Malaria is one of the most important causes of morbidity and mortality in tropical and sub-tropical countries. It is responsible for more than one million deaths each year 1. The estimated annual global incidence of clinical malaria is 500 million cases 2. Recent estimates indicate that more than two billion people are exposed to malaria risk in about 100 countries. Close to 90% of all malaria infections occur in sub-Saharan Africa, where malaria causes an estimated 40% of fever episodes 345. More than 90% of the deaths occur in children under five years of age in Africa 6. Most of the infections and deaths in highly endemic areas occur in children and pregnant women, who have little access to health systems 789. Malaria in children is complicated by anaemia, neurological sequels from cerebral compromise, respiratory distress and sub-optimal cognitive and behavioural development 10.

Malaria transmission varies among communities largely due to environmental factors, such as proximity to breeding sites 11. Many water resources development and management projects result in local outbreaks of malaria and other vector-borne diseases such as schistosomiasis 12, lymphatic filariasis 13 and Japanese encephalitis 14. These outbreaks can be attributed to an increase in the number of breeding sites for mosquitoes, an extended breeding season and longevity of mosquitoes, relocation of local populations to high-risk reservoir shorelines and the arrival of migrant populations seeking a livelihood around the newly created reservoirs 1516171819.

In Ethiopia, approximately 75% of the total area is estimated to be malarious, with 68% of the total population (52 million people) being at risk of infection 16. According to the national health services statistics, malaria is among the top 10 leading causes of morbidity 16. Proximity to micro-dams which were constructed for small irrigation development schemes is considered as one of the risk factors for increased malaria incidence 181920. The actual malaria cases that occur annually throughout the country are estimated to be 4–5 million 21. Malaria is responsible for 30–40% of outpatient visits to health facilities, 10–20% of hospital admissions and 10–40% of severe cases in children under five years of age 22. Most transmission takes place following cessation of rains 23. Previous studies showed that malaria was more prevalent in villages that were close to small irrigation dams than in those farther away 1920. Ethiopia plans to increase its electricity power supply by five-fold over the next five years based on large hydropower dams to fulfill the needs of its people and support the economic growth based on large hydropower dams 24. Ethiopia's power security is already over 85% dependent on hydropower and could grow to over 95% depending on whether all hydropower dams under construction are commissioned. Eight hydropower dams account for over 85% of Ethiopia's existing 767 MW generating capacity. Five additional hydropower sites with a combined capacity of 3,125 MW are currently under construction. Thus, it is important to look at a variety of impacts from the reservoir as it may create health problems and diseases such as malaria, schistosomiasis and lymphatic filariasis that often increase because reservoirs provide habitat for vectors (eg. mosquitoes) and intermediate hosts (eg. snails). Such investigations will also help in planning, designing and monitoring future dams.

Gilgel-Gibe hydroelectric dam, created by impounding the water of the Gilgel-Gibe River in south-western Ethiopia, is currently the largest supply of power (184 MW) in Ethiopia and is operating since 2004. During the construction of the dam, many people were relocated upstream of the reservoir, although some still remain close to the buffer zone (500–800 m from the reservoir edge at full supply level) surrounding the lake. The location of the rural villages near the newly formed reservoir may increase malaria transmission, assuming that this reservoir contributes directly or indirectly to the presence of breeding places for malaria vectors. Studies in various African countries indicate that the flight range of different species of Anopheles ranges from 0.8 km (An. funestus) 25 to an average of 1 to 1.6 km (An. gambiae s.s) 26, and the maximum flight range of anopheline vector mosquitoes is about 3 km 272829.

The current study investigates the possible effects of Gilgel-Gibe hydroelectric dam on malaria transmission and prevalence among children below the age of 10 years, focusing on the distribution of infection in relation to distance of villages from the reservoir shore. Results may further guide the development of appropriate malaria interventions for communities living around the reservoir.

Of the 1,855 children below the age of 10 years examined in this study, 905 (48.8%) were girls and 950 (51.2%) were boys. The mean age of children was 4.7 years and the number of children surveyed from 'at-risk' and 'control' communities was 1,081 (58.3%) and 774 (41.7%), respectively. Of the children in 'at-risk' communities, 528 (48.8%) were boys and 553 (51.2%) girls while in 'control' communities, 377 (48.7%) were boys and 397 (51.3%) were girls. Overall, 194 (10.5%) of the sampled children were positive for malaria, of which, 117 (60.3%) were positive for P. vivax, 76 (39.2%) for P. falciparum and one (0.5%) for both P. vivax and P. falciparum.

Highest P. vivax (60.7%) and P. falciparum (57.9%) positivity rates were observed in October. The P. vivax prevalence varied from 5.9% in children <1 year of age to 6.4% in those 5–9 years of age. The P. falciparum prevalence varied from 4.2% in children <1 year of age to 3.8% in those 5–9 years of age.

Table 1 shows demographic, distance and temporal relationships with malaria infection. The monthly P. vivax point prevalence during the three months ranged from 0.8% to 10.0% and form 2.3% to 5.9% in 'at-risk' and 'control' villages, respectively. Monthly P. falciparum point prevalence during the three months ranged from 2.7% to 6.9% and from 1.2% to 4.0% in 'at-risk' and 'control' villages, respectively (Table 1). The peak prevalence rate for P. vivax was observed in October and gradually decreased during November to December, while the prevalence rate for P. falciparum showed a late increase in December (Figure 1).

The P. vivax prevalence was significantly higher in 'at-risk' communities compared to the 'control' communities in November (OR = 2.00, 95% CI = 1.38, 2.92) and the P. falciparum prevalence was significantly higher in 'at-risk' communities in December (OR = 4.78, 95% CI = 1.03, 22.23) (Table 1). Differences between the two communities in malaria globally (P. vivax and P. falciparum together) were obvious and statistically significant in all months (p < 0.01).

In general, significantly higher malaria prevalence was observed in children living within 3 km from the reservoir than those living farther away (OR = 1.81, 95% CI = 1.17, 2.79 for P. vivax and OR = 2.57, 95% CI = 1.01, 6.57 for P. falciparum) (Table 1). P. vivax prevalence rates differed significantly between 'at-risk' and 'control' communities among children 1–4 years of age (OR = 1.81, 95% CI = 1.21, 2.71) and P. falciparum prevalence rates differed significantly between 'at risk' and 'control' communities among 5–9 years of age (OR = 17.4, 95% CI = 1.22, 249.24).

Moreover, in a multivariate analysis controlling for age, sex and time of data collection, it appeared that children who resided in 'at-risk' villages close to the dam were more likely to have a P. vivax infection than children who resided in 'control' villages (OR = 1.63, 95% CI = 1.15, 2.32) (Table 2). The results in Table 2 further indicate a P. vivax infection difference between boys and girls, however this was not significant at the 0.05 level (p = 0.054). The multivariate analysis indicated that, while controlling for age, sex and time of data collection, children who resided in 'at-risk' villages close to the dam had a higher OR to have P. falciparum infection than children who resided in 'control' villages but this was not significant at the 0.05 level (OR = 2.40, 95% CI = 0.84, 6.88). Finally, while controlling for age, sex and time of data collection, children who resided in 'at-risk' villages close to the dam were at a higher risk to have a Plasmodium infection (P. falciparum and P. vivax combined) than children who resided in 'control' villages (OR = 1.97, 95% CI = 1.24, 3.12) (Table 2).

Figure 2 shows the classification tree for P. vivax reproduced by CART. The children are first split into two groups: those sampled in December (prevalence = 1.5%) and those sampled in October-November (prevalence = 8.2%). The group of children sampled in October-November was further split in children living in 'at-risk' communities (prevalence = 9.9%) and those living in 'control' communities (prevalence = 5.5%). The group of children living in 'control' communities was further split in children of age below 5 years (prevalence = 3.5%) and above 5 years (prevalence = 7.5%). According to the overall discriminatory power (i.e. the relative importance) in the CART analysis, month emerged as the strongest overall discriminating risk factor for a P. vivax infection (Score (Sc) = 100), followed by village type (Sc = 20.21) and age (Sc = 8.73) and sex (Sc = 2.19). The classification tree corresponds well with the P. vivax trends in Figure 1. Indeed, the trends show that in December the P. vivax prevalences in 'at-risk' and 'control' communities are both low and that the difference between 'at-risk' and 'control' communities are especially clear in October-November.

Figure 3 shows the classification tree for P. falciparum reproduced by CART. Children are first split into children living in 'at-risk' communities (prevalence = 5.5%) and those living in 'control' communities (prevalence = 2.2%). The group of children living in 'at-risk' communities was further split in children sampled in November (prevalence = 2.7%) and those sampled in October and December (prevalence = 6.3%). According to the overall discriminatory power in the CART analysis, village type emerged as the strongest overall discriminating risk factor for malaria P. falciparum infection (Sc = 100), followed by month (Sc = 41.3). The other variables, age and sex had a zero-Score. The classification tree corresponds well with the P. falciparum trends in Figure 1. The trends show that P. falciparum prevalences are lower in 'control' communities and that in 'at- risk' communities the prevalences were lower in November.

The 'prevalence fraction (exposed)' PrFe, for malaria (P. vivax and P. falciparum together), measuring the effect of the dam, was calculated using prevalence ratio p(D+|E+)/p(D+|E-) = 7.7/4.4 = 1.75 and is PrFe = (PR-1)/PR. = 0.43. This means that 43% of the malaria occurring in children can be attributed to the dam, assuming that the relationship is causal.

In this study, after controlling for age, sex and time, the prevalence of P. vivax (7.7%) in children who reside with in 3 km of the reservoir created by the Gilge-Gibe hydroelectric dam was significantly higher than in children living in more distant villages (4.4%) although the villages have a similar eco-topography. Plasmodium falciparum prevalence rates in 'at-risk' communities (5.4%) were higher than in 'control' communities (2.2%), but the difference showed no statistical significance (p-value = 0.085). Overall, the Plasmodium prevalence near the reservoir was statistically higher as compared to the Plasmodium prevalence in more distant communities (p-value = 0.013). The main reason for the higher prevalence of malaria among children living close to the reservoir may be due to the man-made ecological transformations, which may influence the presence of mosquito-breeding site and might have an impact on the behaviour, parity rate and longevity of malaria vectors of the study area. A similar study in Cameroon showed a malaria prevalence of 36% in residents living in close proximity to a man-made lake compared with a prevalence of 25% in a village 14 km away 36. In India, a 2.4-fold increase in malaria cases and an over four-fold increase in annual parasite incidence were recorded among children in villages close to a reservoir as compared to more distant villages 37. A high malaria prevalence, up to 47%, was recorded around the Mantali dam in Senegal, constructed to provide hydropower and irrigation, compared to prevalences of 27.3% and 29.6% in two communities downstream the dam 38. Risk factors for malaria infections in the Gilgel-Gibe area might be proximity to the dam, as the low socioeconomic status, the health infrastructure and the malaria control methods appear to be similar in 'at-risk' and 'control' communities.

This study indicates that children between the age of one and four years tend to have a higher malaria prevalence than children below the age of one year (p-value = 0.083, non-significant for P. vivax and P. falciparum together). This could be because the older children, in contrast to younger children, spend outdoors in the evening when peak biting activities of malaria vector mosquitoes are high or the greater use of anti-malarial drugs in early childhood 39. A similar study in Gabon showed lower malaria prevalence in children less than six months (3.7%) than in children at the age of 47 months (47.5%) 40, which was attributed to low number of children less than one year of age and immunity acquired from mothers as difference in the risk of infection among different age groups could be associated to differences in the immunological status. The risk of infection first increases with age and then decreases when the individual reaches a certain degree of immunity due to exposure to the parasite. This was indicated in the study reported in this paper as well. Plasmodium prevalence rates in children between one and four years, below one year and children age of 5–9 years showed no statistically significant differences.

According to several reviews, P. falciparum is the dominant species in Ethiopia, followed by P. vivax, accounting for 60% and 40% of all malaria cases, respectively 1641. In the present study, the predominant species was P. vivax followed by P. falciparum. Plasmodium vivax was found in 117 (60.3%) children, P. falciparum in 76 (39.2%) and mixed infection in one (0.5%) child. The other two Plasmodium species, P. malariae and P. ovale were absent. A similar distribution (69% P. vivax and 31% P. falciparum) was reported in a previous study 42. But, in central Ethiopia, Woyessa et al 43 reported the predominance of P. falciparum during October while P. vivax tends to dominate during November. A parasitological community-based study conducted by Gebreyesus et al 20 on the impact of small irrigation dams on malaria burden in northern Ethiopia also revealed a predominance of P. falciparum. The prevalence of malaria infections varies seasonally, with P. vivax dominating in the dry season (March-June) and P. falciparum peaking in September-October, after the end of the main rainy season 16. Hence, the proportion of malaria cases due to the two parasite species can vary across seasons and localities. Ramos et al 44 reported variability in the distribution of malaria parasites (22.4%–54.7% P. vivax and 40.9%–73.4% of P. falciparum) during different seasons.

The classification trees show that using this non-parametric technique allows obtaining a better insight in the data structure and the available interactions between determinants in their influence on (or relation with) malaria. This was also noted by Thang et al 30. The classification tree results correspond well with the graphical trend observations, indicating that for P. vivax, children can be grouped according to month and children sampled in October-November showed higher prevalences even more when children were living in 'at-risk' communities (prevalence = 9.9%). For P. falciparum, the children living in 'at-risk' communities were grouped together because of higher prevalences. Within 'at-risk' communities especially children sampled in October and December showed a higher prevalence of 6.3%.

In conclusion, this study informs that children living in close proximity to the reservoir created by the newly constructed Gilgel-Gibe dam are at a greater risk of Plasmodium infection than children living further away, possibly due to the creation of new vector habitats around the lakeshore. Epidemiological studies focusing on vector dynamics and socioeconomic, demographic and health behaviour factors could be conducted to identify underlying causes of the spatial pattern of infection reported in this paper.

The authors declare that they have no competing interests.

DY conceptualized the study design, was involved in the coordination, supervision of data collection, data entry, cleaning, analysis, and drafted the manuscript; WL was involved in the design of the survey and reviewed the manuscript; WVB contributed to the discussion and critically reviewed the manuscript; SG was involved in the supervision of the laboratory work and in drafting the manuscript; HK contributed to the study design, and reviewed the manuscript; LD was involved in the interpretation of the statistical analysis and reviewed the manuscript; NS performed the statistical analysis, interpretation and was involved in drafting and revising the manuscript. All authors read and approved the final manuscript