Forecasting hospital pneumonia admissions using influenza surveillance, climate and community data

Influenza peaks around June and December in Singapore every year. Facing an ageing population, hospitals in Singapore have been constantly reaching maximum bed occupancy. The ability to be able to make early decisions during peak periods is important. Tan Tock Seng Hospital is the second largest adult acute care general hospital in Singapore. Pneumonia-related emergency department (ED) admissions are a huge burden to the hospital's resources. The number of cases vary year on year as it depends on seasonal vaccine effectiveness and the population's immunity to the circulating strain.

June 18, 2019

Forecasting Emergency Department Admissions for Pneumonia in Tropical Singapore

Pneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections.

January 19, 2018

Evaluation of Syndrome Algorithms for Detecting Pneumonia Emergency Department Visits

The NYC Department of Health and Mental Hygiene (DOHMH) uses ED syndromic surveillance to monitor near real-time trends in pneumonia visits. The original pneumonia algorithm was developed based on ED chief complaints, and more recently was modified following a legionella outbreak in NYC. In 2016, syndromic data was matched to New York State all payer database (SPARCS) for 2010 through 2015. We leveraged this matched dataset to validate ED visits identified by our pneumonia algorithm and suggest improvements.

January 25, 2018

Computerized Text Analysis to Enhance Automated Pneumonia Detection

Information about disease severity could help with both detection and situational awareness during outbreaks of acute respiratory infections (ARI). In this work, we use data from the EMR to identify patients with pneumonia, a key landmark of ARI severity. We asked if computerized analysis of the free-text of clinical notes or imaging reports could complement structured EMR data to uncover pneumonia cases.

Objective

To improve the surveillance for pneumonia using the free-text of electronic medical records (EMR).

May 22, 2018

A System for Surveillance Directly from the EMR

Hospital acquired infections are a major cause of morbidity, mortality and increased resource utilization. CDC estimates that in the US alone, over 2 million patients are affected by nosocomial infections costing approximately $34.7 billion to $45 billion annually (1). The existing process of detection and reporting relies on time consuming manual processing of records and generation of alerts based on disparate definitions that are not comparable across institutions or even physicians.

Objective:

June 12, 2018

An Early Warning System for Pneumonia and Influenza Mortality in Taiwan

Influenza is a serious disease that seasonality causes substantial but varying morbidity and mortality. In Taiwan, estimates of the influenza mortality burden were based on post-hoc analyses of national mortality statistics and not available until at least six months after the corresponding epidemic. Timely monitoring and early detection of influenza-associated excess mortality can guide antiviral or vaccine interventions and help healthcare capacity planning.

May 02, 2019

Clustering of U.S. Cities Based on Mortality from Influenza and Pneumonia

Influenza is a major cause of mortality. In developed countries, mortality is at its highest during winter months, not only as a result of deaths from influenza and pneumonia but also as a result of deaths attributed to other diseases (e.g. cardiovascular disease). Understandably, much of the surveillance of influenza follows predefined geographic regions (e.g. census regions or state boundaries). However, the spread of influenza and its resulting mortality does not respect such boundaries.

 

Objective

May 02, 2019

Accuracy versus Timeliness for Influenza Detection: A Comparison of Hospital Syndromic Surveillance Data with Discharge Data

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.

July 30, 2018

How Bad Is It? Using Biosurveillance Data to Monitor the Severity of Seasonal Flu

We sought to evaluate the validity of pneumonia and influenza hospitalizations (PI) data gathered by our biosurveillance system.

July 30, 2018

Monitoring Staphylococcus Infection Trends with Biosurveillance Data

Methicillin resistant staphylococcus aureus (MRSA) is a leading cause of skin and soft tissue infections (SSTI). Until recently, S. aureus pneumonia has been considered primarily a nosocomial infection, and was reported infrequently as a cause of severe community-acquired pneumonia. In recent years, there have been several reports of MRSA community-acquired pneumonia cases associated with influenza among healthy individuals resulting in hospitalization or death.

July 30, 2018

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NSSP Community of Practice

Email: syndromic@cste.org

 

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