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American Journal of Epidemiology Vol. 149, No. 12: 1087-1096
Copyright © 1999 by The Johns Hopkins University School of Hygiene and Public Health

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Look before You Leap: Stratify before You Standardize

Bernard C. K. Choi1,2,3,, Nicole A. de Guia2 and Peter Walsh1

1Bureau of Cardio-Respiratory Diseases and Diabetes, Laboratory Centre for Disease Control, Health Canada, Tunney's Pasture Ottawa, ON, Canada
2Department of Public Health Sciences, Faculty of Medicine, University of Toronto Toronto, ON, Canada
3Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa Ottawa, ON, Canada

Reprint requests to Dr. Bernard C. K. Choi, Bureau of Cardio-Respiratory Diseases and Diabetes, Laboratory Centre for Disease Control, Health Canada, PL#1918C3, Tunney's Pasture, Ottawa,ON, Canada K1A 0K9.

This paper presents a mathematical model to show the conditions in which age standardization can be used to summarize age-specific rates for comparison purposes over calendar time. It shows that the conditions for valid comparison depend on the type of measure used for comparison, that is, difference, ratio, or percent change. If the measure for comparison is a difference of the standardized rates at two time points, then the age-specific rates need to maintain a constant rate difference over time for the comparison to be valid. If the measure for comparison is a ratio or percent change of the standardized rates at two time points, then the age-specific rates need to maintain a constant rate ratio over time for the comparison to be valid. Since in reality, as shown by our Canadian empirical data, age-specific rates do not always maintain a consistent pattern over time, it is recommended that one should always stratify the data to look at patterns of age-specific rates before applying age standardization. Am J Epidemiol 1999; 149:1087—96.

confounding; data interpretation, statistical; epidemiologic methods; interaction; models, statistical


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