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Multiple Category-Lot Quality Assurance Sampling: A New Classification System with Application to Schistosomiasis Control

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Olives, Casey, Valadez, Joseph ORCID: https://orcid.org/0000-0002-6575-6592, Brooker, Simon J and Pagano, Marcello (2012) 'Multiple Category-Lot Quality Assurance Sampling: A New Classification System with Application to Schistosomiasis Control'. PLoS Neglected Tropical Diseases, Vol 6, Issue 9, e1806.

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Abstract

Background

Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for
classification of the prevalence of Schistosoma mansoni into multiple categories (#10%, .10 and ,50%, $50%), and semicurtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa.

Methodology

We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n = 15 and n = 25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa.
Principle Findings: Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n = 15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error.

Conclusion/Significance

This work provides the needed analytics to understand the properties of MC-LQAS for assessingthe prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools.

Item Type: Article
Subjects: QX Parasitology > Helminths. Annelida > QX 355 Schistosoma
WA Public Health > WA 105 Epidemiology
WA Public Health > WA 30 Socioeconomic factors in public health (General)
WA Public Health > Statistics. Surveys > WA 950 Theory or methods of medical statistics. Epidemiologic methods
Faculty: Department: Groups (2002 - 2012) > International Health Group
Digital Object Identifer (DOI): https://doi.org/10.1371/journal.pntd.0001806
Depositing User: Users 471 not found.
Date Deposited: 12 Oct 2012 10:46
Last Modified: 06 Sep 2019 11:29
URI: https://archive.lstmed.ac.uk/id/eprint/3057

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