Abstract

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.

Abstract

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.

Abstract

Abstract

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.

Abstract

Abstract

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.