ISDS Conference

Emphasis has been placed on the improvement of existing surveillance systems and developing innovative new surveillance systems around the world after the events of 9/11 in 2001, severe acute respiratory syndrome (SARS) in 2003. Investments have not only been made in traditional public health surveillance systems but also novel approaches such as syndromic surveillance systems. It is important to have timely, relevant evaluations of these systems to understand their usefulness. While most of the published syndromic surveillance systems evaluations looked at technical attributes of the system i.e. accuracy [1]. Other aspects such as utility, acceptability and feasibility[2] as given in the generic Centers for Disease Control and Prevention evaluation framework[3] were not always explicitly addressed. Moreover, most of syndromic surveillance systems are established in developed countries or areas that already have other types of advanced surveillance systems. There are few public reports of the development and implementation of a syndromic surveillance system in rural China.

Objective

To identify the different acceptability groups of village doctors of an integrated syndromic surveillance system (ISS) and to explore factors influencing acceptability from village doctors' perspective before ISS launched.

India is one of the global Ôhot-spotsÕ for emergence and re-emergence of pathogens and propagation of those that are drug resistant. Disease surveillance gained momentum in India only after the outbreaks of cholera in Delhi in 1988 and plague in Surat in 1994, which not only had significant mortality, morbidity and economic consequences. The current key indicator based surveillance system in the country, the Integrated Disease Surveillance Project (IDSP) has evolved from systems that were initiated and scaled up as a response to these outbreaks. IDSP is constrained by challenges of human and material resources and the quality of data generated at the frontline is questionable making it difficult to detect, diagnose, and control outbreaks until they had become quite large. Timeliness and Completeness of weekly reports are the two key SQIs even suggested by the World Health Organization (WHO) to monitor the quality of the surveillance system in the districts and states. The goal of the current study was to assess the validity of these SQIs in predicting the overall surveillance quality in a system where data quality was questionable.

Objective

To assess the validity of the Surveillance Quality Indicators (SQIs), Timeliness and Completeness of the weekly surveillance reports as indicators for overall quality of surveillance system and core and supplementary surveillance functions.

Effective and valid surveillance of syndromes can be extremely useful in the early detection of outbreaks and disease trends. However, medical chart checks without patient identifiers and lack of diagnoses in A08 data has made validation difficult. With the rising availability of electronic health records (EHRs) to local health departments, the ability to evaluate syndromic surveillance systems (SSS) has improved. In LAC, ED data are collected from hospitals and classified into categories based on chief complaints. The most reported syndrome in LAC is the respiratory classification, which is intended to broadly capture respiratory pathogen activity trends. To test the validity of the LAC Department of Public Health (DPH) respiratory syndrome classification, ED syndromic surveillance data were analyzed using corresponding EHRs from one hospital in LAC.

Objective

To compare and validate syndromic surveillance categorization against electronic health records at one hospital emergency department (ED) in Los Angeles County (LAC).

Recent years' informatics advances have increased availability of various sources of health-monitoring information to agencies responsible for disease surveillance. These sources differ in clinical relevance and reliability, and range from streaming statistical indicator evidence to outbreak reports. Information-gathering advances have outpaced the capability to combine the disparate evidence for routine decision support. In view of the need for analytical tools to manage an increasingly complex data environment, a fusion module based on Bayesian networks (BN) was developed in 2011 for the Dept. of Defense (DoD) Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012 this module was expanded with syndromic queries, data-sensitive algorithm selection, and hierarchical fusion network training [1]. Subsequent efforts have produced a full fusion-enabled version of ESSENCE for beta testing, further upgrades, and a software specification for live DoD integration. Beta test reviewers cited the reduced alert burden and the detailed evidence underlying each alert. However, only 39 reported historical events were available for training and calibration of 3 networks designed for fusion of influenza-like-illness, gastrointestinal, and fever syndrome categories. The current presentation describes advances to formalize the network training, calibrate the component alerting algorithms and decision nodes together for each BN, and implement a validation strategy aimed at both the ESSENCE public health user and machine learning communities.

Objective

This presentation aims to reduce the gap between multivariate analytic surveillance tools and public health acceptance and utility. We developed procedures to verify, calibrate, and validate an evidence fusion capability based on a combination of clinical and syndromic indicators and limited knowledge of historical outbreak events.

In North Carolina there has been an escalation of poisoning deaths. In 2011, the number of fatal poisonings was 1,368 deaths, with 91% classified as drug overdoses with the majority of those due to opioid analgesics.[1] Far greater numbers of drug overdoses result in hospitalization, emergency department (ED) or outpatient clinic visits, or resolve without the individual seeking medical attention. Although public health authorities have long employed death data for drug overdose surveillance in NC, little attention has been paid to the use of ED data for this purpose. Through the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT), NC collects information on 99.5% of all acute-care ED visits across the state, primarily for syndromic surveillance purposes. Despite the timeliness and completeness of this data system, drug overdose surveillance is a challenge due to lack of a standardized definition for the positive identification of opioid overdoses. In this study we used NC DETECT ED data to describe visits due to drug, and more specifically, opioid overdoses. Objective: To describe the epidemiologic characteristics for emergency department visits due to drug overdoses in North Carolina.

Reliable detection and accurate scoping of outbreaks of foodborne illness are the keys to effective mitigation of their impacts. However, relatively small number of persons affected and underreporting, challenge the reliability of surveillance models. In this work, we correlate a record of identified outbreaks and sporadic cases of Salmonellosis in humans retained in PulseNet1, and diagnosis codes in hospital claims collected in California from 2006 to 2010. We hypothesize that the data support and reliability of detection could be improved by including cases in which Salmonella infection may be confused2.

Objective

To investigate utility of using inpatient and emergency room diagnoses to detect outbreaks of Salmonellosis in humans. To quantify the impact of including in the analysis cases diagnosed with conditions that may have physiological appearance similar to Salmonellosis.

The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.

Objective

To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.

In light of recent communicable disease outbreaks, the ability of Florida Department of HealthÕs (FDOH) syndromic surveillance system, ESSENCE-FL, to identify emergent disease outbreaks using reportable disease data and algorithms originally designed for emergency department chief complaint data was examined. Preliminary work on this analysis presented last year was recently updated and expanded to include additional diseases, further levels of locale, and detector algorithm comparisons. Cases are entered into Merlin, the Bureau of EpidemiologyÕs secure web-based reporting and epidemiologic analysis system, by all 67 county health departments and the de-identified case data are sent hourly to ESSENCE-FL. These data are then available for ad hoc queries, allowing users to observe unusual changes in disease activity and assist in timely identification of infectious disease outbreaks. Based on system algorithms, weekly case tallies are assigned an increasing intensity awareness status from normal to alert and are monitored by county and state epidemiologists to guide timely disease control efforts, but may not by themselves be definitive actionable information.

Objective

To determine if there is an association between known outbreak activity and ESSENCE generated alerts.