Public health monitoring tools for multiple data streams...Third annual Syndromic Surveillance Conference... Boston, Massachusetts... November 3-4, 2004
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 fo...
| Publicado en: | MMWR: Morbidity & Mortality Weekly Report Vol. 54; pp. 55 - 63 |
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| Autores principales: | , , , |
| Formato: | equations & formulas statistics tables/charts Journal Article |
| Publicado: |
Centers for Disease Control & Prevention (CDC)
2005 Aug 26 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106525172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106525172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01492195 MMW jtl: MMWR: Morbidity & Mortality Weekly Report issn: 01492195 maglogo: N pubinfo: dt: 2005 Aug 26 Supplement vid: 54 pid: 1493 pub: Centers for Disease Control & Prevention (CDC) place: Atlanta, Georgia artinfo: ui: 106525172 106525172 2009033653 NLM16177694 106525172 ppf: 55 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Public health monitoring tools for multiple data streams...Third annual Syndromic Surveillance Conference... Boston, Massachusetts... November 3-4, 2004 aug: au: Burkom HS Murphy S Coberly J Hurt-Mullen K affil: Johns Hopkins Applied Physics Laboratory, 11100 Johns Hopkins Road, MS 8-220, Laurel, MD 20723; Howard.Burkom@jhuapl.edu sug: subj: Data Analysis, Statistical Disease Surveillance Hypothesis Information Systems Algorithms Utilization Chi Square Test Coefficient alpha Correlation Coefficient Descriptive Statistics Null Hypothesis Sensitivity and Specificity Simulations ab: 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 EdgingtonI provide the most robust detection performance across the scenarios tested. As the number of data streams increases, methods based on Hotelling's T[[2]] 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. pubtype: Academic Journal doctype: equations & formulas statistics tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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