Data Sources for Drug Utilization Research in Brazil—DUR-BRA Study

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Medication Errors in Pediatrics: Proposals to Improve the Quality and Safety of Care Through Clinical Risk Management

Medication errors represent one of the most common causes of adverse events in pediatrics and are widely reported in the literature. Despite the awareness that children are at increased risk for medication errors, little is known about the real incidence of the phenomenon. Most studies have focused on prescription, although medication errors also include transcription, dispensing, dosage, administration, and certification errors. Known risk factors for therapeutic errors include parenteral infusions, oral fluid administration, and tablet splitting, as well as the off-label use of drugs with dosages taken from adult literature. Emergency Departments and Intensive Care Units constitute the care areas mainly affected by the phenomenon in the hospital setting. The present paper aims to identify the risk profiles in pediatric therapy to outline adequate preventive strategies. Precisely, through the analysis of the available evidence, solutions such as standardization of recommended doses for children, electronic prescribing, targeted training of healthcare professionals, and implementation of reporting systems will be indicated for the prevention of medication errors.

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

45. COVID-19 Vulnerability and Data Reporting in Africa: Reflections from Cameroon

Background At the beginning of the COVID-19 pandemic there were many questions about vulnerability and data reporting among African countries. We previously found that policymakers in Cameroon value region-specific risk maps for emerging diseases. Therefore, we created regional vulnerability indices for COVID-19 in Cameroon. As the pandemic grew, we aimed to compare how these predictions related to reported COVID-19 cases in Cameroon and whether additional African countries had available data to assess vulnerability for COVID-19. Methods Using data from the Cameroon 2018 Demographic and Health Survey (DHS), we had constructed an epidemiological vulnerability index based on comorbidities potentially associated with COVID-19 severity. Similarly, we had created a healthcare access index. We then compared these indices with regional COVID-19 cases per population from weekly situation reports in Cameroon. Finally, we identified the availability of DHS data and COVID-19 reporting systems in other African countries. Vulnerability Indices for COVID-19 in Cameroon The epidemiological and healthcare access vulnerability indices constructed for Cameroon are shown along with COVID-19 cases per population. Results Adjusting for data reporting limitations, regions in Cameroon that scored higher on the epidemiological vulnerability index were associated with greater COVID-19 cases per population. We also identified regions with mismatches between high epidemiological vulnerability and low healthcare access. COVID-19 data reporting systems varied among African countries. 29/54 (53.7%) of African countries had recurrent situation reports or online dashboards with subnational COVID-19 data in 2020. Meanwhile, 36/54 (66.7%) of African countries had DHS data reported in the last decade. Conclusion We found that vulnerability indices could be a rapid way of identifying populations at risk for emerging diseases such as COVID-19. This method could be used in other countries that have both recent health surveys from programs such as the DHS and subnational reporting of COVID-19 cases. Indices could be useful for decision-making, but they will need to be refined with national expert input. National situation reports and online dashboards provided subnational COVID-19 data in approximately half of African countries. Therefore, increased baseline health surveys as well as expanded reporting of COVID-19 case data could inform future vulnerability assessments in other countries. Disclosures All Authors: No reported disclosures

Endometriosis classification, staging and reporting systems: a review on the road to a universally accepted endometriosis classification

Background: In the field of endometriosis, several classification, staging and reporting systems have been developed. However, endometriosis classification, staging and reporting systems that have been published and validated for use in clinical practice have not been not systematically reviewed up to now. Objectives: The aim of the current review is to provide a historical overview of these different systems based on an assessment of published studies. Materials and Methods: A systematic Pubmed literature search was performed. Data were extracted and summarised. Results: Twenty-two endometriosis classification, staging and reporting systems have been published between 1973 and 2021, each developed for specific and different purposes. There is still no international agreement on how to describe the disease. Studies evaluating different systems are summarised showing a discrepancy between the intended and the evaluated purpose, and a general lack of validation data confirming a correlation with pain symptoms or quality of life for any of the current systems. A few studies confirm the value of the Enzian system for surgical description of deep endometriosis. With regards to infertility, the endometriosis fertility index has been confirmed valid for its intended purpose. Conclusions: Of the 22 endometriosis classification, staging and reporting systems identified in this historical overview, only a few have been evaluated, in 46 studies, for the purpose for which they were developed. It can be concluded that there is no international agreement on how to describe endometriosis or how to classify it, and that most classification/staging systems show no or very little correlation with patient outcomes. What is new? This overview of existing systems is a first step in working towards a universally accepted endometriosis classification.

Mining vaccine adverse events mentions from social media using Twitter as a source (Preprint)

BACKGROUND Traditional monitoring for Adverse Events Following Immunisation (AEFI) relies on various established reporting systems, where there is inevitably a lag between an AEFI occurring and its potential reporting, and subsequent processing of reports. AEFI safety signal detection strives to detect AEFI as early as possible, ideally close to real-time. Monitoring social media data holds promise as a resource for this. OBJECTIVE 1) To investigate the utility of monitoring social media for gaining early insights into vaccine safety issues, by extracting vaccine adverse event mentions (VAEM) from Twitter using natural language processing (NLP) techniques. 2) To document the NLP processes used and identify the most effective of them for successively identifying tweets that contain VAEM, with a view to defining an approach that might be applicable to other similar social media surveillance tasks. METHODS A VAEM-Mine method was developed that combines topic modelling with classification techniques to extract maximal VAEM posts from a vaccine-related Twitter stream, with a high degree of confidence. The approach does not require a targeted search for specific vaccine reactions, but instead identifies any VAEM post within many unrelated posts. RESULTS The VAEM-Mine method successively isolates vaccine adverse event mentions from the massive amount of other vaccine-related Twitter posts, achieving an F1-Score of 0.91 in the classification phase. CONCLUSIONS Social media can assist with detection of vaccine safety signals as a valuable complementary source for monitoring mentions of vaccine adverse events. A social media based VAEM data stream can be assessed for changes to detect possible emerging vaccine safety signals, helping to address the well-recognised limitations of passive reporting systems, including timeliness and under-reporting.