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Platt Richard

Public health departments have a strong interest in monitoring the incidence, care, and complications of gestational diabetes, as it is associated with poor outcomes for infants and increased risk of diabetes type II for mothers. Gestational diabetes rates are also a possible early marker for changes in the incidence of diabetes type II in the general population. However, diabetes is not generally a reportable condition and therefore, public health surveillance is limited to periodic telephone surveys (subject to self-report inaccuracies), sponsored clinical examinations (expensive, small sample size, no information about processes of care), and occasional research studies. Automated analysis of electronic health record data is a promising method to complement existing surveillance tools with longitudinal, continually updated, clinically rich data derived from large populations. We describe a pilot project to automatically survey electronic health record data in order to identify cases of gestational diabetes, describe their patterns of care and complications, and report summary data to the state health department.

 

Objective

To develop an electronic, prospective surveillance system to describe the incidence, care, and complications of gestational diabetes using live electronic health record data from a large defined population.

Referenced File
Submitted by hparton on

Chronic diseases are the leading causes of mortality and morbidity for Americans but public health surveillance for these conditions is limited. Health departments currently use telephone interviews, medical surveys, and death certificates to gather information on chronic diseases but these sources are limited by cost, timeliness, limited clinical detail, and/or poor population coverage. Continual and automated extraction, analysis, and summarization of EHR data could advance surveillance in each of these domains.

Objective

Develop methods for automated chronic disease surveillance and visualization using electronic health record (EHR) data.

Referenced File
Submitted by elamb on

Influenza affects millions of people and causes about 36,000 deaths in the United States each winter. Pandemics of influenza emerge at irregular intervals. National influenza surveillance is used to detect the emergence and spread of influenza virus variants and to monitor influenza-related morbidity and mortality. Existing surveillance consists of seven data types, which are reported weekly. Newly available national electronic data sources created as part of the routine delivery of medical care might supplement current data sources. Nurse call data offer national coverage, are timely, and do not require any extra manual data entry. Using such data for influenza-like illness (ILI) surveillance may lead to earlier detection of ILI in the community, both because people with ILI may call a nurse line before seeking care at a health-care facility and because the data are more timely than existing weekly data.

 

Objective

Our purpose was to compare nurse call data for respiratory and ILI against CDC national influenza surveillance data from the 2004-2005 season by region to determine if the call data were informative and might allow earlier detection of influenza activity.

Referenced File
Submitted by elamb on

There is limited closed-form statistical theory to indicate how well the prospective space-time permutation scan statistic will perform in the detection of localized excess illness activity. Instead, detection methods can be applied to simulated data to gain insight about detection performance. Such results are dependent on the way outbreaks are simulated and the nature of the background data. As an alternative, we explore an empirical approach in which the membership of a large health plan is used to represent a community and detection performance is assessed in samples from the larger group.

 

Objective

Our goal was to assess the impact of sentinel sample size and criteria for a signal on performance of daily prospective space-time permutation detection by comparing results in varying size random samples from a large health plan to results found in the full membership.

Submitted by Sandra.Gonzale… on

Clinician reporting of notifiable diseases has historically been slow, labor intensive, and incomplete. Manual and electronic laboratory reporting (ELR) systems have increased the timeliness, efficiency, and completeness of notifiable disease reporting but cannot provide full demographic information about patients, integrate an array of pertinent lab tests to yield a diagnosis, describe patient signs and symptoms, pregnancy status, treatment rendered, or differentiate a new diagnosis or from follow-up of a known old diagnosis. Electronic medical record (EMR) systems are a promising resource to combine the timeliness and completeness of ELR systems with the clinical perspective of clinician initiated reporting. We describe an operational system that detects and reports patients with notifiable diseases to the state health department using EMR data.

 

Objective

To leverage EMR systems to improve the timeliness, completeness, and clinical detail of notifiable disease reporting.

Referenced File
Submitted by elamb on

National surveillance is used to detect the emergence and spread of influenza virus variants and to monitor influenza-related morbidity and mortality. Nurse telephone triage (“call”) data may serve as a useful complement to traditional influenza surveillance, especially at times or in places traditional surveillance is not operating. It may also be useful to detect increased occurrence of non-influenza respiratory infection.

 

Objective

We compared state-level nurse call data to CDC national influenza surveillance data to determine how well call data performed relative to CDC sentinel provider and viral isolate data. This quantitative analysis extends an earlier semiquantitative regional analysis of the same data.

Referenced File
Submitted by elamb on

CDC is building a public health information grid to enable controlled distribution of data, services and applications for researchers, Federal authorities, local and state health departments nationwide, enabling efficient controlled sharing of data and analytical tools. Federated aggregate analysis of distributed data sources may detect clusters that might be invisible to smaller, isolated systems. Success of the public health grid is contingent upon the number of participating agencies and the quantity, quality, and utility of data and applications available for sharing. Grid protocols allow data owners to control data access, but requires a model to control the level of identifiability of depending upon the user’s permissions. Here, we describe a work currently in progress involving the design and implementation of an ambulatory syndromic surveillance data stream generator for the public health grid. The project is intended to broadly disseminate aggregate syndrome counts for general use by the public health community, to develop a model for sharing varying levels of identifiable data on cases depending upon the user, and to facilitate ongoing development of the grid.

 

Objective

To implement a syndromic surveillance system on CDC’s public health information grid, capable of securely distributing syndromic data streams ranging from aggregate case counts to individual case details, to appropriate personnel.

Referenced File
Submitted by elamb on

Existing statistical methods can perform well in detecting simulated bioterrorism events. However, these methods have not been well-evaluated for detection of the type of respiratory and gastrointestinal events of greatest interest for routine public health practice. To assess whether a syndromic surveillance system can detect these outbreaks, we constructed simulated outbreaks based on public health interest and experience. We then inserted these outbreaks into real data. We assessed whether the simulated outbreaks could be detected using a battery of detection methods, including model-adjusted scan statistics and space-time permutation scan statistics.

 

Objective

We used simulation methods to assess the performance of two distinct anomaly-detection approaches, each under a variety of parameter settings, with respect to their ability to detect outbreaks of commonly occurring events of public health importance.

Referenced File
Submitted by elamb on

Professor Hripcsak rightly points out some of the challenges inherent in disseminating and sustaining robust information systems to automate the detection and reporting of notifiable diseases using data from electronic medical records (EMR). New York City'™s experience with automated tuberculosis identification and notification is a salient reminder that sophisticated technology alone is not enough to ensure broad adoption of automated electronic reporting systems. Substantial resources and ongoing active support by a wide range of public health stakeholders are also essential ingredients. We have attempted to engineer the Electronic medical record Support for Public health (ESP) system to make it suitable for widespread adoption but the ultimate success of this endeavour will depend upon sustained collaboration between many parties including commercial EMR vendors, clinical administrators, state health departments, the Centers for Disease Control and Prevention (CDC), the Council of State and Territorial Epidemiologists (CSTE), and others.

Referenced File
Submitted by elamb on

The utility of syndromic surveillance systems to augment health departments’ traditional surveillance for naturally occurring disease has not been prospectively evaluated.

 

Objective

In this interim report we describe the signals detected by a real-time ambulatory care-based syndromic surveillance system and discuss their relationship to true outbreaks of illness.

Referenced File
Submitted by elamb on
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