Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-29T05:26:51.231Z Has data issue: false hasContentIssue false
  • English
  • Français

Aggregation in schistosomiasis: comparison of the relationships between prevalence and intensity in different endemic areas

Published online by Cambridge University Press:  06 April 2009

H. L. Guyatt ,
T. Smith ,
B. Gryseels ,
C. Lengeler ,
H. Mshinda ,
S. Siziya ,
B. Salanave ,
N. Mohome ,
J. Makwala  and
K. P. Ngimbi
...Show all authors
H. L. Guyatt
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, Socinstrasse 57, Basel CH-4002, Switzerland
T. Smith
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, Socinstrasse 57, Basel CH-4002, Switzerland
B. Gryseels
Affiliation:
Department of Parasitology, University of Leiden, P.B. 9605, 2300 RC Leiden, The Netherlands
C. Lengeler
Affiliation:
Tropical Health and Epidemiology Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WCIE 7HT, UK
H. Mshinda
Affiliation:
Ifakara Centre, PO Box 53, Ifakara, Tanzania
S. Siziya
Affiliation:
Tropical Diseases Research Center, POB 71 769 Ndola, Zambia
B. Salanave
Affiliation:
IFORD, B.P. 1556, Yaoundé, Cameroon
N. Mohome
Affiliation:
OCEAC, B.P. 288, Yaoundé, Cameroon
J. Makwala
Affiliation:
Département de Démographie, Université de Kinshasa, Zaire
K. P. Ngimbi
Affiliation:
Département de Démographie, Université de Kinshasa, Zaire
M. Tanner
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, Socinstrasse 57, Basel CH-4002, Switzerland
Get access Rights & Permissions [Opens in a new window]

Summary

Distributions of the intensities of helminth infections within their host populations are invariably aggregated. In the case of the intestinal nematodes, the degrees of aggregation have been shown to be species specific, and constant for any given species despite geographical variation in study sites. This species-specific aggregation can be quantified and used as a tool in planning control interventions. One practical application is that the prevalence of infection can be used to predict the prevalence of heavy infection and thus the risks of morbidity. This paper investigates the patterns of aggregation in schistosome egg counts in different endemic areas in Africa (data sets were obtained from Burundi, Cameroon, Tanzania, Zambia and Zaire). The analysis demonstrates that the degree of parasite aggregation, for both Schistosoma mansoni and S. haematobium, differs amongst the different study localities. This is probably due to area-specific differences in host exposure and immunity. This implies that for these schistosome species, it is not possible to predict egg count distributions or morbidity levels from prevalence data alone.

Keywords

Schistosoma mansoniSchistosoma haematobiumprevalenceintensityepidemiologycontrol
Type
Research Article
Information
Parasitology , Volume 109 , Issue 1 , July 1994 , pp. 45 - 55
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ali, M. & Byslov, J. (1986). Simplified data collection and analysis in a Schistosoma mansoni endemic area. Tropical Medicine and Parasitology 37, 215–19.Google Scholar
Anderson, R. M. & May, R. M. (1985). Helminth infections of humans: mathematical models, population dynamics and control. Advances in Parasitology 24, 1–101.Google Scholar
Anderson, R. M. & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press.Google Scholar
Anderson, R. M. & Medley, G. F. (1985). Community control of helminth infections of man by mass and selective chemotherapy. Parasitology 90, 629–60.Google Scholar
Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.Google Scholar
Butterworth, A. E., Dalton, P. R., Dunne, D. W., Mugambi, M., Ouma, J. H., Richardson, B. A., Arap Siongok, T. K. & Sturrock, R. F. (1984). Immunity after treatment of human schistosomiasis mansoni. I. Study design, pretreatment observations and the results of treatment. Transactions of the Royal Society of Tropical Medicine and Hygiene 78, 108–23.Google Scholar
Chandiwana, S. K. & Woolhouse, M. E. J. (1991). Heterogeneities in water contact patterns and the epidemiology of Schistosoma haematobium. Parasitology 103, 363–70.Google Scholar
De Vlas, S. J. & Gryseels, B. (1992). Underestimation of Schistosoma mansoni prevalences. Parasitology Today 8, 274–7.Google Scholar
De Vlas, S. J., Gryseels, B., Van Oortmarssen, G. J., Polderman, A. M. & Habbema, J. D. F. (1992). A model for variations in single and repeated egg counts in Schistosoma mansoni infections. Parasitology 104, 451–60.Google Scholar
Dixon, H. (1985). Statistical methods applicable to schistosomiasis control programmes. WHO/SCHISTO/85.81, WHO/ESM/85.1.Google Scholar
Dyke, G. V. & Patterson, H. D. (1952). Analysis of factorial arrangements when the data are proportions. Biometrics 8, 1–12.Google Scholar
Elliott, J. M. (1977). Some Methods for the Statistical Analysis of Samples of Benthic Invertebrates. Freshwater Biological Association, Scientific Publication No. 25.Google Scholar
Gryseels, B. & Nkulikyinka, L. (1988). The distribution of Schistosoma mansoni in the Rusizi Plain. Annals of Tropical Medicine and Parasitology 82, 581–90.Google Scholar
Gryseels, B., Nkulikyinka, K., Kabahizi, E. & Maregeya, E. (1987). A new focus of Schistosoma mansoni in the highlands of Burundi. Annales de la Société Beige de Médecine tropicale 67, 247–8.Google Scholar
Guyatt, H. L. & Bundy, D. A. P. (1991). Estimating prevalence of community morbidity due to intestinal helminths: prevalence of infection as an indicator of the prevalence of disease. Transactions of the Royal Society of Tropical Medicine and Hygiene 85, 778–82.Google Scholar
Guyatt, H. L., Bundy, D. A. P., Medley, G. F. & Grenfell, B. T. (1990). The relationship between the frequency distribution of Ascaris lumbricoides and the prevalence and intensity of infection in human communities. Parasitology 101, 139–43.Google Scholar
Hagan, P., Blumenthal, U. J., Dunn, D., Simpson, A. J. G. & Wilkins, H. A. (1991). Human IgE, IgG4 and resistance to reinfection with Schistosoma haematobium. Nature, London 349, 243–5.Google Scholar
Jordan, P. (1972). Epidemiology and control of schistosomiasis. British Medical Bulletin 28, 55–9.Google Scholar
Katz, N., Chavez, A. & Pellegrino, J. (1972). A simple device for quantitative stool thick-smear technique in schistosomiasis mansoni. Revista do Instituto de Medicina tropical de São Paulo 14, 397–400.Google Scholar
Lengeler, C., De Savigny, D., Mshinda, H., Mayombana, C., Tayari, S., Hatz, C., Degremont, A. & Tanner, M. (1991). Community-based questionnaires and health statistics as tools for the cost-efficient identification of communities at risk of urinary schistosomiasis. International Journal of Epidemiology 20, 796–807.Google Scholar
Lwambo, N. J. S., Bundy, D. A. P. & Medley, G. F. (1992). A new approach to morbidity risk assessment in hookworm endemic communities. Epidemiology and Infection 108, 469–81.Google Scholar
McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models. 2nd Edn. Monographs on Statistics and Applied Probability, vol. 37. London: Chapman & Hall.Google Scholar
Pierce, D. A. & Sands, B. R. (1975). Extra-Bernoulli Variation in Binary Data. Technical report no. 46, Department of Statistics, Oregon State University.Google Scholar
Red Urine Study Group (1994). Identification of high-risk communities for urinary schistosomiasis: a multi-country study. Social and Economic Research Project Reports. WHO/TDR/SER-Report Series, Geneva. (In the Press).Google Scholar
Serc (Statistics And Epidemiology Research Corporation) (1991). EGRET: User's Manual. Seattle: Statistics and Epidemiology Research Corporation.Google Scholar
Tanner, M. (1989). Evaluation of public health impact of schistosomiasis. Tropical Medicine and Parasitology 40, 143–8.Google Scholar
Wilkins, H. A., Blumenthal, U. J., Hagan, P., Hayes, R. J. & Tulloch, S. (1987). Resistance to reinfection after treatment of urinary schistosomiasis. Transactions of the Royal Society of Tropical Medicine and Hygiene 81, 29–35.Google Scholar
World Health Organization (1985). The control of schistosomiasis – report of an expert committee. WHO Technical Report Series 379.Google Scholar
World Health Organization (1993). The control of schistosomiasis. Second report of the WHO Expert Committee. WHO Technical Report Series 830.Google Scholar