A Semi-parametric Two-component “Compound” Mixture Model and Its Application to Estimating Malaria Attributable Fractions

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  • 1 January 2004
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      • Published in RePEc
Abstract
Malaria remains a major epidemiological problem in many developing countries. Malaria is de ned as the presence of parasites and symptoms (usually fever) due to the parasites. In endemic areas, an individual may have symptoms attributable either to malaria or to other causes. From a clinical point of view, it is important to correctly diagnose an individual who has developed symptoms so that the appropriate treatments can be given. From an epidemiologic and economic point of view, it is important to determine the proportion of malaria affected cases in individuals who have symptoms so that policies on intervention programmes can be developed. Once symptoms have developed in an individual, the diagnosis of malaria can be based on analysis of the parasite levels in blood samples. However, even a blood test is not conclusive as in endemic areas, many healthy individuals can have parasites in their blood slides. Therefore, data from this type of studies can be viewed as coming from a mixture distribution, with the components corresponding to malaria and nonmalaria cases. A unique feature in this type of data, however, is the fact that a proportion of the non-malaria cases have zero parasite levels. Therefore, one of the component distribu-tions is itself a mixture distribution. In this article, we propose a semi-parametric likelihood approach for estimating the proportion of clinical malaria using parasite level data from a group of individuals with symptoms. Our approach assumes the density ratio for the parasite levels in clinical malaria and non-clinical malaria cases can be modeled using a logistic model. We use empirical likelihood to combine the zero and non-zero data. The maximum semi-parametric likelihood estimate is more ecient than existing non-parametric estimates using only the frequencies of zero and non-zero data. On the other hand, it is more robust than a fully parametric maximum likelihood estimate that assumes a parametric model for the non-zero data.
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