Electronic influenza monitoring: evaluation of body temperature to classify influenza-like illness in a syndromic surveillance system.

The Centers for Disease Control and Prevention (CDC) defines influenza-like illness (ILI) for its sentinel providers as fever (temperature > or =100.5 degrees F or 37.8 degrees C) and a cough and/or a sore throat in the absence of a known cause other than influenza. For electronic disease surveillance systems, classifying ILI with clinical data that identify only individual aspects of the case definition may add excessive levels of unwanted noise to the system; however, the capability to analyze a patient's body temperature along with other available clinical data (International Classification of Diseases, Ninth Revision codes) could improve diagnostic precision and more accurately classify cases of ILI in a syndromic surveillance system. Developing Boolean algorithms to properly classify true cases of influenza plays an important role toward understanding accurate levels of disease in a community and can also be a key tool for allocating urgent prophylaxis such as antiviral medications during severe outbreaks and pandemics. Results for this study show that elevated body temperature was 40% efficient in correctly predicting laboratory-positive confirmations of influenza (sensitivity) but at the same time was 76% efficient in ruling out influenza (specificity) in the group of sampled members who were tested for influenza.

Race, ethnicity, and insurance as determinants of epidural use: analysis of a national sample survey.

Despite widespread availability of pain interventions in childbirth, for most women, childbirth is associated with labor pain that exceeds expectations. Although epidural is superior to other medical interventions, the choice to use epidural still remains a matter of patient and doctor preference. Whether racial or ethnic characteristics influence preference of physician use or interact with insurance coverage is still unknown. This study used a large national sample of women to measure significant determinants of epidural use in order to discuss disparities in pain management. The findings suggest the need for nurse leaders to foster health policies that are sensitive to diversity and economics.

Continuously rethinking the definition of influenza for surveillance systems: a Dependent Bayesian Expert System.

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.

Bayesian processing of context-dependent text: reasons for appointments can improve detection of influenza.

OBJECTIVE: This article aims to examine whether words listed in reasons for appointments could effectively predict laboratory-verified influenza cases in syndromic surveillance systems. METHODS: Data were collected from the Armed Forces Health Longitudinal Technological Application medical record system. We used 2 algorithms to combine the impact of words within reasons for appointments: Dependent (DBSt) and Independent (IBSt) Bayesian System. We used receiver operating characteristic curves to compare the accuracy of these 2 methods of processing reasons for appointments against current and previous lists of diagnoses used in the Department of Defense's syndromic surveillance system. RESULTS: We examined 13,096 cases, where the results of influenza tests were available. Each reason for an appointment had an average of 3.5 words (standard deviation = 2.2 words). There was no difference in performance of the 2 algorithms. The area under the curve for IBSt was 0.58 and for DBSt was 0.56. The difference was not statistically significant (McNemar statistic = 0.0054; P = 0.07). CONCLUSIONS: These data suggest that reasons for appointments can improve the accuracy of lists of diagnoses in predicting laboratory-verified influenza cases. This study recommends further exploration of the DBSt algorithm and reasons for appointments in predicting likely influenza cases.