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Mass Gathering Surveillance

Description

Public health and medical research on mass gatherings (MGs) are emerging disciplines. MGs present surveillance challenges quite different from routine outbreak monitoring, including prompt detection of outbreaks of an unusual disease. Lack of familiarity with a disease can result in a diagnostic delay; that delay can be reduced or eliminated if potential threats are identified in advance and staff is then trained in those areas. Anticipatory surveillance focuses on disease threats in the countries of origin of MG participants. Surveillance of infectious disease (ID) reports in mass media for those locations allows for adequate preparation of local staff in advance of the MG. In this study, we present a novel approach to ID surveillance for MGs: anticipatory surveillance of mass media to provide early reconnaissance information.

 

Objective

To present the value of early media-based surveillance for infectious disease outbreaks during mass gatherings, and enable participants and organizers to anticipate public health threats.

Submitted by hparton on
Description

The 'Grand Raid de la Reunion' is one of the hardest ultra trails in the world (5,350 competitors in 2012). This one stage race takes place in Reunion Island, a French overseas department in the Indian Ocean. Ultra trails and ultra marathons are intense long-distance running races pushing back human physical abilities' limits. In general terms, studies about these races highlight different severity levels' injuries, from asymptomatic to critical condition [1-4]. No study has yet used syndromic surveillance to study the impact of such sporting events on ED visits. Using a syndromic surveillance approach to monitor sport-related visits could allow an early public health response.

Objective

To estimate the health impact of the 'Grand Raid de la Reunion' (GRR) ultra trail in 2012 on the emergency departments (ED) of Reunion Island.

Submitted by elamb on

Mass gatherings—defined as events attended by a sufficient number of people to strain the planning and response resources of the host state—pose unique surveillance challenges. Attendees can be at greater (or high) risk for injuries due to event activities or volume of people in an unstructured setting. Surveillance can help detect early signs of outbreaks associated with crowding and compromised sanitation.

Submitted by elamb on
Description

Reporting allows for the collection of statistics that show how often disease occurs, which helps researchers identify disease trends and track disease outbreaks. U.S. Navy has a modified list of reportable medical events to accommodate for deployment limiting functions. Reports on all reportable events are submitted to the Naval Disease Reporting System (NDRS). Medical event surveillance is particularly important in the military populations where medical events can have mission-degrading implications and affect troop strength.

Objective

The purpose of the study was to determine whether, through the use of existing electronic laboratory and clinical care databases, it is possible to capture the majority of reportable disease cases, and remove the burden of case finding from the commands through NDRS. Establishment of a more efficient reporting system was proposed to provide more timely disease reporting and aid in active disease surveillance.

Submitted by elamb on
Description

Time series analysis is very popular in syndromic surveillance. Mostly, public health officials track in the order of hundreds of disease models or univariate time series daily looking for signals of disease outbreaks. These time series can be aggregated counts of various syndromes, possibly different genders and age-groups. Recently, spatial scan algorithms find anomalous regions by aggregating zipcode level counts [1]. Usually, public health officials have a set of disease models (for e.g. fever or headache symptom in male adults is indicative of a particular disease). Based on the past experience public health officials track these disease models daily to find anomalies that might be indicative of disease outbreaks. A typical syndromic surveillance system these days will track in the order of 100-200 time series on daily basis using different univariate algorithms like CUSUM, moving average, EWMA, etc.

Let us consider a representative dataset of a state which has 100 zipcodes that monitors 10 syndromes among 3 age groups and 2 genders in emergency rooms. There are a total of 6,000 (100 x 10 x 3 x 2) distinct time series for a particular zipcode, syndrome, age-group and gender. This number already seems too high to monitor daily. Hence most syndromic systems only monitor state level aggregates for all syndromes or a few combinations of syndromes, gender and age-groups.

But most real world disease models are more complex and affect multiple syndromes, or multiple agegroups. We need to analyze more complex streams that aggregate multiple values in the attributes to mine more interesting patterns not seen otherwise. As an example, a massive search could reveal that recently senior female patients having fever and nausea have increased in the north eastern part of the state.

Objective

This paper shows how T-Cubes, a data structure that makes tracking millions of disease models simultaneously feasible, can be used to perform multivariate time series analysis using primitive univariate algorithms. Hence, the use of T-Cube in brute-force search helps identify stronger disease outbreak signals currently missed by the surveillance systems.

Submitted by elamb on
Description

In the spring of 2005, the ISDH began using Electronic Surveillance System for the Early Notification of Community-based Epidemics  (ESSENCE) application to analyze emergency department (ED) chief complaint data for syndromic surveillance purposes.  While granting hospitals and local health departments access to their data through ESSENCE has been desirable since the start of the PHESS project, an aggressive timeline made it necessary to direct all resource capacity toward first establishing hospital ED data connections.  The Marion County Health Department (Indianapolis) was the only LHD in the state with access to its 14 hospitals through ESSENCE.

However, because hospitals and local health departments (except Marion County) did not have access to their data through ESSENCE, any syndromic alert follow-up conducted by the ISDH was accomplished primarily by telephone.   This method, while feasible, was inefficient.  The ISDH felt that alert data follow-up could be greatly facilitated if hospitals and LHDs could view these data through ESSENCE just as the ISDH was doing.

Objective

This paper describes how the Indiana State Department of Health (ISDH) improved response capability by increasing local health department (LHD) and hospital access to syndromic surveillance data as part of the stateís evolving Public Health Emergency Surveillance System (PHESS).

Submitted by elamb on
Description

The lack of a standardized vocabulary for recording CC complicates the collection, aggregation, and analysis of CC for any purpose, but especially for real-time surveillance of patterns of illness and injury. The need for a controlled CC vocabulary has been articulated by national groups and a plan proposed for developing such a vocabulary. To date there has been no comparison of published CC lists.  This study lays the groundwork for a controlled ED CC vocabulary by comparing selected terms from several published ED CC lists.

Objective

The purpose of this study was to compare the most common chief complaints (CC) from a national emergency department (ED) survey, with four published CC lists in order to identify issues relevant to the creation of a controlled ED CC vocabulary.

Submitted by elamb on
Description

Schistosomiasis is a chronic infection caused by flukes belonging to the genus Schistosoma. At least 200 million people, in 74 countries, are infected with the disease and at least 600 million are at risk of infection [1]. Like the majority of the parasitic diseases, schistosomiasis is influenced by human behavior, mainly water use practices and indiscriminate urination and defecation, but also, failure to take advantage of available screening services.

Objective

The purpose of this study was to determine the impact of health education and treatment interventions on the prevalence, intensity and perception of urinary schistosomiasis among school children in three rural communities in Cameroon.

Submitted by elamb on
Description

The existing New York State Department of Health emergency department syndromic surveillance system has used patient’s chief complaint (CC) for assigning to six syndrome categories (Respiratory, Fever, Gastrointestinal, Neurological, Rash, Asthma). The sensitivity and specificity of the CC computer algorithms that assign CC to syndrome categories are determined by using chart review as the criterion standard. These analyses are used to refine the algorithm and to evaluate the effect of changes in the syndrome definitions. However, the chart review (CR) method is labor intensive and expensive. Using an automated ICD9 code-based assignment as a surrogate for chart review could offer a significant cost reduction in this process and allow us to survey a much larger sample of visits.

Submitted by elamb on
Description

The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Typical spatial scan approaches either constrain the search regions to a given shape, reducing power to detect patterns that do not correspond to this shape, or perform a heuristic search over a larger set of irregular regions, in which case they may not find the most relevant clusters. In either case, computation time is a serious issue when searching over complex region shapeso r when analyzing a large amount of data. Analternative approach might be to search over all possible subsets of the data to find the  most relevant pat-terns, but since there are exponentially many subsets, an exhaustive search is computationally infeasible.

Objective

We present a new method of "linear-time subset scanning" and apply this technique to various spatial outbreak detection scenarios, making it computationally feasible (and very fast) to perform spatial scans over huge numbers of search regions.

Submitted by elamb on