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Nelson Noele

Hepatitis C virus (HCV) infection is the most common blood-borne disease in the US and the leading cause of liver-related morbidity and mortality. Approximately 3.5 million individuals in the US were estimated to be living with HCV in 2010 and approximately half of them were unaware that they were currently infected. Among HCV infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represents approximately 75% of current cases. Because of the substantial burden of disease among this age group, CDC expanded its existing HCV risk-based testing recommendations to include a one-time HCV antibody test for all persons born between 1945-1965. The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013.

Objective: Using administrative claims for privately insured and Medicare Advantage enrollees from a large, private, U.S. health plan, we estimated the prevalence of hepatitis C testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the CDC and United States Preventive Services Task Force. This panel will discuss strengths and weaknesses for monitoring hepatitis C testing using alternative data sources including self-reported data, insurance claims data, and laboratory testing data.

Submitted by elamb on

Hepatitis C virus (HCV) infection is the most common blood-borne disease in the US and the leading cause of liver-related morbidity and mortality. Approximately 3.5 million individuals in the US were estimated to have been living with hepatitis C in 2010 and approximately half of them were unaware that they were infected. Among HCV infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represents approximately 75% of current cases. Because of the substantial burden of disease among this age group, CDC expanded its existing hepatitis C risk-based testing recommendations to include a one-time HCV antibody test for all persons born between 1945 and 1965. The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013.

Objective: Using a large nationally representative dataset, we estimated the prevalence of self-reported hepatitis C testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the CDC and United States Preventive Services Task Force.

Submitted by elamb on

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States and HCV incidence has been increasing. Mental illness may impact the likelihood of initial HCV infection, progress and adherence to treatment along the hepatitis C care cascade, and risk of subsequent reinfection for those cured of hepatitis C. The relationship between HCV infection and mental illness is not well understood and many studies have lacked sufficient sample size to adjust for important confounders. We sought to explore the association between chronic HCV infection and mental illness after adjusting for important confounders.

Objective: Using data from the 2011-“2015 IBM MarketScan® Commercial Claims and Encounters, we sought to assess the relationship between mental health outcomes and chronic hepatitis C infection after adjusting for important confounders. Persons with HCV antibody and RNA test results between 2011 and 2015 and continuous enrollment in fee-for-service plans were included in the analysis.

Submitted by elamb on

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Approximately 75% of people infected with chronic HCV were born between 1945 and 1965. Since 2012, the CDC has recommended one-time screening for chronic HCV infection for all persons in this birth cohort (baby boomers). The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013. We estimated the rate of HCV testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort.

Objective: Using the two largest commercial laboratory data sources nationally, we estimated the annual rates of hepatitis C testing among individuals who were recommended to be tested (i.e., baby boomer cohort born between 1945 and 1965) by the CDC and United States Preventive Services Task Force. This panel will discuss strengths and weaknesses for monitoring hepatitis C testing using alternative data sources including self-reported data, insurance claims data, and laboratory testing data.

Submitted by elamb on

Hepatitis C virus (HCV) infection is the most common blood-borne infection in the US, and a leading cause of liver-related morbidity and mortality. Approximately 3.5 million individuals in the US were estimated to have been living with hepatitis C in 2010, and approximately half of them were unaware that they were infected. Among HCV infected individuals, those born between 1945 and 1965 (usually referred to as the baby boomer cohort) represent approximately 75% of current cases. Because of the substantial burden of disease among this age group, CDC expanded its existing hepatitis C risk-based testing recommendations to include a one-time HCV antibody test for all persons born between 1945 and 1965. The United States Preventive Services Task Force (USPSTF) subsequently made the same recommendation in June 2013.

Objective: We estimated the rate of hepatitis C testing between 2011 and 2017 among persons with commercial health insurance coverage and compared rates by birth cohort.

Submitted by elamb on

Hepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Monitoring the burden of chronic HCV infection requires robust methods to identify patients with infection. Insurance claims data are a potentially rich source of information about disease burden, but often lack the laboratory results necessary to define chronic HCV infection. We developed a machine learning-based algorithm to identify patients with chronic HCV infection using health insurance claims alone and compared it a previously developed ICD-9 code-based algorithm.

Objective: We developed a machine learning-based algorithm to identify patients with chronic hepatitis C infection in health insurance claims data.

Submitted by elamb on

Argus is an event-based, multi-lingual, biosurveillance system, which captures and analyzes information from publicly available internet media. Argus produces reports that summarize and contextualize direct, indirect, and enviroclimatic indications and warning (I&W) of human, animal, and plant disease events, and makes these reports available to the system’s users. Early warning of highly infectious animal diseases, like foot-and-mouth disease (FMD), is critical for the enactment of containment and/or prevention measures aiming to curb disease spread and reduce the potential for devastating trade and economic implications.

 

Objective

Our objective is to demonstrate how biosurveillance, using direct and indirect I&W of disease within vernacular internet news media, provides early warning and situational awareness for infectious animal diseases that have the potential for trade and economic implications in addition to detecting social disruption. Tracking of I&W during the 2010 Japan FMD epidemic and outbreaks in other Asian countries was selected to illustrate this methodology.

Referenced File
Submitted by hparton on

Event-based biosurveillance is a practice of monitoring diverse information sources for the detection of events pertaining to human health. Online documents, such as news articles on the Internet, have commonly been the primary information sources in event-based biosurveillance. With the large number of online publications as well as with the language diversity, thorough monitoring of online documents is challenging. Automated document classification is an important step toward efficient event-based biosurveillance. In Project Argus, a biosurveillance program hosted at Georgetown University Medical Center, supervised and unsupervised approaches to document classification are considered for event-based biosurveillance.

 

Objective

This paper describes ongoing efforts in enhancing automated document classification toward efficient event-based biosurveillance. 

Referenced File
Submitted by hparton on

Public health and medical research on mass gatherings (MGs) are emerging disciplines. MGs present surveillance challenges quite different from routine outbreak monitoring, including prompt detection of outbreaks of an unusual disease. Lack of familiarity with a disease can result in a diagnostic delay; that delay can be reduced or eliminated if potential threats are identified in advance and staff is then trained in those areas. Anticipatory surveillance focuses on disease threats in the countries of origin of MG participants. Surveillance of infectious disease (ID) reports in mass media for those locations allows for adequate preparation of local staff in advance of the MG. In this study, we present a novel approach to ID surveillance for MGs: anticipatory surveillance of mass media to provide early reconnaissance information.

 

Objective

To present the value of early media-based surveillance for infectious disease outbreaks during mass gatherings, and enable participants and organizers to anticipate public health threats.

Referenced File
Submitted by hparton on

Argus is an event-based surveillance system which captures information from publicly available Internet media in multiple languages. The information is contextualized and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system's users. In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.

Objective

To demonstrate how event-based biosurveillance can be utilized to closely monitor disease emergence in an isolated rural area, where medical information and epidemiological data are limited, toward identifying areas for public health intervention improvements.

Referenced File
Submitted by elamb on
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