Use of the Chi-square Test to Determine Significance of Cumulative Antibiogram Data
Rocco J. Perla and James Carifio
DOI : 10.3844/ajidsp.2005.162.167
American Journal of Infectious Diseases
Volume 1, Issue 4
An important function of a hospital’s Infectious Disease and Pharmacy programs is to review and compare the most recent antibiogram with that of the previous year to determine if significant changes in antibiotic susceptibility results are noted and to communicate this information and its consequences to the medical staff. However, there are currently no formal analytical (decision-making) models in use to determine if the rate of resistance to an antibiotic from one year to the next has significantly changed more or less than one would expect due to sampling error and test reliability. The purpose of this article, therefore, is to demonstrate the utility of using a well-established and simple nonparametric statistical technique (chi-square) for analyzing annual variations in cumulative antibiogram data and to determine whether such variations are significantly different from chance and to what to degree. The chi-square model outlined here is a simple, practical, quick, low burden and easy to understand and execute approach that greatly improves the analysis of antibiogram data and decisionmaking by practitioners. More work and research is needed to develop additional inferential statistical methods and models that can be applied to antibiogram data.
© 2005 Rocco J. Perla and James Carifio. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.