Analysis of count data using poisson regression

Authors

  • M. Katherine Hutchinson,

    Corresponding author
    1. University of Pennsylvania School of Nursing, 420 Guardian Drive, Philadelphia, Pennsylvania 19104-6096
    • University of Pennsylvania School of Nursing, 420 Guardian Drive, Philadelphia, Pennsylvania 19104-6096.
    Search for more papers by this author
    • Assistant Professor and Associate Director.

  • Matthew C. Holtman

    1. Fels Institute of Government and Department of Criminology, University of Pennsylvania
    Search for more papers by this author
    • Lecturer.


  • This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (www.cpc.unc.edu/addhealth/contract.html).

Abstract

Nurses and other health researchers are often concerned with infrequently occurring, repeatable, health-related events such as number of hospitalizations, pregnancies, or visits to a health care provider. Reports on the occurrence of such discrete events take the form of non-negative integer or count data. Because the counts of infrequently occurring events tend to be non-normally distributed and highly positively skewed, the use of ordinary least squares (OLS) regression with non-transformed data has several shortcomings. Techniques such as Poisson regression and negative binomial regression may provide more appropriate alternatives for analyzing these data. The purpose of this article is to compare and contrast the use of these three methods for the analysis of infrequently occurring count data. The strengths, limitations, and special considerations of each approach are discussed. Data from the National Longitudinal Survey of Adolescent Health (AddHealth) are used for illustrative purposes. © 2005 Wiley Periodicals, Inc. Res Nurs Health 28: 408–418, 2005.

Ancillary