Estimating the number of helminthic infections in the Republic of Cameroon from data on infection prevalence in schoolchildren.

INTRODUCTION: The prevalence of infection with helminths is markedly dependent on age, yet estimates of the total number of infections are typically based on data only from school-aged children. Such estimates, although useful for advocacy, provide inadequate information for planning control programmes and for quantifying the burden of disease. Using readily available data on the prevalence of infection in schoolchildren, the relation between the prevalence of infection in school-aged children and prevalence in the wider community can be adequately described using species-specific models. This paper explores the reliability of this approach to predict the prevalence infection in the community and provides a model for estimating the total number of people infected in the Republic of Cameroon.
METHODS: Using data on the prevalence of helminthic infection in school-aged children in Cameroon, the prevalence of infection in pre-school children and adults was estimated from species-specific linear and logistic regression models developed previously. The model predictions were then used to estimate the number of people infected in each district in each age group in Cameroon.
RESULTS: For Cameroon, if only the prevalence of infection in schoolchildren is used, the number of people infected with each helminthic species will be overestimated by up to 32% when compared with the estimates provided by the species-specific models. The calculation of confidence intervals supports the statistical reliability of the model since a narrow range of parameter estimates is evident. Furthermore, this work suggests that estimation of national prevalence of infection and the number infected will be enhanced if data are stratified by age; this model represents a useful planning tool for obtaining more accurate estimates. Estimates based on data aggregated from three geographical levels (district, regional, and national) show that summarizing prevalence data at the national level will result in biases of up to 19%. Such biases reflect differences in the geographical distribution for the prevalence of each species. DISCUSSION: Developing more accurate estimates requires a better understanding of the differences in the spatial heterogeneity of each species and also better methods of incorporating this information when making estimates.