Public health monitoring tools for multiple data streams.
BACKGROUND
In concert with increased concerns regarding both biologic terrorism and new natural infectious disease threats (e.g., severe acute respiratory syndrome [SARS] and West Nile virus), as a result of advances in medical informatics, various data sources are available to epidemiologists for routine, prospective monitoring of public health. The synthesis of this evidence requires tools to find anomalies within various data stream combinations while maintaining manageable false alarm rates.


OBJECTIVE
The objectives of this report are to establish statistical hypotheses to define the compound multivariate problem of surveillance systems, present statistical methods for testing these hypotheses, and examine results of applying these methods to simulated and actual data.


METHODS
Canonical problems of parallel monitoring and consensus monitoring are considered in this report. Modified Bonferroni methods are examined for parallel monitoring. Both multiple univariate and multivariate methods are applied for consensus monitoring. A multivariate adaptation of Monte Carlo trials, using the injection of epidemic-curve-like signals in the multiple data streams of interest, is presented for evaluation of the various tests.


RESULTS
The Monte Carlo test results demonstrate that the multiple univariate combination methods of Fisher and Edgington provide the most robust detection performance across the scenarios tested. As the number of data streams increases, methods based on Hotelling's T2 offer added sensitivity for certain signal scenarios. This potential advantage is clearer when strong correlation exists among the data streams.


CONCLUSIONS
Parallel and consensus monitoring tools must be blended to enable a surveillance system with distributed sensitivity and controlled alert rates. Whether a multiple univariate or multivariate approach should be used for consensus monitoring depends on the number and distribution of useful data sources and also on their covariance structure and stationarity. Strong, consistent correlation among numerous sources warrants the examination of multivariate control charts.
Burkom
Howard S
HS
Johns Hopkins Applied Physics Laboratory, Laurel, Maryland 20723, USA. Howard.Burkom@jhuapl.edu
Murphy
S
S
Coberly
J
J
Hurt-Mullen
K
K
D000465:Algorithms; D004196:Disease Outbreaks / Q000517:prevention & control*; D015991:Epidemiologic Measurements; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics / Q000295:instrumentation*