Distributional Implications of Alternative Strategic Responses to the Demographic-Epidemiological Transition—An Initial Inquiry

Davidson R.Gwatkin

INTRODUCTION

Over the past decade, transitions in the developing world’s demographic and epidemiological situations have become increasingly obvious. Mortality and fertility have been declining, and disease patterns have been shifting. Illness and death among the young caused by the diarrhea-malnutrition-pneumonia triad have been progressively giving way to newer configurations dominated by chronic and degenerative diseases among adults and the elderly.

These changes raise the possibility that the time has come for a corresponding shift in developing countries health strategies. No longer can one simply assume that it remains adequate to continue stressing oral rehydration, immunization, and related approaches. As the epidemiological and demographic transitions proceed, it would seem reasonable to anticipate a need to become increasingly concerned with preventing strokes and heart attacks as well.

Were societies epidemiologically and demographically homogeneous, the case for such a shift would be straightforward. But societies are not homogeneous. Different groups in them suffer from different kinds of diseases at different ages. A change in focus from one set of diseases and age groups to another could benefit some groups at the expense of others.

This question of who gains and who loses from a change in priorities is

Davidson R.Gwatkin is director of the International Health Policy Program. The author wishes to thank Jun Zhu for his effective research assistance and the many readers of earlier drafts for their valuable suggestions.



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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings Distributional Implications of Alternative Strategic Responses to the Demographic-Epidemiological Transition—An Initial Inquiry Davidson R.Gwatkin INTRODUCTION Over the past decade, transitions in the developing world’s demographic and epidemiological situations have become increasingly obvious. Mortality and fertility have been declining, and disease patterns have been shifting. Illness and death among the young caused by the diarrhea-malnutrition-pneumonia triad have been progressively giving way to newer configurations dominated by chronic and degenerative diseases among adults and the elderly. These changes raise the possibility that the time has come for a corresponding shift in developing countries health strategies. No longer can one simply assume that it remains adequate to continue stressing oral rehydration, immunization, and related approaches. As the epidemiological and demographic transitions proceed, it would seem reasonable to anticipate a need to become increasingly concerned with preventing strokes and heart attacks as well. Were societies epidemiologically and demographically homogeneous, the case for such a shift would be straightforward. But societies are not homogeneous. Different groups in them suffer from different kinds of diseases at different ages. A change in focus from one set of diseases and age groups to another could benefit some groups at the expense of others. This question of who gains and who loses from a change in priorities is Davidson R.Gwatkin is director of the International Health Policy Program. The author wishes to thank Jun Zhu for his effective research assistance and the many readers of earlier drafts for their valuable suggestions.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings of particular interest in health because of the egalitarian tendencies that have typified professional thinking in the field during recent years.1 From an egalitarian perspective, any change is to be welcomed to the extent that its benefits accrue to the poor, and to be resisted to the degree that the gains go instead to the better-off and leave the poor at an even greater disadvantage than before. For those adhering to this viewpoint, is a shift in priorities from communicable diseases among infants and children toward chronic diseases at older ages to be welcomed or resisted? To the extent it is to be resisted, what alternative responses to the demographic and epidemiological transitions might be considered? Such are the questions toward which this exploration into the distributional consequences of different approaches to the Third World’s changing health conditions is directed. THE OVERALL SITUATION Although most of this exploration focuses on the country level that is of particular interest, it can best begin with a brief look at the situation of the Third World as a whole and of the major regions within it. Such a look can provide both a sense of how central a place the demographic and epidemiological transitions deserve in designing health improvement strategies and an initial hint about the possible distributional consequences of these strategies. The inquiry is facilitated by the availability of recent figures produced at the World Bank on cause- and age-specific mortality in the Third World as a whole and in its major regions. A summary of these figures appears in Tables 1A and 1B (Bulatao and Stephens, 1989). The most immediately obvious feature of these tables is the shift in overall age- and disease-specific mortality patterns that they were developed to document. This shift is very large. If mortality trends in the developing world proceed according to the Bulatao-Stephens projections for example, the percentage of total deaths occurring among children less than 1   This thinking is typified by the widely used expression “health for all” and the accompanying belief that an emphasis on the poor and disadvantaged is required to achieve this objective. To many, the greater intergroup equality in health status implied by health for all would also be a more equitable situation than that which currently prevails. For those adhering to such a belief, this is a paper not simply about distribution and equality, but also about equity and justice. However, the equation of equity with equality of health status involves a value judgment that some have felt requires further justification than is possible in the limited space available here. For this reason, the paper focuses on such empirically measurable features of a society’s situation as the intergroup distribution and equality of health status, which are notably less value laden than is the concept of equity. This focus, however, cannot be said to remove all value considerations since the paper’s underlying assumption continues to be that, for whatever reason, reduced intergroup disparities in health status are desirable.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings TABLE 1A Deaths by Age (percent)   Year Region and Age Group 1970 1985 2000 2015 Less Developed Countries 0–14 50.0 42.9 26.7 18.5 15–64 27.8 28.9 33.2 34.3 65+ 22.2 28.2 40.0 47.2 Total 100.0 100.0 99.9 100.0 Life expectancy at birth (years) (57.5) (62.0) (66.0) (68.5) Latin America and the Caribbean 0–14 38.3 33.3 19.2 10.4 15–64 28.7 27.6 30.4 31.1 65+ 33.1 39.0 50.3 58.6 Total 100.1 99.9 99.9 100.1 Life expectancy at birth (years) (62.5) (66.5) (70.5) (72.5) Asia 0–14 48.5 37.1 18.5 10.7 15–64 27.9 30.6 34.2 34.2 65+ 23.5 32.3 47.4 55.1 Total 99.9 100.0 100.1 100.0 Life expectancy at birth (years) (59.0) (64.0) (68.0) (70.0) Middle East and North Africa 0–14 51.6 54.5 34.7 25.8 15–64 28.2 23.9 33.9 37.2 65+ 20.3 21.6 31.4 36.9 Total 100.1 100.0 100.0 99.9 Life expectancy at birth (years) (53.0) (60.0) (64.5) (66.5) Sub-Saharan Africa 0–14 60.3 58.8 50.2 41.9 15–64 26.5 27.0 31.4 34.6 65+ 13.2 14.2 18.4 23.5 Total 100.0 100.0 100.0 100.0 Life expectancy at birth (years) (45.0) (51.5) (57.0) (61.0)   SOURCE: Bulatao and Stephens (1989:44, 61–62).

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings TABLE 1B Death by Cause (percent)   Year Region and Cause of Death 1970 1985 2000 2015 Less Developed Countries Infectious and parasitic diseases 42.1 36.2 25.9 19.4 Neoplasms and circulatory disorders 21.6 26.0 39.6 48.9 Other 36.4 37.7 34.6 31.8 Total 100.1 99.9 100.1 100.1 Life expectancy at birth (years) (57.5) (62.0) (66.0) (68.5 Latin America and the Caribbean Infectious and parasitic diseases 33.3 24.4 15.1 9.3 Neoplasms and circulatory disorders 30.3 35.9 51.6 61.2 Other 36.4 39.7 33.3 29.5 Total 100.0 100.0 100.0 100.1 Life expectancy at birth (years) (62.5) (66.5) (70.5) (72.5 Asia Infectious and parasitic diseases 41.2 33.6 21.3 14.6 Neoplasms and circulatory disorders 22.8 28.5 45.5 55.7 Other 36.0 37.9 33.2 29.7 Total 100.0 100.0 100.0 100.0 Life expectancy at birth (years) (59.0) (64.0) (68.0) (70.0 Middle East and North Africa Infectious and parasitic diseases 41.4 40.4 29.5 23.2 Neoplasms and circulatory disorders 19.1 22.0 32.7 40.8 Other 39.5 37.6 37.8 36.0 Total 100.0 100.0 100.0 100.0 Life expectancy at birth (years) (53.0) (60.0) (64.5) (66.5) Sub-Saharan Africa Infectious and parasitic diseases 48.2 47.2 41.8 36.5 Neoplasms and circulatory disorders 13.9 16.0 20.7 26.7 Other 37.9 36.8 37.5 36.8 Total 100.0 100.0 100.0 100.0 Life expectancy at birth (years) (45.0) (51.5) (57.0) (61.0)   SOURCE: Bulatao and Stephens (1989:44, 15–16).

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings 15 years of age would decline from 50 to 18 between 1970 and 2015, while that attributable to people more than 65 years old would rise from 22 to 47. Similarly, the percentage of deaths caused by infectious and parasitic diseases would fall from 42 to 19, and the percentage resulting from neoplasms and circulatory disorders would climb from 22 to 49. These overall shifts represent only part of the story, however. Of greater interest from a distributional perspective are the interregional differences in the numbers presented, especially differences between high- and low-mortality regions. When these differences are examined, a clear pattern emerges: The poorer a region’s health status, the greater is the importance of deaths at earlier relative to older ages, and from communicable relative to chronic diseases. This pattern is clearly visible in each of the time periods shown in Tables 1A and 1B. In general, when the life expectancy of a region is 50 years, about 60 percent of deaths occur among children under 15 years, about 20 percent at age 65 or above. When a region’s life expectancy is 70 years, the percentage of deaths occurring at less than 15 years is only 10–12, compared to almost 50 percent at age 65 or above. The interregional differences in the cause of death are equally sharp: when a region’s life expectancy is 50 years, approximately 50 percent of deaths are caused by infectious/parasitic diseases and 10–15 percent by neoplasms/circulatory disorders. When a region’s life expectancy is 70 years, the proportions are about 20 percent and 45–50 percent, respectively.2 This pattern suggests that a health strategy focusing on chronic diseases among adults and the elderly might be considerably more relevant for regions with low mortality levels than for high-mortality regions. For example, an emphasis on adult mortality would seem quite sensible for a region in which overall life expectancy is 70 years or so, in which almost 90 percent of deaths take place among people over 15. But its likely efficacy would appear questionable when life expectancy is only 50 and the distinct majority of deaths occurs among infants and children younger than 15. This point appears to have been at least implicitly accepted within the international health profession in that calls for a review of health priorities seem to arise much more frequently with reference to the more advanced Latin American and Asian countries than with respect to high-mortality sub-Saharan African nations. What seems much less well recognized is the possibility that differences analogous to those just noted with respect to 2   The figures presented are estimated from linear regression equations in which the percentage of deaths at a specific age or from a specific cause serves as the dependent variable, and a group’s life expectancy at birth is the independent variable. Each equation is based on 16 observations, one for each of the four regions of the developing world and for each of the four dates covered by the Bulatao and Stephens figures.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings regions might also exist within regions and within the individual countries of which regions are composed. Should analogous intracountry differences exist, a health strategy oriented toward communicable diseases among infants and children would seem much more relevant for a society’s disadvantaged groups than a strategy focusing on chronic diseases of older ages. This raises the possibility that shifting from the former to the latter could benefit the better-off more than the poor, thereby increasing the degree of inequality between them. How great is such a possibility? To find out, it is necessary to turn to the information available about individual countries and about differences in health conditions among population groups within them. RANGE OF INTRACOUNTRY MORTALITY DIFFERENCES The best way to begin the examination is with a look at intergroup differences in overall mortality levels—that is, at differences in mortality at all ages and from all causes together. These differences are of interest both in themselves and, as will be seen later, because of the use to which they can be put in assessing age- and cause-specific patterns. Also, the data that exist about differences in overall levels are considerably better than those available for differences in the distribution of deaths by age and cause; these data make it possible to begin by drawing upon observations taken directly from the countries concerned before proceeding to the necessarily more synthetic data featured in later stages of the discussion. Data based on such direct observations are presented in the appendix to this paper. They represent 14 countries that are broadly representative of the widely diverse conditions found in the Third World, for which relatively recent information is available. For each of the countries there is information on one of four attributes commonly used in discussions of mortality differentials: income, place of residence, women’s educational status, and race. To facilitate comprehension, the figures presented in the appendix focus on one particularly important dimension of intergroup mortality differences: the range between a society’s healthiest and least healthy groups. To the extent feasible, the figures seek to compare the situation of the top 10–20 percent with that of the bottom 10–20 percent of the population in the country concerned.3 The many limitations of the data significantly restrict the uses to which 3   Such a comparison can be meaningfully attempted only with respect to income and place of residence. For women’s educational status and race, the number of groups is too small and the proportion of the population in each is too large and/or too different to permit identification of comparably sized groups at the two ends of the mortality spectrum.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings the figures may legitimately be put.4 The figures can, however, serve to substantiate the ubiquitous presence of significant intrasocietal differences in mortality and to give an initial sense of the general orders of magnitude typically involved. For example, the figures show that in all but one of the fourteen societies covered, infant mortality in the most disadvantaged group is more than twice as high as that of the most advantaged. In four of the fourteen, the difference is three times or more; in two, it is more than fourfold. Similarly, life expectancy of the most privileged groups is 25 percent or more than that of the most disadvantaged in all but three of the societies. In at least one case, the difference is greater 50 percent.5 Beyond this are suggestions with respect to differences in mortality status according to place of residence and education, the two attributes for which the most information is available: The information with respect to place of residence gives rise to a “10–20” rule of thumb. That is, in most settings the life expectancy of the top 10–20 percent of the population as measured by place of residence appears to be somewhere on the order of 10–20 years higher than that of the lowest 10–20 percent of the population defined in a similar manner.6 This rule of thumb, which is broad enough to cover all of the six situations based 4   In addition to the wide variations in intergroup size referred to in the preceding note, these limitations include the multiplicity of attributes used in differentiating among population groups and the reliance on only one statistical measure (i.e., the range) of intergroup disparities. 5   The cross-sectional figures available cannot address the question of whether the differences cited have been widening or narrowing as countries’ epidemiological transitions have proceeded: whether, in other words, the least healthy groups have benefited less or more from the transitions than the groups that were healthiest to begin with. In light of the connection between income and health status, one would expect trends in the distribution of health status to follow at least approximately those of income distribution. If so, the voluminous literature on income distribution trends suggests two possibilities concerning the evolution of disparities in health status. One, derived from the well-known “Kuznets hypothesis” on income distribution, is that disparities would normally widen in the initial stages of development, narrow in the later. The second, based on empirical research into the Kuznets hypothesis, would be that no generalization is possible: that disparities widen in some countries and narrow in others, depending on the particular development and health service strategies adopted. (The findings of the only known examination of this issue, for Mexico, suggest that interregional disparities have been widening, with mortality in areas where it was initially low declining much more rapidly than in regions where it was initially high. This finding, which is giving rise to what the authors call a “health polarization,” is in line with the initial part of the first hypothesis presented above while leaving open the question of whether or when the second part might occur (Bobadilla et al., 1993). 6   The country concerned is divided into 10–20 or so geographical units. The magnitude of the difference between upper and lower groups can normally be expected to vary directly with the number of geographical units being observed, rising as the number increases and declining as the number falls.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings on place of residence in section B of the appendix, implies a two- to fourfold difference in infant mortality between the uppermost and lowest 10–20 percent of the population. The children of the relatively few women with secondary or college education, which qualifies them for consideration as members of their countries’ social and economic elite, can expect to live 10–20 years longer than the children of women with no education, who belong to the core of the most disadvantaged groups in those societies. Here, too, the implied difference in infant mortality is roughly two- to fourfold. This variation of the 10–20 hypothesis suggested above would cover five of the six groups for which figures by education are presented in section C of the appendix. FRAMEWORK Placed in an appropriate framework, this knowledge about differences in overall conditions between a society’s healthiest and least healthy groups can play a central role in the examination of intergroup differences in age-and cause-specific patterns of mortality. The most appropriate framework, of course, is that of the developing country population within which the observed differences exist; however, fully satisfying studies can be undertaken only at a national (or subnational) level. However, an initial look at illustrative country situations based on composite data can be instructive both in introducing an approach of potential relevance for those national studies and in providing findings to guide policies in the interim prior to the studies’ completion. For this purpose, two illustrative populations can serve as the basis for an examination of age- and cause-specific mortality patterns: one population with mortality and fertility levels characteristically found in societies of sub-Saharan Africa, the area of the Third World where overall mortality is highest; the other broadly representative of Latin America, the developing world’s lowest-mortality region. Within each, the age- and cause-specific mortality patterns of the healthiest and least healthy 10–20 percent of the population can be compared. The overall mortality levels of the two societies can be established through reference to the World Bank’s figures cited in Tables 1A and 1B, which showed 1985–1990 life expectancy to be 51.5 years in sub-Saharan Africa, 66.5 years in Latin America and the Caribbean. An upward rounding to allow for increases that have presumably taken place since 1985–1990 leads to the selection of 55.0 years as the average life expectancy of the high-mortality society, 67.5 years as that of the low-mortality society. Based on the 10–20 hypothesis established through the figures for developing countries presented in the appendix, the difference between the most-advantaged 10–20 percent and least-privileged 10–20 percent of the

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings population is set at 15.0 years7, assumed to be centered at the mean. These assumptions yield population groups with life expectancies of 47.5 and 62.5 years in the high-mortality society and life expectancies of 60.0 and 75.0 years in the low-mortality society. The fertility levels are determined through reference to World Bank data. An examination of these data suggests that on average, a woman in an African population group with a life expectancy of 47.5 years will bear about 6.7 children. When life expectancy is 62.5 years, the average number of children will be around 5.1. In Latin America, the corresponding figures for children born are 4.9 (when life expectancy is 60.0 years) and 2.3 (when life expectancy is 75.0 years), respectively (Bulatao et al., 1989, 1990). The same World Bank data are also used to establish the age distribution of each population group. This estimation is done with reference to the three or four national populations whose life expectancies are closest to that of the group concerned: two below, two above. Thus, for example, the age structure for the least healthy group in the high-mortality society is the unweighted average of those of the four African countries whose 1985–1990 life expectancies are closest to the 47.5 years of that group: Mali and Burkina Faso, whose life expectancies of 47.2 and 47.3 years, respectively, are just below 47.5 years; and Senegal and Mozambique, whose life expectancies of 47.6 and 48.1 years are just above. The population distributions of the other groups are determined in an analogous manner, by using data from Africa for the other group in the high-mortality society, and from Latin America and the Caribbean for the two groups in the low-mortality society.8 The resulting population distribution figures appear in Table 2, which also presents the mortality and fertility levels referred to earlier. The higher fertility and mortality of the least healthy groups combine to produce populations that are younger than those of the most healthy groups, particularly in the low-mortality society, with only around 25 percent of the healthiest population being under 15 years of age, compared with more than 40 percent of the least healthy population. At the upper end of the age spectrum, the situation is reversed: more than 9 percent of the healthiest population is 7   This difference is approximately the same as the mean difference of 14.7 years between the highest and lowest groups in the fourteen country examples presented in the appendix, and the mean difference of 15.2 years in the seven examples in which the groups compared represent approximately the uppermost and lowest 10–20 percent of the country’s population. 8   Thanks to Ansley Coale for suggesting this approach based on country data, rather than on stable populations derived from the fertility and mortality rates. The age distributions appearing in stable population tables are based on the assumption that fertility and mortality have remained constant over an extended period prior to the observation date, and as Coale noted, this assumption is not valid for most developing countries, in which mortality and fertility have been declining.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings TABLE 2 Demographic Characteristics of Population Groups With Differing Life Expectancies and Fertility Levels   High-Mortality Country Low-Mortality Country Characteristic Least Healthy 10–20% of Population Healthiest 10–20% of Population Least Healthy 10–20% of Population Healthiest 10–20% of Population Life expectancy (years) 47.5 62.5 60.0 75.0 Total fertility rate 6.7 5.1 4.9 2.3 Distribution by age group 0–14 45.3 42.0 42.6 25.5 15–44 40.9 42.9 42.7 49.0 45–64 10.7 11.3 11.3 16.4 65+ 3.1 3.8 3.4 9.2 Total 100.0 100.0 100.0 100.1 65 years of age or older, compared with much less than 4 percent of the least healthy population. AGE-SPECIFIC MORTALITY Within societies such as those just described, how different are the age-and cause-specific patterns of mortality? What are the implications of these differences for the distributional consequences of alternative health improvement strategies? These questions may be more easily addressed with respect to age, because age at death has been the subject of intense study by demographers over the past several decades. The results of this study cannot be considered fully definitive because it has not yet proceeded to the point at which it can provide reliable direct information about the Third World’s higher-mortality countries. There are, however, model life tables or standardized compilations of age-specific mortality data from developed and advanced developing countries that are generally considered trustworthy. These have long been routinely used by the United Nations, the World Bank, and national statistical offices to assist demographic analyses in a wide range of developing countries with insufficient data of their own. Although obviously less suitable than reliable direct observations, they have withstood the test of time well enough to justify at least a modest degree of confidence in their applicability for the development of broad illustrations such as those pictured here. The figures in Table 3 show what one of the most frequently used sets

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings of model life tables has to say about age-specific mortality in populations with life expectancies corresponding to those of the four groups presently under consideration. Age-specific mortality displays the well-known “U-shaped” pattern featuring high death rates at the youngest and oldest ages, separated by a period of much lower mortality during the intervening years. In each case, death rates for the least healthy are higher at every age. Of particular interest in the present context is the magnitude of the difference between the two groups, which is not constant. In the high-mortality society, for example, the death rate of the high-mortality group’s children aged 1–5 is about 3.6 times that of children in the low-mortality group. This difference declines sharply with age: it is only TABLE 3 Age-Specific Mortality Rates (per 1,000 population) of Population Groups With Differing Life Expectancies   High-Mortality Country Low-Mortality Country Age (1) Least Healthy 10–20% of Population (2) Healthiest 10–20% of Population (3) Ratio (2)/(3) (4) Least Healthy 10–20% of Population (5) Healthiest 10–20% of Population (6) Ratio (5)/(6) (7) 0 148.80 62.49 2.38 74.45 13.31 5.59 1 20.15 5.58 3.61 7.37 0.44 16.79 5 4.50 1.61 2.80 2.00 0.23 8.54 10 3.37 1.24 2.72 1.53 0.20 7.58 15 4.73 1.96 2.42 2.35 0.39 6.04 20 6.39 2.71 2.36 3.24 0.55 5.95 25 7.13 3.00 2.38 3.60 0.58 6.18 30 8.15 3.44 2.37 4.12 0.70 5.92 35 9.43 4.19 2.25 4.96 0.97 5.13 40 11.16 5.42 2.06 6.27 1.55 4.04 45 13.42 7.43 1.81 8.34 2.77 3.01 50 17.97 10.75 1.67 11.87 4.70 2.53 55 24.00 15.76 1.52 17.05 8.10 2.11 60 35.09 24.06 1.46 25.80 13.42 1.92 65 50.31 37.03 1.36 39.16 23.19 1.69 70 75.49 58.64 1.29 61.35 40.08 1.53 75 115.04 93.60 1.23 97.06 69.09 1.40 80 175.81 146.80 1.20 151.54 112.60 1.35 85 270.64 231.02 1.17 237.54 183.27 1.30 90 413.43 360.62 1.15 369.37 295.78 1.25 95 626.93 557.93 1.12 569.41 472.27 1.21 100 949.35 860.99 1.10 875.70 751.40 1.17   SOURCE: Average figures for males and females from West model life tables in Coale and Demeny (1983:46, 50, 53).

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings prevalent category is equal to or less than the cost of preventing a death in the less prevalent categories.13 In view of what is currently known about the cost-effectiveness of dealing with different age- and disease-specific groups (Jamison and Mosley, 1993), the inclusion of cost-effectiveness considerations would seem unlikely to upset many of the conclusions reached in situations where communicable diseases and deaths at an early age appear to deserve highest priority. However, the costs of the traditional approaches to averting deaths among older people from chronic diseases are notoriously high, which raises the possibility that the illustrations in which such diseases emerge dominant overstate the case for according priority to them.14 A fifth consideration concerns the existence of wide differences in the distributional implications of dealing with specific problems within each of the broad age and disease categories covered. Even if a general focus on adults and the elderly or on noncommunicable diseases is less progressive or more regressive than an overall emphasis on communicable diseases or on infants and children, such will not necessarily be the case with respect to each and every noncommunicable condition that occurs primarily at older ages. The figures in Table 6 on deaths resulting from complications of pregnancy illustrate the point. Deaths from this cause, a condition of young adults that is not communicable in the standard sense of the term, represent too small a proportion of total deaths at older ages to justify their use as the basis of any generalization (Bulatao and Stephens, 1989). Unlike other, more frequent causes of death from noncommunicable diseases, however, they clearly affect the least healthy groups far more severely than the healthiest groups: more than twice as severely in the high-mortality population and seven times as severely in the low-mortality one. If comparable figures available were for malnutrition, the results would probably be similar. Such examples serve as important reminders that, although a primary overall focus on communicable diseases and an overall emphasis on infants and children might well be justified, the careful examinations necessary for the formulation of intelligent policies are likely to find at least some activities 13   Otherwise, in a situation of limited resources, a greater number of deaths would be prevented by first directing those resources against that minority of deaths that can be averted at relatively low cost and only then paying attention to the more difficult majority. 14   Unless one assumes that any general shift in priorities toward chronic diseases at older ages is accompanied by a major change in the way in which these diseases are approached. A shift away from high-technology curative approaches that are currently predominant in the treatment of noncommunicable diseases among adults and the elderly, toward the simpler treatment and behavioral measures advocated by reformers, would lead to cost-effectiveness ratios apparently comparable to those of the approaches currently in use for dealing with communicable diseases at younger ages. This shift would reduce or eliminate any overstatement that might exist.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings outside those rubrics that deserve inclusion in a program oriented toward disparity reduction. The sixth consideration is the possibility of modifying the distributional effects of efforts to reduce mortality by targeting specific population groups to benefit from them. By careful targeting, a program with any age- or disease-specific focus can be shaped to benefit the least healthy more than the healthiest. For example, even a tertiary-care facility specializing in rare cardiovascular conditions will benefit the least healthy more than the healthiest and thereby reduce inequalities if it is located in a poor neighborhood and its services are made available only to the poorest residents of that neighborhood. However, the magnitude of the distributional improvement thereby achieved would obviously be far smaller than that which brought about by applying the same resources to an approach more closely aligned with the epidemiological conditions prevailing in the poor community being served. As the example demonstrates, targeting has an extremely important role to play in the development of programs to help the disadvantaged, but it cannot by itself lead to the efficient reduction of disparities that represents the logical objective of such efforts. To reduce disparities, targeting must be used in conjunction with a clear appreciation of the target population’s demographic-epidemiological situation and with cost-effectiveness considerations. CONCLUSION The importance of the considerations presented in the preceding section point to a clear need for further research in many areas to assist in the development of effective approaches to the reduction of differences in health status. One can only urge and hope that such research will proceed as rapidly as possible. In the meantime, thousands of health policymakers will be making hundreds of thousands of decisions concerning health policies that will benefit different groups in different manners. Can guidance, however preliminary and provisional, be drawn from what is now known and presented here in order to help them decide how to respond to the demographic and epidemiological transitions? At the heart of any such guidance would have to be a warning against responding to overall trends such as those portrayed by the data presented in tables 1A and 1B by a general shift in health priorities toward a greater emphasis on problems caused by noncommunicable diseases among adults and the elderly. These data are societal averages. To rely on them is to overlook the differences that exist among groups within society and, in the process, to give as much weight to problems concentrated in society’s most privileged groups as to those of greatest relevance for the least healthy. The

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings information presented here suggests that the least healthy can be much better served by a strategy based on a careful study of their particular needs and that such a strategy is likely to give highest priority to communicable diseases among the young. REFERENCES Bobadilla, J.L., J.Frenk, T.Frejka, R.Lozano, and C.Stern 1993 The epidemiological transition and health priorities. In D.T.Jamison and W.H. Mosley, eds., Disease Control Priorities in Developing Countries. New York: Oxford University Press for the World Bank. Bulatao, R.A., and P.W.Stephens 1989 Estimates and projections of mortality by cause: A global overview, 1990–2015. Unpublished manuscript, World Bank, Washington, D.C. 1992 Estimates and projections of mortality by cause: A global overview, 1970–2015. Policy Research Working Papers. Washington, D.C.: World Bank. Bulatao, R.A., E.Bos, P.W.Stephens, and M.T.Vu 1989 Africa regional population projections (1989–1990 edition). Policy Planning and Research Working Paper. No. WPS 330 (November). Population and Human Resources Department of the World Bank, Washington, D.C. Bulatao, R.A., E.Bos, P.W.Stephens, and M.T.Vu 1990 Latin America and the Caribbean region population projections, 1989–1990 edition. Policy Planning and Research Working Paper. No. WPS 329 (November). Population and Human Resources Department of the World Bank, Washington, D.C. Coale, A.J., and P.Demeny, with B.Vaughan 1983 Regional Model Life Tables and Stable Populations, 2nd ed. New York: Academic Press. Feachem, R., T.Kjellstrom, C.J.L.Murray, M.Over, and M.A.Phillips, eds. 1992 The Health of Adults in the Developing World. New York: Oxford University Press for the World Bank. Jamison, D.T., and W.H.Mosley. 1993 Disease control priorities in developing countries. In D.T.Jamison and W.H. Mosley, eds., Disease Control Priorities in Developing Countries. New York: Oxford University Press for the World Bank.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings APPENDIX Intergroup Differences in Mortality Within Developing Countries A. Populations differentiated by income status Brazil: The highest mortality group for 1970 is based on households with monthly incomes below Cr$100, representing 21.0 percent of the total population. Infant mortality rate (IMR) was 127.4 per 1,000 live births and life expectancy at birth (e0) was 48.9 years. The lowest mortality group is based on households with monthly incomes above Cr$1,000, representing 22.0 percent of the total population. IMR was 60.8 per 1,000 live births and e0 was 62.2 years. IMR relative difference: 2.10 times; IMR absolute difference: 66.6 deaths per 1,000 live births. e0 relative difference: 1.27 times; e0 absolute difference: 13.3 years.   Charles H.Wood and José Alberto Magno de Carvalho, The Demography of Inequality in Brazil, (Cambridge, New York, New Rochelle, Melbourne, Sydney: Cambridge University Press, 1988), p. 190. (Infant mortality derived through application of model life tables to life expectancy figures provided in text.) B. Populations differentiated by place or residence Kenya: The highest mortality group for 1974 is based on residents of Coast and Nyanza Provinces, representing 26.1 percent of the total population. IMR was 140.3 per 1,000 live births and e0 was 46.7 years. The lowest mortality group is based on residents of the Central Province, representing 15.3 percent of the total population. IMR was 58.0 per 1,000 live births and e0 was 62.9 years. IMR relative difference: 2.42 times; IMR absolute difference: 82.3 deaths per 1,000 live births. e0 relative difference: 1.36 times; e0 absolute difference: 16.2 years.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings   W.Henry Mosley, “Will Primary Care Reduce Infant and Child Mortality? A Critique of Some Current Strategies, with Special Reference to Africa and Asia,” in Jacques Vallin and Alan D.Lopez, eds. Health Policy, Social Policy, and Mortality Prospects: Proceedings of a Seminar at Paris, France, February 28-March 4, 1983, (Place of publication not indicated: Ordina Publications for the Institut National d’Etudes Démographiques and the International Union for the Scientific Study of Population, 1985), p. 109. (Infant mortality and life expectancy derived through application of model life tables to figures for child deaths by age 2 per 1000 live births presented in text.) Sudan: The highest mortality group for 1973 is based on residents of Bahr El Ghazal Province, representing 9.4 percent of the total population. IMR was 227.5 per 1,000 live births and e0 was 34.2 years. The lowest mortality group is based on residents of the Khartoum Province, representing 7.8 percent of the total population. IMR was 107.6 per 1,000 live births and e0 was 52.5 years. IMR relative difference: 2.11 times; IMR absolute difference: 119.9 deaths per 1,000 live births. e0 relative difference: 1.54 times; e0 absolute difference: 18.3 years.   Abdul-Aziz Farah and Samuel H.Preston, “Child Mortality Differentials in Sudan,” Population and Development Review, vol. 8, no. 2 (June 1982). (Infant mortality derived through application of model life tables to life expectancy figures presented in text. Figures for percentage of total population calculated from 1973 census.) India: The highest mortality group for 1986 is based on residents of Uttar Pradesh State, representing 16.2 percent of the total population. IMR was 132.0 per 1,000 live births and e0 was 48.0 years. The lowest mortality group is based on residents of Kerala, Maharashtra, and Punjab States, representing 15.3 percent of the total population. IMR was 54.9 per 1,000 live births and e0 was 63.5 years. IMR relative difference: 2.40 times; IMR absolute difference: 77.1 deaths per 1,000 live births. e0 relative difference: 1.32 times; e0 absolute difference: 15.6 years.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings   Office of the Registrar General, Ministry of Home Affairs, Government of India, Registrar General’s Newsletter, vol. XIX, no. 1 (January 1988), p. 16. (Life expectancy derived through application of model life tables to infant mortality figures provided in text. Figures for percentage of total population calculated from 1981 census.) Philippines: The highest mortality group for 1986 is based on residents of Western and Central Mindanao Regions, representing 10.0 percent of the total population. IMR was 101.4 per 1,000 live births and e0 was 53.2 years. The lowest mortality group is based on residents of the National Capital Region, representing 12.9 percent of the total population. IMR was 36.3 per 1,000 live births and e0 as 67.5 years. IMR relative difference: 2.79 times; IMR absolute difference: 65.1 deaths per 1,000 live births. e0 relative difference: 1.27 times; e0 absolute difference: 14.3 years.   Panfila Ching, “Factors Affecting the Demand for Health Services in the Philippines,” (unpublished manuscript, 1989), pp. 10–21, 29. Mexico: The highest mortality group for the period 1982–1988 is based on residents of eight southern states, representing 16.6 percent of the total population. IMR was 92.0 per 1,000 live births and e0 was 55.5 years. The lowest mortality group is based on residents of the seven northern states, representing 14.8 percent of the total population. IMR was 28.0 per 1,000 live births and e0 was 70.2 years. IMR relative difference: 3.29 times; IMR absolute difference: 64.0 deaths per 1,000 live births. e0 relative difference: 1.26 times; e0 absolute difference: 14.7 years.   José-Luis Bobadilla et al., “The Epidemiological Transition and Health Priorities,” draft manuscript prepared for the World Bank Disease Control Priorities Review, December 1989, p. 14a. (Life expectancy derived through application of model life tables to infant mortality figures presented in text. Figures for percentage of total population calculated from 1980 census.)

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings Peru: The highest mortality group for 1967–1968 is based on residents of the Southern Region, representing 18.6 percent of the total population. IMR was 173.0 per 1,000 live births and e0 was 42.0 years. The lowest mortality group is based on residents of the Metropolitan Region, representing 24.4 percent of the total population. IMR was 78.0 per 1,000 live births and e0 was 58.2 years. IMR relative difference: 2.22 times; IMR absolute difference: 96.0 deaths per 1,000 live births. e0 relative difference: 1.39 times; e0 absolute difference: 16.2 years.   Hugo Behm and Alfredo Ledesma, La Mortalidad en Los Primeros Años de Vida en Paises de La America Latina: Peru, 1967–1968, (Serie A. No. 1029) (San José: Centro Latinoamericano de Demografia, Mayo de 1977), pp. 11, 38. C. Populations differentiated by women’s educational status Burundi: The highest mortality group for the period 1981–1987 is based on children of women with no education, representing 80.2 percent of the total population. IMR was 90.0 per 1,000 live births and e0 was 55.8 years. The lowest mortality group is based on children of women with secondary or higher education, representing 2.2 percent of the total population. IMR was 32.0 per 1,000 live births and e0 was 69.2 years. IMR relative difference: 2.81 times; IMR absolute difference: 58.0 deaths per 1,000 live births. e0 relative difference: 1.24 times; e0 absolute difference: 13.4 years.   “Burundi 1987: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 20, No. 3 (May/ June 1989). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) Dominican Republic The highest mortality group for the period 1982–1985 is based on children of women with no education, representing 4.8 percent of the total population. IMR was

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings   102.0 per 1,000 live births and e0 was 53.5 years. The lowest mortality group is based on children of women with higher education, representing 8.4 percent of the total population. IMR was 34.0 per 1,000 live births and e0 was 68.5 years. IMR relative difference: 3.00 times; IMR absolute difference: 68.0 deaths per 1,000 live births. e0 relative difference: 1.26 times; e0 absolute difference: 15.0 years.   “Dominican Republic 1986: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 19, No. 2 (March/April 1988). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) Ecuador: The highest mortality group for the period 1982–1987 is based on children of women with no education, representing 7.8 percent of the total population. IMR was 106.0 per 1,000 live births and e0 was 52.8 years. The lowest mortality group is based on children of women with higher education, representing 9.2 percent of the total population. IMR was 22.0 per 1,000 live births and e0 was 71.9 years. IMR relative difference: 4.82 times; IMR absolute difference: 84.0 deaths per 1,000 live births. e0 relative difference: 1.36 times; e0 absolute difference: 19.1 years.   “Ecuador 1987: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 20, No. 2 (March/April 1989). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) Indonesia: The highest mortality group for the period 1984–1987 is based on children of women with no education, representing 23.2 percent of the total population. IMR was 99.0 per 1,000 live births and e0 was 54.1 years. The lowest mortality group is based on children of women with secondary or higher education, representing 13.1 percent of the total population. IMR was 34.0 per 1,000 live births and e0 was 65.6 years.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings   IMR relative difference: 2.91 times; IMR absolute difference: 65.0 deaths per 1,000 live births. e0 relative difference: 1.27 times; e0 absolute difference: 14.4 years.   “Indonesia 1987: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 20, No. 5 (September/October 1989). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) Senegal: The highest mortality group for the period 1981–1986 is based on children of women with no education, representing 77.2 percent of the total population. IMR was 96.0 per 1,000 live births and e0 was 54.7 years. The lowest mortality group is based on children of women with higher education, representing 9.3 percent of the total population. IMR was 50.0 per 1,000 live births and e0 was 64.7 years. IMR relative difference: 1.92 times; IMR absolute difference: 46.0 deaths per 1,000 live births. e0 relative difference: 1.16 times; e0 absolute difference: 10.0 years.   “Senegal 1986: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 19, No. 6 (November/December 1988). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) Thailand: The highest mortality group for the period 1982–1987 is based on children of women with no education, representing 9.7 percent of the total population. IMR was 54.03 per 1,000 live births and e0 was 63.7 years. The lowest mortality group is based on children of women with secondary or higher education, representing 7.7 percent of the total population. IMR was 19.0 per 1,000 live births and e0 was 72.9 years. IMR relative difference: 2.84 times; IMR absolute difference: 35.0 deaths per 1,000 live births. e0 relative difference: 1.14 times; e0 absolute difference: 9.2 years.

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings   “Thailand 1987: Results from the Demographic and Health Survey,” Studies in Family Planning, Vol. 20, No. 1 (January/February 1989). (Life expectancy derived through application of model life tables to infant mortality figures provided in text.) D. Populations differentiated by race South Africa: The highest mortality group for the period 1981–1985 is based on the black population, representing 68.0 percent of the total population. IMR was ranged from 94.0 to 124.0 per 1,000 live births and e0 ranged from was 49.4 to 55.1 years. The lowest mortality group is based on the white popuation, representing 18.2 percent of the total population. IMR was 12.3 per 1,000 live births and e0 was 75.3 years. IMR relative difference: 7.64–10.08 times; IMR absolute difference: 81.7–111.7 deaths per 1,000 live births. e0 relative difference: 1.37–1.52 times; e0 absolute difference: 20.2–25.9 years.   D. Yach, “Infant Mortality Rates in Urban Areas of South Africa, 1981–1985,” South African Medical Journal, vol. 73 (1988), p. 234. (Life expectancy derived through application of model life tables to infant mortality figures presented in text. Figures for percentage of total population calculated from 1980 census.) NOTES The data here are drawn from the 14 studies referred to in the text. These studies employed a wide range of approaches that required conversion into a standardized format. Three aspects of this conversion process are worthy of note: 1.   None of the studies presented information divided precisely into the population groups desired: that is, the top 10 or 20 percent and the bottom 10 or 20 percent of the total population. To provide workable approximations, an effort was made to identify groups of roughly the same size equaling as closely as possible the highest and lowest 15 percent of each population under study. For this purpose, smaller groups were combined, and weighted averages of the relevant parameters were prepared when necessary. As can be seen and as noted in the text, it was possible to come reasonably close to the desired objective for studies categorizing health status by income and place of residence, but not possible to approach it when the basis of categorization was educational status or race, because of the limited number of categories and uneven distribution of population among them. (A follow-up inquiry is planned to gather information from more countries at different points in time and to organize it in terms of

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The Epidemiological Transition: Policy and Planning Implications for Developing Countries - Workshop Proceedings     a common distributional index—such as the Gini coefficient—in order to permit intercountry and intertemporal comparisons.) 2.   Only two of the fourteen studies (Peru and the Philippines) included both life expectancy and infant mortality figures. One (Kenya, in which mortality was expressed in terms of survival probabilities from birth through age 2) provided neither. The remaining eleven presented either life expectancy or infant mortality, but not both. The missing life expectancy and/or infant mortality figures were estimated using the average of female and male figures taken from the Coale-Demeny West model life tables. An indication of which figures were calculated in this manner is provided in a parenthetical note accompanying each data source. 3.   Four of the studies (India, Mexico, South Africa, and Sudan) lacked the information about group-specific population sizes needed to estimate the proportion of the population. In these cases, the information provided in the studies has been supplemented by census data. Information about the years of the censuses used appears in the parenthetical notes accompanying each relevant data source.