Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-28T07:44:10.603Z Has data issue: false hasContentIssue false
  • English
  • Français

Confounding and the Analysis of Multiple Variables in Hospital Epidemiology

Published online by Cambridge University Press:  02 January 2015

Jonathan Freeman ,
Donald A. Goldmann  and
John E. McGowan Jr.
Jonathan Freeman*
Affiliation:
Hospital Epidemiology Unit, Charming Laboratory, Department of Medicine, Brigham and Women's Hospital, andWest Roxbury VA Hospital, Boston, Massachusetts
Donald A. Goldmann
Affiliation:
Division of Infections Diseases, Children's Hospital, Harvard Medical School, Boston, Massachusetts
John E. McGowan Jr.
Affiliation:
Departments of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia
*
Channing Laboratory, 180 Longwood Avenue, Boston, MA 02115
Get access Rights & Permissions [Opens in a new window]

Abstract

Most information in hospital epidemiology comes from observational studies of hospitalized patients rather than planned experiments, and in such observational studies the characteristics of study patients may vary widely, even within a single hospital. Any comparison between hospital populations will usually contain additional, unintended contrasts among patients with varying degrees of health. Adult patients, for example, may have vastly different underlying diseases, and infants may be of substantially different birth weights. We used both underlying disease and birth weight as indices of the basic severity of illness in order to adjust for confounding by differences in underlying disease in reanalyses of several published studies. We give an example in which differing birth weights among groups of infants compared artifactually double the apparent effect of nosocomial infections as a cause of mortality, and another example in which differing degrees of severity of underlying illness artifactually halve the apparent effect of appropriate antibiotics in preventing death from bacteremia with gram-negative bacilli. We describe simple intuitive methods based on stratification, adapted from chronic disease epidemiology, to remove confounding effects during analyses.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 8 , Issue 11 , November 1987 , pp. 465 - 473
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Barrett-Connor, E: Infectious and chronic disease epidemiology: Separate and unequal? Am J Epidemiol 1979;109:245–249.CrossRefGoogle ScholarPubMed
2. McCabe, WR, Jackson, GG: Gram-negative bacteremia. II. Clinical, laboratory, and therapeutic observations. Arch Intern Med 1962;110:856–864.CrossRefGoogle Scholar
3. Fisher, RA: Statistical Methods for Research Workers, ed 14. New York, Hafner Press, 1970.Google Scholar
4. Armitage, P: Statistical Methods in Medical Research. New York, John Wiley and Sons, 1971.Google Scholar
5. Snedecor, GW, Cochran, WG: Statistical Methods, ed 7. Ames, Iowa State University Press, 1980.Google Scholar
6. Nie, NH, Hull, CH, Jenkins, JG, et al: Statistical Package for the Social Sciences, ed 2. New York, McGraw-Hill, 1975.Google Scholar
7. Cornfield, J: A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast, and cervix. J Natl Cancer Inst 1951;11:1269–1275.Google ScholarPubMed
8. Cochran, WG: Some methods of strengthening the common chi-square tests. Biometrics 1954;10:417–457.CrossRefGoogle Scholar
9. Mantel, N, Haenszel, W: Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959;22:719–748.Google ScholarPubMed
10. Mantel, N: Chi-square tests with one degree of freedom: Extensions of the Mantel-Haenszel procedure, J Am Stat Assoc 1963;58:690–700.Google Scholar
11. Kleinbaum, DG, Kupper, LL, Morgenstern, H: Epidemiologic Research, Principles and Quantitative Methods. Belmont, CA, Lifetime Learning, 1982.Google Scholar
12. Fleiss, JL: Statistical Methods for Rates and Proportions, ed 2. New York, John Wiley and Sons, 1981.Google Scholar
13. Breslow, NE, Day, NE: Statistical Methods in Cancer Research, volume 1, The analysis of case-control studies. Lyons, International Agency for Research on Cancer, 1980.Google Scholar
14. Schlesselman, JJ: Case-Control Studies: Design, Conduct, Analysis. New York, Oxford, 1982.Google Scholar
15. Miettinen, OS: Theoretical Epidemiology: Principles of Occurrence Research in Medicine. New York, John Wiley and Sons, 1985.Google Scholar
16. Rothman, KJ: Modern Epidemiology. Boston, Little Brown & Co, 1986.Google Scholar
17. Hammond, CE, Selikoff, IJ, Seidman, H: Asbestos exposure, cigarette smoking and death rates. Ann NY Acad Sci 1979;330:473–490.CrossRefGoogle ScholarPubMed
18. Miettinen, O: Estimabilily and estimation in case-referent studies. Am J Epidemiol 1976;103:226–235.CrossRefGoogle ScholarPubMed
19. Rothman, KJ: A show of confidence. N Engl J Med 1978;299:1362–1363.CrossRefGoogle ScholarPubMed
20. Rothman, KJ, Boice, JD: Epidemiologic Analysis with a Programmable Calculator. Boston, Epidemiology Resources, 1982.Google Scholar
21. Freeman, J, McGowan, JE: Methodologic issues in hospital epidemiology. 1. Rates, case-finding, and interpretation. Rev Infect Dis 1981;3:658–667.CrossRefGoogle Scholar
22. Freeman, J, McGowan, JE: Methodologic issues in hospital epidemiology. II. Time and accuracy in estimation. Rev Infect Dis 1981;3:668–677.CrossRefGoogle ScholarPubMed
23. Freeman, J, McGowan, JE: Methodologic issues in hospital epidemiology. III. Investigating the modifying effects of time and severity of underlying illness on estimates of cost of nosocomial infection. Rev Infect Dis 1984;6:285–300.CrossRefGoogle ScholarPubMed
24. Hemming, VG, Overall, JC, Britt, MR: Nosocomial infections in a newborn intensive care unit: Results of forty-one months of surveillance. N Engl J Med 1976;294:1310–1316.CrossRefGoogle Scholar
25. Goldmann, DA, Durbin, WA, Freeman, J: Nosocomial infections in a neonatal intensive care unit. J Infect Dis 1981;144:449–459.CrossRefGoogle Scholar
26. Goldmann, DA, Freeman, J, Durbin, WA: Nosocomial infection and death in a neonatal intensive care unit. J Infect Dis 1983;147:635–641.CrossRefGoogle Scholar
27. Kreger, BE, Craven, DE, McCabe, WR: Gram-negative bacteremia. IV. Reevaluation of clinical features and treatment in 612 patients. Am J Med 1980;68:344–355.CrossRefGoogle ScholarPubMed
28. Freeman, J, Hutchison, GB: Duration of disease, duration indicators, and estimation of the risk ratio. Am J Epidemiol 1986;124:134–149.CrossRefGoogle ScholarPubMed
29. Leclair, JM, Freeman, J, Sullivan, BF, et al: Prevention of nosocomial respiratory syncytial virus infections through compliance with glove and gown isolation precautions. N Engl J Med 1987;317:329–334.CrossRefGoogle ScholarPubMed