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Novel algorithm identifies adverse drug events across the seven pediatric development stages

Attendance plummets at LA covid vaccination events

Side effects from pediatric drug treatment are responsible for nearly 10 percent of childhood hospitalizations, with nearly half of those being life-threatening. Despite the need to know more about these drugs and the adverse events they can have on children, little evidence is currently available.

Clinical trials remain the gold standard for identifying adverse drug events (ADEs) for adults, but these have both ethical and methodological concerns for the pediatric population. The rapidly changing biologic and physiologic developments only enhance the challenges of understanding the potential impacts of different drug treatments at various stages of childhood.

Researchers at the Columbia University Irving Medical Center developed a novel algorithm that identified nearly 20,000 ADEs signals (information on a new or known side effect that may be caused by a particular drug) across the seven pediatric development stages and made them freely available. This process is strengthened by a novel approach that allows neighboring development stages to enhance the signal detection power, which helps it overcome limited data within individual stages.

This use of predictive modeling on real-world data can help address a critical gap in healthcare research around the understudied pediatric community.

DBMI associate professor Nicholas Tatonetti and Nick Giangreco, a recent Systems Biology PhD graduate at Columbia University, shared these findings in the study A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development, which was recently published in Med.

For many reasons, children have historically not been included in clinical trials. There are many ethical issues around including children in trials, and there are several limitations when children are included that make it difficult to assess the effectiveness and safety of drugs.”

Nicholas Tatonetti, DBMI associate professor

Because of these factors, few drugs are specifically approved for use in children, though once drugs are approved for adults, physicians can prescribe them “off-label” to children.

“Since drugs are not studied and approved in children directly, physicians must rely on guidelines for adults,” he added. “Essentially treating children as if they were simply small adults is oftentimes an incorrect assumption. This study is an attempt to elucidate systematically what the potential side effects are when drugs are used off label in children.”

The study goes beyond simply differentiating side effects in children from those in adults. It focuses on ADEs across seven developmental stages, starting at term neonatal and going through late adolescence, and it is powered by sharing information from neighboring developmental stages. For example, the development of infants and toddlers is close enough that there will be more shared characteristics than there would be for infants and those in early or late adolescence.

“Previously, children were essentially grouped together,” Tatonetti said. “There were only a few studies that just focused on children, and they basically focused on people 18 and under or 21 and under in one group. The innovation here is using known developmental stages and our newly introduced DGAMs (disproportionality generalized additive models) to improve power and enable that analysis.”

Tatonetti stressed that these signals are not validated and are primarily meant for researchers. Parents should consult with their pediatricians on specific drug side effects.

Giangreco, currently a Quantitative Translational Scientist at Regeneron, noted one of several side effects that were identified by this model.

“One we corroborated that the FDA had found was that montelukast, an asthma drug, was found to elicit psychiatric side effects,” he said. “We saw that in our database as well, but we were able to pinpoint certain developmental stages where the risk was more significant, especially the second year of life.”

The study also integrates pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression are associated with pediatric ADEs.

“This was a biologically-inspired modeling strategy,” Giangreco said. “We used what we knew about biological processes occurring during childhood and formed the modeling strategy. These safety signals came from this prior knowledge of the biological processes that are happening. Our data-driven approach really tried to capture what we thought were the important biologically and physiologically dynamic processes that happen during childhood and use that to tease apart observations across the development stages.”

The model was used on a database of 264,453 pediatric reports in the FDA Adverse Event Reporting System (FAERS). The output of the study is available via KidSIDES, a free and publicly available database of pediatric drug safety signals for the research community, as well as the Pediatric Drug Safety portal (PDSportal), which will facilitate evaluation of drug safety signals across childhood growth and development.

“The primary intention is for other researchers to use it, to follow up on signals they may observe,” Tatonetti said. “If they are experts on a particular drug usage, or particular disease domain and have observed these types of effects, they could follow up on them and be reassured, or could look at what the other evidence is for that effect as we aggregate it together. Clinicians can use it as a gut check. Maybe they saw an effect, or they are wondering if others are seeing this effect, and they can check the PDSPortal to see if others are seeing this effect or to prompt them to write another case report to the FDA.”


Journal reference:

Giangreco, N.P., et al. (2022) A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development. Med.

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Modeling the influence of COVID superspreading events

Study: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / Shutterstock

In a recent study posted to the medRxiv* preprint server, University of Kansas researchers assessed the effect of superspreading events (SSEs) on the United States (US) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak dynamics.

Study: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / ShutterstockStudy: Exploring the Role of Superspreading Events in SARS-CoV-2 Outbreaks. Image Credit: StockTom / Shutterstock


SARS-CoV-2, the novel coronavirus that emerged in late December 2019, has quickly swept over the globe, resulting in over 546 million illnesses and more than 6.3 million fatalities thus far. Coronavirus disease 2019 (COVID-19) has strained the US healthcare network, with several hospitals exceeding or nearing capacity and few limiting services. 

Governments at the state and national levels have responded by issuing guidelines and regulations for decreasing SARS-CoV-2 transmission, including social-distancing directives, mask mandates, stay-at-home instructions, and restrictions on big gatherings. However, insufficient adherence and compliance by the population have affected the efficiency of these laws and regulations, encouraging SSEs, which have assisted the SARS-CoV-2 transmission.

About the study

In the present study, the researchers developed a continuous-time Markov chain (CTMC) model to examine the impact of SSEs on the dynamics of the SARS-CoV-2 outbreak in the US. The authors defined SSEs as social or public events that lead to numerous SARS-CoV-2 infections over a short period.

The current research sought to determine the effect of SSEs compared to non-SSEs on COVID-19 outbreak dynamics, the efficacy of hospitalization and quarantine as containment methods for SSE relative to non-SSE-dominated outbreaks, and the impact of quarantine violation on the efficacy of quarantine for SSE compared to non-SSE-dominated outbreaks.

The investigators simulated a CTMC model for SARS-CoV-2 spread utilizing Gillespie’s direct algorithm under three distinct scenarios: 1) neither hospitalization nor quarantine; 2) quarantine, hospitalization, premature hospital discharge, and quarantine violation; and 3) hospitalization and quarantine but not premature hospital discharge or quarantine violation. They also alter the rate of quarantine violations under realistic hospitalization and quarantine (RHQ) scenarios.


The study results demonstrated that the SARS-CoV-2 outbreaks with SSE dominance were often more variable yet less severe and more prone to extinction than outbreaks without SSE dominance. The authors observed this after eliminating hospitalization and quarantine conditions or upon the inclusion of hospitalization, quarantine, early hospital discharge, and quarantine breach. 

However, the severity of the most catastrophic SSE-dominated outbreaks was higher than the most severe outbreaks without SSE dominance, despite most SSE-dominated outbreaks being less severe. Upon the inclusion of quarantine and hospitalization, while excluding quarantine breach and premature hospital discharge, SARS-CoV-2 outbreaks dominated by SSE were more susceptible to extinction than outbreaks without SSE dominance but were more severe and less variable.

Upon the inclusion of quarantine, hospitalization, premature hospital discharge, and halved quarantine breach, outbreaks dominated by SSE were comparable to when quarantine and hospitalization were included, but quarantine breach and premature hospital discharge were excluded. Besides, when quarantine breach was doubled outbreaks were similar to when quarantine and hospitalization were excluded.

Quarantine and hospitalization were more potent at regulating outbreaks dominated by SSE than those without SSE dominance in all scenarios. Similarly, quarantine breaches and premature hospital discharge were significant for outbreaks dominated by SSE.

SSE-dominated outbreaks were extremely improbable to become extinct when quarantine and hospitalization were excluded. They were moderately unlikely to become extinct when quarantine, hospitalization, premature hospital discharge, and quarantine violation were included. Furthermore, they were highly plausible to become extinct when hospitalization and quarantine were included, but quarantine breach and premature hospital discharge were excluded. 

Moreover, SSE-dominated outbreaks were more likely to become extinct when quarantine violations were halved. However, outbreaks dominated by SSE were less likely to become extinct when quarantine breaches were doubled.


Altogether, the study findings showed that COVID-19 outbreaks dominated by SSE differ noticeably from non-SSE-dominated outbreaks in their severity, variability, and chances of extinction. They also vary, albeit more low-key, from outbreaks dominated by superspreading individuals (SI). The possibility of hospitalization or quarantine and the likelihood of premature hospital discharge or violation of quarantine significantly impact the dynamics of SSE-dominated outbreaks.

Hospitalization and quarantine were substantially effective preventative interventions for COVID-19 outbreaks dominated by SSE. Nevertheless, premature hospital discharge and breach of the quarantine drastically diminished their efficacy. Besides, the team assessed control techniques using the probability of extinction.

The present findings have significant public health consequences, necessitating SARS-CoV-2 modelers must: 1) assess the contribution of SSEs or SIs to COVID-19 spread; and 2) differentiate between SSEs, SIs, and non-SIs/non-SSEs in their models. More research into the combined and individual effects of SSEs and SIs on SARS-CoV-2 outbreak dynamics and the efficacy of control strategies for various kinds of outbreaks were required to guide eradication and containment initiatives.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

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WHO/Europe training course for prison health-care workers: innovation in NCD policy and action

WHO/Europe virtual press briefing: Humanitarian emergency in Ukraine and the wider region

May 2022 online training course

The WHO European Regional Office for Europe, in collaboration with the Yale School of Medicine, has developed an online training course to empower and enhance professional development of national counterparts and clinicians working with prisons and other detention facilities.

The online course will give its participants the knowledge and innovative tools to:

  • review the latest evidence on the burden of noncommunicable diseases (NCD) such as cardiovascular diseases, obesity and overweight, cancer, respiratory diseases, and mental health disorders; and their risk factors;
  • implement successful NCD practices in a prison context;
  • develop further advocacy strategies; and
  • train their peers to deliver the WHO-recommended interventions.

Level and demands

The course is aimed at health professionals specializing in prison environments from any of the 53 Member States of the WHO European Region. Professionals from other regions are also welcome to express their interest in participating.

The training is free of charge for all selected participants.

Course timeline and composition:

The course starts on 10 May 2022 and ends on 24 June 2022.

It will consist of several modules and include educational videos, webinars, practical workshops, and participant activities.

Application deadline:

Please send your expression of interest to participate in the course to Filipa Alves da Costa ( by 5 May 2022. All participants will be notified on further course details by 9 May 2022.


All participants receive a digital certificate after successfully completing the course.