Practical Applications of Disaster Epidemiology

All disasters—whether natural or human generated—have their own unique epidemiology and patterns of health outcomes on the populations they afflict. Commonly defined, epidemiology is the study of the distribution and determinants of health-related states or events in specified populations of interest, and the application of that study to managing and controlling health problems. The science of epidemiology illuminates how specific disasters generate specific expected and sometimes unexpected patterns of morbidity, mortality, and health system damage within a population and civil society. By applying epidemiological methods to crisis-affected populations, the disaster response community has embraced an evidence base for its work and, in so doing, opened the door to more data-driven response strategies and impact measures.

Disaster epidemiology has a role in every phase of the disaster cycle, from the development of prevention strategies during the preparedness phase to needs assessments during the emergency phase to the measurement of disaster response during the postimpact and reconstruction phase. Cognizance of public health effects from given disasters guides preparedness and mitigation efforts. For instance, an earthquake in a dense urban zone will likely damage health system infrastructure at a time when large numbers of the population will seek care for fractures, closed-head injuries, lacerations, and soft-tissue injuries. Often these are multiple and spatially aggregated in places where the health delivery system is destroyed. Within days, a significant number will develop wound infections and renal failure from crush injuries. Surgical services will be stressed providing wound debridement, orthopedic reduction and fixations, and multiple revisions. Long-term mental health issues and physical disabilities will arise, with the potential to disrupt a society’s economy and subsequent ability to repair itself. Advanced understanding of a specific disaster’s epidemiology at all phases can guide disaster managers and relief organizations in planning for an effective response and directing critical resources. More importantly, disaster epidemiology–guided mitigation efforts can reduce the death, disease, and injury burden potentiated by a given disaster.

Historical perspective

The practical applications of disaster epidemiology grew from potent natural disasters of the 1970s and 1980s. These events demonstrated that epidemiological methods could measure and minimize risk, assess the relief effort, describe patterns of morbidity and mortality, and suggest prevention and intervention strategies. The field evolved more fully and became better organized during the 1990 International Decade for Natural Response Reduction, as the international disaster response community, consisting of multilateral organizations and nongovernmental organizations (NGOs), embraced the contribution of disaster epidemiology in providing an evidence-based understanding of response efforts; this led, in part, to the development of standards in humanitarian practice. By the late 1990s, the Sphere Project was born. The consensus-driven initiative, now in its fourth iteration, draws on both empirical evidence and aggregated experience informed by disaster and conflict epidemiology in determining minimum standards in the health, food, nutrition, shelter, water, and sanitation response sectors. Similarly, the Active Learning Network for Accountability and Performance in Humanitarian Action (ALNAP) was established to improve performance, enhance education, and inculcate accountability within the humanitarian enterprise and, in so doing, has adopted qualitative and quantitative epidemiological tools as a means of professionalizing humanitarian response work.

During this time, the Center for Research on the Epidemiology of Disasters (CRED) established a database to track health outcomes in disasters. EM-DAT, the international Emergency Events Database, has archived and tracked more than 18,000 major disasters since 1900. This wealth of data allows donors, disaster managers, disaster researchers, and key policy makers to study comparisons across types of hazards, geopolitical contexts, and vulnerability and resilience factors.

Since the late 1990s, epidemiological methods have also been applied to conflict-affected populations to estimate both direct mortality and indirect mortality. Direct mortality stems from both the conflict’s immediate violence and its incapacitating effect on the health delivery system, such as in Kosovo and Iraq. For instance, in Kosovo, despite the expected levels of deaths due to violence, death from chronic diseases was significant, owing to the affected population’s health demographic, its health profile, and its disconnect from a health system broken by violence, insecurity, and migration. Indirect and excess mortality from preventable and readily treatable causes results from the breakdown of the public health infrastructure due to the conflict, as has been the case in prolonged but less condensed conflicts, such as that in the Democratic Republic of the Congo. In conflict settings as in other population crises, an epidemiological focus guides humanitarian public health responses to populations in dire need and targets political and aid policy makers and human rights advocates.

Current practice

Disaster epidemiology plays a role in all phases of disaster response. Evidence-based decision-making promises to protect vulnerable populations from significant morbidity and mortality if such studies are: conducted in a proactive and timely fashion, well designed to address critical questions for disaster preparedness and response, disseminated to all critical stakeholders, and brought into the preparedness and response discourse. The following types of epidemiological applications are most common in the preparedness, emergency, and response and recovery phases.

Vulnerability Analyses

The key purpose of these analyses is to identify populations at risk from all hazards in an effort to implement preparedness and mitigation strategies, as well as establish a baseline from which recovery efforts can be measured. In addition to disaster managers, a variety of stakeholders concerned with risk and vulnerability, including urban planners and insurance actuaries, rely on these studies. A more detailed discussion can be found elsewhere in this text; the brief mention here is to emphasize the relevance of a population-based science.

Vulnerability is defined as the characteristics and circumstances of a community, system, or asset that make it susceptible to the damaging effects of a hazard; hazards are dangerous phenomena, substances, human activities, or conditions that may cause loss of life, injury, or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage. In epidemiological terms, vulnerability is the physical, social, economic, and environmental factors within a population that puts it at risk from a given hazard. The population’s social sensitivity and adaptive capacity are functions of the potential public health effects of a hazard on a population.

An understanding of population vulnerability in relation to a hazard allows epidemiologists to model risk and potential outcomes, and further delineate the factors that may worsen or mitigate outcomes. For instance, retrospective analyses undertaken in postdisaster phases have identified vulnerable groups and risk factors associated with certain hazards: with extreme heat, individuals with cardiovascular disease and the elderly; with earthquakes, those with mental disorders and moderate physical disabilities; and with a variety of disasters, socioeconomic inequities, , to name a few. Population-based methods are being explored to understand disaster impacts in the interdisciplinary area of human-environment (coupled human and natural systems) research.

Local factors in the human-environment system that affect the population’s ability to absorb the shock of a hazard may include, among others, the degree of foliage degradation and deforestation; the degree of urbanization, density, and land use and development; patterns of employment and livelihoods; the quality and extent of transportation and communications networks; the quality of construction; and the quality and availability of health services delivery. On a national level, factors such as climate change risks, debt-relief policies, zoning policies, the quality of construction codes, the degree of government stability, and the adherence to the rule of law all directly contribute to a population’s vulnerability and reflect critical variables in interdisciplinary population-based public health disaster impact studies.

A population’s perception of risk and its vulnerability in relation to a hazard calls for qualitative epidemiological studies to better understand how disaster managers can customize their planning initiatives and assist their populations with more effective preparedness and response. This is in addition to readily quantifiable risk analysis and modeling. The public’s perception of risk and that of public officials have multiple dimensions and complexities, driven by subjectivity and warranting a qualitative approach. Examples include identifying the constraints of integrating climate change adaptation policies into disaster risk reduction strategies ; understanding public responses to chemical, biological, radiological, or nuclear events ; or coming to consensus on indicators of community postdisaster recovery. Such methods provide the ability to get at the underlying thought processes of all stakeholders and engage multiple disciplines in the discourse.

Rapid Needs Assessments

Rapid assessments seek to determine the magnitude of a crisis, the degree of impact on the population, the status of sector-specific population needs (food, water, sanitation, shelter, health care), vulnerable populations at particular risk, and the state of the disaster response. This requires public health providers to be on the ground characterizing and quantifying the affected population: identifying existing and potential pubic health problems; measuring present and potential impact, especially health and nutritional needs; assessing resources needed, including the availability and capacity of a local response; aiding in planning and guiding an appropriate level of external response; identifying vulnerable groups; and providing baseline data from which the public health system can be restored. Interviewing health workers, reviewing clinic records, or directly observing displaced and nondisplaced settlements and households within settlements are field techniques commonly used to collect data.

Epidemiological data can be gathered through a variety of quantitative, qualitative, and mixed-methods study designs. The critical point is to gain familiarity with the various methods and their appropriate applications. Crises tend to limit the purity of traditional study designs: insecurity, lack of baseline population data for sampling frames, and the need for rapid analysis and dissemination factor into the difficulty of gathering primary population data in these settings. Despite these limitations, crisis epidemiologists have built on a body of literature and devised commonly accepted methodologies for deriving and tracking critical indicators —the qualitative or quantitative criteria used to correlate or predict the value or measure of a program, system, or organization. Such tools inform and guide decision making during the crisis and beyond.

The interagency Standardized Monitoring and Assessment of Relief and Transitions (SMART) initiative has assisted humanitarian practitioners in developing field methodologies to generate and track two key crisis indicators: the nutritional status of children under the age of 5 years and the mortality rate of the population. These two quantitative indicators assess the magnitude and severity of a crisis and are critical in determining if conditions are improving and, by proxy, whether interventions are having an effect.

Mortality rates express the number of deaths within a population of interest per unit of time. Two mortality rates of particular interest in crises are the crude mortality rate (CMR) and the under-age-5 mortality rate (U5MR). The CMR is calculated for an entire population, whereas U5MR signifies a specific vulnerable group (the number of deaths of children under 5 years of age population in a defined time). In a sense, the U5MR is the more sensitive of the two indicators: if mortality rises in the under-5 age group, it portends a rise in overall mortality. Doubling of either or both of these mortality rates from their respective precrisis baselines signals a public health emergency that should alert crisis responders.

The CMR is given by the equation

<SPAN role=presentation tabIndex=0 id=MathJax-Element-1-Frame class=MathJax style="POSITION: relative" data-mathml='CMR=NumberofdeathsinatimeperiodTotalpopulationatmidperiod×KNumberofdaysintimeperiod’>???=Numberofdeathsin?timeperiodTotalpopulationatmidperiod×?NumberofdaysintimeperiodCMR=NumberofdeathsinatimeperiodTotalpopulationatmidperiod×KNumberofdaysintimeperiod
C M R = Number of deaths in a time period Total population at midperiod × K Number of days in time period
where K is a uniform constant by which rates or proportions can be multiplied for purposes of comparison and easy understanding, usually a multiple of 10, such as 1000, 10,000, or 100,000. CMRs are expressed during the emergency phase as deaths per 10,000 people per day, the latter unit used to capture a significant number of deaths in short periods of time if the intensity of mortality is high. As the health crisis of an impact passes into a more stable recovery period, the crude death rate is more commonly used, expressed as deaths per 1000 population per year.

Similarly, age-specific death rates such as the U5MR are used in emergencies to identify subgroups particularly at risk during a disaster and help clarify the mortality nested within the CMR. Similar to the CMR calculation, the U5MR uses the number of deaths in children younger than 5 years of age over a time interval divided by the total population of children younger than 5 years of age at the middle of the specified time period:

<SPAN role=presentation tabIndex=0 id=MathJax-Element-2-Frame class=MathJax style="POSITION: relative" data-mathml='U5MR=Numberofdeathsunder-age-5yearsinatimeperiodTotalunder-age-5yearspopulationatmidperiod×KNumberofdaysintimeperiod’>?5??=Numberofdeathsunderage5yearsin?timeperiodTotalunderage5yearspopulationatmidperiod×?NumberofdaysintimeperiodU5MR=Numberofdeathsunder-age-5yearsinatimeperiodTotalunder-age-5yearspopulationatmidperiod×KNumberofdaysintimeperiod
U 5 M R = Number of deaths under – age – 5 years in a time period Total under – age – 5 years population at midperiod × K Number of days in time period

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Aug 25, 2019 | Posted by in EMERGENCY MEDICINE | Comments Off on Practical Applications of Disaster Epidemiology
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