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Data Analysis

Description

The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) receives daily ED data from 49 of NYC’s 52 hospitals, representing approximately 95% of ED visits citywide. Chief complaint (CC) is categorized into syndrome groupings using text recognition of symptom key-words and phrases. Hospitals are not required to notify the DOHMH of any changes to procedures or health information systems (HIS). Previous work noticed that CC word count varied over time within and among EDs. The variations seen in CC word count may affect the quality and type of data received by the DOHMH, thereby affecting the ability to detect syndrome visits consistently.

Objective

To identify changes in emergency department (ED) syndromic surveillance data by analyzing trends in chief complaint (CC) word count; to compare these changes to coding changes reported by EDs; and to examine how these changes might affect the ability of syndromic surveillance systems to identify syndromes in a consistent manner.

Submitted by teresa.hamby@d… on
Description

The Global Public Health Intelligence Network is a non-traditional all-hazards multilingual surveillance system introduced in 1997 by the Government of Canada in collaboration with the World Health Organization.1 GPHIN software collects news articles, media releases, and incident reports and analyzes them for information about communicable diseases, natural disasters, product recalls, radiological events and other public health crises. Since 2016, the Public Health Agency of Canada (PHAC) and National Research Council Canada (NRC) have collaborated to replace GPHIN with a modular platform that incorporates modern natural language processing techniques to support more ambitious situational awareness goals.

Objective:

To rebuild the software that underpins the Global Public Health Intelligence Network using modern natural language processing techniques to support recent and future improvements in situational awareness capability.

Submitted by elamb on
Description

National initiatives, such as Meaningful Use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. However, electronic reporting has also lead to significant increases in the number of cases reported to public health. In order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring. To meet this challenge, the Utah Department of Health (UDOH)’s Disease Control and Prevention Informatics Program (DCPIP) has developed the Electronic Message Staging Area (EMSA). EMSA is a system capable of automatically filtering, processing, and evaluating incoming electronic laboratory reporting (ELR) messages for relevance to public health, and entering those laboratory results into Utah’s integrated disease surveillance system (UT-NEDSS) without impacting the overall efficiency of UT-NEDSS or increasing the workload of epidemiologists.

Objective:

The objective of this abstract is to illustrate how the Utah Department of Health processes a high volume of electronic data in an automated way. We do this by a series of rules engines that does not require human intervention.

Submitted by elamb on
Description

Twelve years into the 21st century, after publication of hundreds of articles and establishment of numerous biosurveillance systems worldwide, there is no agreement among the disease surveillance community on most effective technical methods for public health data monitoring. Potential utility of such methods includes timely anomaly detection, threat corroboration and characterization, follow-up analysis such as case linkage and contact tracing, and alternative uses such as providing supplementary information to clinicians and policy makers. Several factors have impeded establishment of analytical conventions. As immediate owners of the surveillance problem, public health practitioners are overwhelmed and understaffed. Goals and resources differ widely among monitoring institutions, and they do not speak with a single voice. Limited funding opportunities have not been sufficient for cross-disciplinary collaboration driven by these practitioners. Most academics with the expertise and luxury of method development cannot access surveillance data. Lack of data access is a formidable obstacle to developers and has caused talented statisticians, data miners, and other analysts to abandon the field. The result is that older research is neglected and repeated, literature is flooded with papers of varying utility, and the decision-maker seeking realistic solutions without detailed technical knowledge faces a difficult task. Regarding conventions, the disease surveillance community can learn from older, more established disciplines, but it also poses some unique challenges. The general problem is that disease surveillance lies on the fringe of disparate fields (biostatistics, statistical process control, data mining, and others), and poses problems that do not adequately fit conventional approaches in these disciplines. In its eighth year, the International Society of Disease Surveillance is well positioned to address the standardization problem because its membership represents the involved stakeholders including progressive programs worldwide as well as resource-limited settings, and also because best practices in disease surveillance is fundamental to its mission. The proposed panel is intended to discuss how an effective, sustainable technical conventions group might be maintained and how it could support stakeholder institutions.

Objective

The panel will present the problem of standardizing analytic methods for public health disease surveillance, enumerate goals and constraints of various stakeholders, and present a straw-man framework for a conventions group.

 

Submitted by Magou on
Description

The frequency of disease outbreaks varies as a result of complex biological processes. Analysis of these frequencies can reveal patterns that can serve as a basis for predictions.

Objective:

The goal of this study was to identify the periodicity of seven zooanthroponoses in humans, and set epidemic thresholds for future occurrences.

Submitted by elamb on
Description

Syndromic surveillance is the real-time collection and interpretation of data to allow the early identification of public health threats and their impact, enabling public health action. Statistical methods are used in syndromic surveillance to identify when the activity of indicator ‘signals’ have significantly increased. A wide range of techniques have been applied to syndromic data internationally. As part of the preparation for the 2012 Olympics Public Health England expanded its syndromic surveillance service. As new syndromic systems were introduced, statistical methods were developed and applied for each system, tailored to the particular system challenges at the time, e.g. a lack of historical data, and regular changes to geographical coverage.

Objective

This paper describes the design and application of a new statistical method for real-time syndromic surveillance, used by Public Health England.

Submitted by teresa.hamby@d… on
Description

Regional disease surveillance as well as data transparency and sharing are the global trend for mitigating the threat of infectious diseases. The WHO has already played a leading role in FluNet (http:// www.who.int/influenza/gisrs_laboratory/flunet/en/ ) and DenguNet (http://www.who.int/csr/disease/dengue/denguenet/en/). However, the enterovirus-related infections which caused a high disease burden for pre-school children in South-East Asian regions over the last two decades still lack a comprehensive surveillance system in the region [1]. If the spreading pattern and a possible alert mechanism can be identified and set up, it will be beneficial for controlling hand, foot and mouth disease (HFMD) epidemics in East Asia. In some research findings, the transmission of HFMD was correlated with temperature, relative humidity, wind speed, precipitation, population density and the periods in which schools were open [2]. A delayed temporal trend was also found with the increase in latitude [3,4] . In this study, we tried to apply publicly available weekly surveillance data in Japan, Taiwan and Singapore to evaluate the spatio-temporal evolution of HFMD epidemics and how the weather conditions affect the HFMD epidemics.

Objective

Enterovirus epidemics, especially affecting young children, have occurred in South-East Asia every year. If the epidemic periods are inter-correlated among different areas, early warning signals could be issued to prevent or reduce the severity of the later epidemics in other areas. In this study, we integrated the available surveillance and weather data in East Asia to elucidate possible spatio-temporal correlations and weather conditions among different areas from low to high latitude.

Submitted by Magou on
Description

Health surveillance systems provide important functionalities to detect, monitor, respond, prevent, and report on a variety of conditions across multiple owners. They offer important capabilities, with some of the most fundamental including data warehousing and transfer, descriptive statistics, geographic analysis, and data mining and querying. We observe that while there is significant variety among surveillance systems, many may still report duplicative data sources, use basic forms of analysis, and provide rudimentary functionality.

Objective

To identify analytic gaps and duplication across U.S. government, international agencies, non-profit and academic health surveillance systems, programs, and initiatives in four areas: Analytics, Data Sources, Statistics, and System Requirements.

 

Submitted by Magou on
Description

According to world health organization report 2011, coronary artery diseases are the number one cause of death globally: more people die annually from coronary artery diseases than from any other cause. An estimated 17.3 million people died from coronary artery diseases in 2008, representing 30% of all global deaths. Of these deaths, an estimated 7.3 million were due to coronary heart disease and 6.2 million were due to stroke. Low- and middle-income countries are disproportionally affected: over 80% of coronary artery diseases deaths take place in low- and middle-income countries and occur almost equally in men and women. Populations living in low and middle income countries are exposed to more risk factors associated with coronary artery disease as well as other non-communicable diseases and are less exposed to prevention efforts than people in high income countries.

Objective

To investigate the prevalence of cardiovascular risk factors among patients undergoing elective Coronary Artery Bypass Graft surgery (CABG) in Karachi, Pakistan.

 

Submitted by Magou on