This paper describes an integrated modeling, sensing, and information system for forecasting atmospheric dust episodes and monitoring PM2.5 and PM10 concentrations that aggravate respiratory diseases in the American southwest.
To highlight the key role of Emergency Department syn-dromic surveillance in linking acute care and public health, thus enabling collaborative detection, monitoring and management of a local food borne outbreak.
Poison control centers (PCCs) provide a new source of real-time symptom data that could enhance surveillance systems for foodborne disease outbreaks (FBDOs) through more timely public health department interventions. PCCs provide treatment advice to callers with suspected foodborne illnesses before they seek medical care. The Arizona Poison and Drug and Information Center (APDIC) and the Pima County Health Department (PCHD) are currently evaluating the usefulness of the APDIC’s data collection and triage system to provide early detection of FBDOs in Pima County. Our previous study found that PCC callers with a diarrheal/gastrointestinal syndrome were not duplicative of the cases investigated by PCHD, suggesting that they represent two independent data sets. Evaluating the usefulness of a syndromic surveillance system in terms of its impact on public health is consistent with the CDC’s objectives for improving surveillance. Systems that identify too many cases may overwhelm a health department’s surveillance ability, while too few cases may prevent effective identification of outbreaks.
Objective
This study was designed to test the use of high disease transmission risk criteria in callers to a regional poison control center meeting a predefined case definition for diarrheal/gastrointestinal syndrome as part of an active surveillance program reporting to a county public health department.
Malaria, major leading cause of morbidity and mortality in third world countries has been successfully eliminated from Jamaica since 1965. This, however, is being constantly challenged by lack of sustained vector control activities increased movement of global travellers to and from endemic countries to Jamaica given that the presence of vector “anopheles mosquitoes” that transmit malaria parasites. On December 2006 the first locally transmitted case of malaria was identified in Kingston, the capital city of Jamaica. Due to the impending threat to the country’s economy, such as travel advisory as Jamaica’s main foreign income comes from tourism especially in the western Jamaica, and to health care system. The Ministry of Health stepped up the prevention and control of malaria program. The objectives of the program are (a) early detection of cases and (b) prompt treatment of cases identified.
Objective
This paper evaluates the effectiveness of “active fever surveillance” during malaria outbreak (from December 2006 to June 2007) in western Jamaica.
Methods for locating spatial clusters of diseases are typically variations of the circular scan statistic method. They restrict the number of potential clusters by considering all circular, rectangular, or elliptical regions, and then apply a likelihood ratio test to evaluate the statistical significance of each potential cluster. Because disease outbreaks may have variable shapes, there has been recent interest in developing methods to detect irregularly-shaped clusters. Starting with a neighborhood graph of the administrative regions in the study area, certain sub-graphs are evaluated. These include all connected subgraphs within a circular window and sub-graphs of the minimum spanning tree of a weighted neighborhood graph formed by deleting one edge. These methods restrict the maximum cluster size or identify large clusters having greater likelihood ratios than true clusters in the data, suggesting a limitation of using the likelihood ratio to detect arbitrarily-shaped clusters.
Objective
A method for detecting spatial clusters of diseases of any shape based on the Euclidean minimum spanning tree is described and compared to the circular scan statistic.
Hospital syndromic surveillance data may be a useful tool in detecting increases in influenza-like-illness (ILI) and for monitoring seasonal trends or pandemic activity on a local level. A previous comparison of hospital syndromic surveillance data with ILI surveillance data manually abstracted from emergency department notes revealed that the general respiratory category performed better than symptomspecific subcategories. However, only about half of all patients hospitalized for influenza meet the ILI criteria defined as fever and either cough or sore throat. Hospital discharge data are used retrospectively to determine disease burden, but is not of use for acute monitoring due to the substantial lag time. Knowing how accurately admission data reflect discharge data can assist with interpretation of real or near-real time data streams commonly used in syndromic surveillance systems.
Objective
Timely unplanned hospital admissions data in a general respiratory syndrome category and/or with a pneumonia or influenza admission diagnosis are compared with hospital discharge data to determine accuracy for prediction of influenza disease burden.
Accurate and precise estimation of disease rates for a given population during a specified time frame is a major concern for public health practitioners and researchers in biosurveillance. Many diseases follow distinct patterns; incidence and prevalence of many diseases increase approximately exponentially with age, including many cancers, respiratory infections, and gastroenteritis. With increasing demographic information available in biosurveillance systems leading to more complex and comprehensive disease databases, seeking concise and informative summary measures of disease burden over space and time is becoming more critical for public health surveillance. In this paper we present two summary measures of disease burden in the elderly that simultaneously reflect disease dynamics and population characteristics.
Objective
To better estimate disease burden in the elderly population we illustrate an approach—the Slope Intercept Modeling for Population Linear Estimation (SIMPLE) method—that summarizes age-specific disease rates in the 65+ population using the observed exponential increase in disease rates with age in this dynamic and rapidly growing population subgroup.
Sickness absence is particularly pronounced within health care organizations where job demands and work environment expose workers to an increased risk of illness and injury, potentially leading to an inability to attend work. Health Care Workers (HCWs), especially nurses who are primarily responsible for front-line patient care, are at high risk of acquiring infections from direct patient contact. In addition, there is greater risk of exposure to contaminated human blood and body fluids.
Objective
1) To identify and describe Occupational Health visits (overall and specific conditions) among full-time Kingston General Hospital employees, according to frequency, duration, workplace variables and seasonality. 2) To consider the association between absenteeism and HCW exposure risk to infectious diseases based on a proxy variable defining level of patient contact. 3) To examine the potential for integration of this occupational health data stream into an existing Emergency Department Syndromic Surveillance system.
Our toolkit adds statistical trend analysis, interactive plots, and kernel density estimation to an existing spatio-temporal visualization platform. The goal of these tools is to provide both a quick assessment of the current syndromic levels across a large area and then allow the analyst to view the actual data for a specific region or hospital over a period of time along with an indication as to whether or not a given data point is statistically significant. The sample data used for this toolkit come from over 70 emergency rooms throughout the state of Indiana.
Objective
This paper presents a toolkit designed to aid in the assessment of disease outbreak by visualizing spatiotemporal trends and interactively displaying detailed statistical data.
Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC is a Bayesian network disease outbreak detection system that models 12 diseases. A system that looks only at each day's data might signal an outbreak one day and not signal it the next. Cooper et al. obtained such results when evaluating the ability of PC to detect a laboratory validated outbreak of influenza. We hypothesized that temporal modeling would attenuate this problem.
Objective
A temporal method for outbreak detection using a Bayesian network is presented and evaluated.