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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Visceral leishmaniasis (VL), also known as kala-azar, caused by Leishmania donovani, is an important parasitic disease affecting large populations in the tropics.3,4 On the In-dian subcontinent, the parasite is transmitted by phlebotom-ine sandflies from person to person; in the New World, the Mediterranean region, and East Africa, a reservoir host is involved.5,6 Although VL is restricted to specific localities, little work that uses environmental factors has been performed to explain its focal distribution.7
Some of the most important foci of VL are found in eastern and southern regions of Sudan, where epidemics have claimed the lives of hundreds of thousands of people in the past 20 years.8–10 Previous maps of disease distribution produced, for example, by Hoogstraal and Heynemann11 and Zeese and Franke,12 have been shown to be inadequate for describing the geographic extent of epidemic prone areas. A case in point was the VL epidemic, which killed 100,000 people in an isolated area in the Western Upper Nile region of southern Sudan, an area that lay outside the then-known areas of risk of infection (Figure 1
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). The region is a flat plain, with almost no relief other than small, scattered hills and seasonally flowing watercourses. The principal soil type throughout the region is vertisols. Other soils, which occupy small fractions of the area, include a mixture of alluvial clays, silts, and sands of varying depths on the banks of the seasonal rivers, and rocks, stones, and gravels in some sites. The climate of the region is tropical continental, with an estimated annual rainfall of 400–1,400 mm. The year is sharply divided between the rainy season, June–October, and the dry season, November–May. According to readings at Gedaref station, daily mean minimum temperature is 21.0°C in the rainy season and 18.3°C in the dry season; corresponding maxima are 37.3 and 40.6°C. The natural vegetation of the area is dry savanna woodland. The main indigenous trees in the region are B. aegyptiaca (known locally as "hig-leeg"), A. seyal ("Taleh"), Acacia Senegal ("Hashab"), Acacia mellifera ("Kiter"), Combretum spp., Calotropis procera ("Usher"), as well as some riverine vegetation consisting of Hyphaena thai-baica, Zyzyphus spina-christa, and other trees and bushes. Along the riverbanks, some fruit orchards are found. Dura (Sorghum pupura), sesame (Sesamum orientale), Dokhon (Pennisetum typhodium), and groundnuts (Arachis hypogaea) are grown as cash crops over extensive areas.
The human population of Gedaref State belongs to many ethnic groups, most of whom have a recent history of settlement in the region. Gedaref State remained an unpopulated region (supposedly as a consequence of the presence of VL) until recently, when mechanized agriculture cleared vast areas of woodland.12 Originally, the area was first exploited by nomadic tribes that visited the main rivers during the dry season and traveled north to the Butana region at the onset of rain. The first settlers in the area were the Fellata people, who migrated from the Kano region of Nigeria in 1923–1929. These were then followed by Masaleet, Hausa, Fur, and other West African people who came as laborers in the mechanized agriculture schemes, which were first established at the end of the 1950s and beginning of the 1960s. Further settlement, particularly along the Atbara and Rahad Rivers, followed the continued expansion of mechanized agriculture in this region and the famines that struck western Sudan in 1983–1984. According to the most recent estimate, Gedaref State has a total population of 1,137,642 people (Statistics Department, Ge-daref State, Sudan).
Visceral leishmaniasis cases, human population, and village location data. During the period March 1996–December 2000, > 13,000 VL cases were diagnosed in Gedaref State (Reports of the Ministry of Health, Gedaref State, Sudan). The only intervention program against VL was started in April 1999 by Médecins Sans Frontières—Holland (MSF-Holland), which distributed 350,000 insecticide-treated bed nets to the inhabitants of VL endemic foci.
The case data analyzed in this study were obtained from detailed records of 2 treatment centers established and operated by MSF-Holland in Umkra’a (Um-Elkhair), situated close to River Rahad, and Kassab village, situated close to Gedaref town (Figure 2). The first health center at Umkra’a was opened in March 1996, and since that date, it has become the main treatment center in eastern Sudan, until it was joined, in September 1999, by Kassab. Although a few patients were also diagnosed in Gedaref and Hawata hospitals and other rural dispensaries, most patients have been referred to the 2 MSF VL treatment centers as a result of the high cost of treatment (estimated at US$170 per patient). The inpatient records of each center included the place of residence, age, sex, and the date of admission. Patients reporting to the centers originated from VL-endemic villages throughout eastern and central Sudan. In addition, large numbers of patients treated at the 2 centers arrived from places as far as Bentiu (southern Sudan) and El Fasher (western Sudan), > 1,000 km away from the centers.
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Data on cases and human populations were initially handled within Excel and SPSS (SPSS Inc., Chicago, IL) software to calculate numbers of cases of VL reported to the treatment centers during the epidemiological years Novem-ber 1996–October 1997, November 1997–October 1998, and November 1998–October 1999. Taking into account the opening dates of the health centers and the bed net intervention program of MSF, we based further incidence analysis on the epidemiological year November 1998–October 1999, when most villages had similar access to the health centers and before the large-scale bed net distribution program. Villages from which no cases were reported throughout the period March 1996–December 1999 were considered free of the disease. Data were then entered in a new file containing names of villages, their coordinates, councils, and human population and analyzed to determine annual incidence (per 1,000 people) in different villages. Coordinates of village locations were obtained from readings of a Magellan (Magellan System Corp., San Dimas, CA) global positioning system and from maps produced by South Kassala Agricultural Project.
Environmental data. Environmental data corresponding to the coordinates of each of the study villages were extracted from a number of satellite sources and digital databases by Arcview GIS software with Spatial Analyst (ESRI, 380 New York Street, Redlands, CA 92373-8100, USA, http:// www.esri.com) and the public domain software WINDISP 3 (http://fao.org/giews/english/windisp.html). The U.S. Geological Survey (USGS) hydrologic data set (USGS Web site: http://edcdaac.usgs.gov/gtopo30/hydro/africa.html) was used to obtain a detailed description of the topography of the area, including elevation (digital elevation model), slope, aspect (direction of maximum rate of change in elevation between each cell and its 8 neighbors and representing direction of slope), flow accumulation (defining amount of upstream area draining into each cell), and the compound topographic index (commonly referred to as wetness index).
Information on vegetation status (by use of 10 daily NDVI images) was obtained from data archives of the Vegetation sensor on board the French satellite system SPOT (Satellite Pour l’Observation de la Terre; http://www.spotimage.fr). The annual mean, minimum, maximum, and medium values of NDVI for each grid square (1-km resolution) were calculated for the year 1999. Ten daily images of 10 daily rainfall estimates (5-km resolution) for the years 1996–1998 were obtained from Africa Data Dissemination Service (Web site: http://edcintl.cr.usgs.gov/adds/adds.html) and analyzed by Windisp 3 GIS software to obtain the average annual rainfall for each village. Soil types of different villages were read from a map produced by the South Kassala Agricultural Project and classified in 9 classes. By use of Arcview GIS software, we calculated the distance of each village from each of the 2 treatment centers (Kassab and Umkra’a) and the 2 seasonal rivers (Atbara and Rahad).
Statistical and GIS analysis of environmental and VL incidence data. A univariate correlation analysis was initially undertaken to determine the relationship between incidence of the disease and different environmental variables. Stepwise multivariate analysis was then carried out by binary logistic and linear regressions to determine predictor variables affecting presence and incidence of VL, respectively. To give stronger emphasis on larger villages, the linear regression was weighted by population. For the logistic model, all 190 study villages were used in the analysis. In contrast, the linear regression analysis was carried out on data of 140 villages by excluding large towns, villages with no population data, and places lying within 2 km from the treatment centers. Running selected variables against natural logarithm transformation of the incidence data has further refined the incidence model. The logistic and linear regression models resulting from the analysis were entered into the map calculator module of Spatial Analyst and used to create maps of probability of disease presence and disease incidence.
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RESULTS |
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The general distribution of VL endemic and nonendemic villages in relation to altitude, NDVI, rainfall pattern, and location of rivers and health centers are shown in Figures 3–5. Clear clustering of high incidence villages around the 2 rivers and areas of low altitude and high rainfall zones is noticeable.
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. The location of the treatment centers did not appear to be correlated with the presence (Table 1) or incidence of the disease (Table 2).
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Probability of presence of VL in a village = 1/(1 + e-Z), where
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The estimated coefficients, the standard errors, and the goodness of fit of the binary model are listed in Tables 3a to 3c. The model correctly predicted all observed positive sites as VL-endemic villages. Although none of the 33 negative villages were correctly predicted by the model to be free of infection, there was 82.6% overall accuracy of the model in predicting endemic and nonendemic villages.
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Predicted incidence obtained from this model appeared to correlate closely with the observed data (Figure 7).
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. The 2 models showed that the risk of VL increases in a southeast direction, with places of maximum risk along the banks of Atbara and Rahad Rivers. However, there was a corridor of low-risk areas running parallel to the Atbara River up to the Ethiopian border.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Here, we present the first detailed map of VL in Gedaref State, an important endemic region. Our analysis depended mainly on patients with VL admitted to 2 MSF treatment centers, which were recently established in Gedaref State. We assume that the data of the 2 treatment centers were representative of all areas within the region for the following reasons. First, VL is a serious health hazard, which people recognize to have a fatal outcome if not treated, and most victims need inpatient treatment. Therefore, people were motivated to report to the 2 treatment centers, which offered their services free of charge. Second, treatment of VL is quite expensive, exceeding the annual income of most people in the region, and the drug was not available in other hospitals and dispensaries. Finally, the analysis carried out in the present study showed no significant association between incidence of VL and distance from health centers.
Our results showed that distance from the river, the topography, rainfall, and minimum NDVI are the main environmental variables independently associated with the distribution and incidence of VL in Gedaref State. It is probable that these variables influence the populations of the vector and the reservoir hosts of L. donovani by affecting other microclimatic factors in the area. Phlebotomus orientalis, the vector in the area, is known to thrive in habitats characterized by presence of B. aegyptiaca trees, A. seyal trees, and verti-sols.11,13,15,16,19–22 The vector was also found to inhabit a "climate space" of rainfall 400–1,200 mm and of annual mean maximum daily temperature of ~34–38°C.15,16 Because most of the region is covered by vertisol soils, it is not surprising that this factor did not seem to affect the distribution of the disease within the region (Figure 2).
It is interesting that VL incidence correlated closely with the mean and minimum NDVI and did not appear to have an association with maximum NDVI. The minimum NDVI in this region generally coincides with the sandfly season13 and should reflect the density of trees because most grasses of the area are highly seasonal, flourishing after the start of the rains.
In all analyses carried out in this study, annual rainfall appeared to be the most important predictive variable affecting both the probability of presence and the actual incidence of the disease. Rainfall may affect the vector and the reservoir hosts by affecting the vegetation, the temperature, and the relative humidity. For example, B. aegyptiaca is known to have a core distribution between the 400–800-mm isohytes,23 with localized concentrations in periodically flooded or waterlogged areas with a mean annual rainfall > 900 mm. Similarly, A. seyal has a tendency to concentrate on low-lying areas (< 500 m) with a mean annual rainfall of 200–500 mm.24 Although the elevation did not correlate with VL incidence in the preliminary analysis, it appeared as an important variable when used in the multivariate analysis. This result indicates that in the final analysis, the elevation integrated the effects of many other factors, including distance from the river.
The 2 models developed in this study provide detailed mapping of classified incidence of VL in Gedaref State. The fact that the 2 models were derived from environmental variables gave us the chance to produce a risk map of the disease and predict its burden in areas not covered by the initial data. The risk maps produced from the study should be of great value for planning locations of treatment centers, for finding appropriate places for human settlement, and for deciding where to extend the control programs. Although the models were based on local data pertaining to Gedaref State, we found that they can also provide a good prediction of VL presence and incidence in other areas of Sudan (e.g., Western Upper Nile province, where the disease is transmitted by the same vector; data not shown). We suggest that the novel approach of this study can be used for other parts of the world to predict and map VL transmitted by different vectors. Such studies would provide a global understanding of VL problem and help pri-oritize control programs.25
Received July 13, 2001. Accepted for publication December 3, 2001.
Acknowledgments: We thank Khartoum Office of MSF-Holland and the Ministry of Health of Gedaref State for giving us the permission to analyze and publish their hospital record data. Thanks are due to Dr. Fathi M. Elraba’a, Dr. M. A. Kambal, Dr. S. Abukashawa, Dr. O. F. Osman (Faculty of Science, University of Khartoum), Prof. A. M. El Hassan, and Dr. Ibrahim M. El Hassan (Institute of Endemic Diseases, Univeristy of Khartoum), and Dr. H. Giha (Department of Biochemistry, University of Khartoum) for their help and support.
Financial support: This work was supported by funds from EMRO-Office of World Health Organization (WHO T5/72/6, grant SGS00/ 55), a Shell fellowship from the Liverpool School of Tropical Medicine (D.-E.A.E.) and the Dempster Memorial Trust Fund (M.M.T.).
Reprint requests: Madeleine C. Thomson, International Research Institute for Climate Prediction (IRI), Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, 10964, Telephone: 845-680-4413, Fax: 845-680-4866, E-mail: mthomson{at}iri.columbia.edu. Dia-Eldin A. Elnaiem, Department of Zoology, Faculty of Science, University of Khartoum, Khartoum, P.O. Box 321, Sudan, E-mail: dialnaiem{at}hotmail.com
Authors’ addresses: Dia-Eldin A. Elnaiem and Abdelrafie M. Mekkawi, Department of Zoology, Faculty of Science, University of Khartoum, Sudan. Judith Schorscher, MSF-Holland, Maison Porpi-gna, 64260 Buzy, France. Anna Bendall, University of Greenwich, Chatham, United Kingdom. Valérie Obsomer, Stephen J. Connor, Richard W. Ashford, and Madeleine C. Thomson, Liverpool School of Tropical Medicine, Liverpool, United Kingdom. Maha E. Osman, Institute of Biotechnology, National Centre for Research, Sudan.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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W. A. HAWLEY, F. O. TER KUILE, R. S. STEKETEE, B. L. NAHLEN, D. J. TERLOUW, J. E. GIMNIG, Y. P. SHI, J. M. VULULE, J. A. ALAII, A. W. HIGHTOWER, et al. IMPLICATIONS OF THE WESTERN KENYA PERMETHRIN-TREATED BED NET STUDY FOR POLICY, PROGRAM IMPLEMENTATION, AND FUTURE RESEARCH Am J Trop Med Hyg, April 1, 2003; 68(90040): 168 - 173. [Abstract] [Full Text] [PDF]
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