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      Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

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          Abstract

          Objective

          Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions:

          1. Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?

          2. Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?

          Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n = 15), Infectious Diseases (n = 6), Non-infectious Diseases (n = 4), Medication and Vaccines (n = 3), and Other (n = 5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n = 10), Infectious Diseases (n = 3), Non-infectious Diseases (n = 9), and Other (n = 10).

          Conclusions

          The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

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          Most cited references33

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          Sharing Health Data for Better Outcomes on PatientsLikeMe

          Background PatientsLikeMe is an online quantitative personal research platform for patients with life-changing illnesses to share their experience using patient-reported outcomes, find other patients like them matched on demographic and clinical characteristics, and learn from the aggregated data reports of others to improve their outcomes. The goal of the website is to help patients answer the question: “Given my status, what is the best outcome I can hope to achieve, and how do I get there?” Objective Using a cross-sectional online survey, we sought to describe the potential benefits of PatientsLikeMe in terms of treatment decisions, symptom management, clinical management, and outcomes. Methods Almost 7,000 members from six PatientsLikeMe communities (amyotrophic lateral sclerosis [ALS], Multiple Sclerosis [MS], Parkinson’s Disease, human immunodeficiency virus [HIV], fibromyalgia, and mood disorders) were sent a survey invitation using an internal survey tool (PatientsLikeMe Lens). Results Complete responses were received from 1323 participants (19% of invited members). Between-group demographics varied according to disease community. Users perceived the greatest benefit in learning about a symptom they had experienced; 72% (952 of 1323) rated the site “moderately” or “very helpful.” Patients also found the site helpful for understanding the side effects of their treatments (n = 757, 57%). Nearly half of patients (n = 559, 42%) agreed that the site had helped them find another patient who had helped them understand what it was like to take a specific treatment for their condition. More patients found the site helpful with decisions to start a medication (n = 496, 37%) than to change a medication (n = 359, 27%), change a dosage (n = 336, 25%), or stop a medication (n = 290, 22%). Almost all participants (n = 1,249, 94%) were diagnosed when they joined the site. Most (n = 824, 62%) experienced no change in their confidence in that diagnosis or had an increased level of confidence (n = 456, 34%). Use of the site was associated with increasing levels of comfort in sharing personal health information among those who had initially been uncomfortable. Overall, 12% of patients (n = 151 of 1320) changed their physician as a result of using the site; this figure was doubled in patients with fibromyalgia (21%, n = 33 of 150). Patients reported community-specific benefits: 41% of HIV patients (n = 72 of 177) agreed they had reduced risky behaviors and 22% of mood disorders patients (n = 31 of 141) agreed they needed less inpatient care as a result of using the site. Analysis of the Web access logs showed that participants who used more features of the site (eg, posted in the online forum) perceived greater benefit. Conclusions We have established that members of the community reported a range of benefits, and that these may be related to the extent of site use. Third party validation and longitudinal evaluation is an important next step in continuing to evaluate the potential of online data-sharing platforms.
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            Utilizing social media data for pharmacovigilance: A review.

            Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media.
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              Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak.

              During infectious disease outbreaks, data collected through health institutions and official reporting structures may not be available for weeks, hindering early epidemiologic assessment. By contrast, data from informal media are typically available in near real-time and could provide earlier estimates of epidemic dynamics. We assessed correlation of volume of cholera-related HealthMap news media reports, Twitter postings, and government cholera cases reported in the first 100 days of the 2010 Haitian cholera outbreak. Trends in volume of informal sources significantly correlated in time with official case data and was available up to 2 weeks earlier. Estimates of the reproductive number ranged from 1.54 to 6.89 (informal sources) and 1.27 to 3.72 (official sources) during the initial outbreak growth period, and 1.04 to 1.51 (informal) and 1.06 to 1.73 (official) when Hurricane Tomas afflicted Haiti. Informal data can be used complementarily with official data in an outbreak setting to get timely estimates of disease dynamics.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 October 2015
                2015
                : 10
                : 10
                : e0139701
                Affiliations
                [1 ]Data Sciences and Analytics Group, Pacific Northwest National Laboratory, Richland, Washington, United States of America
                [2 ]International Society for Disease Surveillance, Boston, Massachusetts, United States of America
                [3 ]Commonwealth Scientific and Industrial Research Organization Digital Productivity Flagship, Canberra, Australia
                [4 ]Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States of America
                [5 ]School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, People’s Republic of China
                [6 ]Skoll Global Threats Fund, San Francisco, California, United States of America
                [7 ]Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, United States of America
                [8 ]National Institute of Infectious Diseases, Shinjuku-Ku, Tokyo, Japan
                [9 ]Center of Innovation for Complex Chronic Healthcare, United States Department of Veterans Affairs, Hines, Illinois, United States of America
                IFIMAR, UNMdP-CONICET, ARGENTINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: LECS TLR MAC MC EHYL JAP MS LCS KJS CDC. Performed the experiments: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Analyzed the data: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Contributed reagents/materials/analysis tools: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC. Wrote the paper: LECS TLR MAC MC EHYL JMO JAP MS LCS KJS CDC.

                [¤]

                Current address: Department of Pharmacy Systems, Outcomes, and Policy, College of Pharmacy, University of Illinois at Chicago, Hines, Illinois, United States of America

                Article
                PONE-D-15-21599
                10.1371/journal.pone.0139701
                4593536
                26437454
                9f2eced1-f05d-4617-bad9-04d63b83d22c

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

                History
                : 18 May 2015
                : 15 September 2015
                Page count
                Figures: 2, Tables: 11, Pages: 20
                Funding
                Project management support for this effort was provided by the International Society for Disease Surveillance. Participation of CDC and LECS was supported in part by Pacific Northwest National Laboratory's Laboratory Directed Research and Development Program.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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