Infectious Disease

GAS pharyngitis affects hundreds of millions of individuals globally each year, and over 12 million seek care in the United States annually for sore throat. Clinicians cannot differentiate GAS from other causes of acute pharyngitis based on the oropharynx exam, so consensus guidelines recommend use of clinical scores to classify GAS risk and guide management of adults with acute pharyngitis. When the clinical score is low, consensus guidelines agree patients should neither be tested nor treated for GAS. A prediction model that could identify very-low risk patients prior to an ambulatory visit could reduce low-yield, unnecessary visits for a most common outpatient condition. We recently showed that real-time biosurveillance can further identify patients at low-risk of GAS. With increasing emphasis on patient-centric health care and the well-documented barriers impeding clinicians’ incorporation of prediction models into medical practice, this presents an opportunity to create a patient-centric model for GAS pharyngitis based on history and recent local epidemiology. We refer to this model as the “home score,” because it is designed for use prior to a physical exam.

Objective

1. To derive and validate an accurate clinical prediction model (“home score”) to estimate a patient’s risk of group A streptococcal (GAS) pharyngitis before a health care visit based only on history and real-time local biosurveillance, and to compare its accuracy to traditional clinical prediction models composed of history and physical exam features. 2. To examine the impact of a home score on patient and public health outcomes.

Zanzibar is comprised primarily of two large islands with a population of 1.3 million. Indoor Residual Spraying (IRS) campaigns, distribution of long-lasting insecticide treated bed nets (LLINs), ensuring treatment medication is available, and use of Rapid Diagnostic Tests (RDTs) have reduced Malaria prevalence from 39% in 2005[1] to less than 1% in 2011-2012. This is the third time Zanzibar has been close to eliminating malaria, but there are serious challenges. These include vector resistance to pyrethroids, the shortlived efficacy of LLINs, and resistance to behavior change. Constant traffic with mainland Tanzania and foreign countries also poses the risk of outbreaks. An effective and sustained surveillance and rapid response system is essential to control outbreaks and optimize interventions.

Objective

This presentation aims to share the results of a six-year effort to use mobile health (mHealth) technology to help eliminate malaria from a well-defined geographic area. This presentation will review the history, technology, results, lessons-learned, and applicability to other contexts.

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL) receives daily (or bi-hourly) data from 184 emergency departments (ED) from around Florida. Additionally, 30 urgent care centers submit daily data to the system. These 214 facilities are grouped together in an acute care data source category. Five to six days after the start of each school year in Florida, ESSENCE-FL shows increased respiratory illness visits in the school aged population. Previous analyses of these data have shown that this increase is a result of increased transmission of the common cold among school children. In early September 2014, during this sustained yearly increase in respiratory visits, reports of more severe infection caused by Enterovirus D68 (EV-D68) in children in other parts of the country began circulating. Public health officials in Florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. As is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness. The task of providing situational awareness during this period was complicated by this outbreak coinciding with the start of the school year.

Objective

To provide situational awareness using Florida’s syndromic surveillance system during a 2014 outbreak of EV-D68 in other regions of the country.

Chikungunya virus disease (CHIK) is a mosquito-borne viral infection currently widespread in the Caribbean with the potential for emergence and endemicity in the U.S. via infected travelers and local mosquito vectors. CHIK disease can be severe and disabling with symptoms similar to dengue. CHIK is not a U.S. nationally notifiable disease and tracking travel-associated and locally acquired cases is currently dependent on voluntary reporting via ArboNET. While ArboNET cases are laboratory confirmed and highly specific, ArboNET is a passive surveillance system where representativeness and timeliness may be lacking. In contrast, submitting an electronic bill following HC services is the most mature and widely used form of eHealth. Providers are highly motivated to submit claims for reimbursement and the eHRC process is ubiquitous in the U.S. HC system. HIPAA-compliant eHRCs from provider offices can be captured in e-commerce and consolidated into electronic data warehouses and used for many purposes including public health surveillance. eHRCs are standardized and each claim contains pertinent person, place, and time information as well as ICD-9 diagnostic codes. IMS Health (IMS) is a global HC information company and maintains one of world’s largest eHealth data warehouses that processes ~1 billion provider office eHRCs annually. IMS consolidates eHRCs from >60% of all U.S. office-based providers from all parts of the U.S. The size and predictability of the eHRC flow into the IMS data warehouse supports projections of national estimates and time trends of conditions of interest.

Objective

This paper describes how high-volume electronic healthcare (HC) reimbursement claims (eHRCs) from providers’ offices can be used to supplement Chikungunya surveillance in the U.S.

Decreasing contact between infectious and susceptible people in community settings may reduce influenza transmission. Examining the temporal relationship between the winter holiday break and seasonal influenza activity can provide insight of alternative contact patterns on influenza spread.

Objective

To explore the relationship between influenza-like illness observed by influenza out-patient network and winter holiday breaks in US.

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.

The purpose of this work was to develop a novel method of estimating the amount of influenza-like illness (ILI) in a population, in near-real time, by using a source of information that is completely open to the public and free to access. We investigated the usefulness of data gathered from Wikipedia to estimate the prevalence of ILI in the United States, using data from the Centers for Disease Control and Prevention (CDC) as well as Google Flu Trends.

Introduction

Each year, there are an estimated 250,000–500,000 deaths worldwide that are attributed to seasonal influenza, with anywhere between 3,000–50,000 deaths occurring in the United States of America (US). In the US, the Centers for Disease Control and Prevention (CDC) continuously monitors the level of influenza-like illness (ILI) circulating in the population. While the CDC ILI data is considered to be a useful indicator of influenza activity, its availability has a known lag-time of between 7–14 days. To appropriately distribute vaccines, staff, and other healthcare commodities, it is critical to have up-to-date information about the prevalence of ILI in a population. To this end, we have created a method of estimating current ILI activity in the US by gathering information on the number of times particular Wikipedia articles have been viewed. Not only is the information held within Wikipedia articles very useful on its own, but statistics and trends surrounding the amount of usage of particular articles, frequency of article edits, region specific statistics, and countless other factors make the Wikipedia environment an area of interest for researchers. Furthermore, Wikipedia makes all of this information public and freely available, greatly increasing and expediting any potential research studies that aim to make use of their data.

Utilization of local surveillance data has been shown to help risk stratify patients presenting to the emergency department presenting with GAS pharyngitis or meningitis. (1, 2) Adolescents frequently present to the emergency department (ED) with symptoms that may be associated with a sexually transmitted infection (STI). (3) When ED providers perceive high local rates of STI and low rates of follow-up, empiric treatment is considered. This strategy may result in unnecessary treatment. Knowledge of the local spatial distribution of adolescents with STIs diagnosed in local pediatric emergency departments EDs may enhance risk stratification and allow targeted testing and/or treatment among future ED patients in whom STI is considered.

Objective

(1) To describe the spatial distribution of adolescents with EDdiagnosed STIs in a large urban area with a high prevalence of STI

(2) To compare census block groups and identify “hot spots” of STI.

 

Influenza is a highly contagious, acute respiratory disease that causes periodic seasonal epidemics and global pandemics[1]. Yunnan Province is characterized by poverty, multi-ethnic, and cross-border movement, which maybe be susceptible of influenza (Fig-1). Finding from spatial patter of ILI will promote to control and prevent the respiratory diseases epidemic

Objective

The purpose of the study was to determine spatial clustering of the spread of influenza like illness (ILI) epidemic in Yunnan province, China with the aim of producing useful information for prevention and control measures.

 

Bovine cysticercosis is a zoonotic foodborne disease caused by Taenia saginata involving cattle as the intermediate host and humans as the final host. Humans are infected by eating raw or undercooked meat of infected cattle. Cattle are infected after grazing on pasture infected by human feces. Disease detection in cattle is performed during post-mortem meat inspection at the slaughterhouse through the identification of cysts in muscle tissue. Cysts develop from a viable stage to a degenerated stage in one to nine months, both stages being visible and distinguishable in cattle muscle. Due to the slow development of cysts and the complexity of cattle movements (up to ten different herds from birth to slaughter in France), there is a strong bias to consider the last farm location before slaughter as the location of infection.

Objective

Spatial analysis of infectious diseases enables identification of areas at high risk for infection, a useful tool for implementation of risk-based surveillance. For chronic diseases, the period between infection and detection can be long and when animal movements are important, identifying the place of infection is difficult. The objective of this study is to propose an innovative approach for spatial analysis that takes into account uncertainty regarding the location where animals were infected. An animal-herd-level weighted analysis was used and applied to bovine cysticercosis in France.