Efforts to address the major health, environmental, and social threats that can be found across the globe rely on changes in human behavior. Yet identifying effective and efficient ways to change behavior remains a vexing challenge. To meet this need, investigators need to design and evaluate behavioral intervention strategies in a manner that affords the creation of evidence-based guidelines that specify not only whether interventions work but also how and under what conditions. In this chapter, the design and testing of interventions are situated within the experimental medicine approach. This approach leverages the strength of the experimental method to test how behavior change intervention strategies work and to identify the conditions under which they operate effectively. Moreover, it organizes how investigators specify the questions that underlie the study of behavior change interventions and requires them to articulate precisely what intervention strategy they are using, how they think the strategy operates, and the outcomes it generates. Through the systematic use of this approach, evidence will emerge that addresses practitioners’ prevailing concerns directly – what intervention strategy is the most effective and efficient way to address the problem at hand. This chapter provides an overview of how to implement the experimental medicine approach, describes its key features, and addresses the importance of precision and, finally, considers this approach within a broader set of initiatives that have emerged to support a programmatic approach to the design, evaluation, and implementation of behavior change interventions.

Executive Summary

This chapter addresses changes in weather and climate events relevant to extreme impacts and disasters. An extreme (weather or climate) event is generally defined as the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of the variable. Some climate extremes (e.g., droughts, floods) may be the result of an accumulation of weather or climate events that are, individually, not extreme themselves (though their accumulation is extreme). As well, weather or climate events, even if not extreme in a statistical sense, can still lead to extreme conditions or impacts, either by crossing a critical threshold in a social, ecological, or physical system, or by occurring simultaneously with other events. A weather system such as a tropical cyclone can have an extreme impact, depending on where and when it approaches landfall, even if the specific cyclone is not extreme relative to other tropical cyclones. Conversely, not all extremes necessarily lead to serious impacts. [3.1]

Many weather and climate extremes are the result of natural climate variability (including phenomena such as El Niño), and natural decadal or multi-decadal variations in the climate provide the backdrop for anthropogenic climate changes. Even if there were no anthropogenic changes in climate, a wide variety of natural weather and climate extremes would still occur. [3.1]

A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes, and can result in unprecedented extremes. Changes in extremes can also be directly related to changes in mean climate, because mean future conditions in some variables are projected to lie within the tails of present-day conditions. Nevertheless, changes in extremes of a climate or weather variable are not always related in a simple way to changes in the mean of the same variable, and in some cases can be of opposite sign to a change in the mean of the variable. Changes in phenomena such as the El Nino-Southern Oscillation or monsoons could affect the frequency and intensity of extremes in several regions simultaneously. [3.1]

Perceived susceptibility, one's belief about the likelihood of personal harm, is a key concept in many theories of health behavior (Cummings, Becker, & Maile, 1980; Weinstein, 1993). Such beliefs often turn out to be unrealistically optimistic: People show a consistent tendency to claim that they are less likely than their peers to suffer harm. This optimistic bias in relative risk has been demonstrated with several methods, various age groups, and a wide range of hazards (Weinstein, 1987). In some situations, of course, such optimism may be beneficial (Taylor & Brown, 1988). Positive illusions about the effectiveness of precautions, for example, can sustain attempts to change behavior, and underestimations of risk can protect people from anxiety when there is little they can do to reduce their vulnerability (e.g., Taylor et al., 1992). However, when health problems have not yet appeared and are controllable, a tendency to downplay one's own risk may interfere with appropriate self-protective action.

A few studies have demonstrated that informing people about their susceptibility can increase preventive action (e.g., Blalock, DeVellis, & Afifi, 1990; Wurtele, 1988), but others have been unable to alter risk perceptions (Griffeth & Rogers, 1976; Sutton & Eiser, 1990) or have produced changes too small to result in significant changes in action (Schoenbach, 1987; Siero, Kok, & Pruyn, 1984; Weinstein, Sandman, & Roberts, 1991; Wurtele & Maddux, 1987).

The studies reported here varied the way in which risk-factor information was presented in an attempt to reduce optimistic biases.