Asthma is one of the most prevalent and costly chronic conditions in the United States which cannot be cured. However accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering non-traditional, digital information to perform disease surveillance.
We introduce a novel method of using multiple data sources for predicting the number of asthma related emergency department (ED) visits in a specific area. Twitter data, Google search interests and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, emergency department preparedness, and, targeted patient interventions. Predicting Asthma-Related Emergency Department Visits Using Big Data
Existing methods in the increased availability of information in the Web, in the last years, a new research area has been developed, namely Infodemiology. It can be defined as the “science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy”. As part of this research area, several kinds of data have been studied for their applicability in the context of disease surveillance. Google flu trends exploit the search behavior to monitor the current flurelated disease activity. It could be shown by Carneiro and Mylonakis that Google Flu Trends can detect regional outbreaks of influenza 7–10 days before conventional Centers for Disease Control and Prevention surveillance systems.
Google messages and their relevance for disease outbreak detection has been reported already that especially tweets are useful to predict outbreaks such as a Norovirus outbreak at a university analysed twitter news during the influenza epidemic 2009. They compared the use of the term “H1N1” and “swine flu” over the time. Furthermore, they analysed the content of the tweets (ten content concepts) and validated twitter as a the real time content. They analysed the data via Infovigil an infosurveillance system by using an automated coding. To find out if there is a relationship between automated and manual coding, the tweets were evaluated by a Pearson´s correlation. Chew et al. found a significant correlation between both coding in seven content concept it needs to be investigated whether this source might be of relevance for detecting disease outbreaks in Germany. Therefore, only German keywords are exploited to identify Twitter messages. Further, we are not only interested in influenza-like illnesses as the studies available so far, but also in other infectious diseases (e.g. Norovirus and Salmonella).
Our proposed methods to leverage social media, internet search, and environmental air quality data to estimate ED visits for asthma in a relatively discrete geographic area (a metropolitan area) within a relatively short time period (days) to this end, we have gathered asthma related ED visits data, social media data from Twitter, internet users’ search interests from Google and pollution sensor data from the EPA, all from the same geographic area and time period, to create a model for predicting asthma related ED visits. This work is different from extant studies that typically predict the spread of contagious diseases using social media such as Twitter. Unlike influenza or other viral diseases, asthma is a non-communicable health condition and we demonstrate the utility and value of linking big data from diverse sources in developing predictive models for non-communicable diseases with a specific focus on asthma.
Research studies have explored the use of novel data sources to propose rapid, cost-effective health status surveillance methodologies. Some of the early studies rely on document classification suggesting that Twitter data can be highly relevant for early detection of public health threats. Others employ more complex linguistic analysis, such as the Ailment Topic Aspect Model which is useful for syndrome surveillance. This type of analysis is useful for demonstrating the significance of social media as a promising new data source for health surveillance. Other recent studies have linked social media data with real world disease incidence to generate actionable knowledge useful for making health care decisions. These include which analyzed Twitter messages related to influenza and correlated them with reported CDC statistics validated Twitter as a real-time content, sentiment, and public attention trend-tracking tool. Collier employed supervised classifiers (SVM and Naive Bayes) to classify tweets into four self-reported protective behavior categories. This study adds to evidence supporting a high degree of correlation between pre-diagnostic social media signals and diagnostic influenza case data.