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A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters (2020)

Chapter: 2 Value and Use of Mortality and Morbidity Data

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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
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Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Page 49
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Page 50
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Page 51
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Page 52
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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Page 53
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 54
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 55
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 56
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 57
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 58
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 59
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 60
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 61
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 62
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 63
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
×
Page 64
Suggested Citation:"2 Value and Use of Mortality and Morbidity Data." National Academies of Sciences, Engineering, and Medicine. 2020. A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters. Washington, DC: The National Academies Press. doi: 10.17226/25863.
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2 Value and Use of Mortality and Morbidity Data The act of quantifying mortality and morbidity following a traumatic event, such as a large-scale disaster, holds deep emotional, societal, financial, and logistical value and serves a multitude of different uses for different stakeholders. Accurately quantifying disaster-related mortality and morbidity is a complex and challenging endeavor. The meaningful use of morbidity and mortality data is often undermined by the fact that no uniform framework or standard vocabulary for conceptualizing disaster-related mortality and morbidity data is in use across all jurisdictions, federal and state, local, tribal, and territorial (SLTT) agencies, and professional domains. Data are captured inconsistently, and data that are collected are not being used to their fullest potential due to the siloing of agencies and systems. The first part of this chapter lays out a potential framework for conceptualizing disaster-related mortality and morbidity and introduces updated case definitions developed by the committee. The second part of the chapter discusses the value and meaningful use of mortality and morbidity data by various stakeholders across the disaster lifecycle and provides examples of how these data are currently used or could be used. Chapters 3 and 4 will focus on the analytical and operational challenges and practices related to the collection, reporting, and recording of individual counts and population estimates of mortality and morbidity. CONCEPTUALIZING ALL-CAUSE MORTALITY AND MORBIDITY Significant confusion and disagreement persist across systems and stakeholders regarding what counts as a disaster-related death or morbidity, which profoundly affects the ability to use mortality and morbidity data in meaningful ways. Resolving this discordance and moving toward consistently applied standards and harmonized practices will require more than merely developing simple case definitions, however. It will require taking a broader understanding of disaster-related mortality and morbidity data that considers the context, timing, and methods by which data are collected and recorded as well as considering the methods used to assess and use the data to protect the health of the public. Although a framework with common definitions can be helpful in catalyzing and supporting the adoption of this more comprehensive type of approach, no uniform framework is widely used in current practice. A framework to guide the assessment of mortality and morbidity would provide a methodological structure for more accurately and completely categorizing and reporting those outcomes in a consistent manner. Ideally, such a framework would strike a balance between uniformity and flexibility, would be applicable to all disasters (e.g., small- or large-scale, human- induced or naturally occurring), and would include case definitions that are designed to capture all mortality and morbidity related to the event while also excluding cases that are unrelated PREPUBLICATION COPY: UNCORRECTED PROOFS 2-1

2-2 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY (Combs et al., 1999). A uniform approach for quantitatively describing a disaster’s health impacts would also enable analyses of the effectiveness of disaster management activities across different disasters. A uniform approach could also support: • More consistent assessment of the human impact of disasters across all jurisdictions; • Delivery of adequate resources for recovery; • Forecasting of needs for similar incidents in the future; • Identification of behavioral contributors to disaster-related mortality and morbidity to inform interventions to modify future behavior; • Exploration of population impacts to promote health and offer relevant services and support for prevention and recovery; and • Identification of vulnerable populations and their specific needs to improve services and reduce additional morbidity, injury, and death. To address the need for a uniform approach for conceptualizing and assessing mortality and mortality data following large-scale disasters, the committee developed a framework that can be adopted across all systems and jurisdictions (see Table 2-1). This framework incorporates the two primary methodological approaches for estimating disaster-related mortality and morbidity and builds on a body of literature of analytic prospective and retrospective methodologies (Kishore et al., 2018; Santos-Burgoa et al., 2018; Stephens et al., 2007). TABLE 2-1 Proposed Framework of Approaches for Defining Mortality and Morbidity Following Large-Scale Disasters Total reported mortality and morbidity estimation using individual counts: Individual counts are point-in-time estimates of disaster-related mortality and morbidity derived from reported cases. Term Description Example Direct A death or morbidity directly Deaths from structural collapse, flying debris, radiation attributable to the forces of the exposure, drowning during the event; delayed deaths disaster or a direct consequence of directly related to initial impact (e.g., head injury leading these forces. to coma with eventual death from aspiration pneumonia) Indirect A death or morbidity not from a direct Deaths due to loss of medical or transport services (e.g., impact but due to unsafe or unhealthy death due to lack of access to dialysis); exposure to conditions around the time of the hazards such as chemicals; deaths related to disaster disaster, including while preparing for, response, such as carbon monoxide poisoning from responding to, and during recovery improper use of generators; deaths or illness due to from the disaster. diarrheal disease among shelter residents Partially A death or morbidity that cannot be Death due to drug overdose in a patient who had been attributable definitively tied to the disaster but abstinent and re-started drug use during or immediately where the disaster more likely than not after the disaster; death from myocardial infarction or has played a contributing role in the stroke during a disaster in a patient with pre-existing death. cardiovascular disease; death due to suicide following or during a disaster in a patient with pre-existing mental illness Total mortality and morbidity derived from population estimates: Population estimates are point-in-time estimates of the impact of a disaster at a population level derived using various statistical methods and tools, including sampling. PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-3 Examples: Increase in all-cause mortality in the 4 weeks after a hurricane derived from excess mortality data; increase in myocardial infarctions in the 6 months following a disaster derived from claims data; estimation of population infection rates using serological prevalence studies; increase in asthma exacerbation episodes in the wake of a large wildfire using data from electronic health records. This proposed framework builds on and provides standard meaning to the disaster mortality definitions promoted by the Centers for Disease Control and Prevention and works in tandem with designations for natural and unnatural deaths used by medical examiners and coroners. It is important to note that the case definitions proposed by the committee are aligned with—and not intended to replace—the language already being used in the medical examiner and coroner community to categorize different manners and causes of death. Most states offer five options for coding the manner of death: natural, homicide, suicide, accidental, and undetermined (NAME, 2002). The manner of death is how that injury or illness led to the death, while the cause of death refers to the specific illness or injury that led to death (Washoe County Regional Medical Examiner’s Office, 2020). Natural deaths are those that have internal physiological causes. The term unnatural death is used by medical examiners and coroners to categorize a death that did not occur due to natural causes (IOM, 2003). As discussed below, the committee’s case definition for a direct death will capture only unnatural disaster-related deaths, while the case definitions for indirect deaths and partially attributable deaths can capture both natural and unnatural deaths. Another potential advantage of using the committee’s uniform case definitions and framework is that it offers medical certifiers of individual cases of mortality (e.g., doctors, nurse practitioners) greater autonomy in categorizing a death as being indirectly related or partially attributable to a disaster without triggering a mandatory review by the medical examiner. Chapter 3 provides more detail about the roles of these stakeholders in mortality data collection and reporting. Most significantly, this framework shifts the paradigm for defining a disaster’s health impacts from a singular death toll toward a more inclusive understanding of the complex impact of a disaster on human life. In a major disaster, for example, the total mortality estimate can remain dynamic for years, as individuals succumb to injuries or health conditions that occurred as a result of their exposure to the disaster. Even the number of deaths that can be directly attributed to the force of the disaster (i.e., direct deaths) can change over time, because people injured in the event may eventually die of those same injuries or morbidities years later. Insufficient research exists to define a clear minimum timeline by which disaster-related mortality and morbidity should be tracked; however, setting such guidelines for the capture of data is critical (see Recommendations 3-2 and 3-3 on standards for collection of mortality and morbidity data). Box 2-1 provides an overview of some additional stakeholder considerations that provide further rationale for the use of a uniform approach to assessing disaster-related mortality and morbidity. BOX 2-1 Additional Stakeholder Considerations for Mortality and Morbidity Estimation It is critically important to be aware that stakeholders may have certain vested interests in reducing or increasing the estimated impacts of a disaster. The committee’s intent in moving away from focusing on a single, largely unchanging “death toll” to using morbidity and mortality data to more fully describe the human impact of a disaster over time—including recognizing the potential PREPUBLICATION COPY: UNCORRECTED PROOFS

2-4 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY legitimacy of multiple approaches for assessing mortality and morbidity—is to health avoid confusion or the impression that data are being manipulated for stakeholder-specific purposes. For example, stakeholders with interests in reducing or increasing the estimated impacts of a disaster could potentially—either explicitly or inadvertently—apply different approaches to present different versions of a disaster’s impact. This was a major public concern following Hurricanes Katrina and Maria and, more recently, during the COVID-19 pandemic (see Appendix C). Competing stakeholder interests surround these data, the manipulation of which can pose major policy and public health safety risks, but it is important to realize that variation in estimates might reflect different, but appropriate, methods and targets. Still, while there are legitimate reasons for stakeholders to use different methods for assessing the impact of a disaster, some methods will be more appropriate than others for specific purposes (see Chapters 3 and 4 for methodological best practices for individual counts and population estimation approaches). Risks of inappropriate uses of methods and data can be mitigated if key stakeholders commit to exclusively using and promoting the universal adoption of (1) a methods-based framework for attributing mortality and morbidity to a disaster, (2) standard methods for analyzing the data, and (3) standard operations and practices for data collection, reporting, and use across all jurisdictions and stakeholders. Mortality and morbidity estimates also determine how a disaster is presented in the media. Media coverage of a disaster tends to be very selective and may either over-represent or inadequately account for the severity and number of deaths or morbidities (Tzvetkova, 2017). Estimates made based on media reports during the immediate aftermath of a disaster may not be subsequently updated for completeness or validated (Green et al., 2019). It is important to resist political and media pressures to publicize early case-based mortality counts before they are verified. Establishing clearly defined criteria for case definitions within a standardized approach can allow those providing information to the media to say that (1) this is what has been reported so far, (2) the process of counting and estimating is ongoing, and (3) more information will be provided in the future. Clear communication of disaster-related mortality and morbidity data to the public is essential; an example would be the statements, “According to current reports, the disaster has caused X number of deaths directly and it has contributed to another X number of deaths as of X date.” Data-informed public messaging also helps to avert the spread of rumors and misinformation, given that enormous pressure to provide mortality counts can build in the first hours after a disaster’s impact. Certain states have sought to prevent the spread of rumors immediately following a disaster. For example, Florida’s statewide reporting system centralizes the counting of all deaths at the state level, with individual counties not tasked with counting and reporting their deaths independently. This approach allows initial counts of disaster-related deaths to be communicated to the public by a single source with access to all mortality data from across the state, in addition to assuring consistency and timeliness of data. The Federal Emergency Management Agency also provides a website to dispel commonly held misconceptions surrounding its activities related to morbidity and mortality estimates (FEMA, 2020). Methodological Approaches for Assessing Total Disaster-Related Mortality and Morbidity Disasters are complex events with such multifactorial health consequences that no single number can sufficiently describe a disaster’s health impact. This precludes the possibility of any universal methodological approach that can be used across all disasters to generate an estimation of mortalities or morbidities related to that disaster. This complexity also gives rise to a persistent challenge in the assessment of mortality and morbidity, which is the widespread PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-5 conflation of the outputs of two different but complementary methodological approaches for estimating disaster-related mortality and morbidity: (1) individual counts, which are numbers derived from individual administrative case reports, and (2) population estimates, which are based on statistical approaches. The committee’s proposed framework includes both of these essential methodological approaches. Both approaches provide essential information in the face of a disaster, but they differ in their assumptions, data requirements, strengths, weaknesses, and appropriate uses (see Table 2-2). They are similar in that they pertain to a defined point in time and to a geographical area, and both can be refined over time as the situation evolves or as new data become available. When applied appropriately, the two approaches can help answer different questions, elucidate different sets of risk factors, and uncover different potential points of intervention. Therefore, both individual counts and population estimates contribute to a comprehensive picture of a disaster’s health impacts, which can be used to inform response and recovery and to prepare for future events. Individual counts are estimates that are based on counts of deaths recorded in administrative systems and are valuable for understanding the immediate impact of disasters. However, the accuracy of this approach depends on the completeness with which individual cases are recorded and reported. Depending on the strength and precision of the data collection system to capture information about each bit of data accurately and consistently, individual count methods often fail to capture certain types of disaster-related deaths (e.g., individuals who die of natural causes during a disaster and would not have died but for the disaster). However, individual counting methods, if deployed successfully, can provide an early estimate of the number of reported individual deaths, injuries, and cases of illness that are considered to be directly or indirectly caused by the disaster or partially attributable to it. Operational considerations for the collection, recording, reporting, and use of individual counts can be found in Chapter 3. Unlike individual counts, population estimates of total disaster-related mortality and morbidity are derived by estimating the number of mortalities and morbidities using statistical means, such as representative and complex sampling, survey-based methods or using a variety of excess mortality and morbidity methods (e.g., comparing deaths or illness rates in the disaster- affected population to rates observed in the same population during the previous year or during a relevant time period. Population-based estimation methods are crucial for capturing a full understanding of the impacts of a disaster on health and mortality. These methods are often reported in ways that convey the appearance of less precision (i.e., they provide a point estimate with confidence intervals) compared to reports of individual counting methods (which provide a single number, but no confidence intervals, thereby implying greater certainty around the estimate) and in some applications (e.g., estimates of excess deaths) they cannot distinguish individuals who would have survived in the absence of the disaster from those who would have died during the period regardless. Chapter 4 will explore the landscape of population estimation methods and identify potential best practices for conducting and using these analyses. It is critical to recognize that individual counts are not always superior to population estimates based on samples or vice versa. For some audiences, the term “count” might imply greater precision than the term “estimate,” but this assumption is incorrect and both approaches can produce valuable estimates of the true effect of a disaster, which are useful for different purposes. Importantly, both are estimates in the sense that they are ways of gauging total impact that are inherently incomplete and subject to variability over time and according to the specific methods used. Therefore, focusing solely on individual counts limits the scope of an PREPUBLICATION COPY: UNCORRECTED PROOFS

2-6 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY investigation. A more effective strategy is to apply both approaches to assessing mortality and morbidity, which makes the assessment more valuable in terms of understanding the complex nuances of the disaster’s impact and the population’s vulnerabilities. TABLE 2-2 Strengths, Weaknesses, and Uses for Individual Counts and Population Estimate Approaches Strengths Weaknesses Uses Individual Counts • Can offer a more rapid • Accuracy depends on the • Rapidly identify affected assessment of the immediate strength and precision of the population needs and allocation impact of the disaster at a data system to capture complete of resources. defined point in time. data on reported cases; • Provide rapid updates on impact • Allow for more specific details therefore, more likely to to the public. on the degree of attribution of underestimate impact. • Provides specificity to causes of the mortality—direct, indirect, • Often fail to capture cases that death and morbidity so that and possible or partially lack evidence for inclusion in the interventions may be deployed. related—to the disaster. count (e.g., partially attributable • Development of early public mortalities); therefore, more health messaging. likely to underestimate impact. Population Estimates • Can provide a more • Often substantial lag time in • Offer critical in-depth analyses comprehensive understanding performing these analyses as following the disaster to inform of the impacts of a disaster on compared with individual disaster mitigation and the health of the population. counts. preparedness practices. • Able to capture a broader range • Cannot always distinguish which • Identify population-level trends of disaster-related deaths and individuals would have survived • Provide more complete estimate morbidity, including indirect in the absence of the disaster of total impact across a effects and partially from those who would have died population. attributable effects. during the period regardless. • Often require complex statistical modeling and assumptions. SOURCE: Adapted from Appendix C. Multifactorial Problem Another concept critical to understanding disaster-related mortality and morbidity is that all quantitative assessments developed using either approach represent a description of the disaster’s impact at a specific point in time based on a unique set of conditions and assumptions. These estimates can change over time as more data are gathered, additional mortality and morbidity occurs, new assumptions are developed, and updated analyses are performed. Regardless of the methodological approach applied, the assessment of mortality and morbidity is a complex multifactorial problem that is influenced by time, resources, capability, and the health of the affected population. It is impossible to definitively know the true impact of a disaster on human life. Instead, this report attempts to highlight how the administrative, organizational, logistical, and analytical components associated with each of these approaches can be improved to make counts and estimates more accurate and complete reflections of the disaster’s true effect. PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-7 As a data recording and reporting system matures and is able to more accurately count individual deaths, it will capture progressively more of the deaths that are included in the population-based estimates. This will consequently lessen the disparity between the population estimates of mortality and the number of individual deaths reported, for example. Still, certain categories of indirect and partially attributable mortality and morbidity may always be more difficult to detect in practice at the individual level (e.g., heart attack, stroke) and will tend to be better captured through population estimation methods. Conclusion 2-1: Current terminology and case definitions used to describe disaster-related mortality and morbidity fail to capture the differences in assessment methods used and the totality and temporality of disaster-related deaths and significant morbidity. The lack of a uniform framework for assessing disaster-related health impacts undermines the quality and usability of these data in informing disaster management. Accuracy of Individual Counts and Degree of Attribution The precision of estimates of disaster-related mortality and morbidity made using individual counts depends on the accuracy of decisions that are made about the strength of association of an individual outcome to a disaster. In the case of mortality, medical examiners, coroners, or other medical certifiers1 must consider for each individual death (1) the type of death, (2) the degree to which the death can be attributed to a disaster, and (3) the temporality of the death—“the timescale over which the death is expected and can be attributed to a disaster in the context of different types of disasters” (Green et al., 2019, p. 452). Given the multidimensionality of these judgments, the different parameters by which the responsible parties use to assess the degree of attribution drives variation in the types and quantities of outcomes that are collected and recorded. See Chapter 3 for a discussion of the variation in data collection and recording practices throughout the medicolegal system. Multiple terms have been used to denote the presence and degree of a relationship between a death or injury and a disaster. These are often conflated, resulting in misunderstandings about disasters’ impacts and also poor comparability between mortality and morbidity assessments over time and across disasters. Another fundamental disadvantage of the individual counting approach is the risk of failing to count difficult-to-capture cases, such as natural deaths that would not have occurred but for the disaster. To improve consistency and provide guidance for attributing mortality and morbidity to a disaster using an individual-count approach, the committee developed a set of terms for use throughout the report: namely, an individual reported disaster-related mortality or morbidity can be categorized as directly related, indirectly related, or partially attributable to a disaster. These terms are intended flexible enough yet also to be precise enough to support accurate and consistent decision making on whether a case is related to the disaster’s impact or other consequences and, if so, to what extent (see Table 2-1). Consistent use of these three terms is 1 Medical certifiers are medical professionals with the authority to record a cause of death and sign a death registration following a death, typically in a hospital or a health care setting. Medical certifiers are commonly physicians, nurse practitioners, or physician assistants, although this varies by state based on state law (see Chapter 3 for more information on medical certifiers and death registration). PREPUBLICATION COPY: UNCORRECTED PROOFS

2-8 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY also intended to facilitate more effective communication to the media and public about the different ways that an individual reported death or morbidity can be related to a disaster. Direct Mortality and Morbidity Direct deaths or morbidities are directly attributable to the disaster itself, such as deaths due to blunt force injury or drowning as a consequence of a disaster’s physical force. In the language of cause of death reporting, a direct death is always an unnatural death (Green et al., 2019). Direct deaths tend to be the primary focus of data collection efforts, particularly in the medicolegal death investigation system, because these outcomes are generally the most straightforward to collect and record. However, exclusively capturing direct deaths and failing to account for direct morbidities or for indirect, or partially attributable deaths underestimates the actual short- and long-term impacts of the disaster. Additionally, the attribution of individual direct deaths to a disaster does not have an obvious natural end point. As discussed above, a person who dies as a direct consequence of a disaster should always be considered a direct death or morbidity, even if the death occurs many years later. Box 2-2 provides an overview of data sources for individual counts of disaster-related deaths and morbidity. Indirect Mortality and Morbidity Indirect disaster-related mortality and morbidity data capture natural and unnatural deaths and morbidities that are associated with—but not directly caused by—the event. Essentially, the criteria for an indirect death require that the death would not have occurred “but for” the conditions present due to the disaster. Indirect deaths include outcomes due to unsafe or unhealthy conditions around the time of the disasters or during any phase of the disaster lifecycle that contribute to a death (Combs et al., 1999) (see Table 2-1). For example, carbon monoxide poisoning due to unsafe generator use during a power outage resulting from a hurricane, or deaths due to lack of access to essential medications or treatments, such as dialysis, should be recorded and reported as indirect deaths. Indirect mortality and morbidity data can offer a wealth of information about a disaster’s impact and provide actionable evidence to inform disaster response and on how to prevent deaths during future events. However, accurately capturing and recording indirect deaths can be hampered by subjectivity in determinations by medical examiners, coroners, or medical certifiers and often by incomplete evidence to support attribution at the time the death is certified. Strategies that could help prevent the loss of these valuable data on indirect mortality and morbidity include (1) establishing a common policy and philosophical approach for collecting and recording these data, (2) providing professional training to reduce variation in practice, and (3) developing improved tools and methods for capturing these data. In addition, providing a third category for individuals for whom their mortality or morbidity might be partially attributable to the disaster should help mitigate the risk of losing valuable data on these individuals’ outcomes. Partially Attributable Mortality and Morbidity Partially attributable mortality and morbidity is an intentionally nebulous category compared to direct or indirect deaths or morbidities. It encompasses those deaths or morbidities that cannot be tied to the disaster with a high degree of certainty but where the death, injury, or illness was more likely than not to be at least partially related to the disaster. In other words, the death would be unlikely to have occurred “but for” the disaster, but it cannot be tied definitively to it. For instance, this category would include a person who dies because the disaster caused PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-9 stress or exhaustion that exacerbated a pre-existing chronic condition (Green et al., 2019), such as a person with known heart disease who suffered a heart attack while in a shelter environment. At present there is no term commonly in use to define these types of deaths or morbidities, and because these deaths can be variably labeled as indirect, the lack of a common terminology generates inconsistency and possible bias across jurisdictions or individual certifiers. The committee aims to address this problem with the category of partially attributable deaths. Importantly, this category is not static, and partially attributable deaths or morbidities can be reclassified as indirect deaths or morbidities as more evidence of causation becomes available. BOX 2-2 Data Sources for Individual Counts of Disaster-Related Deaths Data from death certificates are the primary source used to attribute individual deaths directly, indirectly, or partially to a disaster. Death certificate information can be accessed through death investigation reports by medicolegal death investigators, state medical examiners’ and coroners’ systems, physicians and other medical certifiers completing death certificates, and from the electronic death registration systems of state and federal vital statistics departments (Noe, 2018). Access to death certificate information may be delayed during recovery operations, but a number alternative sources of mortality data exist, including funeral home records, emergency medical services scene transport records, hospital medical records, media reports and memorial websites, the Red Cross disaster-related mortality report form, and FEMA’s records of individual funeral benefits distributed (Horney, 2017). Population Estimation Methods Population estimates quantify mortality and morbidity related to a disaster at a population level through statistical analyses and epidemiological approaches to assess the size and characteristics of the population affected. The analytical approaches used to develop population estimates include surveys using representative or complex sampling of affected populations as well as estimates derived by comparing observed deaths or morbidities during the disaster time from to what was observed in a prior time frame or to a comparison population (Green et al., 2019; Kishore et al., 2018; Stephens et al., 2007). Population estimates often include a broader range of disaster-associated effects than are captured by individual counts, which frequently undercount the true number of cases, because population estimates inherently include indirect and partially attributable deaths and morbidities. Thus, for example. population estimates of disaster deaths will often be larger than the number of direct or indirect deaths captured through individual count methods, sometimes by very large margins (see Appendix C for examples). Population estimates have been developed by researchers following many previous disasters. For instance, modeling excess mortality is a statistical approach for conducting population estimates. Models of excess mortality developed following Hurricane Maria in 2017 in Puerto Rico demonstrate the value of comparing immediate all-cause mortality reported in a community during a disaster to a baseline number of deaths that would be expected in the same community from a disaster of that magnitude (Santos-Burgoa et al., 2018). Other analytical approaches, such as those used in other social science fields such as demography and anthropology can be applied to estimate the size of an affected population and develop excess mortality and morbidity estimates, particularly in situations where the affected population is hard to count. For example, mortality estimates following the 1985 Mexico City earthquake were made using a network PREPUBLICATION COPY: UNCORRECTED PROOFS

2-10 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY scale-up method to determine the estimated victim count (Bernard et al., 1991). Sampling approaches and analyses of electronic medical records and related data are especially useful for understanding the long-term, non-fatal consequences of disasters, including mental health issues (see Chapter 4). ATTRIBUTING MORBIDITY TO A DISASTER While the cause of death may sometimes be difficult to determine, mortality itself is easier to define than morbidity. Morbidities related to disasters represent an exceptionally broad range of health outcomes that span physical injuries, chronic and infectious conditions, and psychological impacts in addition to having long-term impacts on communities, health systems, and economies. The physical and mental health effects of disasters can be immediate or delayed (Adeola and Picou, 2012) and can occur as a result of the direct forces of the disaster, such as an injury, or as a result of the conditions brought forth by the disaster. The latter includes such scenarios as the interruption of mental health services or the decline of health maintenance activities (e.g., blood pressure testing or access to prescription drugs), which exacerbate existing vulnerabilities and pre-existing co-morbidities to produce additional morbidity and mortality (Borque et al., 2009; Schnall et al., 2011).2 For example, research following Hurricane Katrina found that the interruption of health maintenance activities was an indicator for additional future morbidity since up to one-half of evacuees seeking shelter in the Astrodome and Red Cross facilities lacked access to their prescription medications (Brodie et al., 2006; Greenough et al., 2008). Common disaster-related morbidities include infectious diseases (Kouadio et al., 2012), chronic diseases exacerbated by disaster conditions (Miller and Arquilla, 2008; Mokdad et al., 2005), and mental health problems, including self-harm and substance abuse, caused or worsened by exposure to intense stressors (McFarlane and Williams, 2012) (see Table 2-3 for an overview of select research on disaster-related morbidities). Further complexity is added by the fact that different disasters produce a different landscape of morbidities (Bourque et al., 2009). A major flood is not expected to produce an increase in burns and a wildfire should not generally lead to an increase in near-drownings. In disasters where large populations are temporarily displaced in close-quartered shelters, gastrointestinal and respiratory infections are likely to be prevalent (Schnall et al., 2011), while terrorist attacks are likely to result in physical and psychological trauma. Chronic diseases such as asthma, cardiovascular conditions, and diabetes and their associated co-morbidities represent a larger proportion of morbidities associated with disasters, particularly environmental disasters, in the United States (Schnall et al., 2011). What should count as a disaster-related morbidity might also be shaped by the intended uses of the data—for example, to address hospital capacity issues, to assess short- or long-term disabilities versus specialty conditions, or to evaluate the impact on the health system more broadly during the response and recovery periods. 2 See Appendix D for an exploration of the causal links across social determinants of health and disaster-related morbidity and mortality through the lens of COVID-19. PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-11 TABLE 2-3 Selected Research on Physical and Psychological Morbidities Associated with Disaster Exposure Morbidity Type Disaster Type Major Findings Carbon monoxide poisoning Hurricane Number of cases, associated with (Bourque et al. 2009) improper use of generators, peaked within 3 days of hurricane landfall. Asthma/other respiratory Terrorism Higher rates of lower-airway symptoms (Bourque et al., 2009) hyper-responsiveness among first responders to Ground Zero, possibly as a result of exposure to airborne contaminants from fires, dust, and equipment exhaust. Water- and vector-borne diseases Flood, tsunami Overcrowding and population (Watson et al., 2007) displacement coupled with disruption or lack of regular sanitation services has led to increases in diseases such as measles, hepatitis, gastrointestinal diseases, and food poisoning, etc. Depression Hurricane, terrorism Depression is considered to be (Goldman et al., 2014) one of the most prevalent disaster-related mental disorders. Substance use disorders and Terrorism Increased rates of cigarette, overdoses (Goldman et al., 2014) alcohol, and marijuana use among New Yorkers following the September 11, 2001, terrorist attacks. Heart attack Hurricane, earthquake Rates of heart attacks and other (Nakagawa et al., 2009; Swerdel cardiovascular conditions et al., 2014) elevated in the months and years following a disaster, compared with pre-disaster levels. Despite the range of information that is possible to collect, the variation in morbidity across disasters, and the ongoing lack of a standard approach for collecting morbidity data, it is likely that a group of key morbidities could be distilled across common disasters (e.g., hurricanes, tornadoes, floods, wildfires, and extreme temperature events). This standard dataset could provide a starting point for the collection of more standardized data points and approaches for data collection and analysis (see Recommendation 3-3 and Conclusion 3-6). Potential morbidities on target for consistent data collection across a range of common disasters should move beyond the definition of “significant morbidity” and be inclusive of morbidities that are known to be associated with those common socioeconomic and environmental conditions prevalent following most disasters. These include morbidities associated with mass displacement, environmental exposure, extreme stress, and lasting infrastructure damage, among PREPUBLICATION COPY: UNCORRECTED PROOFS

2-12 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY others. These data, even if imperfect and incomplete to start, could provide actionable information for disaster management policy and practice. In particular it is well documented that socially disadvantaged and underserved communities suffer disproportionally from disasters and morbidities. More research is needed on how to best mitigate these disparities both before, during, and after a disaster. To achieve this aim, research and decisive action is needed to develop a consensus around which morbidities and other appropriate indicators would be most useful to collect before, during, and after every disaster or specific types of disasters, and what analytical methods and systems should be developed or enhanced to facilitate the collection, analysis, and use of these data (Bourque et al, 2009). More discussion about gaps and potential opportunities for collecting, recording, and using morbidity data for individual counts can be found in Chapter 3. Further discussion of methods for population estimates can be found in Chapter 4. OVERLOOKED VALUE OF MORBIDITY DATA Assessing health outcomes is a critical component of improving rapid responses and recovery following a disaster through the allocation of resources and targeted public health messaging and enhancing prevention and mitigation activities during the inter-disaster period (Schnall et al., 2011). The collection and use of morbidity data, however, is an often overlooked component of the disaster management enterprise, which tends to focus on mortality as an indicator of disaster-related health impacts. When acted on appropriately, morbidity data can help to reduce mortality (i.e., by preventing morbidities from becoming mortalities) and can be used to help shape public health messaging and medical preparedness. For end users in the field of disaster management, in particular, estimates of morbidity resulting from a disaster may actually be of more value than mortality data in informing life-saving mitigation and preparedness activities and in enhancing real-time response. Therefore, an exclusive focus on mortality data, the traditional outcome of interest, at the expense of morbidity data is tantamount to focusing only on the worst case and diverts responders’ attention from efforts that could reduce human suffering and save additional lives. A recent example of the power of morbidity data to prevent additional suffering is the testing and tracking of individual COVID-19 cases throughout the world. In Singapore and Hong Kong, among others place, surveillance data were used successfully in the early months of the pandemic to identify and isolate cases in order to prevent additional mortalities and avoid a drain on medical resources, especially in intensive care units (WSJ, 2020). For hospitals, evidence- based models for quantifying expected morbidities following a disaster may be of much greater value than expected mortality models, because injuries and disease tend to consume a great deal of health system resources. Evaluation of Health System Access, Capacity, and Cost Morbidity data can be used to assess health system functions, costs, and access to care over time in order to support the shifting needs of patients with chronic conditions such as diabetes (Lee et al., 2016) during and after a disaster. For example, in the 6 years following Hurricane Katrina, Peters et al. (2014) noted a three-fold increase in the percentage of admissions to Tulane University Hospital for acute myocardial infarction (Peters et al., 2014). Another 2009 modeling study found that Hurricane Katrina also (1) had a significant effect on diabetes management, (2) had exacerbated existing racial/ethnic health disparities, and (3) had an PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-13 estimated lifetime cost of $504 million for the affected adult population (Fonseca et al., 2009). This research indicates that not only do disaster-related morbidities put pressure on local and regional health care systems but that access to care and the costs of obtaining care are significant and represent variables that could be addressed proactively with better and earlier access to descriptive morbidity data combined with data on related sociodemographic factors. Overall, the disaster management enterprise remains underinvested in understanding morbidity and how using morbidity data can contribute to saving lives, protecting health, and improving health equity, as will be discussed in Chapters 3 and 4. VALUE AND USE OF DATA ACROSS THE DISASTER LIFECYCLE Morbidity and mortality data add value to all phases of the disaster lifecycle, from the immediate aftermath of an event through the recovery and inter-disaster periods. The value of these data and how they can be used optimally will shift over time. For instance, quantifying the health impacts of a disaster can help to determine the disaster’s scale and inform resource deployment at the early stages. During later phases, the data can be used for predictive planning, risk mitigation, and other efforts to improve preparedness and strengthen public health systems to perform better during future disasters. Figure 1-1 in Chapter 1 provides a more detailed description of the use of mortality and morbidity data across the disaster lifecycle. The integration of applied epidemiology into the disaster management can provide a reliable, actionable evidence base for decision makers and other stakeholders (Malilay et al., 2014). However, extracting the value of mortality and morbidity data is dependent on the right data being collected and having the right methods and systems in place to effectively analyze and use the data. This requires determining the types of data with the most value in ensuring people’s well-being at each phase of the disaster management cycle. In terms of their functional value, mortality and morbidity data enable the Federal Emergency Management Agency (FEMA), the Department of Health and Human Services (HHS), and other federal and SLTT agencies involved in a response to (CDC, 2016): • quantify disaster health impacts and ongoing hazards; • detect and track epidemiological trends; • limit further health impacts; • ensure a common operating picture; • inform resource allocation; • shape public messaging and control rumors; • provide support to individuals and families; • target interventions and other public health responses; • monitor and evaluate the effectiveness of the response and recovery efforts; and • evaluate the effectiveness of prevention and mitigation activities and inform preparedness planning Value and Use of Data During the Response and Recovery Period The response and recovery periods include the disaster impact and its immediate aftermath and includes emergency response efforts and efforts to restore infrastructure and PREPUBLICATION COPY: UNCORRECTED PROOFS

2-14 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY services (DOI, 2020). The timing of optimal data use during the response and recovery periods depends on the type of the disaster, its severity, and the areas and populations affected. However, the primary value of data during these periods is to track the evolution of the disaster in order to save lives and limit further deaths and health consequences. For example, the HHS emPOWER program draws on data from the Centers for Medicare & Medicaid Services (CMS) to create dynamic maps, tools, and resources to identify and provide support during disasters to CMS beneficiaries who live alone and are dependent on electricity for their health care needs (ASPR, 2020). The program has been deployed successfully during Hurricane Matthew in Florida (2016) and during the severe wildfires in Los Angeles, California (2017) (Finne, 2018). Accurate real-time and historical mortality and morbidity data are critical for ensuring a common operating picture and real-time situational awareness as a disaster unfolds and recovery efforts begin. All stakeholders need reliable, accurate information with which to inform their decisions. Therefore, a lack of shared information and poor access to data among stakeholders can hinder response efforts. Although mortality and morbidity data present a significant opportunity to better target future planning, mitigation, and response efforts—particularly for vulnerable communities—the usability of these data is reliant on their consistent collection, reporting, and interpretation (see Chapter 3). Targeting Response and Recovery Efforts and Resource Allocation During the immediate response period, reliable real-time mortality and morbidity data can be used to assess and respond to the evolving current needs. For example, the data can guide the strategic allocation of resource in response to situational needs, which can help limit future morbidity and mortality. For example, the Assistant Secretary for Preparedness and Response’s Emergency Support Function #8 (ESF8)—Public Health and Medical Services offers federal support to strengthen SLTT-level disaster response capabilities. Under ESF8, the National Disaster Medical System can respond to spikes in mortality and morbidity to provide human resources in settings where health system capacity and infrastructure have been compromised by a disaster. Disaster medical assistance teams (DMATs) can help triage mass casualties and provide acute care; while disaster mortuary operational response team (DMORT) medical examiners and coroners services can provide standalone morgue operations and human remains identification services (ASPR, 2012). Spikes in mortality can provide real-time evidence for the need for support from DMATs and DMORTs. Rapid increases in initial mortality and morbidity counts could also indicate the need for specific types of health care services or resources. These initial raw data captured immediately following a disaster can also help responders predict and prepare for subsequent waves of mortality and morbidity that often occur during the following days, especially if health delivery systems and transportation infrastructure are incapacitated (Malilay et al., 2014). In addition to the use of real-time data to address current needs, in the immediate aftermath of a disaster historical morbidity data can be used to model expected trajectories and outcomes and to identify vulnerable populations. These models can provide essential details for informing the planning and execution of the response that cannot be derived rapidly from other sources. For example, prospective models based on historical data could predict whether local- level capacity is capable of continuing to manage the current threat or if state or federal resources may be needed. These approaches would be particularly valuable for determining current or future access to basic resources such as food, clean water, and energy in vulnerable communities as well as for identifying and addressing critical gaps in service delivery (Malilay et al., 2014). PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-15 For example, following Hurricane Maria in Puerto Rico, individuals requiring dialysis or diabetes medication were often unable to access them due to a lack of power and clean water and debris blocking access roads (Cordero, 2019). Financial and Emotional Support for Survivors and Families Mortality and morbidity data can also help alleviate the devastating impacts of disasters on individuals and families by guiding the provision of resources and support. Mortality data in particular have major financial implications for individuals and families seeking funeral assistance from FEMA, because one of the eligibility requirements for this support is documentation that the death occurred either directly or indirectly as a result of the disaster impact (NCHS, 2017).3 If a death was not attributed as such by the medical examiner, coroner, or other medical certifier, then the death may be ruled ineligible for funeral assistance, or assistance may be delayed (Bowden, 2018). Data are also used to determine eligibility for other types of resources and support, to guide social and medical benefits and payouts in a standardized way, to streamline assistance programs, and to reduce public confusion about how to access support. For instance, FEMA can use data following a disaster to provide timely individual and public assistance to supplement SLTT resources, such as the crisis counseling program that offers funds for mental health services to communities in disaster-affected areas as defined by the Stafford Disaster Relief and Emergency Assistance Act (Stafford Act) (FEMA, 2016; State of Michigan, 2020). Public Health Emergency Communication and Response Effective public health messaging in a disaster-affected area is shaped by accurate and timely mortality and morbidity data. Timely and clear communication following a disaster keeps the public informed about the unfolding event, protects them from ongoing hazards, and dissuades individuals from taking risks due to a lack of awareness or haste. For example, historical and real-time mortality and morbidity data can be used to identify and communicate risks to the public such as carbon monoxide poisonings, unsafe drinking water sources, fallen electrical wires, or hazardous chemical and environmental exposures (Malilay et al., 2014). These data can also be used to inform the timing and frequency of communicating the existence of digital tools and resources that are available to individuals in their immediate area or in an unfamiliar area to which they have been evacuated. For example, the communication of accessible tools like RxOpen’s real-time pharmacy information could enhance access to critical medications for these groups (RxOpen, 2020). For disaster survivors with chronic health conditions, limited access or a lack of access to essential medications and medical equipment can directly affect immediate and future health. 3 Under the Other Needs Assistance provision of the Individuals and Households Program, FEMA provides financial assistance to help with the cost of uninsured expenses for a death or interment caused directly or indirectly by a presidentially declared emergency or major disaster (see 44 C.F.R. § 206.119(c)(4)). Applicants must submit an official death certificate clearly indicating that the death was attributed to the emergency or disaster, either directly or indirectly, or a signed statement from an SLTT government licensed medical official (e.g., medical examiner or coroner) directly or indirectly attributing the death to the emergency or disaster (FEMA, 2019). PREPUBLICATION COPY: UNCORRECTED PROOFS

2-16 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY Value and Use of Data During the Inter-Disaster Period The inter-disaster period encompasses mitigation, and preparedness efforts that occur in the interim phase between the end of one disaster and the beginning of the next (Malilay et al., 2014). The core value of mortality and morbidity data during this period is to save lives and protect human health during the next disaster (Green et al., 2019). Such data can be used to identify vulnerabilities and exposure-related risk factors, to improve the allocation of resources on a regional level, and to proactively inform the design of interventions to reduce morbidity and mortality from future events (Malilay et al., 2014). In the case of human-caused disasters (such as terrorism, or climate change-related events), better data on health effects and interactions might not only mitigate disaster impacts but even help to prevent future disasters. Building Health System Capacity To support health systems in building response capacity, mortality and morbidity data can be used to predict demands on health care delivery systems, to guide resource and service usage patterns, to strengthen physical infrastructure, and to organize staff and systems to deploy during a future response (Malilay et al., 2014). Gaps in access to care and gaps along the continuum of care can also be identified to improve health systems during and between disasters. Data can be used to assess costs to the medical system at set intervals following a disaster and to perform cost-effectiveness analyses to compare the costs associated with a large-scale disaster response versus the costs of prevention, mitigation, and planning; this can help to prioritize resource allocation and investment in lower-cost planning activities (Malilay et al., 2014). Additionally, mortality data from previous disasters can inform the windows of coverage for FEMA assistance and other benefits for future disasters. Improving Policy and Practice Mortality and morbidity data can be used to muster appropriate levels of support for legislation and funding for programs, infrastructure, and resources. More accurate predictions of excess mortality from certain types of disasters could serve as powerful levers for policy change to prevent such disasters or mitigate their impacts. Furthermore, comparing actual morbidity and mortality data following a disaster to historical data for similar prior disasters can feed back into continuous improvement of preparedness policies and activities (Schnall et al., 2017). Mortality and morbidity data also can be used reflexively to evaluate and improve mortality and morbidity data collection practices. These efforts may include assessing the effectiveness of specific case definitions and data sources with respect to different health outcomes (Malilay et al., 2014) and developing institutional best practices for collecting, using, and sharing data (e.g., standardized practices for assessing morbidity and mortality). Cultivating Community Resilience Protecting the public by investing in community resilience4 is a long-term public health goal, so data should be collected with sufficient granularity to be relevant at the community level. Data can be used to strengthen community resilience to future disasters in myriad ways. In 4 Community resilience is multidimensional, spanning six types of community capital: natural/environmental, buildings and infrastructure, financial and economic capital, human and cultural capital, social capital, and political capital (NASEM, 2019). PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-17 addition to strengthening community-level health systems and emergency management infrastructure, data can be used to understand biopsychosocial and environmental conditions— including the proximal and distal influencers of disparities—that affect a community’s susceptibility to disaster-related mortality and morbidity. Mortality and morbidity data are critical for understanding and mitigating the disproportionate impacts of disasters on vulnerable populations. These populations need to be identified during the inter-disaster period so that their access and functional needs can be supported through targeted interventions and messaging (Browning et al., 2006; Feehan and Salganik, 2016; Lowe et al., 2013; Mace et al., 2018; Wolkin et al., 2015).5 For example, in discussions with community leaders, the committee learned that the response to the Camp Fire that struck Paradise, California, revealed that the city was reliant on the Butte County Health Department for access to public health data. When the county was unable to provide needed data during the disaster and recovery, Paradise officials were forced to look to the local hospital to provide data that could have been used to enhance preparedness and response capacities at the local and county levels. Furthermore, data on disaster-related mortality and morbidity are important for helping communities access resources and support services as well as for supporting fundraising and advocacy. In Florida, for example, the committee learned that data from the state’s robust women, infants, and children program showed a significant disaster-related reduction in the number of certain program participants, after a hurricane, which informed strategies to reengage with specific communities. Demonstrating the value of data collected during disasters can also help to improve transparency and build trust between emergency management and communities. Mortality and morbidity data hold important value for monitoring a community’s progress over time in the recovery period, which can last for years. Longitudinal morbidity data can be used to track long-term health sequelae of disasters, including mortality risk.6 Some of these outcomes are shaped by age, gender, and other sociodemographic factors that have been established by longitudinal studies conducted after Hurricane Katrina (Adams et al., 2011; Adeola and Picou, 2012), Hurricane Maria (Santos-Burgoa et al., 2018), and the 2004 Indian Ocean tsunami (Frankenberg et al., 2014; Ho et al., 2017). Studying relative mortality over time also allows for charting the effects of cumulative stress and chronic disease as the survivors age (Ho et al., 2017). Going forward, standardizing the collection of longitudinal mortality and morbidity data would enable comparisons of specific communities at selected points in time after incidents in order to develop common 6-month, 1-year, 3-year, and 5-year datasets, for example. Stress, posttraumatic stress disorder, depression, and anxiety are common mental health morbidities and co-morbidities after a disaster (Adams et al., 2014; Adhikari Baral and Bhagawati, 2019; Beaglehole et al., 2018; Buttke et al., 2012; Mulchandani et al., 2019; Norris et al., 2002). Using data to better understand the risk factors for these mental health sequelae could help to inform screening, prevention, and intervention efforts in communities affected by disasters (Adams et al., 2014). 5 Medically vulnerable groups include people who are hospitalized, people who have electricity grid–sensitive conditions (e.g., hypertension and diabetes) and people living with infectious diseases such as tuberculosis and AIDS (Bernard et al., 2010). Grid-sensitive conditions are especially common among people in nursing homes and people receiving home care (Banks, 2013; DeSalvo et al., 2014). Socially vulnerable groups include people in financially precarious situations who become homeless and unemployed, leading to indirect deaths by overdose or suicide, as well as local homeless populations. 6 Longer-term physical health consequences are a particular concern when a disaster causes environmental hazards that can persist for years (e.g., oil spills or radiation) (Frankenberg et al., 2014). PREPUBLICATION COPY: UNCORRECTED PROOFS

2-18 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY Ongoing Public Health Communication Another valuable use of mortality and morbidity data is in evaluating how effectively public health messaging is able to drive behavioral change and reduce risk across the disaster management cycle (Malilay et al., 2014). For instance, data on the health consequences of improper generator use could be used to shape messaging and policy in ways to mitigate similar mortality in subsequent disasters (Schnall et al., 2017). Value of Multidimensional Mortality and Morbidity Data As described throughout this chapter, mortality and morbidity data represent a wide variety of uses and values. These data, if accurate and complete, can be used to identify at-risk populations, among other uses, and respond with appropriate actions to support recovery, mitigate root vulnerabilities, and prepare to prevent future harm, which represents great value to the field of disaster management. Critically, mortality and morbidity data alone represent just one category of data and further contextualization of these data with other rich data points, such as race and ethnicity, socioeconomic status, among others, provides for a multidimensional understanding of those same mortality and morbidity data. The integration of these data represent real opportunities to identify the underlying causal pathways and subpopulation inequities existing at the intersection of the social determinants of health (SDOH), disaster exposure, and disaster-related mortality and morbidity, which could in turn allow for the improved design and targeting of resources and programs to the subpopulations in greatest need. The contextualization of morbidity and mortality using SDOH data adds additional value and evidence to foster a stronger and more responsive disaster management enterprise that prioritizes community resilience. The contributory role of SDOH on vulnerability should not be deemphasized. For example, Thomas-Henkel and Schulman (2017) write, “SDOH can account for up to 40% of individual health outcomes, particularly among low-income populations, [and] their providers are increasingly focused on strategies to address patients’ unmet social needs (e.g. food insecurity, housing, transportation, etc.).” Comorbidities are one significant consequence of these unmet social needs (Valderas et al., 2009), which add distinct complexity to the assessment and use of disaster-related mortality and morbidity data. Certain socioenvironmental factors— population density, exposure to pollution, outdoor manual labor—are known to increase biological susceptibility to disease, and those impacts seep into individual treatment decisions and care of various medical conditions (McKibben, 2020). During and after a disaster, these influences are even more pronounced. Other SDOH, such as race and economic status, are known to be associated with persistent inequities in health and are critical to examine alongside disaster-related mortality and morbidity data to provide a foundation of evidence for promoting community resilience. Excluding an assessment of SDOH in the overall assessment of post disaster mortality and morbidity but significantly limits the opportunity to prevent disaster- related mortality and morbidity; an oversight that would have especially deleterious consequences for regions with large communities of disadvantaged and underserved populations and that are also susceptible to natural disasters, such as Puerto Rico, the U.S. Gulf Coast and areas experiencing frequent wildfires (e.g., California). PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-19 RECOMMENDATIONS Mortality and morbidity data related to large-scale disasters represent a poorly tapped resource for critical information to improve the nation’s ability to respond to disasters and save lives. Fundamentally, the lack of a consistently used framework for attributing mortality and morbidity results in the inconsistent collection and reporting of data on the scope and causes of mortality and morbidity over time and across disasters. The committee’s framework responds to this critical gap and is unique in that it balances the value of both individual count and population estimation methods for developing quantitative indicators of total mortality or morbidity and provides updated individual count case definitions to characterize the level of attribution for all deaths related to disasters of all type. Recommendation 2-1: Adopt and Support the Use of a Uniform Framework for Assessing Disaster-Related Mortality and Morbidity The Department of Health and Human Services and the Department of Homeland Security, including the Office of the Assistant Secretary for Preparedness and Response, the Centers for Disease Control and Prevention, the Centers for Medicare & Medicaid Services, and the Federal Emergency Management Agency, should adopt and support the use of a uniform framework for assessing disaster- related mortality and morbidity before, during, and after a disaster by state, local, tribal, and territorial (SLTT) entities; public health agencies; and death investigation and registration systems. To implement this uniform framework nationally, the National Center for Health Statistics in conjunction with state and local vital records offices, medical examiners and coroners, medical certifiers, and all relevant professional associations should jointly adopt and apply this framework to practice, including the routine use of uniform case definitions and data collection, recording, and reporting practices. Additionally, all Stafford Act declarations should require affected states and regions to comply with the reporting requirements for individual count and population estimation approaches as described in the framework. Timely guidance should be disseminated to SLTT entities regarding the proper certification of individual deaths with provision for direct, indirect, and partially attributable deaths following a large-scale disaster. The following terminology and approaches for defining mortality and morbidity following large-scale disasters should be adopted immediately: • Total reported mortality and morbidity estimation using individual counts: Individual counts are point-in-time estimates of disaster-related mortality and morbidity derived from reported cases. o Direct death or morbidity: A death or morbidity directly attributable to the forces of the disaster or a direct consequence of these forces. o Indirect death or morbidity: A death or morbidity not from a direct impact but due to unsafe or unhealthy conditions around the time of the disaster, including while preparing for, responding to, and during recovery from the disaster. o Partially attributable death or morbidity: A death or morbidity that cannot be tied definitively to the disaster but where the disaster more likely than not has played a contributing role in the death. PREPUBLICATION COPY: UNCORRECTED PROOFS

2-20 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY o Unrelated death or morbidity: A death or morbidity that is unassociated or cannot be attributed to the forces of a disaster. • Total mortality and morbidity derived from population estimates: Population estimates are point-in-time estimates of the impact of a disaster at a population level derived using various statistical methods and tools, including sampling. Recommendation 2-2: Report Both Individual Counts and Population Estimates Both individual counts and population estimates should be used as accepted standards for reporting by state, local, tribal, and territorial entities and supported by the federal agencies as indicators of mortality and morbidity to determine the impact of disasters over time. State and federal reporting of total mortality and morbidity estimates following disasters should use both individual counts of direct and indirect deaths and population estimates of mortality and morbidity as these data become available following a disaster. Individual count data should be referred to as reported cases or reported deaths and should not be referred to as reflecting total mortality or a death toll. Total mortality estimates should be derived from population estimation methods, which provide a more complete assessment of overall impacts of large-scale disasters. REFERENCES Adams, V., S. R. Kaufman, T. van Hattum, and S. Moody. 2011. Aging disaster: Mortality, vulnerability, and long-term recovery among katrina survivors. Medical Anthropology 30(3):247–270. Adams, Z. W., J. A. Sumner, C. K. Danielson, J. L. McCauley, H. S. Resnick, K. Grös, L. A. Paul, K. E. Welsh, and K. J. Ruggiero. 2014. Prevalence and predictors of PTSD and depression among adolescent victims of the spring 2011 tornado outbreak. Journal of Child Psychology and Psychiatry 55(9):1047–1055. Adeola, F. O., and J. S. Picou. 2012. Race, social capital, and the health impacts of Katrina: Evidence from the Louisiana and Mississippi Gulf Coast. Human Ecology Review 19(1):10–24. Adhikari Baral, I., and K. C. Bhagawati. 2019. Post traumatic stress disorder and coping strategies among adult survivors of earthquake, Nepal. BMC Psychiatry 19(1):118. ASPR (Assistant Secretary for Preparedness and Response). 2012. Public health emergency—Emergency Support Function #8. https://www.phe.gov/Preparedness/planning/mscc/handbook/chapter7/Pages/emergency.aspx (accessed June 2, 2020). ASPR. 2020. HHS emPOWER program fact sheet. https://empowermap.hhs.gov/HHS%20emPOWER%20Program_Fact%20Sheet_FINAL_v9_508. pdf (accessed June 26, 2020). Banks, L. 2013. Caring for elderly adults during disasters: Improving health outcomes and recovery. Southern Medical Journal 106(1):94–98. Beaglehole, B., R. T. Mulder, C. M. Frampton, J. M. Boden, G. Newton-Howes, and C. J. Bell. 2018. Psychological distress and psychiatric disorder after natural disasters: Systematic review and meta-analysis. British Journal of Psychiatry 213(6):716–722. Bernard, H. R., E. C. Johnsen, P. D. Killworth, and S. Robinson. 1991. Estimating the size of an average personal network and of an event subpopulation: Some empirical results. Social Science Research 20(2):109–121. PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-21 Bernard, H. R., T. Hallett, A. Iovita, E. C. Johnsen, R. Lyerla, C. McCarty, M. Mahy, M. J. Salganik, T. Saliuk, O. Scutelniciuc, G. A. Shelley, P. Sirinirund, S. Weir, and D. F. Stroup. 2010. Counting hard-to-count populations: The network scale-up method for public health. Sexually Transmitted Infections 86(Suppl 2):ii11–ii15. Bourque, L., J. Siegel, M. Kano, and M. Wood. 2009. Morbidity and mortality associated with disasters. In H. Rodríquez, E. L. Quarantelli, and R. R. Dynes (eds.), Handbook of disaster research. Handbooks of sociology and social research. New York: Springer. Pp. 97–112. Bowden, J. 2018. FEMA has approved 3 percent of requests for Puerto Rico funeral aid. The Hill, September 11. https://thehill.com/homenews/administration/406169-fema-has-approved-just-3- percent-of-requests-for-puerto-rico-funeral (accessed June 26, 2020). Brodie, M., E. Weltzien, D. Altman, R. J. Blendon, J. M. Benson. 2006. Experiences of Hurricane Katrina evacuees in Houston shelters: Implications for future planning. American Journal of Public Health. 96(8):1402–1408. doi: 10.2105/AJPH.2005.084475. Browning, C. R., S. L. Feinberg, D. Wallace, and K. A. Cagney. 2006. Neighborhood social processes, physical conditions, and disaster-related mortality: The case of the 1995 Chicago heat wave. American Sociological Review 71(4):661–678. Buttke, D., S. Vagi, A. Schnall, T. Bayleyegn, M. Morrison, M. Allen, and A. Wolkin. 2012. Community assessment for public health emergency response (CASPER) one year following the Gulf Coast oil spill: Alabama and Mississippi, 2011. Prehospital and Disaster Medicine 27(6):496–502. CDC (Centers for Disease Control and Prevention). 2016. A primer for understanding the principles and practices of disaster surveillance in the united states: First edition. Atlanta, GA: Centers for Disease Control and Prevention. Combs, D. L., L. E. Quenemoen, R. G. Parrish, and J. H. Davis. 1999. Assessing disaster-attributed mortality: Development and application of a definition and classification matrix. International Journal of Epidemiology 28(6):1124–1129. Cordero, J. 2019. Session 1: Hurricane Maria in Puerto Rico. Presentation at the August 29, 2019, public meeting of the National Academies of Sciences, Engineering, and Medicine’s Committee on Best Practices for Assessing Mortality and Significant Morbidity Following Large-Scale Disasters, Washington, DC. DeSalvo, K., N. Lurie, K. Finne, C. Worrall, A. Bogdanov, A. Dinkler, S. Babcock, and J. Kelman. 2014. Using Medicare data to identify individuals who are electricity dependent to improve disaster preparedness and response. American Journal of Public Health 104(7):1160–1164. DOI (Department of the Interior). 2020. Natural Disaster Response and Recovery. https://www.doi.gov/recovery (accessed August 1, 2020). Feehan, D. M., and M. J. Salganik. 2016. Generalizing the network scale-up method: A new estimator for the size of hidden populations. Sociological Methodology 46(1):153–186. FEMA (Federal Emergency Management Agency). 2016. Crisis counseling assistance and training program guidnce. https://www.samhsa.gov/sites/default/files/images/fema-ccp-guidance.pdf (acccessed June 26, 2020). FEMA. 2019. Fact sheet—funeral assistance. https://www.fema.gov/media-library-data/1565189678667- 0fdafea4dbca363ed6380cf2ae453aa8/FACTSHEET_FuneralAssistanceFINA2019Compliant.pdf (accessed August 2, 2020). FEMA. 2020. Rumor control. https://www.fema.gov/rumor-control (accessed June 10, 2020). Finne, K. P. 2018. HHS emPOWER program: Translating research into practice. PowerPoint presentation. sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_187829.pdf (accessed June 26, 2020). Fonseca, V. A., H. Smith, N. Kuhadiya, S. M. Leger, C. L. Yau, K. Reynolds, L. Shi, R. H. McDuffie, T. Thethi, and J. John-Kalarickal. 2009. Impact of a natural disaster on diabetes: Exacerbation of disparities and long-term consequences. Diabetes Care 32(9):1632–1638. PREPUBLICATION COPY: UNCORRECTED PROOFS

2-22 A FRAMEWORK FOR ASSESSING MORTALITY AND MORBIDITY Frankenberg, E., M. Laurito, and D. Thomas. 2014. The demography of disasters. In International Encyclopedia of the Social and Behavioral Sciences (Area 3), 2nd ed. Amsterdam, The Netherlands: North Holland. Pp. 1–22. Goldmann, E., and S. Galea. 2014. Mental health consequences of disasters. Annual Review of Public Health 35(1):169-183. https://doi.org/10.1146/annurev-publhealth-032013-182435. Green, H. K., O. Lysaght, D. D. Saulnier, K. Blanchard, A. Humphrey, B. Fakhruddin, and V. Murray. 2019. Challenges with disaster mortality data and measuring progress towards the implementation of the Sendai Framework. International Journal of Disaster Risk Science 10:449–461. Greenough, P. G., M. D. Lappi, E. B. Hsu, S. Fink, Y.–H. Hsieh, A. Vu, C. Heaton, and T. D. Kirsch. 2008. Burden of disease and health status among Hurricane Katrina-displaced persons in shelters: A population-based cluster sample. Annals of Emergency Medicine 51(4):426–432. Ho, J. Y., E. Frankenberg, C. Sumantri, and D. Thomas. 2017. Adult mortality five years after a natural disaster. Population and Development Review 43(3):467–490. Horney, J. 2017. Disaster epidemiology: Methods and applications. London: Academic Press. IOM (Institute of Medicine). 2003. Medicolegal death investigation system: Workshop summary. Washington, DC: The National Academies Press. https://doi.org/10.17226/10792. Kishore, N., D. Marqués, A. Mahmud, M. V. Kiang, I. Rodriguez, A. Fuller, P. Ebner, C. Sorensen, F. Racy, J. Lemery, L. Maas, J. Leaning, R. A. Irizarry, S. Balsari, and C. O. Buckee. 2018. Mortality in Puerto Rico after Hurricane Maria. New England Journal of Medicine 379(2):162– 170. Kouadio, I. K., S. Aljunid, T. Kamigaki, K. Hammad, and H. Oshitani. 2012. Infectious diseases following natural disasters: Prevention and control measures. Expert Review of Anti-infective Therapy 10(1):95–104. Lee, D. C., V. K. Gupta, B. G. Carr, S. Malik, B. Ferguson, S. P. Wall, S. W. Smith, and L. R. Goldfrank. 2016. Acute post-disaster medical needs of patients with diabetes: Emergency department use in New York City by diabetic adults after hurricane sandy. BMJ Open Diabetes Research and Care 4(1):e000248. Lowe, D., K. L. Ebi, and B. Forsberg. 2013. Factors increasing vulnerability to health effects before, during and after floods. International Journal of Environmental Research and Public Health 10(12):7015–7067. Mace, S. E., C. J. Doyle, K. Askew, S. Bradin, M. Baker, M. M. Joseph, and A. Sorrentino. 2018. Planning considerations for persons with access and functional needs in a disaster—Part 1: Overview and legal. American Journal of Disaster Medicine 13(2):69–83. Malilay, J., M. Heumann, D. Perrotta, A. F. Wolkin, A. H. Schnall, M. N. Podgornik, M. A. Cruz, J. A. Horney, D. Zane, R. Roisman, J. R. Greenspan, D. Thoroughman, H. A. Anderson, E. V. Wells, and E. F. Simms. 2014. The role of applied epidemiology methods in the disaster management cycle. American Journal of Public Health 104(11):2092–2102. McFarlane, A. C., and R. Williams. 2012. Mental health services required after disasters: Learning from the lasting effects of disasters. Depression Research and Treatment 2012:970194. Miller, A. C., and B. Arquilla. 2008. Chronic diseases and natural hazards: Impact of disasters on diabetic, renal, and cardiac patients. Prehospital and Disaster Medicine 23(2):185–194. Mensah, G. A., A. H.Mokdad, S. F. Posner, E. Reed, E. J. Simoes, M. M. Engelgau, and Chronic Diseases and Vulnerable Populations in Natural Disasters Working Group. 2005. When chronic conditions become acute: Prevention and control of chronic diseases and adverse health outcomes during natural disasters. Preventing Chronic Disease 2(Spec No):A04. Mulchandani, R., M. Smith, B. Armstrong, C. R. Beck, I. Oliver, and English National Study of Flooding and Health Study Group. 2019. Effect of insurance-related factors on the association between flooding and mental health outcomes. International Journal of Environmental Research and Public Health 16(7):1174. PREPUBLICATION COPY: UNCORRECTED PROOFS

VALUE AND USE OF DATA 2-23 Nakagawa, I., K. Nakamura, M. Oyama, O. Yamazaki, K. Ishigami, Y. Tsuchiya, and M. Yamamoto. 2009. Long-term effects of the Niigata-Chuetsu earthquake in Japan on acute myocardial infarction mortality: An analysis of death certificate data. Heart 95(24)2009–2013. NAME (National Association of Medical Examiners). 2002. A guide for manner of death classification— first edition. https://name.memberclicks.net/assets/docs/4bd6187f-d329-4948-84dd- 3d6fe6b48f4d.pdf (accessed August 1, 2020). NASEM (National Academies of Sciences, Engineering, and Medicine). 2019. Building and measuring community resilience: Actions for communities and the Gulf Research Program. Washington, DC: The National Academies Press. https://doi.org/10.17226/25383. NCHS (National Center for Health Statistics). 2017. A reference guide for certification of deaths in the event of a natural, human-induced, or chemical/radiological disaster. Hyattsville, MD: National Center for Health Statistics. https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg01.pdf (accessed June 26, 2020). Noe, R. 2018. Chapter 5—Applications: Disaster-related mortality surveillance: Challenges and considerations for local and state health departments. In J. A. Horney (ed.), Disaster epidemiology: Methods and applications. London: Academic Press. Pp. 55–63. Norris, F. H., M. J. Friedman, P. J. Watson, C. M. Byrne, E. Diaz, and K. Kaniasty. 2002. 60,000 disaster victims speak: Part I. An empirical review of the empirical literature, 1981–2001. Psychiatry 65(3):207–239. Peters, M. N., J. C. Moscona, M. J. Katz, K. B. Deandrade, H. C. Quevedo, S. Tiwari, A. R. Burchett, T. A. Turnage, K. Y. Singh, E. N. Fomunung, S. Srivastav, P. Delafontaine, and A. M. Irimpen. 2014. Natural disasters and myocardial infarction: The six years after Hurricane Katrina. Mayo Clinic Proceedings 89(4):472–477. RxOpen. 2020. Facilities map. http://rxopen.org (accessed June 26, 2020). Santos-Burgoa, C., J. Sandberg, E. Suárez, A. Goldman-Hawes, S. Zeger, A. Garcia-Meza, C. M. Pérez, N. Estrada-Merly, U. Cólon-Ramos, C. M. Nazario, E. Andrade, A. Roess, and L. Goldman. 2018. Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: A time-series analysis. Lancet Planet Health 2(11):e478–e488. Schnall, A. H., A. F. Wolkin, R. Noe, L.B. Hausman, P. Wiersma, K. Soetebier, S. T. Cookson. 2011. Evaluation of a standardized morbidity surveillance form for use during disasters caused by natural hazards. Prehospital and Disaster Medicine 26(2):90–98. Schnall, A., R. Law, A. Heinzerling, K. Sircar, S. Damon, F. Yip, J. Schier, T. Bayleyegn, and A. Wolkin. 2017. Characterization of carbon monoxide exposure during Hurricane Sandy and subsequent nor’easter. Disaster Medicine and Public Health Preparedness 11(5):562–567. State of Michigan. 2020. Michigan awarded two federal grants to strengthen behavioral health services during COVID-19 crisis. https://www.michigan.gov/coronavirus/0,9753,7-406-98158-526521-- ,00.html (accessed June 26, 2020). Stephens, K. U., Sr., D. Grew, K. Chin, P. Kadetz, P. G. Greenough, F. M. Burkle, Jr., S. L. Robinson, and E. R. Franklin. 2007. Excess mortality in the aftermath of Hurricane Katrina: A preliminary report. Disaster Medicine and Public Health Preparedness 1(1):15–20. Swerdel, J. N., T. M. Janevic, N. M. Cosgrove, J. B. Kostis, and the Myocardial Infarction Data Acquisition System Study Group. 2014. The effect of Hurricane Sandy on cardiovascular events in New Jersey. Journal of the American Heart Association 3(6):e001354. Tzvetkova, S. 2017. Not all deaths are equal: How many deaths make a natural disaster newsworthy? Our World in Data, July 19. https://ourworldindata.org/how-many-deaths-make-a-natural-disaster- newsworthy (accessed June 28, 2020). Washoe County Regional Medical Examiner’s Office. 2020. What is the difference between manner and cause of death? https://www.washoecounty.us/coroner/faq/difference_cause_and_manner_of_death.php (accessed August 1, 2020). PREPUBLICATION COPY: UNCORRECTED PROOFS

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In the wake of a large-scale disaster, from the initial devastation through the long tail of recovery, protecting the health and well-being of the affected individuals and communities is paramount. Accurate and timely information about mortality and significant morbidity related to the disaster are the cornerstone of the efforts of the disaster management enterprise to save lives and prevent further health impacts. Conversely, failure to accurately capture mortality and significant morbidity data undercuts the nation's capacity to protect its population. Information about disaster-related mortality and significant morbidity adds value at all phases of the disaster management cycle. As a disaster unfolds, the data are crucial in guiding response and recovery priorities, ensuring a common operating picture and real-time situational awareness across stakeholders, and protecting vulnerable populations and settings at heightened risk.

A Framework for Assessing Mortality and Morbidity After Large-Scale Disasters reviews and describes the current state of the field of disaster-related mortality and significant morbidity assessment. This report examines practices and methods for data collection, recording, sharing, and use across state, local, tribal, and territorial stakeholders; evaluates best practices; and identifies areas for future resource investment.

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