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International Journal of Epidemiology 2002;31:463-467
© International Epidemiological Association 2002

Theory and Method

A nomogram for single-stage cluster-sample surveys in a community for estimation of a prevalence rate

Rajeev Kumar and Abhaya Indrayan

Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.

Rajeev Kumar, Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi-110 095, India. E-mail: dbmi{at}ucms.ernet.in

Abstract

Background Proper assessment of the magnitude of the problem is essential for devising adequate allocation of available resources and for developing future strategies to combat a disease. The cluster random sampling (CRS) technique is commonly used for rapid assessment of public health problems in developing countries. Our objective is to devise a nomogram that can instantly provide the number of clusters of specified size needed to estimate the prevalence rate of a disease in a community with given precision, ratio of design-effect to cluster size and confidence level. This would be applicable only to single-stage CRS.

Methods We use a logarithmic transformation to linearize the relation between the number of clusters (C) on one side and design-effect (D), cluster size (B), precision (L), anticipated prevalence rate (P) and confidence level ({alpha}) on the other. By using this relation, we construct a nomogram using established methods.

Results A nomogram is obtained that can be used to determine the number of clusters needed in a survey with the help of only a ruler when other parameters are known. This is a 6-in-1 figure as it gives the number of clusters C corresponding to any combination of {alpha} from among the popularly used 0.05, 0.10 and 0.20, and precision 10% of P or 20% of P. Using a very simple calculation, the number of clusters for the other values of {alpha} and L can also be obtained.

Conclusion This nomogram can be a useful aid in instantly providing the number of clusters required to rapidly estimate the prevalence rate of a disease in a community when the ratio of design-effect to cluster size, confidence level, and precision are specified. However, it is not applicable to intervention studies where interest mainly focuses on testing a hypothesis rather than estimation.

Keywords Cluster random sampling, nomogram, number of clusters, rate of homogeneity, precision

Accepted 6 November 2001


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