Surveillance Systems

Processing free-text clinical information in an electronic medical record (EMR) may enhance surveillance systems for early identification of ILI outbreaks. However, processing clinical text using NLP poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems.

Objective

To review the natural language processing (NLP) and technical challenges encountered in an automated influenza-like illness (ILI) surveillance system.

The vaccine preventable diseases (VPDs) of measles and diphtheria in India were responsible for 47% of global measles mortality and 20% of global diphtheria mortality in 2010. We evaluated the VPD surveillance system of Delhi, focusing on measles and diphtheria.

Objective

The specific objective was to evaluate the VPD surveillance system of Delhi, focusing on measles and diphtheria.

Histoplasmosis is an infectious disease caused by a fungus called Histoplasma capsulatum. Fungal spores are found in the soil, mostly associated with bird and bat droppings, and if inhaled can cause lung infection. Histoplasmosis is a reportable disease in Michigan and a case definition was implemented in 2007. Cases are reported into the Michigan Disease Surveillance System (MDSS), a web-based electronic database, and investigated by local health departments (LHD). An evaluation of the histoplasmosis surveillance system was conducted.

In Africa, approximately 13 million cases of measles and 650,000 deaths occur annually, with sub-Saharan Africa having the highest morbidity and mortality (1). Measles infection is endemic in Nigeria and has been documented to occur all year round despite high measles routine and supplemental immunisation coverage (2,3). The frequent outbreaks of Measles in Kaduna State prompted the need for the reevaluation of the Measles case-based surveillance system.

Objective

To evaluate the case-based Measles surveillance system in Kaduna State of Nigeria and identify gaps in its operations.

Each year several thousands contract the seasonal flu, and it is estimated that these viruses are responsible for the deaths of over six thousand individuals [1]. Further, when a new strain is detected (e.g. 2009), the result can be substantially more dramatic [2]. Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data [3]. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two types of multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California.

Objective

Evaluate utility of point of need diagnostic tests in relationship to current standard influenza detection methods.

National Influenza Sentinel Surveillance (NISS) was established in Nigeria in 2006 to monitor influenza occurrence in humans in Nigeria and provide a foundation for detecting outbreaks of novel strains of influenza. Surveillance for influenza-like illness (ILI) and severe acute respiratory infection (SARI) is carried out in 4 sentinel sites. Specimens and epidemiological data are collected and transported 4 days a week from the sentinel sites to the National Influenza Reference Laboratory. At the laboratory, they are tested for influenza A and B viruses and further subtyped if positive for influenza A virus.

Objective

To assess the performance of the surveillance system and identify factors affecting the performance.

In Michigan, both presentations of legionellosis, Pontiac Fever (PF) and Legionnaires’ Disease (LD), are reportable through the Michigan Disease Surveillance System (MDSS), a web-based electronic database. Legionella pneumophila serogroup 1 is responsible for 5090% of cases.1,2 Several diagnostic tests are available with varying sensitivities and specificities. Urinary Antigen testing (UAg) is the most commonly used test but only reliably detects L. pneumophila-1. Culturing is the gold standard test but is limited by antibiotic interference, technical expertise, and time.3 The purpose of this study was to evaluate Michigan’s legionellosis surveillance system and to determine if diagnostic methods influenced case distribution.

Objective

To describe the strengths and weaknesses of Michigan’s legionellosis surveillance system and the influence of diagnostic methods on the temporal and geographic distribution of legionellosis cases in Michigan.

Evaluation of a public health surveillance system is one of the major outputs of the field attachment of the Nigeria Field Epidemiology and Laboratory Training Programme.To conduct this activity, the HIV/AIDS surveillance system in Enugu State, Nigeria was evaluated. The evaluation was conducted from February to March 2014.The objectives of the evaluation were to describe the attributes and process of operation of HIV/AIDS surveillance system in Enugu State, determine if the set objectives for establishing HIV/ AIDS surveillance are being met or not, determine the efficiency and effectiveness of the HIV/AIDS surveillance system and to make appropriate recommendations for improving the surveillance system.

Objective

• To determine the public health importance and relevance of the surveillance system.
• To describe the process of operation and purpose of the system and assess its key attributes.
• To determine the effectiveness and efficiency of the surveillance system.
• To make appropriate recommendations to stakeholders for its improvement.

Hepatitis C is a nationally notifiable viral infection that occurs as a result of parenteral contact with infected body fluids. An estimated 3.5 million persons are currently infected with HCV.1 Infection status is divided into acute (short-term, onset within 6 month of exposure) and chronic (long-term). For most people (75-85%), acute HCV infection leads to chronic infection.2 Those with chronic infection remain relatively asymptomatic until the infection becomes severe enough to be recognized or the infected individual is screened for infection with hepatitis C. Major causes of morbidity and mortality associated with HCV are liver cirrhosis and hepatocellular carcinoma. Treatment is available, but it is expensive and not recommended for some vulnerable populations, such as those with ongoing injection drug use (IDU), who account for the majority of new HCV infections in the United States.3-5 Washington State records cases of both acute and chronic HCV infection, but the system is fragmented.

Objective

To evaluate the surveillance system for hepatitis C virus in Washington State using the Centers for Disease Control and Prevention guidelines for evaluating public health surveillance systems. Based on the findings of the evaluation, recommendations will be made for changes in practice.

Infectious disease remains costly in human and economic terms. Effective and timely disease surveillance is a critical component of prevention and mitigation strategies. The limitations of traditional disease surveillance systems have motivated new techniques based upon internet data sources such as search queries and social media. However, 4 challenges remain before internet-based disease surveillance models can be reliably integrated into an operational system: openness, breadth, transferability, and forecasting. We evaluated a new data source, Wikipedia access logs, in these 4 challenges for global disease surveillance and forecasting

Objective

To explore the use of Wikipedia as a data source for disease surveillance.