Continuously Rethinking the Definition of Influenza for Surveillance Systems

Farrokh Alemi, Martin J. Atherton, David C. Pattie and Manabu Torii

Medical Decision Making, 2013, vol. 33, issue 6, 860-868

Abstract: Objective . In the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), influenza was originally defined by a list of 29 and later by a list of 12 diagnosis codes. This article describes a dependent Bayesian procedure designed to improve the ESSENCE system and exploit multiple sources of information without being biased by redundancy. Methods. We obtained 13,096 cases within the Armed Forces Health Longitudinal Technological Application electronic medical records that included an influenza laboratory test. A Dependent Bayesian Expert System (D-BESt) was used to predict influenza from diagnoses, symptoms, reason for visit, temperature, month of visit, category of enrollment, and demographics. For each case, D-BESt sequentially selects the most discriminating piece of information, calculates its likelihood ratio conditioned on previously selected information, and updates the case’s probability of influenza. Results. When the analysis was limited to definitions based on diagnoses and was applied to a sample of patients for whom laboratory tests had been ordered, the areas under the receiver operating characteristic curve (AUCs) for the previous (29-diagnosis) and current (12-diagnosis) ESSENCE lists and the D-BESt algorithm were, respectively, 0.47, 0.36, and 0.77. Including other sources of information further improved the AUC for D-BESt to 0.79. At the best cutoff point for D-BESt, where the receiver operating characteristic curve for D-BESt is farthest from the diagonal line, the D-BESt algorithm correctly classified 84% of cases (specificity = 88%, sensitivity = 62%). In comparison, the current ESSENCE approach of using a list of 12 diagnoses correctly classified only 31% of this sample of cases (specificity = 29%, sensitivity = 42%). Conclusions. False alarms in ESSENCE surveillance systems can be reduced if a probabilistic dynamic learning system is used.

Keywords: surveillance; algorithm; dependent Bayesian probability models; influenza; dynamic case definition; redundant predictors; reduction of false alarms; robust models (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X13478482 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:33:y:2013:i:6:p:860-868

DOI: 10.1177/0272989X13478482

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().