Estimating the number of helminthic infections in the Republic of Cameroonfrom data on infection prevalence in schoolchildren

Introduction The prevalence of infection with helminths is markedly depende nt on age, yet estimates of the total number of infections are typically ba sed on data only from school-aged children. Such estimates, although useful for advocacy, provide inadequate information for planning control programm es 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 i nfection in the community and provides a model for estimating the total num ber of people infected in the Republic of Cameroon. Methods Using data on the prevelance of helminthic infection in school-aged children in Cameroon, the prevalence of infection in pre-school children a nd adults was estimated from species-specific linear and logistic regressio n models developed previously. The model predictions were then used to esti mate the number of people infected in each district in each age group in Ca meroon. Results For Cameroon, if only the prevalence of infection in schoolchildren is used, the number of people infected with each helminthic species will b e overestimated by up to 32% when compared with the estimates provided by t he species-specific models. The calculation of confidence intervals support s the statistical reliability of the model since a narrow range of paramete r estimates is evident. Furthermore, this work suggests that estimation of national prevalence of infection and the number infected will be enhanced i f data are stratified by age; this model represents a useful planning tool for obtaining more accurate estimates. Estimates based on data aggregated f rom 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 understandi ng of the differences in the spatial heterogeneity of each species and also better methods of incorporating this information when making estimates.