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Seasonality

Influenza causes significant morbidity and mortality, with attendant costs of roughly $10 billion for treatment and up to $77 billion in indirect costs annually. The Centers for Disease Control and Prevention conducts annual influenza surveillance, and includes measures of inpatient and outpatient influenza-related activity, disease severity, and geographic spread. However, inherent lags in the current methods used for data collection and transmission result in a one to two weeks delay in notification of an outbreak via the Centers for Disease Control and Prevention’s website. Early notification might facilitate clinical decision-making when patients present with acute respiratory infection during the early stages of the influenza outbreak.

In the United States, the influenza surveillance season typically begins in October and continues through May. The Utah Health Research Network has participated in Centers for Disease Control and Prevention’s influenza surveillance since 2002, collecting data on outpatient visits for influenza-like illness (ILI, defined as fever of 100F or higher with either cough or sore throat). Over time, Utah Health Research Network has moved from data collection by hand to automated data collection that extracts information from discrete fields in patients’ electronic health records.

We used statistical process control to generate surveillance graphs of ILI and positive rapid influenza tests, using data available electronically from the electronic health records.

Objective

The objective of this study is to describe the use of point-ofcare rapid influenza testing in an outpatient, setting for the identification of the onset of influenza in the community

Referenced File
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Quantifying the spatial-temporal diffusion of diseases such as seasonal influenza is difficult at the urban scale for a variety of reasons including the low specificity of the extant data, the heterogenous nature of healthcare seeking behavior and the speed with which diseases spread throughout the city. Nevertheless, the New York City Department of Health and Mental Hygiene’s syndromic surveillance system attempts to detect spatial clusters resulting from outbreaks of influenza. The success of such systems is dependent on there being a discernible spatial-temporal pattern of disease at the neighborhood (sub-urban) scale.

We explore ways to extend global methods such as serfling regression that estimate excess burdens during outbreak periods to characterize these patterns. Traditionally, these methods are aggregated at the national or regional scale and are used only to estimate the total burden of a disease outbreak period. Our extension characterizes the spatial-temporal pattern at the neighborhood scale by day. We then compare our characterizations to prospective spatial cluster detection efforts of our syndromic surveillance system and to demographic covariates.

 

Objective

To develop a novel method to characterize the spatial-temporal pattern of seasonal influenza and then use this characterization to: (1) inform the spatial cluster detection efforts of syndromic surveillance, (2) explore the relationship of spatial-temporal patterns and covariates and (3) inform conclusions made about the burden of seasonal and pandemic influenza. 

Referenced File
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The burden of asthma on the youngest children in Boston is largely characterized through hospitalizations and self-report surveys. Hospitalization rates are highest in Black and Hispanic populations under age five. A study of children living in Boston public housing showed significant risk factors, including obesity and pest infestation, with less than half of the study population being prescribed daily medication.

Information on asthma visits for children 5 years old or younger was requested by the Boston Public Health Commission Community Initiatives Bureau. The information is being used to establish a baseline for an integrated Healthy Homes Program that includes pest management and lead abatement. There is limited experience in using syndromic surveillance data for chronic disease program planning.

 

Objective

The objective of this study is to report on the use of syndromic surveillance data to describe seasonal patterns of asthma and health inequities among Boston residents, age five and under.

Referenced File
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As the electronic medical record (EMR) market matures, long-term time series of EMR-based surveillance data are becoming available. In this work, we hypothesized that statistical aberrancy-detection methods that incorporate seasonality and other long-term data trends reduce the time required to discover an influenza outbreak compared with methods that only consider the most recent past.

Referenced File
Submitted by teresa.hamby@d… on

Accurately assigning causes or contributing causes to deaths remains a universal challenge, especially in the elderly with underlying disease. Cause of death statistics commonly record the underlying cause of death, and influenza deaths in winter are often attributed to underlying circulatory disorders. Estimating the number of deaths attributable to influenza is, therefore, usually performed using statistical models. These regression models (usually linear or poisson regression are applied) are flexible and can be built to incorporate trends in addition to influenza virus activity such as surveillance data on other viruses, bacteria, pure seasonal trends and temperature trends.

 

Objective

Mortality exhibits clear seasonality mainly caused by an increase in deaths in the elderly in winter. As there may be substantial hidden mortality for a number of common pathogens, we estimated the number of elderly deaths attributable to common seasonal viruses and bacteria for which robust weekly laboratory surveillance data were available.

Referenced File
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Monitoring trends of respiratory illnesses via syndromic surveillance in SC is performed on a daily basis. SC Syndromic Surveillance primarily utilizes emergency department data, and provides situational awareness regarding broad syndrome categories among hospitals in the state. Respiratory illnesses represent a significant public health burden, causing the second highest number of outbreaks reported in SC. Since syndromic surveillance can potentially serve as an earlier indicator of outbreaks,1 it is beneficial to assess seasonality of respiratory illnesses to identify illness clusters early to mobilize a rapid response.

Objective

To assess the temporal patterns of respiratory illnesses in South Carolina (SC) using syndromic surveillance emergency department (ED) data.

Submitted by elamb on

Calls to NHS Direct (a national UK telephone health advice line) which may be indicative of infection show marked seasonal variation, often peaking during winter or early spring. This variation may be related to the seasonality of common viruses. There is currently no routine microbiological confirmation of the cause of illness in NHS Direct callers. Modelling trends in NHS Direct syndromic call data against laboratory data may help by attributing the likely cause of these calls the and surveillance ‘signals’ generated by syndromic surveillance.

Multiple linear regression has been used previously to estimate the contribution of rotavirus and RSV to hospital admission for infectious intestinal disease and lower respiratory tract infections respectively. We applied a similar regression model to NHS Direct syndromic surveillance data and laboratory reports.

 

Objective

To provide weekly estimates of the proportions of NHS Direct respiratory calls attributable to common infectious disease pathogens.

Referenced File
Submitted by elamb on

Influenza is a significant public health problems in the US leading to over one million hospitalizations in the elderly population (age 65 and over) annually. While influenza preparedness is an important public health issue, previous research has not provided comprehensive analysis of season-by-season timing and geographic shift of influenza in the elderly population. These findings fail to document the intricacies of each unique influenza season, which would benefit influenza preparedness and intervention. The annual harmonic regression model fits each season of disease incidence characterized by its own unique curve. Using this model, characteristics of the seasonal curve for each state and each season can be compared. We hypothesize that travelling waves of influenza in the 48 contiguous states differ dramatically in each influenza season.

 

Objective

In surveillance it is imperative that we know when and where a disease first begins. The objective of this study was to examine trends in traveling waves of influenza in the US elderly population. Preparedness for influenza is an important yet difficult public health goal due to variability in annual strains, timing, and shift of the influenza virus. In order to better prepare for influenza epidemics, it is important to assess seasonal variation across individual influenza seasons on a state-by-state basis. This approach will lead to effective interventions especially for susceptible populations such as the elderly.

Referenced File
Submitted by elamb on

The 2003-2004 influenza season was notable for the early, intense and widespread circulation of a Type A drift variant and a resulting rush on vaccine followed by an abrupt decrease in activity by mid-January. By contrast, the 2004-2005 influenza season began with a national vaccine shortage preceding any influenza activity with the resulting need for close monitoring of influenza activity.

The Connecticut Department of Public Health developed its first syndromic surveillance system in September 2001 to monitor for possible bioterrorism events and emerging infections. This system, known as the Hospital Admissions Surveillance System, receives daily reports from all 32 Connecticut acute care hospitals on their total unscheduled admissions in various diagnostic/syndromic categories. Information from one category, pneumonia admissions, has been tracked throughout the last four years as an indicator of influenza activity. The information has been utilized to supplement data from laboratory-confirmed influenza testing. The contrasts between the 2003-04 and 2004-05 influenza seasons provided an opportunity to further examine the specificity of changes in pneumonia admissions as an index of severe influenza activity.

 

Objective

This paper examines the continued usefulness through the 2004-05 influenza season of a hospital admissions-based syndromic surveillance system as a supplement to laboratory surveillance to monitor severe influenza.

Referenced File
Submitted by elamb on

Syndromic surveillance using over the counter (OTC) sales has been shown to provide earlier signals of diarrheal and respiratory disease outbreaks than hospital diagnoses. Under normal circumstances, sales patterns of OTC sales related to gastrointestinal illness (GI) are high in the winter and low in the summer. The Canadian laboratory-based surveillance system that provides weekly counts of reportable bacterial, parasitic and viral isolates by province, has shown that bacterial and parasitic infections tend to be higher in summer and early fall, whereas viral infections (particularly Norovirus and Rotavirus) appear to peak in winter and spring. This suggests that the OTC sales reflect underlying community viral infections rather than bacterial or parasitic infections. If OTC sales are to be considered for use in syndromic surveillance of community GI, the nature of this relationship needs to be clarified. The main objective of this study was to compare temporal distributions of GI-related OTC sales to laboratory-isolate patterns of bacterial, parasitic and viral cases of human GI infections.

 

Objective

To assess if OTC sales of GI related medications are associated with temporal trends of reportable community viral, bacterial and parasitic infections.

Referenced File
Submitted by elamb on
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