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Statistical properties of parasite density estimators in malaria and field applications

jeudi 20 juin 2013, *13h45 - 14h45*

{{Abstract :}}

Malaria is a devastating global health problem that affected 219

million people and caused 660,000 deaths in 2010. Inaccurate estimation of

the level of infection may have adverse clinical and therapeutic

implications for patients, and for epidemiological endpoint measurements.

The level of infection, expressed as the parasite density (PD), is

classically defined as the number of asexual parasites relative to a

microliter of blood. Microscopy of Giemsa-stained thick blood smears

(TBSs) is the gold standard for parasite enumeration. Parasites are

counted in a predetermined number of high-power fields (HPFs) or against a

fixed number of leukocytes. PD estimation methods usually involve

threshold values; either the number of leukocytes counted or the number of

HPFs read. Most of these methods assume that (1) the distribution of the

thickness of the TBS, and hence the distribution of parasites and

leukocytes within the TBS, is homogeneous; and that (2) parasites and

leukocytes are evenly distributed in TBSs, and thus can be modeled through

a Poisson-distribution. The violation of these assumptions commonly

results in overdispersion. Firstly, we studied the statistical properties

(mean error, coefficient of variation, false negative rates) of PD

estimators of commonly used threshold-based counting techniques and

assessed the influence of the thresholds on the cost-effectiveness of

these methods. Secondly, we constituted and published the first dataset on

parasite and leukocyte counts per HPF. Two sources of overdispersion in

data were investigated: latent heterogeneity and spatial dependence. We

accounted for unobserved heterogeneity in data by considering more

flexible models that allow for overdispersion. Of particular interest were

the negative binomial model (NB) and mixture models. The dependent

structure in data was modeled with hidden Markov models (HMMs). We found

evidence that assumptions (1) and (2) are inconsistent with parasite and

leukocyte distributions. The NB-HMM is the closest model to the unknown

distribution that generates the data. Finally, we devised a reduced

reading procedure of the PD that aims to a better operational optimization

and a practical assessing of the heterogeneity in the distribution of

parasites and leukocytes in TBSs. A patent application process has been

launched and a prototype development of the counter is in process.

L’exposé sera donné en anglais.