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Appears in Collections:Faculty of Health Sciences and Sport Conference Papers and Proceedings
Author(s): Andreis, Federico
Furfaro, Emanuela
Mecatti, Fulvia
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Title: Methodological perspectives for surveying rare and clustered population: towards a sequentially adaptive approach
Editor(s): Perna, C
Pratesi, M
Ruiz-Gazen, A
Citation: Andreis F, Furfaro E & Mecatti F (2018) Methodological perspectives for surveying rare and clustered population: towards a sequentially adaptive approach. In: Perna C, Pratesi M & Ruiz-Gazen A (eds.) Studies in Theoretical and Applied Statistics. SIS 2016. Springer Proceedings in Mathematics & Statistics, 227. 48th Scientific Meeting of the Italian Statistical Society, SIS 2016, Salerno, Italy, 08.06.2016-10.06.2016. Cham, Switzerland: Springer, pp. 15-24.
Issue Date: 31-Dec-2018
Date Deposited: 19-Dec-2018
Series/Report no.: Springer Proceedings in Mathematics & Statistics, 227
Conference Name: 48th Scientific Meeting of the Italian Statistical Society, SIS 2016
Conference Dates: 2016-06-08 - 2016-06-10
Conference Location: Salerno, Italy
Abstract: Sampling a rare and clustered trait in a finite population is challenging: traditional sampling designs usually require a large sample size in order to obtain reasonably accurate estimates, resulting in a considerable investment of resources in front of the detection of a small number of cases. A notable example is the case of WHO’s tuberculosis (TB) prevalence surveys, crucial for countries that bear a high TB burden, the prevalence of cases being still less than 1%. In the latest WHO guidelines, spatial patterns are not explicitly accounted for, with the risk of missing a large number of cases; moreover, cost and logistic constraints can pose further problems. After reviewing the methodology in use by WHO, the use of adaptive and sequential approaches is discussed as natural alternatives to improve over the limits of the current practice. A simulation study is presented to highlight possible advantages and limitations of these alternatives, and an integrated approach, combining both adaptive and sequential features in a single sampling strategy is advocated as a promising methodological perspective
Status: VoR - Version of Record
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