OBSERVATIONS 1180
38
TABLE 4. Coefficients for fixed effects and number of levels for random effects used in each model. N(State) includes the District of Columbia and excludes Alaska and Hawaii. Dashes indicate random effects that were not included in the model because their inclusion did not improve model fit at 90% confidence during model iteration. Temperature exposure had little influence on reports of protective behavior while the negative effects of heat on one’s health had large effects across all behaviors. Note that few variables fit the model for Fan Use. This may be due to the beneficial limits of the behavior—using fans above 90º F can worsen conditions (EPA 2006).
39
40 TABLE 5. Results for Checking on Others model. Risk perception and prior experience with heat health symptoms greatly increased the likelihood that Americans will check on their family, friends, and neighbors. Note that there is no spatial variation detected by the county or rural vs. urban spatial levels, or by education for this behavior.
Model Results for Checking on Others
� Std. Error
Fixed Parts
(Intercept) -1.99 ** 0.76
NegHealthEffects_Score 1.64 *** 0.40
RiskPerception_Score 2.11 *** 0.32
WarmSeason_30yr_Average 0.02 0.03
WarmSeason_Anomaly 0.15 0.19
Random Parts
τ00, COUNTY 0.000
τ00, STATE 0.088
τ00, EDU: GENDER: POL. IDEOLOGY 0.140
τ00, REGION 0.017
τ00, INCOME 0.032
τ00, RACE/ETHNICITY 0.081
τ00, POLITICAL IDEOLOGY 0.086
τ00, EDUCATION 0.000
τ00, AGE 0.151
τ00, RURAL v. URBAN 0.000
τ00, GENDER 0.096 Observations 1174
* p<.05 ** p<.01 *** p<.001
41 11. Figures
FIG. 1. Conceptual model used to build a multi-level logistic regression model to investigate heat protective behaviors in the United States. Arrows indicate direction of possible influence or association. Note that both experience variables affect risk perception but risk perception only influences the negative health effects of heat.
42
Stay Indoors Stay
Go to Cooler Place Cooler to Go Fan Use Fan Risk Perception Score Risk Perception
influenced fan use, going to a cooler place, fangoing place, a cooler influenced to use,
Checking on Others on Checking AC Use at Home at Use AC
Dependent Variable Stay Indoors Stay
Go to Cooler Place Cooler to Go Fan Use Fan Negative Health Effects Score Health Effects Negative Coefficients Coefficients of the negative health effects of heat (left) and risk perception (right) for all
Checking on Others on Checking 2.
. health symptoms and risk perception on protective behaviors Influence of heat − health symptoms and risk perception on protective
IG 3 2 1 0 Model Coefficient Model F
AC Use at Home at Use AC risk on perception checking on protectiveinfluence measured more has that behaviors. although Note health others effects than negative health negative effects, and staying indoors much more.
43 Fixed Effects on Checking on Others
5.13 *** Neg Health Effects Score
8.24 *** Risk Perception Score
1.02 Warm Season 30yr Avg
1.16 Warm Season Anomaly
0.5 1 2 5 10 20 50 Odds Ratios FIG. 3. Odds ratios for the fixed effects of checking on others during a heat wave. People with the highest risk perception reported checking on others during a heat wave 27% more than the average American. People with the most prior experience with heat- health symptoms reported checking on others 18% more than the average American. Both warm season effects are not significantly different than the average.
44
FIG. 4. Marginal effects of checking on others during a heat wave. Adults who reported the most prior experience with heat-health symptoms reported checking on friends, family, and neighbors 36% more than adults who had the least experience with these symptoms. Adults who reported the highest risk perception reported checking on others 47% more than adults who did not perceive this risk.
45 CHAPTER 3
IT’S A DRY HEAT: SHIFTING PROFESSIONAL PERSPECTIVES
ON EXTREME HEAT RISK IN UTAH2
1. Abstract
Heat waves are the deadliest natural hazard in the US while also increasing in frequency, intensity, and duration. While heat-related risk is rising, US population growth is occurring in places most exposed to extreme heat and current National Weather Service
(NWS) guidelines to issue heat alerts vary geographically and may not adequately facilitate optimal heat risk communication practices. Moreover, there is little research identifying optimal heat risk communication strategies to reach vulnerable populations.
This study focuses on professional decision making and communication in the context of extreme heat risk in Utah, a state with historically low but increasing risk to heat due to climate change, a growing population, and rising outdoor recreation visitation. We analyze the mental models of decision-makers responsible for forecasting, communicating, and managing heat risk in Utah using interviews with 32 weather forecasters, media broadcasters, and public officials including park managers. Results demonstrate that institutional norms have influenced how forecasters characterize extreme heat in the western region. NWS heat alerts and tools are new and unfamiliar to many Utah decision-makers, especially in the northern metropolitan areas where previous criteria did not warrant heat alert issuance. While experience with NWS heat alerts and tools varied widely among participants, all were familiar with heat protective behaviors
2Co-author: Peter D. Howe
46 and many stated that personal experience with extreme heat influenced their decisions.
Personal experience with extreme heat may be an effective means to communicate heat risk and promote adaptive practices. These insights may be generalizable to other settings where risk is changing and communication strategies are underdeveloped.
2. Background
Heat waves, or extreme heat events, are the deadliest weather-related and natural hazard in the United States (Bernard and McGeehin 2004; Kalkstein and Sheridan 2007;
NWS 2016; Borden and Cutter 2008). Extreme heat events are increasing in frequency, intensity, and duration; this trend is projected to continue due to climate change (Mora et al. 2017a; Romero-Lankao et al. 2014b; Vose et al. 2017). More people are being exposed to extreme heat because US population growth is occurring in the most exposed places (Jones et al. 2015). Utah is among the country’s fastest growing states (US Census
Bureau 2016b) and more recently the US Census Bureau ranked two Utah metropolitan areas in the top ten fastest growing areas: St. George is the fastest growing metro area in the country, and Provo-Orem is the eighth fastest growing (Davidson 2018). St. George, located on the edge of the Mojave Desert, is highly exposed to extreme heat and while the Provo-Orem metro area in northern Utah is less exposed, high temperatures are still common in the summer months. Climate projections estimate that heat waves will continue to grow in intensity and frequency across Utah and the US in general (Romero-
Lankao et al. 2014b; Vose et al. 2017). In 2017, the Salt Lake City metro area—the largest in the state—experienced its hottest summer on record, breaking the all-time high of 107° F with six days consecutively over 100° F and 11 days over 100° F overall
47 (NOAA 2018). Utah is also experiencing increasing visitation to national and state parks that are regularly exposed to extreme heat events (DeMille 2017; Lee 2017; University of
Utah). For example, visitation to Zion National Park and Arches National Park continues to grow and has almost doubled over the past 20 and 25 years, respectively (National
Park Service). A considerable proportion of these visitors are from other states or countries that may not be acclimated to an arid climate or aware of extreme heat in desert environments (Leaver 2018, 2017; Lee 2017; University of Utah)
Despite the serious risk of heat waves, heat wave risk perception is largely understudied. Scholars emphasize that vulnerability is influenced by how one perceives risk (Grothmann and Patt 2005; Jonsson and Lundgren 2015; Slovic 1987; Wilhelmi and
Hayden 2010; Zografos et al. 2016), but few have systematically investigated extreme heat risk perception (Kalkstein and Sheridan 2007; Sampson et al. 2013; Semenza et al.
2008; Sheridan 2007; Howe et al. 2018). Howe and colleagues (Howe et al. 2018) found that heat risk perceptions were generally higher in southern states with greater heat exposure and, at the local level, in neighborhoods with higher social vulnerability. In the same study, the average heat risk perception in Utah was lower than the national average, while Salt Lake County (containing the state capital and a metro area) and Washington
County (containing St. George and Zion National Park) had comparable risk perceptions to the national average and were higher than the rest of the state (Howe et al. 2018).
This study focuses on how extreme heat is perceived and communicated by professionals in Utah to improve communication practices and reduce risk in future heat events. Documented extreme heat events such as the 1995 Chicago heat wave and 2003 heat wave in France demonstrate that perceptions and decisions about extreme heat are
48 influenced by institutional and cultural norms (Poumadère et al. 2005; Klinenberg 2015).
Heat-related illness and death are often preventable because most heat-protective behaviors are simple, quick, and affordable, although some behaviors—such as using air conditioning—are not equally accessible, exacerbating social vulnerability for some.
Despite the preventability of heat-related health consequences, people are frequently unable to promptly identify the onset of heat stroke or heat exhaustion symptoms before serious illness ensues (Harlan et al. 2014). For these reasons extreme heat has been called a “silent killer” (Mishra and Suar 2007; Poumadère et al. 2005; Klinenberg 2015). Some experts are exploring how to measure heat stress more accurately with new technologies and metrics (Kuras et al. 2017; Lee et al. 2016, 2013) but the techniques have not yet been widely used in communication strategies. Hence, successful risk communication is key to educating people to not only recognize and prepare for the dangers of extreme heat but also know how to act quickly and respond appropriately on their own to mitigate heat illness before it becomes serious.
Little research has evaluated the effectiveness of current heat risk communication practices to increase awareness and mobilize adaptive strategies within the US (Hawkins et al. 2016). The National Weather Service (NWS) has initiated internal studies to evaluate the effectiveness of their current heat alert products (Watches, Warnings, and
Advisories) and acknowledges need for improvement (Hawkins et al. 2016). NWS guidelines for issuing heat alerts are written to be flexible to meet the needs of individual
Weather Field Offices (WFOs), but experts at these offices largely recognize that this broad flexibility introduces challenges that create misinformation and confusion among constituents (Hawkins et al. 2016). Most recently, the western region of the NWS has
49 implemented a tool to evaluate heat wave potential in arid regions where traditional heat alert thresholds largely dismissed the possibility of extreme heat. This tool is called
Experimental HeatRisk and takes into account influential factors like climatology, local acclimatization, and duration of the heat event to better evaluate extreme heat for alert issuance in less humid yet still potentially deadly high desert climates (NWS). This tool was implemented in Utah during the summer of 2017 and has not yet been evaluated.
The few external studies that have examined the effectiveness of current NWS heat alert products indicate that warnings must meet specific conditions to elicit behavior response from the general population. Warning messages must come from a credible source and contain information that is considered important to the population (National
Research Council 2013). Likewise, simply hearing a warning does not mean a person will change their behavior (Kalkstein and Sheridan 2007; Lefevre et al. 2015; Sheridan 2007).
If warnings are disseminated too often, people respond less due to the ‘cry wolf’ effect
(Hawkins et al. 2016; Kalkstein and Sheridan 2007; LeClerc and Joslyn 2015). People implement protective behaviors less when warnings trigger positive memories of hot summers (Lefevre et al. 2015). Also, cost constraints can limit a person’s ability to implement strategies like air conditioning (Lefevre et al. 2015; Sheridan 2007).
Since heat risk communication is still not well understood, qualitative social research methods to gather detailed contextual knowledge may provide insight to inform future research. Mental models interviews provide a useful method to gather such information
(Bruin and Bostrom 2013; Morgan et al. 2001; Morss et al. 2015; Slovic 1987). A mental models approach was used to evaluate NWS flash flood alerts in Boulder, Colorado by a set of companion studies (Lazrus et al. 2016; Morss et al. 2015). This example provides a
50 framework to conduct a similar evaluation of NWS heat alerts in Utah. This study is the first to use this approach to understand stakeholder decision-making about heat risk.
Findings may improve NWS heat alerts by exposing communication problems and facilitating recommendations for successful warning response (Bruin and Bostrom 2013;
Morgan et al. 2001; National Research Council 2013). NWS heat alert practices in Utah were investigated in this manner using the following research question:
1) How do stakeholders (those responsible for heat risk messaging)
characterize and make decisions regarding heat risks?
Based on the findings from this question, our objective is to expose knowledge gaps and misconceptions between NWS forecasters and their partners that can be addressed to improve local communication and response, and more effectively promote protective behaviors amongst community members. Academic research for heat wave risk communication and its effect on adaptation practices using this approach has been largely unexplored. Findings may have important implications for application by practitioners and will contribute to the research in hazards, risk, and public health communication.
3. Methods
This study followed a mental models approach to risk communication, following general guidelines established by Morgan and colleagues (Morgan et al. 2001). Using this approach, we investigated different professionals’ perspectives and decisions within the heat risk communication and warning system. A mental models approach helps develop a structured set of interview questions to characterize a system. The approach starts with a draft expert model from the literature, which subsequently serves as a way to organize
51 qualitative findings systematically after conducting interviews. This model represents what researchers expect to find. Any emerging themes or concepts are considered new and may shed light on knowledge and communication gaps. In this study, a draft expert model— the Extreme Heat Risk & Warning System Model (HRSM)—was developed by applying the literature on heat risk and vulnerability (Wilhelmi and Hayden 2010) to a risk communication and warning system scenario (Lazrus et al. 2016; Morss et al. 2015)
(see Appendix F). This model was used to develop the interview questions and also provided an initial set of codes for qualitative analysis after conducting the interviews.
Several additional steps to a mental models approach to risk communication are required to ultimately create and test improved risk messages (see Appendix G). This study focuses on the first two major steps in this process.
a. Sample
Mental model interviews were conducted with 32 professionals from three different domains important for heat risk communication in Utah: namely, six NWS forecasters, four media broadcasters, and 22 public officials. Public officials consisted of professionals from three areas: emergency management (9), public health (6), and parks or protected areas (7). Table 6 demonstrates that the sample represents key professions at different levels of government and geographic location throughout the state. This is important to capture how professionals with different responsibilities, scales of responsibilities, and geographic location may characterize and communicate differently about heat waves. Utah is located in the Intermountain West region of the US, neighboring Nevada to the west, Idaho and Wyoming to the north, Colorado to the east,
52 and Arizona to the south. The climate varies substantially within the state, with the most populated counties to the north located in a semiarid high elevation steppe that experiences warm to hot summers and cold winters. The central and southern part of the state is more rural and includes areas of high desert hot-summer/cold-winter climate, in which several popular national parks are located (including Arches, Canyonlands, Bryce
Canyon, and Capitol Reef National Parks). The southwestern corner of the state, including the St. George metro area and Zion National Park, is on the edge of the Mojave
Desert and is the hottest region of the state with very hot summers and mild winters.
Southern Utah has a history of extreme heat events that have triggered NWS heat alerts
(Excessive Heat Advisories, Watches, and Heat Warnings) whereas northern Utah had its first heat alert, an Excessive Heat Advisory, in the Salt Lake area the summer of 2017
(Herzmann and Iowa State University). The NWS Salt Lake City Weather Field Office
(SLC WFO) is responsible for issuing weather alerts for all counties in the state of Utah and the southwest corner of Wyoming, excluding the four easternmost counties of Utah.
One media market covers the entire state of Utah, so all broadcasters interviewed are responsible for a large geographic area. For these reasons, we chose to interview professionals from different areas of Utah within the SLC WFO boundaries to adequately represent perspectives and communication practices in places with different levels of extreme heat exposure, experience, and responsibility.
Interviewees represented a wide variety of experience and expertise in their jobs. All six forecaster participants were employed as forecasters or managers at the NWS Salt
Lake City WFO. Media broadcaster participants were employed at various news agencies, including local television and radio. All public officials worked in areas of
53 emergency management, public health, or parks, and had job responsibilities related to either responding to extreme heat events or communicating such events and precautionary measures to the public directly or through other agencies. To protect the anonymity of the interviewees, we identify them exclusively by either their professional group, official type, or geographic location.
b. Data collection
Through a partnership with the NWS Salt Lake City WFO, we conducted criterion and snowball sampling to solicit interviews from in-house forecasters and managers with heat alert experience, and partners with whom the WFO regularly works with to communicate weather alerts. Direct contact information for various media professionals and public officials were obtained through this partnership. Organizations with whom the
WFO did not have established collaborations were identified separately and then approached via phone or email in order to better represent various levels of government, geographic location, and agency responsibilities. All interviewees were initially contacted no more than three times via phone or email to elicit participation and subsequent follow- up contacts were pursued to schedule appointments.
The interview protocol was approved as exempt by Utah State University IRB under
Protocol #8615. All interviews were conducted by the first author. The interviewer pre- tested the interview protocol through two practice interviews with relevant professionals and made adjustments to the protocol as indicated. These practice interviews are not included in the sample. Individual semi-structured mental model interviews were then conducted in July and August of 2017. All interviews were audio-recorded with
54 permission of the participant following informed consent and later transcribed (except one, for which detailed hand-written notes were taken). Audio-recorded interviews were transcribed either manually or through Happy Scribe, an automated online tool, (Bastié and Assens 2017), with subsequent editing for quality. Interviews lasted between 39 and
118 minutes (mean: 57 min) and were conducted in person at the most convenient place and time for the participant, most of which took place in their respective workplaces.
In risk communication, mental models are any thought processes or beliefs about a risk or how the world works that guide decisions or actions regarding that risk and through which new information is filtered (Morgan et al. 2001; Lazrus et al. 2016).
Mental model interviews attempt to capture everything that influences a participant’s mental model to ultimately understand misconceptions and knowledge gaps among all parties in order to improve communication. Our interviews inquired about how participants conceptualize extreme heat risk and their decisions regarding this hazard (as well as others’) without establishing any expectation of how their mental models should be structured (Morgan et al. 2001). To accomplish this objective, the interview protocol started with an open-ended format that grew more specific as the interview advanced.
This format elicits mental models on key concepts as the interview progresses without cueing or prompting the participant to build their mental model around specific concepts from the beginning. Thus, in this study, the interviewer started with very broad questions such as “tell me about extreme heat” and “tell me about extreme heat in Utah” to discourage imposing views from outside sources, and followed up with prompts to have the participants elaborate on concepts as they were mentioned. As the interview progressed, more specific concepts were introduced by the interviewer. Such concepts
55 included influences on extreme heat exposure, effects of extreme heat, risks of extreme heat, and if any actions can be taken to prevent or reduce these risks. The interviewer then asked about participants’ decisions during their most recent extreme heat alert or event experience, the specifics about how interviewees communicated the alert or event, and if they had ever seen or used the new Experimental HeatRisk tool described above.
Interviewees were also asked to participate in ranking activities to better understand how they characterize heat risk and practices to reduce this risk. The full interview protocol is available in Appendix H.
c. Coding, model development, and data analysis
The draft HRSM referenced above was used as the basis for creating the interview protocol and developing an initial set of codes to analyze the interview data. The coding scheme was used to code all 32 interviews using ATLAS.ti software with a codebook containing definitions and examples for each code (see Appendix I). When themes emerged from the data that were not in the original HRSM, these concepts were added as new or revised codes in the coding scheme and were incorporated into the HRSM. The finalized HRSM consolidates 112 codes, 19 code groups, and 9 broad code families, and represents the overall, collective mental model for this system (Fig. 5). This procedure integrated perspectives and ideas from different professionals with varying expertise into the analysis and expanded the current expert view on heat risk in general. It also incorporated heat risk for the first time into a communication and warning system model in the hazards literature.
56 All interviews were coded by the same coder in a randomized order between the different professional types. Codes were revised, created, or consolidated until saturation of themes was reached after coding the majority of the interviews, at which point the coding scheme was finalized and used to update the HRSM. A second coder validated the reliability of the coding scheme by coding three interviews, randomly selected from each professional group (forecasters, media broadcasters, and officials). This second coder was trained on the HRSM, coding scheme and definitions, and coded a pilot set of quotes from various interviews to become familiar with the data and codes and receive feedback before starting the coding process. Interrater reliability was calculated based on the number of codes mentioned in each interview. Average Cohen’s kappa value is 0.84, which is within the range of acceptable values for this type of coding (Krippendorff 2004;
Neuendorf 2002). Cohen’s kappa was calculated for each code using ReCal2 (Freelon) and for each interview group using R packages ‘lpSolve’ (Berkelaar and others 2015) and
‘irr’ (Gamer et al. 2012).
Interview coding results were analyzed qualitatively by overarching themes mentioned across the interview sample and specific quotes were selected to feature these broad themes. Coding results were also analyzed quantitatively by whether or not each code was mentioned in an interview, which characterizes what major concepts were and were not described by participants. Certain codes were sub-coded to calculate percentages of agreement and disagreement with certain concepts where necessitated by the concept. Since the coding scheme is hierarchal, coding results were also examined at the broader level of code families, where subcategory codes were included in the general code family. These results were then compared between the three professional groups and
57 their geographic location. Coding results of the three public official types were also examined for patterns between emergency management, public health, and parks officials.
Concepts mentioned in the ranking activities were standardized across all interviews by creating a separate codebook for the ranking questions (see Appendix J). The ranking data were then analyzed in R and Excel to calculate the average ranking of the most common risks of extreme heat for each concept and its standard deviation. Counts were calculated according to the frequency of mentions. This means that concepts that were ranked separately by participants but consolidated into one code counted as additional rankings for the corresponding code to which they belonged. Hence, counts could exceed the total number of interviewees (n = 32). This process was repeated for the most serious risks of extreme heat, and most effective heat risk-reduction practices.
4. Results a. Overall themes and coding results
Three major themes emerged from the interviews: experience, institutional norms, and risk perceptions and attitudes (Fig. 5). Professionals had a wide range of experience with extreme heat alerts (meaning an NWS heat watch, warning, or advisory) but 94% of participants stated they had personally experienced extreme heat and 66% said their experience, whether personal or indirect, affected their decisions and response. One park official said:
“It can be very humbling. It can be very scary. It's an educational experience. There have been times when I did not prepare before I went out. And when I say prepare I mean making sure that my body was in good shape before I even left the car, that I felt as though I was teetering on going from
58 heat exhaustion to shutting down. And alone, on a day that just so happened to be that my radio battery was dead. . . I’ve had a couple of scary events that there are times now when it’s just like, ‘eh, no, I’m not going out.’ It’s sobering.”
Institutional norms were discussed by all six forecasters, explaining how NWS as an agency has recently redefined their view of extreme heat in arid high desert regions. For
NWS as an institution, extreme heat had not been viewed as a high priority risk in the
Intermountain West because few areas met the basic humidity and temperature thresholds. One forecaster stated:
“. . . we had this criteria to issue heat products that was completely unreasonable for our climatology. . . . Forecasters had a perception that heat just wasn’t a problem. ‘It’s hot here in the summer, no big deal.’ So, if you were to ask a forecaster what . . . sort of high impact weather does their forecast area have, they would probably talk about . . . weather related to fires, they’d talk about winter storms, whatever their local climatology has but they’d almost never talk about heat. So, this is all to say it was something that just wasn’t much in our consciousness as an agency from the perspective of the West.”
A new process has been created through the Experimental HeatRisk tool to evaluate extreme heat more accurately in the western US and is starting to shift the agency’s perceptions and attitudes about the dangers of dry heat.
Perceptions and attitudes about extreme heat were also spoken about often on a personal level with most officials recognizing that it is a deadly hazard that must be taken seriously, but it was not the only hazard they have to be concerned about. One emergency manager said:
“So, we face lots of risks in Utah. Some more frequent than others. And it's important that we know all the risks that we face. But I think this is one that is overlooked somewhat. Somewhat. We talk a lot about winter storms and we talk a lot about earthquake . . . But as far as heat, it's just probably a risk that we don't think of very often. So, one that maybe we're not prepared for enough.”
59 Although extreme heat may not receive as much attention as other hazards, officials and media broadcasters were aware and knowledgeable of its risks and impacts. Officials often defined extreme heat by its impacts instead of the physical phenomena that create extreme heat exposure. Regardless of their meteorological knowledge, officials and media broadcasters tended to know the basic signs, symptoms, and treatments for heat illnesses and prevention and preparedness tips for extreme heat (knowledge that was also shared by forecasters).
Overall, there was less experience with official heat alerts in northern Utah amongst public officials (44%) while there was greater use and understanding of heat alerts by officials in southern Utah (100%) where they happen more regularly. Some officials in northern Utah confused heat warnings with Red Flag Warnings which measure hazardous fire weather, a secondary effect of extreme heat. Regardless of experience with heat alerts, most officials stated that they trust National Weather Service forecasts and warning products (68%) and respond accordingly. One emergency manager said:
“If NWS is telling us ‘Yes, this is the way it is.’ OK. We take it as Bible truth. If we're hearing it maybe through some other [source]--they're not weather experts necessarily. ‘Appreciate the heads up.’ Now, let's confirm it through NOAA or NWS or somebody like that.”
Media broadcasters did not tend to prioritize NWS products as consistently as public officials (n = 4, 50%). Contrasting media comments included:
“You didn't ask this but frankly we don't put a whole lot of credence into heat advisories. . . And the reason is ‘It's hot and dry, don't be stupid.’” – Media (Interview #14)
“We have a direct feed from NWS. So, the moment it's issued I'm issuing it on social media and it's top priority in the news.” – Media (Interview #20)
60 Media broadcasters that placed less emphasis on heat alerts cited personal perceptions of heat or the time constraints that require them to prioritize news to what is most relevant and important to the majority of people in the state. One media broadcaster said their viewers’ lack of concern about extreme heat influences its lower prioritization in order to avoid negative viewer response.
Participants emphasized that extreme heat is largely underestimated as a serious health threat by the general population. One park official said:
“I think that it is a hard one for people to wrap their mind around. They’ve been hot before, you know. I’m not sure that understanding of that it can truly kill. Or, maybe [it’s] that ‘it can’t happen to me’ mentality.”
Participants emphasized that many people are largely unaware of how dangerous heat can be and how quickly someone’s health can be affected. To address this mentality, 96% emphasized educating the public on the basic signs, symptoms, treatments, and prevention/preparedness tips to reduce extreme heat risks in Utah, yet 38% said that people, including themselves, do not always apply the knowledge they have to their personal situation:
“I mean knowledge is one thing but taking the action on it is completely different. I think we are all pretty knowledgeable of the things that we're supposed to do when it's hot out. But a lot of us probably don't do them.” – Forecaster
While the impacts of extreme heat, susceptible populations, and the general process for warning and response were stated by all participants, knowledge gaps found in this analysis revolved around awareness of official heat alerts, the Experimental HeatRisk tool, technicalities of how extreme heat occurs, and the relativity of how to define extreme heat. Although some officials may not have completely understood what causes
61 extreme heat, they were attentive to the individual and broader impacts and susceptible populations they should focus on during extreme heat situations, and the appropriate measures to respond. Public officials, particularly in the north, were less aware of excessive heat alerts and relied on standard operating procedures in their general emergency plans to respond to this hazard as a large event. Participants expressed interest in creating more coordinated efforts to educate the public and establish community plans to reduce heat risks but reported constraints to accomplishing such goals.
The quantitative results of the coding scheme and ranking activities provide support for the aforementioned qualitative results and warrant featuring the broad themes and quotations selected. Quantitative results show that professionals were aware of the hazard and its impacts, were influenced by experience and institutional norms, and had varied experience with heat alerts dependent upon geography. Table 7 summarizes the coding results by the main code groups in the model and illustrates how often interviewee groups mentioned certain codes. Mentions of warning information, decisions, and dissemination are high for all groups while the sub-codes explain how aspects of the heat risk warning and communication systems in Utah were new and unfamiliar. Aside from the geographic variation of heat alert experience amongst officials, 68% of public officials had not heard of the Experimental HeatRisk forecasting tool that was launched during the summer in which the interviews were conducted, while all forecasters and media participants were aware of it. Most public officials (91%) talked about NWS products and tools in general, but 95% also relied on forecast and alert information for which they could not remember the source. 31% of participants had never seen anything about what to do during an extreme heat event in any form of media. Furthermore, although 47% of participants
62 mentioned ways that their local government and community has implemented plans or initiatives during extreme heat, when asked if they had ever received anything from their local city about what to do during an extreme heat event, 81% said no.
The Uncertainty/Variability codes indicate that unlike some other hazards, forecasters consistently spoke of having high confidence in extreme heat forecasts days in advance because of the nature of this hazard. Uncertainty for them related more to the specifics of how to interpret the forecasts from the Experimental HeatRisk tool as some results do not coincide with the contextual knowledge they have of the area. Officials and media broadcasters were not concerned about the validity of the information from NWS heat alerts but brought up concerns about how to interpret some of the information contained in the products, the new HeatRisk forecast, and, more commonly, questions were voiced about how to define extreme heat and the relativity of definitions for the term.
b. Rankings
Interviewees were asked to list extreme heat risks in Utah, sort them from most common to least common, and then re-sort them from most serious to least serious. A similar question asked participants to list what individuals can do to reduce their own risks to extreme heat and rank them from most effective to least effective. Average heat risk rankings and risk-reduction practices are found in Tables 8 and 9 respectively. The heat risk rankings have considerable variation amongst participants (Table 8). On average, discomfort/fatigue, dehydration, water accessibility, and heat morbidity were considered the most common risks while heat exhaustion/heat stroke, heat mortality, heat
63 morbidity, and children locked in cars were ranked the most serious. Heat risk reduction rankings had higher agreement among participants (Table 9). Recognizing and treating the signs and symptoms of heat exhaustion and heat stroke, planning, avoiding the hottest time of the day, awareness, and hydration were ranked the most effective practice on average with very similar means and standard deviations. When asked what action was the most effective to reduce extreme heat risk, hydration and awareness had the most votes for the entire sample while differences existed between public officials’ and forecasters’ top ranked practices (Fig. 6).
5. Discussion a. Shifting NWS perspectives
While planning this project we were unaware that NWS had begun to address the institutional norms regarding extreme heat through the western regional office. By collaborating with the SLC WFO, we became aware that the regional office had trained
WFOs in several western states to implement the Experimental HeatRisk tool the same summer we were to conduct interviews. This change proved fortuitous to informing this research on the current state of extreme heat perspectives and systems not only in Utah, but within the broader agency responsible for issuing heat products in the US, the NWS.
By asking about the Experimental HeatRisk tool and being more aware of its implementation and testing, our interviews were able to extract deeper institutional challenges that we had not otherwise suspected regarding perceptions of extreme heat in arid regions of the Intermountain West. NWS professionals have experienced a cultural shift within the agency to recognize and communicate about the dangers of dry heat in
64 this region. Prior to implementing Experimental HeatRisk, although guidelines for heat products were flexible to adjust to each WFO (Hawkins et al. 2016), guidelines tended to not incorporate findings in other areas of science—like public health—that inform how to measure heat risk in differing climates. Hence, the dangers of extreme heat to one’s health in non-humid regions were historically not being considered in the forecasting process. This created an institutional culture that hot weather in high desert regions, like northern Utah, was not perceived to be a problem even though people still suffer heat illness and death in these areas. NWS is now attempting through their new tool to incorporate other variables like local climatology, duration of the event, acclimatization, and cumulative effects of high nightly lows into their warning decisions to capture excessive heat threats in areas considered to be less dangerous because they are not humid and thereby evaluate the seriousness of extreme heat more accurately in these locations. This does not mean all NWS professionals are or will be supportive of this institutional shift. Some forecasters mentioned that other professionals question the strength and validity of acknowledging more excessive heat events and maintain the old adage that high temperatures in arid regions with higher elevation do not matter much.
The process of shifting the agency’s perspectives on the dangers of dry heat will be necessary to successfully elevate awareness of extreme heat risks not only to the public but to other professionals with whom they partner.
Media professionals’ perspectives on extreme heat and how they prioritize these messages were varied, and would be better understood if more broadcaster interviews were obtained. However, public officials’ perspectives and prioritization were more defined. Public officials responsible for further disseminating NWS heat alerts to their
65 constituents, and responding and planning for such events in Utah, trust NWS’s products.
If NWS is unsuccessful in changing the cultural mindset about extreme heat in dry regions, then the extreme heat warning system in these areas may be vulnerable.
Although public officials pay attention to general forecasts and are trained to respond in emergency situations regardless of the issuance of an NWS alert, if NWS does not continue to use these more robust methods, officials may be less prepared for and unable to plan for an extreme heat event because they would not know its magnitude and duration according to findings supported in public health. Our results thereby support the heat risk research community’s call to acknowledge all factors that exacerbate personal heat exposure to subsequently plan and prepare accordingly to minimize illness and death
(Kuras et al. 2017).
Furthermore, as heat waves continue to become more severe, frequent, longer, and affect more people, it becomes vitally important to measure the risk accurately to help officials be prepared to mitigate and respond accordingly to these events in the future.
Investigating how NWS and their partners might adjust their definitions and response plans under a warming climate would be helpful in this process. Although not mentioned specifically by NWS forecasters, it is possible that the observed and/or projected increase in severity and frequency of extreme heat events also influenced NWS administrators to adapt their definitions.
b. Experience as a communication tool
Previous research has found conflicting results about the influence of personal experience on behavior, which tend to be dependent on the hazard and how experience is
66 measured (Demuth et al. 2016; Mishra and Mazumdar 2015; Palm and Hodgson 1992;
Scolobig et al. 2012; Sharma and Patt 2012; Wei et al. 2013; Weinstein 1989; Zaalberg et al. 2009; Silver and Andrey 2013). Some scholars have emphasized the importance of acknowledging how experience influences behavior and what other variables mediate behavior instead of asking if it occurs or not (Demuth et al. 2016; Lindell and Perry 2012;
Sharma and Patt 2012; Siegrist and Gutscher 2008; Wachinger et al. 2013; Zaalberg et al.
2009). Research specific to extreme heat acknowledges that if warnings trigger positive memories of hot summers, people implement protective behaviors less (Lefevre et al.
2015), suggesting that positive experiences with heat encourage people to disregard their vulnerability and not engage in heat-protective actions. This finding then prompts the question of whether negative memories could promote more appropriate response. Our results found that the majority of professionals in the sample stated their personal and indirect experiences with extreme heat encourages them to implement protective actions and promote the seriousness of extreme heat. Appropriately triggering memories of negative health effects of heat may encourage people to take precautions and promote others to do the same. This strategy need not trigger extreme experiences as a scare tactic but simply help people remember how they felt, tell them what the experience means for their health, and encourage them to act to avoid the same consequences in the current heat event. An experimental study designed to test negative heat memories’ influence on protective actions would benefit the scientific community and have practical implications for practitioners responsible for communicating this hazard.
67 c. Uniform beliefs but geographic differences in application
Professionals in Utah were aware of the short- and long-term impacts of extreme heat and know how to mitigate or respond to these impacts. They recognized extreme heat as a serious danger but believed that a large proportion of the Utah population and its visitors substantially underestimate this hazard. Following up with the next step in the mental models approach—which conducts the same interviews with members of the public—would be useful to analyze the validity of professionals’ concerns and address any communication gaps between professionals and members of the public. It is possible that the majority acknowledge the seriousness of the hazard but other constraints make it difficult for some to apply their knowledge. Finding out what those constraints are and what concepts unaware people do not understand would help professionals know what is most important to include in their outreach initiatives and warning messages.
While professionals in Utah were aware of extreme heat and its impacts, experience responding to NWS alerts and extreme heat events is limited to professionals in the southern region. This is partly due to the fact that until the most recent changes through the Experimental HeatRisk tool occurred, the northern area of the state never reached the established criteria for a heat alert. Now that these criteria have been adjusted for the climatology and acclimatization of the area, alerts are more likely to be issued and professionals will accrue experience planning and responding to these events. The recent implementation of this tool and its implications for future NWS heat alerts makes it difficult to measure its impact on officials’ current communication practices when they are still unfamiliar or unaware of the new system. A follow-up study on this area in
68 several years would better tell how professionals view, communicate, and respond to extreme heat when they have more experience on which to draw.
d. Generalizability
Heat waves are a serious and often underestimated hazard (Borden and Cutter 2008;
Howe et al. 2018; Klinenberg 2015; Poumadère et al. 2005). Results of this study may be generalizable to areas where heat waves are underestimated and particularly where heat exposure has been historically low overall but is currently increasing or expected to increase in the coming years. These results may also be helpful in areas where professionals seek to improve overall perceptions of the dangers of extreme heat.
Likewise, areas with similar high desert climatology or similar concerns about high visitation to public places during heat events may use these results to address challenges to incorporating effective heat risk messaging. Findings may also be applicable to areas where other slow developing or less visible hazards ensue (e.g., prolonged drought). This study is also generalizable to other western states currently implementing the
Experimental HeatRisk tool in their WFOs. Utah’s situation is therefore relevant and generalizable to a variety of situations and practitioner settings.
6. Conclusions
Risk communication about extreme heat is somewhat new and developing across
Utah. Utah professionals recognized that the hazard of extreme heat is dependent on personal conditions and definitions. Extreme heat risk communication has historically been focused in the communities and parks of southern Utah where previous NWS thresholds were met. Officials and media broadcasters in the northern region reported the
69 risks of extreme heat but had less experience responding to official alerts. It is likely that official heat alerts will become more commonplace in northern Utah where the majority of the population lives, following the new NWS criteria established through the
Experimental HeatRisk tool. This emphasizes the need for more robust heat exposure metrics to effectively inform large populations at risk in urban settings. The new NWS criteria were established to shift NWS professionals’ perspectives on extreme heat in high desert arid regions like Utah and thereby provide a more accurate warning system for these areas. Future research on the success of this institutional shift by NWS and on stakeholder response to subsequent alerts in the north would help to answer the original research question and objective of how professionals characterize and communicate heat risk, and what recommendations can be given to improve heat risk messaging. Heat risk practices and perspectives in Utah apply broadly to other areas across the world.
Professionals in areas similar to Utah may use these results to support concerns about heat exposure and explore possible areas of miscommunication and needs for education in their own jurisdictions to improve their own planning, warning, and communication strategies for heat waves. Results are particularly helpful for evaluating the effectiveness of the Experimental HeatRisk tool in the NWS western region and could be used to assist other WFOs who wish to improve its implementation.
7. Acknowledgments
We thank the collaborators at NWS, specifically Brian McInerney and Randy
Graham. We also thank Yajie Li for assistance with interrater reliability analysis as a second coder. This study was partially supported by the National Science Foundation
70 under Grant No. 1633756, “NRT: Graduate Climate Adaptation Research that Enhances
Education and Responsiveness of Science at the Management-Policy Interface.”
71 8. Tables officials officials from
profession, profession, their geographic scale of articipants articipants by p
Demographics Demographics of interview sample. Number of agencies.
6. ABLE ABLE T emergency emergency management, public health, and parks professions. All forecasters and media broadcasters cover the who who hold forecasting responsibilities were interviewed, along with four media broadcasters, and 22 majority or all of Utah, while public officials represent various levels of local and state government, and state and professional responsibilities, and the location of their jurisdiction within Utah. Six National Weather Service experts experts Service Weather National Six Utah. within jurisdiction their of location the and responsibilities, professional federal park
72 TABLE 7. Code mention totals by code group and interviewee type. Overall view of main concepts coded by how many interviewees mentioned the concept including sub- codes within each interviewee type starting with forecasters (F), media broadcaster (M), and public officials (O) in the green columns, and then broken down by geographic location in the blue columns: north (N), south (S), and state-wide (SW). The 22 public officials’ mentions are then broken down by the type of agency in the pink columns: emergency management (EM), public health (H), and parks (P). Boxes are shaded to represent zero members of that group mentioned the concept (white) to all group members mentioned the concept (green/blue/pink). Most main concepts were mentioned by the majority of interviewee groups. Few participants mentioned the psychological impacts of extreme heat and no participants mentioned recovery from extreme heat events.
73
TABLE 8. Average rankings for extreme heat risks. Interviewees ranked extreme heat risks according to what they considered as the most common (1) to least common, and most serious (1) to least serious. Heat risks mentioned more than 5 times are listed. Counts were calculated according to the frequency of mentions: concepts ranked separately by participants but consolidated into one code counted as additional rankings for the corresponding code to which they belonged (see Appendix J). Hence, counts could exceed the total number of interviewees (n = 32). Lower means indicate each concept was ranked as more common or more serious. Bolded values indicate means with standard deviations less than 2.0 and italicized less than 1.0. “Heat exhaustion or heat stroke,” and “dehydration” were mentioned the most. On average, “dehydration” was ranked as one of the most common risks while “heat mortality” was strongly agreed upon as the most serious risk of extreme heat. “Children locked in cars” was ranked as one of the most serious risks with high agreement but this risk was mentioned less often. Concept definitions can be found in Appendix J.
74 Average Rankings of Extreme Heat Risks Most Common Most Serious CONCEPT mean s.d. count mean s.d. count Heat Exhaustion or Heat Stroke 3.7 1.8 40 2.5 1.3 40 Dehydration 2.3 1.1 15 4.7 1.9 15 Heat mortality 4.9 2.1 12 1.3 0.5 12 Elderly 3.0 2.0 11 3.2 1.8 11 Health Impacts - General 2.6 2.2 10 2.7 2.1 10 Wildlife 3.3 1.7 10 4.7 2.2 10 Individual Health Characteristics 4.1 1.8 10 3.2 2.2 10 Infrastructure 5.2 2.7 10 3.9 2.1 10 Heat Symptoms/Injuries - Other 3.3 1.7 9 3.8 1.5 9 Young children & infants 3.0 2.4 8 3.0 2.0 8 Pets 4.6 2.0 8 4.9 2.4 8 Water accessibility 2.0 1.3 6 5.2 1.8 6 Children locked in cars 2.5 2.3 6 1.3 0.5 6 Wildfire 3.8 1.5 6 3.8 1.5 6 Sociodemographics 4.5 3.7 6 4.3 3.2 6 Discomfort/Fatigue 2.0 1.2 5 5.8 3.3 5 Pets locked in cars 2.6 2.1 5 2.8 2.5 5 Heat morbidity 2.8 1.8 5 2.2 1.1 5 Domestic Plants & Animals 3.4 1.5 5 5.4 1.1 5 Psychological/Social Impacts 3.4 2.6 5 6.0 1.6 5 Situational: Voluntary Exposure 3.8 3.6 5 3.8 2.4 5 Vehicle damage/diminishment 4.2 1.3 5 5.6 1.1 5 Secondary hazards 5.8 2.4 5 4.8 3.1 5 NOTES: Bold indicates lowest means with s.d. < 2.0 Italics indicates s.d. < 1.0
75 TABLE 9. Average rankings for heat risk reduction practices. Interviewees ranked heat risk reduction practices from most effective (1) to least effective. Practices mentioned more than 5 times are listed. Counts were calculated by the frequency of mentions meaning all items consolidated into each code were counted within each participant’s ranking (see Appendix J). Hence counts can exceed the total number of interviewees (n = 32). Lower means indicate a practice was ranked as more effective. “Hydration” was mentioned the most while knowing how to recognize and treat the signs and symptoms of heat stroke and heat exhaustion was considered on average to be the most effective practice to reduce extreme heat risk. Concept definitions can be found in Appendix J.
Heat Risk Reduction Rankings Most Effective Practices CONCEPT mean s.d. count Hydration 2.5 1.2 26 Awareness 2.4 1.5 16 Avoid hottest time of day 2.4 1.0 15 Lightweight/Light-colored clothing 4.4 1.1 14 Avoid the heat 3.9 3.2 13 Recognize & Treat signs/symptoms of heat exhaustion/stroke 2.2 1.2 12 Preparedness 3.5 2.5 11 Planning 2.3 1.3 9 Social capital 4.2 1.4 9 Know your limitations 3.4 1.6 7 Eat proper food 3.6 1.4 7 Health choices 2.8 1.3 5 Find/Stay in the shade 3.4 1.9 5 Protect 3.6 2.7 5 NOTES: Bold indicates lowest means with s.d. < 2.0
76 9. Figures
77
FIG. 5. Extreme Heat Risk & Warning System Model created in this study (main concepts and sub-concepts). The 11 boxes with solid lines represent the main concepts analyzed in this study. The three boxes in red indicate new concepts that were added to the model from the interview results. Text in red indicates new sub-concepts. The dashed box contains the model concepts related to warning information and warning decisions. Asterisks indicate concepts mentioned by 25% - 49% (*), 50% - 89% (**), 90 – 100% (***) of interviewees. No asterisk indicates concepts that were mentioned by less than 25% of interviewees. Concepts in blue were not mentioned in the interviews at all. 1Indicates percentage was calculated according to the subset of interviewees who were asked these questions. Forecasters’ previous experience issuing heat alerts was originally coded under Warning Process & Decisions.
78
FIG. 6. Top ranked most effective practices by professional group. When participants were asked which of the practices they listed were most effective in reducing extreme heat risk, these practices were selected. More officials ranked awareness as number one but forecaster and media votes gave hydration and awareness the same amount of number one votes overall.
79 CHAPTER 4
CONCLUSIONS
This thesis accomplished multiple objectives through its two distinct studies. First, we now have a better understanding of what may influence protective behaviors among the general population in the United States and that some factors appear to have more influence than others. This study, detailed in Chapter 2 (see Appendix K for co-author permissions), demonstrated that risk perception is a strong predictor of protective behaviors but overall the subjective experience of heat-related health symptoms has a stronger association with a greater number of protective behaviors against extreme heat.
This subjective experience also appeared to have a much stronger relationship than actual heat exposure or local climate. This result supports Dillon and colleagues (2014, 2011) and Sharma and Patt’s (2012) findings that negative impacts of a hazard more effectively predict future protective behavior than other measures of experience. Furthermore, there was little spatial variation in self-reported protective behaviors in the US even though certain regions experience more extreme heat than others. This lack of spatial variation emphasizes the importance of regional acclimatization. Even though some areas experience more extreme heat comparatively to cooler locations, cooler locations can still experience extreme heat according to their climatology to which their populations are not accustomed and are therefore at risk. It also appeared that some demographic groups were more prone to demonstrate altruistic protective behavior—checking on friends, family, and neighbors—than others, namely women and African-Americans. These findings could be incorporated into community outreach initiatives to more effectively
80 respond by both targeting groups that are more willing to act on behalf of others and persuading other groups that are less willing to engage in this behavior. Furthermore, the differences in predictors between personal and altruistic protective behaviors implies that there may be inherent differences in what influences Americans to engage in these categories of behaviors. Future research should consider designing methods to investigate altruistic and personal protective behaviors independently to determine what promotes or impedes them. Findings from such research could help practitioners target improvements in different behaviors more effectively.
The second study, detailed in Chapter 3, showed that the heat risk communication and warning system in Utah is new and unfamiliar in areas with a history of low exposure and few heat alerts, but more established in areas with higher exposure and a history of heat alerts. Professionals spoke little about ways the actual content of alerts could be improved and largely trust NWS products. While this system is vulnerable to newly established NWS criteria and its shifting institutional norms, professionals are aware of extreme heat risks and promote educating populations about them. Professionals’ experience with extreme heat influences their perspectives on the seriousness of this hazard and how they make decisions to communicate and respond to extreme heat events.
Although the overall newness and unfamiliarity of Utah’s heat risk communication and warning system hindered our ability to identify systematic ways to improve their risk messaging practices, we now understand the challenges and strengths to this system. By thoroughly investigating the research question of how warning professionals and their partners characterize and make decisions about heat risks, future studies can more appropriately approach how to incrementally improve heat risk communication practices
81 in Utah and other areas with similar challenges. This chapter further emphasizes that using the negative impacts of heat to expand the definition of heat experience provides a better understanding of what influences people’s decisions. The heat risk community has identified a need for researchers to improve our understanding of personal heat exposure and its implications by measuring it more directly through individual metrics and devices instead of relying on ambient temperatures (Kuras et al. 2017). Yet, even with individualized temperature exposure measurements, the results of this thesis imply that heat exposure is better understood when the negative health effects experienced during such exposure are also included in our analysis of behavioral outcomes. Just as exposure measurements can be improved, heat risk studies can be improved by finding ways to measure and incorporate heat-health symptoms of participants in their analyses.
An overarching theme emerges from these two studies with different purposes: previous experience with the negative effects of heat on one’s health appears to have a strong influence on future response and preparedness. Identifying whether this relationship existed or not was a main objective for this thesis. In the first study, this relationship manifested in the general US public’s efforts to implement various protective behaviors: namely, checking on others during a heat wave, using fans, using AC, going to a cooler place, and staying indoors. People with more experience with heat-related health symptoms were more likely to report implementing these behaviors. In the second study, two-thirds of interviewed Utah professionals responsible for communicating heat risk stated that their experience with heat impacts, whether personally or in their job capacity, influenced their decisions regarding how they communicate heat risk and respond to extreme heat incidents and events. Based on these two studies, it appears that
82 experiencing heat-related illnesses and symptoms make people more aware of the seriousness of extreme heat and encourages them to implement protective behaviors and make decisions that emphasize communicating heat risk and preparedness. Both studies suggest that leveraging the subjective experience of heat on one’s health could be a powerful heat risk communication strategy. Triggering memories of negative experiences with heat, or communicating others’ experience vicariously, may be an effective way to help people recognize their risk and take appropriate action to avoid suffering the negative consequences they experienced previously. Future work that examines how to incorporate memories of negative experiences with heat would benefit the risk communication field and professionals striving to improve adaptive capacity in their communities. Moreover, there are several steps to a mental models approach to improve heat risk communication that should be addressed in future research (see Appendix G)
(Morgan et al. 2001). Completing the steps to the mental models approach in full would provide insight and application to many settings seeking to improve preparedness, perception, and communication about extreme heat risks.
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101
APPENDICES
102 APPENDIX A
SAMPLE DISTRIBUTION MAP
FIG. 7. Survey sample distribution (n = 1330). The survey provider used probabilistic, address-based techniques to assemble a panel from which this nationally representative sample was drawn. The survey, Climate Change in the American Mind: Fall 2015, had an average margin of error of ± 3% at 95% confidence (Leiserowitz et al. 2015). Participant locations have been anonymized through a random jittering process.
103 APPENDIX B
RANDOM EFFECTS ODDS RATIOS
Odds Ratios for random effects in the Checking on Others model from Chapter 2 are listed here on the following pages in this order: State, Region, Rural v. Urban, Age,
Education, Income, Ethnicity/Race, Gender, Political Identity, and the interaction term
(Figures 8 thru 17). For example, Californians are 0.55 times as likely to report checking on others than the national average (53%) while Ohioans are 1.28 times more likely than the average American to report checking on others during a heat wave.
104 CA 0.55 OR 1.06 WA 1.10 NV 0.98 UT 1.14 AZ 1.10 NM 0.81 CO 0.84 WY 0.96 ID 0.92 MT 0.93 TX 0.86 OK 1.02 LA 1.09 AR 0.90 MS 1.14 AL 1.02 TN 1.14 KY 0.89 FL 0.85 GA 1.05 SC 1.11 NC 1.18 WV 1.13 VA 1.05 DC 1.10 MD 0.91 DE 0.86 KS 0.98 NE 1.09 SD 0.90 ND 0.96 MO 1.06 IA 1.02 MN 0.93 WI 1.13 MI 0.86 IL 1.14 IN 1.10 OH 1.28 PA 1.05 NJ 1.20 NY 1.09 CT 1.19 RI 1.05 MA 1.01 VT 0.95 NH 0.95 ME 0.84 0.1 0.2 0.5 1 2 5
FIG. 8. Odds ratios for states.
105
FIG. 9. Odds ratios for regions.
FIG. 10. Odds ratios for metro vs. non-metro areas.
106
FIG. 11. Odds ratios for age groups.
FIG. 12. Odds ratios for education levels.
107
FIG. 13. Odds ratios for income levels.
FIG. 14. Odds ratios for ethnic/racial groups.
108
FIG. 15. Odds ratios for gender.
FIG. 16. Odds ratios for political ideology.
109
Bachelor's degree or higher:Female:Conservative 1.25 Bachelor's degree or higher:Female:Moderate 0.66 Bachelor's degree or higher:Female:Liberal 0.85 Bachelor's degree or higher:Female:Refused 1.02 Bachelor's degree or higher:Male:Conservative 0.91 Bachelor's degree or higher:Male:Moderate 0.87 Bachelor's degree or higher:Male:Liberal 0.83 Bachelor's degree or higher:Male:Refused 0.93 Some college:Female:Conservative 0.89 Some college:Female:Moderate 0.86 Some college:Female:Liberal 0.94 Some college:Female:Refused 1.00 Some college:Male:Conservative 0.94 Some college:Male:Moderate 1.48 Some college:Male:Liberal 0.95 High school:Female:Conservative 0.74 High school:Female:Moderate 1.20 High school:Female:Liberal 1.23 High school:Female:Refused 1.03 High school:Male:Conservative 1.74 High school:Male:Moderate 1.09 High school:Male:Liberal 0.78 High school:Male:Refused 0.77 Less than high school:Female:Conservative 1.13 Less than high school:Female:Moderate 1.77 Less than high school:Female:Liberal 1.17 Less than high school:Female:Refused 1.03 Less than high school:Male:Conservative 0.85 Less than high school:Male:Moderate 0.93 Less than high school:Male:Liberal 0.99 0.2 0.5 1 2 5 10
FIG. 17. Odds ratios for the interaction term. Note that while interaction groups overlap each other and may not vary from one another, female moderates with less than a high school education, and male conservatives with a high school education did not overlap the national average. Male moderates with some college education have an effect size and confidence interval comparable to other key factors noted in the study: females, moderates, Blacks or African-Americans, and less than a high school education.
110 APPENDIX C
PREDICTED PROBABILITIES OF INTERACTION TERM
FIG. 18. Predicated probabilities between Political Ideology and Education for the interaction term including Political Ideology, Gender, and Education. As education increases, Americans check on others during a heat wave less. Moderates check on others more than liberals and conservatives across education levels whereas liberals’ and conservatives’ probability of checking on others converges as their education increases.
111
FIG. 19. Predicated probabilities between Political Ideology and Gender for the interaction term including Political Ideology, Gender, and Education. Male conservatives are predicted to check on others slightly more than male liberals whereas female conservatives are predicted to check on others slightly less than female liberals. Moderate males and females were predicted to check on others significantly more than the other parties.
112 APPENDIX D
ALTERNATIVE DICHOTOMIZATION
TABLE 10. Alternative dichotomization of dependent variables results. The results below reflect dichotomizing the dependent variables with all responses considered as positive except for those who responded ‘Never’. Note that risk perception influences AC Use significantly more than negative health effects but the negative health effects score is still the most consistent predictor for protective behaviors. The climatic indicators of temperature exposure still have negligible association with these behaviors.
113
114 TABLE 11. Checking on others alternative dichotomization results. Model results for Checking on others during a heat wave with an alternative dichotomization show a greater association with risk perception and negative health effects while the variance for the demographic and spatial effects are smaller than the original model including negligible influence of race/ethnicity, education, income, and all spatial variables. This is likely due to the small number of ‘Never’ responses (n = 251) representing several levels.
Alternative Dichotomization Results for Checking on Others
β Std. Error
Fixed Parts
(Intercept) -0.49 0.76
NegHealthEffects_Score 2.13 *** 0.51
RiskPerception_Score 2.56 *** 0.41
WarmSeason_30yr_Average -0.01 0.03
WarmSeason_Anomaly -0.03 0.21
Random Parts
τ00, COUNTY 0.000
τ00, STATE 0.094
τ00, EDU: GENDER: POL. IDEOLOGY 0.023
τ00, REGION 0.000
τ00, INCOME 0.002
τ00, RACE/ETHNICITY 0.000
τ00, POLITICAL IDEOLOGY 0.135
τ00, EDUCATION 0.000
τ00, AGE 0.106
τ00, RURAL v. URBAN 0.000
τ00, GENDER 0.069 Observations 1174
* p<.05 ** p<.01 *** p<.001
115 APPENDIX E
ALTERNATIVE EXPOSURE VARIABLES
TABLE 12. Alternative exposure variables model results. Using Daymet data (Thornton et al. 2018), three alternative exposure variables were created to replace the 2015 temperature anomaly variable in the regression models. These variables represent the number of days that the mean temperature exceeded the 90th, 95th, and 99th percentiles (relative to the 1981-2010 period) for at least two consecutive days at the census tract level, and subsequently averaged by county. The Daymet 2015 warm season average variable is strongly correlated with the PRISM 2015 warm season average variable (r = .096). Results below demonstrate that exposure, as defined by these local percentile indices, had negligible influence on reported protective behaviors, similar to results of the original model using the seasonal temperature anomaly.
116
117 APPENDIX F
EXPERT MODEL (ORIGINAL)
experts. Original expert model. This model was created fromreferencing technical created model. This was literature and expert Original model
20.
. IG F
118 APPENDIX G
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. . 1 2
119 APPENDIX H
INTERVIEW PROTOCOL
Interview Number ______Date ______Time ______(Please state name and position)
Thank you again for agreeing to let me interview you today. Please say what you think or believe about the following questions. Remember that your name will not be associated with your response in our analyses or reports. There are no right or wrong answers, and your answers will be most helpful to us if they are what you really think. If I ask a question that you’ve already answered, please feel free to say so and refer to your previous answer. I’ll be taking notes while you’re talking, and these are to help me follow along and keep track of what you’re saying.
General prompts: You mentioned ___, can you tell me more about that? Does anything else come to mind? You’ve mentioned ___, ___…. , does anything else come to mind? Please say whatever comes to mind. Can you elaborate?
EXTREME HEAT
1. (!) Tell me about extreme or excessive heat … [Prompt:]
2. (!) Tell me about extreme heat events in Utah… [Prompt]
3. (!) [If a county/local/park official, otherwise skip]. Tell me about extreme heat events in (county/city/park) ... [Prompt]
EXPOSURE
4. o [If have mentioned: As you mentioned] What do you think determines whether there is extreme heat in Utah/county/city or not?
5. o [If have mentioned: As you mentioned] Are there particular times when extreme heat is more likely? (If yes: what are the factors that make extreme heat more likely at that/those time(s)?)
120 [Prompt: for times of year, for times of day]
6. Tell me what indications there might be of impending extreme heat in the near future.
7. Is there anything else that might affect the occurrence of extreme heat?
8. Are there any specific locations in Utah/county/city where extreme heat is more likely? (If yes: what are the distinguishing features of those locations that make extreme heat more likely?)
9. Are there factors that influence extreme heat in other places that aren’t relevant here in Utah/county/city (or vice versa)? [Prompt: In other words, what is different about other places in terms of influences on extreme heat?]
EFFECTS IN GENERAL
10. Now, speaking in general, not specific to Utah/ county/city, what risks are there from an extreme heat event? (If needed: Think in general terms, we’ll talk about Utah/county/city in the next section.)
11. What might happen to a person who was exposed to extreme heat?
12. Do any other effects of extreme heat events come to mind?
Ok, now I am going to use these cards to write down your answers for some of the next questions. We will use them later on during this interview.
EFFECTS IN UTAH/County/City
13. What risks are there from extreme heat events in Utah/county/city? If they are similar to risks you already mentioned, that’s ok. (put on BLANK cards)
121 14. Ok, you mentioned (X, Y, Z). I’ve written your responses on these cards. Did I get these right? If not, please feel free to edit them (hand them the cards and a pen). Do any other effects of extreme heat come to mind? [Prompt Is anything or anyone else at risk from extreme heat in Utah/county/city?]
15. Now, please sort them from most likely--that is most common--to least likely to happen in Utah/county/ city, if an extreme heat event occurs. Please think aloud as you sort. (Before moving on, rank the order of the cards in a BLUE pen in the right corner)
16. Now, please sort these effects from the most to the least serious in Utah/county/city (If quiet: Please tell me what’s going through your mind.) (Before moving on, rank the order of the cards in a RED pen in the right corner)
17. Describe how effects of extreme heat might vary? [Effects on people? Effects on property/land?]
18. What determines how effects of extreme heat might vary?
MITIGATION
19. (!) In general, what can or should be done, if anything, to reduce risks from extreme heat events? [Prompt: Anything else?]
20. What can or should be done, if anything, to reduce risks from extreme heat in Utah/county/city?
21. (!) Is there anything that individuals could or should do to reduce their own risks from extreme heat in Utah/county? city? (put on BLANK cards) [Prompt]
22. o [if more than one] Ok, you mentioned (X, Y, Z). I’ve written your responses on these cards. Did I get these right? If not, please feel free to edit them (hand them the cards and a pen). Does anything else come to mind?
122 23. Ok, can you tell me which of these is the most effective in reducing extreme heat risk? Please tell me what’s going through your mind as you compare these.
24. Now, can you rank them from most effective to least effective? Please think aloud as you sort. (Before moving on, rank the order of the cards in a BLUE pen in the right corner)
25. How would you rank risks of extreme heat compared to other risks people face in Utah/county/city? [Prompt: Think in terms of risks, hazards, and other dangers people encounter.]
26. How do the risks people in Utah/county/city face from an extreme heat event compare to risks they face from otherwise hot weather?
27. (!) What information do people need to protect themselves from extreme heat risks? [Prompt]
28. (!) How can a person find out if there is a general risk of extreme heat at a specific location (for example, at work or at home)?
29. (!) How can a person find out if there is an imminent risk of extreme heat at a specific location (for example, at work or at home)?
30. Please tell us how you interpreted “imminent” in that last question?
31. Have you ever worried about extreme heat risks in Utah/county/city?
__Yes __ No o [If yes] What were your worries and how did they come up?
32. (If not mentioned) Have you ever personally experienced extreme heat?
__Yes __ No o [If yes] Tell me about that experience.
123 33. Do others you know worry about extreme heat risks in Utah/county/city?
__Yes __ No o [If yes] What are their worries? [What do they talk about?]
124 EXTREME HEAT ALERT EXPERIENCE - FORECASTERS
Now I’m going to change directions a little and ask you a few questions about how you go about making extreme heat alert decisions. To be sure to cover everything, I will ask some questions that may seem similar. So, you may ask me to repeat a question to clarify the difference. If you feel you’ve already answered a question, please feel free to refer to your previous answers.
34. Have you ever issued an extreme heat alert? This could be a heat watch, warning, advisory, or something similar.
__Yes __ No o [If yes] Describe the most recent one that you issued.
o [If no] Has there been an instance when you could have issued a heat advisory, watch, or warning but chose not to?
__Yes __ No o [If yes] Why did you choose not to issue an alert?
35. o [If said ‘No’ to Q34] Speaking speculatively, how would you go about making a decision to issue an alert now? o [If said ‘Yes’ to Q34] How did you go about making that decision to issue an alert?
36. Can you tell me anything else about this warning (process) decision?
o (Skip if said ‘No to Q34) [If not mentioned ask:] When was this?
o (Skip if said ‘No’ to Q34) [If not mentioned ask:] Can you tell me any more specifics about the alert? [Prompt: For timing, for coverage, additional content?]
37. What (is) was your role in the decision to issue (an) the alert?
38. How (do) did others contribute? [Prompt: others inside the WFO, others outside the WFO]
39. How (would) did you decide what to include and exclude in the alert?
125 40. What information or outside input (would) did you use in deciding to issue the alert? [Prompt]
41. Can you tell me (more) about how you (would use) used this information?
42. Have you ever seen/heard of the Experimental HeatRisk tool?
__Yes __ No o [If yes] Can you describe it for me? o [If no] (See Q87 for description).
43. (Skip if answered ‘No’ to Q42) Did/Do you use it in your decision to issue the/an alert?
__Yes __ No o [If yes] How did/do you use it?
44. Are there ever problems with any of this information?
45. (Will) Do you always have (X, Y, Z, plus the other things you mentioned) available to you when making this type of decision to issue an alert? o [If no] What varies?
46. Are there things you’ve learned from experience that (will affect) affected your decision to issue an alert?
47. Once you created (create) the alert, how did (would) you disseminate it? [Prompt: Are there other ways?]
48. Who do you think used (would use) the alert? [Prompt: Anyone else?]
49. How do you think they interpreted (would interpret) the alert?
50. What do you think they did (would do) with the alert?
126 51. o (Skip if answered ‘No to Q34) Thinking back to that decision to issue an alert, tell me how you felt about it after the fact. Did you evaluate it? [Prompt: Anything else?]
52. o (Skip if answered ‘No to Q34) Tell me how your colleagues felt about the decision. Did they agree with it? [Prompt: colleagues inside the WFO, colleagues outside the WFO]
53. Are there users other than those you mentioned above who would typically use your extreme heat alerts?
54. o (Skip if answered ‘No to Q34) Was this typical of the way you make extreme heat alerts? o [If no: How do you typically make extreme heat alerts?]
55. o (Skip if answered ‘No’ to Q34) Was this typical of the way you disseminate extreme heat alerts? o [If no: What varied in this case?]
56. Can you tell us anything (more) about the role of uncertainty in the decision to issue these alerts?
57. Is there anything we’ve left out that’s important for us to know about decisions to issue alerts for extreme heat?
58. Have you gotten any information from the city/county of ______or other local official about what to do if there is an extreme heat event?
__Yes __ No [If yes] When? [If yes] From whom? [If yes] What information was included?
59. Have you seen a sign, TV, radio, mobile phone, Internet, or social media message with information about what to do if there is an extreme heat event? [Prompt: Either personally or professionally]
127 __Yes __ No [If yes] When? [If yes] Where? [If yes] What information was included?
Any others? (List the forms of media they did not already mention and follow the same questions)
Ok, that’s it. Thank you. I just have one last question for you.
60. Was anything in the interview particularly hard or problematic? Was there anything we didn’t ask that you think we should have asked?
Thank you for your time. We appreciate your participation in our study. We will be interviewing other forecasters in your office and are interested in hearing individuals’ different views, so please do not discuss the questions we’ve asked with your colleagues until we have finished the interviews.
Experimental HeatRisk Tool link:www.wrh.noaa.gov/wrh/heatrisk/?wfo=slc
128 EXTREME HEAT ALERT EXPERIENCE – BROADCAST MEDIA AND LOCAL OFFICIALS
Now I’m going to change directions a little and ask you a few questions about how you go about making extreme heat alert decisions. To be sure to cover everything, I will ask some questions that may seem similar. So, you may ask me to repeat a question to clarify the difference. If you feel you’ve already answered a question, please feel free to refer to your previous answers.
61. In your work, have you ever received or communicated an extreme heat alert or related information? [If no to the above] In your work, have you ever been involved in an extreme heat event or potential extreme heat event? [If no to the above] In your work, have you ever been involved in communicating about or responding to the effects of extreme heat? [If no to the above] Have you ever received extreme heat or extreme heat alert related training or been involved in an extreme heat event exercise? [If no to the above] Have you personally experienced extreme heat or an extreme heat alert [If yes: switch to personal protocol, Q90]? If no experience: skip to question #89.
62. Describe the most recent [extreme heat alert / extreme heat event / extreme heat event training or exercise / response or communication about extreme heat effects] you were involved in professionally.
63. What decisions did you face [regarding the extreme heat alert / regarding the extreme heat event / during the training / during the exercise / when communicating about or responding to the effects]?
64. What did you decide?
65. Can you tell me anything else about your decision(s)?
o [If not mentioned ask] When was this? o [If not mentioned ask] Can you tell me any more specifics about the [alert / extreme heat event / training / exercise / effects response or communication]? [Prompt: For timing, for coverage, additional content?]
66. What was your role in the decision(s)? [Prompt: Anything else?]
129 67. How did others contribute to the decision(s)? [Prompt: others inside the organization, others outside the organization]
68. What forecast or alert information did you receive? [Prompt: From whom?]
69. What other information or outside input did you use in making decisions? [Prompt]
70. Can you tell me (more) about how you used this information?
71. Are there ever problems with any of this information?
72. Are there things you’ve learned from experience that affected your decisions?
73. What alert information did you provide to others? [Prompt: To whom?] [If none, skip to 78]
74. How did you disseminate the alert information? [Prompt: Are there other ways?]
75. Who do you think used the alert information? [Prompt: Anyone else?]
76. How do you think they interpreted the alert information?
77. What do you think they did with the alert information?
78. Thinking back to your decision(s), tell me how you felt about it (them) after the fact. Did you evaluate it (them)? [Prompt: Anything else?]
79. Tell me how your colleagues felt about the decision(s). Did they agree with it (them)? [Prompt: colleagues inside/outside the organization]
130 80. Are there [other] users who would typically use heat alert information from you?
81. Was this typical of the way you make heat alert / heat event decisions? [If no: How do you typically make heat alert / heat event decisions?]
82. Was this typical of the way you disseminate, or decide to not disseminate heat alert information? o [If no: What varied in this case?]
83. Can you tell us anything (more) about the role of uncertainty in the heat alert decisions?
84. Is there anything we’ve left out that’s important for us to know about decisions in heat alert situations?
85. Have you gotten any information from the city/county of ______or other local official about what to do if there is an extreme heat event?
__Yes __ No o [If yes] When? o [If yes] From whom? o [If yes] What information was included?
86. Have you seen a sign or TV, radio, mobile phone, Internet, or social media message with information about what to do if there is an extreme heat event?
__Yes __ No o [If yes] When? o [If yes] Where? o [If yes] What information was included? o Any others? (List the forms of media they did not already mention and follow the same questions)
87. Have you ever seen/heard of the Experimental HeatRisk tool that the National Weather Service is currently testing?
__Yes __ No
131 o [If yes] What do you know about it? o [If no] This tool is supplementary to the official NWS heat watch/warning/ advisory program and is meant to provide continuously available heat risk guidance for decision makers and heat sensitive populations who need to take actions at levels that may be below current watch/warning/advisory levels. If you’d like to see it, I can give you the URL/link after the interview.
88. (Skip if said ‘No’ to Q87) Have you ever used it to disseminate heat risk information or make heat risk decisions?
__Yes __ No o [If yes] How did you use it? o [If no] Why not?
Ok, that’s it. Thank you. I just have one last question for you.
89. Was anything in the interview particularly hard or problematic? Was there anything we didn’t ask that you think we should have asked?
Thank you for your time. We appreciate your participation in our study. We will be interviewing other (officials or members of the broadcast media) and are interested in hearing individuals’ different views, so please do not discuss the questions we’ve asked with your colleagues until we’ve finished the interviews.
Experimental HeatRisk Tool link: www.wrh.noaa.gov/wrh/heatrisk/?wfo=slc
132
EXTREME HEAT ALERT EXPERIENCE – PERSONAL
90. Describe the most recent extreme heat [alert] situation you were involved in personally.
91. How did you go about deciding what to do about the [alert / extreme heat]? [If not mentioned ask] What did you decide?
92. Can you tell me anything else about your decision(s)?
o [If not mentioned ask] When was this? o [If not mentioned ask] Can you tell me any more specifics about the [alert / extreme heat]? [Prompt: For timing, for coverage, additional content?]
93. What was your role in deciding what to do?
94. How did others contribute to the decision(s)?
95. What forecast or alert information did you receive? [Prompt: From whom?]
96. What other information or outside input did you use in deciding what to do? [Prompt]
97. Can you tell me (more) about how you used this information?
98. Were there any problems with this information?
99. Are there things you have learned from experience that affected your decisions?
100. Thinking back to the decision(s) you made, tell me how you felt about it (them) after the fact. [Prompt: Anything else?]
133 101. Tell me how others felt about the decision(s). Did they agree with it them)?
102. Can you tell us anything (more) about the role of uncertainty in your decisions?
103. Is there anything we’ve left out that’s important for us to know about your decisions in response to an extreme heat event or extreme heat alerts?
104. Have you gotten any information from the city/county of ______or other local official about what to do if there is an extreme heat event?
__Yes __ No o [If yes] When? o [If yes] From whom? o [If yes] What information was included?
105. Have you seen a sign, TV, radio, mobile phone, Internet, or social media message with information about what to do if there is an extreme heat event?
__Yes __ No o [If yes] When? o [If yes] Where? o [If yes] What information was included?
o Any others? (List the forms of media they did not already mention and follow the same questions)
106. Have you ever seen/heard of the Experimental HeatRisk tool that the National Weather Service is currently testing?
__Yes __ No o [If yes] What do you know about it? o [If no] This tool is supplementary to the official NWS heat watch/warning/ advisory program and is meant to provide continuously available heat risk guidance for decision makers and heat sensitive populations who need to take actions at levels that may be below current watch/warning/advisory levels. If you’d like to see it, I can give you the URL/link after the interview.
134 107. (Skip if said ‘No’ to Q106) Have you ever used it to disseminate heat risk information or make heat risk decisions? __Yes __ No o [If yes] How did you use it? o [If no] Why not?
Ok, that’s it. Thank you. I just have one last question for you.
108. Was anything in the interview particularly hard or problematic? Was there anything we didn’t ask that you think we should have asked?
Thank you for your time. We appreciate your participation in our study. We will be interviewing other (officials or members of the broadcast media) and are interested in hearing individuals’ different views, so please do not discuss the questions we’ve asked with your colleagues until we’ve finished the interviews.
Experimental HeatRisk Tool link: www.wrh.noaa.gov/wrh/heatrisk/?wfo=slc
135 APPENDIX I
INTERVIEW CODEBOOK
TABLE 13. Finalized interview codebook.
Code Code Definition examples EXPOSURE elements that create, contribute to, (climate, or the sun, without exacerbate, or measure physical exposure explaining more) to extremely hot temperatures mentioned in very general terms exposure: Geographic Factors geographic and topographic (elevation, desert landscape, characteristics that exacerbate existing desert environment, bare or red heat or are result of long-standing hot rock, canyon, little vegetation) conditions exposure: Timing & Duration time of year and/or length of time that a (hottest time of day, hottest heat event occurs, including seasonality, months of year, early heat in anomalously/unseasonably warm, or June, breaking records, above average, breaking records anomalously or unseasonably warm, above average for time of year exposure: Meteorolgocial meteorological characteristics that (high pressure systems, high Factors contribute to creating extreme heat pressure ridges, weather events patterns, monsoon pattern, storm systems) exposure: Predictive Models discuss models that predict or explain the (forecast models, results of meteorological factors that create heat forecasts events exposure: Climatology statements about historical temperature data, climatological averages, general statements about climate or climatology exposure: Climate Variability statements about the variability of (La Niña/El Niño oscillations, climate or weather as it relates to heat ocean currents, climate events variability) exposure: Climate Change statements about climate change as it (climate change, global relates to global warming in general or warming, earth is warming influencing heat wave/extreme heat overall) occurrence exposure: Definitions of Discuss varying definitions of heat wave Extreme Heat or extreme heat exposure: Force of God/Nature statements about heat events caused by nature, God, or other element outside influence of man exposure: Urbanization urbanization and land development contribution to heat exposure including exacerbating effects of urban heat island effect or mentioning it, even if the name used is incorrectly exposure: Temperature Reference to temperatures as part of (increasing temperatures, 115 extreme heat degrees, exposure: Temp Night Lows Reference to high nightly low (night temp is higher, 75 temperatures as part of extreme heat degrees at night
136 exposure: Humidity Level Reference to humidity as part of extreme heat exposure: Cumulative effects how effects of extreme heat exposure are (cumulative effects of poor air cumulative or delayed and can quality, cumulative effects of start/continue after the heat event heat on health--body needs including secondary effects time to recover and if can't the effects carry over to next day, algae/algal blooms results from long periods of high heat) exposure: Secondary hazards other hazards that are exacerbated by (air pollution, ozone increase, extreme heat but don't mention if wildfire risk, disease vectors, cumulative or not algal bloom) exposure: other contributions to localized heat that are (car exhaust, traffic, AC not land development machines increasing localized humidity) ADAP_CAP mitigation & preparedness efforts, plans, and actions to adapt to extreme heat, prevent injury, and respond appropriately - GENERAL adap_cap: Social Capital use of neighborhood, family, friend, or coworker networks to warn, check on, prepare each other for heat events, usually via personal contact adap_cap: Education & educational materials and messages (oral Outreach or written) from authorities/officials on health effects of heat, how to recognize and respond to symptoms, how to prepare and prevent health effects & their programs/procedures associated with sharing this info & protecting/responding to citizens adap_cap: Comm. Resources & social programs that subsidize AC bills or Public Assistance assist citizens to pay for/obtain AC units in homes; access to AC via cooling centers, other public buildings; distribute fans PLANS_GOALS Plans and goals by officials to prepare, respond, and adapt to heat events in their jurisdiction including communication about heat risk plans_goals: should Ideas about plans, goals, interventions that should be implemented by a group/society (not individual/public) but currently are not or not known to be implemented plans_goals: Comm. & Local - Knowledge of notification to community YES by Local officials' plans and goals to prepare, respond, and adapt to heat events in their jurisdiction plans_goals: Comm. & Local - Not known or no notification to NO community by local officials plans or goals to prepare, respond, and adapt to heat events plans_goals: Federal, State, Federal, Regional, & State officials' plans Region and goals to prepare, respond, communicate and adapt to heat events and heat risk in their jurisdiction
137 KAP Interviewee's knowledge and attitudes about extreme heat as they relate to individual or group practices used to mitigate extreme heat & its effects, to recognize & respond to its effects, and preventative measures and coping strategies for heat. KAP: 2nd order beliefs Interviewee's opinion of public’s (incl. business & NGOs) knowledge, attitudes, and practices regarding extreme heat: what they perceive public thinks/does (or not) to prevent, adapt, and respond to heat risks. Also, includes opinion of media’s general willingness/efforts to publicize KAP about extreme heat versus other risks, NOT what they have seen them do or say about heat risk. KAP: Media Reach - NO Interviewee has not seen/heard/received any messages from media about knowledge, attitudes or practices regarding heat risk KAP: Media Reach - YES Interviewee has seen/heard/received any messages from media about knowledge, attitudes or practices regarding heat risk. Includes descriptions of these specific messages. KAP: do not apply when someone’s personal actions do not align with their knowledge or training for extreme heat risk prevention and response SENSTVTY Individual characteristics that make someone more susceptible to negative health effects including unawareness of heat risk - GENERAL senstvty: Ind. Health Health or bodily conditions that make cardiovascular conditions, Characteristics someone more susceptible to heat- medications, out of shape, related illnesses diabetes senstvty: Age Age ranges that make someone more (kids, infants, babies, small susceptible to heat-related illnesses, children, elderly, older people) namely the elderly and young children senstvty: Unawareness Unawareness of heat risk or how body is (unaware, don't realize, don't affected by heat thus attributing health recognize heat is affecting effects to other causes and thereby being them, don't know/understand, more susceptible to heat impacts underestimate senstvty: Acclimatization How the body becomes accustomed to (acclimatization, acclimated, the climate conditions they are regularly acclimatized, aren't used to the exposed to and does not adjust to drastic heat here, not changes in climate exposure immediately. accustomed/unaccustomed senstvty: New Specific groups who are new to or visiting (visitors, tourists, new move- the area and either unaware or ins) unaccustomed to extreme heat conditions VULNRBLTY External conditions that increase susceptibility to experiencing heat stress and related illnesses - GENERAL
138 vulnrblty: exertion Physically exerting oneself in extreme heat regardless of activity or no mention of activity. vulnrblty: Situational: Voluntary Voluntarily choosing to expose self to extreme heat or not (i.e., exercising, hiking, running an errand) whether aware/prepared or not and/or possibility of being stuck/stranded in heat (i.e., car breaks down, get lost & run out of water); not influenced by routine or obligation like work outside, team practice. vulnrblty: Situational: Situations that obligate people/pets to be (work outside, high school Obligatory exposed to extreme heat to fulfill athlete; child/pet locked in a responsibilities/duties, or, are dependent car) on others decisions. vulnrblty: Sociodemographics Sociodemographic conditions that expose (lower socioeconomic status people to more hot conditions; unable to (SES), can't afford AC, poor, access resources to engage in protective homeless) actions vulnrblty: Timing Timing of heat makes a group of people vulnerable such as when community events occur (i.e., marathons, fairs) or holidays when people are out (i.e., 4th of July) vulnrblty: Isolation Living alone or other situations that neighbor being forgotten, no isolate someone from social capital or valid driver’s license to go to community resources cooler place vulnrblty: Neighborhood Safety things that make people concerned to lack of green space in open windows or go outside to seek neighborhood, high crime, gang shelter in cooler locations because there activity is nothing close by or it is dangerous/unsafe IMPACTS positive and negative impacts of extreme heat events on any time or spatial scale - GENERAL impacts: Infrastructure & effects on buildings, vehicles, Lifelines airports/planes, AC, electricity, and utility functionality during heat events impacts: Broad-Scale Very significant impacts to a community or structure that has cascading effects for human mortality and resources, including increased risk of wildfire. Community level impacts to infrastructure, economy, neighborhoods, social networks impacts: Environmental impacts of heat on land, animals (not pets), plant life, ecosystems, water accessibility impacts: None or minor Interviewee says there are no or minor impact of heat impacts: Positive positive impacts from heat events impacts: Pets impacts of heat on pets impacts: People statements about impacts of heat on people in general, not specifying which kind of impacts or particular populations at risk otherwise defined.
139 impacts: Timeframe statements describing the impacts of heat long-term, short-term, in the having a more long-term or short-term long run, beyond individual, effect timeline, immediate effect vs. long-term IMP HEALTH general statements of negative impacts of heat on individual health imp health: Heat morbidity heat-related illnesses and all symptoms from discomfort to heat stroke not resulting in death imp health: Heat mortality death due to heat, including if specified death due to heat stroke imp health: sun References to sunburn as an impact of heat rashes, allergic reactions to extreme heat or use sunscreen to avoid sun/heat, sunburn, sunscreen, effects of extreme heat. Also, reference to sunblock skin conditions to sun. IMP PSYCHOLOGICAL psychological impacts of heat stress on irritable, agitated, cranky, short humans including its association with temper, more violent, riots violence, riots IMP ECONOMIC economic impact of heat on businesses, agencies and personal finance - GENERAL, not specified imp Economic: business/service economic impact of heat on businesses' and agencies' ability to do their work (i.e., less efficient because need to take more breaks) and/or its financial impact (i.e., costs more to provide service) imp Economic: personal economic impact of heat on personal electric bill goes up finances IMP RECOVERY impact of heat on recovery process of community WARNING Warning information, decision process, and dissemination of NWS heat alerts - GENERAL warning: NWS products & tools any NWS products, tools, or programs (i.e., Experimental Heat Risk used to create warnings and evaluate Tool, Warnings, Advisories, heat threat Recommender Tool) warning: Exp. HeatRisk Tool Use, awareness, or knowledge of Experimental HeatRisk Tool warning: Exp. HeatRisk Tool - No knowledge of Experimental HeatRisk NO Tool warning: Trust information statements about trusting (or not) source information from NWS warnings. Includes receiving feedback from partners and public about NWS messaging warning: Content the content of an official NWS warning message warning: Access Modes of access to warning information and barriers to access it (i.e., language barriers, no TV) whether it's an official NWS alert or not. warning: non-specified forecast weather forecast information about info extreme heat without specifying the source of the forecast and/or not an official NWS heat alert
140 WARN DECISION the warning decision process and decisions in response to a NWS warning or heat event - GENERAL warn decision: alert Expert has issued an official extreme heat alert whether a heat watch, warning, or advisory: should only be coded once in an expert interview warn decision: Forecasters how and what influenced forecasters to make a warning decision warn decision: Contributors who else contributed to a forecaster or official's decisions to warn or not and how warn decision: how and what influence media to warn Response/Receive independently, with NWS warning, or not at all; how officials, private sector, and citizens respond to warning or heat event; & who receives (uses) the warning/alert WARN DISSEM how NWS warnings are disseminated by different parties - GENERAL warn dissem: NWS how NWS disseminates their warnings to their partners and the public warn dissem: Officials if and how public officials disseminate NWS warnings warn dissem: Media if and how media disseminate NWS warnings warn dissem: Intermediary if and how NWS warnings are disseminated by NGO's, businesses, citizens UNCERT_VARIAB Opinion about limitations of data used to classify heat events (and its impacts, i.e., effects of algae blooms), relative definitions of heat wave, interpretation of heat products - GENERAL Uncert/Variab: Issues with Issues with information/data; statements information about confidence level that models or data provide in making heat event decisions Uncert/Variab: Issues with Questions of how heat waves are defined. definition Vagueness of such varying and broad definitions Uncert/Variab: Issues with Issues with interpretation of warning interpretation message, ExpHeatRisk Tool, heat risk levels, etc. EXPRNCE Experience with extreme heat in general, not specifying how it affected them, others, or their surroundings. Including lack of experience professionally exprnce: personal Direct, personal experience of heat- related health symptoms affecting their body & if they attribute personal experience of secondary effects of heat to heat exprnce: personal: NO No admittance to experiencing negative health effects of extreme heat, or no experience with extreme heat in general.
141 exprnce: indirect Knowing someone else who has experience heat-related health symptoms and/or witnessing that experience exprnce: prof treat Treating others personally with heat- related health symptoms as part of professional capacity exprnce: prev issue References to previous experience issuing official extreme heat alerts contributing to warning decisions exprnce: prof alert Professionally received or communicated official extreme heat alert: should only be coded one time in an interview exprnce: prof event Been involved in an extreme heat event or potential event as part of profession: should only be coded one time in an interview exprnce: prof communicate professionally communicated or responded to effects of extreme heat: should only be coded one time in an interview exprnce: prof training professionally participated in extreme heat training or exercise: should only be coded one time in an interview exprnce: subjective reference to heat being experienced or felt differently by different people although exposed to the same conditions AFF_DECISION how experience influenced their decisions or not and what they learned from it - GENERAL aff_decision: learned experience with extreme heat influenced their decisions and/or what they learned from this experience that influences their decisions aff_decision: no experience in general does not influence their decisions INST_NORMS Statements about how extreme heat is viewed and treated as a work group - GENERAL inst_norms: Process - NEW Stating whether process for extreme heat protocol is different than before or new inst_norms: Process - OLD what the process for extreme heat protocol followed in the past and was typical inst_norms: Perception - dry Institutional ideas about dry heat and its heat uniqueness as a hazard due to misperceived risk of dry climates like deserts inst_norms: Perception - Institutional ideas about where extreme Location of extreme heat heat occurs in relation to Utah, whether it occurs in Utah and where in Utah inst_norms: Perception - heat as Thoughts, as an institution, about where hazard extreme heat ranks as a hazard compared to other hazards
142 PERCEPTION personal perceptions and attitudes of (serious, dangerous, not a big how extreme heat is viewed as a hazard deal) and belief of its meaning in general Perception: dry heat personal ideas about dry heat and its uniqueness as a hazard due to misperceived risk of dry climates like deserts Perception: Location of extreme personal ideas about where extreme heat heat occurs in relation to Utah, whether it occurs in Utah and where in Utah Perception: heat as hazard personal thoughts about where extreme heat ranks as a hazard compared to other hazards Perception: diff. extreme Whether and how interviewee differentiates between extreme heat and otherwise hot weather Perception: worry self interviewee has worried or does worry about extreme heat in UT and why, what are their worries Perception: worry others knowledge of others who worry about extreme heat in UT and why, what are their worries
143 APPENDIX J
RANKING CODEBOOKS
TABLE 14. Ranking codebook for questions 15-16
Code Code Definition SENSTVTY Individual characteristics that make someone more susceptible to negative health effects Ind_HC Health or bodily conditions that make someone more susceptible to heat-related illnesses, i.e., cardiovascular conditions, medications, out of shape elderly elderly, old young young, children, babies acclimatization How the body becomes accustomed to the climate conditions they are regularly exposed to and does not adjust to drastic changes in climate exposure immediately. new Groups who are new to or visiting the area and either unaware or unaccustomed to extreme heat conditions (i.e., visitors, tourists, new move-ins) VULNRBLTY External conditions that increase susceptibility to experiencing heat stress and related illnesses exertion Physically exerting oneself in extreme heat regardless of activity or no mention of activity. SitVoluntary Voluntarily choosing to expose self to extreme heat or not (i.e., exercising, hiking, errand) whether aware/prepared or not and possibility of being stuck/stranded in heat (i.e., car breaks down, get lost & run out of water); not influenced by routine or obligation like work outside, team practice. sociodemo Sociodemographic conditions that expose people to more hot conditions (i.e., lower SES, can't afford AC, poor, homeless) SitObligatory Situations that obligate people/pets to be exposed to extreme heat to fulfill responsibilities/duties (i.e., work outside, high school athlete), or, are dependent on others decisions (child/pet locked in a car). locked_pet pets locked in cars locked_child children/infants locked in cars
144 timing Timing of heat makes a group of people vulnerable such as when community events occur (i.e., marathons, fairs) or holidays when people are out (i.e., 4th of July) IMPACTS positive and negative impacts of extreme heat events on any time or spatial scale infrastructure general statements of effects on buildings, AC, electricity, and utility functionality during heat events. NOT vehicles, planes, helicopters, gear services_needed effects cause demand in public services (e.g., increased S&R) vehicles vehicles, planes, helicopters, including functionality of gear energy increased energy use BroadScale community level impacts to infrastructure, economy, neighborhoods, social networks; Very significant impacts to a community or structure that has cascading effects for human mortality and resources, incl. increased risk of wildfire enviro general statements on impacts of heat on landscape and resources drought drought water increased water use wildlife wildlife or animals when not specified as domestic plantlife wild or non-domestic plantlife, wilderness, forest, etc. domP/A domestic plants/animals, i.e., crops, gardens, lawns, plants, livestock, NOT pets pets impacts of heat on pets people statements about impacts of heat on people in general, not specifying which kind of impacts or particular populations at risk otherwise defined. HEALTH general statements of negative impacts of heat on individual health morbidity general reference to heat-related illnesses and injuries heat_injury specific symptom other than those coded (e.g., syncope, heat cramps, dizziness, nausea, disoriented/distracted, heat stress, overheating) discomfort/fatigue includes decreased physical activity, can't sleep at night, fatigue, lethargic, discomfort dehydration mentions of dehydrated, not hydrating, incl. hyponatremia and sweating
145 E/S heat exhaustion or heat stroke and serious symptoms of these conditions: rapid heart rate, altered mental state, respiratory failure, unconscious mortality death due to heat, including if specified death due to heat stroke sun References to sunburn as an impact of extreme heat or use sunscreen to avoid effects of extreme heat. Also, reference to skin conditions to sun. PSYCHOLOGICAL psychological impacts of heat stress on humans (i.e., irritable, agitated, cranky, short temper, cranky, unhappy) including its association with violence, riots ECONOMIC economic impact of heat on businesses, agencies and personal finance business/service economic impact of heat on businesses and agencies ability to do their work (i.e., less efficient because need to take more breaks) and/or its financial impact (i.e., costs more to provide service) GeogFac geographic and topographic characteristics that exacerbate existing heat or are result of long-standing hot conditions SecHaz other hazards that are exacerbated by extreme heat (i.e., air pollution, disease vectors, algae blooms) except wildfire risk wildfire increased wildfire risk, smoke from wildfires, etc. temperature Reference to temperatures as part of extreme heat risk Duration length of day or length of exposure increases risk ADAP_CAP not prepared
146 TABLE 15. Ranking codebook for questions 23-24
Code Code Definition social_capital use of neighborhood, family, friend, or coworker networks to warn, check on, prepare each other for heat events, both directions volunteer volunteerism associated with informing, preparing, educating, about heat risk through organizations instead of through own initiatives avoid_heat stay out of sun/heat, does not specify indoors or shade or both, limit outside activity/high temp locations, take actions to mitigate/avoid sunscreen use sunscreen hydration drink lots of water, stay hydrated, hydrate well before you go out, replace fluids & electrolytes clothing lightweight, light-colored clothing adjust_AC adjust AC when not at home to avoid stress on infrastructure and brown/black out during heat events awareness awareness of current temperature/time of day and forecasts, understand forecasts, awareness of what extreme heat means and knowledge of what should/should not do during it, base choices on heat problem_solve come up with own solution to mitigate heat/cool off (summer shears) shade stay in/find shade stay_indoors stay inside, avoid going out, shelter low_effort minimal exertion, don't push yourself outside, avoid excessive activities outdoors preparedness general statements of being prepared for activities plan to do readiness statements about being ready for unexpected exposure (i.e., keep water in the car) health_choices things that can be done to improve health/stamina (lose weight, stop smoking, fitness program) know_limits know limits of own health conditions and stamina, medications pace pace self when working/hiking, take breaks more often, rest most, slow down protect check surface temp for children/pets, keep eye on them, limit access to cars, look before you lock cooler_place go to a cooler place, know about plans for cooling stations planning plan ahead for exposure/exertion, emergency plan of what to do if there is a problem, know terrain/climate/time of year AC_access access to adequate AC, stay in AC R&T_SS recognize signs and symptoms of heat illness in own body, know & be able to treat the signs and symptoms & recognize in others mitigate reduce carbon footprint
147 proper_food avoid diuretics, maintain salts, have snacks common_sense use common sense outside but know reference to specific practices follow heed warnings, follow instructions, trust authorities’ suggestions navigation skills to navigate in backcountry/terrain to avoid getting lost, map skills duration length of exposure to heat or heat event time_of_day adjustments to do activities/work during coolest parts of day, avoid hottest parts of day
148
APPENDIX K
PERMISSION LETTERS
149
October 2, 2018
Utah State University School of Graduate Studies 0900 Old Main Hill Logan, UT 84322-0900
To Whom It May Concern:
I hereby give my permission, as co-author, for Emily D. Esplin to use our work “Can you take the heat? Heat-induced health symptoms influence protective behaviors” as a chapter in her thesis. I understand that reference to my co-authorship will be made within the appropriate thesis chapter.
Regards,
Jennifer R. Marlon, Ph.D. Research Scientist School of Forestry and Environmental Studies Yale University 150
October 1, 2018
Utah State University School of Graduate Studies 0900 Old Main Hill Logan, UT 84322-0900
To Whom It May Concern:
I hereby give my permission, as co-author, for Emily D. Esplin to use our work “Can you take the heat? Heat-induced health symptoms influence protective behaviors” as a chapter in her thesis. I understand that reference to my co-authorship will be made within the appropriate thesis chapter.
Regards,
Anthony Leiserowitz, Ph.D. Director, Yale Program on Climate Change Communication Senior Research Scientist School of Forestry and Environmental Studies Yale University