DIMACS/DyDAn Workshop: Investigation of Disease Clusters:
Transitioning to the 21st Century and Beyond*
May 6 - 8, 2008
DIMACS/DyDAn Center, CoRE Building, Rutgers University
- Organizers:
- Andrew Lawson, University of South Carolina, alawson at gwm.sc.edu
- Daniel Wartenberg, Robert Wood Johnson Medical School, dew at eohsi.rutgers.edu
Presented under the auspices of the Special Focus
on Computational and Mathematical Epidemiology and the Center for Dynamic Data Analysis
(DyDAn).
*Funded by DIMACS and the UMDNJ Academic Partnership
for Environmental Public Health Tracking
(1 U19 EH000 CDC grant).
Disease clusters, defined as local excesses of disease in space, time
or space and time, represent an important but vexing problem in public
health. Clusters are usually identified by community residents who
believe that some unusual circumstance leading to unexpected illness
has befallen their families, friends or neighbors. Clusters of
leukemia are reported most often, although clusters of other cancers,
birth defects and other adverse health outcomes are also
reported. While there are many protocols to assess whether a given
cluster is etiologic, i.e., due to an identifiable cause, there is no
clear consensus about how best to conduct an investigation and reach a
scientifically valid conclusion. A variety of statistical issues
confront investigators of clusters. For example, since cluster reports
typically are based on a handful of cases, it is possible that the
observed excess is simply due to random variation, particularly if the
investigator fails to adjust for multiple comparisons. On the other
hand, the statistical power of traditional cluster analysis methods is
fairly low, likely resulting in many false negatives which might cause
investigators to miss true, etiologic clusters. In addition,
assumptions are made about the amount of disease expected because
large data sets are generally not available at a scale that would
enable investigators to determine background rates of disease, such as
at the census block, census tract or zip code. Some recent methods
have begun to look at approaches for conducting prospective
surveillance by analyzing data collected for each time unit (e.g.,
year) it is collected. These methods offer the opportunity the
overcome some of the statistical limitations of traditional cluster
analyses and provide a more appropriate perspective for health
officials to use in responding to community concerns. The workshop
will bring together mathematicians, biostatisticians, epidemiologists
and public health officials to develop an approach that, while
statistically rigorous, is able to address the concerns of the public.
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Document last modified on June 6, 2005.