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Integrating Climate Change Adaptation into Public Health Practice: Using Adaptive Management to Increase Adaptive Capacity and Build Resilience

[do action=”authors”]Jeremy J. Hess1,2,3, Julia Z. McDowell1,2, George Luber1[/do][do action=”affiliations”]1 Climate and Health Program, Division of Environmental Hazards and Health Effects, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA, 2 Department of Environmental Health, Rollins School of Public Health, and, 3 Department of Emergency Medicine, Emory University School of Medicine, Emory University, Atlanta, Georgia, USA[/do]

[do action=”citations”]Environ Health Perspect 120:171-179 (2012). [online 13 October 2011][/do]



[do action=”abstract”]

Background: Climate change is expected to have a range of health impacts, some of which are already apparent. Public health adaptation is imperative, but there has been little discussion of how to increase adaptive capacity and resilience in public health systems.

Objectives: We explored possible explanations for the lack of work on adaptive capacity, outline climate–health challenges that may lie outside public health’s coping range, and consider changes in practice that could increase public health’s adaptive capacity.

Methods: We conducted a substantive, interdisciplinary literature review focused on climate change adaptation in public health, social learning, and management of socioeconomic systems exhibiting dynamic complexity.

Discussion: There are two competing views of how public health should engage climate change adaptation. Perspectives differ on whether climate change will primarily amplify existing hazards, requiring enhancement of existing public health functions, or present categorically distinct threats requiring innovative management strategies. In some contexts, distinctly climate-sensitive health threats may overwhelm public health’s adaptive capacity. Addressing these threats will require increased emphasis on institutional learning, innovative management strategies, and new and improved tools. Adaptive management, an iterative framework that embraces uncertainty, uses modeling, and integrates learning, may be a useful approach. We illustrate its application to extreme heat in an urban setting.

Conclusions: Increasing public health capacity will be necessary for certain climate–health threats. Focusing efforts to increase adaptive capacity in specific areas, promoting institutional learning, embracing adaptive management, and developing tools to facilitate these processes are important priorities and can improve the resilience of local public health systems to climate change.

[/do][do action=”abstract”]Key words: adaptive management, climate change, climate change adaptation, public health, public health administration[/do]

[do action=”notes-rule-above”]Address correspondence to J.J. Hess, Department of Emergency Medicine, Steiner Building, First Floor, 49 Jesse Hill Jr. Dr., Atlanta, GA 30303 USA. Telephone: (404) 251-8851. Fax: (404) 688-6351. E-mail:[/do][do action=”notes”]J.H. serves as a consultant for the CDC. The other authors declare they have no actual or potential competing financial interests.[/do][do action=”notes”]

We thank K. Ebi for helpful discussions regarding adaptive management during the article’s preparation, as well as reviewers at the Centers for Disease Control and Prevention (CDC).

[/do][do action=”notes”] Received 02 February 2011; accepted 13 October 2011; online 13 October 2011.[/do]

Public Health Adaptation to Climate Change

Many potential climate change health impacts have been established, and several are already evident (McMichael et al. 2004). Climate change is expected to increase the burden of climate-sensitive diseases such as heat-related illness, vector-borne disease, diarrheal disease, injuries from extreme events, and respiratory diseases (Campbell-Lendrum and Woodruff 2006). Although the developing world is most at risk (Patz et al. 2007), industrialized countries are also ill prepared (Ebi et al. 2009; Maibach et al. 2008; O’Neill et al. 2010). Indeed, the event with the most dramatic health impact attributed to climate change thus far, the European heat wave of 2003, occurred in the ostensibly well-prepared industrialized world, illustrating the disastrous effects of extreme weather made more likely by climate change (Stott et al. 2004), by a relatively unprepared public health sector [World Health Organization (WHO) 2009], and by high levels of both population exposure (Poumadere et al. 2005) and susceptibility (Rey et al. 2009).

Public health institutions at all operational scales will need to consciously modify their approaches to both science and practice in anticipation of climate change health impacts, and much has been written on various aspects of these issues. Several articles have outlined climate change as a public health concern, advancing the public health community’s awareness (Frumkin et al. 2008; Haines et al. 2006; Patz et al. 2005). Others have explored climate change epidemiology and risk assessment (Campbell-Lendrum and Woodruff 2006; Kovats et al. 2005; McMichael 2001) by examining fundamental scientific questions and by providing epistemological insights. Still others have clarified methodologies and practical strategies for conducting vulnerability and impact assessments (Ebi et al. 2006), assessed relevant environmental health frameworks (Füssel 2008), and articulated guidelines for climate impact and adaptation assessments and advanced research agendas (Portier et al. 2010).

Despite this progress, however, with notable exceptions (Ebi and Burton 2008; Ebi and Semenza 2008; Ebi et al. 2005, 2006; Huang et al. 2011; Jackson and Shields 2008), there has been little discussion of how public health organizations should implement and manage the process of planned adaptation. Huang et al. (2011) note that this includes both enhancing adaptive capacity, that is, the resources for adaptation and the ability to use them effectively and efficiently, and implementing adaptive actions. They also offer several suggestions for overcoming likely barriers. Apart from this significant work, however, there has been relatively little discussion of how to increase public health’s adaptive capacity or how this process could increase public health’s resilience.

Literature from other sectors provides some general guidance for building adaptive capacity. Besides resource availability, many other factors are important, including social and human capital, attention to institutional decision making and information management, and processes for spreading risk (Yohe and Tol 2002). Also key is the promotion of social learning, which means building collective knowledge through social interactions (Berkhout et al. 2006; Pahl-Wostl 2009) and integrating learning into management (McDaniels and Gregory 2004; Social Learning Group 2001). Other literature highlights the connection between high levels of adaptive capacity and resilience in socioeconomic systems, emphasizing that such systems have the capacity to retain their essential structure and function after significant disruption, to reorganize, and to learn (Folke 2006). Despite their relevance to public health adaptation, these insights have not been fully synthesized for the public health context specifically.

This article uses a substantive, interdisciplinary literature review to identify strategies for expanding public health’s adaptive capacity through an emphasis on learning and changes in management frameworks. Our review explores possible explanations for the relative dearth of work on adaptive capacity in public health and potential implications for policy and practice; highlights several climate-sensitive health threats that may overwhelm public health’s adaptive capacity; reviews the role of learning in building adaptive capacity; and considers how adaptive management—a strategy that integrates learning and management—might increase adaptive capacity, thereby fostering the development of more resilient public health systems.

Perspectives on Public Health’s Adaptive Capacity

Apart from highlighting the need for additional resources in many settings, adaptive capacity has not been a major focus of the climate–health literature. There are two possible explanations, each based on a particular take on climate change as a public health stressor. Reviewing these explanations lends insight into differing perspectives on how best to build adaptive capacity and facilitate public health adaptation.

The first explanation is that climate change is not likely to require substantial changes to public health practice other than increased investment and program expansion, that is, increasing resources and implementing adaptive measures are the primary means of increasing adaptive capacity. This perspective holds that climate change will primarily amplify known public health stressors, which affect vulnerable populations most dramatically. Because human health is already heavily managed via extensive infrastructure (Füssel 2008), it follows that established practices will likely be sufficient if given the appropriate mandate, adequate funding, and support. By extension, effective adaptation will be characterized primarily by investments that reinforce essential public health services (Frumkin et al. 2008). This perspective affirms public health’s readiness contingent on sufficient support and puts the emphasis on bolstering rather than reconfiguring public health practice.

The second explanation is that innovations in public health practice are likely necessary to enhance adaptive capacity, but a broad literature base that supports this contention is not yet available. This dearth of literature may be because innovative strategies have not yet materialized in many locations, perhaps because adaptation tends to occur in response to the stimulus of extreme events (Berrang-Ford et al. 2011), or because such strategies have not yet made their way into the literature. The contention that innovative strategies will be required is based on a concern that climate change, which could jeopardize critical infrastructure and destabilize various systems that maintain public health, may represent a categorically distinct public health stressor. Thus, novel frameworks, strategies, and tools are required to help manage systemic risk. Rather than affirming the conventional approach that adaptation will primarily entail program expansion, the innovation perspective, recognizing its limitations, highlights its limits, particularly the potential for systemic instability to undermine public health gains. In focusing on potential failures, this innovation-oriented view emphasizes novel management strategies in addition to standard public health programming to enhance adaptive capacity.

Although these two narratives are not mutually exclusive, there is tension between them, for several reasons. First, the two perspectives lead to different funding priorities. Given current budget constraints, funding new initiatives is likely to come at the expense of other programming, and investments without clear near-term payoffs are hard to sell. Even in settings with a well-developed infrastructure, climate change adaptation competes, often unsuccessfully, with other urgent public health concerns (Ebi et al. 2009; Maibach et al. 2008). Second, particularly in regions with less public health infrastructure, many believe that adaptation should be secondary to more immediate concerns, such as basic public health services and essential medicines. Third, most public health institutions and health care systems have chosen to rely on existing infrastructure and all-hazards preparedness rather than investing in innovations when increased risks have yet to materialize (Hess et al. 2009). Fourth, a management-oriented, systems-based, long-view approach to public health is logistically difficult to pursue because it requires secure long-term funding, interdisciplinary and intersectoral collaboration, and integrated information management, which existing funding and administrative structures inadvertently discourage (Füssel 2008; Haines et al. 2009).

Identifying Public Health Impacts That Exhibit Distinct Climate Sensitivity

Determining the relative merit of the two perspectives is a primary challenge for public health practitioners interested in increasing adaptive capacity and developing resilient public health systems. Because climatic stressors, population vulnerabilities, and public health capacities are variably distributed, this determination will be context specific (Hess et al. 2008). Although multiple guidelines can help clarify needs in particular contexts, however, none satisfactorily addresses the full range of policy questions (Füssel 2008), and there are no criteria to help determine how to portion investments between bolstering current activities and developing innovative programming.

Identifying areas where vulnerability is particularly high—threats that exhibit distinct climate sensitivity—can help clarify where efforts to increase adaptive capacity should be focused. Criteria for identifying these threats include

  1. High population vulnerability to hydrometeorological hazards (i.e., high levels of exposure and susceptibility and low adaptive capacity), such that increases in the frequency and severity of such hazards will significantly increase overall risk (Ebi et al. 2006; Keim 2008; Schneider et al. 2007). One example is systems in which recurrent flooding, combined with other exposures that erode household coping capacity (O’Brien and Leichenko 2000; Webster and Jian 2011), undermines long-term adaptive capacity and increases cumulative risk (Tapsell et al. 2002).
  2. The potential for extreme events associated with climate change to present hazards outside the coping range (Yohe and Tol 2002) of a given public health system. The probability of the European heat wave of 2003, for instance, was significantly increased by anthropogenic emissions (Stone et al. 2009; Stott et al. 2004) and by imposed stresses outside the coping capacity of the public health system (Lagadec 2004; Poumadere et al. 2005).
  3. The likelihood that increasingly severe and frequent hazards associated with climate change could undermine or compromise systems and infrastructure and have significant population health impacts (Gerber 2007; McDaniels et al. 2008). For example, more frequent heat waves increase reliance on mechanical air conditioning, increasing electricity demand and thus the probability of cascading grid failure.
  4. The likelihood that climate change will fundamentally alter basic ecosystem services important to public health (Myers and Patz 2009; Schroter et al. 2005). Examples are abundant, including ecosystem shifts driving increased bioaccumulation of toxins such as mercury and polychlorinated biphenyls (Carrie et al. 2010) and the potential for groundwater salinity as a result of saltwater intrusion from sea level rise (Khan et al. 2008).
  5. The likelihood that climate change will result in abrupt ecosystem shifts (Walther 2010) favoring the introduction or reemergence of diseases for which effective surveillance and management practices are not yet in place. An example of this is the 2004 outbreak of Vibrio parahaemolyticus associated with Alaskan oysters harvested during an unusually warm period, which abruptly shifted the northernmost range of the endemic area for this disease by 1,000 km (McLaughlin et al. 2005).

Applying these criteria to major public health concerns, for example, tobacco use, teen pregnancy, and health-care–associated infections, it is clear that many do not currently exhibit distinct climate sensitivity and are not likely to in the near future. Although climate change may affect the distribution of some health outcomes that are not distinctly climate sensitive (e.g., road traffic injuries may worsen with changes in precipitation), bolstering existing programming may well be sufficient to address these shifting threats. Similarly, in lower resource settings, emphasizing basic service provision will likely be most strategic. In moderate to high resource settings, however, other strategies to enhance adaptive capacity may be more strategic.

Arguments for Focusing on Distinctly Climate-Sensitive Threats

For several reasons, identifying public health challenges exhibiting distinct climate sensitivity is important for building adaptive capacity in settings where basic needs are already addressed.

First, it will focus effort on the subset of problems requiring substantial innovation and collaboration, and this focus can help address known barriers to adaptation. Other sectors have identified a need for focused innovative adaptation efforts (Smithers and Blay-Palmer 2001), and health is likely to be similar. Indeed, public health has previously identified challenges in need of focused innovation, such as the articulation of the Grand Challenges in Global Health and corresponding funding of innovative strategies to address these challenges (Cohen 2005; Varmus et al. 2003). As other sectors have shown, some catalytic innovations—which use novel technologies or strategies to bring goods or services to whole new populations—can result in both improved population outcomes and lower costs (Christensen et al. 2006), an appealing prospect in a time of worsening budgetary constraints.

Second, a focused approach could minimize friction between the climate–health community and other areas of public health. Although this friction has not proven a significant impediment to date, there are several instances—such as the contentious debate around climate change and malaria (Chaves and Koenraadt 2010; Reiter 2001)—in which the emphasis on climate change has been seen as an inappropriate distraction from established, evidence-based efforts at disease prevention and control.

Third, such a focus may be strategic from a policy perspective, because it allows climate–health advocates to highlight the need for general investment in public health infrastructure, particularly in resource-poor settings, where “adaptation to climate change is essentially a matter of basic public health protection” (Campbell-Lendrum et al. 2007), as well as specific climate–health programming for issues of greatest concern. This may prove attractive to policy makers who craft health adaptation portfolios in the developing world, where a strong case can be made for general investment in public health to reduce climate-related and other risks.

Management Challenges for Distinctly Climate-Sensitive Public Health Concerns

Developing effective adaptations to distinctly climate-sensitive health threats presents a host of management challenges, including uncertainty in climate projections and future socioeconomic conditions; financial challenges and other maldistribution of existing adaptive capacity; limits in technological advancement and dissemination; institutional arrangements that limit the scope of collaborative efforts and accumulation of evidence about effective adaptation; limits on social capital at the community level; and uninformed or inaccurate perceptions of individual risk (Huang et al. 2011). Two other issues, scale and complexity, are also significant.

The scale issues that complicate adaptation are both temporal and spatial. Temporal concerns include the need to focus on short-term planning for discrete events, such as a severe heat wave, and longer-term needs for strategies to reduce hazardous exposures and increase resilience (McMichael and Dear 2010; Sherwood and Huber 2010). Spatial concerns arise from mismatches between hazard distributions, political and administrative boundaries, and resource availability. The issue of spatial scale and climate has been explored more thoroughly in the ecological (Clarke et al. 2007; Seo et al. 2009), agricultural (Baron et al. 2005), and modeling (Diffenbaugh et al. 2005) literature than in public health, although examinations of heat hazards at various scales (Harlan et al. 2006; Stone et al. 2010) and synchrony of cholera outbreaks (Constantin de Magny et al. 2007) suggest how this research may unfold.

Complexity is perhaps the most pervasive concern. Huang et al. (2011) note one aspect of this issue in their discussion of limits to individual cognition and risk perception. The issue of complexity extends well beyond individual cognition, however, to a host of systems concerns related to managed socioecosystems, from cities to fisheries, whose complex dynamics, including delays, positive and negative feedbacks, stock-and-flow relationships (Sterman 2000, 2008), and thresholds (Codeco et al. 2008), complicate management. Climate change has introduced additional uncertainty into these dynamics and highlighted the need for new strategies to understand and manage such systems, emphasizing the need for an approach that fully captures impacts and facilitates informed management (Grabs et al. 2007; Howden et al. 2007). This echoes a general trend toward systems-based investigation in environmental health (Gohlke and Portier 2007) and public health in general (Diez Roux 2011), risk management (Bea et al. 2009), ecology (Montoya and Raffaelli 2010), and economics (Polasky and Segerson 2009). Recent operations research in public health has also come to similar conclusions about other aspects of the public health system (Van Wave et al. 2010), insights that need to be applied to climate change adaptation.

The Role of Learning

These management challenges highlight the need for strategies that embrace uncertainty and emphasize learning (Sterman 2006). Scholarly work on learning theories, such as experiential learning (Kolb 1984) and transformative learning (Mezirow 1995), emphasize concrete learning cycles, learning by doing, and the ways learning feeds into reinterpretation of value structures. The learning loop framework (Argyris and Schön 1978) integrates these insights and divides learning into three categories based on the extent to which the learning promotes transformative change in management strategies.

Single-loop learning focuses on improving the efficiency of action by reconciling differences between what is expected and what is observed (Pelling et al. 2007), for example, whether a dike is high enough to contain anticipated flooding. Double-loop learning considers whether management strategies are appropriate (Flood and Romm 1996), for example, whether dikes are the most appropriate strategy in the context of changing precipitation distributions. Triple-loop learning examines underlying principles and value systems (Pelling et al. 2007) and power relationships (Flood and Romm 1996) to explore the range of possible management options, for example, new approaches to governance, participatory risk management, and planning aimed at robust actions instead of strategies that are optimal for particular constituents or conditions (Pahl-Wostl 2009).

Each type of learning is relevant for public health adaptation. There are a host of strategies for facilitating institutional learning and for incorporating learning into management (Armitage et al. 2008). In particular, the adaptive management framework is a potentially useful approach for increasing adaptive capacity by increasing learning at all levels and reorienting management approaches to distinctly climate-sensitive health threats.

Adaptive Management and Its Potential

Adaptive management was developed as an iterative method for managing natural resource systems where linear approaches had failed (Holling 1978) as a result of the systems’ wide range of responses to management choices, the managers’ difficulty understanding the systems’ dynamics (Linkov et al. 2006), and the dynamic interplay between managers, stakeholders, interventions, and system responses (Henriksen and Barlebo 2008). To manage these systems, ecosystem managers needed an iterative process that acknowledged complexity and uncertainty, emphasized ongoing learning, and allowed for continuous stakeholder input. Adaptive management was created in response to these needs (Whicker et al. 2008).

The National Research Council’s (2004) guide to adaptive management emphasizes six primary elements: a) management objectives that are regularly revisited and revised, b) a model of the system(s) being managed, c) a range of management choices, d) monitoring and evaluation of outcomes, e) mechanisms for incorporating learning into future decisions, and f) a collaborative structure for stakeholder participation and learning. These steps are diagrammed in Figure 1. The process allows for an approach tailored to the unique specifics of each system and situation and integrates management and learning instead of consigning them to different domains (Murray and Marmorek 2003).

Figure 1: The adaptive management cycle.The steps in the process are shown in the circles, the arrows indicate the direction of the process flow, and the central spiral emphasizes the goal of arriving at a robust consensus based on a shared set of objectives developed through the iterative process. Adapted from Whicker et al. (2008).

Figure 1

The adaptive management cycle.The steps in the process are shown in the circles, the arrows indicate the direction of the process flow, and the central spiral emphasizes the goal of arriving at a robust consensus based on a shared set of objectives developed through the iterative process. Adapted from Whicker et al. (2008).

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Adaptive management has yet to secure a significant place in the public health toolbox, although several agencies have used it to engage a wide range of environmental health concerns, sometimes coupled with structured decision analysis processes (Linkov et al. 2006). Adaptive management has been difficult to implement in certain instances, although a systematic review suggests that difficulties primarily stem from application of the framework in inappropriate contexts (Gregory et al. 2006). This review suggests that adaptive management is most appropriate in circumstances in which modeling and decision-making scales are matched and external factors are considered, there is explicit consideration of uncertainties, stakeholders agree on metrics of cost and risk, and stakeholders are sufficiently engaged and provide adequate institutional support.

In regard to climate change, as Ebi (2011) has noted, adaptive management closely parallels frameworks for general climate change adaptation (Lim et al. 2005) and public health adaptation (Ebi and Semenza 2008). It has been used to explore issues related to ecosystem management (Prato 2010), watersheds (Pulwarty and Melis 2001), emissions trading (Satterstrom et al. 2007), and air quality monitoring (Stubbs and Lemon 2001). In its “active” form, which facilitates analysis of multiple decision possibilities, adaptive management appears to have significant potential for public health adaptation efforts, particularly at the local to regional scale.

Adaptive Management of Distinctly Climate-Sensitive Health Threats

Many of the essentials of adaptive management—modeling complex, dynamic problems; interacting with a wide range of stakeholders; and an evidence-based, iterative approach to decision making—are familiar to public health. The process is perhaps most akin to evidence-based medicine and its cousin, evidence-based public health (Brownson et al. 2009; Eriksson 2000). As with these approaches, embracing the entire paradigm confers several advantages over a disjointed approach.

The potential of adaptive management and the tools required are perhaps best conveyed through an example. Of the various hazards associated with climate change, extreme heat events (EHEs) are the best studied and among the most urgent. Although considerable uncertainty regarding heat morbidity remains, we have a solid understanding of heat–mortality functions (Basu 2009; Ishigami et al. 2008; Kovats et al. 2008; Pirard et al. 2005) and a rapidly evolving understanding of the factors that put populations at risk, from physiological susceptibility (Ellis 1972, 1976; Kilbourne et al. 1982) to exposure (Basu 2009; Harlan et al. 2006; Ishigami et al. 2008; Reid et al. 2009) to aspects of the built (Clarke 1972; Sheridan and Dolney 2003; Silva et al. 2010; Stone et al. 2010) and social environments (Klinenberg 2002; Rey et al. 2009), as well as a sense of several successful interventions at multiple levels (Hajat et al. 2010a, 2010b; O’Neill et al. 2009, 2010; Semenza 2006; Sheridan 2006; Silva et al. 2010; WHO 2009).

Models are fundamental to adaptive management and can be relatively straightforward conceptual models that distill the system into key components or more complex computer-based models (Ebi 2011). Integrated assessment models (IAMs) are examples of the latter and are often used to facilitate decision making and to assess impacts of potential interventions. These models draw from multiple disciplines to capture system behavior (Chan et al. 1999). Such frameworks have been developed only for certain climate-sensitive health outcomes, and there are currently no IAMs for heat specifically, although some models of urban heat impacts are being developed (Dawson et al. 2009). Team-based modeling efforts to organize and focus group thinking are also relevant (Vennix 1996). Examples of these types of models, and other tools, are presented in Table 1.

Table 1: Steps in the adaptive management cycle, central actions in each step, and tools useful for completing the central actions.

Table 1

Steps in the adaptive management cycle, central actions in each step, and tools useful for completing the central actions.

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In the case of heat, the urban environment is a particularly relevant system, for several reasons: most of the world’s population now lives in cities (United Nations Population Programme 2004); cities have high concentrations of people vulnerable to heat-related injury (Campbell-Lendrum and Corvalán 2007; Hess et al. 2008); urban environments amplify heat exposure at several levels (Campbell-Lendrum and Corvalán 2007; Patz et al. 2005; Stone et al. 2010); EHE response plans are typically administered at the metropolitan level (Bernard and McGeehin 2004); and municipal health authorities are often underprepared for EHEs (Bernard and McGeehin 2004; O’Neill et al. 2010).

Despite the lack of an IAM for urban heat, we can outline an adaptive management process focused on EHEs and consider how this process might evolve iteratively as uncertainties regarding the climate system, health communications, exposure determinants, population susceptibility, and the response to various potential interventions are clarified.

Assessment. Assessment is the first step of the adaptive management process (Figure 1). This is one type of vulnerability assessment, for which multiple theoretical frameworks and methodologies are available. In the case of heat, several components of risk, from hazard frequency and severity to population exposure and susceptibility, must be assessed. EHE risk results from the interaction of various factors at multiple scales, as depicted in Figure 2. Using the natural hazards risk formula to incorporate hazard probability, hazard exposure, and population susceptibility (Malilay et al. 1997), taking care to incorporate social factors affecting vulnerability (Sullivan and Meigh 2005), can help organize these components. A wide range of stakeholders should be engaged, from neighborhoods to emergency medical responders to city planners to electrical and water utilities, in order to assess dynamics affecting both exposure and response. Substantial literature provides insight into effective strategies for stakeholder engagement (Lim et al. 2005).

Figure 2: Components of heat-related morbidity and mortality risk operative at various spatial scales. AC, air conditioning.

Figure 2

Components of heat-related morbidity and mortality risk operative at various spatial scales. AC, air conditioning.

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Planning. Planning prepares for real-world implementation and often uses IAMs. Response activities incorporated into the model should parallel exposures, that is, strategies to change land use and urban form at the mesoscale (Clarke 1972; Golden 2004; Shimoda 2003); building materials, vegetation, and other factors affecting sensible heat at the neighborhood and street levels (Jenerette et al. 2007; Silva et al. 2010); home visitation and other social capital strategies at the neighborhood level (Luber and McGeehin 2008; Wolf et al. 2010); and strategies for changing the home and other environments and relocation of susceptible people (O’Neill et al. 2005). Planning should also incorporate a range of possible futures and be tailored to stakeholder inputs. Improved forecasts that are downscaled to a finer geographic scale can help to limit uncertainty.

Certain tools allow practitioners to organize information on the hazard and population at risk in order to prioritize responses. Vulnerability mapping, for example, allows for visual rendering of relative population vulnerability in relation to hazards and response infrastructure (Li et al. 2010; Morrow 1999). The maps should be used to identify a range of possible interventions to incorporate into the IAM. Decision support tools, including software tools, documents, and work processes, are designed to help practitioners and policy makers evaluate decisions available to them and the potential impacts of those decisions across complex systems, but few tools for selecting adaptation options are available (Pyke et al. 2007).

Stakeholders should also heavily influence the selection of adaptation options. Adaptation requires a new level of cross-sectoral planning, and other sectors are increasingly acknowledging the need to incorporate health (Kashyap 2004) and vice versa (Cole et al. 2007). In the case of extreme heat, electricity generation for air conditioning is a primary concern, and water and forestry are also important. Dynamic models to simulate such interconnected relationships have not been well developed in public health but are increasingly important. Adaptive management must consider scenarios in which other sectors that typically facilitate public health are not fully functional, and alternatives must be modeled and explored. Importantly, research has shown that the primary threat to such systems is the inability of managers to reorganize and recover from significant stressors (Bodin and Norberg 2005; Bunce et al. 2009), highlighting the role of intersectoral collaboration and communication in the planning process.

Implementation. Implementation occurs at various time, geographic, and administrative scales. For instance, implementation of strategies focused on hard infrastructure (e.g., changes in the built environment) will occur at longer time scales than those focused on changes in vegetation, outreach programs, and implementation of early warning systems. From an administrative perspective, implementation will take place via established networks, although adaptive management should result in more interdisciplinary, transsectoral implementation efforts. Implementation will require integration of several dynamic information streams tracking exposures (Webster and Jian 2011), population response to early warnings (Basher 2006; Ebi and Schmier 2005; Kashyap 2004), and assets available for response. A wide variety of decisions must be made at different administrative levels (Luber and McGeehin 2008), such as how predictions will be made, what variables will be tracked, how warnings will be conveyed, thresholds for triggering warning messages (Hajat et al. 2010b; Metzger et al. 2010), and strategies for acting on preparedness plans (Balbus et al. 2008) and communicating warnings (Ebi 2007).

Monitoring. Monitoring provides data fundamental to learning in adaptive management (Holling 1978). Monitoring should be planned early in the process (Ebi 2011) and capture relevant data. Monitoring for EHE management would ideally capture shifts in exposures and modifying factors at various levels, changes in demographics, urban form, and outcome, such as heat morbidity and mortality rates. Syndromic surveillance of symptoms of heat-related illness can be analyzed in real time, for instance, to detect significant increases in these symptoms even before diagnoses are confirmed and reported to public health agencies, facilitating earlier response and ongoing changes in tactics as an outbreak progresses (Josseran et al. 2009). Other exposure indicators are also important, sometimes using remote sensing (Johnson et al. 2009). Other longer-term indicators should be tracked at larger geographic and administrative scales, if possible. In the United States, this might include health indicators that are or soon will be tracked at state and national levels (English et al. 2009). Particular attention should be paid to vulnerable populations (Balbus and Malina 2009). Monitoring should also capture system interactions and capacity. For instance, both short- and medium-term electrical power generation capacity are important determinants of EHE adaptation; although utilities monitor capacity, there is little coordination to increase public health preparedness.

Evaluation. Evaluation in adaptive management is explicitly focused both on the efficacy of the intervention (management objectives) and on increasing understanding of the system being managed (learning objectives) (Satterstrom et al. 2007). This introduces the need for statistical support of pre- to postassessments in an iterative process, often involving Bayesian frameworks (Henriksen and Barlebo 2008). Such pre- to postassessment is fundamentally probabilistic and requires both managers and stakeholders be educated on this approach, although it is often intuitive even for stakeholders without significant specific training (Webster and Jian 2011).

Carrying through the extreme heat example, several issues can complicate evaluation efforts. Often multiple interventions are mounted concurrently, as was the case after the European heat wave of 2003, making it difficult to parse their relative contributions. Moreover, because of constantly shifting baseline conditions, it is difficult to generate baseline estimates of disease burden. However, comparing one extreme event with another can give some indication of efficacy, as with the 2003 and 2006 heat waves in Europe, where the later heat wave resulted in far lower mortality after significant prevention measures were taken (Fouillet et al. 2008).

Adjustment. Adjustment is crucial to adaptive management. The adjustment phase is when future decisions regarding management and research are made, linking to the next cycle (Figure 1). During adjustment, stakeholders are again actively engaged, results of the initial management decisions are conveyed, and stakeholders and system managers convey input regarding the next cycle. Adjustment is thus a process of information synthesis and communication as well as enhanced decision making and the point at which significant learning occurs (Bormann et al. 2007). Adjustment also has important implications for the social integration of stakeholders, which has been shown to improve resilience to climate change in other sectors (Tompkins and Adger 2004).

Adjustment is also where the cycle is at greatest risk. Reviews of adaptive management efforts have shown that inattention to key social learning elements—particularly rapid knowledge acquisition, effective information management, and explicit attention to creating shared understandings among diverse stakeholders—are key culprits (McLain and Lee 1996). This is a concern in any discipline, but public health, with its emphasis on the social determinants of health and integration within community based organizations, has a set of tools for facilitating such processes (Baker et al. 2005; Rowitz 2004). Coupled with appropriate tools for managing information flow within and between organizations and a strong stakeholder commitment to the process, these tools are crucial for the adjustment phase.

Tools to Facilitate Adaptive Management

Many tools are available to facilitate adaptive management (Table 1), falling into three categories: assessment tools for identifying and locating hazards and vulnerable populations; tools to model, project, or evaluate specific climate-related health threats using scenarios; and decision support tools to evaluate adaptation options. In addition to these three categories, it will be crucial to refine tools for evaluating public health adaptive management efforts, for which several methods are available (McFadden et al. 2011), and for performing cost–benefit analyses of adaptive management efforts. Currently there is no comprehensive, centralized tool repository, although such a resource could maximize diffusion of innovations.


To date much of the climate–health literature has focused on establishing and projecting climate change health impacts. This work has shown that certain distinctly climate-sensitive health threats are very likely to pose challenges outside public health’s coping range. The question of how to increase public health capacity has received less attention. Our findings suggest that management of these threats is likely to require innovative strategies acknowledging that the systems protecting public health have limited resources and are dynamic, incompletely understood, and subject to multiple stakeholders. Institutional learning at multiple levels is key to increasing adaptive capacity, and adaptive management is a potentially useful framework. Its components are familiar, but the coordinated process and the use of modeling in iterative decision making are relatively new. Several helpful tools are available but must be revised for new contexts, and significant gaps remain (Table 1). Developing a centralized tool repository should be a high priority and, along with increased focus on learning, modeling, and adaptive management, will help increase the resilience of local public health systems.

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  1. Argyris C, Schön D 1978. Organizational Learning: A Theory of Action Perspective. Reading, PA:Addison-Wesley.
  2. Armitage D, Marschke M, Plummer R. 2008. Adaptive co-management and the paradox of learning. Glob Environ Change 18(1):86–98.
  3. Baker EA, Metzler MM, Galea S. 2005. Addressing social determinants of health inequities: learning from doing. Am J Public Health 95(4):553–555.
  4. Balbus J, Ebi K, Finzer L, Malina C, Chadwick A, McBride D, et al 2008. Are We Ready? Preparing for the Public Health Challenges of Climate Change. Environmental Defense Fund, National Association of County and City Health Officials, Center of Excellence in Climate Change Communication Research at George Mason University. Washington, DC:Environmental Defense Fund.
  5. Balbus JM, Malina C. 2009. Identifying vulnerable subpopulations for climate change health effects in the United States. J Occup Environ Med 51(1):33–37.
  6. Baron C, Sultan B, Balme M, Sarr B, Traore S, Lebel T, et al. 2005. From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact. Philos Trans R Soc Lond B Biol Sci 360(1463):2095–2108.
  7. Basher R. 2006. Global early warning systems for natural hazards: systematic and people-centred. Philos Transact A Math Phys Eng Sci 364: 1845. 2167–2182.
  8. Basu R. 2009. High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ Health 8:40.; doi:10.1186/1476-069X-8-40 [Online 16 September 2009]
  9. Bea R, Mitroff I, Farber D, Foster H, Roberts K. 2009. A new approach to risk: the implications of E3. Risk Manage 11:30–43.
  10. Berkhout F, Hertin J, Gann D. 2006. Learning to adapt: organisational adaptation to climate change impacts. Clim Change 78:135–156.
  11. Bernard SM, McGeehin MA. 2004. Municipal heat wave response plans. Am J Public Health 94(9):1520–1522.
  12. Berrang-Ford L, Ford J, Paterson J. 2011. Are we adapting to climate change? Glob Environ Change 21(1):25–33.
  13. Bodin O, Norberg J. 2005. Information network topologies for enhanced local adaptive management. Environ Manage 35(2):175–193.
  14. Bormann BT, Cunningham PG, Brookes MH, Manning VW, Collopy MW 2007. Adaptive Ecosystem Management in the Pacific Northwest. Portland, OR:U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.
  15. Brownson RC, Fielding JE, Maylahn CM. 2009. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health 30:175–201.
  16. Bryan BA, Kandulu J, Deere DA, White M, Frizenschaf J, Crossman ND. 2009. Adaptive management for mitigating Cryptosporidium risk in source water: A case study in an agricultural catchment in South Australia. J Environ Manage 90(10):3122–3134.
  17. Bunce M, Mee L, Rodwell LD, Gibb R. 2009. Collapse and recovery in a remote small island—a tale of adaptive cycles or downward spirals? Glob Environ Change 19(2):213–226.
  18. California Department of Public Health 2009. Climate Change: Maps of Heat Vulnerability in California. Available:​5 [accessed 6 October 2011]
  19. Campbell-Lendrum D, Corvalán C. 2007. Climate change and developing-country cities: implications for environmental health and equity. J Urban Health 84(3): suppli109–i117.
  20. Campbell-Lendrum D, Corvalán C, Neira M. 2007. Global climate change: implications for international public health policy. Bull World Health Organ 85(3):235–237.
  21. Campbell-Lendrum D, Woodruff R. 2006. Comparative risk assessment of the burden of disease from climate change. Environ Health Perspect 114:1935–1941.
  22. Carrie J, Wang F, Sanei H, Macdonald R, Outridge P, Stern G. 2010. Increasing contaminant burdens in an arctic fish, Burbot (Lota lota), in a warming climate. Environ Sci Technol 44:316–322.
  23. Center for Climate and Energy Solutions 2011. State Adaptation Plans. Available at​one/in_the_states/adaptation_map.cfm [accessed 6 October 2011].
  24. Chan NY, Ebi KL, Smith F, Wilson TF, Smith AE. 1999. An integrated assessment framework for climate change and infectious diseases. Environ Health Perspect 107:329–337.
  25. Chaves LF, Koenraadt CJM. 2010. Climate change and highland malaria: fresh air for a hot debate. Q Rev Biol 85(1):27–55.
  26. Christensen CM, Baumann H, Ruggles R, Sadtler TM. 2006. Disruptive innovation for social change. Harv Bus Rev 84(12):94–101.
  27. Clarke A, Johnston NM, Murphy EJ, Rogers AD. 2007. Antarctic ecology from genes to ecosystems: the impact of climate change and the importance of scale. Philos Trans R Soc Lond B Biol Sci 362(1477):5–9.
  28. Clarke J. 1972. Some effects of the urban structure on heat mortality. Environ Res 5:93–104.
  29. Codeco CT, Lele S, Pascual M, Bouma M, Ko AI. 2008. A stochastic model for ecological systems with strong nonlinear response to environmental drivers: application to two water-borne diseases. J R Soc Interface 5(19):247–252.
  30. Cohen J. 2005. Public health. Gates Foundation picks winners in Grand Challenges in Global Health. Science 309(5731):33–35.
  31. Cole BL, Fielding JE, Cole BL, Fielding JE. 2007. Health impact assessment: a tool to help policy makers understand health beyond health care. Annu Rev Public Health 28:393–412.
  32. Constantin de Magny G, Guegan J-F, Petit M, Cazelles B. 2007. Regional-scale climate-variability synchrony of cholera epidemics in West Africa. BMC Infect Dis 7:20.; doi:10.1186/1471-2334-7-20 [Online 19 March 2007]
  33. Dawson R, Hall J, Barr S, Batty M, Bristow A, Carney S, et al. 2009. City-scale integrated assessment of climate impacts, adaptation and mitigation. IOP Conf Ser Earth Environ Sci 6:332008.; doi:10.1088/1755-1307/6/33/332008 [Online 9 March 2009]
  34. Diez Roux AV. 2011. Complex systems thinking and current impassess in health disparities research. Am J Public Health 101(9):1627–1634.
  35. Diffenbaugh NS, Pal JS, Trapp RJ, Giorgi F. 2005. Fine-scale processes regulate the response of extreme events to global climate change. Proc Natl Acad Sci USA 102(44):15774–15778.
  36. Ebi K. 2007. Towards an early warning system for heat events. J Risk Res 10(5):729–744.
  37. Ebi K. 2011. Climate change and health risks: assessing and responding to them through “adaptive management.” Health Aff 30(5):924–930.
  38. Ebi K, Balbus J, Kinney PL, Lipp E, Mills D, O’Neill MS, et al. 2009. U.S. funding is insufficient to address the human health impacts of and public health responses to climate variability and change. Environ Health Perspect 117:857–862.
  39. Ebi KL, Burton I. 2008. Identifying practical adaptation options: an approach to address climate change-related health risks. Environ Sci Policy 11:359–369.
  40. Ebi KL, Kovats RS, Menne B. 2006. An approach for assessing human health vulnerability and public health interventions to adapt to climate change. Environ Health Perspect 114:1930–1934.
  41. Ebi KL, Schmier JK. 2005. A stitch in time: improving public health early warning systems for extreme weather events. Epidemiol Rev 27:115–121.
  42. Ebi KL, Semenza JC. 2008. Community-based adaptation to the health impacts of climate change. Am J Prev Med 35(5):501–507.
  43. Ebi K, Smith J, Burton I, eds 2005. Integration of Public Health with Adaptation to Climate Change: Lessons Learned and New Directions. Leiden:Taylor & Francis.
  44. Ebi KL, Teisberg TJ, Kalkstein LS, Robinson L, Weiher RF. 2004. Heat watch/warning systems save lives: estimated costs and benefits for Philadelphia 1995–98. Bull Am Meteorol Soc 85:1067–1073.
  45. Ellis FP. 1972. Mortality from heat illness and heat-aggravated illness in the United States. Environ Res 5(1):1–58.
  46. Ellis FP. 1976. Mortality and morbidity associated with heat exposure. Int J Biometeorol 6(2): suppl36–40.
  47. English PB, Sinclair AH, Ross Z, Anderson H, Boothe V, Davis C, et al. 2009. Environmental health indicators of climate change for the United States: findings from the State Environmental Health Indicator Collaborative. Environ Health Perspect 117:1673–1681.
  48. Eriksson C. 2000. Learning and knowledge-production for public health: a review of approaches to evidence-based public health. Scand J Public Health 28(4):298–308.
  49. FEMA (Federal Emergency Management Association) 2011. Hazus. FEMA’s Methodology for Estimating Potential Losses from Disasters. Available: [accessed 6 October 2011]
  50. Flood R, Romm N 1996. Diversity Management: Triple Loop Learning. Chichester:Wiley.
  51. Folke C. 2006. Resilience: the emergence of a perspective for social-ecological systems analyses. Glob Environ Change 16(3):253–267.
  52. Fouillet A, Rey G, Wagner V, Laaidi K, Empereur-Bissonnet P, Le Tertre A, et al. 2008. Has the impact of heat waves on mortality changed in France since the European heat wave of summer 2003? A study of the 2006 heat wave. Int J Epidemiol 37(2):309–317.
  53. Frumkin H, Hess J, Luber G, Malilay J, McGeehin M. 2008. Climate change: the public health response. Am J Public Health 98(3):435–445.
  54. Füssel H-M. 2008. Assessing adaptation to the health risks of climate change: what guidance can existing frameworks provide? Int J Environ Health Res 18(1):37–63.
  55. Gerber BJ. 2007. Disaster management in the United States: examining key political and policy challenges. Policy Stud J 35(2):227–238.
  56. Gohlke JM, Portier CJ. 2007. The forest for the trees: a systems approach to human health research. Environ Health Perspect 115:1261–1263.
  57. Golden J. 2004. The built environment induced urban heat island effect in rapidly urbanizing arid regions—a sustainable urban engineering complexity. Environ Sci 1:321–349.
  58. Grabs W, Tyagi AC, Hyodo M. 2007. Integrated flood management. Water Sci Technol 56(4):97–103.
  59. Gregory R, Ohlson D, Arvai J. 2006. Deconstructing adaptive management: criteria for applications to environmental management. Ecol Appl 16(6):2411–2425.
  60. Haines A, Kovats RS, Campbell-Lendrum D, Corvalan C. 2006. Climate change and human health: impacts, vulnerability and public health. Lancet 367(9528):2101–2109.
  61. Haines A, McMichael AJ, Smith KR, Roberts I, Woodcock J, Markandya A, et al. 2009. Public health benefits of strategies to reduce greenhouse-gas emissions: overview and implications for policy makers. Lancet 374(9707):2104–2114.
  62. Hajat S, O’Connor M, Kosatsky T. 2010a. Health effects of hot weather: from awareness of risk factors to effective health protection. Lancet 375(9717):856–863.
  63. Hajat S, Sheridan SC, Allen MJ, Pascal M, Laaidi K, Yagouti A, et al. 2010b. Heat-health warning systems: a comparison of the predictive capacity of different approaches to identifying dangerously hot days. Am J Public Health 100(6):1137–1144.
  64. Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L. 2006. Neighborhood microclimates and vulnerability to heat stress. Soc Sci Med 63:2847–2863.
  65. Henning KJ. 2004. What is syndromic surveillance? MMWR Morbid Mortal Wkly Rep 53: suppl5–11.
  66. Henriksen H, Barlebo H. 2008. Reflections on the use of Bayesian belief networks for adaptive management. J Environ Manage 88(4):1025–1036.
  67. Hess J, Heilpern K, Davis T, Frumkin H. 2009. Climate change and emergency medicine: impacts and opportunities. Acad Emerg Med 16(8):782–794.
  68. Hess J, Malilay J, Parkinson AJ. 2008. Climate change: the importance of place. Am J Prev Med 35(5):468–478.
  69. Holling C 1978. Adaptive Environmental Assessment and Management. New York:Wiley.
  70. Howden SM, Soussana JF, Tubiello FN, Chhetri N, Dunlop M, Meinke H. 2007. Adapting agriculture to climate change. Proc Natl Acad Sci USA 104(50):19691–19696.
  71. Huang C, Vaneckova P, Wang X, FitzGerald G, Guo Y, Tong S. 2011. Constraints and barriers to public health adaptation to climate change. Am J Prev Med 40(2):183–190.
  72. Ishigami A, Hajat S, Kovats RS, Bisanti L, Rognoni M, Russo A, et al. 2008. An ecological time-series study of heat-related mortality in three European cities. Environ Health 7:5.; doi:10.1186/1476-069X-7-5 [Online 28 January 2008]
  73. Jackson R, Shields KN. 2008. Preparing the U.S. health community for climate change. Annu Rev Public Health 29:57–73.
  74. Jenerette GD, Harlan SL, Brazel A, Jones N, Larsen L, Stefanov WL. 2007. Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecol 22(3):353–365.
  75. Johnson D 2011. NASA ROSES Projects: Using NASA Data and Models to Improve Heat Watch Warning Systems for Decision Support. Universities Space Research Association. Available:​earth_science/nasa_roses_projects [accessed 6 October 2011]
  76. Johnson D, Wilson J, Luber G. 2009. Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data. Int J Health Geogr 8(1):57.; doi:10.1186/1476-072X-8-57 [Online 16 October 2009]
  77. Josseran L, Caillere N, Brun-Ney D, Rottner J, Filleul L, Brucker G, et al. 2009. Syndromic surveillance and heat wave morbidity: a pilot study based on emergency departments in France. BMC Med Inform Decis Mak 9:14.; doi:10.1186/1472-6947-9-14 [Online 20 February 2009]
  78. Kashyap A. 2004. Water governance: learning by developing adaptive capacity to incorporate climate variability and change. Water Sci Technol 49(7):141–146.
  79. Keim M. 2008. Building human resilience: the role of public health preparedness and response as an adaptation to climate change. Am J Prev Med 35(5):508–516.
  80. Khan A, Mojumder S, Kovats S, Vineis P. 2008. Saline contamination of drinking water in Bangladesh. Lancet 371:385.; doi:10.1016/S0140-6736(08)60197-X [Online 2 February 2008]
  81. Kilbourne EM, Choi K, Jones TS, Thacker SB. 1982. Risk factors for heatstroke. A case–control study. JAMA 247(24):3332–3336.
  82. Klinenberg E 2002. Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago:University of Chicago Press.
  83. Kolb DA. 1984. Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ:Prentice-Hall.
  84. Kovats RS, Campbell-Lendrum D, Matthies F. 2005. Climate change and human health: estimating avoidable deaths and disease. Risk Anal 25(6):1409–1418.
  85. Kovats RS, Ebi KL, Menne B 2003. Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change. Health and Global Environmental Change No. 1. Geneva:World Health Organization.
  86. Kovats RS, Hajat S. 2008. Heat stress and public health: a critical review. Annu Rev Public Health 29:41–55.
  87. Lagadec P. 2004. Understanding the French 2003 heat wave experience: beyond the heat, a multi-layered challenge. J Conting Crisis Manag 12(4):160–169.
  88. Li F, Bi J, Huang L, Qu C, Yang J, Bu Q. 2010. Mapping human vulnerability to chemical accidents in the vicinity of chemical industry parks. J Hazard Mater 179(1–3):500–506.
  89. Lim B, Spanger-Siegfried E, Burton I, Malone E, Huq S, eds 2005. Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies and Measures. Cambridge:Cambridge University Press.
  90. Linkov I, Satterstrom FK, Kiker G, Batchelor C, Bridges T, Ferguson E. 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management: recent developments and applications. Environ Int 32(8):1072–1093.
  91. Luber G, McGeehin M. 2008. Climate change and extreme heat events. Am J Prev Med 35(5):429–436.
  92. Maibach EW, Chadwick A, McBride D, Chuk M, Ebi KL, Balbus J. 2008. Climate change and local public health in the United States: preparedness, programs and perceptions of local public health department directors. PLoS One 3(7):e2838.; doi:10.1371/journal.pone.0002838 [Online 30 July 2008]
  93. Malilay J, Henderson A, McGeehin M, Flanders WD. 1997. Estimating health risks from natural hazards using risk assessment and epidemiology. Risk Anal 17(3):353–358.
  94. McDaniels T, Chang S, Cole D, Mikawoz J, Longstaff H. 2008. Fostering resilience to extreme events within infrastructure systems: characterizing decision contexts for mitigation and adaptation. Glob Environ Change 18(2):310–318.
  95. McDaniels T, Gregory R. 2004. Learning as an objective within structured decision processes for managing environmental risks. Environ Sci Technol 38(7):1921–1926.
  96. McFadden JE, Hiller TL, Tyre AJ. 2011. Evaluating the efficacy of adaptive management approaches: is there a formula for success? J Environ Manage 92(5):1354–1359.
  97. McLain RJ, Lee RG. 1996. Adaptive management: promises and pitfalls. Environ Manage 20(4):437–448.
  98. McLaughlin JB, DePaola A, Bopp CA, Martinek KA, Napolilli NP, Allison CG, et al. 2005. Outbreak of Vibrio parahaemolyticus gastroenteritis associated with Alaskan oysters. N Engl J Med 353(14):1463–1470.
  99. McMichael AJ. 2001. Global environmental change as “risk factor”: can epidemiology cope? Am J Public Health 91(8):1172–1174.
  100. McMichael A, Campbell-Lendrum D, Kovats RS, Edwards S, Wilkinson P, Wilson T, et al 2004. Global climate change. In: Comparative Quantification of Health Risks (Ezzati M, Lopez A, Rodgers A, Murray C, eds). Geneva:World Health Organization, 1543–1649.
  101. McMichael AJ, Dear KBG. 2010. Climate change: heat, health, and longer horizons. Proc Natl Acad Sci USA 107(21):9483–9484.
  102. Metzger KB, Ito K, Matte TD. 2010. Summer heat and mortality in New York City: how hot is too hot? Environ Health Perspect 118:80–86.
  103. Mezirow J 1995. Transformation theory in adult learning. In: In Defense of the Life World (Welton MR, ed). Albany, NY:State University of New York Press, 39–70.
  104. Montoya J, Raffaelli D. 2010. Climate change, biotic interactions and ecosystem services. Philos Trans R Soc Lond B Biol Sci 365(1549):2013–2018.
  105. Morrow BH. 1999. Identifying and mapping community vulnerability. Disasters 23(1):1–18.
  106. Murray C, Marmorek D 2003. Adaptive management and ecological restoration. In: Ecological Restoration of Southwestern Ponderosa Pine Forests (Freiderici P, ed). Washington, DC:Island Press, 417–428.
  107. Myers SS, Patz JA. 2009. Emerging threats to human health from global environmental change. Annu Rev Environ Resour 34(1):223–252.
  108. National Research Council 2004. Adaptive Management for Water Resources Project Planning. Washington, DC:National Academies Press.
  109. O’Brien K, Leichenko R. 2000. Double exposure: assessing the impacts of climate change within the context of economic globalization. Glob Environ Change 10: 2000. 221–232.
  110. O’Neill MS, Carter R, Kish JK, Gronlund CJ, White-Newsome JL, Manarolla X, et al. 2009. Preventing heat-related morbidity and mortality: new approaches in a changing climate. Maturitas 64(2):98–103.
  111. O’Neill MS, Jackman DK, Wyman M, Manarolla X, Gronlund CJ, Brown DG, et al. 2010. U.S. local action on heat and health: are we prepared for climate change? Int J Public Health 55(2):105–112.
  112. O’Neill MS, Zanobetti A, Schwartz J. 2005. Disparities by race in heat-related mortality in four US cities: the role of air conditioning prevalence. J Urban Health 82(2):191–197.
  113. Pahl-Wostl C. 2009. A conceptual framework for analysing adaptive capacity and multi-level learning processes in resource governance regimes. Glob Environ Change 19(3):354–365.
  114. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. 2005. Impact of regional climate change on human health. Nature 438(7066):310–317.
  115. Patz J, Gibbs H, Foley J, Rogers J, Smith K. 2007. Climate change and global health: quantifying a growing ethical crisis. Ecohealth 4(4):397–405.
  116. Pelling M, High C, Dearing J, Smith B. 2007. Social learning and adaptive capacity: a relational understanding of adaptive capacity to climate change within organizations. Environ Plan A 40(4):867–884.
  117. Pirard P, Vandentorren S, Pascal M, Laaidi K, Le Tertre A, Cassadou S, et al. 2005. Summary of the mortality impact assessment of the 2003 heat wave in France. Eurosurveill 10(7):153–156.
  118. Polasky S, Segerson K. 2009. Integrating ecology and economics in the study of ecosystem services: some lessons learned. Ann Rev Resource Econ 1(1):409–434.
  119. Portier C, Thigpen-Tart K, Hess J, Luber G, Maslak T, Radtke M, et al 2010. A Human Health Perspective on Climate Change. Research Triangle Park, NC:Environmental Health Perspectives, National Institute of Environmental Health Sciences.
  120. Poumadere M, Mays C, Le Mer S, Blong R. 2005. The 2003 heat wave in France: dangerous climate change here and now. Risk Anal 25(6):1483–1494.
  121. Prato T. 2010. Sustaining ecological integrity with respect to climate change: a fuzzy adaptive management approach. Environ Manage 45(6):1344–1351.
  122. Pulwarty RS, Melis TS. 2001. Climate extremes and adaptive management on the Colorado River: lessons from the 1997–1998 ENSO event. J Environ Manage 63(3):307–324.
  123. Pyke C, Bierwagen B, Furlow J, Gamble J, Johnson T, Julius S, et al. 2007. A decision inventory approach for improving decision support for climate change impact assessment and adaptation. Environ Sci Policy 10(7–8):610–621.
  124. Reid CE, O’Neill MS, Gronlund CJ, Brines SJ, Brown DG, Diez-Roux AV, et al. 2009. Mapping community determinants of heat vulnerability. Environmental Health Perspectives 117:1730–1736.
  125. Reiter P 2001. Climate change and mosquito-borne disease. Environ Health Perspect 109(suppl)1:141–161.
  126. Rey G, Fouillet A, Bessemoulin P, Frayssinet P, Dufour A, Jougla E, et al. 2009. Heat exposure and socioeconomic vulnerability as synergistic factors in heat-wave-related mortality. Eur J Epidemiol 24(9):495–502.
  127. Rowitz L. 2004. Ten tools for practice learning. J Public Health Manag Pract 10(4):368–370.
  128. Satterstrom FK, Linkov I, Kiker G, Bridges T, Greenberg M 2007. Adaptive management: a review and framework for integration with multi-criteria decision analysis. In: Reclaiming the Land: Rethinking Superfund Institutions, Methods and Practices (Macey J, Cannon J, eds). New York:Springer, 89–117.
  129. Schneider SH, Semenov S, Patwardhan A, Burton I, Magadza CHD, Oppenheimer M, et al 2007. Assessing key vulnerabilities and the risk from climate change. In: Climate Change 2007: Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK:Cambridge University Press, 779–810.
  130. Schroter D, Cramer W, Leemans R, Prentice IC, Araujo MB, Arnell NW, et al. 2005. Ecosystem service supply and vulnerability to global change in Europe. Science 310(5752):1333–1337.
  131. Semenza JC, March TL, Bontempo BD. 2006. Community-initiated urban development: an ecological intervention. J Urban Health 84(1):8–20.
  132. Seo C, Thorne JH, Hannah L, Thuiller W. 2009. Scale effects in species distribution models: implications for conservation planning under climate change. Biol Lett 5(1):39–43.
  133. Sheridan SC. 2006. A survey of public perception and response to heat warnings across four North American cities: an evaluation of municipal effectiveness. Int J Biometeorol 52(1):3–15.
  134. Sheridan SC, Dolney TJ. 2003. Heat, mortality, and level of urbanization: measuring vulnerability across Ohio, U.S.A. Clim Res 24:255–265.
  135. Sherwood S, Huber M. 2010. An adaptability limit to climate change due to heat stress. Proc Natl Acad Sci USA 107(21):9552–9555.
  136. Shimoda Y. 2003. Adaptation measures for climate change and the urban heat island in Japan’s built environment. Build Res Inform 31(3–4):222–230.
  137. Silva HR, Phelan PE, Golden JS. 2010. Modeling effects of urban heat island mitigation strategies on heat-related morbidity: a case study for Phoenix, Arizona, USA. Int J Biometeorol 54(1):13–22.
  138. Smithers J, Blay-Palmer A. 2001. Technology innovation as a strategy for climate adaptation in agriculture. Appl Geogr 21(2):175–197.
  139. Social Learning Group 2001. Learning to Manage Global Environmental Risks. Vo1 2. A Functional Analysis of Social Responses to Climate Change, Ozone Depletion, and Acid Rain (Clark W, Jaeger J, van Eijndhoven J, Dickson N, eds). Cambridge, MA:MIT Press.
  140. Sterman JD 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston:Irwin/McGraw-Hill.
  141. Sterman JD. 2006. Learning from evidence in a complex world. Am J Public Health 96(3):505–514.
  142. Sterman JD. 2008. Economics. Risk communication on climate: mental models and mass balance. Science 322(5901):532–533.
  143. Stone B Jr, Hess J, Frumkin H. 2010. Urban form and extreme heat events: are sprawling cities more vulnerable to climate change than compact cities? Environ Health Perspect 118:1425–1428.
  144. Stone DA, Allen MR, Stott PA, Pall P, Min S-K, Nozawa T, et al. 2009. The detection and attribution of human influence on climate. Annu Rev Environ Resour 34(1):1–16.
  145. Stott P, Stone D, Allen M. 2004. Human contribution to the European heat wave of 2003. Science 432(7017):610–614.
  146. Stratus Consulting 2007. Adaptation Decision Matrix. Available:​ploads/​m [accessed 6 October 2011]
  147. Stubbs M, Lemon M. 2001. Learning to network and networking to learn: facilitating the process of adaptive management in a local response to the UK’s National Air Quality Strategy. Environ Manage 27(3):321–334.
  148. Sullivan C, Meigh J. 2005. Targeting attention on local vulnerabilities using an integrated index approach: the example of the climate vulnerability index. Water Sci Technol 51(5):69–78.
  149. Tapsell SM, Penning-Rowsell EC, Tunstall SM, Wilson TL. . 2002. Vulnerability to flooding: health and social dimensions. Philos Transact A Math Phys Eng Sci 360: 1796. 1511–1525.
  150. Tizio BV/Netherlands Environmental Assessment Agency 2011. MyM: Visual Simulation Tool. Available: [accessed 6 October 2011]
  151. Tompkins E, Adger W. 2004. Does adaptive management of natural resources enhance resilience to climate change? Ecol Soc 9(2):10.
  152. UNFCCC (United Nations Framework Convention on Climate Change) 2010. Subsidiary Body for Scientific and Technical Advice. Synthesis report on efforts undertaken to monitor and evaluate the implementation of adaptation projects, policies, and programmes and the costs and effectiveness of completed projects, policies and programmes, and views on lessons learned, good practices, gaps and needs. Available:​ta/eng/05.pdf [accessed 6 October 2010]
  153. United Nations Population Programme 2004. World Population Prospects: The 2004 Revision Population Database. Geneva:United Nations.
  154. University of Delaware Disaster Research Center 2011. Puerto Rico Disaster Decision Support Tool (DDST). Available: [accessed 6 October 2011]
  155. Van Wave TW, Scutchfield FD, Honore PA. 2010. Recent advances in public health systems research in the United States. Annu Rev Public Health 31:283–295.
  156. Varmus H, Klausner R, Zerhouni E, Acharya T, Daar AS, Singer PA. 2003. Grand challenges in global health. Science 302(5644):398–399.
  157. Vennix J 1996. Group Model Building: Facilitating Team Learning using System Dynamics. Chichester, UK:Wiley.
  158. Walther GR. 2010. Community and ecosystem responses to recent climate change. Philos Trans R Soc Lond B Biol Sci 365(1549):2019–2024.
  159. Webster PJ, Jian J. 2011. Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world. Philos Transact A Math Phys Eng Sci 369: 1956. 4768–4797.
  160. Whicker JJ, Janecky DR, Doerr TB. 2008. Adaptive management: a paradigm for remediation of public facilities following a terrorist attack. Risk Anal 28(5):1445–1456.
  161. WHO 2009. Improving Public Health Responses to Extreme Weather/Heat-Waves—EuroHEAT. Technical Summary. Copenhagen:World Health Organization Regional Office for Europe.
  162. Wolf J, Adger WN, Lorenzoni I, Abrahamson V, Raine R. 2010. Social capital, individual responses to heat waves and climate change adaptation: an empirical study of two U.K. cities. Glob Environ Change 20(1):44–52.
  163. Yohe G, Tol R. 2002. Indicators for social and economic coping capacity—moving toward a working definition of adaptive capacity. Glob Environ Change 12(1):25–40.

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