Vaccine Vigilance System : Considerations on the Effectiveness of Vigilance Data Use in COVID-19 Vaccination

FARP project "Selection of biomarkers in ME/CFS for patient stratification and treatment surveillance / optimisationi".(1) Background: The safety of medicines has been receiving increased attention to ensure that the risks of taking medicines do not outweigh the benefits. This is the reason why, over several decades, the pharmacovigilance system has been developed. The post-authorization pharmacovigilance system is based on reports from healthcare professionals and patients on observed adverse reactions. The reports are collected in databases and progressively evaluated. However, there are emerging concerns about the effectiveness of the established passive pharmacovigilance system in accelerating circumstances, such as the COVID-19 pandemic, when billions of doses of new vaccines were administered without a long history of use. Currently, health professionals receive fragmented new information on the safety of medicines from competent authorities after a lengthy evaluation process. Simultaneously, in the context of accelerated mass vaccination, health professionals need to have access to operational information—at least on organ systems at higher risk. Therefore, the aim of this study was to perform a primary data analysis of publicly available data on suspected COVID-19 vaccine-related adverse reactions in Europe, in order to identify the predominant groups of reported medical conditions after vaccination and their association with vaccine groups, as well as to evaluate the data accessibility on specific syndromes. (2) Methods: To achieve the objectives, the data publicly available in the EudraVigilance European Database for Suspected Adverse Drug Reaction Reports were analyzed. The following tasks were defined to: (1) Identify the predominant groups of medical conditions mentioned in adverse reaction reports; (2) determine the relative frequency of reports within vaccine groups; (3) assess the feasibility of obtaining information on a possibly associated syndrome—myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). (3) Results: The data obtained demonstrate that the predominant medical conditions induced after vaccination are relevant to the following categories: (1) “General disorders and administration site conditions”, (2) “nervous system disorders”, and (3) “musculoskeletal and connective tissue disorders”. There are more reports for mRNA vaccines, but the relative frequency of reports per dose administered, is lower for this group of vaccines. Information on ME/CFS was not available, but reports of “chronic fatigue syndrome” are included in the database and accessible for primary analysis. (4) Conclusions: The information obtained on the predominantly reported medical conditions and the relevant vaccine groups may be useful for health professionals, patients, researchers, and medicine manufacturers. Policymakers could benefit from reflecting on the design of an active pharmacovigilance model, making full use of modern information technologies, including big data analysis of social media and networks for the detection of primary signals and building an early warning system.publishersversionPeer reviewe

The Potential of Social Media Intelligence to Improve Peoples Lives: Social Media Data for Good

In this report, developed with support from Facebook, we focus on an approach to extract public value from social media data that we believe holds the greatest potential: data collaboratives. Data collaboratives are an emerging form of public-private partnership in which actors from different sectors exchange information to create new public value. Such collaborative arrangements, for example between social media companies and humanitarian organizations or civil society actors, can be seen as possible templates for leveraging privately held data towards the attainment of public goals

A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

abstract: Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR being generated. A case control study has been performed on a data set of twitter timelines of women that announced their pregnancy, the goals of the study is to compare the ADR risk of medication usage from each medication category during the pregnancy. In addition, to evaluate the prediction power of this approach, another important aspect of personalized medicine was addressed: the prediction of medication usage through the identification of risk groups. During the prediction process, the health information from Twitter timeline, such as diseases, symptoms, treatments, effects, and etc., is summarized by the topic modelling processes and the summarization results is used for prediction. Dimension reduction and topic similarity measurement are integrated into this framework for timeline classification and prediction. This work could be applied to provide guidelines for FDA drug risk categories. Currently, this process is done based on laboratory results and reported cases. Finally, a multi-dimensional text data warehouse (MTD) to manage the output from the topic modelling is proposed. Some attempts have been also made to incorporate topic structure (ontology) and the MTD hierarchy. Results demonstrate that proposed methods show promise and this system represents a low-cost approach for drug safety early warning.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Mining social media data for biomedical signals and health-related behavior

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc

Analyzing Adverse Events from Publicly Available Web Sources

Data mining for drug-reaction associations is a major topic in the pharmaceutical industry. Historically the focus has been on using privately owned and maintained datasets consisting of information that has been transformed via the FDA Adverse Event Reporting System (FAERS) and privatized reporting systems that house the data from clinical trials. Our focus will be on building a pipeline that demonstrates an open source solution for building a drug’s safety profile from data collection through signal detection. In contrast this pipeline primarily uses the openFDA and social media data available through Reddit with all analysis being done in the R statistical programming language. The aim was to collect the information available in these public sources and apply popular data mining methodologies used to identify and predict the occurrence of adverse events. The results show the ability of the openFDA and social media sites to create real-time drug safety occurrence profiles by applying the same statistical methods applied in clinical trials. Social media will be shown to provide the best results when applied to prescribed daily use medications compared to common over-the-counter drugs or last line of defense medications. The information and results reported in this paper are not intended or implied to be a substitute for professional medical advice, diagnosis, or treatment. Do not delay seeking medical treatment or advice because of something you have read in this paper

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Pharmacovigilance of pregnancy exposures to medicinal products focusing on the risk of orofacial clefts

Background: It is important to obtain robust scientific information on possible safety concerns related to the use of drugs during pregnancy in post-approval settings. Since pregnant women are actively excluded from trials in the clinical development of most products, at the time of the drug entry in the market meaningful human data on the effects of that drug during pregnancy are rarely available. There are approximately 5 million pregnancies in the EU each year, and about 1 in every 10 women of childbearing age is pregnant each year. Insufficient information for management of maternal disease during pregnancy can have teratogenic impact on fetus. Aim and objectives: This reach comprises three studies, in the first study; the goal was to evaluate the maternal use of medicines and the associated risks of cleft lip and/or palate in fetus and to link this to the accuracy and currency of safety information available in prescribing information. The second area of research was aimed at identifying and exploring social and digital media to understand patients’ experiences regarding medicine use during pregnancy. Last, but not least, I contributed to the development of an enhanced pharmacovigilance programme for analysing drug exposure during pregnancy and outcomes in neonate. Method: Firstly, I identified medication-induced risk factors for oral clefts with safety signal detection and safety signal evaluation techniques. Then I assessed the completeness of the safety information for pregnancy exposures in the Summary of Product Characteristics and the Patient Information in the UK and the US. In second study, the content of posts concerning pregnancy and use of medicines in online pregnancy forums was analysed using artificial intelligence in the form of natural language processing and machine learning algorithms. Third, the PRIM (PRegnancy outcomes Intensive Monitoring) system was developed as an enhanced pharmacovigilance data collection method. This was used to improve the quality and content of prospective case reports using sets of targeted checklists, structured follow-up, a rigorous process of data entry and data quality control, and programmed aggregate analysis. Results: For 12 antiepileptic drugs studied there was a statistical disproportionality in individual case safety reports indicative of an increased risk of cleft lip and/or palate. There are inconsistencies between the UK and US safety labels, despite the same evidence being available for assessment. The second study showed that in social media forums many pregnant women with MS shared profound uncertainties and specific concerns about taking medicines during the reproductive period. There was evidence of concealment of information with health care professionals; however, the same evidence was shared with a peer group. The PRIM method of enhanced pharmacovigilance has yielded substantially more information on the safety of fingolimod exposure during pregnancy than has been achieved via the regulatory authority-mandated pregnancy registry. Conclusion: Use of medicines during pregnancy is an important topic for public health. There is a significant need to provide inclusive, unbiased, up to- date information to prescribers and women of childbearing age concerning the use of medicines in pregnancy and postpartum during breastfeeding. Information must be provided in a timely manner by a trusted source and patients should have access to health care professionals with the relevant expertise and knowledge. It is important that the full anonymised data set, along with evidence-based conclusions are made publicly available to inform decision-making

Using social media for air pollution detection-the case of Eastern China Smog

Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently