The Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) conduct post-licensure vaccine safety monitoring using the Vaccine Adverse Event Reporting System (VAERS), a spontaneous (or passive) reporting system. This means that after a vaccine is approved, CDC and FDA continue to monitor safety while it is distributed in the marketplace for use by collecting and analyzing spontaneous reports of adverse events that occur in persons following vaccination. Various methods and statistical techniques are used to analyze VAERS data, which CDC and FDA use to guide further safety evaluations and inform decisions around vaccine recommendations and regulatory action. VAERS data must be interpreted with caution due to the inherent limitations of passive surveillance. VAERS is primarily a safety signal detection and hypothesis generating system. Generally, VAERS data cannot be used to determine if a vaccine caused an adverse event. VAERS data interpreted alone or out of context can lead to erroneous conclusions about cause and effect as well as the risk of adverse events occurring following vaccination. CDC makes VAERS data available to the public and readily accessible online. We describe fundamental vaccine safety concepts, provide an overview of VAERS for healthcare professionals who provide vaccinations and might want to report or better understand a vaccine adverse event, and explain how CDC and FDA analyze VAERS data. We also describe strengths and limitations, and address common misconceptions about VAERS. Information in this review will be helpful for healthcare professionals counseling patients, parents, and others on vaccine safety and benefit-risk balance of vaccination.

RV5 was associated with approximately 1.5 (95% CI, 0.2 to 3.2) excess cases of intussusception per 100,000 recipients of the first dose. The secondary analysis of RV1 suggested a potential risk, although the study of RV1 was underpowered. These risks must be considered in light of the demonstrated benefits of rotavirus vaccination. (Funded by the Food and Drug Administration.).

PurposeDefining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.MethodsWe reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.ConclusionEvidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision‐makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram.A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

Adenosine is a neuroprotective agent that inhibits neuronal activity and modulates neurotransmission. Previous research has shown adenosine gradually accumulates during pathologies such as stroke and regulates neurotransmission on the minute-to-hour time scale. Our lab developed a method using carbon-fiber microelectrodes to directly measure adenosine changes on a sub-second time scale with fast-scan cyclic voltammetry (FSCV). Recently, adenosine release lasting a couple of seconds has been found in murine spinal cord slices. In this study, we characterized spontaneous, transient adenosine release in vivo, in the caudate-putamen and prefrontal cortex of anesthetized rats. The average concentration of adenosine release was 0.17±0.01 µM in the caudate and 0.19±0.01 µM in the prefrontal cortex, although the range was large, from 0.04 to 3.2 µM. The average duration of spontaneous adenosine release was 2.9±0.1 seconds and 2.8±0.1 seconds in the caudate and prefrontal cortex, respectively. The concentration and number of transients detected do not change over a four hour period, suggesting spontaneous events are not caused by electrode implantation. The frequency of adenosine transients was higher in the prefrontal cortex than the caudate-putamen and was modulated by A1 receptors. The A1 antagonist DPCPX (8-cyclopentyl-1,3-dipropylxanthine, 6 mg/kg i.p.) increased the frequency of spontaneous adenosine release, while the A1 agonist CPA (N6-cyclopentyladenosine, 1 mg/kg i.p.) decreased the frequency. These findings are a paradigm shift for understanding the time course of adenosine signaling, demonstrating that there is a rapid mode of adenosine signaling that could cause transient, local neuromodulation.

Fast-scan cyclic voltammetry (FSCV) can detect small changes in dopamine concentration; however, measurements are typically limited to scan repetition frequencies of 10 Hz. Dopamine oxidation at carbon-fiber microelectrodes (CFMEs) is dependent on dopamine adsorption, and increasing the frequency of FSCV scan repetitions decreases the oxidation current, because the time for adsorption is decreased. Using a commercially available carbon nanotube yarn, we characterized carbon nanotube yarn microelectrodes (CNTYMEs) for high-speed measurements with FSCV. For dopamine, CNTYMEs have a significantly lower ΔEp than CFMEs, a limit of detection of 10 ± 0.8 nM, and a linear response to 25 μM. Unlike CFMEs, the oxidation current of dopamine at CNTYMEs is independent of scan repetition frequency. At a scan rate of 2000 V/s, dopamine can be detected, without any loss in sensitivity, with scan frequencies up to 500 Hz, resulting in a temporal response that is four times faster than CFMEs. While the oxidation current is adsorption-controlled at both CFMEs and CNTYMEs, the adsorption and desorption kinetics differ. The desorption coefficient of dopamine-o-quinone (DOQ), the oxidation product of dopamine, is an order of magnitude larger than that of dopamine at CFMEs; thus, DOQ desorbs from the electrode and can diffuse away. At CNTYMEs, the rates of desorption for dopamine and dopamine-o-quinone are about equal, resulting in current that is independent of scan repetition frequency. Thus, there is no compromise with CNTYMEs: high sensitivity, high sampling frequency, and high temporal resolution can be achieved simultaneously. Therefore, CNTYMEs are attractive for high-speed applications.