Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-05-05T02:23:53.372Z Has data issue: false hasContentIssue false
  • English
  • Français

The Analysis of Disease Clusters, Part II: Introduction to Techniques

Published online by Cambridge University Press:  02 January 2015

David Birnbaum ,
G.M. Jacquez ,
R. Grimson ,
L.A. Waller  and
D. Wartenberg
G.M. Jacquez*
Affiliation:
BioMedware, Ann Arbor, Michigan
R. Grimson
Affiliation:
Department of Preventive Medicine, State University of New York at Stony Brook, Stony Brook, New York
L.A. Waller
Affiliation:
University of Minnesota, School of Public Health, Minneapolis, Minnesota
D. Wartenberg
Affiliation:
Robert Wood Johnson Medical School, Piscataway, New Jersey
*
BioMedware, 516 North 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 the second part 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 6 , June 1996 , pp. 385 - 397
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.Grimson, RC. Assessment of risk trends and patterns. Proceedings of the 1989 Public Health Conference on Records and Statistics. National Center for Health Statistics, DHHS publication no. PHS 90-1214;1989:327–333.Google Scholar
2.Grimson, RC, Rose, RD. A versatile test for clustering and a proximity analysis of neurons. Methods Inf Med 1991;30:299–303.Google Scholar
3.Grimson, RC, Aldrich, TE, Drane, JW. Clustering in sparse data and an analysis of rhabdomyosarcoma incidence. Stat Med 1992;11:761–768.Google Scholar
4.Jacquez, GM. Stat! Statistical software for the clustering of health events [software manual]. Ann Arbor, MI: BioMedware; 1994.Google Scholar
5.Grimson, RC. Disease cluster tests based on the maximum occupancy frequency. Proceedings of the Section on Epidemiology, American Statistical Association; 1994:64–69.Google Scholar
6.Naus, JI. The distribution of the size of the maximum cluster of points on a line. J Am Stat Assoc 1965;60:532–538.Google Scholar
7.Wallenstein, S. A test for detection of clustering over time. Am J Epidemiol 1980;104:576–584.Google Scholar
8.Wallenstein, S, Neff, N. An approximation for the distribution of the Scan statistic. Stat Med 1987;6:197–207.Google Scholar
9.Glaz, J. Approximations for the tail probabilities and moments of the scan statistic. Stat Med 1993;12:1845–1852.Google Scholar
10.Larsen, RJ, Holmes, CL, Heath, CW. A statistical test for measuring unimodal clustering: a description of the test and of its application of cases of acute leukemia in metropolitan Atlanta, Georgia. Biometrics 1973;29:301–309.Google Scholar
11.Simes, RJ. An improved Bonferroni procedure for multiple tests of significance. Biometrika 1986;73:751–754.Google Scholar
12.Wallenstein, S, Gould, MS, Kleinman, M. Use of the statistics to detect time-space clustering. Am J Epidemiol 1989;130:1057–1064.Google Scholar
13.Cliff, AD, Ord, JD. Spatial Processes, Model and Application. London, England: Pion; 1981.Google Scholar
14.Cuzick, J, Edwards, R. Spatial clustering for inhomogeneous populations (with discussion). J R Stat Soc 1990(series B);52:73–104.Google Scholar
15.Jacquez, GM. Cuzick and Edward's test when exact locations are unknown. Am J Epidemiol 1994;140:58–64.Google Scholar
16.Moran, PAP. Notes on continuous stochastic phenomena. Biometrica 1950;37:17–23.Google Scholar
17.Oden, N. Adjusting Moran's I for population density. Stat Med 1995;14:17–26.Google Scholar
18.Knox, G. The detection of space-time interactions. Appl Stat 1964;13:25–29.Google Scholar
19.Barton, DE, David, FN. The random intersection of the two graphs. In: David, FN, ed. Research Papers in Statistics. New York, NY: John Wiley and Sons; 1966.Google Scholar
20.Barton, DE, David, FN, Merrington, M. A criterion for testing contagion in both time and space. Ann Hum Genet 1965;29:97–102.Google Scholar
21.Smith, PG, Pike, MC. Generalization of two tests for the detection of household aggregation of disease. Biometrics 1976;32:817–828.Google Scholar
22.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.Google Scholar
23.Meighan, SS, Knox, G. Leukemia in childhood: epidemiology in Oregon. Cancer 1965;18:811–814.Google Scholar
24.Mainwaring, D. Epidemiology of acute leukemia in the Liverpool area. Br J Prev Soc Med 1966;20:189–194.Google Scholar
25.Albeck, H, Coleman, N, Nielsen, , et al.Space-time clustering of nasophar yngeal carcinoma in Greenland Eskimos. Br J Cancer 1985;52:909–914.Google Scholar
26.Alderson, MR, Nayak, R. A study of space-time clustering of Hodgkin's disease in the Manchester region. Br J Prev Soc Med 1971;25:168–173.Google Scholar
27.Kryscio, RJ, Meyers, MH, Presiner, ST, et al.The space-time distribution of Hodgkin's disease in Connecticut, 1940-1969. J Natl Cancer Inst 1973;50:1107–1110.Google Scholar
28.Silcocks, PBS, Murrels, T. Space-time clustering and bone tumors: application of Knox's method to data from a population-based cancer registry. Int J Cancer 1987;40:769–771.Google Scholar
29.Doll, R. An epidemiological perspective of the biology of cancer. Cancer Res 1978;38:3573–3583.Google Scholar
30.Lloyd, S, Roberts, CJ. A test for space clustering and its application to congenital limb defects in Cardiff. Br J Prev Soc Med 1973;27:188–191.Google Scholar
31.Roberts, CH, Laurence, KM, Lloyd, S. An investigation of space and space-time clustering in a large sample of infants with neural tube defects born in Cardiff. Br J Prev Soc Med 1975;29:202–204.Google Scholar
32.Glass, AG, Mantel, N, Gunz, FW, et al.Time-space clustering of childhood leukemia in New Zealand. J Natl Cancer Inst 1971;47:329–336.Google Scholar
33.Alperovitch, A, Hesse, C, Lazar, P, et al.Temporal-spatial distributions of leukemia and haematosarcoma in seven French regions. Int J Epidemiol 1974;3:209–218.Google Scholar
34.Klauber, MR. A study of clustering of childhood leukemia by hospital of birth. Cancer Res 1968;28:1790–1792.Google Scholar
35.Glass, AG, Mantel, N. Lack of space-time clustering of childhood leukemia, Los Angeles County, 1960-64. Cancer Res 1969;29:1995.Google Scholar
36.Klauber, MR, Mustacchi, P. Space-time clustering of childhood leukemia in San Francisco. Cancer Res 1970;30:1969–1973.Google Scholar
37.Klauber, MR. Two-sample randomization tests for space-time clustering. Biometrics 1971;27:129–142.Google Scholar
38.Klauber, MR. Space time clustering tests for more than two samples. Biometrics 1975;27:129–142.Google Scholar
39.Jacquez, GM. A k-nearest neighbor test for space-time interaction. Stat Med 1995. In press.Google Scholar
40.Besag, J, Newell, J. The detection of clusters in rare diseases. J R Stat Soc 1991;154(series A):143–155.Google Scholar
41.Stone, RA. Investigations of excess environmental risks around putative sources: statistical problems and proposed test. Stat Med 1988;7:649–660.Google Scholar
42.Bithell, JF. Statistical methods for analyzing point-source exposures. In: Elliot, P, Cuzick, J, English, D, Stern, R, eds. Geographical and Environmental Epidemiology: Methods for Small-Area Studies. Oxford, England: Oxford University Press; 1992.Google Scholar
43.Waller, LA, Turnbull, BW, Clark, LC, Nasca, P. Chronic disease surveillance and testing of clustering of disease and exposure: application to leukemia incidence and TCE-contaminated dumpsites in upstate New York. Environmetrics 1992;3:281–300.Google Scholar
44.Waller, LA, Turnbull, BW, Clark, LC, Nasca, P. Spatial pattern analyses to detect rare disease clusters. In: Lange, N, Ryan, L, Billard, L, Brillinger, D, Conquest, L, Greenhouse, J, eds. Case Studies in Biometry. New York, NY: John Wiley and Sons; 1994:3–23.Google Scholar
45.Lawson, AB. On the analysis of mortality events associated with a prespecified fixed point. J R Stat Soc 1993;156(series A):363–377.Google Scholar
46.Breslow, NE, Day, NE. Statistical Methods in Cancer Research, Volume II: The Design and Analysis of Cohort Studies. Lyon, France: International Agency for Research on Cancer; World Health Organization IARC publication no. 82; 1987.Google Scholar
47.Waller, LA, Lawson, AB. The power of focused tests to detect disease clustering. Stat Med 1995;14:2291–2308.Google Scholar
48.Bithell, JF. The choice of test for detecting raised disease risk near a point source. Stat Med 1995;14:2309–2322.Google Scholar
49.Waller, LA, Turnbull, BW. The effect of scale on tests of disease clustering. Stat Med 1993;12:1869–1884.Google Scholar
50.Le, N, Petkau, JA, Rosychuk, R. Surveillance of clustering near point sources. Stat Med. In press.Google Scholar
51.Diggle, PJ. A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point. J R Stat Soc 1990;153(Series A):349–362.Google Scholar
52.Bithell, JF. An application of density estimation to geographical epidemiology. Stat Med 1990;9:691–701.Google Scholar
53.Lawson, AB, Williams, FLR. Applications of extraction mapping in environmental epidemiology. Stat Med 1993;12:1249–1258.Google Scholar
54.Schulman, J, Selvin, S, Merrill, DW. Density equalized map projections: a method for analyzing clustering around a fixed point. Stat Med 1988;7:491–505.Google Scholar
55.Grimson, RC. Disease clusters, exact distribution of maxima and P-values. Stat Med 1993;12:1773–1794.Google Scholar
56.Grimson, RC, Oden, N. Disease clusters in structured environments. Stat Med. In press.Google Scholar
57.Grimson, RC. Combinatorial tests for concordance and a comparison of series of asthma attacks. Stat Med. In press.Google Scholar
58.Grimson, RC. Epidemiologic Assessment of an Hypothesized Cancer Cluster Among Former Residents of the Irving/O'Neill Residence Hall Complex. State University of New York at Stony Brook, Department of Preventive Medicine Technical Report, October 1993.Google Scholar
59.Johnson, NL, Katz, S. Urn Models and Their Application. New York, NY: John Wiley and Sons, Inc; 1977.Google Scholar
60.David, FN, Barton, DE. Combinatorial Chance. London, England: Charles Griffin and Co; 1962.Google Scholar
61.Mielke, PW, Siddiqui, MM. A combinatorial test for independence of dichotomous responses. J Am Stat Assoc 1965;60:437–441.Google Scholar
62.Eicker, PJ, Siddiqui, MM, Mielke, PW. A matrix occupancy problem. Ann Math Stat 1972;43:988–996.Google Scholar
63.Mantel, N. Approaches to a health research occupancy problem. Biometrics 1974;30:355–362.Google Scholar
64.Walter, SD. A generalization of a matrix occupancy problem. Biometrics 1976;32:371–375.Google Scholar
65.Walter, SD. Some generalizations of the committee problem. Can J Stat 1979;7:1–10.Google Scholar
66.Davies, P. Some statistical tests for constrained occupancy problems. Biometrics 1983;39:19–725.Google Scholar
67.Mathen, KK, Chakraborty, PN. A statistical study on multiple cases of disease in households. Sankhya: The Indian Journal of Statistics 1950;10:387–392.Google Scholar
68.Walter, SD. On the detection of household aggregation of disease. Biometrics 1974;30:525–538.Google Scholar
69.Klauber, MR, Angulo, JJ. Variola minor in Braganca Raulista County—1956: space-time interactions among variola minor cases in two elementary schools. Am J Epidemiol 1974;99:65–74.Google Scholar
70.Jacquez, GM. Disease cluster tests for imprecise space-time locations. Stat Med. In press.Google Scholar
71.Day, R, Ware, JH, Wartenberg, D, et al.An investigation of a reported cancer cluster in Randolph, Massachusetts. J Clin Epidemiol 1988;42:137–150.Google Scholar
72.Diggle, PJ. A point process modeling approach to raised incidence of a rare phenomenon in the vicinity of a pre-specified point. J R Stat Soc series 1990;153(A):349–362.Google Scholar
73.Lyon, JL, Klauber, MR, Graff, W, Chiu, G. Cancer clustering around point sources of pollution: assessment by case-control methodology. Environ Res 1981;25:29–34.Google Scholar
74.Whittemore, AS, Friend, N, Brown, BW, Holly, EA. A test to detect clusters of disease. Biometrika 1987;74:631–635.Google Scholar
75.Pinkel, D, Dowd, JE, Bross, IDJ. Some epidemiological features of malignant solid tumors of children in Buffalo, NY area. Cancer 1963;16:28–33.Google Scholar
76.Weinstock, MA. A generalized scan statistic test for the detection of clusters. Int J Epidemiol 1981;10:289–293.Google Scholar
77.Openshaw, S, Craft, AW, Charlton, M, Birch, JM. Investigation of leukaemia clusters by use of a geographical analysis machine. Lancet 1988;1:272–273.Google Scholar
78.Turnbull, BW, Iwano, EJ, Burnett, WJ, Howe, HL, Clark, LC. Monitoring for clusters of disease: application to leukemia incidence in upstate New York. Am J Epidemiol 1990;132:14S–22S.Google Scholar