Background
Malaria parasite transmission and clinical disease are characterized by important microgeographic variations, often between adjacent villages, households or families [
1‐
7]. This local heterogeneity is driven by a variety of factors including genetic [
6,
8], distance to potential breeding sites [
9‐
12], housing construction [
2,
5,
11,
13,
14], presence of domestic animals near the household [
15,
16], and socio-behavioural characteristics [
3,
10,
11,
17]. While seldom prioritized in the planning of malaria control by national programmes, the understanding of the microepidemiology of malaria is important to the design of effective small-area interventions [
3,
4] particularly in areas of unstable or very low transmission where risk is over-dispersed and highly focal [
18].
To assess space-time local heterogeneity of disease, techniques that detect the presence of statistically significant small-area disease clusters are often used [
5,
7,
19‐
21]. Some of the earliest use of disease cluster analysis was in the detection of distribution patterns of rare conditions such as cancers [
20,
22] and more recently applied to infectious diseases including dengue [
23], filariasis [
24], sleeping sickness [
25] and West Nile virus [
26]. The space-time clustering of malaria has also been described but mainly in moderate to high transmission settings [
2,
5,
27‐
30]. There are very few descriptions and quantification of space-time clustering of infection, disease or hospitalization from malaria in areas of unstable low or very low transmission settings. Here, the spatial and temporal clustering of
Plasmodium falciparum infections in 88 villages surveyed each year from 1999 to 2009 in the Gezira state, a generally very low unstable transmission area of the northern Sudan, is examined.
Discussion
Eleven years of data on
P. falciparum infection prevalence among children aged two to ten years in 88 villages in Gezira state were assembled. Over this study period, overall infection prevalence reduced from approximately five-fold to just below 2% in 2009 with infection rates dropping to 4% by 2000, the second year of the study, and remaining below this level in all subsequent years (Table
1 and Figure
2). Importantly, using the Kulldorff scan statistics [
20,
37] it was possible to show the presence of spatial-only and space-time clustering of infection prevalence in Gezira state. In each year, there was at least one significant primary spatial-only cluster containing from one to 26 villages and a single overall single significant primary space-time cluster (Table
2, Figures
3 and
4). The mean prevalence of infection in the villages contained in the primary spatial-only or space-time clusters was consistently higher than that of the overall data or of those villages outside the clusters throughout the study period. All spatial-only clusters either overlapped or were contained with the primary space-time cluster in all the years except in 2007, a year in which the cluster was of indeterminate radius.
The analysis of spatial and temporal clustering of malaria has predominantly been examined in areas of stable or low stable endemic transmission [
2,
28,
29]. This study, however, represents spatial-temporal cluster analysis of malaria infections in a very low unstable transmission area where disease risk manifests as "hotspots" and is associated with occasional epidemics. The results have a number of implications for malaria control in Gezira state. First, the primary space-time cluster identified in this study is located on the southern tip of the state in an area near a sugar plantation close to the Sennar dam which irrigates the very large Gezira scheme. In addition, almost all the spatial-only clusters observed in each year were either within or intersected the primary space-time cluster in the south of Gezira state, indicating a highly focal concentration of infections which can be addressed by focused targeting of interventions. Second, while it is difficult to empirically determine the reason for the peaks in infection prevalence in an otherwise declining trend over the study period using the available data, it is interesting that these coincided with the period when reports of vector resistance to malathion-deltamethrin emerged and its use was discontinued in 2002; when vector resistance to pyrethroid-permethrin had increased to substantial levels in 2006 just before its replacement with bendiocarb in 2007; and in 2008 when the IRS programme was wound up (Figure
2). Therefore, malaria control in Gezira needs to maintain and scale-up its efforts on prevention of disease among the agricultural population in the Gezira irrigation scheme and those along the southern and central Blue Nile River, expand control efforts to the neighbouring Sennar state where the Sennar dam is located.
Although the data assembled for this study provide a useful basis for tracking the changing infection prevalence among the population in Gezira state, there are some caveats and opportunities for strengthening future surveillance. First, the temporal resolution of the data of one survey each year limits the analysis of seasonal peaks of transmission and potentially misses a significant proportion of infections. Second, understanding the microgeographic (households and individual) heterogeneity of malaria infections has potentially important implications for small-area targeting of malaria control. The survey data, however, could not be assembled at the household or individual levels because hardcopy survey data at household and individual level could not be located from the GMCP archives. Future surveys should be digitized to make them amenable to detailed microgeographic analyses that account for potential risk factors such as age, housing structure, behavioural and environmental variables. Third, in the context of elimination and given the low levels of malaria transmission, data on all ages examining not only the asexual stage infections but also the sexual stages are required [
38]. Finally, in areas of low malaria transmission microscopy or rapid diagnostic tests have low detection rates [
39,
40] thereby underestimating the overall infection prevalence. Alternative approaches such as long-term active and passive case-detection from a spatially representative sample of communities and health facilities and use of polymerase chain reaction (PCR) [
39] to detect low level infections and serological markers to assess Plasmodium antibody exposure [
40,
41] should be explored. One of the best examples of such a detailed investigation is represented by the 11 year longitudinal study of malaria in one village, Daraweesh in Eastern Sudan [
42], which provided important observations on the epidemiology [
43], seasonality [
44], presence of sub-microscopic chronic
P. falciparum infections [
45]; ethnic and genetic susceptibility [
42,
46] and immunity [
47] to malaria under conditions of very low transmission intensity.
Conclusion
This study demonstrates the potential of space-time clustering techniques to identify areas of high malaria infection rates in an area of generally low transmission in Gezira state. The success of malaria elimination in the Gezira, the stated aim of the GMFI, depends critically on sustained control and the establishment of high quality surveillance to measure disease patterns. All of these are linked to the availability of adequate funding but there are already shortfalls in the financing of both control and epidemiological surveillance [
33]. The development of better surveillance systems to document any changes in malaria epidemiology of the disease should consider the establishment of health facility, school and community sentinel sites for the prospective assembly of high quality active and passive case detection data.
Acknowledgements
We are grateful to the many individuals at the Blue Nile Health Project and Gezira State Malaria Control Programme of the Federal Ministry of Health; the University of Gezira and the University of Khartoum who helped with the assembly of the parasite prevalence survey data. We also thank Drs Philip Bejon, Pete Gething and Anand Patil for their advice at preliminary analysis stages comments on earlier versions of the manuscript. We are grateful to Victor Alegana for his help with mapping of survey locations. The study received funding from the Gezira State Malaria Control Programme with support from the World Health Organization and the Global Fund to Fight AIDS, Tuberculosis and Malaria. AMN is supported by the Wellcome Trust as a Research Training Fellow (#081829). RWS is supported by the Wellcome Trust as Principal Research Fellow (#079080) that also supported SEM's internship at the KEMRI/WTRP, Nairobi. This work forms part of the output of the Malaria Atlas Project (MAP,
http://www.map.ox.ac.uk), principally funded by the Wellcome Trust, UK. AMN and RWS also acknowledge support from the Kenya Medical Research Institute.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SEM was responsible for data assembly, cleaning, analysis, interpretation and production of the final manuscript; BYMN was responsible overall supervision of survey data assembly; SMB was responsible overall supervision of survey data assembly; IH was responsible overall supervision of survey data assembly; RWS was responsible for overall scientific management, interpretation and preparation of the final manuscript; AMN was responsible for overall statistical analysis, interpretation and production of the final manuscript. All authors read and approved the final manuscript.