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Outpatients visits

Description

Group A beta-hemolytic Streptococcus (GABHS) has caused outbreaks in recruit training environments, where it leads to significant morbidity and, on occasion, has been linked to deaths. Streptococcal surveillance has long been a part of military recruit public health activities. All Navy and Marine Corps training sites are required to track and record positive throat cultures and rapid tests on weekly basis. The Navy and Marine Corps have used bicillin prophylaxis as an effective control measure against GABHS outbreaks at recruit training sites. Though streptococcal control program policies vary by site, a minimum prophylaxis protocol is required and mass prophylax is indicated when local GABHS rates exceed a specific threshold. Before July 2007, prophylaxis upon initial entry was required between October and March, and when the local rate exceeded 10 cases per 1000 recruits. In July 2007, the Navy instituted a policy of mass prophylaxis upon initial entry throughout the year. Evaluation of GABHS cases before and after implementation of the new policy, including overall rates, identification of outbreaks, and inpatient results will help enhance the Navy’s ability to evaluate threshold levels, provide  systematic/standardized monitoring across the three recruit sites, and inform prophylaxis and monitoring strategies.

 

Objective

To compare trends of GABHS among recruits before and after changes in prophylaxis implementation using electronic laboratory and medical encounter records.

Submitted by hparton on
Description

Influenza causes significant morbidity and mortality, with attendant costs of roughly $10 billion for treatment and up to $77 billion in indirect costs annually. The Centers for Disease Control and Prevention conducts annual influenza surveillance, and includes measures of inpatient and outpatient influenza-related activity, disease severity, and geographic spread. However, inherent lags in the current methods used for data collection and transmission result in a one to two weeks delay in notification of an outbreak via the Centers for Disease Control and Prevention’s website. Early notification might facilitate clinical decision-making when patients present with acute respiratory infection during the early stages of the influenza outbreak. 

In the United States, the influenza surveillance season typically begins in October and continues through May. The Utah Health Research Network has participated in Centers for Disease Control and Prevention’s influenza surveillance since 2002, collecting data on outpatient visits for influenza-like illness (ILI, defined as fever of 100F or higher with either cough or sore throat). Over time, Utah Health Research Network has moved from data collection by hand to automated data collection that extracts information from discrete fields in patients’ electronic health records.

We used statistical process control to generate surveillance graphs of ILI and positive rapid influenza tests, using data available electronically from the electronic health records. 

 

Objective

The objective of this study is to describe the use of point-of-care rapid influenza testing in an outpatient, setting for the identification of the onset of influenza in the community. 

Submitted by hparton on
Description

A comprehensive electronic medical record (EMR) represents a rich source of information that can be harnessed for epidemic surveillance. At this time, however, we do not know how EMR-based data elements should be combined to improve the performance of surveillance systems. In a manual EMR review of over 15 000 outpatient encounters, we observed that two-thirds of the cases with an acute respiratory infection (ARI) were seen in the emergency room or other urgent care areas, but that these areas received only 15% of total outpatient visits. Because of this seemingly favorable signal-to-noise ratio, we hypothesized that an ARI surveillance system that focused on urgent visits would outperform one that monitored all outpatient visits.

Submitted by hparton on
Description

A comprehensive electronic medical record (EMR) represents a rich source of information that can be harnessed for epidemic surveillance. At this time, however, we do not know how EMR-based data elements should be combined to improve the performance of surveillance systems. In a manual EMR review of over 15 000 outpatient encounters, we observed that two-thirds of the cases with an acute respiratory infection (ARI) were seen in the emergency room or other urgent care areas, but that these areas received only 15% of total outpatient visits.1 Because of this seemingly favorable signal-to-noise ratio, we hypothesized that an ARI surveillance system that focused on urgent visits would outperform one that monitored all outpatient visits.

Submitted by Magou on
Description

http://Google.org developed a regression model that used the volume of influenza-related search queries best correlated with the proportion of outpatient visits related to influenza-like illness (ILI) model to estimate the level of ILI activity. For calibration, the model used ILINet data from October 2003 to 2009, which report weekly ILI activity as the percentage of patient visits to health care providers for ILI from the total number patient visits for the week. Estimates of ILI in 121 cities were added in January 2010.

 

Objective

This paper compares estimates of ILI activity with estimates from the Centers for Disease Control’s ILINet from October 2008 through March 2010.

Submitted by hparton on
Description

Microorganisms resistant to antibiotics (ABX) increase the mortality, morbidity and costs of infections. In the absence of a drug development pipeline that can keep pace with the emerging resistancemechanisms, these organisms are expected to threaten public health for years to come. Because exposure to ABX promotes the development of bacterial resistance, health care providers have long been urged to avoid using antibiotics to treat conditions that they are unlikely to improve, including many uncomplicated acute respiratory infections. We asked if interposing clinical decision support software at the time of electronic order entry could adjust ABX utilization toward consensus guidelines for these conditions. 

Submitted by hparton on
Description

The South Carolina Aberration Alerting Network (SCAAN) is a collaborative network of syndromic systems within South Carolina. Currently, SCAAN contains the following data sources: SC Hospital Emergency Department chief-complaint data, Poison Control Center call data, Over-the-Counter pharmaceutical sales surveillance, and CDC’s BioSense biosurveillance system. The Influenza-like Illness Network (ILINet) is a collaboration between the Centers for Disease Control, state health departments and health care providers. ILINet is one of several components of SC’s influenza surveillance.

 

Objective

This paper compares the SCAAN hospital-based fever–flu syndrome category with the South Carolina Outpatient ILINet provider surveillance system. This is the first comparison of South Carolina’s syndromic surveillance SCAAN data with ILINet data since SCAAN’s deployment.

Submitted by hparton on
Description

To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.

 

Objective

To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.

Submitted by elamb on
Description

We started an experimental syndromic surveillance using 1)OTC and 2)outpatients visits, in the last year and included 3)ambulance transfer from this year so as to early detect bioterrorism attack (BTA). 

Submitted by elamb on