Biomedical subjects

Howard S Burkom

Publications and source records attributed to Howard S Burkom.

3 recordsLinked to original sources

PubMed2005

Public health monitoring tools for multiple data streams.

INTRODUCTION: 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. OBJECTIVES: 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. CONCLUSION: 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.

Algorithms↗

PubMed2004

Role of data aggregation in biosurveillance detection strategies with applications from ESSENCE.

INTRODUCTION: Syndromic surveillance systems are used to monitor daily electronic data streams for anomalous counts of features of varying specificity. The monitored quantities might be counts of clinical diagnoses, sales of over-the-counter influenza remedies, school absenteeism among a given age group, and so forth. Basic data-aggregation decisions for these systems include determining which records to count and how to group them in space and time. OBJECTIVES: This paper discusses the application of spatial and temporal data-aggregation strategies for multiple data streams to alerting algorithms appropriate to the surveillance region and public health threat of interest. Such a strategy was applied and evaluated for a complex, authentic, multisource, multiregion environment, including >2 years of data records from a system-evaluation exercise for the Defense Advanced Research Project Agency (DARPA). METHODS: Multivariate and multiple univariate statistical process control methods were adapted and applied to the DARPA data collection. Comparative parametric analyses based on temporal aggregation were used to optimize the performance of these algorithms for timely detection of a set of outbreaks identified in the data by a team of epidemiologists. RESULTS: The sensitivity and timeliness of the most promising detection methods were tested at empirically calculated thresholds corresponding to multiple practical false-alert rates. Even at the strictest false-alert rate, all but one of the outbreaks were detected by the best method, and the best methods achieved a 1-day median time before alert over the set of test outbreaks. CONCLUSIONS: These results indicate that a biosurveillance system can provide a substantial alerting-timeliness advantage over traditional public health monitoring for certain outbreaks. Comparative analyses of individual algorithm results indicate further achievable improvement in sensitivity and specificity.

Algorithms↗

PubMed2003

Biosurveillance applying scan statistics with multiple, disparate data sources.

Researchers working on the Department of Defense Global Emerging Infections System (DoD-GEIS) pilot system, the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE), have applied scan statistics for early outbreak detection using both traditional and nontraditional data sources. These sources include medical data indexed by International Classification of Disease, 9th Revision (ICD-9) diagnosis codes, as well as less-specific, but potentially timelier, indicators such as records of over-the-counter remedy sales and of school absenteeism. Early efforts employed the Kulldorff scan statistic as implemented in the SaTScan software of the National Cancer Institute. A key obstacle to this application is that the input data streams are typically based on time-varying factors, such as consumer behavior, rather than simply on the populations of the component subregions. We have used both modeling and recent historical data distributions to obtain background spatial distributions. Data analyses have provided guidance on how to condition and model input data to avoid excessive clustering. We have used this methodology in combining data sources for both retrospective studies of known outbreaks and surveillance of high-profile events of concern to local public health authorities. We have integrated the scan statistic capability into a Microsoft Access-based system in which we may include or exclude data sources, vary time windows separately for different data sources, censor data from subsets of individual providers or subregions, adjust the background computation method, and run retrospective or simulated studies.

Bioterrorism↗