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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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In 2001, the total area under irrigation worldwide was estimated at 272 million hectares (ha) compared with 139 million ha in 1961 (http://apps.fao.org/page/collections?subset=agriculture). Concurrently, it is estimated that at least 40,000 large dams (i.e., defined as impoundments more than 15 meters high or storing more than 3 million m3 of water) and 800,000 small dams have been built worldwide. The majority of the large dams serve irrigation purposes. Most of the large dams were constructed after 1950, during the post-war development era, when large-scale infrastructures were regarded as symbols of patriotic pride and technological advance. More than 400,000 km2 have been inundated by reservoirs worldwide.5 These ecologic transformations go hand-in-hand with the creation of new mosquito breeding sites. Water resources development is usually also coupled with demographic changes, and thus alters human-vector-parasite contact patterns. The potential for negative health impacts of water projects must also be juxtaposed with the positive effect that dams and irrigation schemes contribute substantially to renewable energy production, food security, and social and economic development. This, in turn, can provide rural households with greater capacity to purchase essential commodities, including drugs and insecticide-treated nets (ITNs), as well as improved access to health care services and education.
Reliable analyses of environmental risks to health are fundamental for the prevention and control of diseases, for evidence-based guidance of health policy and planning, and for the promotion of intersectoral action for the reduction of transmission. However, to our knowledge, an in-depth analysis of the malaria burden attributable to the development and operation of water projects has not been carried out.
In this report, we present the outcomes of a systematic review of the literature spanning the past 25 years by linking malaria prevalence and incidence data in relation to major water projects, with an emphasis on irrigation and large dams. The global database on the effect of small dams and flood control is inadequate to support generic conclusions from a systematic review. Our primary objectives are 1) to estimate the size of the populations at risk of malaria due to their proximity to irrigation schemes and large dams, and 2) to assess the impact of irrigation and large dams on the burden of malaria at global and regional scale. We use the 14 sub-regions articulated in the statistical analyses of the annual World Health Report of the World Health Organization (WHO).2
In the next section, we describe our data sources and methodology for producing estimates of the sizes of at-risk populations and the impact of large dams and irrigation schemes on the burden of malaria. Detailed illustrations of our calculations are given in Appendix 1. After presenting our results in the subsequent section, we conclude with a discussion of a myriad of unresolved issues that need to be addressed if the impact of major water projects on the burden of malaria is to be estimated with greater precision than is currently feasible. The requirement for such measurement is directly connected to ongoing policy debates about the pressing need for defensible health impact assessments associated with development projects quite generally.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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Malaria-endemic countries according to WHO sub-regions.
We used the recent WHO classification of countries into 14 epidemiologic sub-regions, which is based on a combination of WHO regions, and child and adult mortality rates, as described in the annexes of the annual World Health Report.2 From this list we included only those countries with high and moderate malaria transmission and excluded countries with sporadic malaria risk (e.g., Kazakhstan). The countries included in our review are located in 10 of the 14 sub-regions in the WHO classification and are listed in Table 1
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We gathered statistics on population assigned to mixed irrigation schemes (areas that combine cropping with livestock with at least 10% of the area irrigated) from a global data set of irrigated areas.6 To have a second range (since the irrigation population provided by Thornton and others6 might be overestimated by a factor as high as 10), we based our calculations on the irrigated area of each country and a hypothetical average population density of 200 people/km2 in the irrigated areas. The later figure is justified as follows. Although rural population densities vary from province to province and country to country, in general irrigation schemes are well-developed and highly attractive areas, and the villages might be even overcrowded. For example, in the Bura and Mwea irrigation schemes in Kenya, population densities of 223 people/km2 and 320 people/km2 have been reported, whereas the overall population density in Kenya is several-fold lower, namely 54 people/km2 as of 2002.7,8
To determine the population living in proximity to irrigation schemes in malaria-endemic areas, we retrieved data for each country on the percentage of the population living in malaria risk areas. The sources of these data are given in Table 1
. We then determined for each country the population at risk by multiplying the sizes of the irrigation populations by the fraction of the population living in malaria-endemic areas.
Populations at risk of malaria due to their proximity to reservoirs of large dams. Components of dam sites include the reservoir, upper catchment area, irrigation schemes, and flood plains. To estimate the size of the at-risk populations of malaria, we focus on the environment immediately surrounding the reservoir.
In a first step, we got an estimate of the population density near dam sites stratified by WHO sub-regions that are endemic for malaria by collecting information on displacement and resettlement of population in relation to the size of the reservoir for many dams for 8 of the 10 relevant sub-regions (Table 2).9,10 For each individual dam we standardized the calculated population density according to the year 2000, using the average annual rate of change of the rural population.11 We then calculated the median for each relevant WHO sub-region.
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Figure 1 shows how we estimated the area of risk near dam reservoirs. A detailed description of our calculations of the at-risk populations of malaria and 2 examples are given in Appendix 1.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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. As detailed earlier, the current review focuses on irrigation schemes and large dam sites. In principle, proximity to a dam, including command areas and/or an irrigation scheme, implies proximity to new bodies of standing water that can serve as Anopheles larval development sites. Whether this general expectation is realized largely depends on the ecology of the local vectors. In particular, it requires that the new bodies of standing water have pH, sunlight or shade, surrounding vegetation, turbidity, etc., compatible with the larval habitats for at least one local vector species. Consequently, the creation of new breeding sites might have an effect on the development of vector species and survival rates, and tradeoffs among them in terms of their role in local transmission. In addition, dams and irrigation schemes operate in the presence of diverse combinations of preventive and curative interventions against malaria. The details of these intervention packages vary substantially from one location to another in the malarious regions of the world. Interventions and also the social and economic changes that occur will affect the pool of parasites in humans and the human-vector-parasite contact patterns.
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. Between 1,000 (Epupa dam, Namibia) and 1.2 million people (Three Gorges dam, People’s Republic of China; currently under construction) have been, or will be, displaced due to large dam building. When we standardized the populations from the year of construction of the individual dams to the year 2000, we found that the densities are as low as 1.2 people/km2 and as high as 2,478 people/km2. The median population density calculated for each relevant WHO sub-region ranges from 25.8 people/km2 in WHO sub-region 2 to 764 people/km2 in WHO sub-region 7.
Irrigated areas, large dam sites, malaria burden, and people at risk in endemic WHO sub-regions.
Sub-Saharan Africa (WHO sub-regions 1 and 2).
Table 3 summarizes estimated DALYs lost due to malaria, total surface area, agricultural area, irrigated area, rice-harvested area, as well as total population, irrigation population, and irrigation population in malaria-endemic areas ("population at risk"). At present, irrigated agriculture or rice harvested areas are marginal in WHO sub-regions 1 and 2 because they represent only 0.2–0.5% of the total surface area. While some countries have virtually no areas under irrigation (e.g., Central African Republic = 0.02%), irrigation is more pronounced in others (e.g., South Africa = 1.5%). However, as irrigation provides an opportunity for agriculture in arid areas and stabilizes yields in regions with unpredictable rainfall (e.g., Sahel), irrigated areas continue to grow in sub-Saharan Africa: the predicted irrigation potential of WHO sub-regions 1 and 2 is 39.3 million ha (Table 3). This represents a 10-fold increase of the current irrigated area.
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and Appendix 1).
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). The cross-village comparisons assume that prior to the water project, the two sets of sites were approximate ecologic replicates in terms of factors influencing malaria transmission. None of the 11 studies in stable transmission areas found a higher malaria prevalence in the irrigated villages compared with non-irrigated villages. For example, in the Kou valley in Burkina Faso, malaria prevalence rates ranged from 16% to 58% in an irrigated village, compared with 35–83% in a non-irrigated village.13 In two study villages in Senegal, children were found to have a malaria prevalence of 8.7% in the irrigated village and 16.5% in the non-irrigated village.14 Furthermore, in Mali, a two-fold reduction in the annual malaria incidence was observed after the implementation of irrigation, although rice cultivation changed transmission from seasonal to perennial.15 A lower malaria incidence or prevalence in the irrigated villages when compared with non-irrigated villages (the so-called "paddies paradox"16) has been explained by improved socioeconomic status, effective vector control programs, or changes in health-seeking behavior in the irrigated villages.17 In addition, as described earlier, the epidemiologic setting, in particular the entomologic parameters, are key contextual determinants whether an irrigation project causes an increase in the malaria incidence or prevalence. A water resource development project may also cause a change toward less endophilic and anthropophilic malaria vectors, thereby resulting in a lower vectorial capacity, as for example the replacement of Anopheles funestus by An. arabiensis.16 In addition, a greater larval competition in irrigated areas might result in reduced adult longevity. Furthermore, in irrigated villages in sub-Saharan Africa, high An. gambiae and An. arabiensis densities were correlated with low anthropophily, a decrease in the parity rate, low sporozoite indices, and low mosquito survival rates.18 Again, significant use of ITNs or antimalarial drugs due to a greater wealth in irrigated villages, or a higher compliance to ITN use, often driven by the nuisance caused by a higher mosquito density, might play an important part in these findings.19 Another explanation for lower malaria transmission in irrigated villages might also be differing presence of cattle in the villages. Domestic animals are often kept close to the house and ITNs might divert mosquitoes away to the unprotected animals.17,19
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We found only three studies assessing the impact of large dams in WHO sub-regions 1 and 2 (Table 6). No malaria transmission was observed in a village near the Gleita dam in Mauritania in 1984 in the fifth month of the dry season, although the malaria situation in the region is unstable.22 In Cameroon, a malaria prevalence of 36% was observed near the Bamendjin dam compared with a malaria prevalence of 25% in a village located 14 km away from the dam.23 In addition, year round malaria transmission was observed in villages near the Manantali dam reservoir in Mali, which were previously characterized by seasonal transmission.24
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We estimate that of the 637.3 million people living in WHO sub-regions 1 and 2, approximately 9 million people (1.4%) live close to irrigation schemes (Table 3). Approximately two-thirds of these people live in malaria-endemic areas, and thus are at a risk of the disease. In addition, 3.1 million people are living near large dam sites in malaria endemic areas. In Figure 3, we depict these key numbers in relation to the malaria burden.
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, the impact of irrigation schemes or a large dam site on malaria depends on several contextual determinants since personal protective measures, access to effective treatment, and acquired immunity factors strongly counterbalance negative effects. Although studies on large dam sites in sub-Saharan Africa are rare, we can conclude that irrigation projects and dam sites in general might not present a risk to inhabitants of stable malaria areas, in particular when control programs have been launched simultaneously. It has recently been estimated that approximately 10% of the African population lives in epidemic, unstable, malaria risk areas.26 We therefore assume that currently approximately 0.9 million people live near irrigation and large dam sites in unstable malaria transmission areas. Without the implementation of malaria control programs, inhabitants, particularly young children, in these settings are at high risk of disease-associated morbidity and mortality. Since the irrigated area in sub-Saharan Africa is anticipated to increase strongly, in particular in arid and semi-arid environments, this number is likely to increase significantly.
Southeast Asia (WHO sub-regions 11 and 12).
In contrast to WHO sub-regions 1 and 2, irrigated agriculture plays a much greater role in southeast Asia: 10.6% of the total surface area is currently irrigated, mainly for rice production. The irrigated area is expected to further grow significantly, potentially up to 22.4% of the total area (Table 3). A total of 4,431 large dams have been built in the selected countries of WHO sub-regions 11 and 12; the large majority of them in India (n = 4,010) (Table 4). At maximum capacity the reservoirs constitute a total area of 53,265 km2. Between 145.1 and 771 million people have been assigned to irrigation schemes, and 122.9–659.6 million (7.7–41.5% of the total population) live in malaria-endemic areas. We furthermore estimate that 10.9 million people are at risk of malaria due to large dam sites (Figure 3). However, this number of people at risk may be overestimated because dams and irrigation schemes are not distributed homogenously between malaria-endemic and non-endemic areas. For example, in the eastern belt and coastal belt of India, characterized by large areas under irrigation, the risk of acquiring malaria is very small.27
An estimated 6.0% of the estimated global malaria burden rests in WHO sub-regions 11 and 12.2 Whether irrigation and dam sites present a risk factor for malaria in these sub-regions again depends on contextual determinants, which make the attribution of the fraction to these potential risk factors presently impossible. First, there is a great diversity of vectors in WHO sub-regions 11 and 12 and several of these (e.g., siblings of An. culicifacies or An. stephensi) have limited breeding in irrigated rice fields.27 Conversely, a shift in vector species composition may occur. In addition, the review of studies in these two WHO sub-regions has shown that the local setting, malaria endemicity, the deployment of control programs, and knowledge on the disease were key determining factors.
We retrieved studies that assessed the impact of surface irrigation projects in India28–31 or Sri Lanka.32–34 We are not aware of studies assessing the impact of irrigation on malaria prevalence or incidence in Bangladesh, Bhutan, Indonesia, Myanmar, Nepal, the People’s Democratic Republic Korea, or Thailand.
Sharma and others have analyzed data over a 21-year period, commencing in 1963 in 25 states of India, representing state-wide annual parasite incidence and the area under rice irrigation. Significant positive associations were only found in the two states of Punjab and Nagaland.27 However, paddy cultivation did not cover a huge area and the relationship, which was generally poor, was only found when both sets of data were pooled at the state level.27,31 Studies focusing on individual irrigation projects have demonstrated the impact irrigation has on malaria: after the implementation of the Mahi-Kadana project in India, the annual parasite index increased from 0.01 in 1961 to 37.9 in 1976. As a consequence, a malaria control program was stepped up. Two years later, the annual parasite index in the Mahi-Kadana irrigation project had decreased to 11.4.29 In Meerut and Gurgaon, the incidence in canal irrigated villages increased up to ninefold.28 Of particular concern are reports of malaria outbreaks due to irrigation schemes from areas that have been only mildly prone to malaria, e.g., the Thar desert in the Rajastan State of India. As many as 13 epidemic outbreaks have been reported in this area up to 2002 because extensive irrigation has altered the physiography and malaria transmission parameters. An. culicifacies, which was previously unknown in the desert, has taken over from the original vectors, causing a high percentage of the Plasmodium falciparum malaria.31
In Sri Lanka, a five-fold higher malaria incidence was reported following the introduction of the Mahaweli Systems H and B.33 Another study comparing the malaria prevalences in four villages, two relatively new villages and two ancient villages, of which two were irrigated and two non-irrigated, showed a prevalence of 4.8% in the irrigated compared with 2.5% in the non-irrigated villages. However, the new villages, in irrigated but also non-irrigated areas, had much higher malaria prevalences compared with the old villages, which was explained by changing livelihoods, less knowledge on malaria, and fewer personal protection measures in the new villages.32 In a more recent study, irrigated rice cultivation in the Uda Walawe region was found to have a lower malaria risk than non-irrigated areas.34 As in the African cases discussed before, these claims also presume that the two groups of communities were approximate ecologic replicates prior to the introduction of irrigation. Several studies have assessed the impact on dam building in southeast Asia (Table 6). For example, the Bargi dam in India has been studied in considerable detail: after the construction of the Bargi dam, a 2.4-fold increase in malaria cases and a more than four-fold increase in annual parasite incidence among children were recorded in villages closer to the dam (head end) compared with more distant villages (tail end). In addition, there was a strong increase in the prevalence rates in partially submerged villages, as seen from routinely collected malaria data in the nearby hospital.35,36
Again, integrated vector management or other control interventions were found to have a strong influence on the malaria transmission parameters. For example, a study carried out in Uttaranchal, India comparing the parasitologic indices in a dam area with those in forest or plain areas showed a prevalence and annual parasite incidence of 0 in the dam area. An elevated economic status, indoor residual spraying, and more awareness of malaria risk were reported to be the main factors accounting for the lack of malaria transmission at the dam site.37 In addition, in Thailand, no increase of malaria incidence was observed near the Nong Wai dam and the Ubol Ratana dam. However, this is probably because all walls inside of houses were sprayed with DDT compared with the Srinagarind dam, where an increase in malaria prevalence was reported, but where there was no mention of any vector control measures.38,39
Eastern Mediterranean (WHO sub-regions 6 and 7).
Irrigated areas range from 0.04% of the total surface area in Djibouti, 0.45% in Somalia, 0.94% in Yemen, 1.5% in Sudan, 4.6% in the Islamic Republic of Iran, 6.2% in Afghanistan, and 8% in Iraq to 22% in Pakistan. These percentages correspond to an estimated irrigation population ranging from 0.09% in Yemen to 73% in Pakistan. We allocated between 71.5 and 143.4 million people (with the great majority in Pakistan) to irrigation (Table 3); 49.4–116 million of these live in malaria-endemic areas.
There are 156 large dams located in these countries, which report malaria as a health problem and are part of WHO sub-region 7. The majority of these large dams are located in Pakistan (n = 71) and the Islamic Republic of Iran (n = 66). We estimate that in these regions 1.9 million people live within the estimated mosquito flight range of 2,509 km2, and thus might be at risk of acquiring malaria (Table 4). We could not retrieve data on the size of the reservoirs for the 38 large dams in Saudi Arabia (WHO sub-region 6).
The Eastern Mediterranean (WHO sub-regions 6 and 7) have 4.8% of the current estimated global malaria burden.2 New water resource development projects were reported to increase malaria transmission in Afghanistan and in the Gezira scheme in Sudan.27,40 However, this data is insufficient to determine the attributable fraction of irrigation and large dam sites to the malaria burden; thus, further studies are warranted.
The Americas, Europe, and the Western Pacific sub-regions (WHO sub-regions 4, 5, 9, and 14).
Only 1.3% of the global malaria burden is currently estimated to occur in WHO sub-regions 4, 5, 9, and 14.2 Irrigated areas account for less than 1% (WHO sub-region 4) and up to 6.9% (WHO sub-region 9) of the total surface area, as shown in Table 3. A large population (170.4–982.5 million people) can be associated with irrigation. However, the majority of these individuals live in parts of the countries where no malaria transmission occurs (e.g., in the non-malarious parts of China); only 1.2–3.3% of the irrigation population (26.5–69.4 million people) is estimated to live in malaria-endemic areas.
A total of 4,079 large dams have been constructed in the countries of WHO sub-regions 4, 5, 9, and 14 and are included in our review. The countries with the highest number of large dams in these regions are China (n = 1,905), Turkey (n = 625), Brazil (n = 594), and Mexico (n = 536). The reservoir areas range from 385 km2 (large dams of WHO sub-region 5) to 58,480 km2 (large dams of WHO sub-region 12). We estimate that a total of 2.3 million people are living close enough to reservoirs in endemic areas; thus, they are at risk of malaria transmission (Table 4 and Figure 3).
We retrieved only three studies assessing the impact of irrigation on the malaria incidence and prevalence in the selected countries of these WHO sub-regions. The first is a recent study conducted in a dry coastal area of Peru, where malaria incidence was found to be five-fold higher in villages where houses were located closely to fields and irrigation canals compared with villages in the dry areas.41 The second study was carried out in the Lao People’s Democratic Republic. The malaria infection rate was higher in villages surrounded by rice fields compared with non-irrigated villages.42 Finally, in Turkey, the implementation of a network of irrigation channels and a subsequent domestic migration from malaria-endemic regions to the area caused a serious epidemic outbreak.43
The health impacts of three large Brazilian dams, namely 1) the Balbina power plant, 2) the Itaipú dam, and 3) the Tucuruí Hydropower dam have been studied in detail. We summarize data on the malaria incidence before and after their construction in Table 6. An increase of malaria cases was reported at all three sites.44–47 Overall, these studies show that despite a limited malaria burden and a small population at risk, irrigation and large dam sites might have a strong influence on disease parameters in WHO sub-region 4.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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It was not possible to quantify the attributable fraction of the malaria burden due to dam building and irrigation for the individual WHO sub-regions (e.g., using the methodology of comparative risk assessment)48 due to many confounding factors and the scarcity of the currently available global database. Sadly, even the extensive report authored by the World Commission on Dams, derived from 17 exhaustive reviews on dams, allocates a mere 2 pages to health.49 At a given location, if malaria incidence or prevalence data are available both before and after the introduction of a dam and/or an irrigation scheme, we can ascertain the impact of the environmental transformation. However, most extant studies based their results on the comparison of two villages. Care is needed in the interpretation of these results because many studies comparing malaria rates in villages proximal to a water resource development project with villages that are relatively distant do not give a clear picture of the extent to which nearby villages were approximate ecologic matches/replicates of the distant villages prior to the introduction of the water resource project. There might be subtle differences in ecologic, epidemiologic, and socioeconomic features; thus, resulting in different transmission characteristics, even in neighboring villages. In addition, the possible presence of multiple malaria control interventions in the two sets of localities makes clear interpretation of claims about impact of dams and irrigation schemes on at-risk population difficult to interpret, since most studies do not give sufficient attention to this issue.
Our calculations depended on a number of assumptions and they are therefore inevitably subject to a level of uncertainty. The possibility that we have overestimated the risk cannot be ruled out. First, we assumed that the whole population assigned to irrigation in malaria-endemic areas is at risk of the disease. However, not all forms of irrigation actually present a risk for the local population. There are three common classes of irrigation systems, namely 1) pressurized distribution as in sprinkler or trickle systems, 2) gravity flow distribution as in surface irrigation, and 3) subsurface irrigation. If they are well maintained, sprinkler irrigation, drip irrigation, and subsurface irrigation provide irrigation water without creating suitable breeding sites for Anopheles vectors.50
Second, we did not include annual fluctuations of the water level of the reservoir, which in turn has important implications for the estimation of the population at risk from large dam sites. At the end of the low water period, the area of the reservoir, and thus the mosquito flight range area is, in general, considerably reduced. For example, the reservoir area of the Manantali dam in Mali decreases from 477 km2 to 275 km2 at the minimum operating level of the dam.24 Furthermore, not every dam reservoir might actually be a good breeding site for malaria vectors. Each Anopheles species is characterized by specific habitat preferences, including exposure to sunlight, turbidity of the water, presence of vegetation, pH, and nitrate and phosphate concentrations of the water.51 These environmental factors are specific for each dam and its shoreline. In addition, settlement around the reservoirs might not be possible at certain locations due to topography and other reasons.
Third, we have assumed that the population densities around dams are similar to the ones of the resettled communities and that the population near the dam is subjected to the same population growth as the rural areas of the respective countries. New villages might have been constructed further away than the 2 km (estimated mosquito flight range, Appendix 1) from the dam sites and, consequently, the population would not be at risk attributable to the dam. Conversely, dam sites are characterized by marked demographic impacts, in particular during the construction and early operational phases. These sites attract visitors, fishermen, and farmers who often have low immunities to malaria. Thus, during construction, the population might be larger than before. As the dam ages, however, temporary workers leave and the population density consequently decreases.
Fourth, since a geo-referenced database exists only for the African dams, it is difficult to determine the exact population at risk from dams in countries that are only partially endemic for malaria on the remaining continents. Without knowledge of the geographic coordinates, we would have presumed that 
20% or more than 100 of the 539 South African large dams and their reservoirs are located in malaria-endemic areas. In reality, however, only 25 (< 5%) of these are located in areas where malaria transmission occurs. Similarly, calculation of the irrigation population at risk in partially malaria-endemic countries is based on the assumption that the total population and the population living near irrigation schemes are equally at risk of the disease.
It is also conceivable that we might have underestimated the actual population at risk of malaria from water resource development, which is justified on the following grounds. First, we could not include the impact of a large dam on malaria downstream of the project site. However, the change of the water regimen can stretch for many kilometers and strongly influence larval breeding.
Second, it is unfortunate that no systematic inventory of small dams and only very few studies assessing their cumulative impact on malaria are currently available. Their impact on the frequency and transmission dynamics of malaria could be significant because their total shoreline is much greater when compared with large dams. For example, 1,110 Nigerian small dams were described to have an area of 400,000 ha compared with a surface area of 116,000 ha of 34 large dams. Furthermore, an estimated 15,000 small dams have been constructed in Zimbabwe, and more than 50,000 small dams were built in Kenya within three years during the late 1950s.52
Finally, studies investigating the consequences of the construction of flood control, water projects for recreational purposes, or pumps and drains for water supply and sanitation on malaria have, to our knowledge, not been conducted. It follows that no estimates of their impact on malaria could be presented in this review.
When irrigation schemes and dams are proximal to areas of unstable transmission, integrated multiple-intervention malaria control holds promise for mitigation. In several of the studies, which we have reviewed here, malaria control programs, consisting mainly of early diagnosis and treatment, residual spraying, or distribution of ITNs, have been successfully conducted. It is important to note that environmental management presents an additional option for malaria control in such settings. For example, vector control by means of water management has been carried out with success for several decades, particularly in areas where malaria is unstable. The first studies on intermittently irrigated rice fields, which led to greatly reduced Anopheles densities and often increased rice yields, were carried out more than 70 years ago.53 At the same time, elimination of mosquito breeding sites has been achieved in rivers and streams of Sri Lanka and Malaysia by means of different types of siphons and small dams.54 Significant reductions of Anopheles breeding sites has been achieved in the reservoirs of the Tennessee River Valley by implementation of several types of environmental and water management measures. Among them was an integrated operating rule consisting of a fluctuation cycle with an amplitude of 0.3 meters over 7–10-day periods.24,55
We conclude that future water resource development projects should include in-depth assessment of potential health effects, positive or negative, including malaria, where this disease is endemic. Indeed, institutionalization of health impact assessments for development projects quite generally, analogous to environmental impact assessments, would lead to information requirements that could fill many of the data gaps described in this report.56 Introduction of sound monitoring and surveillance systems proximal to such water projects would facilitate systematic evaluation of the impact of these ecosystem interventions over time. This, in turn, would greatly improve our understanding of the role of dams and irrigation systems in either promoting or reducing malaria transmission. In addition, mitigation strategies to alleviate potential negative health effects, of which malaria might be only one component, are mandatory to reduce the current burden of malaria in settings near irrigation or dam projects, particularly in areas where malaria transmission is unstable.
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APPENDIX 1 ESTIMATION OF AT-RISK POPULATION DUE TO PROXIMITY OF LARGE DAM SITES (TWO EXAMPLES FOR WORLD HEALTH ORGANIZATION (WHO) SUB-REGIONS 2 AND 4) |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIX 1 ESTIMATION OF...REFERENCES |
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), we calculated for each dam the base b of the reservoir according to b = A/l, where A represents the area of the reservoir and l the length of the dam (both parameters have been obtained from the World Register of Dams12). To get an idea of the shape of the reservoirs (whether the rectangle has a long or short perimeter), we then calculated for each group the median of the ratio b/l [A/(l x l)] of the dams’ reservoirs. We found that those dams with a reservoir size <1 km2 were close to square shapes (median ratio b/l = 2.5). Those dams with reservoir sizes >100 km2 had a much longer base than length (median b/l = 0637.4).
We assumed a mosquito flight range of 2 km, which is justified on the following grounds. First, some individual mosquito species might have a very long flight range, up to 12 km have been reported for Anopheles sinensis; however, the great majority (66.5%) of An. sinensis were recaptured 1–3 km from their release points.70 Second, An. darlingi and An. albimanus were abundant in houses <1 km from the river and not present in houses further away.71 Third, WHO has suggested to locate villages 1.5–2 km from the edge of the reservoir, which has proven successful in reducing malaria incidence.72 Thus, we calculated the area 2 km around the hypothetically rectangular reservoirs for all four groups applying the following formula: Area at risk = 2 x (b x 2) + 2 x (l x 2) + 22
(Figure 1).
For the 226 large dams in WHO sub-region 2, we determined a total flight range area of 11,578 km2 (mean = 51 km2/reservoir). Since we have no data on the reservoirs of the remaining 120 dams in this WHO sub-region, we assume that they have a similar average flight range, namely 51 km2/reservoir. Consequently, the estimated total area for all 346 registered large dams in WHO sub-region 2 is 17,726 km2. Using the percentage of population in malaria-endemic regions for each country, we obtained an estimate of the mosquito flight range around large dams in endemic areas. In our example of WHO sub-region 2, we assume that 94% of the dams’ surface areas are located in endemic areas.
Multiplication of the mosquito flight range in malaria-endemic areas with the obtained population density for WHO sub-region 2 (25.8 persons/km2; Table 2) gave an estimate of the at-risk population of 429,887.
For WHO sub-regions 4–14 we classified all large dams in these countries into only two categories (since there are several thousand dams), namely 1) area of the reservoirs 
100 km2, and 2) area of the reservoirs >100 km2. For each group we calculated the area of a 2-km mosquito flight range with the aid of two hypothetical rectangles of A1 = 15 l x l (for the small reservoirs 100 km2) and A2 = 500 l x l (for the large reservoirs >100 km2) (see example for WHO sub-region 4 below). The calculation of the people at risk has been conducted as described above for WHO sub-region 2.
| Reservoir sizes (x103 m2) | Number of dams* | Total size of reservoirs (x103 m2) | Median base/length of dam | Area flight range at full water level (x103 m2) | Area flight range for all large dams at full water level (x103 m2) |
Area flight range for dams located in malaria-endemic areas at full water level (x103 m2) | At-risk population at full water level |
| 25–960 | 100 | 47,396 | 2.5 | 73,414 | |||
| 1,010–8,700 | 95 | 279,681 | 7.1 | 215,808 | |||
| 10,000–91,050 | 23 | 706,151 | 36.1 | 668,796 | |||
| 120,000–5,100,000 | 8 | 11,005,700 | 637.4 | 10,620,121 | |||
| 226 | 12,038,928 | 11,578,139 | 17,725,823 | 16,662,273 | 429,887 | ||
| * Only the 25 South African dams located in malaria-endemic areas have been included. | |||||||
| No data were available on the area of the reservoir of 120 dams. |
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| Reservoir sizes (x103 m2) | Number of dams | Total size of reservoirs (x103 m2) | Estimated median base/length of dam | Area flight range (x103 m2) (full water level) | Area flight range for all dams (x103 m2) (full water level)* | Area flight range for endemic dams (x103 m2) (full water level) | At risk population (full water level) |
| 25–100,000 | 516 | 4,643,105 | 15 | 1,138,457 | |||
| 100,000– | 53 | 23,772,133 | 500 | 13,830,507 | |||
| 569 | 28,415,238 | 14,968,964 | 36,541,108 | 12,789,387 | 383,670 | ||
| * No data were available on the area of the reservoir of 820 dams. | |||||||
Received April 9, 2004. Accepted for publication August 13, 2004.
Financial support: This work was part of the project Burden of Water-Related Vector-Borne Diseases: An Analysis of the Fraction Attributable to Components of Water Resources Development and Management, which was kindly funded by the World Health Organization. Marcia Caldas de Castro is grateful to the Center for Health and Wellbeing at Princeton University and Jennifer Keiser and Jürg Utzinger to the Swiss National Science Foundation (Project no. PMPDB-106212 and PPOOB–102883) for financial support.
Authors’ addresses: Jennifer Keiser, Marcel Tanner, and Jürg Utzinger: Swiss Tropical Institute, PO Box, CH-4002 Basel, Switzerland. Marcia Caldas de Castro Department of Geography, University of South Carolina, Callcott Hall-125, Columbia, SC 29208. Michael F. Maltese, St. Antony’s College, Oxford University, Oxford OX2 6JF, United Kingdom. Robert Bos, Water, Sanitation and Health (WSH/PHE), World Health Organization, Avenue Appia 20, CH-1211 Geneva 27, Switzerland. Burton H. Singer, Office of Population Research, Wallace Hall, Princeton University, Princeton, NJ 08544.
Reprint requests: Jennifer Keiser, Swiss Tropical Institute, PO Box, CH-4002 Basel, Switzerland, Telephone: 41-61-225-2666, Fax: 41-61-225-2678, E-mail: jennifer.keiser{at}unibas.ch.
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