Study area

This study focused on SSA—geographically, the area of the African continent that lies south of the Sahara Desert. It consists of all African countries except Morocco, Algeria, Tunisia, Libya, and Egypt [20]. This region accounts for the greatest burden of malaria in the world where Plasmodium falciparum, the most severe of the malaria parasite species that infect humans, is predominant. Annually, an estimated 174 million cases occur in this region [8]. Malaria transmission is generally stable in western and central Africa, unstable in much of eastern Africa and unstable to absent in southern Africa [21] (Fig. 1).

Fig. 1 Map showing spatial distribution of existing and planned dams in Africa with respect to the 2010 malaria stability indexing (E no. existing dams, P no. planned dams) (adapted from Kibret et al. [22]) Full size image

Data collection

The present study used available databases to quantify the impact of dams on malaria. Dam databases were used to collect information on the number and location of dams across SSA. Population data and malaria prevalence databases were used to estimate population at risk around dams in different eco-epidemiological settings of SSA.

Data on existing and planned African dams

To identify and locate existing and planned dams in SSA, georeferenced locations of individual dams (both existing and planned) were obtained from the FAO African dams database [23] and the International Rivers database [24]. Data on water storage capacity, dam height and reservoir surface area were obtained from the ICOLD World Register of Dams [25] and the Global Reservoirs and Dams (GRanD) database [26]. Locations and parameters of additional dams were obtained from a number of journal articles, project reports and dissertations.

Overall, georeferenced locations and dam parameters were gathered for a total of 1268 existing dams (out of an estimated total of over 2000 [7]) and 78 planned dams (out of an estimated total of 150 [24]) in SSA. A planned dam was defined as a dam currently under construction or planned for construction in the next 5 years. While the number of existing and planned dams for which locations could be found was below the known total of each, the set of existing and planned dams mapped for this study is the most extensive yet utilized in an analysis of the malaria impacts of dams in SSA.

Estimating reservoir perimeters

Data on reservoir perimeter are necessary to estimate the population at risk of malaria due to a dam. However, these data are not easily available for most dams. Thus reservoir perimeter was estimated using a method proposed by Lehner et al. [27] and Keiser et al. [18]. First, it was assumed that reservoirs have a rectangular shape [18]. Length of reservoir (LR) was calculated for each dam according to LR = A/LD, where A represents the surface area of the reservoir and LD the length of the dam. A and LD were obtained from the World Register of Dams and FAO database, respectively. Then, the perimeter of the reservoir was estimated as 2LR + 2LD. For each dam, the calculation was based on the reservoirs’ maximum water storage (i.e., the reservoir at full supply level, when the surface area is at a maximum).

The ‘rectangular’ reservoir shape assumption was validated using known reservoir perimeters from the literature. The actual shape of 11 reservoirs, derived from the literature review, indicated that dams with reservoir size >1000 sq km had a much longer reservoir length than dam length (median LR/LD = 65.4) while dams with reservoir size <100 sq km had comparable dam and reservoir lengths (median LR/LD = 4.2). This supports the ‘rectangular’ reservoir shape approach in the present study, which assumed a much greater length than width.

It is also recognized that in many cases the reservoir water level varies substantially throughout the year and this will change both the perimeter of the reservoir shoreline and the relative distance of the shoreline to communities. However, data on fluctuations in reservoir water levels are not generally available, so it was not possible to make allowance for any temporal variability in reservoir surface area.

Data on malaria transmission stability

The Gething et al. [21] classification was utilized to characterize the epidemiological settings in which the dams were located. This is defined as:

Stable transmission in areas with annual P. falciparum infection rates (PfIR) greater than 0.1 cases per 1000 population;

Unstable transmission in areas with annual PfIR between 0 and 0.1 cases per 1000 population;

No malaria in areas having zero annual PfIR.

Data on malaria transmission

The Malaria Atlas Project (MAP) database was used to produce annual predictions of spatial PfIR rates at high resolution (1 × 1 km grid) [21]. MAP is an initiative founded in 2005 to generate new and innovative methods of mapping malaria risk and has continuously updated georeferenced PfIR surveys since 2005. The updated version, completed on 1 June 2010, consisted of 22,212 quality-checked and spatiotemporally unique malaria prevalence survey data points. The 2010 dataset was used to determine annual PfIRs for populations at different distances from reservoirs in areas of stable and unstable transmission [21]. All dams classified as ‘existing’ in this study were commissioned before 2010. Additional data were obtained from literature review and the World Health Organization [28].

Literature review

A systematic review of the peer-reviewed literature, dissertations and technical reports was carried out, with an emphasis on published research findings from assessments of the impact of large dams on malaria transmission. Articles were searched mostly through PubMed using the combination of keywords such as ‘malaria’, ‘Anopheles vector’, ‘dams’, ‘mosquito breeding’, ‘reservoir shoreline’ and ‘sub-Saharan Africa’. Relevant references cited by each reviewed study were also examined. Pertinent book chapters and websites (e.g., 27) were also consulted. Two types of studies were included: (1) those that assessed epidemiological (malaria prevalence or incidence) and/or entomological (malaria mosquito bionomics, density and vectorial capacity) variables before and after the construction of a dam; and, (2) those that compared dam/reservoir villages and non-dam/reservoir settings with similar social and eco-epidemiological settings were included. Studies without a control comparison design were excluded from this review to ensure causality in the environmental factors responsible for changes in malaria transmission in nearby villages.

A total of 17 studies showing the effects of 11 large dams on malaria incidence and/or vector breeding in SSA were found. The impact of dams on malaria was analysed in relation to areas of stable and unstable transmission.

Data analysis

Mapping dams and malaria

The distribution of existing and planned dams was overlaid on the malaria stability index map using ArcGIS and the number of large dams in each malaria stability category (stable, unstable and no malaria) was determined. ArcGIS was used to produce all the maps and for population estimates.

Estimating the population at risk around dams

To estimate the population at risk at different distances from a dam and its associated reservoir, high resolution (1 × 1 km) Worldpop Project population distribution database [29] was used. Population at risk was estimated as all persons living within a 5-km distance of the reservoir, upstream of a dam. The impact of a dam on malaria was assumed to be negligible beyond 5 km due to mosquitoes’ limited flight range [30].

Malaria incidence around dams

Using the MAP database, annual PfIR was computed for four distance cohorts (i.e. <1, 1–2, 2–5, 5–9 km). These cohorts lie within the same climatic region at each of the dam sites. The 5–9 km cohort was taken as the control group for each dam: the assumption being that malaria incidence in this zone equated to what would have occurred in the other distance cohorts if the dam had not been built. The Odds Ratio (OR) (i.e., the ratio of malaria in each cohort relative to the control) was calculated to compare the PfIR among the cohorts. The annual number of malaria cases for each cohort was calculated by multiplying PfIR by the population present in that cohort.

Since the population density varies among the cohorts, the difference between PfIRs between at risk cohorts (<1, 1–2 and 2–5 km) and the control cohort (5–9 km) was compared as follows [31]:

$$z = \frac{{(\hat{\rho }_{1} - \hat{\rho }_{2} ) - 0}}{{\sqrt {\hat{\rho }(1 - \hat{\rho })\left( {\frac{1}{{n_{1} }} + \frac{1}{{n_{2} }}} \right)} }}$$

where, \(\hat{\rho }_{1}\) is the PfIR (in per cent) in the at risk cohort, \(\hat{\rho }_{2}\) is the PfIR (in per cent) in the control cohort, \(\hat{\rho }\) is the odds ratio of \(\hat{\rho }_{2}\) and \(\hat{\rho }_{1}\), and n 1 and n 2 are population size of at risk and control cohorts, respectively, z is a value on the Z-distribution.

Determining the increased cases associated with dams

The annual number of malaria cases associated with current and future dams was determined for unstable and stable transmission areas. The number of annual malaria cases attributable to dams was estimated by calculating the difference in the number of annual malaria cases for communities less than 5 km and for communities greater than 5 km (i.e., 5–9 km) from the reservoir, allowing for differences in population size. The annual PfIR calculated for 5–9 km was applied to the <5-km cohorts multiplied by the population in the <5-km cohorts. In the planned dams, the rate of malaria case increase between at risk (<5 km) and control (5–9 km) cohorts in the existing dams was taken to predict the potential increase in malaria cases in planned dams after dam construction—with differential rates for stable and unstable areas. No adjustment was made for the population growth that often accompanies dam construction.

Validating with evidence from literature

A total of 17 dam-malaria studies focused on 11 dams explored relationships between dams and malaria. For the 11 dams where literature is available, results from the MAP-based analysis and those reported in the literature were compared. Due to limitations in the results reported in the literature, it was not possible to determine malaria incidence for the four distance cohorts used in the MAP-based analyses. However, the data were sufficient to enable the range of malaria prevalence and OR to be calculated for those living close (<3 km) to dams and further away (>3 km) in stable and unstable areas. For the MAP data, two distance groups were recreated [<3 and >3 km (3–6 km)] to enable comparison with the literature dataset. The OR of malaria prevalence from MAP and literature were compared using the Chi square test. Statistical analyses were done using statistical software, SPSS version 22 (SPSS Inc, Chicago, IL, USA). The level of significance was determined at the 95 % confidence interval (P < 0.05).