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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance 7 The Human and Economic Burden of Malaria INTRODUCTION The cost of malaria can be measured in lives lost, in time spent ill with fever, and in economic terms. Money spent on preventing and treating malaria, the indirect costs of lost wages, time home from school, and time spent caring for sick children, adds up at the personal level. In the public sector, large fractions of health sector budgets are spent on malaria control and treatment. And at the macroeconomic level, a heavy national burden of malaria dampens economic development, sometimes subtly, but pervasively. All of these effects are recognized and accepted widely, but their magnitude has been poorly documented. Studies of the effects of malaria have most often been motivated by a desire to understand the costs of the disease to individuals and society, and frequently to justify public expenditures to diminish the burden. This type of work has only grown in importance, as competition for resources is ever more explicit, both within the spectrum of malaria activities (research vs. control, prevention vs. treatment) and between malaria and other diseases. THE HEALTH BURDEN OF MALARIA IN AFRICA In Africa, most people are born, live, and die without leaving a trace in the official record. Don de Savigny, Tanzania Essential Health Interventions Project
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance Estimates of Deaths from Malaria Deaths are not well counted in much of the world, and the situation is worst where people are poorest—which is where malaria takes its greatest toll. This has begun to change—Snow and colleagues (2003) date recognition of the importance of reliable estimates to the World Health Organization’s (WHO’s) Global Burden of Disease program of the early 1990s—but the figures available are still approximate. Some 50 years ago, Leonard Bruce-Chwatt estimated the annual African death toll from malaria at one million. The figure was an extrapolation to the continent based on the civil registration of deaths in Lagos in 1950 (Bruce-Chwatt, 1952). Other numbers have been produced since then—between 0.5 million and 2 million deaths per year—using a variety of more and less evidence-based methods (Sturchler, 1989; Greenwood, 1990; WHO, 1996; Schwartlander, 1997). The most plausible current estimates come from Snow and colleagues, and their work on the Burden of Malaria in Africa (BOMA) project (Snow et al., 2003), which builds on the MARA (Mapping Malaria Risk in Africa) risk mapping, as well as mining of a wide range of other data sources. Their best estimate is 1,144,572 deaths attributable directly to malaria in Africa in the year 2000 (Table 7-1). WHO’s most recent estimate of malaria deaths is similar—a worldwide total of 1,124,000 deaths due directly to malaria in 2001, of which about 970,000 would have been in Africa (WHO, 2002). The estimate also includes about 90,000 malaria deaths in Southeast Asia, 56,000 in the Eastern Mediterranean region, 11,000 in the Western Pacific, and about 1,000 in the Americas. While in the same overall range, the estimates do differ in their distribution among age groups: Snow and colleagues figures suggest that about 65 percent of deaths among children under 5, and the corresponding figure from WHO is much higher at 86 percent. Estimates of Malaria Cases There are no direct counts of the number of cases of malaria that occur each year. A very wide range of estimates has been made, using a variety of definitions. The lack of precision is problematic for this report, because an estimate of how many courses of malaria treatment are used is needed to estimate how much a global ACT subsidy will cost. The number of cases and the number of treatments are not the same but are related. Malaria cases may be overestimated because few cases are definitively diagnosed before treatment (whether prescribed or self-selected), so quite a large number of fevers that are not malaria may be counted as such. The number also may be underestimated because people may get no treatment, either through choice or economic default, and in any case, episodes of malaria are not kept track of formally anywhere.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance TABLE 7-1 Estimated Malaria-Specific Mortality during 2000, best estimate and IQR range 0-4 years 5-14 years 15+ years Total Southern Africa malaria risk (Class 4) 266 [164–430] 482 [297–779] 1,129 [695–1,824] 1,877 [1,156–3,033] Rest of Africa—low stable/ epidemic risk (Classes 2+3) 57,688 32,588 49,079 139,355 Rest of Africa—stable endemic risk (Class 4) 684,364 [541,330–1,068,723] 182,113 [76,072–319,274] 136,863 [84,399–214,419] 1,003,340 [701,801–1,1602,415] Total 742,318 [541,494–1,069,153] 214,701 [76,369–320,053] 187,071 [85,094–216,253] 1,144,572 [702,957–1,605,448] IQR = Interquartile range, which represents the range of values from the 25th percentile to the 75th percentile, essentially the range of the middle 50% of the data. Because it uses the middle 50%, the IQR is not affected by extreme values. Classification of areas: Class 1: no human settlement, or unsuitable climate for malaria transmission. Class 2: populations exposed to marginal risks of malaria transmission, uncommon in an average year. Class 3: populations exposed to acute seasonal transmission with a tendency toward epidemics. Class 4: populations exposed to stable, endemic malaria transmission. In southern Africa (Namibia, Swaziland, South Africa, Botswana, Zimbabwe) Class 4 areas, malaria still poses a risk but its extent and transmission potential are determined by aggressive vector control. SOURCE: Snow et al. (2003). Snow and colleagues, using the general pattern of risk and population as they used to estimate deaths for the Disease Control Priorities Project, also estimated the number of malaria episodes likely to occur in Africa in a year (the year 2000). In this case, they used the BOMA database to search for prospective studies of the incidence of fever (i.e., axillary or rectal body temperature ≥ 37.5°) since the 1980s.1 Studies were included if they met the following criteria: 1 Prospective studies have been carried out as part of controlled trials or for descriptive purposes. In such studies, a defined population is monitored continuously or periodically.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance They were cross-sectional studies with at least two rounds of observations on a single cohort to reflect seasonal differences in risk. Subjects were recruited randomly from the community, and not from a clinic population. For cohorts involved in randomized trials, only the control arm participants (i.e., those not receiving a study intervention) were included. Clinical episodes were defined by quantified parasite levels (although levels differed among studies, ranging from 1,000 to 10,000 parasites/µl blood). Definitions and methods of detection differed among the studies, and it was not possible to standardize them; some lack of comparability and imprecision had to be accepted. The results are summarized in Table 7-2, ending with a median estimate of 213,549,000 episodes per year in Africa (with the estimate ranging from 134,322,000 to 324,617,000). Among young children in stable endemic areas, data from 28 studies led to a median estimate of 1,424 episodes per 1,000 children each year (i.e., nearly 1.5 episodes per child). The TABLE 7-2 Estimated Number [IQR] of Clinical Attacks (thousands) of Malaria during 2000 0-4 years 5-14 years 15+ years Total Southern Africa malaria risk (Class 4) 60 [20–265] 109 [36–479] 255 [84–1,122] 424 [140–1,866] Rest of Africa—low stable/epidemic risk (Classes 2+3) 4,007 [2,752–4,756] 6,310 [4,333–7,488] 6,291 [4,320–7,466] 16,608 [11,405–19,710] Rest of Africa—stable endemic risk (Class 4) 104,452 [61,468–158,952] 67,658 [44,145–112,610] 24,407 [16,880–31,479] 196,517 [122,493–303,041] Totals 108,519 [64,240–163,982] 74,077 [48,514–120,577] 30,953 [21,284–40,067] 213,549 [134,322–324,617] SOURCE: Snow et al. (2003).
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance 19 studies that included older children led to an estimate of 587 per 1,000 (on average, about one episode every other year). The median estimate for non-pregnant adults (based on only seven studies) was 107 per 1,000 people each year. Rates in low-transmission and fringe areas were correspondingly lower. The Effect of Antimalarial Drug Resistance on the Burden of Malaria Given the fragmentary statistics on malaria morbidity and mortality, it may seem presumptuous to attempt an assessment of how the spread of resistance to chloroquine and sulfadoxine-pyrimethamine (SP) has affected these measures over the past decades. There is, in fact, little direct information on how the number of cases of malaria has changed in the most endemic areas. What has been documented, however, are stark bellwethers of worsening conditions: a well-documented instance is the malaria epidemic of 1999-2000 in KwaZulu Natal, South Africa, which was a direct result of failing antimalarial drugs—SP had a failure rate of 88 percent. Other factors—increased vector resistance to the pyrethroid insecticides that were introduced in 1996, and the reinvasion of the highly anthropophilic Anopholes funestus vector—further exacerbated this epidemic (Muheki et al., 2003). The introduction of ACTs (and other control measures) brought the epidemic under control. And the question of changes in mortality has been addressed by thorough reviews of the data that do exist, in two major efforts. The first, the long view across the 20th century, used a variety of historical records from the BOMA project (Snow et al., 2001). The other case is an examination of trends in the 1980s and 1990s on a finer scale, contrasting East and West Africa (Korenromp et al., 2003), using the relatively uniform data reported in African Demographic Surveillance Systems (DSS). Trends Through the 20th Century The BOMA project provides the best opportunity to chart malaria’s past in Africa, and how drug resistance has affected its course over the final decades of the 20th century. The BOMA project began in 1998 with the aim of assembling in a single database all available evidence on morbidity, disability, and mortality associated with falciparum malaria in Africa, starting as far back as possible. The data come not only from the usual electronic databases, but from hand searches of early, unindexed papers in English and French tropical medicine journals, and a mass of unpublished material from local and regional conference proceedings, libraries, and Ministries of Health records (Snow et al., 2001). In 2001, Snow and colleagues selected as much information as possible
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance from the BOMA database to inform an analysis of malaria mortality in Africa from 1900 through the 1990s (Snow et al., 2001). They took mortality reports from areas with documented, stable endemic transmission, where the prevalence of parasitemia in children was at least 30 percent, and which had recorded both all-cause mortality and malaria-specific mortality. Thirty-nine studies in 13 countries of sub-Saharan Africa 2 met the criteria, spanning the period from 1912 to 1995. A year-by-year analysis was not possible with these scattered, sparse data. Instead, the time span was divided into three periods, corresponding approximately to changes of probable significance to malaria control. The period before 1960 represents a time of limited access to primary health care and hence, to effective antimalarial drugs. From 1960 until 1990, after the beginning of independence for most countries, health care expanded across Africa and chloroquine became widely available, both from health services and as self-medication. The 1990s saw the widespread emergence of chloroquine resistance in many parts of Africa. The picture painted by these data suggests a continuing downward trend in total child mortality over the three periods, but a downward and then ascending course for malaria-specific mortality, with the lowest rates in the middle period, and similar rates pre-1960 and post-1990 (Figure 7-1). With a 34 percent decline in overall mortality from before 1960 into the 1990s, and the fall and rise of malaria death rates into the 1990s, the proportion of all deaths due to malaria first fell from 18 percent pre-1960 to 12 percent in 1960-1990 but rose to 30 percent during the 1990s. Snow and colleagues (2001) cite data from Tanzania, Senegal, and Kenya comprising more detailed time series, which are consistent with the findings overall. The data used to describe these trends, are by their nature, limited and not entirely comparable. The pre-1960 data are mainly from colonial Anglophone Africa, where malaria deaths were tracked through civil notification systems operating in defined populations. The pre-1960 systems probably missed a greater proportion of deaths than the later prospective surveillance studies, which have high rates of ascertainment of the fact of death. However, identification of deaths from “malaria” may actually have been more accurate in the earlier period because deaths often were followed up by a medical officer to determine their cause. The later surveillance systems rely on assigning cause of death retrospectively, mainly through verbal autopsies. The declines in childhood mortality during the second half of the 20th 2 Senegal, The Gambia, Guinea Bissau, Sierra Leone, Ghana, Nigeria, Benin, Democratic Republic of Congo, Burundi, Uganda, Kenya, Tanzania, and Malawi.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance FIGURE 7-1 Malaria mortality and all cause mortality per 1000 children under 5 years in three time periods: pre-1960, 1960–1989, and 1990–1995. Central thick lines represent median estimates, box height represents 25%–75% quartile range, and extensions represent upper and lower confidence intervals. SOURCES: Trape and Marsh, 2001; Snow, Trape, and Marsh, 2001. century are well documented and accepted. The biggest declines have been in deaths from diarrheal disease, widely attributed to the development and dissemination of oral rehydration therapy, largely through WHO-sponsored programs. Declines also occurred in deaths from pneumonia and other infectious diseases. The declines are not uniform, and there are even places where trends are reversing in overall child mortality, but by and large, the trends have been positive. Malaria appears to be an exception. How can the observed trends in malaria mortality among African children be explained? The positive effects on total mortality are a plausible result of expanded basic health services, including vaccinations, the widespread adoption of oral rehydration therapy, expanded access to antibiotics, and at least in places, improved sanitation and general living condi-
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance tions. In the mid-20th century, chloroquine would have counted among new beneficial drugs, even in the absence of a major campaign against malaria. The rise in malaria-specific mortality coincides with the spread of chloroquine-resistant strains of falciparum malaria. Although it is virtually impossible to demonstrate a causal link, in the absence of any other compelling reason, chloroquine resistance is the most plausible explanation for the increase. Trends in the 1980s and 1990s Resistance to chloroquine first became apparent in Africa in the late 1970s, more than a decade after its appearance in Asia. By 1990, resistant strains had been reported from all the endemic countries in Africa (Trape, 2001). The spread of drug resistance was implicated in the previous section as responsible for reversing the improvement in childhood mortality from malaria. The inference was made because mortality patterns in large population groups coincided with the general patterns of drug resistance, but the measurements of drug resistance were not available to make the link definitive. There are data—albeit limited—that address the question more directly, although again, not definitively. Trape (2001) identified population-and hospital-based studies that recorded annual malaria mortality in Africa, based on continuous monitoring. He chose for analysis all those that spanned a period during which chloroquine resistance was known to be emerging in that area. Some of the hospital-based studies also had information on the prevalence of severe malaria over time. The remainder of this section is based on Trape’s report. Population-Based Studies Three studies tracked childhood malaria mortality in populations over the period in which chloroquine resistance emerged, from the mid-1980s to the mid-1990s. All were in Senegal, in different climatic areas: Mlomp area (rain forest), Niakhar area (Sahel), and Bandafassi area (savanna). In all three areas, the malaria mortality rates increased from the early to the later periods (Table 7-3). They more than doubled in all three sites and in Mlomp, increased 10-fold. Over the same period, standardized clinical protocols for assessing drug failures were carried out at least twice. In all cases, the degree of resistance intensified. In one other place, Bagamoyo, on the Tanzania coast, deaths were investigated in 1984-1985, and again in 1992-1994 (i.e., not continuous, as in Senegal), bounding a period of increasing chloroquine resistance. Overall child mortality remained the same, but the proportion attributed to malaria was twofold higher in the later period.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance TABLE 7-3 Childhood Malaria Mortality in Three Sites in Senegal Year first CQ therapy failures reported Mlomp (hypoendemic) 1985-1989 1990-1992 1993-1995 1990 <5 malaria death rate (per 1,000) 0.5 3.4 5.5 Resistance from standardized surveys RII/RIII: 36% (1991) RII/RIII: 46% (1995) Niakhar (mesoendemic) 1984-1991 1992-1995 1992 <10 malaria death rate (per 1,000) 4.0 8.2 Resistance from standardized surveys RII/RIII: 10% (1993) RII/RIII: 29% (1996) Bandafassi (holoendemic) 1984-1992 1993-1995 1993 <5 malaria death rate (per 1,000) 4.2 11.4 Resistance from standardized surveys RII/RIII: 6% (1994); RII/RIII: 16% (1995) SOURCE: Trape, 2001. Hospital-Based Evidence Data from hospitals in several countries offer another glimpse at trends in malaria incidence and death, with definite limitations. A hospital population consists only of those who decide—often through a complex decision-making process—to seek care there. Trends in the proportion of people with malaria who go to a hospital may relate to changes in incentives for hospital-based versus other sites of care (e.g., the population may know that hospitals are out of drugs at a particular time, a competing provider may be more attractive, or money for the hospital is unavailable in a given season or year) and changes in the incidence of other diseases, as well as to changes in the epidemiology of malaria. Hospitals also have advantages because of their range of expertise and technologies, and their record keeping. Malawi. Malawi has national records for hospital admissions and deaths. From 1978 through 1983, the incidence of admissions for malaria
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance for children under 5 years of age more than doubled. Throughout the period, about 5 percent of those children died (Khoromana et al., 1986). The spread of chloroquine resistance in Malawi during this time was well documented. Tanzania. The mission hospitals in Tanzania recorded steep increases in the proportion of admissions for malaria from 1968 through 1985. In the 1970s, about 10 percent of admissions were for malaria, and by the mid-1980s, had risen to 23 percent (Kilama and Kihamia, 1991). This also coincides with the rapid increase in chloroquine-resistant malaria in Tanzania. Congo. In the hospital that was the main referral center in Kinshasa, the proportion of overall pediatric admissions for malaria increased each year, from 29.5 percent in 1982, to 56.4 percent in 1986, and the proportion of deaths increased from 4.8 to 15.3 percent over the same period (Greenberg et al., 1989). Again, this was the period during which chloroquine resistance emerged and spread quickly in Kinshasa. Malaria admissions and deaths at the four hospitals in Brazzaville were studied for the years 1983-1989, during which (in 1985) chloroquine resistance was first detected there. From 1983 through 1986, pediatric admissions for malaria increased from 22 to 54 percent of all pediatric admissions, and then remained stable. Deaths from cerebral malaria more than doubled from the first to the second half of the study period (Carme et al., 1992). Nigeria. A similar pattern was seen in the pediatric emergency room of Calabar Hospital in Nigeria, where the number of cases of malaria-related convulsions doubled during the years 1986 through 1988. In 81 percent of these cases, chloroquine was ineffective (Asindi et al., 1993). Other Types of Evidence from Hospital-Based Studies Various other studies, including one of trends in severe anemia in Kenya, and another of changes in hospital case fatality rates for malaria after a switch from chloroquine to SP, provide corroborating evidence that chloroquine resistance has led to increases in severe malaria and malaria deaths (Trape, 2001). THE ECONOMIC BURDEN OF MALARIA It has long been recognized that a malarious community is an impoverished community. T. H. Weller, Nobel Laureate in Medicine, 1958
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance We remain woefully ignorant of the social and economic effect of malaria in those countries of the world where it is prevalent. Andreano and Helminiak, 1988 … from the preventive point of view this [the cost of malaria] is perhaps the most important question before us; because, obviously, it governs the question of the expenditure which may be demanded for the anti-malarial campaign. Ronald Ross, 1911 The human costs of malaria are high, in lives that are lost and many more that are diminished. The immediate monetary costs of treating and trying to prevent disease are obvious and large, for governments and families. Those costs are far from the whole economic story, though. Malaria’s presence has—subtly, and overtly—influenced the nature of economic activities that define levels of development, and ultimately health and wellbeing in the broadest sense. For centuries, malaria’s pervasive effects have been recognized, and people have tried to estimate the costs in economic terms (Box 7-1). Ideally, to understand the influence of malaria, one would start with a top-down approach, deriving dollar figures that represent the aggregate economic effects of malaria in a nation or region—the macroeconomic approach—and then working from the ground up, uncovering the detailed chains of causation leading to various streams of cost—the microeconomic approach. Such a coherent economic picture of the whole and its parts is not what exists today, however. The information is richer in quantity and quality than it was in 1991, when the Institute of Medicine last reported on this topic (IOM, 1991), but the knowledge base remains small compared with the size of the problem. In fact, it is not only results that are lacking but the methods for generating them. In a recent review of approaches to evaluating the economic burden of malaria, Malaney (2003) observes: … the state of the art for costing a disease like malaria has not progressed to the point where a dominant paradigm can be said to exist. Rather there are competing schools of thought, each of which directly addresses some piece of the puzzle at the expense of leaving some other aspect of the problem for a competing methodology.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance BOX 7-3 Conly’s Classic Microeconomic Study in Paraguaya In the early 1970s, Conly spent 22 months in the field observing and recording data on malaria (and health more generally), work of all kinds, and finances among farmers in Paraguay in newly-settled areas (Conly, 1975). She divided the families into three groups: “little malaria,” “moderate-incidence,” and “much malaria.” The divisions, to a great extent, reflected immune status, with the “much malaria” families being the most recently arrived from nonmalarious areas, and thus most vulnerable immunologically in all age groups. Conly found immediate effects of malaria on the work a family was able to do, as well as more far-reaching effects. During the period of illness, agricultural labor input declined, crops were neglected, and work done was less efficient than when people were well. In many cases, families were able to make up the lost work, and harvests did not suffer. But she also demonstrated that the threat of malaria caused families to shift from more labor-intensive (and valuable) crops to less labor-intensive ones, to minimize the risk of loss in case malaria kept family members from working at key times, particularly during the harvest. Conly describes the effects on “much malaria” families during their first year of farming: These farmers, even after making use of all available hands, skilled or not, were unable to do enough even to protect their immediate returns. It was not merely a matter of letting the condition of the farm deteriorate in a way that would mean much catching up later on: it was a question of losing crops that had been planted, of not being able to stop the weeds from stunting and choking the growing crops, of being unable to strip off the tobacco leaves at the right moment or get the burst of pods of cotton in before the rain. … In the intervals between bouts of illness these farmers—and their wives, children, parents and other relatives as well—worked long hours, but their yields at harvest time … were 36 percent lower than they should have been. By the second year, they began to adapt their farming in various ways and no doubt, also acquired some immunity to malaria, which would have protected at least some individuals from the debilitating episodes of the first year. This study has the strength of collecting data continuously over nearly two years. Most subsequent work has been based on shorter periods, and on individuals’ recall of illness episodes and their consequences. a This description of Conly’s work is taken from Gomes (1993). Pakistan (Khan, 1966), based on approximately 4.2 million people experiencing malaria per year, of whom 2.5 million were assumed to be workers. The costs included direct costs of treatment for everyone plus lost workdays (valued at an average daily wage rate) for the workers. This totaled to 81 million rupees, which was about 0.75 percent of GNP. Two studies have estimated total household direct and indirect costs. In Malawi, the total annual household cost was estimated at about US$40,
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance which was 7 percent of household income (Ettling et al., 1994). Total household costs were estimated at 9-18 percent of annual income for small farmers in Kenya, and 7-13 percent in Nigeria (Leighton and Foster, 1993). One multicountry study has attempted an Africa-wide estimate of direct plus indirect costs of malaria based on extrapolations from four case studies of areas in Burkina Faso, Chad, Congo, and Rwanda. The totals reported were US$1,064 million overall, which translates to US$3.15 per capita and 0.6 percent of total sub-Saharan Africa GDP (in 1987, inflated to 1999 U.S. dollars) (Shepard et al., 1991). Two of the country case studies estimated household plus government costs (including the direct costs of treatment, but not prevention; and including indirect mortality costs). The national cost per capita was estimated at US$1.55 in Burkina Faso (Sauerborn et al., 1991), and US$3.87 in Rwanda (Ettling and Shepard, 1991), figures that were roughly equivalent to 3.5 days of individual production. Drawbacks of the Human Capital Method for Estimating Costs of Malaria The studies reported here vary widely in the details of the methodology, their sources of data, and in their perspectives. Comprehensive studies of this type are difficult to conceive and to carry out, given inherent limits on data available in the places most affected, general difficulties in carrying out research in such places, and a lack of funding for such studies. Even when done well, however, the methods often require assumptions about such things as employment levels (which affect whether wages are actually lost), the value of leisure time, and substitutability of work by other families. Some of the aspects not generally captured by the human capital method relate to coping strategies adopted by families, which affect their economic well-being in ways not usually measured through direct and indirect costs usually measured. Coping Strategies with Effects Not Captured by the Human Capital Method In addition to paying for malaria prevention and treatment with cash-on-hand, and losing wages and other productive labor, families cope with malaria in ways that diminish their overall economic wellbeing. They respond not only to actual episodes of illness, but to the risk of illness with “anticipatory” coping strategies. These are often ignored in quantitative analyses because they may be hidden, and are at best, difficult to quantify. Families faced with paying extraordinary costs for malaria treatment may:
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance TABLE 7-4 Calculations of Productivity Costs of Morbidity from a Malaria Episode Using the Wage Rate Method Authors Country Method of Valuing Time Atkins (1995) The Gambia Marginal value of work time on crops cultivated by women Asenso-Okeyre et al (1997) Ghana Average agricultural wage differential by age and gender Bruce-Chwatt (1963) Nigeria (psychiatric patients, Lagos) – Cropper et al. (1999) Ethiopia 50% or 100% of the average daily wage Ettling et al. (1991) Rwanda 85% of average rural wage Ettling et al. (1994) Malawi Mean per capita income from household survey Gazin et al. (1988) Burkina Faso (factory) – Guiguemde et al. (1997) Burkina Faso Mean annual income per worker by occupational group and age Leighton and Foster (1993) Kenya Weighted average wage rate in agricultural, service and industrial sectors Leighton and Foster (1993) Nigeria Weighted average wage rate in agricultural, service and industrial sectors Miller (1958) West African men – Nur and Mahran (1988) Gezira, Sudan – Sauerborn et al. (1991) Burkina Faso Market value of average output per person for main produce in each of three seasons SOURCE: Chima et al., 2003.
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance Time Lost per Episode by Sick Adult Time Lost per Episode by Carrier or Sick Child Average Indirect Cost per Episode (1999 US$) – 2.16 h per day per child for 4 days plus time lost seeking treatment – 5 days 5 days 57.63 2.6 days (untreated) – – 18 days (total of 21 days lost, taking account of labor substitution) 2 days (total of 12 days lost, taking account of labor substitution) children $6–23 for adults & teenagers; $3–12 for (including net effects of labor substitution) 1 day for each hospitalized day plus 3 additional days 1 day for each hospitalized day plus 1 additional day – 2.7 days 1.2 days $1.54 for patients over 10 years of age; $0.68 for patients age 10 and under 3.5 days – – 4 days (all ages; 73% were <5) 1.2 days $4.21 2–4 days (plus allowance for lower productivity for 2 days) 2–4 days – 1–3 days (plus allowance for lower productivity for 2 days) 2–4 days – 4.2 days per episode – – 6 days of disability, plus 5 at 50% productivity – – Mild illness 1 day; severe 5 days 1/3 of adult illness time $0.73
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance deplete savings; sell assets important to the household asset base, e.g., livestock; receive cash gifts from family and friends; or take out loans, which can lead to serious debt. When a family member is sick with malaria, other family or community members may compensate for the loss of work time. Where unemployment or underemployment is common, the loss may be fully compensated without difficulty, so the economic loss might be less than otherwise estimated. This is particularly true in agricultural and other nonwage communal settings, but can even be the case for wage laborers whose family members substitute for them. On the other hand, the family of a small-scale farmer who falls ill may not be able to complete the work he would have done. In any case where children are substituting for adult laborers, they also may lose time from school, which is rarely quantified. The anticipatory coping strategies are more subtle, but can have major economic consequences. In a labor market where illness is common, these may include limiting staff specialization or maintaining labor reserves to ensure a sufficient workforce, both of which reduce overall average productivity. Families may limit their investments to maintain cash for malaria emergencies, or invest in assets that can be easily turned into cash. Families also may modify their farming practices, to avoid crops that require intensive work during the high-transmission seasons. Another family coping strategy is having larger numbers of children to ensure that a reasonable number will survive, with all of the obvious and subtle economic consequences entailed. Malaney (Malaney, 2003) observes that the human capital approach “strains under the weight of a disease that affects entire societies on an effectively permanent basis,” a context for which these methods were not originally designed. But she also proposes ways of beginning to broaden the human capital approach to integrate these effects. The Willingness-to-Pay Approach to Disease Valuation Individuals and families regularly make decisions with economic consequences, like deciding to seek treatment for disease as opposed to watching and waiting, deciding whether or not to buy a bednet to prevent malaria, and deciding to plant one crop versus another with different labor requirements and different economic yield when harvested. Clearly, economic realities constrain these decisions, although some are forced upon families, such as a child in convulsions, who must be given attention. The willingness-to-pay approach attempts to estimate the costs of dis-
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance ease by determining the value people would place on avoiding it. The assumption is that this approach would incorporate the burden of treatment and prevention, of lost productivity, lost leisure time, and the value of not having to cope with malaria in other ways. But the way in which this valuation happens is subject to individual interpretation, and assumes that people’s economic choices are rational and related to costs and other consequences. Nonetheless, the amount people are willing to pay to avoid disease—if captured in a reliable way—is a useful measure of how important the disease is, and can help make it a priority for public policy. Studies of this type are very difficult to carry out reliably. The study described here is one of the few examples in malaria. The Tigray Study Malaria is endemic to the Tigray region of northern Ethiopia, with seasonal transmission throughout the rainy season (June through September), peaking toward the end of it (October and November). Malaria is a visible health problem to those who live there. The government encourages community control measures (mainly limiting mosquito breeding sites). It also carries out spraying, and trains community health workers to recognize and treat malaria. Cropper and her colleagues studied the monetary value that households in Tigray place on preventing malaria through a “willingness-to-pay” scenario. They also made conventional “cost of illness” estimates for malaria (direct and indirect costs), in order to compare those figures with how much people were willing to pay to prevent malaria. If accurate, the amount that people are willing to pay to prevent malaria would capture the value placed on the economic losses as well as the pain and suffering caused by malaria, within the financial means of real families. Study Design The survey took place in January 1997 in the Tembien sector of Tigray region, where most people live by subsistence agriculture, plus some income from livestock. One person in each participating household (selection of survey households is described below) was interviewed with a three-part questionnaire. The first and third parts were the same everywhere: first, a “cost-of-illness” section with questions about the household’s current health status, knowledge of malaria, and expenditures on malaria prevention and treatment, mainly in the previous 2 years. The other constant section asked about socioeconomic characteristics of the household and its members, including education, income, assets, occupation, and housing construction. The middle section measured willingness to pay, with questions that varied
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance in order to build a coherent story. To do this, respondents were introduced to one of two scenarios: one regarding a hypothetical malaria vaccine that would prevent the disease entirely for one year, and the other, about bednets. For each scenario, a respondent was asked whether they would buy the intervention (the vaccine or the bednet) at a given price, and how many they would buy, recognizing that a dose of vaccine would protect one person, and a single bednet could be used for one or more people. The price each respondent was given for the intervention was one of five, assigned randomly, so that estimates could be made about the effect of price on willingness to pay. Eighteen study villages were chosen (to represent a range of malaria incidence), and randomly assigned either the vaccine (12 villages) or the bednet (six villages) scenario. The target was to interview one respondent in 50 households chosen in each village (although it was not possible to select random samples of households because of a lack of records, the sampling method appears to have been unbiased). Of the intended 900 interviews, 889 were completed but 41 were dropped from the sample because the respondent was not familiar with malaria. This left 569 in the vaccine sample (about 114 for each of the five price levels) and 279 in the bednet sample (about 56 for each price level). Results Malaria and the Cost of Illness Malaria was common. More than half of the respondents (58 percent) reported having malaria in the previous 2 years; in half of the households, another adult (besides the respondent) had had malaria, and in half, a child or teenager had had malaria. Using the detailed information collected on these episodes, the costs of malaria per episode and per household were calculated (Table 7-5). Direct costs to the household included out-of-pocket expenditures to see a health practitioner, buy medicines, and pay for transportation, amounting to about US$1.60 for an adult episode, and about half that for a child. Indirect costs of illness are based on productive time lost by patients themselves, other family members caring for patients, and family members substituting for patients at their work. The average adult loss per episode was 21 days. Dollar values were assigned to the days lost, for adults, equivalent to the average daily wage for a healthy, unskilled worker. Under the “high productivity” assumption this amounted to US$24 for an adult, and under the “low productivity” assumption, half that amount. The rates for teenagers were half the adult rates, and for children, one quarter. Willingness to Pay to Prevent Malaria As would be expected, for both the hypothetical vaccine and bednets, the lower the price offered to respon-
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance TABLE 7-5 Cost of Malaria to Households in Tigray, Ethiopia Direct Costs (1997 US$) Indirect Costs Total Annual Costs (1997 US$) Age Group Consulta Medicineb Transport Total Direct Workdays Lostc Average Cost (1997 US$)d High Prod Low Prod Adults 1.30 .20 .10 1.60 21 7-24 Teenagers .80 .20 .04 1.10 26 7-23 Children .60 .20 .02 .82 12 4-12 Household 31 9 aOut-of-pocket costs to see a practitioner. bOut-of pocket costs of medicines. cAverage number of days of productivity loss per episode. dAverage cost of illness per episode, using high- and low-productivity assumptions. SOURCE: Cropper et al. (1999).
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Saving Lives, Buying Time: Economics of Malaria Drugs in an Age of Resistance dents, the more said they would buy, and the number they would buy was greater the lower the price (keeping in mind that each respondent was offered the intervention at one specific price). For vaccines, at the lowest price offered (US$0.80), three-quarters of households would buy at least one dose, but at the highest price (US$32), only 10 would buy at least one. For bednets, at the lowest price (US$1.30), about 80 percent of households would acquire at least one, and at the highest price (US$16), about 40 percent. These results were compared with the cost-of-illness figures using three different models (see original paper for details), with similar results: people appear to be willing to pay about three times the annual cost of illness for total malaria prevention (the vaccine). For partial prevention (bednets), willingness to pay was less, about 72 percent of the value for vaccines. Comments People living in malarious areas are willing to spend dearly—according to this study, about 15 percent of their annual household income—to prevent malaria. This is two to three times the expected annual economic losses per household from malaria (and this does not count the amount spent by government). The amount itself does not necessarily have an economic basis, but it makes clear that people view malaria as much more than simply the sum of its obvious economic consequences, which themselves are substantial. Summing Up The “true” economic costs of malaria are undeniably large, but just how large is not known. Admittedly, the information base is small, which accounts for part of the problem, but the methods themselves are not as well developed as needed. Adding up all of the effects from the best microeconomic studies using the human capital method, the totals do not begin to approach the magnitude of effect seen with top-down macroeconomic approaches. The human capital method, as practiced, underestimates costs in ways that are identifiable, as well as some that have yet to be defined. With macroeconomic methods, it is impossible to know whether other factors, inadequately controlled for, are inflating the numbers. Understanding both the magnitude of malaria’s economic effects as well as its operative pathways will accomplish two goals. It will better place malaria in its appropriate economic context and it will improve strategies by which to combat it.
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Representative terms from entire chapter: