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Infectious Disease

Traditional public health practice has relied on public health surveillance of disease to detect outbreaks in an effort to mitigate their effects. Often the earlier an outbreak is detected, the greater the mitigation of its effects. The logical extension of this relationship is to predict outbreaks before they occur. A predictive model for an emerging infectious disease would forecast, when and where an outbreak of a given disease will occur, well before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases and is still in their infancy.

Objective

This paper addresses the problem of predicting outbreaks of diseases of military importance in a chosen region of the world, one to several months in advance.

Referenced File
Submitted by uysz on

The United States outpatient Influenza-like Illness Surveillance Network (ILINet) is one of the five systems used for influenza surveillance in the United States. In Pennsylvania, ILINet providers are asked to report, every Monday, the total number of patients seen for any cause, and the number of patients with influenza-like illness (ILI) by age group. In order to encourage timely reporting, weekly reminders along with a data summary were sent to all sentinel providers postoutbreak recognition. Through the study period, recruitment of new sentinel sites was done through local health departments, health alerts, and training sessions. Sentinel providers were not restricted from submitting specimens to the state lab before and after the outbreak, whereas non sentinel providers had strict restrictions.

Objective

The objective of this study is to describe changes in influenza-like illness (ILI) surveillance, eight weeks before and after the 2009 A/H1N1 pandemic influenza outbreak. We examined changes in provider recruitment, composition, reporting of ILI, and we characterize ILI data in terms of timeliness, and ILI baselines by type of sentinel provider.

Referenced File
Submitted by teresa.hamby@d… on

GI disease outbreaks can be focal (for example, restaurant associated), generalized (for example, seasonal rotavirus increases) or intermediate (for example, widely disseminated contaminated commercial products). Health departments (HDs) are commonly notified of focal outbreaks by passive reporting, whereas generalized outbreaks in non-institutional settings are seldom reported as clusters. Intermediate outbreaks are often detected via laboratory testing, which may be subjected to backlogs and delays. Healthcare systems routinely collect in EMRs clinical data related to GI disease, such as ambulatory care diagnoses, that could be exploited for surveillance. Multiple syndromic and laboratory data sources could potentially be used to prospectively detect generalized and intermediate GI disease outbreaks for situational awareness and possible epidemiological investigation.

Objective

To identify which syndromic and laboratory-based data streams from electronic medical records (EMRs) may be used to detect gastrointestinal (GI) disease outbreaks in a timely manner.

Referenced File
Submitted by teresa.hamby@d… on

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.

Submitted by elamb on

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. 

Submitted by elamb on

In 2012, an outbreak of Mycobacterium chelonae infections in tattoo recipients in Rochester, NY was found to be associated with premixed tattoo ink contaminated before distribution.1 In May 2012, a case of M. chelonae was reported in a New York City (NYC) resident who received a tattoo with ink alleged to have been diluted with tap water. When a second case of M. chelonae in a tattoo recipient was reported in March 2013, an investigation was initiated. M. chelonae is not reportable in NYC other than in clusters reported by providers or laboratories. To determine if there were additional tattoo-associated M. chelonae infections, we searched for cases using NYC ED syndromic surveillance.

Objective

To investigate tattoo-associated skin infections due to Mycobacterium chelonae using Emergency Department (ED) syndromic surveillance.

Submitted by elamb on

The 122 Cities Mortality Reporting System (CMRS) has been used for pneumonia and influenza monitoring in the U.S. since the early 20th century. The 122 CMRS is regarded as the timeliest source of mortality data, with the majority of deaths being reported to the system within two weeks. However, while it excels at timeliness it lacks detail, accuracy and completeness. Deaths are counted during the week that the death certificate was filed and not during the week in which the death occurred and the system only covers approximately 25% of the U.S. population. Also, while the standard case definition for 122 CMRS is a death in which pneumonia or influenza is listed anywhere on the death certificate; not all sites follow this definition (i.e. some sites only use pneumonia or influenza listed only as the underlying cause of death) [1]. 

Objective

To increase the accuracy, completeness, and detail of data as well as decrease the resources needed to conduct pneumonia and influenza mortality surveillance in the U.S.

Submitted by elamb on

Pandemic 2009 H1N1 influenza and recent H7N9 influenza outbreaks made the public aware of the threat of influenza infection. In fact, annual influenza epidemic caused heavy disease burden and high economic loss around the world [1, 2]. Although the virological surveillance provided the high sensitivity and specificity for testing results, the timeliness and the cost of the test were not feasible for extensive public health surveillance. In addition, traditional sentinel physician surveillance also encountered many challenges such as the representativeness and reporting bias. The seamless surveillance system without extra labor reporting would be the ideal approach. Taiwan had as high as 99% of health insurance coverage. The real-time monitoring of the ILI clinical visits in the communities could reflect the severity of influenza epidemics. In this study, we used an innovative two-stage approach for detecting aberrations during 2009 pandemic influenza in Taiwan.

Objective

This study proposed a two-stage approach for early detection of aberrations of influenza-like illness (ILI) using the small-area based claim data of outpatient and emergency room visit.

Submitted by elamb on

Clostridium difficile (CD) is an important cause of antibiotic and hospital-associated infection. This preventable infection also plays a major role in hospital readmissions, mostly in the elderly, leading to the CMS implementing rules to penalize hospitals with higher rates, in the Hospital Value Based Purchasing Program1.

Objective

To profile the demographic characteristics of the Medicare Advantage(MA) population with this infection and analyze trends in readmissions, mortality, emergency room(ER) visits and observation room(OR) stays from 2008-2011.

Submitted by elamb on
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