Malaria continues to be one of the world’s most serious public health problems. Around the world, malaria is worsening the effects of infectious disease. The disease affects about 300 to 500 million people worldwide and contributes to about 3000 pediatric deaths per day [1
]. While globally the pace of decline in malaria mortality rate has accelerated since 2000, when the Millennium Development Goal (MDG6) target of reversing the incidence of malaria (between 2000 and 2015) was established, in some sub-Saharan African countries no reduction has been achieved [4
]. It is estimated that malaria is accountable for 20%–30% of hospital admissions and about 30%–50% of outpatient consultations [5
Increasingly, sub-Saharan African countries suffer from a “double burden” of pervasive malaria, economic and social burden in costs of health care, days lost in education, absenteeism, dwindled productivity, along with loss of investment and tourism. One study by Smith examined the extent to which malaria acts as a risk factor for all-cause mortality in African children less than five years of age and found that Plasmodium falciparum
infection prevalence more than doubled all-cause mortality (p
= 0.0001) [6
Recently, researchers have shown an increased interest in knowing the duration, start and end of the malaria transmission season in order to help plan high-impact malaria control strategies [7
]. Compared to low-elevation areas, most tropical highlands have had little or no malaria [11
] because of cool temperatures and seasonally dry conditions that limit malaria transmission [12
]. However, despite this, “epidemics can still occur in response to abnormally warm temperatures and wet conditions” [8
]. For example, in one study, which included data on climatic variability and number of malaria outpatients in seven highland sites in Ethiopia, Kenya, and Uganda, climate variability was associated with malaria epidemics [10
]. Temperature and rainfall may influence the intensity of malaria transmission in the highlands [13
]. Studies have indicated that malaria transmission rates tend to increase with temperature up to a threshold level [8
Several studies documenting a link between climate and malaria reported lagged associations of climate variables including temperature and rainfall with malaria cases over time periods ranging from weeks to months [10
]. With the exception of a few studies which focus on malaria in children [18
], most research in the field have focused on the general population. The existing work on the spatial prevalence of malaria in children in the highlands of Africa, for example, has concentrated on regions within a country, addressing only part of the more general issue of geographic variation. To achieve a better understanding of this question, a growing number of studies have used more complex analytic approaches to examine geographical malaria risk [21
] and its determinants [25
]. These studies have steadily reported small but significant effects of geographic variation.
The objectives of this study, therefore, are to describe local geographic variation in pediatric malaria cases across Burundi and to determine whether these differences are independent of Burundi’s four principal seasons, which are two rainy seasons (February to May and September to November) and two dry seasons (June to August and December to January). It is important to analyze the incidence of malaria at the seasonal scale, between wet and dry season, due to the widely established relationship between rainfall and malaria incidence [9
]. Additionally, due to the proximity of Burundi to the equator there is limited variability in temperatures. However, there are spatial variations in annual average temperatures from 16 °C in the higher elevation areas in the north to 20 °C in the central plateau and 23 °C in the low lying areas near Lake Tanganyika.
2. Materials and Methods
2.1. Study Setting
Burundi, located in East-Central Africa, is one of the most densely populated regions in central Africa. It has a tropical highland climate with significant daily temperature fluctuations varying greatly between regions mainly due to differences in altitude [14
]. Burundi is comprised of eighteen provinces (Figure 1
), each named after their respective capital with the exception of Bujumbura Rural. The newest province, Rumonge, was created on 26 March 2015 from five districts previously belonging to the provinces of Bujumbura Rural and Bururi.
2.2. Data Source
We gathered data retrospectively from 26 hospitals representative of the regions of Burundi. We reviewed malaria admissions records of local hospitals. All children who were aged less than 5 years during the time period studied, admitted in the Departments of Pediatrics for symptoms of malaria were included in the study. Data on the daily number of admissions for children admitted for malaria in different hospital wards from 1 June 2010 through 31 December 2012 were collected. Variables included age, gender, area of residence, admission date, discharge date, principal and secondary diagnoses, hospital and hospital ward admitted (e.g., pediatrics). We calculated hospital admission rates based on Encounter ID, allowing separate admissions encounters by the same patient to be linked. For example, because a child may be treated more than once for malaria, an algorithm was developed using the Encounter ID and other identifiers (e.g., gender, address, and date of birth) so that only the first encounter in each year is used to calculate admission rate. Cases with incomplete records, such as dates of health facility visit, age, address, results of diagnosis, were excluded from the analysis. We analyzed a complete year of data from December 2011 to December 2012.
2.3. Case Definition
In Burundi, malaria treatment is based on both clinical signs (microscopy or rapid diagnostic tests (RDTs) [28
] and symptoms. Cases are registered in preformatted registration books (log books) at health care levels and reported both weekly and monthly to next higher level of health management system. Malaria in children was diagnosed based on the guidelines in the International Classification of Diseases (ICD-9-CM) ninth revision, and the definition included all malaria cases.
2.4. Ethics Statement
This study was approved by the University of Faculty of Medicine at the University of Burundi based on ethical procedures set by the Ministère de la Santé Publique et de la lutte contre le SIDA (ethics approval number not applicable). Results were reported in an aggregated manner (at the district level).
2.5. Statistical Analyses
In order to analyze the incidence of pediatric malaria cases in Burundi, we took into consideration all cases less than or equal to 5 years of age. All cases were classified into the four main seasons for each year.
Data management and statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were calculated for all variables. Incidence rate, was calculated as follows: the number of number of new cases occurring during a given time (2012)/population at risk during the same time period × 10n
. According to the United Nations Children’s Emergency Fund (UNICEF), there were 1,838,500 children under 5 in 2012. To reveal spatial patterns of pediatric malaria in Burundi, we employed the hot spot cold spot (HSCS) analysis technique using ArcGIS version 10.2 (Esri, Redlands, CA, USA). We calculated the Getis-Ord Gi* statistic [29
], which reveals a location or a small area within an identifiable boundary showing localized concentration or clustering of incident. Only positive spatial autocorrelation is taken into consideration (e.g., features that are similar to each other). It facilitates the comparison between clusters of similar values that are high or low compared to the mean [29
Lastly, in view of the defining role of elevation in the overall climate patterns, the role of elevation on the observed spatial patterns of pediatric malaria was analyzed using the Geographically Weighted Regression (GWR) technique to reveal the local level relationship at the seasonal scale. The main equation used in GWR is:
PM(u,v) = β0(u,v) + β1(u,v)Elevation + ε(u,v)
where (u,v) are the locational coordinates of a particular point on a surface. This technique takes into consideration the local variations in the rates of change with resulting coefficients calculated by the model for each specific location [32
]. It is particularly useful for taking into consideration non-stationarity in spatial relationships among different variables [33
]. Specifically, the various parameter estimates are calculated through weighting methods, in which the contribution of an observational site to the analysis is weighted in accordance with its locational proximity to the specific site being considered. Therefore, the weighting of an observation is not constant, but rather a function of its exact location. The analysis was performed using the count of malaria cases for the years 2011 to 2012 aggregated at the district level, with district centroids as latitude and longitude.
In order to reveal the role of elevation on the pediatric malaria cases, we mapped the local R square values and the beta coefficients. We used an adaptive kernel method, which allows for larger bandwidths associated with points that are widely spaced. The bandwidth minimization for the model was determined with the Akaike Information Criteria (AIC) using a bi-square function. Additional information on these techniques may be found elsewhere [34
Our results showed that the hotspots of pediatric malaria were predominantly in the northern half of Burundi, mostly concentrated in Kayanza, Kirundo, and Cibitoke provinces, which also overlaps with the more densely populated regions [35
]. These results are in agreement with Checchi and collengues’ [36
] findings which showed high malaria caseloads in the Burundian highlands. Another study conducted in Burundi found malaria mortality rates above emergency thresholds in the highland population [37
The annual incidence of pediatric malaria was persistently greater among boys than among girls, who were found to be more vulnerable to malaria during 2011–2012. A study in Nigeria also reported similar results [38
]. Likewise, a previous study by Nyarko and Cobblah [39
] conducted in Ghana reported that the 21% of malaria incidence was among males compared to 19% for their female counterparts. It is possible, therefore, that boys and girls are vulnerable to malaria in different ways that are shaped by their behavioral and socially determined roles within their communities and families. Guo [40
] reported similar results in Guangdong, with a higher incidence among males than among females. Children under five years old are disproportionately affected by malaria with 78% malaria deaths occurring in children under five years of age in 2012 [41
]. Sub-Saharan Africa has the largest burden of malarial disease, with approximately 229,486 deaths under five years of age.
The role of elevation was more pronounced in the higher elevation areas in the northern half of the country during all seasons. Pediatric malaria incidence was higher in the west and in higher elevation regions than in the east. During the drier months, due to the relative absence of the role of larger scale circulation patterns, the role of elevation was more distinct across the entire country, including southern Burundi. These results are in accordance with recent studies indicating an increase in the incidence of malaria in the highlands of east Africa during warmer years [42
]. For example, Bødker and collengues [45
] showed that low temperatures in the Usambara Mountains of Tanzania averted malaria parasite development in mosquitoes during the cool season rains, but highland transmission was higher during the warm dry season when vector densities were low. These results are also in accordance with recent studies which link increased altitude and malaria distribution in warmer years as well as an increased malaria burden in densely populated highlands of Africa [42
A possible explanation for the high prevalence of pediatric malaria in the northern region of Burundi may be that transimission is generated by an increase in vector density as a result of either proliferation of artificial breeding sites or high rainfall in the beginning of the warm season. A conceptual model of potential malaria risk factors in the Burundi highlands and demonstrated that Anopheles density was the best predictor for high malaria prevalence [46
]. There are, however, other possible explanations. It may be that the physical divide created by the Mitumba Mountains in a north south orientation, creates a climatological divide between the east and west. In addition, the northwestern part of Burundi is characterized by a steep terrain, which leads to a more localized role of elevation on pediatric malaria incidence.
In 2012, major parts of the Sahel region experienced severe droughts, while flooding events were reported from Southern Africa. Notably, the role of altitude was less pronounced at the local scale in 2011, with positive coefficients observed for the entire region. Due to the rugged uneven topography in Burundi there are substantial spatial inter-annual variations in malaria incidence malaria. Elevation data used for this study proved to be a good proxy variable for prevailing seasonal conditions.
The hotspots of pediatric malaria coincide with the more densely populated regions of Burundi in Kayanza, Cibitoke, and Kirundo. The role of elevation was more pronounced in the western highland region and the moderate elevation areas in the east during all seasons. The role of elevation was more dominant in the low lying regions in the south during the dryer months when the effect larger circulation patterns in the form of the Inter Tropical Convergence Zone and teleconnections like ENSO are limited.
The high prevalence of pediatric malaria in northern Burundi has public health implications. We observed that Burundi can be divided in three regions—northeast, northwest and southern region, an important finding for spatially targeting malaria control interventions and programs. This finding suggests that the populated regions of northeast and northwest which are also characterized by high population density (Figure 1
) and intensive subsistence farming practice, will require spatially targeted malaria control strategies. Residents of these districts spend more time outside tending to the crops and are likely to be exposed to malaria infected mosquitoes.
Despite the absence of station level climate data, the results of our study reveal a clear inter-annual seasonality in the spatial patterns of pediatric malaria incidence. Although these results differ from some published studies suggesting that vector densities and transmission intensity in the adjacent lowland areas are generally much higher than in the highlands [8
], they are consistent with other recent studies [47
]. Our results provide further support for the hypothesis that with rising temperatures and increasing trends in extreme precipitation events, the spatial spread and incidence of malaria in the highlands of Africa will continue to increase. In addition, with warmer climate conditions the upward spatial expansion of the hotspots of pediatric malaria in higher elevation areas will rise, if proper intervention measures are not put in place in a timely manner.
A limitation of this study is its retrospective design and data collected only from major hospitals.Vegetation data, which could also favor malaria transmission [52
], was not assessed in the models because of lack of high resolution spatial data.We did not collect data on mosquito larvae and are unable to associate infective mosquitos to malaria incidence. However, our results indicate elevation was associated with an increased number of pediatric malaria cases in the west and in higher elevation regions of Burundi. This information is important from a public health standpoint and for spatial targeting of malaria control programs and interventions. Due to the lack of availability of station level climate data, more detailed role of climate conditions on the spatial patterns of pediatric malaria incidence could not be assessed. Further investigation into socio-biophysical factors is strongly recommended.