“The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis”

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.</p

“The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis”

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.</p

The effectiveness of COVID-19 vaccines to prevent long COVID symptoms:staggered cohort study of data from the UK, Spain, and Estonia

Background: Although vaccines have proved effective to prevent severe COVID-19, their effect on preventing long-term symptoms is not yet fully understood. We aimed to evaluate the overall effect of vaccination to prevent long COVID symptoms and assess comparative effectiveness of the most used vaccines (ChAdOx1 and BNT162b2). Methods: We conducted a staggered cohort study using primary care records from the UK (Clinical Practice Research Datalink [CPRD] GOLD and AURUM), Catalonia, Spain (Information System for Research in Primary Care [SIDIAP]), and national health insurance claims from Estonia (CORIVA database). All adults who were registered for at least 180 days as of Jan 4, 2021 (the UK), Feb 20, 2021 (Spain), and Jan 28, 2021 (Estonia) comprised the source population. Vaccination status was used as a time-varying exposure, staggered by vaccine rollout period. Vaccinated people were further classified by vaccine brand according to their first dose received. The primary outcome definition of long COVID was defined as having at least one of 25 WHO-listed symptoms between 90 and 365 days after the date of a PCR-positive test or clinical diagnosis of COVID-19, with no history of that symptom 180 days before SARS-Cov-2 infection. Propensity score overlap weighting was applied separately for each cohort to minimise confounding. Sub-distribution hazard ratios (sHRs) were calculated to estimate vaccine effectiveness against long COVID, and empirically calibrated using negative control outcomes. Random effects meta-analyses across staggered cohorts were conducted to pool overall effect estimates. Findings: A total of 1 618 395 (CPRD GOLD), 5 729 800 (CPRD AURUM), 2 744 821 (SIDIAP), and 77 603 (CORIVA) vaccinated people and 1 640 371 (CPRD GOLD), 5 860 564 (CPRD AURUM), 2 588 518 (SIDIAP), and 302 267 (CORIVA) unvaccinated people were included. Compared with unvaccinated people, overall HRs for long COVID symptoms in people vaccinated with a first dose of any COVID-19 vaccine were 0·54 (95% CI 0·44–0·67) in CPRD GOLD, 0·48 (0·34–0·68) in CPRD AURUM, 0·71 (0·55–0·91) in SIDIAP, and 0·59 (0·40–0·87) in CORIVA. A slightly stronger preventative effect was seen for the first dose of BNT162b2 than for ChAdOx1 (sHR 0·85 [0·60–1·20] in CPRD GOLD and 0·84 [0·74–0·94] in CPRD AURUM). Interpretation: Vaccination against COVID-19 consistently reduced the risk of long COVID symptoms, which highlights the importance of vaccination to prevent persistent COVID-19 symptoms, particularly in adults. Funding: National Institute for Health and Care Research.</p

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model

Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to &gt;85% of drug records in all but one of the assessed databases. Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.</p

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model

Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to &gt;85% of drug records in all but one of the assessed databases. Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.</p

Incident Use of Hydroxychloroquine for the Treatment of Rheumatoid Arthritis and Systemic Lupus Erythematosus During the COVID‐19 Pandemic

Objective: We studied whether the use of hydroxychloroquine (HCQ) for COVID‐19 resulted in supply shortages for patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Methods: We used US claims data (IQVIA PHARMETRICS® Plus for Academics [PHARMETRICS]) and hospital electronic records from Spain (Institut Municipal d'Assistència Sanitària Information System [IMASIS]) to estimate monthly rates of HCQ use between January 2019 and March 2022, in the general population and in patients with RA and SLE. Methotrexate (MTX) use was estimated as a control. Results: More than 13.5 million individuals (13,311,811 PHARMETRICS, 207,646 IMASIS) were included in the general population cohort. RA and SLE cohorts enrolled 135,259 and 39,295 patients, respectively, in PHARMETRICS. Incidence of MTX and HCQ were stable before March 2020. On March 2020, the incidence of HCQ increased by 9‐ and 67‐fold in PHARMETRICS and IMASIS, respectively, and decreased in May 2020. Usage rates of HCQ went back to prepandemic trends in Spain but remained high in the United States, mimicking waves of COVID‐19. No significant changes in HCQ use were noted among patients with RA and SLE. MTX use rates decreased during HCQ approval period for COVID‐19 treatment. Conclusion: Use of HCQ increased dramatically in the general population in both Spain and the United States during March and April 2020. Whereas Spain returned to prepandemic rates after the first wave, use of HCQ remained high and followed waves of COVID‐19 in the United States. However, we found no evidence of general shortages in the use of HCQ for both RA and SLE in the United States

Árboles viejos como indicadores de biodiversidad de vertebrados forestales amenazados de la provincia de Salamanca (España)

Ancient trees abundance has been compared with distribution of threatened forest vertebrate species at the province of Salamanca (Spain). A significant correlation between both parameters has been observed for the following considered species: imperial eagle (<em>Aquila adalberti</em>), black vulture (<em>Aegypus monachus</em>), iberian lynx (<em>Lynx pardinus</em>), red kite (<em>Milvus milvus</em>) and a group of forest bats (<em>Miniopterus schreibersii, Myotis bechsteinii, Myotis emarginatus, Myotis mystacinus, Myotis myotis, Nyctalus lasiopterus, Nyctalus noctula, Rhinolophus euryale, Rhinolophus ferrumequinum</em> y <em>Rhinolophus mehelyi</em>). It has been proved that there is an increase of threatened forest vertebrates biodiversity along with increasing ancient trees density at the municipalities of the province. Therefore, we can deduce that ancient trees density is a good indicator parameter of the conservation status of the forest ecosystem and it is essential for the maintenance of these endangered species.<br><br>Se ha comparado la abundancia de árboles viejos en la provincia de Salamanca (España) con la distribución de las especies de vertebrados forestales amenazados presentes, observándose que existe una correlación significativa entre ambos parámetros para las siguientes especies estudiadas: águila imperial (<em>Aquila adalberti</em>), buitre negro (<em>Aegypus monachus</em>), lince ibérico (<em>Lynx pardinus</em>), milano real (<em>Milvus milvus</em>) y un grupo de especies de quirópteros forestales (<em>Miniopterus schreibersii, Myotis bechsteinii, Myotis emarginatus, Myotis mystacinus, Myotis myotis, Nyctalus lasiopterus, Nyctalus noctula, Rhinolophus euryale, Rhinolophus ferrumequinum</em> y <em>Rhinolophus mehelyi</em>). Se ha demostrado que existe un incremento de la biodiversidad de vertebrados forestales amenazados paralelo al aumento de la densidad de árboles viejos en los municipios de la provincia. Por ello, se puede deducir que la densidad de árboles viejos es un buen parámetro indicador del estado de conservación del ecosistema forestal y clave para el mantenimiento de dichas especies amenazadas

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research

Differential body composition effects of protease inhibitors recommended for initial treatment of HIV infection: A randomized clinical trial

This article has been accepted for publication in Clinical Infectious Diseases ©2014 The Authors .Published by Oxford University Press on Clinical Infectious Disease 60.5. DOI: 10.1093/cid/ciu898Background. It is unclear whether metabolic or body composition effects may differ between protease inhibitor-based regimens recommended for initial treatment of HIV infection. Methods. ATADAR is a phase IV, open-label, multicenter randomized clinical trial. Stable antiretroviral-naive HIV-infected adults were randomly assigned to atazanavir/ritonavir 300/100 mg or darunavir/ritonavir 800/100 mg in combination with tenofovir/emtricitabine daily. Pre-defined end-points were treatment or virological failure, drug discontinuation due to adverse effects, and laboratory and body composition changes at 96 weeks. Results. At 96 weeks, 56 (62%) atazanavir/ritonavir and 62 (71%) darunavir/ritonavir patients remained free of treatment failure (estimated difference 8.2%; 95%CI -0.6 to 21.6); and 71 (79%) atazanavir/ritonavir and 75 (85%) darunavir/ritonavir patients remained free of virological failure (estimated difference 6.3%; 95%CI -0.5 to 17.6). Seven vs. five patients discontinued atazanavir/ritonavir or darunavir/ritonavir due to adverse effects. Total and HDL cholesterol similarly increased in both arms, but triglycerides increased more in atazanavir/ritonavir arm. At 96 weeks, body fat (estimated difference 2862.2 gr; 95%CI 726.7 to 4997.7; P=0.0090), limb fat (estimated difference 1403.3 gr; 95%CI 388.4 to 2418.2; P=0.0071), and subcutaneous abdominal adipose tissue (estimated difference 28.4 cm2; 95%CI 1.9 to 55.0; P=0.0362) increased more in atazanavir/ritonavir than in darunavir/ritonavir arm. Body fat changes in atazanavir/ritonavir arm were associated with higher insulin resistance. Conclusions. We found no major differences between atazanavir/ritonavir and darunavir/ritonavir in efficacy, clinically-relevant side effects, or plasma cholesterol fractions. However, atazanavir/ritonavir led to higher triglycerides and total and subcutaneous fat than darunavir/ritonavir and fat gains with atazanavir/ritonavir were associated with insulin resistanceThis is an Investigator Sponsored Research study. It was supported in part by research grants from Bristol‐Myers Squibb and Janssen‐Cilag; Instituto de Salud Carlos III (PI12/01217) and Red Temática Cooperativa de Investigación en SIDA G03/173 (RIS‐EST11), Ministerio de Ciencia e Innovación, Spain. (Registration number: NCT01274780; registry name: ATADAR; EUDRACT; 2010‐021002‐38)

Non-motor symptom burden in patients with Parkinson's disease with impulse control disorders and compulsive behaviours : results from the COPPADIS cohort

The study was aimed at analysing the frequency of impulse control disorders (ICDs) and compulsive behaviours (CBs) in patients with Parkinson's disease (PD) and in control subjects (CS) as well as the relationship between ICDs/CBs and motor, nonmotor features and dopaminergic treatment in PD patients. Data came from COPPADIS-2015, an observational, descriptive, nationwide (Spain) study. We used the validated Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease-Rating Scale (QUIP-RS) for ICD/CB screening. The association between demographic data and ICDs/CBs was analyzed in both groups. In PD, this relationship was evaluated using clinical features and treatment-related data. As result, 613 PD patients (mean age 62.47 ± 9.09 years, 59.87% men) and 179 CS (mean age 60.84 ± 8.33 years, 47.48% men) were included. ICDs and CBs were more frequent in PD (ICDs 12.7% vs. 1.6%, p < 0.001; CBs 7.18% vs. 1.67%, p = 0.01). PD patients had more frequent previous ICDs history, premorbid impulsive personality and antidepressant treatment (p < 0.05) compared with CS. In PD, patients with ICDs/CBs presented younger age at disease onset, more frequent history of previous ICDs and premorbid personality (p < 0.05), as well as higher comorbidity with nonmotor symptoms, including depression and poor quality of life. Treatment with dopamine agonists increased the risk of ICDs/CBs, being dose dependent (p < 0.05). As conclusions, ICDs and CBs were more frequent in patients with PD than in CS. More nonmotor symptoms were present in patients with PD who had ICDs/CBs compared with those without. Dopamine agonists have a prominent effect on ICDs/CBs, which could be influenced by dose