Impact of Users’ Experience with a Web-Based Reporting Portal on Timeliness and Quality of Reporting

The New York City Department of Health and Mental Hygiene’s (NYC DOHMH) Division of Disease Control (DDC) conducts surveillance of more than 90 specific diseases and conditions and relies on both provider reports and electronic laboratory reports for data. While laboratory reports provide vital laboratory data and represent the majority of the surveillance data that DOHMH receives, they are not always timely or sufficient to confirm a case. Provider reports, in contrast, contain data often not available in laboratory reports and can be more prompt than laboratory reports.

January 21, 2018

Profile: Karachi Health and Demographic Surveillance System of Pakistan (KHDSS)

The Karachi Health and Demographic Surveillance System was set up in year 2003 by the Department of Pediatrics and Child Health of the Aga Khan University, Karachi, Pakistan, in four peri-urban low socioeconomic communities of Karachi and covers an area of 17.6 square kilometers.(Figure 1).

Objective:

January 25, 2018

Comparison of National and Local Syndromic Surveillance Data - Cook County, IL, 2017

In 2005, the Cook County Department of Public Health (CCDPH) began using the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) as an emergency department (ED)-based local syndromic surveillance program (LSSP); 23 (100%) of 23 hospitals in suburban Cook County report to the LSSP. Data are transmitted in delimited ASCII text files (i.e., flat files) and contain a unique patient identifier, visit date and time, zip code, age, sex, and chief complaint. Discharge diagnosis and disposition are optional data elements.

January 21, 2018

Implementation of a Facility Based County Surveillance System Using Epi Info

Surveillance in nursing homes (Enserink et al., 2011) and day care facilities (Enserink et al., 2012) has been conducted in the Netherlands, but is not commonly practiced in the United States (Buehler et al., 2008). Outbreaks of illnesses within these facilities are required to be reported to the Epidemiology Program, however a small fraction of outbreaks reported come from LTCFs. Without regular communication between LTCFs and the Epidemiology Program, it is likely that many outbreaks are going unreported due to lack of awareness of the reporting requirements by facility staff.

January 25, 2018

Correlation of Tweets Mentioning Influenza Illness and Traditional Surveillance Data

The use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source.

January 21, 2018

Evaluation of Syndrome Algorithms for Detecting Pneumonia Emergency Department Visits

The NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements.

January 25, 2018

Enhancing Epidemic Detection Using Syndromic Surveillance and Early Notification Methods

Early Notification Detection Systems have taken a critical role in providing early notice of disease outbreaks. To improve the detection methods for disease outbreaks, many detection methods have been created and implemented. However, there is limited information on the effectively of syndromic surveillance in Thailand. Knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in Thailand.

Objective:

January 21, 2018

Epi Evident: Biosurveillance to Monitor, Compare, and Forecast Disease Case Counts

The Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks.

January 25, 2018

Public Health Decisions Using Point of Care Data from Open Source Systems in Africa

Ministries of Health in Low and Middle Income Countries (LMIC) are making or trying to make public health decisions for infectious disease conditions like HIV using data garnered from sentinel events and disease tracking in the community. The process of gathering and aggregating data for these case-based reports for is, in all too often a cumbersome or paper-based process. The Center for Disease Control (CDC) was interested in prototyping and piloting approaches that could improve the efficiency and reliability of case reports in resource-constrained environments.

January 21, 2018

Are the French SAMU data relevant for health surveillance?

The syndromic surveillance SurSaUD® system developed by Sante© publique France, the French National Public Health Agency collects daily data from 4 data sources: emergency departments (OSCOUR® ED network), emergency general practioners (SOS Medecins network), crude mortality (civil status data) and electronic death certification including causes of death. The system aims to timely identify, follow and assess the health impact of unusual or seasonal events on emergency medical activity and mortality.

January 25, 2018

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