Statistical properties of parasite density estimators in malaria.
Malaria is a global health problem responsible for nearly one million deaths every year around 85% of which concern children younger than five years old in Sub-Saharan Africa. In addition, around 300 million clinical cases are declared every year. The level of infection, expressed as parasite densit...
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doaj-8cb2774d89974922afecdb17d188eeeb2021-03-03T20:24:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0183e5198710.1371/journal.pone.0051987Statistical properties of parasite density estimators in malaria.Imen HammamiGrégory NuelAndré GarciaMalaria is a global health problem responsible for nearly one million deaths every year around 85% of which concern children younger than five years old in Sub-Saharan Africa. In addition, around 300 million clinical cases are declared every year. The level of infection, expressed as parasite density, is classically defined as the number of asexual parasites relative to a microliter of blood. Microscopy of Giemsa-stained thick blood films is the gold standard for parasite enumeration. Parasite density estimation methods usually involve threshold values; either the number of white blood cells counted or the number of high power fields read. However, the statistical properties of parasite density estimators generated by these methods have largely been overlooked. Here, we studied the statistical properties (mean error, coefficient of variation, false negative rates) of parasite density estimators of commonly used threshold-based counting techniques depending on variable threshold values. We also assessed the influence of the thresholds on the cost-effectiveness of parasite density estimation methods. In addition, we gave more insights on the behavior of measurement errors according to varying threshold values, and on what should be the optimal threshold values that minimize this variability.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23516389/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Imen Hammami Grégory Nuel André Garcia |
spellingShingle |
Imen Hammami Grégory Nuel André Garcia Statistical properties of parasite density estimators in malaria. PLoS ONE |
author_facet |
Imen Hammami Grégory Nuel André Garcia |
author_sort |
Imen Hammami |
title |
Statistical properties of parasite density estimators in malaria. |
title_short |
Statistical properties of parasite density estimators in malaria. |
title_full |
Statistical properties of parasite density estimators in malaria. |
title_fullStr |
Statistical properties of parasite density estimators in malaria. |
title_full_unstemmed |
Statistical properties of parasite density estimators in malaria. |
title_sort |
statistical properties of parasite density estimators in malaria. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
Malaria is a global health problem responsible for nearly one million deaths every year around 85% of which concern children younger than five years old in Sub-Saharan Africa. In addition, around 300 million clinical cases are declared every year. The level of infection, expressed as parasite density, is classically defined as the number of asexual parasites relative to a microliter of blood. Microscopy of Giemsa-stained thick blood films is the gold standard for parasite enumeration. Parasite density estimation methods usually involve threshold values; either the number of white blood cells counted or the number of high power fields read. However, the statistical properties of parasite density estimators generated by these methods have largely been overlooked. Here, we studied the statistical properties (mean error, coefficient of variation, false negative rates) of parasite density estimators of commonly used threshold-based counting techniques depending on variable threshold values. We also assessed the influence of the thresholds on the cost-effectiveness of parasite density estimation methods. In addition, we gave more insights on the behavior of measurement errors according to varying threshold values, and on what should be the optimal threshold values that minimize this variability. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23516389/?tool=EBI |
work_keys_str_mv |
AT imenhammami statisticalpropertiesofparasitedensityestimatorsinmalaria AT gregorynuel statisticalpropertiesofparasitedensityestimatorsinmalaria AT andregarcia statisticalpropertiesofparasitedensityestimatorsinmalaria |
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