Presented July 27, 2017.
The inferences we make from data can only be as good as the quality of the data; making sure that we are receiving timely, quality data is important. In this presentation, Mark White will describe a number of functions that he has written to perform data quality checks on Kansas emergency department records from NSSP’s BioSense Platform.
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 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.
On October 1, 2015, the number of ICD codes will expand from 14,000 in version 9 to 68,000 in version 10. The new code set will increase the specificity of reporting, allowing more information to be conveyed in a single code. It is anticipated that the conversion will have a significant impact on public health surveillance by enhancing the capture of reportable diseases, injuries, and conditions of public health importance that have traditionally been the target of syndromic surveillance monitoring. For public health departments, the upcoming conversion poses a number of challenges, including: 1) Constraints in allocating resources to modify existing systems to accommodate the new code set, 2) Lack of ICD-10 expertise and training to identify which codes are most appropriate for surveillance, 3) Mapping syndrome definitions across code sets, 4) Limited understanding of the precise ICD-10 CM codes that will be used in the US Healthcare system, and 5) Adjusting for changes in trends over time that are due to transitions in usage of codes by providers and billing systems. To accommodate the ICD-9 to ICD-10 transition, the Centers of Disease Control and Prevention (CDC) partnered with the International Society of Disease Surveillance (ISDS) CoP to form a workgroup to develop the Master Mapping Reference Table (MMRT). This tool maps over 130 syndromes across the two coding systems to assist agencies in modifying existing database structures, extraction rules, and messaging guides, as well as revising established syndromic surveillance definitions and underlying analytic and business rules.
Objective
This roundtable will provide a forum for the syndromic surveillance Community of Practice (CoP) to discuss the public health impacts from the ICD-10-CM conversion, and to support jurisdictional public health practices with this transition. It will be an opportunity to discuss key impacts on disease surveillance and implementation challenges; and identify solutions, best practices, and needs for technical assistance.
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.
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.
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
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.
The EpiCenter syndromic surveillance platform currently uses Java libraries for time series analysis. Expanding the data quality capabilities of EpiCenter requires new analysis methods. While the Java ecosystem has a number of resources for general software engineering, it has lagged behind on numerical tools. As a result, including additional analytics requires implementing the methods de novo.
The R language and ecosystem has emerged as one of the leading platforms for statistical analysis. A wide range of standard time series analysis methods are available in either the base system or contributed packages, and new techniques are regularly implemented in R. Previous attempts to integrate R with EpiCenter were hampered by the limitations of available R/Java interfaces, which were not actively developed for a long time.
An alternative bridge is via the PostgreSQL database used by EpiCenter on the backend. An R extension for PostgreSQL exists, which can expose the entire R ecosystem to EpiCenter with minimal development effort.
Objective To demonstrate the broader analytical capabilities available by making the R language available to EpiCenter reporting