Planning a sample for an epidemiological survey

Daniela Tomcíková (), Daniela Cocchi (), Barbara Bordoni () and Antonio Marzocchi ()
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Daniela Tomcíková: Institut Biostatistiky a Analýz, Masarykova Univerzita, Brno, Czech Republic
Daniela Cocchi: Università di Bologna
Barbara Bordoni: Policlinico S. Orsola-Malpighi, Bologna
Antonio Marzocchi: Università di Bologna

No 11, Quaderni di Dipartimento from Department of Statistics, University of Bologna

Abstract: This work illustrates the joint use of a pilot study and an administrative data base for designing a probabilistic sample for an epidemiological survey. The target is to estimate the prevalence of an asymptomatic disease, the aortic valve stenosis (AS), in the elderly population of the city of Bologna, Italy. The novelty of the study is to reach the target population of elderly patients via a sample of their general practitioners (GPs). The pilot study was conducted in San Giovanni in Persiceto, a town in the province of Bologna. Overall information on patients and their GPs are available in the Azienda Unità Sanitaria Locale di Bologna (AUSL) data sets. Since the disease is asymptomatic, the sampling plan is designed to estimate the number of suspected patients that will be sent to further echocardiographic (ECO) examination. The probabilistic sampling plan aims at controlling the sources of randomness, via an appropriate clustering of the population of GPs. The number of practitioners to sample is fixed in advance. The subpopulations of patients to screen are also defined in advance and assigned to doctors. In this way the potential sources of randomness, due to the individual choices of doctors out of the definition of the experiment, are avoided. The number of elderly patients per doctor has been identified, from the pilot study, as an important factor able to influence the proportion of suspected patients sent to further examination. This feature is the leading factor of the sampling design, together with the clustering of the AUSL Bologna territory in NCPs, which emerges from the AUSL data set.

Keywords: Survey sampling; ratio and regression sampling estimator; finite population; epidemiology; cardiology. (search for similar items in EconPapers)
Pages: 38
Date: 2011
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