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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence INTRODUCTION Fertility and reproductive health issues more broadly have tended to be of low priority in humanitarian crises (Wulf, 1994; Palmer et al., 1999). Public attention is drawn by information concerning the magnitude of refugee flows, of death tolls, and of numbers of injuries. Reproductive health has been regarded as a longer term issue that could safely be put on the back burner during the crisis phase of an emergency, when issues of providing adequate food, clean water, and shelter, plus treating acute infectious diseases of crowding, take priority. When reproductive health has been a priority, attention has focused on issues around sexually transmitted diseases and sexual violence. Although these are entirely appropriate priorities, little attention has been paid to the consequences for fertility, the major determinant of medium-term population dynamics, of humanitarian crises. The number of studies of fertility in refugee or displaced person populations has been very limited (Hynes et al., 2002). In an important change for the international community, the International Conference on Population and Development (ICPD) in 1994, with its increased emphasis on human rights and reproductive health, explicitly included reference to reproductive health of both internally displaced persons and refugees and asylum seekers (United Nations, 1994). As a result of the increasing concern for the circumstances of refugees to which the ICPD recommendations were responding, more studies have become available in
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence the past few years about fertility and reproductive health in displaced populations. However, concepts, data collection methods, and analytical methods vary widely, as do the circumstances of humanitarian crises (used broadly here to cover war, population displacements, famine, natural disasters, etc.), making comparisons and generalizations difficult. The purpose of this paper is to review what evidence there is concerning the effects of humanitarian crisis on fertility, with a view to identifying common patterns that may exist across settings and be of value in guiding responses to future crises. I will start by adapting a conceptual framework for this purpose and reviewing data collection strategies and analyses. CONCEPTUAL FRAMEWORK The intermediate variables framework provides an appropriate structure for examining the effects of population displacement on fertility. As proposed by Davis and Blake (1956), reproduction is determined by three necessary processes: intercourse, conception, and parturition. As the authors state it: “In analyzing cultural influences on fertility, one may well start with the factors directly connected with these three processes. Such factors would be those through which, and only through which, cultural conditions can affect fertility” (authors’ emphasis, p. 211). Whereas Davis and Blake were interested in cultural factors, of interest here is the impact of humanitarian crises and population displacement, but the principle is the same: change in the intermediate variables is necessary and sufficient for fertility change, so any effect of crisis must work through one or more of these variables. Within each process, Davis and Blake identify the following variables (modified slightly for present purposes): Process 1: Intercourse Formation and dissolution of unions: age at entry into sexual unions, permanent celibacy, and amount of reproductive time spent between or after unions as a result of divorce, separation, desertion, or widowhood. Exposure to intercourse within unions: voluntary abstinence, involuntary abstinence including temporary separations, and coital frequency. Exposure to intercourse outside unions: coercive sex, commercial sex.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence Process 2: Conception given intercourse Involuntary infecundity (starvation, disease). Use or nonuse of contraception. Voluntary temporary infecundity (breast-feeding). Voluntary permanent infecundity (sterilization, other medical procedures). Process 3: Successful delivery given conception Spontaneous intrauterine mortality. Intentional intrauterine mortality. Note that infanticide is not included. Demographers measure fertility in terms of live births, so infant deaths (and infanticide as a component of such deaths) do not directly affect fertility. It is possible that there may be some misreporting of infanticide as stillbirths in crisis situations, but evidence of such misreporting is nonexistent. Bongaarts (1977, 1982) simplified the Davis-Blake framework into four intermediate variables associated with the vast majority of the variation of fertility between and within populations: (1) proportions currently married at each age weighted by marital fertility at that age; (2) proportions using contraception of varying levels of effectiveness; (3) postpartum infecundity associated with breast-feeding or postpartum abstinence; and (4) the extent of induced abortion. Bongaarts (1982) demonstrated that the remaining factors either do not have a large effect on fertility (e.g., spontaneous intrauterine mortality) or do not vary much between populations (e.g., permanent sterility). However, Bongaarts’ arguments are for fertility in a “business-as-usual” setting, whereas humanitarian crises are anything but business as usual. I will thus retain the broader range of intermediate variables proposed by Davis and Blake, in order to be sure that abnormal circumstances are covered. The key characteristics of intermediate variables are that they are both necessary and sufficient for fertility change, and that the direction of change in fertility resulting from a change in an intermediate variable is unambiguous. Thus any background, cultural, or other factor must operate through one or more of the variables, and it must have an effect on fertility whose direction (if not size) through that variable is unambiguous.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence LIKELY EFFECTS OF HUMANITARIAN CRISES THROUGH INTERMEDIATE VARIABLES The effects of humanitarian crises on fertility are likely to depend to some extent on the stage of demographic transition reached by the population. Humanitarian crises vary widely in nature and setting, but they will be characterized by the affected population experiencing one or more of the following adverse consequences: direct exposure to violence, witnessing violence, loss of family members, displacement, food scarcity, increased exposure to communicable diseases, reduced access to health services, plus a range of other socioeconomic setbacks. Effects on fertility will depend on the adverse consequences experienced and on the characteristics of the affected population. In a largely natural fertility population (that is, a population whose fertility-related behaviors do not vary substantially by achieved family size), effects on fertility can be expected to be largely involuntary, except perhaps for changes in the incidence of induced abortion. In a population with a substantial level of fertility control, in contrast, changes in use of contraception in response to changes in fertility preferences (concerning both timing and ultimate family size) resulting from the crisis may be substantial. In the short run, effects through intercourse are likely to reduce fertility. A humanitarian crisis is likely to delay entry into a sexual union and to increase the risk of spousal separation or union dissolution, with a downward effect on fertility. A crisis is unlikely to have much effect on permanent celibacy. Within unions, involuntary abstinence through temporary separation is likely to increase, and in severe cases coital frequency may be reduced, again exerting a downward effect on fertility. Intercourse outside unions may increase, however, either through coercion or commercial sex, with the possibility of increased extramarital childbearing. Effects through risk of conception are less clear-cut. Although moderate malnutrition appears to have little effect on fecundability (Bongaarts, 1980), starvation clearly reduces conception probabilities, sharply reducing fertility in serious famines (Stein et al., 1975). Fecundability may also be affected by sexually transmitted diseases (including HIV) and certain other infections, such as malaria, the prevalence of which may increase in crowded camp conditions. In populations with substantial voluntary fertility control, use of contraception may be interrupted by a crisis, but couples may also choose to avoid pregnancy during difficult times. Breast-feeding may be continued for longer periods, increasing postpartum amenorrhea, but it
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence may be truncated by increased risk of infant death. Although women of reproductive age appear to have an above-average propensity to move in response to a crisis, young children are sometimes underrepresented in camp populations (Holck and Cates, 1982), either because they never started a move or because they died en route. Although data concerning risks of intrauterine mortality are very weak, there is no compelling evidence that malnutrition or stress has a substantial effect on risks of spontaneous pregnancy loss. Induced abortion, in contrast, might reasonably be expected to increase during a crisis if abortion is within the nexus of conscious choice, thus reducing fertility. In the long run, the directions of effect become even less clear. Long-term refugee populations may well marry early, because of lack of attractive alternatives, and may prefer large families for political reasons; for example, fertility has remained very high in the Palestinian population over several decades (Fargues, 2000). Couples may also react to long-term insecurity either by increasing childbearing (to make up for child losses, as a source of future security, or to maintain political influence) or by reducing it as a result of pessimism about the future. DATA SOURCES Statistically developed countries obtain information about fertility levels and trends from virtually complete birth registration. However, registration of births in many developing countries is not complete, and a humanitarian crisis is likely to have an additional adverse effect on an existing registration system. Thus civil registration data are not generally useful sources of data about the fertility effects of severe crises (although they may be useful for studying the effects of economic fluctuations on fertility). Demographic data for refugee or displaced populations used to come largely from camp registers. However, over the past two decades, the use of data from household surveys has become much more common. Such surveys have been conducted in camp populations, in self-settled refugee populations, and in nationally representative samples. Regardless of source, however, such errors as misreporting of age, of dates, or even wholesale omission of events often affect data quality in developing countries. The accuracy of data from camp registration systems is likely to vary for a variety of reasons, among them number of staff, duration of existence, and the nature of entitlement systems. A recent review of reproductive health indicators in 52 postemergency phase camps in seven countries
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence found that the crude birth rate was positively associated with the number of local health staff per 1,000 persons and negatively associated with the age of the camp (Hynes et al., 2002). Data quality may also be affected by the services offered. If food and other entitlements are linked to registers, there are risks of multiple registrations of residents to receive increased resources, of inclusion of persons who are not true residents, and of omission of events that remove persons, such as deaths. Births are likely to be fully or more than fully recorded, but the size of the resident population may be exaggerated. If entitlements are not linked to the register, events (such as births at home) that occur outside a facility are likely to be underrecorded, as is the resident population. Bias is possible in both cases, although the direction of the bias is not clear. An additional problem with register data is limited content: the age distribution of the population has in some cases been recorded or presented only in the broad categories of under age 5 and age 5 and over, a situation that does not permit adequate age standardization of crude rates. Retrospective surveys have collected data on fertility primarily by asking women about their own childbearing experience. The simplest question simply asks each woman about the number of children she has given birth to. It is well established that omission of births, perhaps largely children who died before the interview, is a common problem, particularly among older women (Brass et al., 1968). Also, such an aggregate birth history provides no information about the timing of births and is thus of no value for tracking short-term changes in fertility. Current fertility has often been estimated from a question about recent childbearing—either whether or not a woman had a birth in the 12 months before interview or for the date of her most recent live birth. This approach has uniformly been found to underestimate fertility, either because of a misperception of the time period involved or as a result of reporting events as occurring further in the past than was actually the case (United Nations, 1983). Also, data from a single survey provide no information about changes over time or in response to a crisis. In order to get more information, some surveys have used a “truncated” birth history, collecting information about all births in a defined time period (for instance, the 5 years before the interview) or about some number of recent births, such as the last three births. The time-truncated birth history has been found to underestimate fertility greatly, and birth histories truncated by some number of births are likely to be affected by errors of date recording that are also likely to reduce recent fertility. Full-
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence birth histories, whereby a woman is asked for the date of birth of each live-born child (and age at death if he or she has died), is the method of choice for fertility surveys today and is used by all Demographic and Health Surveys (DHS). Even such full-birth histories, however, are affected by errors of date reporting, which may shift births from one time period into another (Brass and Rashad, 1992). The DHS have noted a tendency to shift births out of the time period covered by extra questions about the health of young children (Arnold and Blanc, 1990), and fertility trends from birth histories have been found often to exaggerate fertility declines, as a result of reporting births further in the past than they actually occurred. That said, full-birth histories are not much more time-intensive in terms of field work than, say, a truncated history focusing on the last three births, but they allow much more flexibility in analysis and provide a much stronger basis for internal consistency checks to identify possible errors. An additional problem with birth histories, whether aggregate, truncated, or full, is that they are obtained from interviews with surviving women. A woman who has died or moved out of a sampled area cannot provide information. Thus the data collected will reflect any sample selection biases: survivor bias may be expected to minimize the severity of the effects of a crisis on fertility. Although there is no straightforward way around this problem, an indirect approach, based on asking women about the survival or otherwise of their sisters, and an aggregate pregnancy history for each sister, might provide an indication of the magnitude of any selection bias. However, this approach has not been tried in the field. In some cases, the only information available about a population is its age distribution. An approximate measure of fertility can be calculated as the child-woman ratio, the ratio of children under 5 to women ages 15 to 44. This measure may be affected by high child mortality and also by typical patterns of age misreporting (United Nations, 1983). The bottom line is that there is no universally satisfactory way of collecting information about fertility during a humanitarian crisis. This is one reason why relatively little is known about the effects of crisis on fertility, and a further reason for treating empirical results with some caution. Retrospective measures, in particular, are subject to biases in terms of event dating that may create spurious fluctuations in numbers of births over time; much greater confidence in time patterns can be felt if two different surveys, held at different times, show similar fluctuations for the same time periods.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence FERTILITY MEASURES Demographers are obsessive about two things: age and exposure time. We obsess about age because demographic processes are highly age-dependent, with the result that quite small differences in age distributions can have a substantial effect on summary measures, such as the crude birth rate. Failure to take account of age distributional factors can give rise to highly misleading comparisons. We obsess about exposure time in part because we obsess about age (and thus need to get exposure to risk in different age ranges correct) and partly because demographic events take place over time, so that roughly speaking twice as many events take place over a 1-year period as over a 6-month period. Controlling for exposure time is thus a critical part of any meaningful comparison. As a result of these two obsessions, demographers tend to describe processes in terms of age-specific occurrence-exposure (O-E) rates, whereby numbers of occurrences (pregnancy outcomes and particularly live births, for this paper) to women of a particular age or age group during a time period are related to the total exposure time of women at that age or age group during that time period. The measure of exposure time is usually expressed in person-years, and using this standard is convenient for the purposes of comparison with, for example, more normal conditions. However, the choice is arbitrary. It is important to note that the measure of exposure time used has no necessary connection to the length of time over which measurement lasts: observation over 10 days results in 0.0274 person-years of exposure, and results can perfectly well be presented on an annualized basis. In situations in which age-specific O-E fertility rates can be calculated, the summary fertility measure of choice is the Total Fertility Rate (TFR). The TFR represents the average number of children a woman would have if exposed throughout her reproductive life to a given set of age-specific fertility rates. It is calculated by summing such fertility rates across all ages of childbearing, and is unaffected by the age distribution of the population. It is often the case that the data available simply do not permit the calculation of age-specific O-E rates. Births may not be recorded by age, or they may be recorded in terms of very wide age groups. If no age information is available for either births or population, the crude birth rate (CBR), calculated as births in a period divided by the midperiod population multiplied by the length of the period in years (in most cases an adequate approximation for exposure time), is all that can be calculated. It must be remembered,
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence however, that the birth rate may be affected by the age and sex distribution of the population. If some information is available on the age and sex distribution of the population (but not for births), a somewhat more specific measure, the general fertility rate (GFR), can be calculated by dividing the births by the female population ages 15 to 44. In normal populations, the GFR is quite insensitive to typical variations in age distributions, but displaced populations are likely to be abnormal, and the GFR needs to be examined carefully. For comparisons between populations, indirect standardization using the full age distribution is preferable to the GFR, and if any age of mother detail at all is available for births, direct standardization (as described, for example, in Preston et al., 2001) is better still. A current estimate of GFR that may be less biased by date of event errors than estimates based on retrospective reports of recent births can be derived from information on current pregnancy. All (ever-married) women of reproductive age can be asked whether they are currently pregnant. The proportion reporting a current pregnancy is typically too low, possibly because of unrecognized pregnancies, but the degree of underreporting seems to be fairly constant across settings (Stanton, 2004). Figure 1 plots proportions currently pregnant (PCP) against the GFR for the preceding 3 years for 114 DHS around the world. The association is clearly quite close, and the regression line (which accounts for 99 percent of variance) GFR = 24.30*PCP – 0.492*PCP2 provides a simple way to obtain a current fertility measure. It is interesting to note that the relationship is slightly curvilinear: reporting of current pregnancy may be slightly better, or pregnancy loss somewhat lower, in low-fertility than in high-fertility populations. EVIDENCE OF IMMEDIATE FERTILITY EFFECTS A crisis or displacement can have a short-term effect on fertility only through the third (gestation) process of the conceptual framework, since pregnancies will already have occurred. Process 3 has only two variables, spontaneous and intentional intrauterine mortality. The majority of the evidence concerning spontaneous pregnancy loss is from famines and suggests that there is little effect. Mosley (1978) cites evidence from both The Netherlands in 1944-1945 and Bangladesh 1974-1975 that severe famine had no measurable effect on spontaneous pregnancy loss, although the data
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence FIGURE 1 Relationship between general fertility rate (in 3 years before survey) with proportion currently pregnant (at time of survey) for 114 DHS. SOURCE: Stanton (2004). are not strong. Intentional intrauterine mortality could have a very large effect, but there is very little evidence on this issue. Mosley (1978) cites Chowdhury and Chen (1977) as showing that there was a significant increase in induced abortion in Matlab Thana during the Bangladesh famine of 1974-1975, in a population in which induced abortion at the time was probably quite unusual. The monthly data on pregnancy outcomes in Matlab, collected by regular household visits by field workers, represent perhaps the only prospective data source concerning monthly miscarriages, stillbirths, and live births during a famine in the developing world. The pattern of miscarriages in comparison to births does not seem to provide strong evidence for changes in overall pregnancy loss. Figure 2 shows the monthly deviations from the seasonally adjusted trends in live births, stillbirths, and miscarriages over the period January 1974 to December 1977 for each outcome. The price of rice rose throughout 1974, peaking in the first quarter of 1975 at over 200 percent of initial levels (Mosley, 1978) . As can be seen, live births and stillbirths show very
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence FIGURE 2 Monthly deviations in live births, stillbirths, and miscarriages from seasonally adjusted trends, Matlab, January 1974 to December 1977. SOURCE: Cholera Research Laboratory. Demographic Surveillance System—Matlab. Registration of Demographic Events, Reports 4 to 7, 1974 to 1977, Dhaka, Bangladesh. similar patterns, both dipping below expected values from August 1974 and remaining low until May 1976. If the decline in births was the result of reduced conceptions, the apparent drop by August 1974 is surprisingly early, but the more pronounced drop in May 1975, continuing to January 1976, is consistent with reduced conceptions at the peak of the famine. Miscarriages, presumably including induced abortions in these data, fall well below expected values as early as March 1974 and remain low until September 1975. These data certainly give no support to the idea that miscarriages might have increased as a result of the famine, and it is not until September 1975 that they give any support to the idea that induced abortions might have increased (but the fact that miscarriages increased to “normal” levels by September 1975, 9 months before births recovered, may suggest some induced abortion in the later stages of the famine). The pattern of miscarriages both falling and then recovering before births suggests that it was changes in conceptions that were responsible for both.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence bodians resettled in the United States and the data from the Sakaeo camp in Thailand, with the additional inference that fertility recovered very fast once the refugees reached camps in Thailand. Gordon uses child-woman ratios to examine fertility trends among arriving refugees over the period 1979 to 1986. These ratios fall sharply for arrivals from Vietnam, fluctuate around a high level for arrivals from Laos, and rise steeply for arrivals from Cambodia. SUMMARY AND CONCLUSIONS Data concerning the fertility impact of humanitarian crises and conflict have improved substantially over the past three decades, but they are still not adequate to answer all questions of importance. Short-term effects can operate only through Process 3 (increased pregnancy loss) factors. There is little evidence to support a strong effect on spontaneous abortion, and evidence concerning induced abortion is largely anecdotal. Where short-term effects appear to be large (such as in the Sakaeo refugee camp in Thailand in 1979-1980) the likelihood is that the population arriving at the camp had been severely malnourished for a substantial period prior to moving. The population of Sakaeo was also selected (in terms of age and marital status) for low fertility. Medium-term effects can also operate through Process 1 (intercourse) or Process 2 (conception) factors. The strongest data, from civil registration or surveillance data, concern the effects of famine and suggest that fertility can drop dramatically during famine, primarily as a result of reduced fecundability (although reduced coital frequency cannot be ruled out). Temporary separation must have an impact in the case of population displacement, but clear evidence for what may be a rather weak effect is lacking. Long-term effects of conflict on fertility appear to be very limited. Populations that have experienced long-term conflicts (such as Angola) have fertility levels very similar to those of neighboring populations that have been less affected. In the Middle East, it seems likely that prolonged belligerence between Israelis and Palestinians has contributed to fertility levels higher than they would otherwise have been. This review does not identify a uniformly satisfactory way to collect data about the demographic consequences of crisis. Sound demographic methodology, controlling insofar as possible for age and exposure effects, is important when feasible, but continuous recording of events generally
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence breaks down in a conflict, and retrospective reporting seems to involve too much noise in the data to identify patterns. Certain general conclusions about data collection can be drawn, however. Since age is such a crucial demographic variable, which must be controlled for to make valid comparisons, registration systems should attempt to collect and tabulate detailed information on age both for the population and for events (age at death, age of mother for births). Ideally, 5-year age groups should be used. At the very least, for fertility studies, it is necessary to record the size of the female population of reproductive age, usually taken as 15 to 44. This information will be very useful for planning the provision of reproductive health services as well. Registration systems should also ensure that enough staff are available to record events and should guard against gradual decline in quality over time. If data are to be extracted from a register for a recent period, the period chosen should be a full year to avoid possible seasonality problems. Periodic sample surveys, ideally including full birth histories, should also be implemented to provide a check on the registration system. Average numbers of children ever born by women classified by age provide a useful basis for data evaluation. Sample surveys can provide useful fertility and reproductive health indicators from surprisingly small samples of no more than 3,000 households. A full-birth history is recommended: either time-limited or birth-limited truncation is likely to result in underestimates of recent fertility, as well as providing no basis for evaluating data quality. A final point concerns data publication. Some reports consulted for this review provided data only in graphical form; good practice requires that the basic numbers be shown somewhere. Other papers showed summary measures but referred only to ratios of numbers for some categories. Again, interpretation may depend on having the raw numbers in such cases. Finally, I would encourage field staff conducting studies in this area to report results using standard measures, that is, to report birth and other rates in terms of person-years of exposure. Such reporting makes comparisons between studies, and between populations in crisis and those in more normal circumstances, much easier. Although it is not hard to convert a measure expressed per 10,000 population per day, or per 1,000 population per month, into the more normal format per 1,000 person-years, it is unnecessary and tends to give the erroneous impression that the measure itself is in some way inherently different, rather than just differently scaled.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence ACKNOWLEDGMENT I wish to acknowledge useful discussion and comments of participants on an earlier draft of this paper presented at the National Research Council’s Workshop on Fertility and Reproductive Health in Humanitarian Crises, Washington DC, October 23-24, 2002, as well as the confidential comments of three reviewers. REFERENCES Agadjanian, V., and N. Prata 2002 War, peace and fertility in Angola. Demography 39(2):215-231. Arnold, F., and A. Blanc 1990 Fertility Levels and Trends. (DHS Comparative Studies, No. 2.) Columbia, MD: Institute for Resource Development. Ashton, B., K. Hill, A. Piazza, and R. Zeitz 1984 Famine in China, 1958-61. Population and Development Review 10(4):613-645. Bicego, G., A. Chahnazarian, K. Hill, and M. Caymittes 1991 Trends, age patterns and differentials in childhood mortality in Haiti (1960-1987). Population Studies 45(2):235-252. Blacker, J.G.C., M. Afzal, and A. Jalil 1989 The estimation of fertility from distributions of births by order. Application to the Pakistan Demographic Survey. In IUSSP International Population Conference, New Delhi. Liege, Belgium: International Union for the Scientific Study of Population. Bongaarts, J. 1977 A dynamic model of the reproductive process. Population Studies 31(1):59. 1980 Does malnutrition affect fertility? A summary of evidence. Science 208:564-569. 1982 The fertility-inhibiting effects of the intermediate fertility variables. Studies in Family Planning 13(6/7):179-189. Brass, W. 1969 Disciplining demographic data. In Proceedings of the IUSSP International Population Conference, London. Liege, Belgium: International Union for the Scientific Study of Population. Brass, W., A.J. Coale, P. Demeny, D.F. Heisel, F. Lorimer, A. Romaniuk, and E. van de Walle 1968 The Demography of Tropical Africa. Princeton, NJ: Princeton University Press. Brass, W., and H. Rashad 1992 Evaluation of the reliability of data in maternity histories. In A. Hill and W. Brass (eds.), The Analysis of Maternity Histories. Liege, Belgium: International Union for the Scientific Study of Population. Centers for Disease Control 1983 International notes surveillance of health status of Kampuchean refugees—Khao-I-Dang Holding Center, Thailand, December 1981-June 1983. Morbidity & Mortality Weekly Report 32(31):412-415.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence Central Statistical Authority and ORC Macro 2001 Ethiopia Demographic and Health Survey 2000. Addis Ababa, Ethiopia, and Calverton, MD: Central Statistical Authority and ORC Macro. Centre for Research on the Epidemiology of Disasters 1997 Reproductive Health Needs of Refugees: Evidence from Three Camps in Ethiopia. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters, Department of Public Health, Université Catholique de Louvain. Chowdhury, A.K.M.A, and L.C. Chen 1977 The Dynamics of Contemporary Famine. (Report No. 47.) Dacca, India: Ford Foundation. Curlin, G.T., L.C. Chen, and S.B. Hussain 1976 Demographic crisis: The impact of the Bangladesh civil war (1971) on births and deaths in a rural area of Bangladesh. Population Studies 30(1):87-105. Davis, K., and J. Blake 1956 Social structure and fertility: An analytic framework. Economic Development and Cultural Change 4(4):211. Fargues, P. 2000 Protracted national conflict and fertility change: Palestinians and Israelis in the twentieth century. Population and Development Review 26(3):441- 482. Galloway, P. 1988 Basic patterns in annual variations in fertility, nuptiality, mortality and prices in pre-industrialized Europe. Population Studies 42:275-303. Gordon, L.W. 1989 The missing children: Mortality and fertility in a Southeast Asia refugee population. International Migration Review 23(2):219-237. Holck, S.E., and W. Cates, Jr. 1982 Fertility and population dynamics in two Kampuchean refugee camps. Studies in Family Planning 13(4):118-124. Hynes, M., M. Sheik, H.G. Wilson, and P. Spiegel 2002 Reproductive health indicators and outcomes among refugee and internally displaced persons in postemergency phase camps. Journal of the American Medical Association 288(5):595-603. International Centre for Migration and Health no date Pregnancy outcome among displaced and non-displaced women in Bosnia and Herzegovina. In Report of the Technical Working Group on Reproductive Health and Pregnancy Outcome Among Displaced Women. Geneva, Switzerland: International Centre for Migration and Health. Khlat, M., M. Deeb, and Y. Courbage 1997 Fertility levels and differentials in Beirut during wartime: An indirect estimation based on maternity registers. Population Studies 51:85-92. Lee, R. 1990 The demographic response to economic crisis in historical and contemporary populations. Population Bulletin of the United Nations 29:1-15. Lindstrom, D.P., and B. Berhanu 1999 The impact of war, famine and economic decline on marital fertility in Ethiopia. Demography 36(2):247-261.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence McGinn, T. 2000 Reproductive health of war-affected populations: What do we know? Family Planning Perspectives 26(4):174-180. Médecins Sans Frontières 1997 Refugee Health: An Approach to Emergency Situations. New York: MacMillan Education LTD. Mosley, W.H. 1978 The effects of nutrition on natural fertility. In H. Leridon and J. Menken (eds.), Natural Fertility. Liege, Belgium: International Union for the Scientific Study of Population. Palloni, A., K. Hill, and G.P. Aguirre 1996 Economic swings and demographic changes in the history of Latin America. Population Studies 50:105-132. Palmer, C.A., L. Lush, and A.B. Zwi 1999 The emerging international policy agenda for reproductive health services in conflict settings. Social Science and Medicine 49:1689-1703. Preston, S.H., P. Heuveline, and M. Guillom 2001 Demography: Measuring and Modeling Population Processes. Oxford, England: Blackwell Publishers. Stanton, C. in press Methodological issues in the measurement of birth preparedness in support of safe motherhood. Evaluation Review. Stein, Z., M. Susser, G. Saenger, and F. Marolla 1975 Famine and Human Development: The Dutch Hunger Winter of 1944- 1945. New York: Oxford University Press. United Nations 1983 Manual X: Indirect Techniques for Demographic Estimation. (Population Studies, No. 81, Department of International Economic and Social Affairs.) New York: United Nations. 1994 Report of the International Conference on Population and Development, Cairo, 5-13 September 1994. A/CONF.171/13/Rev.1. Available: http://www.unfpa.org2/icpd/icpd_poa.htm [ March 31, 2004] 1996 Population and Development. New York: United Nations. Weeks, J.R., R.G. Rumbaut, C. Brindis, C.C. Korenbrot, and D. Minkler 1989 High fertility among Indochine refugees. Public Health Reports 104(2):143-150. Wulf, D., ed. 1994 Refugee Women and Reproductive Health Care: Reassessing Priorities. New York: Women’s Commission for Refugee Women and Children, International Rescue Committee. Zakharia, L.F., and S. Tabari 1997 Health, work opportunities and attitudes: A review of Palestinian women’s situation in Lebanon. Journal of Refugee Studies 10(3):411-429.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence ANNEX: RAW NUMBERS UNDERLYING FIGURES 1 TO 3
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence Raw Numbers Underlying Figures 1 to 3 Number of Births Month of Birth 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 January 95 84 102 101 130 106 113 83 165 123 118 106 February 100 100 115 92 122 136 157 72 126 133 111 97 March 89 88 133 98 107 132 138 69 120 124 110 141 April 142 113 138 121 155 143 232 97 150 162 128 148 May 101 90 133 67 129 125 151 126 156 110 152 149 June 115 109 133 97 136 115 164 129 171 129 130 153 July 123 116 127 115 135 143 194 154 173 176 116 137 August 108 92 123 101 159 111 159 156 136 128 116 143 September 105 93 125 85 87 97 129 144 129 111 92 154 October 84 69 119 69 95 97 116 116 120 111 82 147 November 52 62 75 61 63 76 77 112 126 117 80 184 December 65 64 92 64 85 87 87 104 116 123 96 122 SOURCE: Rwanda DHS 2000: Births by Month and Year.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence Pregnancy Outcomes by Month: Matlab Demographic Surveillance System Miscarr. Still-births Live Births Miscarr. Still-births Live Births Miscarr. Still-births Live Births Miscarr. Still-births Live Births January 34 33 1,067 32 28 838 63 26 614 78 45 1,188 February 44 34 940 31 21 711 71 22 577 76 37 1,009 March 31 20 931 50 15 695 93 27 638 91 38 973 April 38 18 811 42 22 603 93 29 501 94 36 813 May 42 35 791 50 16 466 108 29 668 123 31 739 June 54 34 743 57 23 347 103 29 752 94 48 801 July 46 29 811 52 18 490 117 36 898 105 40 886 August 48 22 805 50 16 586 114 39 1,013 84 36 1,042 September 50 34 1,004 65 18 795 79 64 1,281 71 49 1,146 October 31 44 1,128 57 18 780 73 57 1,367 75 63 1,328 November 31 33 1,155 61 30 637 68 55 1,487 75 59 1,352 December 29 41 1,027 71 26 613 57 56 1,375 48 47 1,111
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence ABOUT THE AUTHOR Kenneth Hill has been a professor and director of the Hopkins Population Center since 1995. His research interests have been in the development of demographic measurement methods (particularly for demographic outcomes that are hard to measure, such as child and adult mortality, unmet need for family planning, undocumented migration); the measurement of child mortality (with particular emphasis on tracking national trends and linking them to other changes); the exploration of links between demographic parameters and economic crisis; the impact of policy and programs on demographic change; the role of gender preferences on child health behaviors and fertility; the demography of Sub-Saharan Africa; the role of development, particularly child mortality change, on fertility decline; and the measurement of demographic parameters for populations undergoing complex emergencies. His publications include “Demographic Techniques: Indirect Estimation” (International Encyclopedia of Social and Behavioral Sciences, 2001); “Interrupting HIV Transmission in Africa” (Foreign Policy, 2001); Levels and Trends in Child Mortality in the Developing World (with R. Pande, 2001); The Quick and the Dead in Zimbabwe: Replacement and Insurance Effects on Fertility (with R. Marindo and M. Mahy, 1997); “Demographic Responses to Economic Shocks: The Case of Latin America” (with A. Palloni, in The Peopling of the Americas, Vol. 3, 1992). He has a Ph.D. in demography from the London School of Hygiene and Tropical Medicine.
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War, Humanitarian Crises, Population Displacement, and Fertility: A Review of Evidence The Committee on Population was established by the National Academy of Sciences (NAS) in 1983 to bring the knowledge and methods of the population sciences to bear on major issues of science and public policy. The committee’s work includes both basic studies of fertility, health and mortality, and migration and applied studies aimed at improving programs for the public health and welfare in the United States and in developing countries. The committee also fosters communication among researchers in different disciplines and countries and policy makers in government and international agencies. The Roundtable on the Demography of Forced Migration was established by the Committee on Population of the National Academy of Sciences in 1999. The Roundtable’s purpose is to serve as an interdisciplinary, nonpartisan focal point for taking stock of what is known about demographic patterns in refugee situations, applying this knowledge base to assist both policy makers and relief workers, and stimulating new directions for innovation and scientific inquiry in this growing field of study. The Roundtable meets yearly and has also organized a series of workshops (held concurrently with Roundtable meetings) on some of the specific aspects of the demography of refugee and refugee-like situations, including mortality patterns, demographic assessment techniques, and research ethics in complex humanitarian emergencies. The Roundtable is composed of experts from academia, government, philanthrophy, and international organizations. Other Publications of the Roundtable on the Demography of Forced Migration Psychosocial Concepts in Humanitarian Work with Children: A Review of the Concepts and Related Literature (2003) Initial Steps in Rebuilding the Health Sector in East Timor (2003) Malaria Control During Mass Population Movements and Natural Disasters (2003) Research Ethics in Complex Humanitarian Emergencies: Summary of a Workshop (2002) Demographic Assessment Techniques in Complex Humanitarian Emergencies: Summary of a Workshop (2002) Forced Migration and Mortality (2001)
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Representative terms from entire chapter: