Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-20T08:29:18.088Z Has data issue: false hasContentIssue false
  • English
  • Français

The Analysis of Disease Clusters, Part I: State of the Art

Published online by Cambridge University Press:  02 January 2015

David Birnbaum ,
G.M. Jacquez ,
L.A. Waller ,
R. Grimson  and
D. Wartenberg
G.M. Jacquez*
Affiliation:
BioMedware, Ann Arbor, Michigan
L.A. Waller
Affiliation:
The University of Minnesota, Minneapolis, Minnesota
R. Grimson
Affiliation:
Department of Preventive Medicine, State University of New York at Stony Brook, Stony Brook, New York
D. Wartenberg
Affiliation:
Robert Wood Johnson Medical School, Piscataway, New Jersey
*
BioMedware, 516 N State St, Ann Arbor, MI 48104
Get access Rights & Permissions [Opens in a new window]

Abstract

Public health professionals often are asked to investigate apparent clusters of human health events, or “disease clusters.” A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is part I of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious diseases in the hospital environment.

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 17 , Issue 5 , May 1996 , pp. 319 - 327
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1996

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. Rothman, KJ. A sobering start for the Cluster Busters' conference. Am J Epidemiol 1989;1990:132(suppl):6S–13S.CrossRefGoogle Scholar
2. Neutra, RR. Counterpoint from a cluster buster. Am J Epidemiol 1990;132:1–8.CrossRefGoogle ScholarPubMed
3. Snow, J. Snow on Cholera. New York, NY: Hafner; 1965.Google Scholar
4. Centers for Disease Control. Pneumocystic pneumonia—Los Angeles. MMWR 1981;30:250–252.Google Scholar
5. Waxmeiler, RJ, Stringer, W, Wagoner, JK, et al. Neoplastic risk among workers exposed to vinyl chloride. Ann NY Acad Sci 1976;271:40–48.CrossRefGoogle Scholar
6. Bender, AP. On disease clustering. Am J Public Health 1987;77:742. Letter.CrossRefGoogle ScholarPubMed
7. Schulte, PA, Ehrenberg, RL, Signal, M. Investigation of occupational cancer clusters: theory and practice. Am J Public Health 1987;77:52–56.CrossRefGoogle ScholarPubMed
8. Caldwell, GG. Twenty-two years of cancer cluster investigations at the Centers for Disease Control. Am J Epidemiol 1990;132(suppl):43S–47S.CrossRefGoogle ScholarPubMed
9. Devier, JR, Brownson, RC, Bagby, JR, et al. A public health response to cancer clusters in Missouri. Am J Epidemiol 1990;132(suppl):23S–31S.CrossRefGoogle ScholarPubMed
10. Fiore, BJ, Hanrahan, LP, Anderson, HA. State health departments response to disease cluster reports: a protocol for investigation. Am J Epidemiol 1990:14S–22S.Google Scholar
11. Centers for Disease Control. Guidelines for investigating clusters of health events. MMWR 1990;39(RR-11):1–23.Google Scholar
12. National Conference on Clustering of Health Events. Am J Epidemiol 1990;132(suppl S1):S1–S202.CrossRefGoogle Scholar
13. Proceedings of the workshop on statistics and computing in disease clustering, Port Jefferson, NY, July 23-24, 1992. In: Jacquez, GM, Grimson, RR, Kheifets, L, Wartenberg, DE, eds. Stat Med 1993;19,20:1751–1968.Google Scholar
14. Proceedings of the Conference on Statistics and Computing in Disease Clustering, Vancouver, British Columbia, July 21-22, 1994. In: Jacquez, GM, Grimson, RR, Kheifets, L, Waller, L, Wartenberg, DE, eds. Stat Med. In press.Google Scholar
15. Grufferman, S. Hodgkin's disease. In: Schottenfeld, D, Fraumeni, JF, eds. Cancer Epidemiology and Prevention. Philadelphia, PA: WB Saunders Co; 1982.Google Scholar
16. Enterline, PE. Evaluating cancer clusters. Am Ind Hyg Assoc J 1985;46:B10–B13.CrossRefGoogle ScholarPubMed
17. Cuzick, J, Edwards, R. Spatial clustering for inhomogeneous populations (with discussion). J R Stat Society, Series B. 1990;52:73–104.Google Scholar
18. Oden, N, Grimson, RJ. Adjusting Moran's I for population density. Stat Med 1995;14:17–26.CrossRefGoogle ScholarPubMed
19. Chen, R, Mantel, N, Klingberg, M. A study of three techniques for time-space clustering in Hodgkin's disease. Stat Med 1984;3:173–184.CrossRefGoogle ScholarPubMed
20. Wartenberg, D, Greenberg, M. Detecting disease clusters: the importance of statistical power. Am J Epidemiol 1990;132:156S–166S.CrossRefGoogle ScholarPubMed
21. Pike, MC, Smith, PG. A case-control approach to examine disease for evidence of contagion, including diseases with long latent periods. Biometrics 1974;30:263–279.CrossRefGoogle ScholarPubMed
22. Waller, LA, Jacquez, GM. Disease models implicit in statistical tests of disease clustering. Epidemiol. In press.Google Scholar
23. Wartenberg, D, Greenberg, M. Solving the cluster puzzle: clues to follow and pitfalls to avoid. Stat Med 1993;12:1763–1770.CrossRefGoogle Scholar
24. Holland, PW. Statistics and causal inference (with discussion). J Am Stat Assoc 1986;81:945–970.CrossRefGoogle Scholar
25. Robins, JM, Greenland, S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol 1986;123:392–402.CrossRefGoogle ScholarPubMed
26. Greenland, S. Randomization, statistics, and causal inference. Epidemiol 1990;1:421–429.CrossRefGoogle ScholarPubMed
27. Rubin, DB. Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. Biometrics 1991;47:1213–1234.CrossRefGoogle ScholarPubMed
28. Grimson, RC, Aldrich, TE, Drane, JW. Clustering in sparse data and an analysis of rhabdomyosarcoma incidence. Stat Med 1992;11:761–768.CrossRefGoogle Scholar
29. Moulton, LH, Foxman, B, Wolfe, RA, Port, FK. Potential pitfalls in interpreting maps of stabilized rates. Epidemiol 1994;5:297–301.CrossRefGoogle ScholarPubMed
30. Benneyan, JC. An introduction to using statistical process control within health care. In: Proceedings of the First International Applied Statistics in Medicine Conference, Dallas, TX; 1995. In press.Google Scholar
31. Benneyan, JC. The importance of modeling discrete data in SPC. In: Proceedings of the 10th International Conference of the Israel Society for Quality, Israel; 1994:640–646.Google Scholar
32. Benneyan, JC. Design of statistical g control charts for nosocomial infection. In: Proceedings of the First International Applied Statistics in Medicine Conference; Dallas, TX; 1995. In press.Google Scholar
33. Marshall, RJ. A review of methods for the statistical analysis of spatial patterns of disease. J R Stat Soc Assoc 1991;154 part 3:421–441.CrossRefGoogle Scholar
34. Wartenberg, D, Greenburg, M. Characterizing cluster studies: a review of the literature. Presented at the Conference on Statistics and Computing in Disease Clustering; Vancouver, British Columbia, Canada; July 21-22, 1994.Google Scholar
35. Maskarinec, G. Investigating increased incidence of events in the Islands: a Hawaii Department of Health perspective. Stat Med 1996. In press.Google Scholar