Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in monitoring emergency department (ED) visits for suspected opioid overdoses.
Adverse Drug Events
Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in monitoring emergency department (ED) visits for suspected overdoses of any drug.
Description
Nationally, vaccine safety is monitored through several systems including Vaccine Adverse Event Reporting System (VAERS), a passive reporting system designed to detect potential vaccine safety concerns. Healthcare providers are encouraged to report adverse events after vaccination to VAERS, whether or not they believe that the vaccine caused the adverse event. The 2009 Pandemic H1N1 influenza vaccine became available in the United States in October 2009. By January 2010, Center for Disease Control and Prevention (Atlanta, GA, USA) estimated that 61 million persons across the United States had received the vaccine. As of January 2010, an estimated 28% of the North Carolina population greater than or equal to six months of age had been vaccinated against 2009 H1N1.
Objective
The objectives of this study were: (1) to compare trends in vaccine adverse events identified through emergency department (ED) diagnosis codes and reports from the VAERS, and (2) to determine whether 2009 H1N1 vaccine adverse events identified through VAERS could also be identified using ED diagnosis codes.
Referenced File
Description
The research reported in this paper is part of a larger effort to achieve better signal-to-noise ratio, hence accuracy, in pharmacovigilance applications. The relatively low frequency of occurrence of adverse drug reactions leads to weak causal relations between the reaction and any measured signal. We hypothesize that by grouping related signals, we can enhance detection rate and suppress false alarm rate.
Objective
ICD-9 codes are commonly used to identify disease cohorts and are often found to be less than adequate. Data available in structured databasesFlab test results, medications etc.Fcan supplement the diagnosis codes. In this study, we describe an automated method that uses these related data items, and no additional manual annotations to more accurately identify patient cohorts.
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Description
Singulair (MONTELUKAST SODIUM) is a Leukotriene receptor antagonist, indicated to prevent asthma attacks in adults and children. It is also used to relieve allergies in adults and children. Singulair was approved by the FDA in February 20, 1998. In March 2008 the FDA informed healthcare professionals of investigating the possible association between Singulair usage and behavior/mood changes, suicidality and suicide. First Life Research [FLR] identifies, analyzes, indexes and aggregates user generated content by collecting billions of testimonials from social networks. It utilizes cutting edge technologies for massive data aggregation, and applies advanced Natural Language Processing (NLP) techniques for continuous analyses, in order to convert this unstructured data into refined information. With a large population sharing experiences regarding health issues and treatments online via social media platforms [Health 2.0], generating novel data sets comprised of massive unstructured user generated content of health reports. This collective intelligence is referred to as the 'Wisdom of the Crowd' or the 'Crowd Trial'. Unlike regulated formal post marketing reports, the crowd trial takes place spontaneously, continuously and on a very large scale. This Crowd Trial provides a snapshot of health trends and has become a proxy of post-market clinical trials of medications and other therapies.
Objective
The purpose of this case report is to demonstrate how applying an additional data source originated from e-patient reports, helps support drug surveillance and Pharmacovigilance processes.
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Description
Adverse drug events (ADEs) are a major cause of morbidity and mortality. However, post-marketing surveillance systems are passive and reporting is generally not mandated. Thus, many ADEs go unreported, and it is difficult to estimate and/or anticipate side effects that are unknown at the time of approval. ADEs that are reported to the FDA tend to be severe, and potentially common, but less serious side effects are more difficult to characterize and document. Drugs with a high risk of harm outweighing the therapeutic value have recently been subjected to a greater level of interest with the Food and Drug Administration's Risk Evaluation and Mitigation Strategies (REMS). However, no rapid method to detect if the REMS produce the desired effect and assessment of the impact is conducted by the drug manufacturer. Increasingly, Americans have been turning to the internet for health related information, largely by the use of search engines such as Google. The volume of searches for drugs and ADEs provides a unique insight about the interest in various medications and side effects as well as longitudinal changes.
Objective
To investigate the use of search volume data from Google Insight for the detection and characterization of adverse drug events.
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Description
Adverse drug events (ADEs) are a significant source of morbidity and mortality. The majority of post-marketing surveillance for ADEs is passive. Information regarding ADEs is reported to the medical community in peer-reviewed journals. However, in most cases there is significant lag in the publication of peer-reviewed articles concerning ADEs. Within medical journals, our intuition is that letters to the editor may provide the earliest reports of ADEs. They often report single case reports or a collection of cases and usually precede more formal investigations and reports. Although these letters may contain useful and timely information, the challenge is that letters to the editor may be "buried" inside print journals. Furthermore, they may be more difficult to find and access even when using electronic searches because unlike other published reports, there is no corresponding abstract to view. Due to the lack of an abstract, detection depends almost exclusively upon words in a title, or manually applied Medical Subject Headings (MeSH). We propose that searching the full text of letters to the editor can provide a faster and perhaps more complete detection of ADEs compared to searches based on MeSH terms or titles alone.
Objective
Our objective was to explore the intuition that letters to the editor in leading medical journals contain early signals about adverse drug events. We explored this with letters in two leading journals.
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