Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences.

BACKGROUND: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. OBJECTIVE: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. METHODS: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. RESULTS: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. CONCLUSIONS: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues.

Diversion and Illicit Sale of Extended Release Tapentadol in the United States.

OBJECTIVE: Prescription opioid analgesics are commonly prescribed for moderate to severe pain. An unintended consequence of prescribing opioid analgesics is the abuse and diversion of these medications. Tapentadol ER is a recently approved centrally acting analgesic with synergistic mechanisms of action: µ-opioid receptor agonism and inhibition of norepinephrine reuptake. We assessed the amount of diversion and related cost of obtaining tapentadol IR (Nucynta®) and tapentadol ER (Nucynta ER®) as well as other Schedule II opioid medications in street transactions in the United States using the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS®) System. METHODS: The Drug Diversion Program measures the number of cases opened by 260 drug diversion investigators in 49 states. StreetRx(TM) uses a crowd-sourcing Website to collect the prices paid for licit or illicit drugs. RESULTS: The population-based rates of diversion were 0.003 (tapentadol IR), 0.001 (tapentadol ER), and 1.495 (other Schedule II opioid tablets) reports per 100,000 population. The tapentadol ER rate was lower than the other Schedule II opioid tablets (P < 0.001) and tapentadol IR (P= 0.004). Diversion rates based on drug availability were 0.03 (tapentadol IR), 0.016 (tapentadol ER), and 0.172 (other Schedule II opioid tablets) per 1,000 prescriptions dispensed. The median street price per milligram was $0.18 (tapentadol IR), $0.10 (tapentadol ER), and $1.00 (other Schedule II opioid tablets). DISCUSSION: Our results indicate that tapentadol ER is rarely sold illicitly in the United States. When sold illicitly, tapentadol ER costs less than other Schedule II opioid products.

Comparing timeliness, content, and disease severity of formal and informal source outbreak reporting.

BACKGROUND: Infectious disease surveillance has recently seen many changes including rapid growth of informal surveillance, acting both as competitor and a facilitator to traditional surveillance, as well as the implementation of the revised International Health Regulations. The present study aims to compare outbreak reporting by formal and informal sources given such changes in the field. METHODS: 111 outbreaks identified from June to December 2012 were studied using first formal source report and first informal source report collected by HealthMap, an automated and curated aggregator of data sources for infectious disease surveillance. The outbreak reports were compared for timeliness, reported content, and disease severity. RESULTS: Formal source reports lagged behind informal source reports by a median of 1.26 days (p=0.002). In 61% of the outbreaks studied, the same information was reported in the initial formal and informal reports. Disease severity had no significant effect on timeliness of reporting. CONCLUSION: The findings suggest that recent changes in the field of surveillance improved formal source reporting, particularly in the dimension of timeliness. Still, informal sources were found to report slightly faster and with accurate information. This study emphasizes the importance of utilizing both formal and informal sources for timely and accurate infectious disease outbreak surveillance.

A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives.

BACKGROUND: Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. OBJECTIVE: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. METHODS: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. RESULTS: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal. CONCLUSIONS: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.

Global capacity for emerging infectious disease detection.

The increasing number of emerging infectious disease events that have spread internationally, such as severe acute respiratory syndrome (SARS) and the 2009 pandemic A/H1N1, highlight the need for improvements in global outbreak surveillance. It is expected that the proliferation of Internet-based reports has resulted in greater communication and improved surveillance and reporting frameworks, especially with the revision of the World Health Organization's (WHO) International Health Regulations (IHR 2005), which went into force in 2007. However, there has been no global quantitative assessment of whether and how outbreak detection and communication processes have actually changed over time. In this study, we analyzed the entire WHO public record of Disease Outbreak News reports from 1996 to 2009 to characterize spatial-temporal trends in the timeliness of outbreak discovery and public communication about the outbreak relative to the estimated outbreak start date. Cox proportional hazards regression analyses show that overall, the timeliness of outbreak discovery improved by 7.3% [hazard ratio (HR) = 1.073, 95% CI (1.038; 1.110)] per year, and public communication improved by 6.2% [HR = 1.062, 95% CI (1.028; 1.096)] per year. However, the degree of improvement varied by geographic region; the only WHO region with statistically significant (α = 0.05) improvement in outbreak discovery was the Western Pacific region [HR = 1.102 per year, 95% CI (1.008; 1.205)], whereas the Eastern Mediterranean [HR = 1.201 per year, 95% CI (1.066; 1.353)] and Western Pacific regions [HR = 1.119 per year, 95% CI (1.025; 1.221)] showed improvement in public communication. These findings provide quantitative historical assessment of timeliness in infectious disease detection and public reporting of outbreaks.

Crowdsourcing black market prices for prescription opioids.

BACKGROUND: Prescription opioid diversion and abuse are major public health issues in the United States and internationally. Street prices of diverted prescription opioids can provide an indicator of drug availability, demand, and abuse potential, but these data can be difficult to collect. Crowdsourcing is a rapid and cost-effective way to gather information about sales transactions. We sought to determine whether crowdsourcing can provide accurate measurements of the street price of diverted prescription opioid medications. OBJECTIVE: To assess the possibility of crowdsourcing black market drug price data by cross-validation with law enforcement officer reports. METHODS: Using a crowdsourcing research website (StreetRx), we solicited data about the price that site visitors paid for diverted prescription opioid analgesics during the first half of 2012. These results were compared with a survey of law enforcement officers in the Researched Abuse, Diversion, and Addiction-Related Surveillance (RADARS) System, and actual transaction prices on a "dark Internet" marketplace (Silk Road). Geometric means and 95% confidence intervals were calculated for comparing prices per milligram of drug in US dollars. In a secondary analysis, we compared prices per milligram of morphine equivalent using standard equianalgesic dosing conversions. RESULTS: A total of 954 price reports were obtained from crowdsourcing, 737 from law enforcement, and 147 from the online marketplace. Correlations between the 3 data sources were highly linear, with Spearman rho of 0.93 (P<.001) between crowdsourced and law enforcement, and 0.98 (P<.001) between crowdsourced and online marketplace. On StreetRx, the mean prices per milligram were US$3.29 hydromorphone, US$2.13 buprenorphine, US$1.57 oxymorphone, US$0.97 oxycodone, US$0.96 methadone, US$0.81 hydrocodone, US$0.52 morphine, and US$0.05 tramadol. The only significant difference between data sources was morphine, with a Drug Diversion price of US$0.67/mg (95% CI 0.59-0.75) and a Silk Road price of US$0.42/mg (95% CI 0.37-0.48). Street prices generally followed clinical equianalgesic potency. CONCLUSIONS: Crowdsourced data provide a valid estimate of the street price of diverted prescription opioids. The (ostensibly free) black market was able to accurately predict the relative pharmacologic potency of opioid molecules.

Integration and visualization of host-pathogen data related to infectious diseases.

MOTIVATION: Infectious disease research is generating an increasing amount of disparate data on pathogenic systems. There is a growing need for resources that effectively integrate, analyze, deliver and visualize these data, both to improve our understanding of infectious diseases and to facilitate the development of strategies for disease control and prevention. RESULTS: We have developed Disease View, an online host-pathogen resource that enables infectious disease-centric access, analysis and visualization of host-pathogen interactions. In this resource, we associate infectious diseases with corresponding pathogens, provide information on pathogens, pathogen virulence genes and the genetic and chemical evidences for the human genes that are associated with the diseases. We also deliver the relationships between pathogens, genes and diseases in an interactive graph and provide the geolocation reports of associated diseases around the globe in real time. Unlike many other resources, we have applied an iterative, user-centered design process to the entire resource development, including data acquisition, analysis and visualization. AVAILABILITY AND IMPLEMENTATION: Freely available at http://www.patricbrc.org; all major web browsers supported. CONTACT: cmao@vbi.vt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010-2011.

BACKGROUND: The objective of this study was to investigate the use of novel surveillance tools in a malaria endemic region where prevalence information is limited. Specifically, online reporting for participatory epidemiology was used to gather information about malaria spread directly from the public. Individuals in India were incentivized to self-report their recent experience with malaria by micro-monetary payments. METHODS: Self-reports about malaria diagnosis status and related information were solicited online via Amazon's Mechanical Turk. Responders were paid $0.02 to answer survey questions regarding their recent experience with malaria. Timing of the peak volume of weekly self-reported malaria diagnosis in 2010 was compared to other available metrics such as the volume over time of and information about the epidemic from media sources. Distribution of Plasmodium species reports were compared with values from the literature. The study was conducted in summer 2010 during a malaria outbreak in Mumbai and expanded to other cities during summer 2011, and prevalence from self-reports in 2010 and 2011 was contrasted. RESULTS: Distribution of Plasmodium species diagnosis through self-report in 2010 revealed 59% for Plasmodium vivax, which is comparable to literature reports of the burden of P. vivax in India (between 50 and 69%). Self-reported Plasmodium falciparum diagnosis was 19% and during the 2010 outbreak and the estimated burden was between 10 and 15%. Prevalence between 2010 and 2011 via self-reports decreased significantly from 36.9% to 19.54% in Mumbai (p = 0.001), and official reports also confirmed a prevalence decrease in 2011. CONCLUSIONS: With careful study design, micro-monetary incentives and online reporting are a rapid way to solicit malaria, and potentially other public health information. This methodology provides a cost-effective way of executing a field study that can act as a complement to traditional public health surveillance methods, offering an opportunity to obtain information about malaria activity, temporal progression, demographics affected or Plasmodium-specific diagnosis at a finer resolution than official reports can provide. The recent adoption of technologies, such as the Internet supports self-reporting mediums, and self-reporting should continue to be studied as it can foster preventative health behaviours.

New technologies for reporting real-time emergent infections.

Novel technologies have prompted a new paradigm in disease surveillance. Advances in computation, communications and materials enable new technologies such as mobile phones and microfluidic chips. In this paper we illustrate examples of new technologies that can augment disease detection. We describe technologies harnessing the internet, mobile phones, point of care diagnostic tools and methods that facilitate detection from passively collected unstructured data. We demonstrate how these can all assist in quicker detection, investigation and response to emerging infectious events. Novel technologies enable collection and dissemination of epidemic intelligence data to both public health practitioners and the general public, enabling finer temporal and spatial resolution of disease monitoring than through traditional public health processes.

Fentanyl panic goes viral: The spread of misinformation about overdose risk from casual contact with fentanyl in mainstream and social media.

BACKGROUND: Fueled by misinformation, fentanyl panic has harmed public health through complicating overdose rescue while rationalizing hyper-punitive criminal laws, wasteful expenditures, and proposals to curtail vital access to pain pharmacotherapy. To assess misinformation about health risk from casual contact with fentanyl, we characterize its diffusion and excess visibility in mainstream and social media. METHODS: We used Media Cloud to compile and characterize mainstream and social media content published between January 2015 and September 2019 on overdose risk from casual fentanyl exposure. RESULTS: Relevant content appeared in 551 news articles spanning 48 states. Misinformed media reports received approximately 450,000 Facebook shares, potentially reaching nearly 70,000,000 users from 2015-2019. Amplified by erroneous government statements, misinformation received excess social media visibility by a factor of 15 compared to corrective content, which garnered fewer than 30,000 shares with potential reach of 4,600,000 Facebook users. CONCLUSION: Health-related misinformation continues to proliferate online, hampering responses to public health crises. More evidence-informed tools are needed to effectively challenge misinformed narratives in mainstream and social media.

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.

Use of unstructured event-based reports for global infectious disease surveillance.

Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.

HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports.

OBJECTIVE: Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks. DESIGN: This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans. MEASUREMENTS: We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage. RESULTS: As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure. CONCLUSION: HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.

Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis.

BACKGROUND: The Food and Drug Administration (FDA) issues drug safety communications (DSCs) to health care professionals, patients, and the public when safety issues emerge related to FDA-approved drug products. These safety messages are disseminated through social media to ensure broad uptake. OBJECTIVE: The objective of this study was to assess the social media dissemination of 2 DSCs released in 2013 for the sleep aid zolpidem. METHODS: We used the MedWatcher Social program and the DataSift historic query tool to aggregate Twitter and Facebook posts from October 1, 2012 through August 31, 2013, a period beginning approximately 3 months before the first DSC and ending 3 months after the second. Posts were categorized as (1) junk, (2) mention, and (3) adverse event (AE) based on a score between -0.2 (completely unrelated) to 1 (perfectly related). We also looked at Google Trends data and Wikipedia edits for the same time period. Google Trends search volume is scaled on a range of 0 to 100 and includes "Related queries" during the relevant time periods. An interrupted time series (ITS) analysis assessed the impact of DSCs on the counts of posts with specific mention of zolpidem-containing products. Chow tests for known structural breaks were conducted on data from Twitter, Facebook, and Google Trends. Finally, Wikipedia edits were pulled from the website's editorial history, which lists all revisions to a given page and the editor's identity. RESULTS: In total, 174,286 Twitter posts and 59,641 Facebook posts met entry criteria. Of those, 16.63% (28,989/174,286) of Twitter posts and 25.91% (15,453/59,641) of Facebook posts were labeled as junk and excluded. AEs and mentions represented 9.21% (16,051/174,286) and 74.16% (129,246/174,286) of Twitter posts and 5.11% (3,050/59,641) and 68.98% (41,138/59,641) of Facebook posts, respectively. Total daily counts of posts about zolpidem-containing products increased on Twitter and Facebook on the day of the first DSC; Google searches increased on the week of the first DSC. ITS analyses demonstrated variability but pointed to an increase in interest around the first DSC. Chow tests were significant (P<.0001) for both DSCs on Facebook and Twitter, but only the first DSC on Google Trends. Wikipedia edits occurred soon after each DSC release, citing news articles rather than the DSC itself and presenting content that needed subsequent revisions for accuracy. CONCLUSIONS: Social media offers challenges and opportunities for dissemination of the DSC messages. The FDA could consider strategies for more actively disseminating DSC safety information through social media platforms, particularly when announcements require updating. The FDA may also benefit from directly contributing content to websites like Wikipedia that are frequently accessed for drug-related information.

Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.

INTRODUCTION: The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data. OBJECTIVE: Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS). METHODS: A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS. FINDINGS: A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS. CONCLUSIONS: An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Social Media Listening for Routine Post-Marketing Safety Surveillance.

INTRODUCTION: Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources. OBJECTIVES: The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance. METHODS: A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information. RESULTS: In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information. CONCLUSION: Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.