Digital Pharmacovigilance: the medwatcher system for monitoring adverse events through automated processing of internet social media and crowdsourcing

Thesis (Ph.D.)--Boston UniversityHalf of Americans take a prescription drug, medical devices are in broad use, and population coverage for many vaccines is over 90%. Nearly all medical products carry risk of adverse events (AEs), sometimes severe. However, pre- approval trials use small populations and exclude participants by specific criteria, making them insufficient to determine the risks of a product as used in the population. Existing post-marketing reporting systems are critical, but suffer from underreporting. Meanwhile, recent years have seen an explosion in adoption of Internet services and smartphones. MedWatcher is a new system that harnesses emerging technologies for pharmacovigilance in the general population. MedWatcher consists of two components, a text-processing module, MedWatcher Social, and a crowdsourcing module, MedWatcher Personal. With the natural language processing component, we acquire public data from the Internet, apply classification algorithms, and extract AE signals. With the crowdsourcing application, we provide software allowing consumers to submit AE reports directly. Our MedWatcher Social algorithm for identifying symptoms performs with 77% precision and 88% recall on a sample of Twitter posts. Our machine learning algorithm for identifying AE-related posts performs with 68% precision and 89% recall on a labeled Twitter corpus. For zolpidem tartrate, certolizumab pegol, and dimethyl fumarate, we compared AE profiles from Twitter with reports from the FDA spontaneous reporting system. We find some concordance (Spearman's rho= 0.85, 0.77, 0.82, respectively, for symptoms at MedDRA System Organ Class level). Where the sources differ, milder effects are overrepresented in Twitter. We also compared post-marketing profiles with trial results and found little concordance. MedWatcher Personal saw substantial user adoption, receiving 550 AE reports in a one-year period, including over 400 for one device, Essure. We categorized 400 Essure reports by symptom, compared them to 129 reports from the FDA spontaneous reporting system, and found high concordance (rho = 0.65) using MedDRA Preferred Term granularity. We also compared Essure Twitter posts with MedWatcher and FDA reports, and found rho= 0.25 and 0.31 respectively. MedWatcher represents a novel pharmacoepidemiology surveillance informatics system; our analysis is the first to compare AEs across social media, direct reporting, FDA spontaneous reports, and pre-approval trials

Automated Vocabulary Discovery for Geo-Parsing Online Epidemic Intelligence

Background Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as "geo-parsing". A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.Results Given that human readers perform this kind of task by using both their lexical and contextual knowledge, we developed an approach which relies on a relatively small expert-built gazetteer, thus limiting the need of human input, but focuses on learning the context in which geographic references appear. We show in a set of experiments, that this approach exhibits a substantial capacity to discover geographic locations outside of its initial lexicon.Conclusion The results of this analysis provide a framework for future automated global surveillance efforts that reduce manual input and improve timeliness of reporting

Surveillance for Neisseria meningitidis Disease Activity and Transmission Using Information Technology

Background While formal reporting, surveillance, and response structures remain essential to protecting public health, a new generation of freely accessible, online, and real-time informatics tools for disease tracking are expanding the ability to raise earlier public awareness of emerging disease threats. The rationale for this study is to test the hypothesis that the HealthMap informatics tools can complement epidemiological data captured by traditional surveillance monitoring systems for meningitis due to Neisseria meningitides (N. meningitides) by highlighting severe transmissible disease activity and outbreaks in the United States. Methods Annual analyses of N. meningitides disease alerts captured by HealthMap were compared to epidemiological data captured by the Centers for Disease Control’s Active Bacterial Core surveillance (ABCs) for N. meningitides. Morbidity and mortality case reports were measured annually from 2010 to 2013 (HealthMap) and 2005 to 2012 (ABCs). Findings HealthMap N. meningitides monitoring captured 80-90% of alerts as diagnosed N. meningitides, 5-20% of alerts as suspected cases, and 5-10% of alerts as related news articles. HealthMap disease alert activity for emerging disease threats related to N. meningitides were in agreement with patterns identified historically using traditional surveillance systems. HealthMap’s strength lies in its ability to provide a cumulative “snapshot” of weak signals that allows for rapid dissemination of knowledge and earlier public awareness of potential outbreak status while formal testing and confirmation for specific serotypes is ongoing by public health authorities. Conclusions The underreporting of disease cases in internet-based data streaming makes inadequate any comparison to epidemiological trends illustrated by the more comprehensive ABCs network published by the Centers for Disease Control. However, the expected delays in compiling confirmatory reports by traditional surveillance systems (at the time of writing, ABCs data for 2013 is listed as being provisional) emphasize the helpfulness of real-time internet-based data streaming to quickly fill gaps including the visualization of modes of disease transmission in outbreaks for better resource and action planning. HealthMap can also contribute as an internet-based monitoring system to provide real-time channel for patients to report intervention-related failures.National Library of Medicine (U.S.) (Grant 5 R01 LM010812-04

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Background: Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective: The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods: We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results: Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion: Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation

Characteristics of US public schools with reported cases of novel influenza A (H1N1)

Objective The 2009 pandemic of influenza A (H1N1) has disproportionately affected children and young adults, resulting in attention by public health officials and the news media on schools as important settings for disease transmission and spread. We aimed to characterize US schools affected by novel influenza A (H1N1) relative to other schools in the same communities. Methods A database of US school-related cases was obtained by electronic news media monitoring for early reports of novel H1N1 influenza between April 23 and June 8, 2009. We performed a matched case–control study of 32 public primary and secondary schools that had one or more confirmed cases of H1N1 influenza and 6815 control schools located in the same 23 counties as case schools. Results Compared with controls from the same county, schools with reports of confirmed cases of H1N1 influenza were less likely to have a high proportion of economically disadvantaged students (adjusted odds ratio (aOR) 0.385; 95% confidence interval (CI) 0.166–0.894) and less likely to have older students (aOR 0.792; 95% CI 0.670–0.938). Conclusions We conclude that public schools with younger, more affluent students may be considered sentinels of the epidemic and may have played a role in its initial spread.National Institute of Allergy and Infectious Diseases (U.S.) (R21AI073591-01)National Institutes of Health (U.S.)Canadian Institutes of Health Research (PAN-83152)Canadian Institutes of Health Research (CAT-86857)Google (Firm) (Research Grant

Analisis Proses Seleksi Tenaga Kerja Di De Boliva Café Surabaya Town Square

Penelitian ini dilakukan di De Boliva Café Surabaya Town Square. Tujuan dalam penelitian ini adalah untuk mengetahui proses seleksi tenaga kerja. Teknik analisis yang digunakan dalam penelitian ini adalah analisis kualitatif deksriptif. Hasil analisis menunjukkan bahwa proses seleksi tenaga kerja di De Boliva adalah seleksi curriculum vitae (CV) beserta surat lamaran, tes tulis, wawancara video, dan wawancara akhir

Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project

John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases