Pneumonia in US hospitalized patients with influenza-like illness: BioSense, 2007-2010.

We used data from BioSense, a national electronic surveillance system, to describe pneumonia in hospitalized patients with influenza-like illness (ILI). Ninety-five hospitals from 20 states reported ICD-9-CM-coded inpatient final diagnosis data during the study period of September 2007 to February 2010. We compared the characteristics of persons with and without pneumonia among those with ILI-related hospitalizations. BioSense captured 26 987 ILI-related inpatient hospitalizations; 8979 (33%) had a diagnosis of pneumonia. Analysis of trends showed highest counts of pneumonia during the 2007-2008 season and the second 2009 pandemic wave. Pneumonia was more common with increasing age. Microbiology and pharmacy data were available for a subset of patients; 107 (5%) with pneumonia had a bloodstream infection and 17% of patients were prescribed antiviral treatment. Our findings demonstrate the potential utility of electronic healthcare data to track trends in ILI and pneumonia, identify risk factors for disease, identify bacteraemia in patients with pneumonia, and monitor antiviral use.

Investigating the effect of high spring incidence of pandemic influenza A(H1N1) on early autumn incidence.

A pandemic H1N1 infection wave in the USA occurred during spring 2009. Some hypothesized that for regions affected by the spring wave, an autumn outbreak would be less likely or delayed compared to unaffected regions because of herd immunity. We investigated this hypothesis using the Outpatient Influenza-like Illness (ILI) Network, a collaboration among the Centers for Disease Control and Prevention, health departments, and care providers. We evaluated the likelihood of high early autumn incidence given high spring incidence in core-based statistical areas (CBSAs). Using a surrogate incidence measure based on influenza-related illness ratios, we calculated the odds of high early autumn incidence given high spring incidence. CBSAs with high spring ILI ratios proved more likely than unaffected CBSAs to have high early autumn ratios, suggesting that elevated spring illness did not protect against early autumn increases. These novel methods are applicable to planning and studies involving other infectious diseases.

ESSENCE II and the framework for evaluating syndromic surveillance systems.

INTRODUCTION: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II) is a prototype syndromic surveillance system for capturing and analyzing public health indicators for early detection of disease outbreaks. OBJECTIVES: This paper presents a preliminary evaluation of ESSENCE II according to a CDC framework for evaluating syndromic surveillance systems. METHODS: Each major topic of the framework is addressed in this assessment of ESSENCE II performance. RESULTS: ESSENCE captures data in multiple formats, parses text strings into syndrome groupings, and applies multiple temporal and spatio-temporal outbreak-detection algorithms. During a recent DARPA evaluation exercise, ESSENCE algorithms detected a set of health events with a median delay of 1 day after the earliest possible detection opportunity. CONCLUSIONS: ESSENCE II has provided excellent performance with respect to the framework and has proven to be a useful and cost-effective approach for providing early detection of health events.

Evaluation of syndromic surveillance systems--design of an epidemic simulation model.

INTRODUCTION: The paucity of outbreak data from biologic terrorism and emerging infectious diseases limits the evaluation of syndromic surveillance systems. Evaluation using naturally occurring outbreaks of proxy disease (e.g., influenza) is one alternative but does not allow for rigorous evaluation. Another approach is to inject simulated outbreaks into real background data, but existing simulation models generally do not account for such factors as spatial mobility and do not explicitly incorporate knowledge of the disease agent. OBJECTIVE: The objective of this analysis was to design a simulated anthrax epidemic injection model that accounts for the complexity of the background data and enables sensitivity analyses based on uncertain disease-agent characteristics. MODEL REQUIREMENTS AND ASSUMPTIONS: Model requirements are described and used to limit the scope of model development. Major assumptions used to limit model complexity are also described. Available literature on inhalational anthrax is reviewed to ensure that the level of model detail reflects available disease knowledge. MODEL DESIGN: The model is divided into four components: 1) agent dispersion, 2) infection, 3) disease and behavior, and 4) data source. The agent-dispersion component uses a Gaussian plume model to compute spore counts on a fine grid. The infection component uses a cohort approach to identify infected persons by residential zip code, accounting for demographic covariates and spatial mobility. The disease and behavior component uses a discrete-event approach to simulate progression through disease stages and health-services utilization. The data-source component generates records to insert into background data sources. CONCLUSIONS: An epidemic simulation model was designed to enable evaluation of syndromic surveillance systems. The model addresses limitations of existing simulation approaches by accounting for such factors as spatial mobility and by explicitly modeling disease knowledge. Subsequent work entails software implementation and model validation.