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Validation

Description

The HL7 messaging standard, version two that was implemented by most vendors and public health agencies did not resolve all systems’ interoperability problems. Design and tool implementation for automated machine-testing messages may resolve many of those problems. This task also has critical importance for rapid deployment of electronic public health systems.

 

Objective

This document describes the Public Health Information Network efforts on the development of the messaging quality framework, a flexible framework of services and utilities designed to assist public health partners with preparing and communicating quality, standard electronic messages.

 

Submitted by hparton on
Description

Hospital discharge data received by public health agencies has a reporting lag time of greater than six months. This data is often used retrospectively to conduct surveillance to assess severity of illness and outcome, and for evaluating performance of public health surveillance systems. 

With the emergence of Health Information Exchanges and Regional Health Information Organizations (RHIOs), inpatient data can be available to public health in near real-time. However, there currently are no established public health practices or information systems for conducting routine surveillance in the inpatient setting. 

Through a contract with the Centers for Disease Control and Prevention, New York State Department of Health

initiated the development of a statewide public–health Health Information Exchanges with New York RHIOs. Daily

minimum biosurveillance data set data-exchange implementation, and evaluation efforts were focused on one RHIO (RHIO A) and one participating hospital system composed of five acute-care facilities.

 

Objective

The objective of this paper is to assess the potential utility of inpatient minimum biosurveillance data set data obtained from RHIOs for pneumonia and influenza surveillance.

Submitted by hparton on
Description

The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) obtains electronic data from 153 Veterans Affairs (VA) Medical Centers plus outpatient clinics in all 50 states, American Samoa, Guam, Philippines, Puerto Rico, and U.S. Virgin Islands. Currently, there is no centralized VA reporting requirement for nationally notifiable infectious conditions detected in VA facilities. Surveillance and reporting of cases to local public health authorities are performed manually by VA Infection Preventionists and other clinicians. In this analysis, we examined positive predictive value of ICD-9-CM diagnosis codes in VA ESSENCE to determine the utility of this system in electronic detection of reportable conditions in VA.

 

Objective

To determine the utility of ICD-9-CM diagnosis codes in the VA ESSENCE for detection and public health surveillance of nationally notifiable infectious conditions in veteran patients.

Submitted by hparton on
Description

The evolution of a communicable disease in a human population is not entirely predictable. However, the spreading process can be assumed to vary smoothly in time. The time-dependent infection process can be linked to observations of the epidemic’s evolution by convolving it with a stochastic delay model. In retrospective analyses of epidemics, when the observations are the dates of exhibition of patients’ symptoms, the delay is the incubation period. In case of biosurveillance data, the delay is caused by incubation and a (hospital) visit delay, modeled as independent random variables. A model for observational error is also required. The time-dependent infection/spread rate may be inferred from observations by a deconvolution process. The smooth temporal variation of the infection rate allows its representation using a low dimensional parametric model, and the inference may be performed with relatively little data. For large outbreaks, the data may be available early in the epidemic, allowing timely modeling of the outbreak. Short-term forecasts using the model could thereafter be used for medical planning.

 

Objective

We present a statistical method to characterize an epidemic of a communicable disease from a time series of patients exhibiting symptoms. Characterization is defined as estimating an unobserved, time-dependent infection rate and associated parameters that completely define the evolution of an epidemic. The problem is posed as one of Bayesian inference, where parameters are inferred with quantified uncertainty. The method is demonstrated on synthetic and historical epidemic data. 

Submitted by hparton on
Description

In Montreal, notifiable diseases are reported to the Public Health Department (PHD). Of 44, 250 disease notifications received in 2009, up to 25% had potential address errors. These can be introduced during transcription, handwriting interpretation and typing at various stages of the process, from patients, labs and/or physicians, and at the PHD. Reports received by the PHD are entered manually (initial entry) into a database. The archive personnel attempts to correct omissions by calling reporting laboratories or physicians. Investigators verify real addresses with patients or physicians for investigated episodes (40–60%). 

The Dracones qualite (DQ) address verification algorithm compares the number, street and postal code against the 2009 Canada Post database. If the reported address is not consistent with a valid address in the Canada Post database, DQ suggests a valid alternative address.

 

Objective

To (1) validate DQ developed to improve data quality for public health mapping and (2) identify the origin of address errors.

Submitted by hparton on
Description

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.

Submitted by elamb on
Description

Public health surveillance systems are constantly facing challenges of epidemics and shortage in the health care workforce. These challenges are more pronounced in developing countries, which bear the greatest burden of disease and where new pathogens are more likely to emerge, old ones to reemerge and drug-resistant strains to propagate. In August 2008, a mobile phone based surveillance system was piloted in 6 of the 23 districts in the state of AP in India. Health workers in 3832 hospitals and health centers used mobile phones to send reports to and receive information from the nationwide Integrated Disease Surveillance Project (IDSP). Like in many other states, the IDSP in AP is facing many operational constraints like lack of human resource, irregular supply of logistics, hard to reach health facilities, poor coordination with various health programs and poor linkages with non-state stakeholders. The mobile phone based surveillance system was an attempt to tackle some of the barriers to improving the IDSP by capitalizing on the exponential growth in numbers as well as reach of mobile phones in the state. Promising results from the pilot of the system led AP state to extend it to about 16,000 reporting units in all 23 districts. This study evaluates how the system has affected the efficiency and effectiveness of IDSP in the state.

Objective

To assess the impact of use of mobile phones use on the efficiency and effectiveness of the Integrated Disease Surveillance Project (IDSP) in the state of Andhra Pradesh (AP)

Submitted by elamb on
Description

Syndromic surveillance systems use electronic health-related data to support near-real time disease surveillance. Over the last 10 years, the use of ILI syndromes defined from emergency department (ED) data has become an increasingly accepted strategy for public health influenza surveillance at the local and national levels. However, various ILI definitions exist and few studies have used patient-level data to describe validity for influenza specifically.

Objective

Estimate and compare the accuracy of various ILI syndromes for detecting lab-confirmed influenza in children.

Submitted by elamb on
Description

Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.

 

Objective

To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.

Submitted by elamb on
Description

With the increase in GPS enabled devices, pin-point spatial data is an obvious future growth area for cluster detection research. The FBSSS handles binary labelled point data, but requires Monte Carlo testing to obtain inference [1]. In the Bayesian Poisson SSS [2], Monte Carlo is replaced by use of historic data, manifoldly speeding up processing. Following [2], [3] derived the BBSSS, replacing historic data with expert knowledge on cluster relative risk. This paper compares the spatial accuracy of BBSSS and FBSSS using new measure [4] which, being independent of inference level, permits direct comparison between Bayesian and frequentist methods. To compare the spatial accuracy of a Bayesian Bernoulli spatial scan statistic (BBSSS) and the frequentist Bernoulli spatial scan statistic (FBSSS), using benchmark trials.

Submitted by elamb on