helminth infections, Africa, population, PHOTOGRAMMETRIC ENGINEERING, Brooker, Cameroon, Brookerand Michael, Royal Society of nopica1 Medicine, World Health Organization, REMOTE SENSING, intestinal nematode, Trichuris trichiura, schistosome, mass treatment, nematode infections, Ascaris lumbricoides, Geneva, Switzerland, satellite sensor data, environmental variables, anthelminthic treatment, helminth infection, human infections, S. Brooker, NOAA-AVHRR Data, spatial scale, Goddard Space Flight Center, control programs, Oxford University Press, Raoult Ratard, Simon I. Hay, geographic information systems, population map, spatial distribution, schistosomes, Imperial College School of Medicine, infections, population size, Logistic regression models, independent variables, regression models, empirical data, Louisiana Department of Health and Hospitals, disease transmission, M. Bavia, R. Ratard, Schistosoma haematobium, priority areas, Idrisi Project, schistosomiasis, Wellcome Trust Prize Fellowship, Active Archive Center, Schistosoma mansoni, Journal of Infectious Diseases, M.S. Abdel Rahman, International Journal of Remote Sensing, WellcomeTrust Advanced Training Fellowship, References Anderson, National Oceanic and Atmospheric Administration, Blackland Research Center Report, environmental variability, Bundy, S. Greenland, Modern Epidemiology, schoolchildren, urinary schistosomiasis, A. Montresor, Partnership for Child Development, Red Urine Study Group, M. Shafik, J. Spilsbury, Economic Research Project Reports, Psychological Bulletin, Human schistosomiasis, M.M. El Bahy, J.C. McCarroll, Don Bundy, Journal of Geophysical Research, geographical information systems
Using NOAA-AVHRR Data to Model Human Helminth Distributions in PlanningDiseaseControl in Cameroon, West Africa
Simon Bmker, Simon I. Hay, Louis-Albert Tchuem Tchuente, and Raoult Ratard
Abstract Human helminth infections [intestinal nematode infections such as Ascaris lumbricoides, Trichuris trichiura, and hookworm, and schistosome infections such as Schistosoma haematobium and S. mansoni] affect more than a quarter of the world's population, with potential consequences for the health and nutritional and educational development
of infected individuals. The advent of broad-spectrum anthelminthic drugs that are cheap, safe, and simple to deliver has meant that control has become a viable option for many countries. Because helminth infections patterns are highlyheterogeneous, methods to identify priority areas for intervention against intestinal nematode and schistosome will enhance the eflcacy of control. This paper describes the use of NOAA-AVHRR data to develop logistic regression models that predict the probability of infection prevalence greater than 50 percent, and thus warrant mass treatment for intestinal nematodes and schistosomes, according to WHO'S criteria. Moreover, by overlaying the resulting risk maps on population surfaces, it is possible to estimate the school-aged population size requiring mass treatment and also provide an estimate of program costs. Introduction More than fifty years ago, a seminal paper entitled "This Wormy World" (Stoll,1947)showed that helminth infections, including schistosome and intestinal nematode species, were among the most common of human infections. Today, these infections still affect more than a quarter of the world's population (Chan et al., 1994;Bundy, 1997),with potential consequences for children's physical and intellectual development (Stephenson, 1987;Watkins and Pollitt, 1997). The advent of broad-spectrum anthelminthic drugs that are cheap, safe, and simple to deliver has meant helminth control has become a viable option for many countries. The World Health Organization
(WHO) presently recommends mass anthelminthic treatment in areas where infection prevalence (proportion of community infected) is 50 percent or greater (WHO, S. Brooker is with the ~ i ~ a r t m e ; of Infectious Disease Epidemiology, Imperial College School of Medicine, Norfolk Place, London WG IPG, United Kingdom ([email protected]
). S. Hay is with the Trypanosomiasis and Land Use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford 0x1 ~ P SU, nited Kingdom. L.-A.Tchuem Tchuent6 is with the Centre for Schistosomiasis & Parasitology, P.O. Box 7244, Yaound6, Cameroon. R. Ratard is with the Louisiana Department of Health
and Hospitals, Office of Public Health, 1201 Capitol Access Road, P.O. Box 629, Baton Rouge, LA 70821-0629.
1995a).Praziquantel is used to treat the schistosomes (Schistosoma haematobium and S. mansoni), and intestinal nematodes (Ascaris lumbricoides, Trichuris trichiura, hookworm) are treated by the benzimidazole drugs, albendazole and mebendazole (WHO, 1995b).Studies have shown that these treatments can be safely and effectively combined (Savioli et al., 1997;Olds et al., 1999),and the WHO recommends joint delivery in areas where both groups of parasites occur (WHO, 1995a).It has been suggested that the overlap in the geographical distribution
of each group of species is sufficiently large to justify combined treatment (Bundy et al., 1991),but more detailed analysis suggests uneven and often non-overlapping distributions within countries (Brooker et al., 1999).This indicates a more refined approach to combined control is required, whereby target communities are identified separately for intervention against schistosomes and intestinal nematodes, and drugs are distributed according to local needs, thus reducing delivery costs and the prospect of drug resistance
. In an effort to better understand the distribution of species, Geographic information
systems (GIS)are increasingly being used to collate and map available helminth survey data available from the formal and "grey" literature (Brooker et al., 2000a). Such information on the distribution of infection will be central to successfully addressing the key operational questions of reliably estimating the target population numbers at risk (Brookeret al., 2000b), stratifying areas by prevalence to prioritize areas for control, and estimating overall drug needs and costs. Although there is comprehensive information on helminth distributions in some African countries, empirical survey data are available for only a third of administrative districts across the continent (Brookeret al., 2000a). To help fill the gap in empirical data, remotely sensed (RS) satellite sensor data and interpolated meteorological surfaces are being used to predict the distributions of a variety of Infectious Diseases
(Malone et al., 1997; Hay et al., 2000; Rogers, 2000;Lindsay and Thomas, 2000; Malone et al., 2001).For helminth species, years of field studies have documented the influence of climate and environmental variables on the distribution of helminth infections (Appleton, 1978;Brown, 1994; Crompton, 1994),and RS-derivedenvironmental variables and meteorological variables are of potential use in predicting the occurrence of significant transmission (Brookerand Michael, 2000). The present study uses environmental data derived frommeteorological satellite sensors and interpolated meteoro- Photogrammetric Engineering & Remote Sensing Vol. 68, No. 2, February 2002, pp. 175-179. 0099-1112/02/6800-175$3.00/0 O 2002 American Society for Photogrammetry and Remote Sensing
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logical data to model the distribution of schistosome and intestinal nematode infections in Cameroon. These distributions are then used to quantify the population requiring treatment and to estimate the financial costs of school-based programs using single species or combined control options. Methods Epidemiological Data Prevalence data were collected during a nation-wide survey of helminth infection in 1985-1987 originally aggregatedat the district level
(Ratard et al., 1990;Ratard et al., 1991).These data are stratified at the school level here because schoolchildren are the primary targets for treatment and the educational infrastructure is usually used to deliver treatment. The original study was designed to provide data on the nationwide distribution of helminth infections using a stratified, random-cluster sampling procedure with the primary school
as the basic sampling unit. In the north of the country where school enrollment kaslow, all children from the apprdpriate age group living in the communitv were invited to participate [Ratard et al., 1990). Urine and stoil samples were &aminid by sedimentation and the Kato-Katz thick smear technique, respectively. The location of schools was obtained by transcribing coordinates from 1:25,000-scalemaps used in the original survey.Data on intestinal nematode species (Ascaris Iumbricoides and Richuris trichiura) for 18,260school children aged 10to 19years in 402 schools and data on schistosome species (Schistosomahaematobium) for 19,524children in 303 schools were collected. Satellite Sensor Data Land SurfaceTemperature (LST)and the normalized difference vegetation index (NDVI) information were derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration
's (NOAAp)olar-orbiting meteorological satellites (Cracknell, 1997)using Price (1984)and Tucker (1979)procedures, respectively. Daily data at 8- by 8-km spatial resolution were first processed for the period 1985through 1998to exclude unreliable pixels due to extreme sun and sensor viewing angles and cloud contamination (see Hay and Lennon, 1999). Single monthly images were then maximum-value composited (Holben,1986). Minimum, mean, and maximum values
of these data were extracted for each pixel that corresponded to the location of the parasitological surveys. image processing
was performed using the Earth Resources data analysis
System (ERDAS) Imagine 8.4'" (ERDAS,Inc.,Atlanta, Georgia). Other Environmental Data Interpolated rainfall surfaces were taken from the Spatial Characterization Tool (Corbettand O'Brien, 1997),and an interpolated digital elevation model (DEM) of Asica was obtained from the Global Land Information System (GLIS) of the United States geological survey
(EROSData Center,1996). Population Data District population data were derived from a 1990 national population forecastbased on the 1987national census (Deichmann, 1996),and were projected to 2001using annual specific growth rates obtained from the United States Census Bureau
(2001). Statistical Analysls To examine the relationship between environmental variables and the need for mass treatment, schools were classified as having prevalence above or below 50 percent, WHO'S treatment threshold (WHO recommends that mass treatment is warranted if the prevalence in a school exceeds 50 percent infection).
Logistic regression models were developed to identify significant environmental variables affecting the transmission of infection. A potential problem in developing regression models usine environmental vari-ables is that many are highly intercorrelagd so that it is difficult to separate the-effectsif ihe inDependent variable
s statistically (Morgenstern, 1998). TO reduce the dimensionality of these collinear variables, we first selected those variables likely to have greater biological significance on infection transmission (Brookerand Michael, 2000). Second, the remaining variables were added to the models in a stepwise fashion, and the statistical fits of alternative models were compared using the residual deviance of models includ- ing and excluding correlated variables using a x2distribution (Venablesand Ripley, 1999).Analysis was done using S-Plus 4.5 Professional Release 2 (Math Soft, Seattle, Washington). The best-fit logistic regression models were then used to map the probability of infection prevalence being greater than 50 percent using Idrisi Version 2 (The Idrisi Project, Worcester, Massachusetts). To define whether a district would be a priority area for control, we have used an arbitrary criteria based on whether the average logistic regression probability is greater than 0.5 within a district. On this basis, the number of schoolaged children who would receive mass treatment for intestinal nematodes was estimated. The population size for praziquantel and albendazole treatment was calculated by overlaying the predictive maps of infection prevalence on a population map. For albendazole we used a combined estimate of infection prediction for either A, lumbricoides or II: trichiura. Cost Data Detailed prospective cost analyses have been conducted for school-based anthelmintic programs in Ghana and Tanzania (Partnership for Child Development, 1999a).The cost of mass distribution of a single dose of albendazole for intestinal nematodes to schoolchildren by their teachers was US$0.03 in both countries. The cost of delivering praziquantel for schistosomes-which required targeti
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TABLE1. REGRESSION COEFFICIENTDSESCRIBINTGHE LOGISTIC REGRESSION MODELLST. = !AND SURFATECMEPERATURNED; VI = NORMALIZED DIFFERENCVEEGETATIONINDEX
Residual deviance P (<)
S. haematobium Constant Maximum LST Rainfall A. lumbricoides Constant Maximum LST Maximum NDVI Rainfall rl: trichiura Constant Maximum LS'I Mean LST
-228.16 0.14 0.02 75.31 -0.05 0.04 -0.002 142.4 -0.003 -0.17
58.1 32.6 21.3 549.4 294.5 286.6 256.1 525.9 181.3 155.5
0,001 0,001 0.001 0.004 0.001 0.001 0.001
In contrast to the models for A. lumbricoides and I: trichiura, maximum LSThas a positive effect on S. haematobium. In Cameroon, the predominant snail species are Bulinus senegalensis in the north of the country and Bu. globosus and Bu. truncatus in the south (Wright, 1959;Greer et al., 1990).The former is found principally in semi-permanent water bodies and can survive the dry season by aestivation. By contrast, the two southern species tend to occur in more peGanent water bodies. These features of snail distributions suggest that the model for S. haematobium is in fact predicting the distribution of Bu. Senegalensis.Specifically,the model is predicting habitats suitable for this species, i.e., areas with semi-permanent water bodies which arose from periodic drying out due to high temperatures. Moreover,the rarity of water points in the north leads to a concentration of human water contacts with fewer water points available, thus increasing the risk of transmission. In the south, by contrast, human water contacts are more dispersed among numerous water bodies, decreasing the risk of transmission. Estimates of Treatment PopulationSize and Program Costs Thebest-fitregressionmodels were then used to generateprobability maps of infection prevalence greater than 50 percent for S. haematobium, A. lumbricoides, and T trichiura (Figure1). These maps indicate that different areas would warrant mass treatment with albendazole than those requiring mass treatment with praziquantel-combined control would not be justified in most areas. We estimate that 5.8 million school-aged children in 33 of 49 districts in Cameroon would warrant mass treatment with albendazole.Using the model for S. haematobium,we estimate
Figure 1. Predicted probability of having infection preva lence greater than 50 percent. (a) S. haematobium. (b)A. lumbricoides. (c) T. trichiura.
no districts would warrant mass treatment at the 50 percent threshold. However, because there will be heterogeneity of prevalence within districts and there should be flexibility in the treatment thresholds to suit local needs, the analysis was rerun using a 20 percent prevalence threshold. On this basis, we estimate that 1.8million school-agedchildren in nine districts would receive mass treatment with praziquantel. For S. haematobium, an effective approach to help locate high-risk communities/schools requiring mass treatment within at-risk areas has been the use of blood in urine questionnaires (Red Urine Group, 1995; Partnership for Child Development, 1999b).To identifypriority areas for questionnaire surveys, the present broad-scale ecological predictions can provide relevant information. Estimates of treatment costs were developed based on the predicted target population size. Using the Tanzania cost data as a lower estimate and the Ghana cost data as an upper estimate, we suggest that the cost of control for A. lumbricoides and I: trichiura (using albendazole) would be US$0.18 million and for S. haematobium (using praziquantel) would be US$ 0.39 to 1.24million. Discussion and Conclusions These results indicate that RS and meteorologicaldata offer the opportunity to investigate the distribution of intestinal nematode and schistosome infection and some of the ecological fac- tors that limit transmission, for purposes of predicting infec- - tion distributions. This will prove valuable for health planners in the majority of low-income countries where there is a lack of detailed empirical survey data. Equally important for national health planning, the results will help provide estimates of control program costs of delivering anthelmintics through the school system. This has important implications for the efficient allocation of scarce health resources. Previously,the sensitivity of helminth transmission to climate variation, and the use of models using N O A A - A m data to predict infection patterns, have been described for S.mansoni (Malone et al., 1994;Malone et al., 2001). In Egypt, Malone et al. (1994)used 1-krnresolution data to derive maps of diurnal temperature differences (dT),which indicate surface and sub-surfacemoisture contained in soil and plant canopy and hence may act as a surrogate for the abundance of the snail vector, Biomphalaria alexandrina, whereby wetter and more suitable habitats for Bi. alexandrina corresponded to lower dT values. They found that low values of dT are associated with increased snail abundance in wet areas with a slow current flow, and is closely mirrored in the patterns of S.mansoni prevalence. Malone et al. (2001)also used 1-kmA= data to produce maps of LST and NDVI to study the distribution of S.mansoni in Ethiopia. They found that annual composite maximum LST values of 20 to 33OC and wet season values of 18 to 29°C defined the distribution of S. mansoni prevalence greaterthan 5 percent in Ethiopia, and used these limits to predict infection risk within the country. In an analysis of survey data from Tanzania (Brookeret al., 2001),we have used 8-km AVHRR to develop predictive models of S. haematobium. We found that the model allows reasonable discrimination between high- and low-prevalence schools, at least within those geographical areas
in which they were originally developed, and performs reasonably well in other coastal areas, but performspoorly in comparison in the Great Lakes area of Tanzania. Despite these applications for S. mansoni and S.haematobium, we believe that there are no published studies using satellite sensor data to predict distributions of intestinal nematodes in Africa. Although the present analysis uses the example of Cameroon because of the geographically detailed data available for the country, the approach can be extended to other countries in Africa. The potential of such an approach will, however, re-
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F e b r u a r y 2 0 0 2 177
main undefined until further studies are undertaken which consider several issues. The first issue relates to the problem of spatial scale (Wiens,1989; Levin, 1992; Walsh et al., 1999). The problem is that many biological responses are scale-dependent (Wiens, 1989), and observed associations between disease transmission and environmental variables vary as the scale changes. In the context of helminth control, the rationale for prediction is to provide information on the spatial patterns of infection and disease at the administrative level at which control resources are likely to be mobilized, usually the district level. Furthermore, although RS data are available at fine spatial scales,the satellite systems most widely used in the R s com- munity are those with a broad spatial scale of 1to 8km. We used AVHRR 8-km satellite sensor data to model the probability of areas having infection of 50 percent or greater and warranting mass treatment with anthelmintics. Other studies conducted at different scales may reach different conclusions (Walsh et al., 1999). A further issue is that different environmental variables may impact upon helminth transmission in different areas (Appletonand Gouws, 1996; Brooker and Michael, 2000) and, in the case of schistosomiasis, different snail species may be differently affected by environmental variability responsible for disease transmission (Malone et al., 2001; Brooker et al., 2002).The development of separate or modified models of spatial distribution
of infection will provide the basis for a wider and more detailed analysis of the population size at risk of infection and allow for the more targeted and rational implementation of control programs in Africa. The challenge lies however in defining the spatial envelope in which developed models can be applied and where different models are required. This is an area of ongoingresearch. In summary, the models developed here provide health planners with a means of predicting the geographicaldistribution of intestinal nematodes and schistosomes for the purposes of targeting control and for estimating likely program costs. With the development of further models, the approach could provide, for the whole of Africa, reasonable predictions of the need to mass treat with albendazole and praziquantel, and identify areas where a single or combined control approach is warranted. This will provide a potentially valuable planning tool for planning and implementing control programs aimed at reducing the disease burden due to helminth infections among African school children. Acknowledgments We thank Andrew Roddam,David Rogers,and William Wint for providing statistical and GIs advice. We are also grateful to Don Bundy for comments on the manuscript. The original survey work in Cameroon was funded by USAID.S. Brooker and S.I. Hay are supported by a Wellcome Trust Prize Fellowship (#062692)and WellcomeTrust Advanced Training Fellowship (#056642), respectively.Data used in this study include data produced through funding from the Earth Observing System Pathfinder Program of NASA'sMission to Planet Earth in cooperation with the National Oceanic and Atmospheric Administration. The data were provided by the Earth Observing System Data and Information System (EOSDIS)D, istributed Active Archive Center (DAAC) at the Goddard Space Flight Center, which archives, manages,and distributes this dataset. References Anderson
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