Surveillance Systems

Cancer Registration is the systematic collection of data on cancers and the use of such information for action. Population-based cancer registration is not common practice in developing countries. Ghana has had no population-based cancer registry till 2012 when the Kumasi Cancer Registry was established. Established initially as a hospital-based cancer registry, the KsCR has made significant strides in the collection and analysis of data on cancers in Kumasi. We describe the operations of the registry and provide information on data analysis from information collected by the registry for the three year period 2012 to 2014.

Objective

To describe the operations and review data from the Kumasi Cancer Registry for the period 2012 to 2014

Previous research identifies social media as an informal source of near-real time health data that may add value to disease surveillance systems by providing broader access to health data across hard-toreach populations. This indirect health monitoring may improve public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. The Philippines consists of over 7,000 islands and is prone to meteorological (storms), hydrological (floods), and geophysical disasters (earthquakes and volcanoes). In these situations, evacuation centers are used for safety and medical attention and often house up to 50K people each for 2 or more months, sometimes with unclean water sources and improper sanitation. Consequently, these conditions are a perfect venue for communicable disease transmission and have been proposed to cause disease outbreaks weeks after the original disaster occurred. Coined the social media capital of the world1, the Philippines provides a perfect opportunity to evaluate the potential of social media use in disease surveillance.

Objective

To determine the potential of Twitter data as an early warning of a likely communicable disease outbreak following a natural disaster, and if successful, develop an open-source algorithm for use by interested parties.

The sanitary-epidemiological monitoring system in Kazakhstan is passive and statistical in nature. Due to the modern threats, activation and emergence of new and recurring diseases (corona virus, Ebola, etc.) it is vital to transition from current epidemiological surveillance approaches to new prognostic, epi risk probability assessment, and bio risk management technologies, and in addition to urgent response develop preventive measures procedures to minimize the consequences of potential epi outbreaks.

Objective

Development of a concept for sanitary-epidemiological monitoring system reinforcement in Kazakhstan based on the deployed electronic surveillance system, risk assessment and management approach, and establishment of a Situational Center. 

Since 2004, the French syndromic surveillance system Oscour® has been implemented by the national institute for public health surveillance (InVS) and is daily used to detect and follow-up various public health events all over the territory [1]. Beginning with 23 ED in 2004, the coverage and data quality have permanently been increasing until including about 650 ED in August 2015. Initially based on a voluntary participation of ED, a mandatory transmission has been decided in July 2013, with major modification on the structural organization of the data transmission in some regions and on coding practices of the new ED. Besides this juridical context, the system is based on automatically data collection by ED physicians without recording added information for public health surveillance. This represents the main theorical condition to ensure stability and quality, even in case of occurrence of major public health events susceptible to drastically increase the workload [2].

Objective

Identification of the main factors influencing the stability and the quality of the French Emergency departments (ED) syndromic surveillance system.

The CDC defines a foodborne outbreak as two or more people getting the same illness from the same contaminated food or drink. These illnesses are often characterized as gastroenteritis until the causative agent is identified (bacterial or viral). Due to the globally interconnected food distribution system, local foodborne disease outbreaks often have global impacts. Therefore, the rapid detection of a gastroenteritis outbreak is of utmost importance for effective control. Situational awareness is important for early warning or detection of a disease outbreak, and tools that provide such information facilitate mitigation actions by civil/military health professionals. We have developed the Surveillance Window app (SWAP), a web based tool that can be used to help understand an unfolding outbreak. The app matches user input information to a library of historical outbreak information and provides context. This presentation will describe our analysis of global civilian and military gastrointestinal outbreaks and the adaptation of the SWAP to enhance situational awareness in the event of such outbreaks.

Objective

The objectives of this project are to identify properties that influence the progression of an outbreak, evaluate the ability of a property-based algorithm to differentiate between military and civilian outbreaks and different pathogens, and develop a decision support tool to enhance situational awareness during an unfolding outbreak.

As of 2012, 3,400 000 million people (all ages) are living with HIV in Nigeria. The estimated new HIV infections is 260,000 and estimated AIDS death is 240,000.The reported number of adults on ART(Anti-retroviral treatment) was 459,465 and the ART coverage based on WHO guideline was 36%.The number of pregnant women living with HIV who received antiretroviral for preventing motherto-child –transmission was 33,323 and the percentage coverage was 17%. Enugu State has the highest prevalence (6.5%) of HIV/ AIDS in the South East and the fourth in Nigeria.To implement the commitments in the 2011 United Nations Political Declaration on HIV and AIDS and increase progress towards universal access to HIV prevention, treatment, care and support, Nigeria has developed the president’s Comprehensive Response Plan (PCRP). PCRP aims to bridge the current gap in service provision and funding. It assesses needs and gaps, identifies focus areas, and set targets for Prevention of mother to child transmission (PMTCT), ART and HIV Counseling and Testing (HCT) services. We determined the implementation of these preventive services by health care providers in Enugu State.

Objective

• To determine the percentage and trends of newly diagnosed HIV positive pregnant women

• To determine the percentage of pregnant women that are counseled tested with result.

• To determine the percentage and trend in the uptake ART among HIV positive pregnant women.

• To determine the average no of individual that are counseled and tested for HIV.

• To determine the average no of individual that are HIV positive

• To estimate the average no of individual currently on ART, newly started on ART and those enrolled into HIV care.

Population health relies on tracking patients through a continuum of care with data from disparate sources. An assumption is made that all records of a patient from all the sources are connected. As was realized during the process of operationalizing algorithms for population health, not all patient records are connected. Disconnected records negatively impact results: from individual patient care management through population health’s predictive analytics. An enterprise master patient index (EMPI) system can be employed to connect a patient’s records across disparate systems, but it requires comprehensive tuning to maximize the number of connected records. This presentation describes how one large healthcare integrated delivery network tuned their EMPI system to maximize the number of connected patient records across all sources.

The Infectious Disease Epidemiology Section (IDEpi) within the Office of Public Health (LaOPH) conducts syndromic surveillance of emergency departments by means of the Louisiana Early Event Detection System (LEEDS). LEEDS accepts ADT (admit-discharge transfer) messages from participating hospitals, predominately A04 (registration) and A03 (discharge), to obtain symptom or syndrome information on patients reporting to hospital emergency departments. Capturing the data using discharge messages (A03) only could result in a delay in receipt of data by LaOPH, considering the variability in the length of stay of a patient in the ED.

Objective

To explore the difference between the reported date of admission and discharge date using discharge messages (A03), from hospital emergency departments participating in the Louisiana Early Event Detection System (LEEDS.

Nontyphoidal Salmonella, consisting of >2,500 distinct serotypes, is the leading bacterial agent of foodborne illness in the U.S., causing an estimated 1 million infections per year. In NYC, interviews of all case-patients (N≈1,100 annually) are attempted to support outbreak investigation and control. Salmonella clusters in NYC are typically identified either by notification from PulseNet, CDC, or other health departments or by a weekly analysis using the historical limits method. More systematic and timely cluster detection could inform resource prioritization and improve the effectiveness of public health interventions. We initiated daily analyses in May 2015 to detect spatio-temporal clusters by serotype among cases since February 23. In July 2015, an analysis was added to detect purely temporal clusters among cases since May 1.

Objective

To prospectively identify serotype-specific clusters of salmonellosis in New York City (NYC).

CRE are multidrug-resistant bacteria associated with up to 50 percent mortality in infected persons. CRE are increasingly problematic in Illinois healthcare facilities, especially long-term acute care hospitals (LTACHs); therefore, Illinois implemented the eXtensively Drug-Resistant Organism (XDRO) registry (www.xdro. org). Mathematical models have identified patient sharing between healthcare facilities as a mechanism for regional spread, and the importance of each facility within a network can be quantified using social network analysis. Degree centrality is a measure representing the number of facilities with which a facility has shared at least one patient, and hence, a measure of “risk” of receiving a CRE colonized patient. Eigenvector centrality is more sophisticated in that it quantifies how well a given node is connected to other “wellconnected” nodes. We expect that facilities that have high degree and/or eigenvector centrality – and, thus, higher “risk” of encountering a CRE colonized patient – will have higher incidence of CRE, as will facilities that share patients with LTACHs. Understanding facilitylevel characteristics that predict higher CRE rates will enhance the XDRO registry’s usefulness as a surveillance tool.

Objective

To enhance CRE surveillance and communication by incorporating social network measures to quantify patient sharing between facilities.