Eating disorders (ED) are serious mental illnesses that affect millions of people around the world and entail considerable personal, family, and social costs. It is a hidden public health problem with a great population impact, of which the magnitude in our environment and its characteristics are unknown.
This project aims to characterize and determine the magnitude of eating disorders (ED) in Catalonia, and to describe the social inequalities of people diagnosed with ED in primary care or admitted to hospital.
Specific objectives:
1. To estimate the incidence and prevalence of ED in Catalonia among the individuals diagnosed in primary care or admitted to hospital between 2010 and 2024.
2. To examine, sex, age, nationality, rural/urban setting, and socioeconomic inequalities of ED diagnosis in Catalonia among the individuals who visited primary care or were admitted to the hospital between 2010 and 2024.
3. To identify comorbidities and lifestyle factors associated with ED diagnosed in primary care or admitted to the hospital between 2010 and 2024 in Catalonia.
Study design:
We will conduct a population-based cohort study using individual-level routinely collected Electronic Health Records (EHR) obtained from SIDIAP database.
The study will compromise two consecutive parts:
• Population-level cohort study: This part will estimate the incidence and prevalence of ED in the whole cohort and by specific subgroups (objective 1 and 2)
• Patient-level characterisation: This part aims to estimate comorbidities and associated factors among individuals diagnosed with ED (Obejctive 3).
Study period:
The study period will be from 1st of January 2010 until the end of data availability.
Statistical analyses:
We will estimate monthly incidence and prevalence rates with 95% CI of eating disorders over the study period (2010-2023) in the overall population. Incidence and prevalence will be calculated for the overall of ED and separately for each outcome (anorexia nervosa, bulimia nervosa, binge eating disorder, avoidant/restrictive eating disorder and unspecified eating disorders).
We will stratify our analyses on incidence and prevalence by age, sex, nationality, socioeconomic status (MEDEA), residence type (urban / rural) and other somatic comorbidities. Incidence rate ratios (IRR) with 95%CI will be calculated for each subgroup to compare the differences in the incidence of each strata using negative binomial regression models.
We will explore the effects of explanatory variables such other mental conditions diagnosis (neurodevelopmental disorder, alcohol and substance use disorders, mood and anxiety disorders, personality disorders, obsessive compulsive disorder, impulsivity disorders),, obesity, diabetes and lifestyle factors such as smoking and alcohol risk for each ED.
Finally, we will also conduct a large-scale characterization of the ED cohort to identify the medical conditions in the individuals’ history and compared them to a random subsample of individuals in SIDIAP.

Rationale and Background
Systemic lupus erythematosus (SLE) is a multisystem autoimmune disorder of connective tissue characterized by autoantibodies that target nuclear antigens, remissions and flares, and a highly variable clinical presentation, disease course, and prognosis. The disease course is more severe in childhood-onset compared to adult-onset SLE, with higher prevalence of morbidities and lower survival rates.
Therefore, to review new drug applications, it would be important for the European Medicines Agency (EMA) to understand the current clinical practice of treating SLE in paediatric population and differences with the treatment in adult population.
Research question and Objectives
The overall objective of this study is to characterise paediatric and adult patients with SLE diagnosed in the period 2013-2022, and to study the treatments they received in this same period.
Research Methods
Study design
A series of cohort studies will be conducted, including newly diagnosed SLE and new user (of SLE treatments) cohorts.
Population
The source population will include all individuals eligible in the database between 01/01/2013 and 31/12/2022. Additional eligibility criteria will be applied for each study objective: Cohort 1) at least 365 days of prior history available before date of new SLE diagnosis will be applied for large-scale characterisation (objectives 1 and 2); Cohort 2) a washout period of 365 days at the treatment ingredient level will be applied to capture new users of SLE treatment (objectives 5 and 6).
Variables
The main exposure of interest is the treatment of SLE: treatment/s initiated at index date, 1 to 30, 31 to 90, 91 to 180, 181 to 365 days, and 366 days to any time after new diagnosis of SLE. A pre-specified list of SLE treatments will be generated (objectives 3, 4, 5, and 6).
All co-morbidities and co-medications will be used for large-scale patient characterisation, identified as concept/code and descendants. A list of pre-specified co-morbidities and co-medications will also be described.
Data sources
1. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
2. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
3. Institut Municipal Assistencia Sanitaria Information System (IMASIS), Spain
4. Clinical Data Warehouse of Bordeaux University Hospital (CDWBordeaux), France
5. Clinical Practice Research Datalink (CPRD) GOLD, United Kingdom (UK)

Rationale and Background:
Gastroparesis is a medical condition characterized by delayed gastric emptying, causing symptoms like postprandial fullness, nausea, vomiting, and upper abdominal pain. It affects individuals across different age groups, encompassing both paediatric population and adults. Pharmacotherapy, particularly medication with prokinetic properties, has been used to manage symptoms, often through off-label use.

Research question and Objectives:
Research question
What is the real-life use of medicines with prokinetic properties in children and adults diagnosed with gastroparesis?
Study objectives
1.To determine the incidence and prevalence of use of medications with prokinetics properties in the paediatric population stratified by calendar year, age categories, sex and country/database during the study period (2012 – 2022)
2.To determine the incidence and prevalence of use of medications with prokinetics properties in adults, stratified by calendar year, age categories, sex and country/database during the study period (2012 – 2022)
3.To describe the characteristics (in terms of age, sex) of children and adults initiating treatment with any of the prokinetic drugs of interest stratified by indication of use.
4.To determine the dose, formulation, route of administration , indication of use and setting (inpatient vs outpatient ) at time of treatment initiation of any of the prokinetic drugs of interest. In addition, the cumulative treatment duration will be estimated.

Research Methods:
Study design
-Population-level cohort study (Objective 1 and 2, Population-level drug utilization study of medication with prokinetic properties)
-New drug user cohort study (Objective 3 and 4, Patient-level drug utilization analyses regarding summary characterization, dose, formulation, route of administration (oral or parenteral), duration and indication of use of medication with prokinetic properties)
Population
-Population-level utilization of medication with prokinetic properties: Population-level drug utilization analyses will include all individuals registered in the respective databases between 2012 and 2022, with at least 1 year of data visibility prior they become eligible for study inclusion. This requirement of at least 1 year of data history will not hold for children < 1 year. -Patient-level drug utilization: Patent-level drug utilization analyses will include new users of medication with prokinetic properties in the period between 2012 and 2022 (or latest date available, whatever comes first), with at least 1 year of data visibility prior to index date, and no use of the respective medication with prokinetic properties in the previous 1 year. This requirement of at least 1 year of data history will not hold for children < 1 year. Variables: Drug of interest -Macrolide antibiotics: Erythromycin (not used for the treatment of infection – i.e. prescriptions with code for infection in the +7/-7 days around the prescription date will be excluded) -Prokinetic agents: Metoclopramide, Cisapride, Domperidone, Clebopride, Itopride and Cinitapride Condition of interest: -Gastroparesis and other conditions/indications Data sources: 1.Clinical Data Warehouse of Bordeaux University Hospital (CHUBX), France 2.Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 3.IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 4.IQVIA LBD Belgium, Belgium 5.Institut Municipal Assistencia Sanitaria Information System (IMASIS), Spain 6.Integrated Primary Care Information Project (IPCI), The Netherlands 7.The Information System for Research in Primary Care (SIDIAP), Spain Sample size: No sample size has been calculated for this drug utilization study, as our primary focus is to examine drug utilization of medications with prokinetics properties, irrespective of the sample size. Based on a preliminary feasibility assessment, the expected number of records for medication with prokinetic properties in the databases included in this study will be approximately up to 276,000 in children and up to 1,655,000 in adults. Data analyses: Population-level utilization of medication with prokinetic properties: Annual period prevalence of medications with prokinetics properties use and annual incidence rates per 100,000 person years will be estimated in the paediatric population and in adults. The statistical analyses will be performed based on OMOP-CDM mapped data using the “IncidencePrevalence” R package. Patient-level drug utilization: Large-scale patient-level characterization will be conducted at index date including patient demographics and co-medication. Index date will be the date of the first prescription of the specific medications with prokinetics properties for each person. Frequency of indication/comorbidities will be assessed at index date and for any time up to 2 years for infants/toddlers and any time up to 5 years for the other age categories. Treatment duration will be estimated for the first treatment era and the minimum, p25, median, p75, and maximum will be provided. Dose/strength of prescribed or dispensed medication with prokinetic properties will be expressed as the minimum, p25, median, p75 and maximum. Route of administration will be estimated for the first treatment era and provided as proportion. The statistical analyses will be performed based on OMOP-CDM mapped data using the “DrugUtilization” R package. For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts obscured.

Rationale and Background:

In clinical trials involving patients with major depressive disorder, participants who start treatment may experience intercurrent events (IEs) during follow-up, such as treatment discontinuation, switch to alternative therapies, or changes in background/concomitant therapies (e.g., sleep aids). The ICH E9(R1) guideline defines these IEs as events that occur after treatment initiation and influence the interpretation of the outcome of interest or after which the outcome no longer exists (e.g., death).
While target estimands in these trials may adopt a treatment policy or composite strategy to handle these IEs, it is crucial to recognize that the rate at which these intercurrent events occur significantly impacts the interpretation of estimated treatment effects.
To gain a more comprehensive understanding of the external validity of clinical trials in this indication, it is essential to assess whether the rate of occurrence of these IEs is similar in real-life settings compared to what is observed in the clinical trials. By obtaining such insights, the results of this study aim to provide valuable information regarding the generalizability of clinical trial findings to real-world scenarios.

Research question and Objectives:

Research question
What is the incidence of treatment-related intercurrent events (IEs) common in clinical trials in patients with major depressive disorder?

Study objectives
1. To examine the proportion of patients with newly diagnosed major depressive disorder who start treatment with antidepressants (NSRIs, SSRIs, or other anti-depressants), as well as those who switch or discontinue treatment at specific intervals (4, 6, 8, 12, and 24 weeks after treatment initiation), stratified by age, sex, and country/database during the study period (2013 – 2022).
2. To estimate the duration of antidepressant use in patients with newly diagnosed major depressive disorder who initiate treatment with antidepressants (NSRIs, SSRIs, or other antidepressants), stratified by age, sex, and country/database during the study period (2013 – 2022).
3. To assess the proportions of patients with newly diagnosed major depressive disorder who initiate, switch, or discontinue treatment with psycholeptics (antipsychotics, anxiolytics, hypnotics, and sedatives) at specific intervals (4, 6, 8, 12, and 24 weeks) after starting antidepressant therapy, stratified by age, sex, and country/database during the study period (2013 – 2022).

Research Methods:

Study design
•Patient-level characterisation (Objective 1 and 3, Patient-level characterization of use patterns and sequences, including initiation, discontinuation, and switching, of antidepressants and psycholeptics in patients with newly diagnosed major depressive disorder).
•Patient-level drug utilization (Objective 2, Patient-level drug utilization analyses to assess the duration of antidepressant use in patients with newly diagnosed major depressive disorder).

Population
Patient-level characterisation: Patient-level characterisation analyses will include all patients with newly diagnosed with major depressive disorder who are aged 12 years and above in the respective databases from 2013 to 2022 (or the latest available date if earlier), with a minimum of 1 year of data visibility before their diagnosis, and no previous record of major depressive disorder in the year preceding their diagnosis.

Patient-level utilization: Patient-level drug utilization analyses will include all patients aged 12 years and above with newly diagnosed major depressive disorder who are new users of any of the antidepressant class of interest in the respective databases from 2013 to 2022 (or the latest available date if earlier), with a minimum of 1 year of data visibility before the index date, and no record of using the respective antidepressants in the year preceding the index date.

Variables
Drug class of interest:
•Non-selective monoamine reuptake inhibitors
•Selective serotonin reuptake inhibitors
•Other antidepressants (excluding esketamine and Hyperici herba)
•Concomitant medications – Psycholeptics
-Antipsychotics
-Anxiolytics
-Hypnotics and sedatives
Condition of interest:
•Major depressive disorder (MDD)

Data sources
1.Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom
2.IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
3.Institut Municipal Assistencia Sanitaria Information System (IMASIS), Spain
4.Integrated Primary Care Information Project (IPCI), The Netherlands
5.The Information System for Research in Primary Care (SIDIAP), Spain

Sample size
No sample size was calculated for this study, as our primary focus is to examine the use pattern of antidepressants and psycholeptics in adolescents/adults with newly diagnosed MDD, regardless of the sample size. Based on a preliminary feasibility assessment, the expected number of MDD records in the included databases for this study will be approximately 380,000.

Data analyses
Patient-level characterisation of incident MDD: The number and percentage of patients with newly diagnosed MDD initiating, switching, or discontinuing treatment with antidepressants (objective 1) and psycholeptics (objective 3) following date of MDD diagnosis (for treatment initiation of antidepressants and at 4, 6, 8, 12, and 24 weeks after starting antidepressant therapy will be depicted using Sunburst plots and Sankey diagrams. The statistical analyses will be performed based on OMOP-CDM mapped data using the “TreatmentPatterns” R package.

Patient-level drug utilization of antidepressants: The patient-level drug utilization of antidepressants will involve estimating the duration (mean, median, quantiles 25% and 75%, minimum and maximum) of antidepressant use in patients with newly diagnosed MDD during the first treatment era. The index date will be determined as the date of the first prescription of the specific antidepressant class for each individual. Statistical analyses will be conducted using the “DrugUtilization” R package based on OMOP-CDM mapped data.
For all analyses, results will be reported with a minimum cell count of 5, and any counts smaller than 5 will be obscured to ensure privacy and confidentiality.

Objectives: The main aim of this project is to estimate time trends in prevalence and incidence rates, and short- and long-term survival of site-specific cancers in the OHDSI network.
Design: This study will be a multinational observational cohort study and will be conducted using a network of large real world data sources that have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
Setting: Population-based, electronic health records, claims and registry data from primary and secondary care.
Participants: Individuals with no prior history of cancer (for incidence and survival analyses only), and who have been on the database for at least 1 year before study entry.
Outcomes: Prevalent and incident cancer diagnoses and overall as well as 1-, 5-, and 10-year survival of site-specific cancers.
Data analyses: The OHDSI Cohort Diagnostics package will be used to assess the fitness of use of cancer data on each database. We will calculate prevalence (PR) and incidence rates (IR) with 95% confidence intervals (95%CI) for each year and study period by dividing the number of ever and first recorded cases of cancer, respectively, by 1,000 person-years of follow-up, overall and stratified by demographics and relevant comorbidities. The overall and 1-, 5-, and -10-year survival rates will be calculated as the percentage of people who have been diagnosed with cancer and are still alive during the study period as well as one or five years after diagnosis, respectively, per year and stratified by pre-defined subgroups. To assess the incidence trend over time, we will calculate the IRs in 5 year periods and then calculate the incidence rate ratios (IRRs) and their corresponding 95%CI to analyze the differences in incidence between the defined time periods.

Rationale and Background:

Idiopathic inflammatory myopathies (IIM) are rare and diverse autoimmune disorders characterized by muscle inflammation, weakness, and extramuscular manifestations affecting organs like skin, lungs, heart, and joints (Lundberg 2021, Sasaki 2018). The subgroups include dermatomyositis, antisynthetase syndrome, immune-mediated necrotizing myopathy, inclusion body myositis, polymyositis, and overlap myositis (Lundberg 2021). Despite their rarity, understanding the epidemiology of these disorders is essential to identify patterns and determinants.
Currently, there are no approved specific therapies for dermatomyositis (DM) and polymyositis (PM) based on randomized controlled trials. These diseases are challenging because of their associated morbidity and mortality (Aggarwal et al., 2017). Classification criteria developed by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR) help identify major IIM subgroups. Diagnostic tools involve elevated muscle-derived enzymes in serum, antinuclear antibodies, muscle biopsy, electromyography, and MRI (Lundberg 2021, Papadopoulo 2023).
The pathogenesis, treatment responses, and organ involvement vary among IIM subtypes, necessitating a deeper understanding of molecular pathways and auto-antigens (Lundberg 2021, Findlay 2015). Glucocorticoids are commonly used as first-line treatment, often combined with immunosuppressive agents like methotrexate, azathioprine, and others (Oldroyd 2022, Sasaki 2018). Rituximab shows promise in refractory cases (Valiyil R 2010-, Mok 2007). Although TNF’s role is implicated, anti-TNF treatments’ efficacy is limited (Lundberg 2021).
Pediatric cases require special consideration. Juvenile idiopathic inflammatory myopathies affect children and young individuals, involving muscles, skin, and other organs. Differences exist between juvenile and adult forms in terms of pathogenesis, autoantibody profiles, and treatment responses. Consensus guidelines help guide diagnosis and management (Belluti et al.).
In conclusion, idiopathic inflammatory myopathies encompass a spectrum of rare autoimmune disorders affecting muscles and various organs. Understanding their epidemiology, classification, diagnostic criteria, and treatment approaches is essential for improving patient outcomes and tailoring treatments, especially in paediatric cases.

Research question and Objectives:

The overall objective of this study is to describe and characterise dermatomyositis (DM), polymyositis (PM) and their juvenile forms (JDM and JPM), in terms of prevalence, natural history of the disease, disease severity, and treatment.

The specific objectives of this study are:
1. To estimate the yearly prevalence of DM and PM in adult (18+ years) and paediatric populations (less than 2 years, 2 to less than 6 years, 6 to less than 12 years, 12 to less than 18 years) overall and by sex.
2. To characterise patients and describe age at disease onset, for DM, PM, JDM and JPM.
3. To describe the occurrence in adults and children of biomarker measurements (e.g. creatinine kinase, tests for myositis auto-antibodies – INF-b levels, INF type I gene signature) before, at the time, and after a diagnosis of DM, PM, JDM and JPM.
4. To describe the occurrence of clinical manifestations (muscle inflammation, muscle weakness, connective tissue disease overlap, presence of calcinosis in children) before, at the time, and after a diagnosis of DM, PM, JDM and JPM.
5. To describe disease severity including organ involvement (skin, joints, lung, heart, GI tract) before, at the time, and after a diagnosis of DM, PM, JDM and JPM.
6. To describe treatment administered (including combinations and sequences) after a diagnosis of DM, PM, JDM and JPM

All results will be reported by database, overall, and by study periods (2006-2013, 2013-2020, and 2020-2022), and stratified by age and sex when possible.

Research Methods:

-Study design
Cohort study. We will include cohorts of first diagnosed DM, PM, JDM, JPM and new user cohorts of their treatments (for objective 6).

-Population
The source population will include all individuals eligible in the database between 01/01/2006 and end of the available date in each database. For objective 1, all patients active in the database at the start of all calendar year will be included. For objectives 2-5, two cohorts with be characterised, one with a 90-day prior history requirement from diagnosis date, and one without this requirement. For objective 6, a washout period of 365 days at the treatment ingredient level will be applied to capture new users of DM, PM, JDM and JPM treatment.

-Variables
DM, PM, JDM and JPM will be assessed as first occurrence of the codes specified in Annex 1. Additional age criteria, <18 year old at time of first diagnosis will be applied for JDM and JPM and >18 at time of first diagnosis for DM and PM. Co-morbidities and co-medications will be used for large-scale patient characterisation, identified as concept/code and descendants. A list of pre-specified co-morbidities, measurements, clinical manifestations, and severity markers will also be characterised and is included in Annex 1. Treatments of DM, PM, JDM, JPM will be identified using the codes included in Annex 1.

-Data sources
1. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
2. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
3. Clinical Data Warehouse of Bordeaux University Hospital (CDW Bordeaux), France
4. Clinical Practice Research Datalink (CPRD) GOLD, United Kingdom (UK)
5. Estonian Biobank (EBB), Estonia

-Sample size
No sample size has been calculated as this is a descriptive Disease Epidemiology Study where we are interested in the characteristics of all incident DM, PM, JDM and JPM patients. Based on a preliminary feasibility assessment the expected number of patients in the included databases for this study will be approximately 6,000 for DM, and 5,000 for PM. We will define JDM and JPM based on codes and age at diagnosis as they are likely to not be coded specifically as juvenile, so we are not able to determine a concrete sample size for the juvenile forms (we expect around 3-10% of them to be juvenile).

-Data analyses
Point prevalence of each outcome of interest (DM, PM, JDM, JPM), with every individual deemed to have the diagnosis from first occurrence until end of follow-up calculated on an annual basis as of the 1st January for each year, estimated overall and stratified by age and sex.
Age and sex at time of DM, PM, JDM, JPM diagnosis (index date) will be described for each of the generated study cohorts (Objective 2). Large-scale patient-level characterisation will be conducted for objectives 3 to 5. Occurrence of co-morbidities, measurements, clinical manifestations, and severity markers will be assessed for anytime –and up to 365 days before index date, for 364 to 91, for 90 to 31, and for 30 to 1 day before index date, and at index date. We will also report them for 1 to 90, 91 to 180, 181 to 365 days, 366 to 1095, 1096 to 1825 days, and 1826 days to any time post index date.
The number and percentage of patients receiving each of a pre-specified list of DM, PM, JDM and JPM treatments (see Appendix 1) and treatment combinations will be described at index date, 1 to 90, 91 to 180, 181 to 365 days, 366 to 1095, 1096 to 1825 days, and 1826 days to any time post index date. Additionally, sunburst plots and Sankey diagrams will be used to describe treatment patterns and sequences over time (objective 6).
For all continuous variables, mean with standard deviation and median with interquartile range will be reported. For all categorical analyses, number and percentages will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5”. All analyses will be reported by country/database, overall and stratified by age groups and sex when possible (minimum cell count reached). Additionally, to capture treatments availability and changes over time, analyses will be further stratified by study periods (2006-2013, 2013-2020, and 2020-2022).

Rationale and background: Coronavirus disease-2019 (COVID-19) patients are at increased risk of venous and arterial thromboembolic events. SARS-CoV-2 variants have evolved during the COVID-19 pandemic with the dominant variant being Omicron now (as of December 2021). Information relating to thromboembolic risk and its impact on COVID-19 largely relates to COVID-19 variants occurring earlier during the pandemic. Therefore, evidence used to contextualize coagulopathy risk in light of vaccination may now differ. There is a need to better understand the risks of thromboembolic events among patients with COVID-19 associated with the Omicron variant, their impact on prognosis, and whether risk factors for such events remain the same, overall and in the context of prior COVID-19 infection, SARS-CoV-2 vaccination and among certain subgroups.
This study is one of the five use cases selected in the pilot project to test and inform HealthData@EU frameworks. HealthData@EU pilot project is the European Health Data Space (EHDS) Pilot project that aims to investigate and establish an infrastructure and data ecosystem for the secondary use of health data to facilitate research, innovation and better policy making; and assess the ability to scale towards a Union-wide infrastructure, as a core component of the EHDS.

Objectives:
1. To estimate the background incidence rate of venous and arterial thromboembolic events among the general population pre-pandemic population.
2. To estimate the incidence rate of venous and arterial thromboembolic events among patients with COVID-19 within 30-, 60-, 90- and 180-days during the Omicron period, stratified by prior SARS-CoV-2 vaccination and prior infection status.
3. To estimate the incidence rate of venous and arterial thromboembolic events among patients with SARS-CoV-2 vaccination within 30-, 60-, 90- and 180-days, stratified by prior infection status.
4. To estimate a) the association between clinical risk factors and prior SARS-CoV-2 vaccination on the incidence rate of venous and arterial events among people with COVID-19 and b) the impact that thromboembolic events have on worsening severity of COVID-19 during the Omicron period.
5. To estimate incidence rate ratios for venous and arterial thromboembolic events among patients with COVID-19 and different SARS-CoV-2 vaccine doses compared to the background population, using incidence rates estimated in objectives 1 to3.

Study type: population-level cohort
Study population:
1) Pre-pandemic cohort to calculate background rates of venous and arterial thromboembolic events in the general population (year 2017-2019).
2) People with COVID-19 during the time when OMICRON was the dominant variant (coded or test positive). All participants are required to be visible in the data source since 1st January 2020 to have full records on infection and vaccination history.
3) People vaccinated against SARS-CoV-2 (+/- during the period when OMICRON was the dominant variant). All participants are required to be visible in the data source since 1st January 2020 to have full records on infection and vaccination history.
For allcohorts, individuals with recent VTE or ATE event (defined as having a diagnosis of VTE or ATE within 183 days prior to index date, with sensitivity analysis of using any time prior and 91-days as wash-out periods.) will be excluded.

Analysis:
Standardized incidence rate ratio (SIR) compared to the background population will be estimated using indirect standardization.
Cohorts will be stratified by prior COVID-19 infection occurrence and prior SARS-CoV-2 vaccination (COVID-19 and vaccine cohorts only), age, sex. All cohorts will additionally be stratified by whether patients are immunocompromised on the index date.

Rationale and Background:

The extended mandate of EMA reinforcing the role of the Agency in crisis preparedness and management of medicinal products and medical devices became applicable on 1st March 2022 (Regulation on EMA’s extended mandate becomes applicable | European Medicines Agency (europa.eu)).
EMA is now responsible for monitoring medicine shortages that might lead to a crisis situation, as well as reporting shortages of critical medicines during public health emergencies (PHE). Such shortages would make it difficult or impossible to meet the treatment needs of individual patients or populations. The Agency has also the mandate to coordinate responses of EU / EEA countries to shortages of critical medical devices and in-vitro diagnostics in crisis situations.
Scientific and commercial data on monthly prescriptions of medicines that may be critical in PHE can help understanding trends and seasonal variations. In conjunction with time series and forecasting models, as well as data on medicines supply, such data will contribute to the on-going efforts of the Agency to better monitor and coordinate its response to shortages of critical medicines.
This study aims at generating monthly prescription rates of selected medicines over the last 10 years and to fit Autoregressive Integrated Moving Average (ARIMA) prediction models to such data.

Research question and objectives:

This study aims to characterise the incidence of use (prescription or dispensation) of 11 antibiotics used for public health emergencies that are considered at risk of shortages in order to understand trends, cycles and seasonality in the use of those medicines; and to forecast short-term prescription rates of such medicines under assumed scenarios, which could help anticipate and prevent potential shortages, or manage them.
The general research question is: What are the monthly prescription rates of selected medicines of importance for public health emergencies over the last 10 years?
The specific objectives of this study are:
(i) To estimate monthly incidence rates of use (prescription or dispensation) of the 11 selected medicines during a 10-year period from the most recent data available, stratified by age and sex, in each of the databases.
(ii) To conduct time series modelling by fitting an ARIMA model to data generated in objective 1 for short-term (6-month) forecasting.

Research Methods:

Study design
• Population level cohort study (Objective 1, Population-level drug utilisation study on antibiotics)
• Population level cohort study (Objective 2, Time series modelling based on the Population-level drug utilisation study on antibiotics)
Population
Population-level drug utilisation of antibiotics: All individuals present in the database in the last 10 years of available data will be included in the analysis after 30 days of database history. For this population, incidence of use of antibiotics will be explored.

Variables
Drugs of interest: list of 11 antibiotics that may be critical in PHE
Calendar month, age, and sex will be used for stratification.

Data sources
1) IQVIA LPD Belgium (Primary Care Database)
2) CPRD Gold (UK, Primary Care Database)
3) SIDIAP (Spain, Primary Care Database)
4) IMASIS (Spain, Secondary Care Database)
5) IQVIA DA (Germany, combination of primary and secondary care (outpatient visits) database).

Sample size
No sample size has been calculated. Based on a preliminary study feasibility assessment the expected number of prescriptions in the period investigated is expected to be between <1Kand 25M across the five data sources considered. Data analyses Population-level drug utilisation study on antibiotics: monthly incidence rates of antibiotic use per 100,000 person-year, as described in section 8.7.5.1 – Population-level drug utilisation study. Time series modelling: forecast of the 6-month incidence rates of antibiotic use after the end of available data in the data source using AutoRegressive Integrated Moving Average (ARIMA) models for time series analysis, as described in section 8.7.5.2.

Rationale and Background
The research agenda of the Vaccine Monitoring Platform jointly coordinated by EMA and the European Centre for Disease Prevention and Control (ECDC) includes the continuous assessment of COVID-19 vaccine effectiveness.
COVID-19 vaccines were authorised for use in the European Union. These vaccines, and any (future) adapted vaccines, would therefore benefit from post-authorisation studies to provide real-world evidence to guide regulatory and vaccination policies. A recent post-authorisation study performed in the Nordic countries, where near real-time data is available, showed that receipt of a bivalent BA4/5 mRNA booster as a fourth dose provides 67.8% protection against COVID-19 related hospitalisation. There is also evidence that effectiveness starts to wane after a few months. For regulatory purposes, such data are especially useful for the most recent variants, including XBB and later.
There is mounting evidence on post-acute outcomes of SARS-CoV-2 infection. This can include very specific outcomes such as cardiovascular events or the incidence of new-onset diabetes, or broader definitions such as the WHO clinical case definition for post COVID-19 condition. Data are needed regarding the COVID-19 vaccines effectiveness at preventing these outcomes. This is pertinent for the most recent variants, but equally important for older variants.

Objective(s)
To generate additional evidence on the effectiveness of COVID-19 vaccines at preventing severe COVID-19 and post-acute outcomes of SARS-CoV-2 infection.

Specifically, this study has 6 objectives:
1. To assess the effectiveness of COVID-19 vaccination for the prevention of severe COVID-19 related outcomes (COVID-19 related hospitalisation or COVID-19 related death)
2. To assess waning of the effectiveness of COVID-19 vaccination for the prevention of severe COVID-19 related outcomes (COVID-19 related hospitalisation or COVID-19 related death)
3. To assess the effectiveness of COVID-19 vaccination for the prevention of all-cause mortality in the 3- and 6-months following discharge for COVID-19 related hospitalisation
4. To assess the effectiveness of COVID-19 vaccination for the prevention of new-onset type 1 Diabetes Mellitus in the 12 months after a SARS-CoV-2 infection
5. To assess the effectiveness of COVID-19 vaccination for the prevention of new-onset type 2 Diabetes Mellitus in the 12 months after a SARS-CoV-2 infection
6. To assess the effectiveness of COVID-19 vaccination for the prevention of cardiovascular events in the 12 months after a SARS-CoV-2 infection

Research Methods
Study design: Population-level cohort studies

Data sources:
• Clinical practice Research Datalink (CPRD) GOLD, United Kingdom
• Integrated Primary Care Information Project (IPCI), The Netherlands
• The Information System for Research in Primary Care (SIDIAP), Spain

Additional databases can be added as part of a routine repetition of this study once they are successfully onboarded for DARWIN EU and meet feasibility requirements for this study.

Exposure:
Covid-19 vaccines BNT162b2 (Comirnaty) and mRNA-1273 (Spikevax), particularly the number of received vaccine doses per brand.
For those databases where this information is available, the 4th dose (2nd booster) will be stratified for monovalent (original strain) or adapted, bivalent (original strain + omicron ba1 or original strain + omicron ba4/5).
Analyses will be conducted separately for each vaccine brand.

Primary outcomes of interest:
1) Outcomes assessed from start of rollout of 4th vaccine dose/ 2nd booster dose program onwards:
1. COVID-19 related hospitalisation
2. COVID-19 related death
2) Outcome/s during periods with dominance of any SARS-CoV-2 variants:
3. All-cause mortality in the 3 months after discharge from a COVID-19 hospitalisation
4. All-cause mortality in the 6 months after discharge from a COVID-19 hospitalisation
5. Incidence of new-onset type 1 Diabetes Mellitus beyond the first 30d after SARS-CoV-2 infection
6. Incidence of new-onset type 2 Diabetes Mellitus beyond the first 30d after SARS-CoV-2 infection
7. Incidence of cardiovascular events (cerebrovascular disorders, dysrhythmias, ischemic and non-ischemic heart disease, pericarditis, myocarditis, heart failure and thromboembolic disease) in the 12 months after a SARS-CoV-2 infection
COVID-19 related hospitalisation (outcome 1, part of outcomes 3 and 4) is not available for IPCI and CPRD but will only be assessed in SIDIAP.

Study population:
All subjects aged 12 years and older, with at least 365 days of data availability before index date (ID) [ID defined as the date of the latest vaccine dose administered] AND data availability from 12/2020 onwards (i.e. the time when the roll-out of the vaccination campaign started) in the respective database will be included.

All studies will be carried out comparing 8 cohorts, which we defined based on varying degrees of vaccine exposure and period of predominant SARS-CoV-2 variant.

Outcomes 1-2 will be assessed in cohorts 1-2 (and 3-4 where available), outcomes 3-7 will be assessed in cohorts 5-8.
Unvaccinated groups will not be used as a comparator in our study for vaccine effectiveness research because they may be very different from vaccinated individuals regarding their risk of infection with SARS-Cov-2. This study therefore focusses on the association of varying degrees of vaccine exposure and COVID-19 related outcomes.

Study period:
Period of “start of roll-out 4th dose/2nd booster dose onwards”: from 01/08/2022 – last available data for each database.
Period of “XBB variant or later dominant”: 01/03/2023 – last available data for each database. Note: This period is not covered by any of the data cuts onboarded for DARWIN EU at the time of protocol submission.
Period of “any variant dominant”: 01/01/2021 (when the wider roll-out of the vaccination campaign started) – last available data for each database.

Statistical analyses:
All analyses will be conducted separately for each database, and will be carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data. For each analysis, we will subsequently pool effect estimates across databases using random effect meta-analyses, I^2 for heterogeneity will be reported.
Cell counts <5 will be suppressed to comply with the database’s privacy protection regulations.

Rationale and Background:

Severe acute respiratory infection (SARI) caused by respiratory syncytial virus (RSV) has gained recognition as a global health problem with a high burden of disease. In children under 5 years, it is estimated that 3.6 million hospital admissions, and 101,400 deaths were attributable to RSV worldwide in 2019. RSV infection also represents a substantial health burden in older adults. It is estimated that 470,000 hospitalisations, and 33,000 in-hospital deaths in =60-year-old adults were attributable to RSV-related disease in high-income countries.
There have been substantial advances in the development of RSV vaccines, with several prophylactic candidates in late-phase clinical development. As of July 2023, the European Medicines Agency (EMA) has recommended granting a marketing authorisation for Arexvy and Abrysvo vaccines for use in the European Union. Arexvy is indicated for active immunisation for the prevention of lower respiratory tract disease caused by RSV virus in adults = 60 years. Abrysvo is indicated for the prevention of lower respiratory tract disease caused by RSV through: (a) passive protection in infants from birth through 6 months of age following maternal immunisation during pregnancy, (b) active immunisation of adults = 60 years. It is expected that more of these vaccines, using different platforms, will be approved by EMA in the coming year(s). Therefore, accurate information about RSV burden in high-risk groups is essential for decision-making to support the continuous assessment of their benefit/risk profile.
This study is expected to generate evidence that is complementary to the work carried out by European initiatives such as RESCEU; and PROMISE More importantly, the objective is to explore the feasibility of capturing adequate RSV-specific endpoints in the DARWIN EU® data sources (for example, availability of laboratory testing data) that may pave the way for effectiveness studies once the vaccines are being deployed and along their lifecycle, as part of the research agenda of the EU Vaccine Monitoring platform, a collaboration between EMA and the ECDC.

Research question and Objectives:

Research question
What are the age-specific disease frequencies, hospitalization rates, and mortality rates of Respiratory Syncytial Virus (RSV) infection in European countries over the past decade?

Study objectives
Objective 1: To estimate the incidence of RSV-related hospitalisation in the general population, stratified by year and age groups, during the period from January 1, 2013, to December 31, 2022.
Objective 2: To estimate the duration of RSV-related hospitalisation among patients diagnosed with RSV infection, stratified by year and age groups, between January 1, 2013, and December 31, 2022.
Objective 3: To estimate the prevalence of RSV-related intensive care unit (ICU) admissions among patients diagnosed with RSV infection, stratified by year and age groups, between January 1, 2013, and December 31, 2022.
Objective 4: To estimate the prevalence of RSV co-infections with other respiratory pathogens, specifically Influenza Viruses, Rhinoviruses, SARS-CoV-2, Parainfluenza Viruses, Adenoviruses, Metapneumovirus, and Enteroviruses, in the general population, stratified by year and age groups, during the period from January 1, 2013, to December 31, 2022.
Objective 5: To estimate RSV-related mortality rates among patients diagnosed with RSV infection, stratified by year and age groups, between January 1, 2013, and December 31, 2022.

Research Methods:

Study design:
Retrospective cohort study.
•Population-level cohort: Population-level descriptive epidemiology of the incidence of RSV-related hospitalisation (Objective 1), and prevalence of RSV co-infections with other respiratory pathogens (Objective 4) in the general population.
•Patient-level cohort: Patient-level characterisation to estimate duration of RSV-related hospitalisation (Objective 2), prevalence of RSV-related ICU admissions (Objective 3), and RSV-related mortality rates (Objective 5) in patients with diagnosed with RSV infection.

Population:
Population-level descriptive epidemiology: This analysis will include all individuals in the respective databases from 2013 to 2022 (or the latest available date if earlier), with a minimum of 1 year of data visibility prior to study entry date. However, this additional requirement will not be applicable to children aged <1 year. Patient-level characterization: This analysis will include all patients diagnosed with RSV infection between 2013 and 2022 (or the latest available date if earlier), with at least 1 year of data availability prior to their diagnosis. However, this additional requirement will not be applicable to children aged <1 year. Variables Drug of interest: None Condition of interest: RSV infection identified through SNOMED disease codes and LOINC laboratory test codes. Outcomes of interest: Study outcomes will include RSV-related hospitalisation, ICU admission, mortality rate, and co-infection with other respiratory pathogens (Influenza Viruses, Rhinoviruses, SARS-CoV-2, Parainfluenza Viruses, Adenoviruses, Metapneumovirus, and Enteroviruses). Data sources: 1. Clinical Data Warehouse of Bordeaux University Hospital (CHUBX), France 2. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 3. Estonian Biobank (EBB), Estonia 4. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 5. Institut Municipal Assistencia Sanitaria Information System (IMASIS), Spain 6. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain Sample size: No sample size was calculated for this study as our primary objective is to describe the age-specific incidence rates of RSV-related disease outcomes in Europe, irrespective of the sample size. Based on a preliminary feasibility assessment, the estimated number of individuals with RSV infection in the included databases varied, ranging from 1,000 (CPRD GOLD) to 16,400 (SIDIAP). Additionally, specific counts for other databases are as follows: 6,100 (EBB), 6,700 (CHUBX), 9,100 (IQVIA DA Germany), and 9,800 (IMASIS). Data analysis: Data analysis will be conducted to estimate the number and rates of hospitalisation due to RSV infection (Objective 1) and the number and percentage of individuals with RSV co-infection with other respiratory pathogens (Objective 4) within the general population. Furthermore, the number and percentage of ICU admissions will be estimated among patients hospitalised due to with RSV infection (Objective 3). The statistical analyses will be performed on OMOP-CDM mapped data using the IncidencePrevalence R package, and stratified by age, calendar year and database. RSV-related mortality rates (Objective 5) will be calculated using the Kaplan-Meier (KM) method and data on time at risk of RSV-related death, defined as within 30 days of RSV infection. Results will be reported as plots of the estimated survival curves as well as the estimated probability of survival at 30 days. The statistical analysis will be performed on OMOP-CDM mapped data using the CohortSurvival R package, and stratified by age, calendar year and database. The duration of hospitalization will be calculated in patients with RSV infection. This will include key metrics such as the median, interquartile range (p25 and p75), maximum, and minimum days of hospitalization. Results will be provided stratified by age, calendar year and database. For all analyses a minimum cell counts of 5 will be used when reporting results, with any smaller counts obscured.