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


CONCLUSIONS
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.
Kumar
Rajeev
R
Division of Biostatistics and Medical Informatics, University College of Medical Sciences, Dilshad Garden, Delhi, India. dbmi@ucms.ernet.in
Indrayan
Abhaya
A
D016000:Cluster Analysis; D006801:Humans; D015995:Prevalence; D012494:Sampling Studies