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FORCED MIGRATION & MORTALITY 1 Understanding Mortality Patterns in Complex Humanitarian Emergencies Charles B. Keely, Holly E. Reed, and Ronald J. Waldman The term complex humanitarian emergency is widely used to describe a particular type of disaster: a situation in which a large civilian population is affected by a combination of civil or international war, or a gross attempt to restructure the state or society (such as a genocide), leading to large-scale population displacement with accompanying deterioration of living conditions (such as food, potable water, shelter, and sanitation) creating the potential for a significant increase in mortality typically during some limited period of time, but sometimes lasting much longer. 1 Man-made complex humanitarian emergencies have existed throughout history. A small and arbitrary subset of examples includes events like the Roman attack on Carthage, the Goths' attack on Rome, and conquests by Islamic and Crusader forces. In the 20th century, complex humanitarian emergencies include the Holocaust in Europe in the 1930s and 1940s, the Bengal famine of 1943, and the murder or expulsion of the Chinese from Indonesia in the 1960s. Examples of complex humanitarian emergencies in even more recent years include wars, ethnic cleansing, forced migration, and genocide occurring in places as varied as Somalia, Bosnia, Rwanda, Kosovo, Sierra Leone, and East Timor. One justification for a detailed review of mortality in such situations is the widespread assumption among the health and assistance communi- 1 This definition is adapted from Toole and Waldman (1997). It has been somewhat modified to take a wider variety of complex humanitarian emergencies into account.
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FORCED MIGRATION & MORTALITY ties that “(t)he crude mortality rate (CMR) most accurately represents [in a single measure] the health status of emergency-affected populations ” (Toole and Waldman, 1997). Mortality is indeed a valuable event to measure in emergencies; although it refers to only one dimension, it is a useful summary measure of the scale of the crisis and its impact, as well as the performance of those working to provide aid. Mortality estimates can be highly inaccurate, but they are often better and more easily captured than other health indicators, which may be subject to different definitions and cultural interpretations. There are many other potential outcomes of complex humanitarian emergencies, including morbidity, a possible change in fertility, migration, changes in family and household structures, broader societal changes, psychological effects, and potential cultural shifts. Mortality, however, has so far been one of the most easily and accurately measured indicators in an emergency setting. Since the mid-1980s, therefore, mortality rates have become a basic indicator in complex humanitarian emergencies (Hansch, 1999). Concern for human life raises many questions about the causes, consequences, correlates, and measurement of mortality in complex humanitarian emergencies: How do mortality patterns differ in different kinds of complex humanitarian emergencies? How do mortality rates differ between refugee and internally displaced populations? How do mortality patterns differ in various types of geographic settings? How do mortality patterns differ by gender, age, or other groupings? How do mortality patterns in complex humanitarian emergencies differ from (or are similar to) “normal” mortality patterns? How does the distance traveled by refugees affect mortality? How does the length of a crisis affect mortality? How does food insecurity affect mortality? and What are the effects of various humanitarian interventions on mortality? The case studies in this volume and the collected wisdom based on several decades of relief aid in emergencies provide a good starting point for understanding mortality patterns in complex humanitarian emergencies. However, much of this knowledge is based on data collected in camp settings and must be adapted for different situations. There are still many issues that remain unresolved and many new issues that must be examined. It is also important to realize the potential policy and program
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FORCED MIGRATION & MORTALITY implications of such research. If researchers gain a better understanding of mortality patterns in emergencies and their underlying causes, then this may point to new interventions and/or improvements to current interventions that could reduce mortality in future emergencies. Many of the public health policies and recommendations that humanitarian assistance agencies use today are a direct result of the findings of research conducted in emergency settings in earlier decades. This introductory overview presents some key definitions and a crude typology of complex humanitarian emergencies, summarizes current knowledge about mortality in complex humanitarian emergencies, outlines some of the new contexts that may affect complex emergencies, and discusses how data constraints affect existing knowledge. Finally, the contents of the volume are briefly previewed and some potential next steps are presented. We have also included an appendix of five case studies of mortality patterns in complex humanitarian emergencies, compiled by Steve Hansch. The appendix further illustrates some of the points made in this paper with reference to the difficulties of obtaining even rough estimates of mortality in complex humanitarian emergencies during or immediately following a crisis when assistance needs critically depend on these estimates. It may also serve to enrich some readers' understanding of the nature of complex humanitarian emergencies. DEFINITIONS AND TYPOLOGY Definitions “A disaster may be defined as a relatively acute situation created by man-made, geophysical, weather-related, or biological events that adversely impacts on the health and economic well being of a community to an extent that exceeds the local coping capacity” (Toole and Waldman, 1997: 284). Complex humanitarian emergencies2 are distinguished from acute natural disasters because population displacement and the lack of basic services available to a migrating population result in indirect or secondary health and mortality effects to a degree not usually present in a natural disaster. The disruption of services and life generally can often be addressed with some rapidity, especially if the population remains more or less in place. The difference between a complex emergency and a natural disaster is not necessarily in the mortality rate per se. Natural disasters can result in huge loss of life as a result of earthquake, weather, 2 For the sake of brevity, the term “complex humanitarian emergency” will simply be “complex emergency” throughout the rest of the chapter.
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FORCED MIGRATION & MORTALITY or other natural causes. Complex emergencies, in addition to being caused by human beings, typically involve large-scale population displacements and the disruption of normal life to an extent that is beyond the means of typical coping mechanisms of a society. People may be displaced either within a country—internally displaced persons (IDPs)—or between one or more countries—refugees. It is the unusual and threatening conditions brought on by the disruption of society that lead to negative health and mortality consequences for such populations. The concept crude mortality rate (CMR) is discussed frequently throughout the volume, which demographers often refer to as the crude death rate (CDR). The concept denotes the number of deaths in a given period of time divided by an estimate of the population at risk of dying during that period (Shryock and Siegel, 1976). In this chapter, we will refer to the number of deaths per 10,000 population per day as the daily crude mortality rate or CMR, and to the number of deaths per 1,000 population per year as the annual crude death rate or CDR. The two expressions are convertible by multiplying the CDR (more familiar to demographers) by 36.5 to obtain the CMR (more familiar to epidemiologists working in complex emergency situations). Baseline mortality is the “normal” mortality level in a given population. Epidemiologists often refer to a “return to baseline level,” which indicates a stabilization of the situation and potential end to the mortality crisis. However, with refugee or internally displaced populations, it is often difficult to define the baseline, because the population of comparison may not be clearly defined, populations may have chronically high mortality rates due to ongoing conflict and other problems, and surveillance may have started well into the period of elevated mortality. Typology Grouping various complex emergencies into distinct categories may help emergency aid organizations to identify the types of assistance that are most likely to be needed early in a crisis. One such typology distinguishes between five types of crises based on their settings and patterns of population risk (Hansch, 1999). Rural Famine or Refugee Paradigm: This is the model on which most relief work has traditionally been based. Populations are expected to be rural, poor, and illiterate, with low vaccination coverage and high chronic malnutrition, and they are generally housed in high-density camps. Mortality is often due to communicable diseases compounded by malnutrition. Deaths generally occur disproportionately among children less than five years of age. Examples of this type of crisis include: Biafra, Nigeria,
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FORCED MIGRATION & MORTALITY in 1968; the Sahel in 1973, and Sudan, Ethiopia, and Somalia in the late 1980s and early 1990s. Ethnic Cleansing or Genocide: This type is increasingly common and is characterized by armed forces (sometimes assisted by civilians) attacking large numbers of civilians in an effort to kill or displace them. Mortality is due in large part, if not mainly, to physical injury, not communicable diseases or malnutrition. Disability and mental health trauma are other important consequences of this type of emergency. Examples of this type of emergency include: Rwanda in 1994; Bosnia in the early 1990s, and Kosovo in 1999. Urban Services Collapse or Urban Depopulation: This type of crisis occurs when generally healthy and well-nourished populations who are dependent on urban services become refugees due to war. Mortality is usually due to chronic diseases and lack of sophisticated health systems (i.e., kidney dialysis machines). This type of crisis has occurred within larger crises in Somalia, Bosnia, and Kosovo. Conflict Among Combatants: Most mortality occurs among armed combatants due to battle injuries, landmines, collateral damage, or communicable diseases associated with the effects of war. This type of emergency includes: Cambodia and Angola in the 1980s and 1990s (where landmines were a significant mortality risk) and Chechnya. Short-Onset, Short-Duration Natural Disaster: Hurricanes, tornadoes, and earthquakes can create high mortality rates at the beginning of a crisis based on physical trauma or environmental exposure. However, these types of disasters can lead to longer-term problems such as famine and disease if they are not addressed immediately. Examples include: floods in Bangladesh and earthquakes in Mexico and South America. This type of emergency is not discussed in detail in this volume because it is generally caused more by natural than political factors. Clearly, these categories are rarely completely distinct and often overlap, but may be useful in a debate about how the nature of complex emergencies are evolving over time (see below). CURRENT KNOWLEDGE Extent of the Problem Although complex emergencies have been occurring for centuries, systematic data on the numbers of forced migrants in the world have only been available for approximately the past 40 years. The number of refugees and IDPs in the world has increased dramatically during the past four decades. As Figure 1-1 shows, by the end of 1998, there were over
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FORCED MIGRATION & MORTALITY FIGURE 1-1 Global trends in refugees and internally displaced persons, 1964-1999. United States Committee for Refugees and United Nations High Commissioner for Refugees, various years. 11.6 million refugees, almost twice as many as there were 36 years ago. Yet in recent years, since about 1991, the number of refugees has generally declined, despite a brief rise in the late 1990s (United Nations High Commissioner for Refugees, 2000). Meanwhile, the number of IDPs has grown quite rapidly, reaching over 25 million by 1994, although this figure also declined slightly in the late 1990s to about 17 million by 1998. The map that follows page 18 shows that, refugees and internally displaced persons are located around the globe—in Africa, Central and South America, Eastern Europe, the Middle East, and Central and Southern Asia (United States Committee for Refugees, 2000). Due to the political nature of flows of refugees and internally displaced persons, one must acknowledge not only the effect of global and local political events, but also the willingness of states and international organizations to count persons as refugees and IDPs. This varies with circumstances; therefore, interpretations of trends in the number of forced migrants require caution. Although not every refugee or IDP in the world is currently affected by a complex emergency, complex emergencies do produce forced migrants. The number of complex emergencies has also increased over the
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FORCED MIGRATION & MORTALITY past decade. In 1989, there were 14 ongoing emergencies; in 1992, there were 17. By 1996, there were 24 ongoing complex emergencies in the world, and there were about 30 by the end of 1999 (Natsios, 1997; United States Committee for Refugees, 2000). However, the realignment of state boundaries and the creation of additional states in places such as the former Soviet Union and former Yugoslavia may have some effect on these statistics. This apparent increase in emergencies has been accompanied by a parallel increase in emergency foreign aid expenditures by the United States. In 1989, the U.S. spent $300 million in bilateral aid for foreign disasters and crises. By 1994, it was spending $1.3 billion. There is a corresponding trend in multilateral expenditures for emergency assistance. Between 1984 and 1989, for example, the World Food Program spent 25 to 40 percent of its annual assistance budget on relief activities. By 1992-1993, this was up to over 60 percent (Natsios, 1997). However, it is important to note that an increase in emergency foreign aid does not necessarily translate into an increase in overall foreign aid. Levels of Mortality In complex emergencies, the crude mortality rate (CMR) is often expressed as the number of deaths per 10,000 population per day during the acute phase of an emergency. Calculating a daily rate has been considered to be appropriate since conditions can change dramatically on a daily basis and the large base of 10,000 per day is used to express events in whole numbers. In developing countries, the median crude death rate (CDR) for the total population is 9 deaths per 1,000 per year (Population Reference Bureau, 2000). This translates into a daily rate of 0.25 deaths per 10,000. A threshold of 1.0 per 10,000 per day is widely used as the benchmark of elevated mortality, on the recommendation of the Centers for Disease Control and Prevention (1992). This threshold of 1 per 10,000 per day is equivalent to an annual CDR of 36.5 per 1,000.3 3 Throughout the rest of this chapter, the term crude death rate (CDR) will be used to refer to a rate of deaths per 1,000 population per year, while the term crude mortality rate (CMR) will be used to refer to a rate of deaths per 10,000 population per day. It should be noted, however, that although the threshold CMR of 1 death per 10,000 per day is widely used, it is unclear how elevated this really is. Mortality in the early stages is most likely to affect vulnerable groups like the chronically ill, the malnourished, and the population under five years of age. Since CMRs are calculated for the whole population, they do not show decomposition by age groups. If mortality is to a large extent confined to the under-five population, and if deaths take place within the first months after flight, then a return to baseline mortality measured as a CMR may indicate that the surviving population has achieved mortality rates lower than the pre-flight levels. For example, in Baidoa, Somalia, in 1992, about 75 percent of children under five years of age died in a six-month period and the percentage of children under five years of age in the population dropped from 18.3 percent to 7.8 percent (Moore et al., 1993). However, an occurrence like this does not change the life expectancy for survivors; it means that those who were at the greatest risk of dying have already died, and therefore the mortality rate may be lower than it was before the emergency. It may also be possible that the provision of food, shelter, sanitation, immunizations, and basic primary care may increase the life expectancy for the remaining population and therefore, result in lower mortality rates for survivors compared to their baseline experience. In any such event this must be offset by the traumatic experiences suffered by these populations during war, famine, flight, and refuge.
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FORCED MIGRATION & MORTALITY Although the CMR and CDR are essentially the same concept, there are reasons for preferring to use one rather than the other. Demographers have traditionally favored longer reference periods for demographic rates as they are generally interested in average mortality over a period of time. Epidemiologists working in emergencies, however, are interested in the “instantaneous” rate. Therefore they use the daily rate (CDR) 4 to observe rapid changes in the mortality rate which shows whether or not the situation is stabilizing. Elevated CMRs in complex emergencies vary widely. Table 1-1 , based on data in Toole and Waldman (1997) provides a dozen examples of CMRs and CDRs expressed in terms of daily rates per 10,000 and annual rates per 1,000. The table has the virtue of providing information on emergencies in different parts of the world around the same time period, as well as estimates for some of the same countries at different times and estimates for refugee populations from the same origin country in different asylum countries. The data shown in Table 1-1 indicate a daily CMR on a base of 10,000 of over 1 in all of the cases given. The range is between 1.2 in the case of Mozambicans in Malawi in June 1992 to some of the highest levels ever measured —between 19.4 to 30.9 deaths per 10,000 per day at the height of the Rwandan crisis in July 1994. The Rwandan levels, if sustained, would have meant that every refugee would have been dead in less than a year. (The level of 1,127.9 per 1,000 per year means annihilation in less than a year.) The heavy reliance on data collected from camp populations may distort understanding of the levels and trends of mortality among the total refugee and internally displaced populations. Camp populations may benefit from earlier and more effective assistance interventions that lead to a reversal of the high mortality levels associated with the emergency and result in a more quickly stabilized situation in terms of food, 4 Note that the so-called “daily” rate may not actually be a daily rate as it is often based on the average mortality experience over a number of days. It still gives a sense of the mortality levels in relatively “real time,” however.
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FORCED MIGRATION & MORTALITY TABLE 1-1 Estimated Daily Crude Mortality Rates (CMRs) and Annual Crude Death Rates (CDRs) in Selected Refugee Populations, 1990-1994 Date Asylum Country Origin Country Daily CMR a Annual CDR b July 1990 Ethiopia Sudan 2.3 84.0 June 1991 Ethiopia Somalia 4.6 167.9 March-May 1991 Turkey Iraq 4.1 149.7 March-May 1991 Iran Iraq 2.0 73.0 March 1992 Kenya Somalia 7.3 266.5 March 1992 Nepal Bhutan 3.0 109.5 June 1992 Bangladesh Burma 1.6 58.4 June 1992 Malawi Mozambique 1.2 43.8 August 1992 Zimbabwe Mozambique 3.5 127.8 December 1993 Rwanda Burundi 3.0 109.5 August 1994 Tanzania Rwanda 3.0 109.5 July 1994 Zaire Rwanda 19.4-30.9 708.1-1,127.9 a Expressed as deaths per 10,000 per day. b Expressed as deaths per 1,000 per year. Source: Toole and Waldman (1997: Table 2) shelter, sanitation, and other basic needs. On the other hand, camp situations may increase the risk of subsequent mortality due to infectious diseases. Although delivering assistance in camps may be more manageable for providers, it may not be more effective for recipients. Under certain circumstances, self-settlement among a host population may be more effective (Van Damme, 1995). Complex emergencies rarely continue indefinitely. In most cases, international organizations, national governments, nongovernmental organizations, and others intervene to provide some stability for the refugee population, and minimal services are aimed at reduction of mortality, reduction of morbidity, and other threats to life. What the data in Table 1-1 underscore is that the acuteness of the challenge, as indicated by CMRs, varies enormously from situation to situation. Internally displaced persons, who often face the same difficult survival conditions as refugees who have crossed an international border, also face the prospect of elevated mortality. Because of considerations of sovereignty and the absence of international agreements about the provision of protection and assistance to victims of persecution and war who remain in their own country, internally displaced persons are less likely to receive international assistance that might meet survival needs and provide a modicum of stability. Although mortality data on internally displaced populations are scarce, most of the situations for which data are available display very high mortality rates. As shown in Table 1-2 , the
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FORCED MIGRATION & MORTALITY TABLE 1-2 Estimated Daily Crude Mortality Rates (CMRs) and Annual Crude Death Rates (CDRs) Among Internally Displaced Persons, 1990-1994 Date Country Daily CMR a Annual CDR b January-December 1990 Liberia 2.3 84.0 April 1991-March 1992 Somalia (Merca) 4.5 164.3 April-November 1992 Somalia (Baidoa) 16.7 609.6 April-December 1992 Somalia (Afgoi) 5.4 197.1 April 1992-March 1993 Sudan (Ayod) 7.6 277.4 April 1992-March 1993 Sudan (Akon) 4.5 164.3 April 1992-March 1993 Bosnia (Zepa) 1.0 36.5 April 1993 Bosnia (Sarajevo) 1.0 36.5 May 1995 Angola (Cafunfo) 8.2 299.3 February 1996 Liberia (Bong) 5.4 197.1 a Expressed as deaths per 10,000 per day. b Expressed as deaths per 1,000 per year. Source: Adapted from Toole and Waldman (1997: Table 3). CMR in Baidoa, Somalia, in 1992 was almost 17 deaths per 10,000 per day and in both Sudan in 1992-1993 and Angola in 1995, it was over 7 per 10,000 per day. Crude mortality rates among Muslims in Bosnia during the war in 1993 were about four times the baseline level (Toole et al., 1993). Stages of a Crisis The data in Table 1-1 highlight the degree to which mortality can rise in crisis situations but reveal nothing about patterns of mortality over the various stages of a particular complex emergency. Each complex emergency is typically different from the last: different logistics, different politics, different social context, etc. However, some generalizations are possible. Figure 1-2 shows the classic rural famine/refugee paradigm pattern, which is a refinement of an inverted U-shaped pattern. Note the sharp increase at the beginning of the crisis (Phase 1), followed by the peak mortality rate (Phase 2) and then a relatively rapid decline (Phase 3), and stabilization (Phase 4). These distinctions should be based not so much on absolute measurements, but on patterns. In other words, in an emergency, population parameters—including mortality—may be quite unstable—either fluctuating or rapidly changing due to interventions or other reasons. The post-emergency phase is usually marked by more stable mortality rates, even though they might remain unacceptably el-
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FORCED MIGRATION & MORTALITY FIGURE 1-2 Model of mortality change in a forced migration situation. Source: Reed et al., 1998, Figure 2. evated. However, stabilization is what signals the time to shift programming from life-saving interventions to longer-term ones. Typically, the period of flight and the time immediately after arrival in a place of asylum are the periods of highest mortality. In 1992, in Chambuta camp, Zimbabwe, for example, Mozambican refugees who had been in the camp for less than one month had a CMR of 8 per 10,000, which was four times that of those who had been in the camp for one to three months and 16 times the baseline (Centers for Disease Control and Prevention, 1993a). In Goma, Zaire, among Rwandan refugees, the average daily CMR from July 14 to August 14, 1994, was between 19.5 and 31.2 per 10,000. This was more than 30 times the baseline rate (Goma Epidemiology Group, 1995). The rate at which mortality rates decline varies across populations, and the speed of mortality reduction also depends on the rates of mortality and/or out-migration of specific groups at high risk for mortality. For example, the initial high mortality rates of Cambodian refugees in Thailand in 1979 declined to baseline levels in about one month (Toole and
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FORCED MIGRATION & MORTALITY Increasing Attention to the Quality of Relief Another change in the context of complex emergencies has to do with setting standards for assistance and protection. International organizations and NGOs have been working to create and implement regulations for relief aid, especially under the Sphere Project. 8 Many NGOs have joined this project, which focuses on setting minimum standards for aid, training workers to implement these standards, evaluating assistance programs, and creating accountability. These new trends are helping to ensure that the level and quality of assistance provided in emergency settings is monitored (International Federation of the Red Cross and Red Crescent Societies, 1998). And standards are critical in continued work to reduce morbidity and mortality in crisis settings. These steps are crucial in the new climate of reduced foreign aid funding. Emergency aid is still high—almost three times its 1990 level— but within a context of an overall decrease in development aid, crises continue to flourish (International Federation of the Red Cross and Red Crescent Societies, 1998). Furthermore, NGOs are under continuous pressure to prove that their funds are being put to work in an efficient and effective manner to save lives. A Growing Appreciation of Information Needs The need for further research and information on complex emergencies is now becoming quite clear to many NGOs, international agencies, states, donors, and scholars. Several universities around the world have established centers for research and training on how to deal with crisis situations. NGOs are forming partnerships with these centers to create standards, evaluate their own work, and learn new ways to implement relief efforts more effectively. Much is already known about mortality in complex emergencies, but that knowledge is not complete. Much remains unknown about complex emergencies and issues surrounding appropriate responses, including ethical issues, management of specific diseases, and understanding how to treat specific populations. Reproductive health and mental health are 8 “The Sphere Project was launched by a group of humanitarian agencies . . . to develop a set of universal minimum standards in core areas of humanitarian assistance. The aim of the Project is to improve the quality of assistance provided to people affected by disasters and to enhance the accountability of the humanitarian system in disaster response” (Sphere Project, 2000).
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FORCED MIGRATION & MORTALITY two of the most important areas in need of further study (Waldman and Martone, 1999). In order to improve understanding, response, and assistance to forced migrants, research and collaboration must continue. The case studies in this volume are an example of a step towards more and better knowledge of mortality in complex emergencies. DATA ISSUES While the international assistance community is confident about the general course of responses to complex emergencies to reduce mortality and is beginning to understand the new contexts for complex emergencies, much more still needs to be known about mortality levels and trends and measurement of them. Why is it important to focus on collecting good mortality data in emergencies? Approximate data are generally sufficient for preliminary assessment of a crisis situation and for mobilizing public support and resources. However, as situations develop, the need for more precise data increases. Relief workers must be able to better estimate the population's needs and evaluate their own performance to ensure the best quality relief and the least morbidity and mortality (Reed et al., 1998). Nevertheless, excess mortality data are often the result of crude attempts to obtain approximate estimates. In addition to the generally difficult conditions for data collection in ongoing emergencies, there are a number of other issues that hinder the development of more reliable, comparative data on mortality that would improve the analysis and understanding of trends in demographic processes among forced migrants caught in complex emergencies. As Figure 1-9 (in the Appendix) and the appendix illustrate, even after crises end there is continued uncertainty about the total excess mortality during the crises. Uniform Protocols for Data Collection Relief workers, particularly medical personnel, seek the baseline information needed to respond to the most pressing health problems and develop monitoring systems. However, in the chaotic situation of a complex emergency, these systems are often poorly coordinated and sometimes even duplicate information. Many times, there is no general agreement or protocol on what data to collect or the appropriate methods to follow to ensure quality, interpretation, and comparability in order to assess the severity of problems and to provide markers for assessing progress over time. Field personnel need better systems of data collection to generate the information they need to plan, even in rudimentary ways, their response to the specific problems of a complex emergency. It is not that data collection as it is currently done is without merit. Much knowl-
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FORCED MIGRATION & MORTALITY edge of mortality in complex emergencies results from such data collection by the medical community. However, questions of quality and comparability retard efforts to accumulate a body of knowledge that would facilitate sophisticated analysis of the determinants and pace of mortality change under stressful situations. Denominators The estimation of a population at risk in the construction of any demographic rate seems deceptively simple, but unfortunately it is often wrong and/or the result of compromise. In emergency situations, the basic estimation of the total number of refugees, which is needed to construct even a crude mortality rate, is elusive. The difficult conditions of emergency situations can make producing even rudimentary estimates an extreme challenge (see appendix). In addition, there are several specific reasons for population overestimation in crises. The leaders of displaced persons may try to hide those who are not legitimate refugees (those who have been involved in war crimes or military operations) in with the rest of the displaced population and thus inflate the numbers. Refugees may also try to register themselves more than once in order to gain more food rations. Out-migrations and deaths may also be underreported for the same reasons. When refugees are located within a host country community, local residents may register as refugees in order to obtain food and medical aid. Finally, refugee events are quite fluid and change rapidly. On the other hand, refugee and displaced populations may be underestimated for a variety of reasons. Refugees who are self-settled among local populations may be difficult to count because they are hidden or continue to be on the move. If relief workers are not permitted access to the populations, then they are likely to misestimate their numbers. Those who are sick, impoverished, or malnourished may be hidden or cut off from the rest of the group and therefore not counted. Thus, estimates of the same events taken from different sources often vary greatly. Perhaps the most familiar example of this is the different population estimates of the Rwandan refugees in Goma, Zaire, in 1994. Estimates from different agencies and NGOs ranged from 500,000 to 800,000, making it impossible to determine the mortality rate with any accuracy (Goma Epidemiology Group, 1995). In many situations, therefore, even if there is confidence in the estimated number of deaths, it is not a foregone conclusion that one can estimate the mortality rate with any confidence in the result (see also the case studies on Afghanistan and North Korea in the appendix).
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FORCED MIGRATION & MORTALITY Composition of Denominators Even more demanding than estimating the total population is obtaining information on the composition of a given population. Of special interest in the study of mortality is age composition because of the vulnerability of children less than five years of age in developing countries. This age group is vulnerable even under normal circumstances, but much more so in situations of conflict, violence, and displacement (Davis, 1996). The age composition of a refugee population can have very important effects on the crude mortality rate. A population with a higher proportion of young children and elderly (like many developing country populations) will probably have a higher crude mortality rate than a population with a middle-aged distribution, because children under five years of age will probably experience higher mortality rates. Whether mortality is “excess” or not and why mortality is “elevated” are both a function of a population's age composition. This is a problem in many emergencies because only crude mortality rates are collected and therefore nothing is known about age- and sex-specific mortality rates. Even when age-specific mortality rates are known, they are generally only broken down into two categories: children under five years of age and others, which does not permit careful analysis. Thus, age composition may explain some “excess” mortality. However, as noted above, if large proportions of children under the age of five, who may be over-represented in the refugee population, die in the early stages of a complex emergency, then the converse of excess mortality may occur. It is possible that the remaining population might appear to have lower than normal mortality because of the age composition of the surviving population. Heavy loss of vulnerable populations in an acute phase of an emergency, followed by the availability of assistance (including some assistance elements, such as vaccinations, that may not normally be available), may result in mortality levels for survivors that are significantly lower than those in the pre-emergency situation. There are various scenarios about the effects of early deaths on different groups within various populations that may affect subsequent mortality patterns. What is lacking is systematic data on these situations and analysis of mortality patterns within different populations. In short, mortality rates in all emergencies should be standardized for age and sex, which requires some ability to decompose the population by these characteristics. If this is not done, then reliance on crude mortality rates as the major indicator of the severity of a complex emergency can lead to incorrect conclusions.
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FORCED MIGRATION & MORTALITY Collecting Mortality Data There are many ways to collect mortality data in refugee settings, including burial site surveillance, collecting information from hospital and burial records, community-based reporting systems, and population surveys. However, none of these methods is flawless. Some of the reasons why data may be inaccurate are: Poorly representative population sample surveys; Failure of families to report all deaths for fear of losing food ration entitlements; Inaccurate estimates of affected populations for the purpose of calculating mortality rates; and Lack of standard reporting procedures (Toole and Waldman, 1997: 287). Mortality rates are often underestimated because of deaths being undercounted and populations being overestimated. Secure and well-organized refugee camps seem to have generally produced the best estimates, while it is very difficult to get good mortality data on scattered populations and internally displaced persons. Mortality may be skewed in one direction or another because those with the highest risk of death are drawn to camps where there is food and medical attention or because those with the highest risk of death are in areas with the least access to the relief aid (Toole and Waldman, 1997). It is very difficult to compare mortality survey results from different settings because of the huge variation in methods. In Somalia between 1991 and 1993, 23 field surveys were found to have extreme differences in populations, sampling methods, units of analysis, computation of rates, and analysis techniques (Boss et al., 1994). However, it is hoped that efforts like the Sphere Project will increase the comparability between data from different settings. Sampling Sampling is the process whereby researchers determine a subset of the population under study from which to collect data that will hopefully be representative of the entire population. If the sample is properly drawn, then one should be able to make inferences about a population based on the characteristics of a sample. Although sampling is already a challenging enterprise under normal circumstances, in complex emergencies and refugee settings, it becomes even more difficult. In addition to “normal” issues that may bias the sample, because the total population is
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FORCED MIGRATION & MORTALITY often unknown and unreachable, it is very difficult to obtain a representative and unbiased sample in an emergency setting. Again, the major bias of current knowledge of demographic processes among refugee and internally displaced populations is the heavy reliance on information gathered from populations in camps. This is because of the relative ease of sampling and collecting data in a camp setting, where the sampling frame, or total population, is known, or area samples of a confined population are used. However, some scholars have argued that over 60 percent of Africa's refugees do not reside in camps; they live among the population in host countries (Harrell-Bond, 1994; Van Damme, 1995). What is known about refugee mortality may not hold true for noncamp populations. The reality is that the potential differences between these two populations are unknown because most information comes from camp situations where refugees are collectively aided by relief and protection agencies. Furthermore, because estimates of the total size of a refugee population are so difficult to obtain, any attempts to sample from this more or less unknown universe become problematic. Sampling can move from a concern with population parameters to sampling geographically or spatially. In camp settings, such approaches have been implemented by dividing space into coordinated blocks and collecting data within specific blocks or sampling areas (Médecins Sans Frontières, 1997). In non-camp settings, such techniques are less useful unless one has knowledge of the spatial distribution of refugees among the host population. Therefore, other nonrandom sampling methods, such as snowball sampling, where one finds one refugee who then identifies other refugees to be included in the sample, must be used. However, these types of sampling techniques often mean that some refugees and demographic events, like deaths, may be missed. Recall The effect of the experience of a complex emergency on people's ability to recall events, and whether it is more of an issue than in normal situations, is unknown. Depending on specific cultural beliefs about death, psychosocial trauma, and other issues, it may be quite difficult to get an accurate estimate of mortality based on a population survey in an emergency setting. The impact of recall on monitoring and surveillance is not trivial because it is important in trying to develop baseline parameters. Issues of data quality, interpretation, and methodology are not limited to those mentioned above. These are examples of issues that come up again and again in discussions of mortality patterns in complex emergencies. In many published papers, there is only a brief allusion in the form
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FORCED MIGRATION & MORTALITY of a caveat for interpretation of data. Progress in understanding levels, trends, patterns, determinants, and consequences of mortality in complex emergencies requires attention to these technical issues from demographers, epidemiologists, and statisticians. Although the issues may seem sterile and esoteric, they have a large impact on what is known and consequently how relief workers are likely to respond to crisis situations. OVERVIEW OF THE VOLUME This introduction covers a broad amount of territory about information on mortality in complex emergencies and related data issues. It provides a basic overview of the state of knowledge, the gaps that need attention, and aspects of the social and operational situation that affect data collection, interpretation, and application. The case studies in this volume look at the specific examples of Rwanda, Kosovo, North Korea, and Cambodia. These case studies are drawn from four different regions and examine four different types of crises. They try to provide a best estimate of what we know but also illustrate concretely the issues reviewed above and the need for progress in the knowledge base used to address complex emergencies. In the first case study, Dominique Legros, Christopher Paquet, and Pierre Nabeth describe the flight of Rwandan refugees into the forests of Eastern Zaire (now the Democratic Republic of the Congo) and discuss mortality at various stages of the forced migration that occurred following the 1994 genocide. Using a combination of surveillance systems and retrospective mortality surveys, they estimate mortality rates for the same refugee population at four different points in time and in four different geographic locations. The pattern that emerges is quite disturbing; by the final estimation, only about 20 percent of the original refugee population remained and the rest were either dead or missing. The authors also discuss the merits and drawbacks of both mortality estimation methods. Brent Burkholder, Paul Spiegel, and Peter Salama examine these same methods—surveillance and retrospective surveys—in an entirely different population: Albanian Kosovar refugees in March to June 1999. One set of data was collected from surveillance systems that were operational in refuge areas in Albania and the Former Yugoslav Republic of Macedonia (FYROM) during the refugee crisis. The second data set was collected in Kosovo in September 1999, after the majority of the refugees had returned home. The authors compare and contrast the results of these two efforts and find that overall mortality in the Kosovo crisis was relatively low. The different nature of the populations and the crisis in a more developed region raises several methodological issues about esti-
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FORCED MIGRATION & MORTALITY mating mortality, such as the importance of chronic diseases, reproductive health, and psychosocial trauma. In the third case study, Court Robinson, Myung Lee, Ken Hill, and Gilbert Burnham use indirect estimation techniques to estimate mortality rates among an isolated population suffering from famine: North Korea. By interviewing North Korean migrants who crossed the border into China in search of food about their own household experiences and the experiences of a sibling, nonmigrant household, they were able to estimate mortality rates. Although the sample is not representative, the study gives insight into what is happening inside North Korea. The final case was not originally presented at the workshop, but commissioned afterwards. Patrick Heuveline describes a variety of data sources and techniques that can be used to estimate the total excess mortality during the Cambodian crisis of 1975 to 1979. Survey and census data are discussed, but ultimately the focus is again on indirect estimation techniques, including demographic projection methods to attempt to estimate total excess mortality and decomposition methods to get at age- and cause-specific mortality. Finally, in his reflections on the four case studies, Manuel Carballo ponders the difficulty and necessity of collecting statistics in emergency situations. He reminds practitioners and researchers alike that each crisis is a unique event and must be understood not only on the basis of its similarities to other events, but on the basis of its specificity. NEXT STEPS What are some potential topics for future research on these issues? There are many issues that researchers and practitioners should examine as they continue to work on understanding mortality patterns in complex emergencies: Increase and improve the collection of data by age, sex, and other characteristics in complex emergencies to enhance our understanding of mortality patterns for population subgroups; Examine mortality patterns by age group and compare these patterns to those of populations that are not in crisis; Improve techniques for the evaluation of humanitarian interventions by NGOs and other aid organizations; Improve our understanding of the long-term consequences of complex emergencies on morbidity and mortality, including psychosocial and reproductive health; and Document, compare and validate methods for rapid assessment techniques in emergencies.
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FORCED MIGRATION & MORTALITY These are only a few of the potential research and data needs for learning more about mortality in complex emergencies. The volume signifies an increased appreciation of the need for data and the shallowness of the knowledge base about demographic processes among displaced populations. In addition to continued research on mortality, topics such as information on reproductive health and fertility and mental illness are beginning to be studied in forced migrant populations. Perhaps with a new appreciation of the utility of this information, more attention will be given to improving the quality of research on refugees and IDPs. With improved data and analysis, policies and programs can be created and adjusted accordingly to best assist forced migrants in each situation. REFERENCES Abiri, E. 2000 The securitisation of migration . Unpublished Ph.D. dissertation, Department of Peace and Development Research, Gothenburg University . Boss, L.P., M.J. Toole, and R. Yip 1994 Assessments of mortality, morbidity, and nutritional status in Somalia during the 1991-1992 famine. Journal of the American Medical Association 272:371-376. Centers for Disease Control and Prevention 1990 Update: Health and nutritional profiles of refugees—Ethiopia, 1989-1990 . Morbidity and Mortality Weekly Report 39 : 707-709, 715-718 . 1991 Public health consequences of acute displacement of Iraqi citizens, March-May 1991 . Morbidity and Mortality Weekly Report 40(26) : 443-446 . 1992 Famine-affected, refugee, and displaced populations: Recommendations for public health issues . Morbidity and Mortality Weekly Report 41 : RR-13 . 1993a Mortality among newly arrived Mozambican refugees: Zimbabwe and Malawi, 1992 . Morbidity and Mortality Weekly Report 42 : 468-469 , 475-477 . 1993b Status of public health: Bosnia and Herzegovina, August-September 1993 . Morbidity and Mortality Weekly Report 42 : 973, 979-982 . Coale, A.J. , P. Demeny , and B. Vaughan 1983 Regional Model Life Tables and Stable Populations , 2nd edition . San Diego, CA : Academic Press, Inc. Cohen, R. 1998 Recent trends in protection and assistance for IDPs . Pp. 3-9 in J. Hampton, ed., Internally Displaced People: A Global Survey . London : Earthscan Publications, Global IDP Survey, and Norwegian Refugee Council . Davis, A.P. 1996 Targeting the vulnerable in emergency situations: Who is vulnerable? Lancet 348 : 868-871 . Elias, C.J. , B.H. Alexander , and T. Sokly 1990 Infectious disease control in a long-term refugee camp: The role of epidemiologic surveillance and investigation . American Journal of Public Health 80(7) : 824-828 . Goma Epidemiology Group 1995 Public health impact of Rwandan refugee crisis. What happened in Goma, Zaire, in July 1994? Lancet 345 : 339-344 .
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FORCED MIGRATION & MORTALITY Hansch, S. 1999 The Evolution of Mortality Patterns in Complex Emergencies . Unpublished paper presented at Workshop on Mortality Patterns in Complex Emergencies, National Academy of Sciences, November 19, 1999, Washington, D.C. Harrell-Bond, B. 1994 Pitch the tents . The New Republic September 19-26 . International Federation of the Red Cross and Red Crescent Societies 1998 World Disasters Report 1998 . Oxford, UK : Oxford University Press . Jean, F., ed. 1993 Life, Death, and Aid: The Médecins Sans Frontières Report on World Crisis Intervention . London and New York : Routledge . Kushner, T. , and K. Knox 1999 Refugees in an Age of Genocide: Global, National and Local Perspectives during the Twentieth Century . London : Frank Cass . Médecins Sans Frontières 1997 Refugee Health: An Approach to Emergency Situations . London : Macmillan . Moore, P.S. , A.A. Marfin , L.E. Quenemoen , B.D. Gessner , and Y.S. Ayub 1993 Mortality rates in displaced and resident populations of Central Somalia during the famine of 1992 . Lancet 341 : 935-938 . Natsios, A.S. 1997 U.S. Foreign Policy and the Four Horsemen of the Apocalypse: Humanitarian Relief in Complex Emergencies . Westport, CT : Praeger Publishers and the Center for Strategic and International Studies . Newland, K. 1999 The decade in review . Pp. 14-21 in World Refugee Survey 1999 . Washington, D.C. : Immigration and Refugee Services of America . Noji, E.K., ed. 1997 The Public Health Consequences of Disasters . New York : Oxford University Press . Person-Karell, B. 1989 The relationship between child malnutrition and crude mortality among 42 refugee populations . Unpublished master's thesis . Atlanta, GA : Emory University . Population Reference Bureau 2000 World Population Data Sheet . Washington, DC : Population Reference Bureau . Reed, H. , J. Haaga , and C. Keely, eds. 1998 The Demography of Forced Migration: Summary of a Workshop . Washington, DC : National Academy Press . Rogers, R. , and E. Copeland 1993 Forced Migration: Policy Issues in the Post-Cold War World . Medford, MA : Tufts University . Shryock, H.S. , J.S. Siegel, and associates 1976 The Methods and Materials of Demography . Condensed edition by Edward G.Stockwell . San Diego, CA : Academic Press . Sphere Project 2000 Humanitarian Charter and Minimum Standards in Disaster Response . [Online]. Available: http://www.sphereproject.org [December 19, 2000]. Stein, J.G. 2000 New challenges to conflict resolution: Humanitarian nongovernmental organizations in complex emergencies . Pp. 383-419 in International Conflict Resolution After the Cold War . Committee on International Conflict Resolution , Paul C. Stern and Daniel Druckman, eds. Washington, DC : National Academy Press .
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FORCED MIGRATION & MORTALITY Toole, M.J. , and R. Bhatia 1992 A case study of Somali refugees in Hartisheik A camp, eastern Ethiopia: Health and nutrition profile, July 1988-June 1989 . Journal of Refugee Studies 5 : 313-326 . Toole, M.J. , S. Galson , and W. Brady 1993 Are war and public health compatible? Lancet 341 : 935-938 . Toole, M.J. , R.J. Steketee , R.J. Waldman , and P. Nieburg 1989 Measles prevention and control in emergency settings . Bulletin of the World Health Organization 67 : 381-388 . Toole, M.J. , and R.J. Waldman 1988 An analysis of mortality trends among refugee populations in Somalia, Sudan, and Thailand . Bulletin of the World Health Organization 66(2) : 237-247 . 1990 Prevention of excess mortality in refugee and displaced populations in developing countries . Journal of the American Medical Association 263 : 3296-3302 . 1997 The public health aspects of complex emergencies and refugee situations . Annual Review of Public Health 18 : 283-312 . United Nations High Commissioner for Refugees 2000 Refugees and Others of Concern to UNHCR: 1999 Statistical Overview . Geneva : Registration and Statistical Unit, Programme Coordination Section, United Nations High Commissioner for Refugees . United States Committee for Refugees 2000 World Refugee Survey 2000 . Washington, DC : Immigration and Refugee Services of America . Van Damme, W. 1995 Do refugees belong in camps? Experiences from Goma and Guinea . Lancet 346 : 360-362 . Wæever, O. , B. Buzan , M. Kelstrup , and P. Lemaitre 1993 Identity, Migration, and the New Security Agenda in Europe . New York : St. Martin's Press . Waldman, R. , and G. Martone 1999 Public health and complex emergencies: New issues, new conditions . American Journal of Public Health 89(10) : 1483-1485 . Watson, F. , I. Kulenovic , and J. Vespa 1995 Nutritional status and food security: Winter nutrition monitoring in Sarajevo, 1993-1994 . European Journal of Clinical Nutrition 49 : S23-S32 . World Bank 1991 World Development Report 1991: The Challenge of Development . New York : Oxford University Press . 1992 World Development Report 1992: Development and the Environment . New York : Oxford University Press . 1994 World Development Report 1994: Infrastructure for Development . New York : Oxford University Press . 1995 World Development Report 1995: Workers in an Integrating World . New York : Oxford University Press . 1996 World Development Report 1996: From Plan to Market . New York : Oxford University Press .
Representative terms from entire chapter: