Respiratory

Within the UK, previous syndromic surveillance studies have used statistical estimation to describe the activity of respiratory pathogens. The Emergency Department Syndromic Surveillance System (EDSSS) was initially developed in preparation of the London 2012 Olympic and Paralympic Games and has continued as a standard surveillance system, with expanding coverage across England and Northern Ireland. All reporting to this system is completely passive, with no extra work required within the ED. The data collection includes the diagnosis for each attendance, where available, using the coding system in use locally. The coding varies by ED with ICD- 10, Snomed-CT and the less detailed NHS Accident and Emergency Diagnosis Tables all in use. The use of diagnosis coding systems with differing levels of detail creates the need to have a variety of syndromic indicators to make best use of the data received.

We aim to describe the trends in respiratory attendances, and their comparison to the known circulating pathogens identified though laboratory surveillance to establish if any single syndromic indicator may be attributed to any one pathogen in particular. We also aim to describe the flexibility in the development of EDSSS syndromic indicators to best fit the data received.

Objective

Can syndromic surveillance using standard emergency department data collected using automated daily extraction be used to describe and alert the onset of the seasonal activity of named respiratory pathogens within the community?

National studies estimate that respiratory syncytial virus (RSV) is responsible for one in 38 emergency department (ED) visits for children < 5 years old. The Council for State and Territorial Epidemiologists position statement (13-ID-07): “RSV-Associated Pediatric Mortality” advocates for improved RSV surveillance including monitoring of RSV-associated pediatric mortality and hospitalizations. The goal of that data collection is to establish prevaccine baselines to evaluate vaccine effectiveness should one become available. As RSV is not reportable in Florida, RSV surveillance relies on a small subset of all Florida hospital laboratories to report data in aggregate and calculation of percent positive of all tests for RSV performed. These data assess virus activity, and do not allow for assessment of morbidity or age-specific analysis. Moreover, this data is not complete or timely, most often becoming available a minimum of a week after the testing was conducted. Florida’s RSV surveillance efforts guide clinical decision making and insurance reimbursements. Florida’s RSV seasonality not only differs from the nation but there is strong variation among five distinct regions, as exemplified by southeast Florida where the RSV season is year round. In Florida, pre-approval of prophylactic treatment by insurance companies is tied to seasonality.

Objective

In Florida, pre-approval of prophylactic treatment by insurance companies is tied to seasonality. Previous analyses determined that Florida’s syndromic surveillance system (Electronic Surveillance System for the Early Notification of Community-based Epidemics [ESSENCE-FL]) was capable of monitoring Florida’s statewide RSV seasonality. This analysis aims to determine if ESSENCE-FL can also be used to describe RSV and RSV-associated hospitalizations in children < 5 years by region and season.

The Child Health Epidemiology Reference Group (CHERG) has predicted around 43 million pneumonia cases in India. It is recognized that for huge nation like India, which accounts for 23% of global pneumonia burden, the national estimates may hide regional disparities. In this context, we have generated Indian state specific burden of severe pneumonia, pneumococcal pneumonia and pneumonia deaths through use of mathematical model.

Objective

This presentation highlights the use of mathematical model to estimate burden of disease in absence surveillance data. We estimated the burden of severe pneumonia, pneumococcal pneumonia and pneumonia deaths in Indian states using a mathematical model through application of vaccine probe methodology and attributable fraction.

During the winter months, Utah experiences a temperature inversion which traps pollutants, such as fine particle pollution (PM 2.5), in the Salt Lake Valley. A previous study determined the impact of inversion on ED visits for asthma, however similar phenomena have yet to be examined using the BioSense 2.0 syndromic surveillance system. While similar studies utilize a time-stratified case-crossover design, the absence of individually identifiable information on the dashboard precludes the utilization of this methodology. Using BioSense 2.0 frontend data, an ecological study design may allow for analyses to determine the impact of inversion on ED visits for respiratory syndrome and subsyndromes from submitting facilities in Salt Lake County, UT.

Objective

To determine the association between emergency department (ED) visits for the respiratory syndrome and subsyndromes and air quality indices for fine particle pollution in Salt Lake County, UT using frontend BioSense 2.0 data.

Seasonal rises in respiratory illnesses are a major burden on primary care services. Public Health England (PHE), in collaboration with NHS 111, coordinate a national surveillance system based upon the daily calls received at the NHS 111 telehealth service. Daily calls are categorized according to the clinical ‘pathway’ used by the call handler to assess the presenting complaints of the caller e.g. cold/flu, diarrhoea, rash.

Objective

We compared weekly laboratory reports for a number of seasonal respiratory pathogens with telehealth calls (NHS 111) to assess the burden of seasonal pathogens on this syndromic surveillance system and investigate any potential for providing additional early warning of seasonal outbreaks.

Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics modeling studies are commonly used to estimate the impact of influenza on mortality.

Objective

To estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures.

ARIs have epidemic and pandemic potential. Prediction of presence of ARIs from individual signs and symptoms in existing studies have been based on clinically-sourced data. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. Thus, the viral information that comes from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms. Participatory data — information that individuals today can produce on their own — enabled by the ubiquity of digital tools, can help fill this gap by providing self-reported data from the community. Internet-based participatory efforts such as Flu Near You have augmented existing ARI surveillance through early and widespread detection of outbreaks and public health trends.

Objective

To evaluate prediction of laboratory diagnosis of acute respiratory infection (ARI) from participatory data using machine learning models

Human MERS-CoV was first reported in September 2012. Globally, all reported cases have been linked through travel to or residence in the Arabian Peninsula with the exception of cases associated with an outbreak involving multiple health care facilities in the Republic of Korea ending in July 2015. While the majority of MERS-CoV cases have been reported in the Arabian Peninsula, several cases have been reported outside of the region. Most cases are believed to have been acquired in the Middle East and then exported elsewhere, with no or rare instances of secondary transmission. Two cases of MERS-CoV were exported to the United States and identified in May 2014. One of these cases traveled from Saudi Arabia to Florida.

DOH conducts regular surveillance for MERS-CoV through the investigation of persons with known risk factors. PUIs have most often been identified by physicians reporting directly to local health departments and by DOH staff regularly querying ED and UCC chief complaint data in ESSENCE-FL. ESSENCE-FL currently captures data from 265 EDs and UCCs statewide and has been useful in identifying cases associated with reportable disease and emerging pathogens. 

Objective

To retrospectively identify initial emergency department (ED) and urgent care center (UCC) visits for Florida’s Middle East respiratory syndrome coronavirus disease (MERS-CoV) patients under investigation (PUIs) in the Florida Department of Health’s (DOH) syndromic surveillance system, the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL), using information gathered from PUI case report forms and corresponding medical records for the purpose of improving syndromic surveillance for MERS-CoV. The results of this study may be further utilized in an effort to evaluate the current MERS-CoV surveillance query. 

Public Health England (PHE) uses syndromic surveillance systems to monitor for seasonal increases in respiratory illness. Respiratory illnesses create a considerable burden on health care services and therefore identifying the timing and intensity of peaks of activity is important for public health decision-making. Furthermore, identifying the incidence of specific respiratory pathogens circulating in the community is essential for targeting public health interventions e.g. vaccination. Syndromic surveillance can provide early warning of increases, but cannot explicitly identify the pathogens responsible for such increases.

PHE uses a range of general and specific respiratory syndromic indicators in their syndromic surveillance systems, e.g. “all respiratory disease”, “influenza-like illness”, “bronchitis” and “cough”. Previous research has shown that “influenza-like illness” is associated with influenza circulating in the community1 whilst “cough” and “bronchitis” syndromic indicators in children under 5 are associated with respiratory syncytial virus (RSV)2, 3. However, the relative burden of other pathogens, e.g. rhinovirus and parainfluenza is less well understood. We have sought to further understand the relationship between specific pathogens and syndromic indicators and to improve estimates of disease burden. Therefore, we modelled the association between pathogen incidence, using laboratory reports and health care presentations, using syndromic data. 

Objective

To improve understanding of the relative burden of different causative respiratory pathogens on respiratory syndromic indicators monitored using syndromic surveillance systems in England. 

The ICD-9 codes for acute respiratory illness (ARI) and pneumonia/influenza (P&I) are commonly used in ARI surveillance; however, few studies evaluate the accuracy of these codes or the importance of ICD-9 position. We reviewed ICD-9 codes reported among patients identified through severe acute respiratory infection (SARI) surveillance to compare medical record documentation with medical coding and evaluated ICD-9 codes assigned to patients with influenza detections.