Asthma

The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached "High" or "Very High" levels on the UK Daily Air Quality Index (DAQI). We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.

Objective:

To explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.

Submitted by elamb on Sun, 01/21/2018 - 21:05

Recently, a growing number of studies have made use of Twitter to track the spread of infectious disease. These investigations show that there are reliable spikes in traffic related to keywords associated with the spread of infectious diseases like Influenza [1], as well as other Syndromes [2]. However, little research has been done using Social Media to monitor chronic conditions like Asthma, which do not spread from sufferer to sufferer. We therefore test the feasibility of using Twitter for Asthma surveillance, using techniques from NLP and machine learning to achieve a deeper understanding of what users Tweet about Asthma, rather than relying only on keyword search.

Objective

We present a Content Analysis project using Natural Language Processing to aid in Twitter-based syndromic surveillance of Asthma

Submitted by rmathes on Fri, 01/19/2018 - 14:50

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE-FL) receives daily (or bi-hourly) data from 184 emergency departments (ED) from around Florida. Additionally, 30 urgent care centers submit daily data to the system. These 214 facilities are grouped together in an acute care data source category. Five to six days after the start of each school year in Florida, ESSENCE-FL shows increased respiratory illness visits in the school aged population. Previous analyses of these data have shown that this increase is a result of increased transmission of the common cold among school children. In early September 2014, during this sustained yearly increase in respiratory visits, reports of more severe infection caused by Enterovirus D68 (EV-D68) in children in other parts of the country began circulating. Public health officials in Florida, as well as the media, questioned whether children in the state were being infected by this virus capable of causing more severe illness, especially among asthmatics. As is the case with many incipient outbreaks, syndromic surveillance played an integral role in early efforts to detect the presence of this illness. The task of providing situational awareness during this period was complicated by this outbreak coinciding with the start of the school year.

Objective

To provide situational awareness using Florida’s syndromic surveillance system during a 2014 outbreak of EV-D68 in other regions of the country.

Submitted by uysz on Wed, 12/20/2017 - 09:34

Public Health England has developed a suite of syndromic surveillance systems, collecting data from a number of health care sources, and linking to public health action to try and improve the public health benefit of the surveillance.1 We aim to describe this national syndromic service, highlighting the flexibility of the systems in responding to a range of environmental incidents.

Objective

To deliver a national syndromic surveillance service, linking analytical and statistical methods with public health action to provide surveillance support for national public health programmes monitoring the spread of infectious diseases and the health impact of environmental incidents in England.

Submitted by rmathes on Mon, 10/23/2017 - 08:58

In June 2004, the French syndromic surveillance system based on the ED has been implemented by the French institute for public health surveillance (InVS), starting with 23 ED. In August 2014, about 600 ED (40,000 daily attendances) are included in the Oscour network, recording 80% of the national total attendances.

Asthma is one of the about 60 syndromic indicators monitored each day and followed all over the year.

This indicator presents important fluctuations and can be influenced by several environmental and infectious but also societal factors. Particularly factors like air pollution are known to have both short and long term impact on asthma while thunderstorms are associated with acute outbreaks of asthma.

Objective

Description of the temporal pattern of the daily number of attendances in emergency departments (ED) for asthma in Paris area and identification of the main factors influencing this indicator.

Submitted by teresa.hamby@d… on Tue, 10/03/2017 - 10:28

Public health agencies and researchers have traditionally relied on the Behavioral Risk Factor Surveillance System (BRFSS) and similar tools for surveillance of non-reportable conditions. These tools are valuable but the data are delayed by more than a year, limited in scope, and based only on participant self-report. These characteristics limit the utility of traditional surveillance systems for program monitoring and impact assessments. Automated surveillance using electronic health record (EHR) data has the potential to increase the efficiency, breadth, accuracy, and timeliness of surveillance. We sought to assess the feasibility and utility of public health surveillance for chronic diseases using EHR data using MDPHnet. MDPHnet is a distributed data network that allows the Massachusetts Department of Public Health to query participating practices’ EHR data for the purposes of public health surveillance (www.esphealth.org). Practices retain the ability to approve queries on a case-by-case basis and the network is updated daily.

Objective

To assess the feasibility of tracking the prevalence of chronic conditions at the state and community level over time using MDPHnet, a distributed network for querying electronic health record systems

Submitted by Magou on Tue, 08/08/2017 - 21:51

This paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. The goal is to enhance investigation capabilities of day-to-day population health monitors.

Submitted by ctong on Fri, 03/24/2017 - 11:22

Use case for the Analytic Solutions for Real-Time Biosurveillance: Models for Forecasting Asthma Exacerbations in Urban Environments consultancy held March 30-31, 2016 at the Boston Public Health Commission (BPHC).

Problem Summary

Submitted by ctong on Fri, 03/24/2017 - 11:14

Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Models for Forecasting Asthma Exacerbations in Urban Environments consultancy held March 30-31, 2016 at the Boston Public Health Commission (BPHC).

Problem Summary

Submitted by ctong on Thu, 03/23/2017 - 16:34

Common colds are one of the principal causes of severe exacerbations in asthmatic people, reflected in epidemic-like waves of asthma hospitalizations. Most studies do not estimate the effect of infectious causes of exacerbations, and cannot account for how this risk changes through time.

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