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spaces previously isolated by railroad tracks.

REBUILDING BY DESIGN

To tackle the food insecurity that plagued New Orleans before Katrina(achallengethatcontinues there and in many communities across the nation), one enterpris- ing resident turned empty lots in the devastated Lower Ninth Ward, recognized as a food desert by the US Department of Agri- culture, into community or- chards. Through a new nonprofit organization, these rejuvenated lots provide healthy food to resi- dents who need it, teach people to grow their own produce, and serve as a source of community unity and pride.

Events like Katrina provide a unique opening for innovation and creativity in building com- munities that are both healthier and more socially vibrant than they were before disaster struck. Indeed, after Superstorm Sandy devastated communities along the East Coast, the groundbreaking Rebuild by Design contest called on innovators and experts across

sectors to work with community members to envision, design, and build solutions to the region’s most complex challenges. Atten- tion to community factors that affect health figured significantly in those solutions.

Today, less than a month after Harvey, Irma, and Maria delivered back-to-back blows, funding and resources are coming into communities in Texas, Florida, and Puerto Rico. Amid the flurry of first response and the outpouring of compassion and support from across the nation and the world, we hope affected communities will capitalize fully on the rebuilding opportunities before them by using the funds and resources strategically. The blueprint should not be the status quo; it should be a vision for an infrastructure that also supports optimal health and resilience for every community.

REBUILD OUR COMMUNITIES AS WE WANT THEM

Far too many communities in the United States are

suboptimally healthy and lack adequate health-supporting infrastructure, such as housing, high-quality health care, strong networks that prevent social iso- lation, and easy access to healthy, affordable food. Although no one wishes a disaster on any com- munity, we know that, inevitably, they will continue to occur—and withthemwillcomeopportunity. Asthefamoussayinggoes:“Never let a serious crisis go to waste.” When planning for disasters, we should also plan for what we want our communities to look like as they recover—including careful consideration of what will promote and sustain good health.

Political leaders, health offi- cials, preparedness and response professionals, and community organizations should act now to develop a shared vision of opti- mal health for their community. A long-term plan for health and resilience should be a fore- thought, rather than an after- thought, when a disaster occurs. A detailed framework to support this kind of planning was rec- ommended in a 2015 consensus report from the Institute of Medicine (now the National Academy of Medicine), and

resources are available through the federal government’s National Disaster Preparedness Framework (https://www.nap. edu/read/18996/chapter/1; https://www.fema.gov/ national-disaster-recovery- framework).

As Harvey and Irma focus our national consciousness on the deadly impact of natural disasters, each of us should consider what can be done to make our com- munities safer,healthier,andmore resilient places to live.

Victor J. Dzau, MD Nicole Lurie, MD, MSPH

Reed V. Tuckson, MD

CONTRIBUTORS The authors contributed equally to this editorial.

REFERENCES 1. Institute of Medicine Committee on Post-Disaster Recovery of a Community’s Public Health, Medical, and Social Ser- vices. Healthy, Resilient, and Sustainable Communities After Disasters: Strategies, Opportunities, and Planning for Recovery\ Committee on Post-Disaster Recovery of a Com- munity’s Public Health, Medical, and Social Services; Board on Health Sciences Policy, In- stitute of Medicine of the National Academies. Washington, DC: National Academies Press; 2015.

Climate Change, Hurricanes, and Health

See also Zolnikov, p. 27; Lichtveld, p. 28;

Rodrı́guez-Dı́az, p. 30; and Dzau et al., p. 32.

The year 2017 has seen a devastating series of hurricanes across the Caribbean, Central America, and the United States— Harvey in August, Irma and Maria in September, and Nate in October. The first three caused devastation along their paths and reached the United States as Category 4 hurricanes.

Inevitably, there has been dis- cussion on the role of climate change in increasing the severity of tropical storms generally and this series of hurricanes specifically.

We address the causal attri- bution of severe and extreme weather events to climate change and the associated health

consequences. This attribution is of primary scientific interest but comes with evident political implications.

CAUSAL ATTRIBUTION OF EXTREME WEATHER EVENTS

The broad community of at- mospheric scientists has brought increasing attention to the causal attribution of extreme weather events to human activities.1 The underlying approaches will be

ABOUT THE AUTHORS Alistair J. Woodward is with the Department of Epidemiology and Biostatistics, School of Public Health, University of Auckland, New Zealand. Jonathan M. Samet is with the Colorado School of Public Health, University of Colorado, Aurora.

Correspondence should be sent to Jonathan M. Samet, Dean and Professor, Colorado School of Public Health, Office of the Dean, 13001 East 17th Place, MS B119, Aurora, CO 80045 (e-mail: Jon.Samet@ucdenver.edu). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.

This editorial was accepted October 16, 2017. doi: 10.2105/AJPH.2017.304197

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https://www.fema.gov/national-disaster-recovery-framework
https://www.fema.gov/national-disaster-recovery-framework
https://www.fema.gov/national-disaster-recovery-framework

 

familiar to those knowledgeable about causal attribution in public health, particularly the adoption of the potential outcomes framework, which compares what is observed with what is expected under an alternative scenario of no exposure to the factor of interest. This hy- pothetical state of no (or an al- ternative to reality) exposure is referred to as the counterfactual, that is, counter to the facts.2

An analogy in public health is the comparison of lung cancer risk in smokers to the counter- factual risk that smokers would have had as never smokers. In the attribution of weather events to climate change, different coun- terfactuals are relevant to differ- ent questions. One example is the current climate, as it is affected by human activities, compared with past climate conditions. Another is the comparison of “business as usual” scenarios—that is, con- tinuing on the present trajectory of increasing emissions of green- house gases—with alternative futures in which emissions plateau and then decline.

FREQUENCY AND SEVERITY OF STORMS

In approaching the attribution of storms and other extreme weather events to climate change, atmospheric scientists estimate probabilities of causa- tion, a notion familiar to public health scientists. For example, we generally accept that it is not possible to determine whether smoking caused a particular case of lung cancer, but we do know that the odds of this being the case are very high (about 8:1 in an American male lifetime cigarette smoker). On this basis, we can estimate the likelihood that the particular case resulted

from smoking and hence the population-wide benefits of re- ducing or eliminating altogether tobacco smoking. Climate sci- entists have adopted this ap- proach and emphasize that the question is not “Did climate change cause event X?” but “By how much did climate change increase the chance that event X would occur?”3

The approach taken for this estimation is parallel to that used in epidemiology to estimate the at- tributable risk in those exposed to a factor (i.e., the attributable risk in exposed =(PE – P0)/PE, where PE is the probability of the outcome in those exposed and P0 is the probability in the unexposed). For hurricanes and climate change, PE couldbetheprobabilityofmoreor of more severe hurricanes in the setting of climate change, and P0 is the probability associated with the counterfactual scenario.

ATTRIBUTION AND LIABILITY

In public health, attribution and liability are closely linked and form a basis for policy action and, in some instances, compensation. In some legal settings, proof of causation is judged on the basis of “more likely than not,” meaning that the outcome rests on estab- lishing the presence of exposure because of a relative risk greater than 2. Climate scientists have put their toes into the same water, for example, in exploring the issue of responsibility for extreme events such as the 2003 European heatwave.1

The attribution of events such as Harvey and Irma is more dif- ficult than is attribution in the lung cancer example because of the difference between climate and weather. Exposure estimates (analogous to the presence or

absence of smoking) relate to climate—what prevails in the long run—but the outcomes are acute weather events, and these are qualitatively different phe- nomena. The relation between weather and climate is complex, and modeling different counter- factuals (e.g., storm frequency in a world without human- induced climate change) is not straightforward.

Precipitation is especially dif- ficult to simulate, because it de- pends on much tighter space and time scales than apply to tem- perature and is heavily influenced by local physical processes such as convection.4 Nonetheless, such modeling is difficult but not impossible; climate models are now capable of simulating the incidence and intensity of tropi- cal cyclones, with and without greenhouse loading, and dis- tinguishing to some extent the influences of natural variability (such as the occurrence of El Nino events) from anthropo- genic forcing.

A recent modeling study of this kind examined cyclone ac- tivity in the western north Paci- fic area in 2015 and linked the extreme energy levels that were observed to human-induced cli- mate change. This and other studies have concluded that climate change makes high- intensity storms more likely, but it is less certain that the overall frequency of storms is affected.5

ATTRIBUTING HEALTH IMPACTS

Attributing health impacts is even more complex than is at- tributing weather events, because many variables are relevant aside from the meteorological condi- tions.6 There is no single method for this task. If there were

sufficient data, it might be pos- sible to proceed in steps, de- termining first, for example, whether a rise in greenhouse gas emissions increased the proba- bility of very high temperatures and, second, to what extent ex- cess mortality may be attributed to observed high temperatures. Other health outcomes, such as geographic spread of vector- borne disease and water-borne infections in warming seas, may require different analytic ap- proaches, including pattern matching and argument from understanding disease mechanisms.7

For hurricanes, modeling health impacts is challenging because the impacts of storms are modified strongly by local cir- cumstances. The health losses that result from the storms can be attributed, in part, to the lack of effective and general adaptation to extreme weather. In Houston, Texas, for instance, there were features of the city, such as urban expansion over wetlands and a landscape dominated by im- pervious surfaces, that made the flooding worse than it would have been otherwise.

Despite these complexities, the recent storms provide a powerful reminder, absent modeling, that hurricanes di- rectly and indirectly increase mortality and lead to long-term increases in morbidity. Media accounts document many deaths from physical injury and drowning: access to clean water has been interrupted for millions as has the availability of electric power; elderly nursing home residents died in Florida from heat exposure; and needed and life-sustaining medical services were lost by many because hos- pitals closed and dialysis units could not operate. For the longer term, people face loss of property, water-damaged homes, and loss

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of livelihood, and there may be persisting economic and psy- chosocial consequences. Puerto Rico seems at particular risk in this regard.

The hurricanes of 2017 are consistent with model-based projections of more severe weather associated with climate change. Theresulting devastation has reached broadly; Puerto Rico and other Caribbean islands will need years to recover. These storms offer another moment to begin to address climate change and its implications, yet the En- vironmental Protection Agency administrator Scott Pruitt has said that it would be “too insensitive” to have that discussion now. The storms’ victims may wish that action had been taken decades ago.

Alistair J. Woodward, MMedSci, PhD, MB

Jonathan M. Samet, MD, MS

CONTRIBUTORS The authors contributed equally to this editorial.

ACKNOWLEDGMENTS This editorial was supported by the GEOHealth Hub for research and training inEasternAfricaandfundedbytheFogarty International Center, National Institutes of Health (grant U2RTW010125).

REFERENCES 1. Otto FEL. Extreme events: the art of attribution. Nat Clim Chang. 2016;6(4): 342–343.

2. Glass TA, Goodman SN, Hernán MA, Samet JM. Causal inference in public health. Annu Rev Public Health. 2013;34: 61–75.

3. National Academies of Sciences, En- gineering, and Medicine. Attribution of Extreme Weather Events in the Context of Climate Change. Washington, DC: Na- tional Academies Press; 2016.

4. Stott PA, Stone DA, Allen MR. Human contribution to the European heatwave of 2003. Nature. 2004;432(7017):610–614.

5. Herring SC, Hoell A, Hoerling MP, Kossin JP, Schreck CJ III, Stott PA. Explaining extremes of 2015 from a cli- mate perspective. Bull Am Meteorol Soc. 2016;97(12):S1–S3.

6. Zhang W, Vecchi GA, Murakami H, et al. Influences of natural variability and anthropogenic forcing on the extreme 2015 accumulated cyclone energy in the western north Pacific. Bull Am Meteorol Soc. 2016;97(12):S131–S135.

7. Mitchell D, Heaviside C, Vardoulakis S, et al. Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ Res Lett. 2016; 11(7):074006.

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