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An Introduction to Time-Trend Analysis

Published online by Cambridge University Press:  02 January 2015

David Birnbaum ,
John W. Ely ,
Jeffrey D. Dawson ,
Jon H. Lemke  and
Jon Rosenberg
John W. Ely*
Affiliation:
Department of Family Practice, University of Iowa Hospitals and Clinics, Iowa City, Iowa
Jeffrey D. Dawson
Affiliation:
Department of Preventive Medicine and Environmental Health, University of Iowa College of Medicine, Iowa City, Iowa
Jon H. Lemke
Affiliation:
Department of Preventive Medicine and Environmental Health, University of Iowa College of Medicine, Iowa City, Iowa
Jon Rosenberg
Affiliation:
Division of Communicable Disease Control, California Department of Health Services, Berkeley, California
*
Department of Family Practice, University of Iowa Hospitals and Clinics, 2109 Steindler Bldg, Iowa City, IA 52242
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Abstract

Healthcare professionals often are presented with data that appear to indicate an upward or downward trend over time. For example, admissions of acquired immunodeficiency syndrome (AIDS) patients appear to be increasing, cesarean section rates appear to be decreasing, or nosocomial pneumonia rates appear to be increasing. Critical decisions sometimes are based on such trends, which often are presented without a statistical analysis. Those responsible for decision making may be left wondering whether these apparent trends represent only chance variation. Graphs showing trends over time generally present one of three kinds of outcome data: counts (eg, three AIDS admissions), proportions (eg, 10 cesarean sections per 100 total deliveries), or person-time data (eg, 13 cases of nosocomial pneumonia per 10,000 patient days). Using familiar examples and a minimum of technical language, we illustrate the analysis of time trends.

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 18 , Issue 4 , April 1997 , pp. 267 - 274
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1997

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