Elena Roel Herranz

Rationale and background
Depression, self-harm, suicidal ideation, attempt, and suicide are prevalent health conditions causing significant healthcare utilisation, morbidity, and mortality. Safety concerns on the use of medicines in these populations or as causes of these conditions are common. A better understanding on how well data on these areas are captured will be important to inform the feasibility of conducting RWD studies on these populations or their use as outcomes in DARWIN EU® studies, including the potential validity of phenotypes.
Objectives
1. What is the proportion of individuals with the following types of recording in the DARWIN EU® network: suicide (completed), suicide attempt, suicidal ideation, self-harm, depression, symptom measurement data (PHQ9 depression scales), procedures (psychotherapy and electrotherapy), and depression remission
2. To characterise reporting of suicide, suicide attempt, suicidal ideation, self-harm, and depression within the Darwin EU® network in terms of:
a) Median number (and IQR) of suicides, suicide attempts, suicidal ideation, self-harm, depression, symptom measurement, and resolution reporting per individual within the study period.
b) Rates of suicide, suicide attempt, suicidal ideation, self-harm, and depression, symptom measurement, and resolution reporting within the study period and per calendar year.
c) Explore the values of the symptom measurement data
3. What are the characteristics of individuals with records of suicide, suicide attempt, suicidal ideation, self-harm, and depression in terms of demography, use of concomitant medications (antidepressants, antipsychotics, benzodiazepines, antipsychotics, stimulants, hypnotics), comorbidities (schizophrenia, anxiety disorder, substance use disorder, personality disorders), procedures, and lifestyle factors.
Note: SIDIAP can support the achievement of most of the study’s objectives; however, objectives deemed unfeasible due to data limitations will not be executed (for instance, SIDIAP does not capture cause of mortality)
Methods
Study design
• A population-level descriptive epidemiology study will be conducted to address objectives 1, 2b, and 2c.
• An individual-level characterisation study will be conducted to address objectives 2a and 3.
Population
For objective 1, 2b, and 3c, the study population will include all individuals present in the data source during the study period 01/01/2015 to 31/12/2024 (or to the end of available data) and with at least 365 days of data source history prior to index date. For objective 2a and 3, the study population will include individuals with a first occurrence of suicide (completed), suicide attempt, suicide ideation, self-harm, or depression in the study period with at least 365 days of prior observation.
Variables
Outcome:
Suicide (completed), composite suicide-related events (suicide, suicide attempt, suicide ideation, self-harm), composite fatal suicide-related events, composite non-fatal suicide-related events, suicide attempt, suicidal ideation, self-harm, depression, measurement occurrence of Patient Health Questionnaire-9 (PHQ9) depression scale, healthcare referral and hospital admission, electrotherapy, and psychotherapy.
Relevant covariates:
Demographic characteristics, drug prescriptions (antidepressants, antipsychotics, benzodiazepines, stimulants, hypnotics), conditions (schizophrenia, anxiety disorder, substance use disorder, personality disorders), procedures (psychotherapy, electrotherapy), and observation occurrences (smoking, obesity)
Relevant covariates will be considered at any time prior to the index date.
Statistical analysis
Characteristics will be described by means of pre-specified characterisation. Covariates of interest will be reported as counts and proportions. Yearly incidence rates per 100,000 person-years and period prevalence (proportion) of the outcomes will be estimated in the general population, overall and stratified by age categories and sex. Incidence rates will be given together with 95% Poisson confidence intervals. The statistical analyses will be performed based on OMOP CDM mapped data using IncidencePrevalence and CohortCharacteristics R packages.

Background
Climate change leading to extreme events represent pressing challenges for humanity. Short- term exposure
to ambient air pollution and temperature has been associated with exacerbations of chronic respiratory
diseases, yet significant knowledge gaps remain regarding specific diseases, such as interstitial lung diseases
and bronchiectasis, and potential adaptation strategies to decrease the risk of exacerbations.
Objectives
To investigate the short-term effects of air pollution and temperature, including their co- occurrence, on
chronic respiratory diseases exacerbations and sick leaves in Catalonia; to evaluate potential adaptation
strategies, including the role of inhaled corticosteroids, statins, SGLT-2 inhibitors, and vaccines in modifying
these effects; to incorporate patient perspectives to understand coping mechanisms against climate change
stressors and disease burden.
Methods
Using a well-characterised cohort with detailed environmental exposure data, we will analyze associations
between air pollution and temperature, and chronic respiratory diseases exacerbations, accounting for
hospitalisations, mortality, and sick leaves. We will apply advanced statistical modelling to disentangle
exposure-outcome relationships, assess effect modification, and integrate a qualitative research approach
to capture patient experiences.
Expected results
Identification of environmental triggers of chronic respiratory disease exacerbations, evaluation of
protective effects of pharmacological and behavioural adaptation measures, and generation of evidence to
inform public health policies and clinical guidelines.

Background: Non-communicable diseases (NCDs) are a major global health challenge, contributing to significant morbidity, mortality, and healthcare burden worldwide. Monitoring NCDs is essential for tracking disease prevalence, evaluating trends, and assessing the impact of preventive and control measures. In response to this need, the World Health Organization (WHO) developed the NCD Facility-Based Monitoring Guidance, which provides a framework and a set of core and optional indicators to assess patient care and health programs at the primary care level. Leveraging electronic health records (EHRs) can facilitate reliable and timely monitoring, enabling data-driven policy and healthcare decisions.
Objective: We aim to demonstrate how primary care EHR data can be used to monitor NCDs in Europe using the WHO-proposed indicators.
Methods: A population-based descriptive epidemiological study will be conducted using routinely collected data from multiple European databases willing to participate. Databases that have already committed to participate include SIDIAP (Spain) and IPCI (Netherlands). The study will cover the period from January 1, 2010, to the most recent data available. Specifically, the study will (1) assess the feasibility of estimating the WHO-defined indicators using European databases mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), (2) develop phenotypes for selected indicators, and (3) estimate prevalence rates of these indicators over time, stratified by age, sex, socioeconomic status, nationality, and Charlson Comorbidity Index.
Expected results: This study will demonstrate the feasibility of using EHR data to systematically track NCD indicators across Europe. The findings will support data-driven decision-making for policymakers and healthcare providers, contributing to improved NCD prevention and management strategies.

Rationale and background
Clozapine is an effective treatment for treatment-resistant schizophrenia and Parkinson’s disease psychosis, but it carries a risk of severe haematological complications, including neutropenia and agranulocytosis. Emerging evidence suggests that the risk is highest in the initial months of treatment, yet stringent haematological monitoring requirements remain in place throughout long-term use. These requirements may hinder clinical practice, leading to underuse, early treatment discontinuation, or reluctance to initiate therapy. This study aims to provide epidemiological evidence on the incidence and timing of clozapine-associated neutropenia and agranulocytosis across Europe.
Research question and objectives
Research question
What is the incidence of agranulocytosis and neutropenia over time in new users of clozapine?
Study objectives
1. To estimate the incidence rates of agranulocytosis and neutropenia in consecutive weekly and monthly intervals following the new initiation of clozapine treatment, overall and stratified by age, sex and country/database.
2. To estimate the Kaplan-Meier curves for the time to onset of agranulocytosis and neutropenia in individuals initiating clozapine treatment, overall and stratified by age, sex and country/database.
3. To characterise individuals initiating clozapine treatment in terms of demographics and pre-specified conditions related to the indication for clozapine use. Results will be stratified by country/database.
4. To determine the treatment duration for clozapine use. Results will be stratified by country/database.
Methods
Study design
• Population-level cohort study (Objective 1, Population-level descriptive epidemiology of agranulocytosis and neutropenia in new users of clozapine).
• Cohort analysis (Objective 2, Patient-level characterisation to the time of onset of agranulocytosis and neutropenia in individuals initiating clozapine treatment).
• New drug user cohort (Objective 3 and 4, Patient-level drug utilisation regarding demographics, pre-specified conditions related to clozapine initiation and treatment duration).
Study period
1st of January 2010 to 31st of December 2024 (or latest available data
Population
Population-level descriptive epidemiology: Population-level descriptive epidemiology will include all new users of clozapine registered in the respective databases between 1st of January 2010 and 31st of December 2024. Eligible individuals must have at least 1 year of data visibility prior to becoming eligible for study inclusion and no history of clozapine use. Children <1 year of age will be excluded. Patient-level characterisation: Patient-level characterisation will include all new users of clozapine registered in the respective databases between 1st of January 2010 and 31st of December 2024. Eligible individuals must have at least 1 year of data visibility prior to becoming eligible for study inclusion and no history of clozapine use. Additionally, to ensure sufficient follow-up, only individuals who initiated clozapine treatment at least 1 year before the end of the available data will be included. Children <1 year of age will be excluded. Patient-level utilisation of clozapine: Patient-level drug utilisation analysis will include all new users of clozapine in the period between 1st of January 2010 and 31st of December 2024, with at least 1 year of data visibility prior to becoming eligible for study inclusion and no history of clozapine use prior to the index date. For treatment duration analysis, to ensure sufficient follow-up, only individuals who initiated clozapine treatment at least one year before the end of the available data will be included. Variables Drug of interest: Clozapine. Outcomes of interest: Agranulocytosis and neutropenia following the initiation of clozapine treatment. Agranulocytosis and neutropenia will be defined separately (narrow definition) and additionally as a combined outcome (broad definition). To ensure that only incident cases are captured, individuals with a prior history of agranulocytosis or neutropenia will be excluded. Data source 1. Finnish Care Register for Health Care (FinOMOP-HILMO), Finland 2. Croatian National Public Health Information System (NAJS), Croatia 3. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 4. The Information System for Research in Primary Care (SIDIAP), Spain Sample size No sample size has been calculated a this is a descriptive study. Statistical analysis Population-level descriptive epidemiology: Incidence rates of newly diagnosed agranulocytosis and neutropenia will be estimated following clozapine treatment initiation (objective 1). These rates will be expressed as the number of individuals with the newly diagnosed outcome of interest following clozapine initiation per 1,000 person-years. Incidence rates will be calculated for consecutive weekly (0-7 days, 8-14 days, 15-21 days etc.) and monthly intervals (0-30 days, 31-60 days, 61-90 days etc.) since the initiation of clozapine treatment (index date), with a maximum follow-up period of 24 months for reporting both weekly and monthly estimates. The statistical analysis will be performed based on OMOP-CDM mapped data using “IncidencePrevalence” R package. The results will be reported overall and stratified by age, sex and country/database. Patient-level characterisation: Kaplan-Meier curves for the time of onset of agranulocytosis and neutropenia will be estimated in individuals initiating clozapine treatment (objective 2). This analysis will be conducted using “CohortSurvival” R package based on OMOP-CDM mapped data. The results will be stratified by age, sex and country/database. Patient-level utilisation of clozapine: Characterisation including age and sex will be assessed at the date of new (incident) prescription of clozapine (index date) (objective 3). The frequency of pre-specified conditions related to clozapine initiation will be assessed at any time prior to 1 day before index date, 365 days prior to 1 day before index date and at the index date. Duration of treatment will be calculated and summarised providing the minimum, quartiles and maximum, where available (objective 4). Statistical analyses will be conducted using the “CohortCharacteristics” and “DrugUtilisation” R package based on OMOP-CDM mapped data. The results will be stratified by country/database. For all analyses a minimum cell counts of 5 will be used when reporting results, with any smaller counts obscured.

Rationale and Background
The WHO 2023 AWaRe classification (who.int) of antibiotics for evaluation and monitoring of use classifies 258 antibiotics into 3 categories (Access/Watch/Reserve) according to their impact on antimicrobial resistance. The Watch list includes antibiotic classes that have higher resistance potential and includes most of the highest priority agents among the Critically Important Antimicrobials for Human Medicine and/or antibiotics that are at relatively high risk of selection of bacterial resistance. These medicines should be prioritized as key targets of stewardship programs and monitoring. The DARWIN EU®_ P1-C1-003 study focused on the Watch category and this study is now repeated to include more recent data and more data sources.
This study will improve our understanding of the use of antibiotics in the Watch category in routine health care delivery, including indication, treatment duration and trends over time. The results will contribute to the EU efforts to monitor use of antibiotics as part of the global fight against antimicrobial resistance.
Research question and Objectives
Research question
What is the incidence of prescription of the antibiotics in the ‘Watch’ category, including indication and treatment duration, from 2012 to 2024, stratified by demographic characteristics, calendar year/month, and country?
Objectives
1. To investigate the incidence of use of antibiotics (from the WHO AWaRe Watch category) stratified by age, sex, calendar year/month, and data source during the study period 2012-2024.
2. To characterise antibiotic (WHO AWaRe Watch category 2023) use by duration of use over the study period 2012 to 2024.
3. To characterise antibiotic use (WHO AWaRe Watch category 2023) by indication of use over the period 2012 to 2024 stratified by calendar year.
Research Methods
Study design
? Population level cohort study (Objective 1, Population-level drug utilisation study on Watch category
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antibiotics)
? New drug user cohort study (Objective 2 and 3, Patient-level drug utilisation analysis with regard to duration and indication of antibiotic use)
Population
Population-level utilisation of antibiotics (objective 1): All individuals present in the data source in the period between 01/01/2012 and 31/12/2024 will be included in the analysis after 365 days of data source history.
Patient-level antibiotic utilisation (objectives 2 and 3): All new users of antibiotics (i.e. no use of the antibiotic of interest in the preceding 30 days) in the period between 01/01/2012 and 31/12/2024, with at least 365 days of visibility prior to the date of their first antibiotic prescription.
Study period
Study period will start from 2012 until the end of available data. In the NAJS data source, accurate data will be available from 2017 on.
Variables
Exposures
All antibiotics from the WHO AWaRe Watch category.
Outcome
n/a
Relevant covariates
Age groups, sex, calendar year/month, predefined conditions of interest.
Data sources
1. Clinical Data Warehouse of Bordeaux University Hospital (CDW Bordeaux), France
2. Danish Data Health Registries (DK-DHR), Denmark
3. Finnish Care Register for Health Care (FinOMOP-HILMO), Finland
4. Institut Municipal Assistència Sanitària Information System (IMASIS), Spain
5. Integrated Primary Care Information Project (IPCI), The Netherlands
6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
7. Nacionalni Javnozdravstveni Informacijski Susta (NAJS), Croatia
8. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
9. Semmelweis University Clinical Data (SUCD), Hungary
Sample size
No sample size has been calculated as this is an exploratory study which will not test a specific hypothesis.
Data analyses
Population-level antibiotic use: Yearly and monthly incidence rates of antibiotics use per 100,000 person months (PMs) will be estimated. Overall incidence rates will be reported as well as stratified by age, sex, calendar year/month, and country/database. Incidence rates will be reported together with 95% Poisson confidence intervals.
Patient-level antibiotic use: Proportions of indication of use at index date will be assessed. Index date will be the date of each prescription of the specific antibiotic for each person. Cumulative treatment duration will be estimated and the minimum, p25, median, p75, and maximum will be provided.
The statistical analyses will be performed based on OMOP-CDM mapped data using “IncidencePrevalence” and “DrugUtilizationCharacteristics” R packages. A minimum cell counts of 5 will be used when reporting results, with any smaller count reported as “<5”.

Rationale and background
The WHO 2023 AWaRe classification (who.int) of antibiotics for evaluation and monitoring of use classifies 258 antibiotics into 3 categories (Access/Watch/Reserve) according to their impact on antimicrobial resistance.
The ‘Access’ category includes antibiotics that are recommended as first- or second-line treatments for a wide range of common infectious diseases. These antibiotics are generally active against a broad spectrum of commonly encountered, susceptible pathogens and have a relatively lower risk of promoting antimicrobial resistance compared to agents in other categories. The WHO emphasizes that ‘Access’ antibiotics should be widely available, affordable, and of assured quality across all healthcare settings to ensure equitable treatment, especially in low- and middle-income countries. As of the 2023 update, the ‘Access’ group includes 84 antibiotics, such as amoxicillin and doxycycline, which are used to treat high-burden infections like pneumonia and urinary tract infections.(1, 2) Promoting the use of ‘Access’ antibiotics for appropriate indications is a key component of global antimicrobial stewardship strategies aimed at reducing the need for broader-spectrum agents and mitigating the spread of resistance.
The DARWIN EU®_ P1-C1-003 study focused on the Watch category but there is now interest in including also the other category (‘Access’) to characterise the use of most antibiotics, and increased focus on the indication for use.
This study will improve the understanding of the use of antibiotics in routine health care delivery, including indication, treatment duration and trends over time. The results will contribute to the EU efforts to monitor use of antibiotics as part of the global fight against antimicrobial resistance.
Research question and objectives
Research question
What is the incidence of prescription of the antibiotics in the ‘Access’ category, including indication and treatment duration, from 2012 to 2024, stratified by demographic characteristics, calendar year/month, and country?
Objectives
1. To investigate the incidence of use of antibiotics (from the WHO AWaRe ‘Access’ category) stratified by age, sex, calendar year/month, and country/database during the study period 2012-2024.
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2. To characterise antibiotic (WHO AWaRe ‘Access’ category 2023) use by duration of use over the study period 2012 to 2024.
3. To characterise antibiotic use (WHO AWaRe ‘Access’ list 2023) by indication of use over the period 2012 to 2024 stratified by calendar year.
Methods
Study design
• Population level cohort study (Objective 1, Population-level drug utilisation study on ‘Access’ category antibiotics)
• New drug user cohort study (Objectives 2 and 3, Patient-level drug utilisation analysis with regard to duration and indication of antibiotic use)
Population
Population-level utilisation of antibiotics (objective 1): All individuals present in the database in the period between 01/01/2012 and 31/12/2024 will be included in the analysis after 365 days of database history.
Patient-level antibiotic utilisation (Objectives 2 and 3): All new users of antibiotics (i.e. no use of the antibiotic of interest in the preceding 30 days) in the period between 01/01/2012 and 31/12/2024, with at least 365 days of visibility prior to the date of their first antibiotic prescription.
Study period
Study period will start from 2012 until the end of available data. In the NAJS data source, accurate data will be available from 2017 on.
Variables
Exposures
All antibiotics from the WHO AWaRe ‘Access’ category.
Outcome
n/a
Relevant covariates
Age groups, sex, calendar year/month, predefined conditions of interest.
Data source
1. Danish Data Health Registries (DK-DHR), Denmark
2. Finnish Care Register for Health Care (FinOMOP-HILMO), Finland
3. The Integrated Primary Care Information (IPCI), the Netherlands
4. IQVIA Disease Analyser (DA) Germany, Germany
5. National Public Health Information System (NAJS), Croatia
6. Information System for Research in Primary Care (SIDIAP), Spain
Sample size
No sample size has been calculated as this is an exploratory study which will not test a specific hypothesis.
Statistical analysis
Population-level antibiotic use: Yearly and monthly incidence rates of antibiotic prescriptions per 100,000 person-months (PMs) will be estimated. Overall incidence rates will be reported as well as stratified by age, sex, calendar year/month, and country/database. Incidence rates will be reported together with 95% Poisson confidence intervals.
Patient-level antibiotic use: Proportions of indication of use at index date will be assessed. Index date will be the date of each prescription of the specific antibiotic for each person. Cumulative treatment duration will be estimated and the minimum, p25, median, p75, and maximum will be provided.
The statistical analyses will be performed based on OMOP-CDM mapped data using “IncidencePrevalence” and “DrugUtilizationCharacteristics” R packages. A minimum cell counts of 5 will be used when reporting results, with any smaller count reported as “<5”.

Rationale and background
Antipsychotic drugs are indicated for the management of schizophrenia and bipolar disorder. They are also used in adults to manage behavioural and psychological symptoms of dementia (BPSD) with the recommendation to be discontinued after BPSD symptoms resolve. Antipsychotic drugs can be classified into typical and atypical antipsychotics with different recommendations for their use. For example, guidelines recommend the preferential use of atypical antipsychotics when required for the management of BPSD.[1, 2]
Safety concerns in adults have previously led to regulatory warnings and risk communications over their use.[3, 4] Antipsychotic drugs have been associated with several adverse drug reactions, particularly in the elderly. Somnolence, hypotension, extrapyramidal side effects and gait abnormalities are well-recognized side effects that may in turn contribute to the risk of falls and fracture in elderly persons.[1] Similarly, cardiovascular adverse effects, falls and injuries may increase mortality.
Antipsychotics are sometimes used in children and adolescents; however, not all antipsychotics have been approved for use in children and adolescents and if prescribed their use would be considered off-label. A prior study reported an increased use of antipsychotics between 2008 and 2017 in the paediatric populations of Catalonia (35.7%), Norway (45.1%) and Sweeden (57.6%).[5] Likewise, in England, the use of antipsychotics in patients between 3 and 18 years doubled between 2000 and 2019.[6]
This study aims to provide an overview of antipsychotic prescribing in the children in databases from Europe, and to describe the characteristics of children initiating antipsychotics. This will provide a benchmark to understand current clinical practice over their use in children and adolescents and help to understand whether off-label use may occur.
Research question and objectives
1. To characterise children with a first prescription of an antipsychotic in each database in terms of age, sex, comorbidities and indication of use.
2. To measure trends in the incidence/prevalence of antipsychotic prescribing in children overall, by typical/atypical grouping and separately for 11 drug substances in each database. Results would be stratified by calendar year, age and sex.
3. To characterise first use of antipsychotic initiation in children (overall, by typical/atypical use, and by the 11 prespecified drug substances) in terms of dose and duration in each database. Results will be
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stratified by age and sex.
Methods
Study design
1. A new user cohort study will be used to describe patient-level characterisation of antipsychotic users.
2. A population-level cohort study will be used to assess incidence rates of antipsychotic use.
3. A new user cohort study will be conducted to describe patient-level antipsychotic utilisation.
Population
The study cohort will comprise all paediatric individuals between 1- and 18-years old present in the database during the study period (i.e., 2013-2023).
Additional eligibility criteria for patient-level antipsychotic characterisation and drug utilisation, and for the calculation of incidence rates will be applied, where a minimum follow-up of 365 days of data availability
will be required to exclude individuals with a prior use of the respective drug of interest (i.e., when overall, no prior use of any of the common antipsychotics will be required; when stratified by specific antipsychotic drug, no prior use of the specific antipsychotic will be required).
Variables
Exposure of interest: all antipsychotics under the ATC code N05A (overall, and by typical/atypical grouping); and eleven prespecified drug substances that cover most antipsychotics prescribed in the paediatric population (selected based upon feasibility in the data sources of interest): risperidone, aripiprazole, olanzapine, quetiapine, paliperidone, chlorprothixene, clozapine, methotrimeprazine (levomepromazine), pipamperone, sulpiride and promazine.
Data sources
• BIFAP (Spain, Primary Care Database)
• DK-DHR (Denmark, National Registry)
• InGef RDB (Germany, Claims Database)
• IPCI (Netherlands, Primary Care Database)
• NAJS (Croatia, National Claims Registry) [Only Objectives 1 and 2]
• NLHR (Norway, National Registry)
• SIDIAP (Spain, Primary Care Database)
Statistical analysis
1. Patient-level characterisation study: characterisation of patient-level features for new users of antipsychotics will be calculated overall, by typical/atypical, and by the 11 pre-specified drug substances for each database, including description of age (mean [SD] and median [IQR]), age groups [N,%] and sex [N,%] at index date (date of first prescription of the antipsychotic of interest),comorbidities [N,%] recorded -7 days, -30 days or any time before (index date), and indications of use [N,%] recorded -30 days or any time before (index date).
2. Population-level drug utilisation study: annual incidence and annual period prevalence estimates will be calculated for antipsychotic treatment overall, by typical/atypical and by the 11 pre-specified drug substances for each database.
3. Patient-level drug utilisation study: patient-level characterisation of new antipsychotic users will be conducted at index date (date of first prescription of the antipsychotic of interest) for overall, by typical/atypical and by the 11 pre-specified drug substances for each database, including median [IQR] prescribed or dispensed initial and cumulative dose of antipsychotics, and median [IQR] treatment duration.
For all analyses a minimum cell counts of 5 will be used when reporting results, with any smaller counts will be noted as <5.

Background
Initially, mRNA COVID-19 vaccines were recommended during pregnancy; however, pregnant individuals were excluded from early clinical trials, resulting in limited data on vaccine efficacy and safety for this group. Subsequent observational studies to date have indicated that mRNA vaccines are safe during pregnancy, showing no increased risk of complications such as miscarriage, preterm delivery, stillbirth, or birth defects. Nevertheless, these studies often involve small sample sizes, limiting the ability to evaluate rare adverse events and the effects of different vaccine doses.
Objectives
To leverage 4 real-world-data sources to, first, understand adverse events occurring during pregnancy, and second, assess whether mRNA COVID-19 vaccines increase the risk of these adverse events.
Methods
Data Sources
The study will include participants registered in primary care data from the United Kingdom, primary care data linked with hospital discharge data from Catalonia (Spain), and nationwide linked health registries from Norway and Sweden. All databases will have been mapped to the OMOP common data model facilitating federated analysis pipeline.
Study design
For the first objective, the study design is a population-level descriptive epidemiology study. We will estimate background incidence rates of the outcomes occurring during pregnancy, and compare baseline characteristics between pregnant women who had the outcome and those outcome-free.
The study design for the second objective is a cohort study following the target trial emulation framework. We will compare pregnant women receiving a 1st, 2nd, or booster (3rd or subsequent doses) COVID-19 vaccine during pregnancy (with an mRNA vaccine), compared to pregnant women eligible for the same dose but not yet vaccinated with it.
Outcomes
Outcomes of interest in the study are:
1) 15 Adverse Events of Special Interest (AESI): Deep vein thrombosis, Pulmonary embolism, Myocardial infarction, Ischaemic stroke, Bell’s palsy, Encephalitis, Guillain Barré Syndrome, Transverse Myelitis, Haemorrhagic stroke, Myocarditis or pericarditis, Thrombosis with Thrombocytopenia, Immune Thrombocytopenia, Anaphylaxis, Narcolepsy, Disseminated Intravascular Coagulation
2) 13 Maternal Adverse Events (MAE): Miscarriage, Stillbirth, Preterm labour, Eclampsia, HELLP syndrome,
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Antepartum Haemorrhage, Dysfunctional labour, Postpartum haemorrhage, Postpartum endometritis, Maternal death, Gestational diabetes (only objective 1), Ectopic pregnancy (only objective 1), Pre-eclampsia (only objective 1)
Outcome time-window in the first objective is any-time during pregnancy for all outcomes, except post-partum and maternal death outcomes (follow-up will be extended 6 to 12 weeks postpartum according to the definition) and miscarriage (before gestational week 20).
In the second objective, we are interested in AESI occurring in the 42 days after vaccination, or until the end of pregnancy, whichever comes first. For the MAE, outcome-time is given by their definition: up until week 19 for miscarriage, 6 weeks after pregnancy end for post-partum endometriosis and maternal death, 12 weeks after pregnancy end for postpartum haemorrhage, and up until the end of pregnancy for the rest. As a secondary analysis, we will look at AESI 42 days after index date regardless of pregnancy end.
Study population
For the first objective, the study population will include all pregnancies starting between 1st January 2018, and up to 9 months before end of data. Pregnant women aged 12 to 55 years old at pregnancy start day and with 365 days or more of previous follow-up will be included. Pregnant women with an occurrence of the outcome of interest in the wash-out period relative to pregnancy start date will be excluded. Index date will be set at pregnancy start date.
For the second objective, women pregnant during the enrolment period, aged 12 to 55 years at pregnancy start date, and with 365 days or more of previous observation in the database will be eligible to enter the study. Women with an occurrence of any of the outcomes of interest during the outcome-specific washout window relative to pregnancy start will be excluded.
Enrolment period will be different for each exposure of interest. When assessing the 1st dose of an mRNA COVID-19 vaccine during pregnancy, the enrolment period will span from 01/04/2021 to 28/02/2022. When studying the 2nd dose during, the enrolment period runs from 01/05/2021 to 31/03/2022. Finally, when the exposure of interest is the booster dose during pregnancy, the enrolment period goes from 01/10/2021 (when 1st booster was approved) to 12 months before the data cut-off.
For each day within the enrolment period, pregnant women receiving the exposure (vaccine dose) of interest will enter the exposure cohort. Index date for exposed will be exposure date. Simultaneously, a sample of eligible comparators (pregnant women who have not yet received the exposure) will enter the comparator cohort if they match on gestational age (2-week band) and maternal age (2-year band). Index date for sampled matched comparators will be the same as that of the exposed counterpart.
Follow-up
For the first objective, follow-up will be from day 1 after index date, until the earliest of: 1) outcome of interest, 2) end of time-window to observe the outcome of interest, or 3) death or end of available data.
Follow-up for the second objective, will start at 1 day after the index date until the earliest of the following events: 1) outcome of interest, 2) end of time-window to observe the outcome of interest, 3) death or end of available data, 4) occurrence of a truncating events that precludes the outcome of interest (e.g., miscarriage for postpartum haemorrhage), and 5) subsequent COVID-19 vaccination.
In sensitivity analysis, follow-up will also be stopped at COVID-19 infection.
Data analysis
For the first objective, incidence rates will be estimated for calendar year and month and stratified by age group (12-17, 18-34, and 35-55 years) and gestational trimester (0 to 90, 91 to 180, and 181 and more days). Additionally, overall pregnant incidence rates across the study period will be estimated, and stratified by gestational trimester for outcomes occurring during pregnancy (AESI and pre-eclampsia, eclampsia, HELLP syndrome, gestational diabetes, and ectopic pregnancy). For each outcome, a cohort of all pregnant women who experienced the outcome will be identified, with index date set at incident diagnosis date (“outcome cohort”). Then, a matched cohort will be created with 1:1 matching on age and gestational age, for each outcome cohort. Pre-selected baseline characteristics and large-scale characteristics will be summarised for both cohorts (outcome and matched cohorts) at index date and before. Standardised Mean Differences (SMDs) will be calculated to compare baseline characteristics between cohorts. Matching will be used to provide a better context for the outcome-cohort, rather than to achieve conditional exchangeability.
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For the second objective, SMDs will be calculated between exposed and comparator cohorts. An SMD =0.1 will indicate adequate covariate balance. 68 Negative Control Outcomes (NCOs) will be used to detect residual confounding.
If the sampled cohorts are not balanced, Propensity Score (PS) overlap weighting will be used to create comparable cohorts. The PS model will include variables on demographics (age, location, index date), pregnancy-related (gestational time at enrolment, previous pregnancies, complications during pregnancy, obesity, smoking status and alcohol use), healthcare utilisation (days in observation, healthcare visits…), and pre-existing conditions (all history) and medications (last 180 days) selected through LASSO regression. SMD and NCO will be used to assess observed and unobserved confounding in the weighted population. If required, PS-modeling will be iterated until balance is achieved.
Incidence Rate Ratios will measure the relative incidence of the outcomes of interest between exposed and comparator cohorts. Empirical calibration will adjust estimates and confidence intervals if NCOs indicate residual confounding. Database-specific estimates will be pooled through random-effects meta-analysis for overall results.

Rationale and background
Asthma affects 3-8% of pregnant women and often changes in severity during pregnancy. Uncontrolled asthma poses risks such as pregnancy loss, low birth weight, and perinatal mortality, making continued treatment crucial. Studies have shown inconsistent adherence to asthma treatments among pregnant women and variations in prescribing patterns.
Research question and objectives
Research questions
This study aims to explore the trends in use of asthma medication in the year before, during and year after pregnancy from 2010 until 2023, in order to update our knowledge on the current trend of asthma medication use among pregnant women.
Objectives
The specific objectives of this study are:
1. To estimate the prevalence of use of asthma medication in the year before, during and the year after pregnancy
2. To estimate the proportion of women discontinuing the asthma treatment before or during pregnancy, and among those who discontinued the proportion restarting the treatment
3. To characterise pregnant women with prior asthma diagnosis
Methods
Study design
• Patient-level DUS
• Population-level DUS
Population
The study population will include all pregnant women present in the Netherlands and Spain primary care databases during the study period 01/01/2010 to 31/12/2023 or to the end of available data, with prior asthma diagnosis and with at least 365 days of database history prior to the index date (i.e., start date of pregnancy).
Variables
Exposure(s): Asthma medication
Outcome(s): Prevalence rate of asthma medication use the year before, during and after pregnancy
Proportion of women discontinuing and restarting use of asthma drugs Characteristics of pregnant women with asthma
Relevant covariate(s):
• Age
• Smoking status
• History of respiratory conditions (assessed at index date)
o Chronic rhinosinusitis (+/- nasal polyposis)
o Allergic rhinitis
o Respiratory infections
• Metabolic and cardiovascular
o Obesity: Body mass index: body mass index may be stratified in groups (such as <27, 27-39, =40) o Gastroesophageal reflux disease o Diabetes mellitus o Thyroid dysfunction • Mental health o Depression o Anxiety • Inflammatory conditions o Atopic dermatitis Data source 1. Integrated Primary Care Information (IPCI), Netherlands 2. The Information System for Research on Primary Care (SIDIAP), Spain Sample size. No sample size has been calculated Statistical analysis Prevalence of use of asthma medication and the proportion of pregnant women discontinuing and restarting treatment with asthma drugs will be estimated one year before and one year after end of pregnancy and per trimester during pregnancy. Characteristics of pregnant women with asthma will be described using mean (+ ± SD), median (+ range) or number of person (N, %). In addition, covariates of interest will also be reported as counts and proportions. The statistical analyses will be performed based on OMOP-CDM mapped data using Darwin R packages.

Rationale and background
Art.31 CHMP referral in 2016 on metformin and use in patients with reduced kidney function led to PI updates including introduction of a warning and interaction with concomitant use with iodinated agents for patients undergoing imaging procedures.
Based on the evidence reviewed in the PSUSA, it was considered premature to delete the warning and interaction in the metformin PIs, at this stage. PRAC suggested the possibility to conduct a RWD study to gather more data on this specific issue and understand better if further regulatory action would be deemed necessary Research question and objectives
1. To phenotype and characterise ICA in patients with type 2 diabetes initiating metformin
2. To phenotype AKI and CKD stage using diagnosis codes and eGFR measurements among patients
with type 2 diabetes initiating metformin
3. To characterise patients with type 2 iabetes initiating treatment of metformin in terms of:
a. Demographics (age, sex)
b. Recorded comorbidities
c. Recorded duration from first diabetes diagnosis
d. Previous procedures with ICA
e. CKD stage (most recent in past year)
4. To characterise patients with type 2 diabetes with a first procedure requiring ICA with ongoing
metformin use in terms of previous history of:
a. Demographics (age, sex)
b. Comorbidities
c. Recorded duration from first diabetes diagnosis
d. CKD stage
e. ICA type
f. Time from metformin initiation to first procedure requiring ICA
5. To quantify the occurrence of renal dysfunction and of acute diabetes decompensation among
patients with type 2 diabetes with a first procedure requiring ICA during metformin use specifically:
a. AKI
b. Lactic acidosis
c. Diabetic ketoacidosis
Methods
Study design
New user cohort study
Population
Two cohorts:
1) Metformin new users: new users of metformin between 01/01/2014 and 31/12/2024 (or latest date
available), with a diagnosis of diabetes and at least 365 days of history prior to the date of their first
metformin prescription and no prior use of metformin.
2) ICA new use: first procedure requiring an ICA between 01/01/2014 and 31/12/2024 (or latest date available), with metformin initiation during the study period, ongoing metformin use, previous diagnosis of diabetes and 365 days of prior history before metformin initiation. ICA new use population (Cohort 2) is a subset of the metformin new users (Cohort 1).
We will exclude patients with a record of AKI, ketoacidosis or diabetic acidosis within a year of index date.
Variables
Exposures: ICA, metformin
Outcomes:
• AKI
• Lactic acidosis
• Diabetic ketoacidosis
Covariates for characterisation:
• Demographics (age, sex)
• Comorbidities: Anxiety, asthma, CKD, chronic liver disease, chronic obstructive pulmonary disorder (COPD), dementia, gastroesophageal reflux disease (GERD), heart failure, human immunodeficiency virus (HIV), hypertension, hypothyroidism, inflammatory bowel disease, malignant neoplastic disease, myocardial infarction, osteoporosis, pneumonia, rheumatoid
arthritis, stroke, venous thromboembolism
• Duration of diabetes (days from initial diagnosis of type 2 diabetes to index date)
• Previous use of ICA
• CKD stage
• ICA type
• Time from metformin initiation to first procedure requiring ICA
Objective 5: Survival analyses will be stratified by
• ICA indication/type
• CKD stage (1-5)
Data source
1. SIDIAP (Spain, Primary Care Database)
2. FinOMOP-HILMO (Finland, National Registry)
3. DK-DHR (Denmark, National Registry)
4. CPRD GOLD (United Kingdom [UK], Primary Care Database)
Statistical analysis
Phenotyping and characterisation of ICA, and AKI/CKD using diagnosis codes and eGFR measurements, will be done for patients with type 2 diabetes initiating metformin.
Patient-level characterisation will be conducted in databases based on data availability.
Patient-level characterisation of new metformin users will be conducted at index date (date of first prescription), including patient demographics, comorbidities, time since diabetes diagnosis, previous use of ICA, and CKD stage.
Patient-level characterisation of first procedure requiring ICA during ongoing metformin use will be conducted at index date (date of first procedure), including patient demographics, comorbidities, time since diabetes diagnosis, CKD stage, ICA type and time from metformin initiation to first procedure requiring ICA.
We will estimate Kaplan Meier survival functions to describe the probability of outcome occurrence and median survival to outcomes of interests, specially, AKI, lactic acidosis, diabetic ketoacidosis among patients with type 2 diabetes with a first procedure requiring ICA during metformin use stratified by CKD stage, and ICA type.
For all analyses a minimum cell counts of 5 will be used when reporting results, with any smaller counts will be noted as <5.

Rationale and background
Meningococcal vaccines are recommended in the immunisation schedule in targeted children and
adolescent population to preventing Invasive Meningococcal Disease (IMD). Various Meningococcal vaccines
have been developed to target distinct serogroups of the bacteria Neisseria Meningitidis including groups A,
B, C, W and Y responsible for IMD. The current vaccination schedule in countries within the Europe have
recommended the uptake of three doses Meningococcal group B (MenB) vaccines for children at age 2, 4
and 12 months, single dose of MenC or Haemophilus influenzae type b/Meningococcal group C (Hib/MenC)
vaccines at age 12 months and quadrivalent Meningococcal groups ACWY (MCV4) vaccines as a single dose
schedule in adolescent between 14 and 18 years of age as the main vaccination schedule for the prevention
of severe meningococcal infection. This study aims to generate comprehensive evidence on the coverage of
these separate types of meningococcal vaccines within the target population across six countries within
Europe.
Research question and objectives
The general objective of this study is to examine the coverage of meningococcal vaccines routinely
administered in countries across Europe for preventing IMD in eligible individuals.
The specific objectives of this study are:
1. To examine the coverage of MenB vaccine in children at age one and two years by dose received (1
dose, 2 doses and 3 doses)
2. To examine the coverage of MenC or Hib/MenC conjugate vaccines in children at age two years
3. To examine the coverage of MCV4 vaccines in individuals at age 18 years
4. To estimate the coverage of specific brand of MenB vaccines (Bexsero® and Trumemba®) in
individuals aged two years and MCV4 vaccines (Menveo® and Nimenrix®) in individuals aged 18 years
Methods
Study design
Population-level drug utilisation study (DUS)
Population
The study population will include all individuals aged one year and two years at the index date of separate
observation windows for assessing the coverage of MenB vaccines and individuals aged 18 for assessing the
coverage of MCV4 vaccines.
Variables
Data source
1. Base de Datos para la Investigación Farmacoepidemiológica en el Ámbito Público (BIFAP), Spain
2. Clinical Practice Research Datalink (CPRD) GOLD, United Kingdom (UK)
3. Danish Data Health Registries (DK-DHR), Denmark
4. Finnish Care Register for Health Care (FinOMOP-HILMO), Finland
5. InGef Research Database (InGef), Germany
6. Croatian National Public Health Information System (NAJS), Croatia
7. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
Statistical analysis
Objective one of this study examines the point prevalence of individuals who have received different
number of doses of MenB vaccines (1 dose, 2 doses and 3 doses) in separate age groups (age 1 years and 2
years of age). Objective 2 examines the point prevalence of eligible individuals aged two who have received
at least one dose of MenC or Hib/MenC vaccines. Objective three examines the point prevalence of MCV4
recipient in individuals at 18 years of age. Objective four of this study examines the point prevalence of
MenB and MCV4 vaccines by brand (MenB: Bexsero®, Trumemba®; MCV4: Menveo®, Nimenrix®). The
analyses stated above will be conducted in separate quarterly and yearly observation windows.

To date, asthma prevalence has been mainly assessed from self or parental-reported questionnaires in cross-sectional studies, implying recall bias. Despite its noteworthy prevalence, its age-specific evolving incidence among children and adolescents remains unclear and region-specific data from Catalonia is scarce.
In this context, comprehensive, population-based data sources such as routinely collected electronic health records (EHR) that capture real-time healthcare information and minimise recall bias, can offer a valuable alternative.
The objective of this study is to investigate time trends in the prevalence and incidence of asthma, among individuals from 0 to 18 years in Catalonia, Spain. We will conduct a population-based open cohort with individual level data from the Information System for Research in Primary Care (SIDIAP) mapped to the OMOP-CDM from 2008 to 2024. We will estimate overall and annual incidence and prevalence of asthma among children and adolescents overall and stratified by sex, age, nationality and socioeconomic status. We will estimate crude and age-standardised rates of asthma. We will also investigate changes in time trends over the study period.
Our study will enable to identify temporal trends in asthma in Catalonia and will inform preventive strategies targeted to specific groups that are more vulnerable, helping to address asthma disparities across different demographics.

Title
DARWIN EU® – Trends in utilisation of Attention-Deficit Hyperactivity Disorder (ADHD) Medications
Rationale and background
A first off-the-shelf study has been performed in 2024 at the request of the SPOC Working Party (responsible for monitoring and reporting events that could affect the supply of medicines in the EU) that has been monitoring shortages of different medicines to treat Attention-Deficit Hyperactivity Disorder (ADHD), mainly due to an increased demand in multiple markets, production constraints related to raw material availability, new regulatory approvals for some medicines, and changes in the competitive
landscape. The main products under monitoring are lisdexamfetamine and methylphenidate, but three more have the indication in Europe (atomoxetine, dexamfetamine and guanfacine). Currently, the situation has improved slightly in the EU and there are no critical shortages. However, it is anticipated that constraints in the supply will continue throughout 2024. The initial study was conducted to better anticipate potential shortages and its impact on appropriate patient management, as it is important to assess the evolution of prescriptions over time and get an overview of how these ADHD medicines are used across Europe. The Spanish Regulatory Authority (AEMPS) after having seen the results of the initial study has asked the EMA if we could repeat the study with additional data partners, especially Spanish ones as the network expanded in this country since the first FA request.
Research question and objectives
The overall aim of this study is to characterise the use of ADHD medications in the period of 2010 to 2023.
The specific objectives are:
1. To estimate the monthly and yearly period prevalence of use of each ADHD medicine, overall and stratified by age and sex in each database.
2. To estimate the monthly and yearly incidence of use of each ADHD medicine, overall and stratified by age and sex in each database.
3. Among new users of each ADHD medicine, to identify the indication at the time of the initial of the prescribing/dispensing, overall and stratified by age and sex.
4. Among new users of each ADHD medicine, to estimate the initial dose, cumulative dose, and time on treatment of the initial medication, overall and stratified by age and sex.
5. Among new users of any ADHD medicine, to estimate the total treatment duration, number of prescriptions overall and by medicine, stratified by initial medicine, age, and sex.
6. To identify the treatment pathway of each individual who initiated an ADHD medicine, including treatment add-on, switch, and concurrent medication/co-prescribing, stratify by calendar time of initiation, age, and sex.
Methods
Study design
Population-level drug utilisation study (Objectives 1 and 2)
Patient-level utilisation study (Objectives 3 – 6, new user cohort study)
Population
In the population-level utilisation of ADHD medications (prevalence, incidence), all people aged 3 years and older, registered in the respective databases since 1st of January of 2010 to the latest available data, with at least 365 days of prior data availability, will be included.
In the patient-level utilization of ADHD medications, new users will be identified using the first record of any of the ADHD medications of interest within the study period, having no previous records for the study medication during the 12 months before cohort entry.
Variables
Drugs of interest: Five approved medications for the treatment of ADHD in Europe: methylphenidate, dexamphetamine, lisdexamfetamine, atomoxetine and guanfacine.
Data source
• SIDIAP, covering primary care and linked hospital data from Catalonia, Spain
• BIFAP, covering Spanish primary care
• DK-DHR, linked national registries from Danmark
• NLHR, linked national registries from Norway
• InGef RDB, national wide claims data with primary and secondary care from Germany
• SMPA-GU, linked national registries from Sweden
Statistical analysis
Objectives 1 to 2 are population-level drug utilisation study, monthly and yearly period prevalence and incidence use of each ADHD medications will be estimated, overall and stratified by age group and sex.
Objectives 3 to 6 are patient-level drug utilisation study. In Objectives 3 and 4, new user cohorts will be constructed for each ADHD medicine with pre-defined washout period, characteristics of user will be described, indication for the initial prescribing/dispensing will be estimated, overall and stratified by age and sex. Initial dose, cumulative dose, and length of the treatment will be calculated. In Objectives 5 and 6, we will construct new user cohorts of any ADHD medicine, estimate the total treatment duration and number
of prescriptions. Treatment pathways will be defined and proportion of individuals in each path and the length of each treatment stage will be reported.
For all analyses, a minimum cell counts of 5 will be used when reporting results, with any smaller counts will be noted as “<5”.

Rationale and background
Cystic fibrosis (CF) is a progressive genetic disorder associated with significant morbidity and premature mortality, primarily affecting the respiratory and gastrointestinal systems. It leads to chronic lung infections, pancreatic insufficiency, and other complications requiring comprehensive, lifelong management. This study aims to generate epidemiological evidence on the clinical haracteristics and monitoring of individuals diagnosed with CF across Europe between 2015 and 2024.
Research question and objectives
Research question:
What are the demographic and clinical characteristics of individuals diagnosed with cystic fibrosis (CF) in Europe between 2015 and 2024?
Study objectives:
1. To characterise individuals newly diagnosed with CF in terms of demographics, pre-specified comorbidities, Pseudomonas aeruginosa colonisation, CF screening, genotyping test and CFTR modulator treatment use, overall and stratified by paediatric and adult populations.
2. To characterise timing and availability of key clinical measurements including Forced Expiratory Volume (FEV), height, weight, Body Mass Index (BMI) measurements, sweat chloride levels and genotyping tests in
individuals who initiated any CFTR modulator treatment after CF diagnosis, overall and stratified by paediatric and adult populations.
3. To estimate the background incidence rates of pre-specified events of special interest: susceptibility for influenza virus infections, cataract, depression, anxiety and hepatotoxicity by treatment, in the CF population, overall and stratified by paediatric and adult populations and by calendar year.
4. To measure the incidence of pulmonary exacerbation among individuals newly diagnosed with CF overall and stratified by country/database, paediatric and adult populations, sex and time since diagnosis (one and two years post-diagnosis).
Methods
Study design
This retrospective cohort study aims to characterise individuals newly diagnosed with CF in terms of demographics, Pseudomonas aeruginosa colonisation, pre-specified comorbidities, CF screening test and genotyping test and CFTR modulator treatment use (objective 1), clinical characteristics including key clinical measurements (FEV, height, weight, BMI), sweat chloride levels and genotyping tests (objective 2) and to estimate the incidence of selected events of special interest and pulmonary exacerbation (objective 3 and 4).
Study period
1st January 2015 to 31st December 2024 (or latest available).
Study population
New CF diagnosis cohort (objective 1, 3 and 4): The study population will include all individuals with a first recorded diagnosis of CF in the period between 1st January 2015 and 31st December 2024 (or latest available). To ensure sufficient follow-up, only individuals diagnosed no later than 180 days prior to the end of data availability in each database will be included. Eligible individuals must have at least one year of data visibility prior to the date of CF diagnosis. The requirement of one year prior data availability will not hold or children below 1 year of age.
New CFTR modulator user cohort (objective 2): The study population will include individuals with a first diagnosis of CF in the period between 1st January 2015 and 31st December 2024 (or latest available) who initiated any CFTR modulator therapy after CF diagnosis. To ensure sufficient follow-up, only individuals diagnosed with CF at least 180 days before the end of data availability in each data source will be included.
Eligible individuals must have at least one year of prior data visibility prior to the date of CF diagnosis and no use of CFTR modulator therapy in one year preceding treatment initiation. This requirement will not hold for children < 1 year of age. Condition of interest: cystic fibrosis Variables Medication of interest: CFTR modulator therapy CFTR modulator therapy WHO ATC classification code · Ivacaftor R07AX02 · Ivacaftor and lumacaftor R07AX30 · Ivacaftor and tezacaftor R07AX31 · Ivacaftor, tezacaftor and elexacaftor R07AX32 · Deutivacaftor, tezacaftor and vanzacaftor R07AX33 Pre-specified comorbidities: depression, anxiety, sleeping disorders, pregnancy, congenital adverse events, diabetes, liver morbidity or mortality (if not feasible overall mortality), bone content (or measurements of bone content), gastro-intestinal complaints including constipation, acid reflux and abdominal pain. Events of special interest: susceptibility for influenza virus infections, cataract, depression, anxiety, hepatotoxicity by treatment, pulmonary exacerbations. Data sources 1. Assistance Publique – Hôpitaux de Marseille (APHM), France 2. Hospital Universitario 12 de Octubre (H12O), Spain 3. Norwegian Linked Health Registry data (NLHR), Norway 4. Research Repository @Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (POLIMI), Italy 5. The Information System for Research in Primary Care (SIDIAP), Spain 6. Semmelweis University Clinical Data (SUCD), Hungary Statistical analysis Descriptive characterisation will be performed at the patient level (objective 1). Age and sex at the time of the first recorded diagnosis of CF will be reported. The index date is defined as the date of the first recorded CF diagnosis for each individual. The number and percentage of individuals with a record of the prespecified condition of interest, colonisation with Pseudomonas aeruginosa, CF screening, genotyping test or CFTR modulator use will be assessed during pre-defined time windows. The statistical analysis will be conducted using “CohortCharacteristics” R package based on OMOP-CDM mapped data. Clinical characterisation (objective 2) will include age and sex at the date of incident CFTR modulator initiation. Additionally, the number and proportion of individuals initiating CFTR modulator treatment with available record of clinical measurements (FEV, height, weight, BMI) at index date and during pre-defined time windows, will be reported overall and stratified age. If available, clinical measurements (FEV, height, weight, BMI) will be summarised using minimum, quartiles and maximum values at index date and during pre-defined time windows, overall and stratified age groups. Similarly, number and proportion of individuals initiating CFTR modulators with a record of sweat chloride and genotyping tests will be reported at the index date and during the pre-defined time windows, overall and stratified by age. Results of sweat chloride levels and genotyping tests (if available) will also be reported, overall and stratified by age. The statistical analysis will be conducted using “CohortCharacteristics” R package based on OMOP-CDM mapped data. Incidence rates of pre-specified events of special interest (objective 3) will be estimated following CF diagnosis. The pre-specified events of interest are susceptibility for influenza virus infections, cataract, depression, anxiety and hepatotoxicity by treatment. These incidence rates will be expressed as the number of individuals with the event of interest following new CF diagnosis per 1,000 person-years of the individuals fulfilling the inclusion and exclusion criteria. Incidence rates will be reported overall and stratified by paediatric and adult populations and calendar year. The statistical analyses will be performed based on OMOP-CDM mapped data using the “IncidencePrevalence” R package. Incidence rates of newly diagnosed pulmonary exacerbation (objective 4) will be estimated following CF diagnosis. The results will be expressed as the number of individuals with the pulmonary exacerbation per 1,000 person-years of the individuals fulfilling the inclusion and exclusion criteria. Incidence rates will be calculated for consecutive yearly intervals since the CF diagnosis (index date), with a maximum follow-up period of 24 months. Incidence rates will be stratified by paediatric and adult populations and sex. These statistical analyses will be performed based on OMOP-CDM mapped data using the “IncidencePrevalence” R package. For all analyses a minimum cell counts of 5 will be used when reporting results, with any smaller counts obscured.

Rationale and background
Acute myeloid leukaemia (AML) is an aggressive haematological malignancy characterised by the uncontrolled proliferation of myeloid precursors. It primarily affects older adults but is also diagnosed in children. Despite advances in diagnostics and treatment, survival remains poor, especially among elderly patients. Diagnosis relies on blast counts and genetic markers, with evolving classifications emphasising molecular features. Standard treatment includes induction chemotherapy and, in eligible patients, haematopoietic stem cell transplantation. Although novel targeted therapies show promise, further evidence is needed to support their safety. This study aims to generate real-world evidence on AML incidence, patient characteristics, treatment patterns, and survival, utilising routinely collected healthcare data, to inform both clinical and regulatory decision-making.
Research question and objectives
Research questions
What real-world data and evidence is currently available to contextualise treatment choices and outcomes in AML patients?
Objectives
This study aims to estimate the incidence of AML and characterise patients with AML in terms of comorbidities, diagnostic tests, treatments, and survival.
Specific objectives are:
1. To estimate the annual incidence of AML in children (<18 years old) and adults (=18). 2. To characterise AML patients in terms of the comorbid conditions, medications, procedures, and diagnostic measurements. 3. To describe treatment patterns (including hematopoietic stem-cell transplantation) in AML patients. 4. To estimate overall survival 1, 3, and 5 years for patients with AML. Methods Study design Retrospective cohort studies will be conducted using routinely collected health data from five databases across four European countries. A population-level descriptive epidemiology study will address annual AML incidence (objective 1), and a patient-level characterisation study will address the remaining objectives (objectives 2 to 4). Population Objective 1: All individuals present in the respective databases during the study period (January 1, 2015, to MODEL DE SOL·LICITUD 2 IMP-126-CT Versió 07 December 31, 2024, or until the end of available data). Objectives 2–4: All patients diagnosed with AML within the same period. Variables Sex (female/male), age (continuous and age groups 0 to 17; =18; 18 to 39; 40 to 59; 60 to 79; =80 years), calendar year, study period (2015–2020, 2021–2024), medications, comorbidities, procedures, and diagnostic measurements. Outcomes Diagnosis of AML (objective 1), death from any cause (objective 4) Data sources 1. Croatia: Croatian National Public Health Information System (NAJS) 2. Germany: InGef Research Database (InGef RDB) 3. Netherlands: Netherlands Cancer Registry (NCR) 4. Spain: The Information System for Research on Primary Care (SIDIAP) 5. Multiple countries: HARMONY - Acute Myeloid Leukaemia (HARMONY-AML) Study size No sample size has been calculated, as this is an exploratory study that will not test a specific hypothesis. Statistical analysis Population-level descriptive epidemiology: Annual incidence rates per 100,000 person-years will be estimated overall and stratified by sex and age. The statistical analyses will be performed based on OMOP CDM mapped data using the “IncidencePrevalence” package. Only NAJS, InGef, and SIDIAP databases will be used in this objective. Patient-level characterisation: Demographics (age and sex) will be described at the time of AML diagnosis. Large-scale patient-level characterisation will be conducted at any time before, at the date of AML diagnosis, and two years after, and describe demographics, comorbidities, medications, and procedures, using the “CohortCharacteristics” package. All treatments at the ingredient level and combinations of pre-specified medicines of interest initiated in patients with AML will also be described using the “TreatmentPatterns” package. Overall survival probabilities and restricted mean survival time (RMST) at 1, 3, and 5 years, as well as median survival, will be calculated using the “CohortSurvival” package. Results will be presented with 95% confidence intervals. A minimum cell count of 5 will be applied to all reporting results, with any smaller count reported as “<5”.

Rationale and background
Few studies on obesity and weight management have used anthropometric measurements, largely due to methodological challenges. This study aims to assess the availability and completeness of data on obesity, weight, and obesity-related variables across the network of DARWIN EU® data partners. These findings will inform the viability of conducting further Real-World Data (RWD) studies on obesity and related comorbidities.
Research question and objectives
1. What proportion of individuals within the DARWIN EU® network have records of obesity-related conditions, measurements, observations, and procedures?
2. What are the median number and values of anthropometric measurements and lifestyle factors recorded in the DARWIN EU® network?
3. How do the demographic and obesity-related conditions, medications, procedures, and lifestyle factors of individuals with a high body mass index (BMI) compare to those of individuals with a recorded condition of obesity within the DARWIN EU® network?
Objectives
1. To assess the proportion of individuals with records of obesity-related conditions, measurements, lifestyle factors, and procedures within the DARWIN EU® network
2. To characterise reporting of anthropometric measurements and lifestyle factors within the DARWIN EU network in terms of Median number (IQR) of BMI, weight, cholesterol, waist circumference, diet, and physical activity records per individual within the study period
3. To summarise the median value (min-max, Q1, and Q3) of BMI and weight measurements
4. To assess changes in the rate of BMI and weight measurements over time
5. To compare the characteristics of individuals with obesity based on disease codes versus those with obesity defined by BMI cutoff value in terms of demography, comorbidity, use of concomitant medications, procedures, and lifestyle factors
Methods
Study design
We will perform a retrospective cohort study with three components: (i) to assess the proportion of individuals with obesity-related variables (objective 1); (ii) to perform a patient-level characterisation to describe the characteristics of individuals with anthropometric, lab, and lifestyle measurements (objective 2); and (iii) a patient-level characterisation to describe the characteristics of individuals with obesity (objective 3).
Population
The study population will include all individuals present in the database during the study period 01/01/2010 to 31/12/2024 (or to the end of available data) and with at least 365 days of database history prior to index date (except for individuals in hospital data sources).
Variables
Outcome:
For prevalence estimation, outcomes will include condition records of obesity and measurements of BMI, weight, height, cholesterol, and waist circumference.
Relevant covariates:
This study will characterise by the following: occurrences of BMI, weight, height, cholesterol, and waist circumference measurements; the value of the measurements of BMI and weight; condition records of obesity, diabetes mellitus type 2, hypertension, ischemic heart disease, and chronic kidney disease; drug records of Glucagon-like peptide-1 (GLP-1) receptor agonists, Orlistat, Metformin, and Naltrexone-bupropion; procedure record of bariatric surgery; and observations of diet, exercise, and smoking status.
Data sources
1. Estonia: Estonian Biobank (EBB)
2. Italy: Research Repository @Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico (POLIMI)
3. Spain: Hospital Universitario 12 de Octubre (H12O)
4. Spain: Platafoma de Recerca en Informació Sanitària de les Illes Balears (PRISIB)
5. Spain: The Information System for Research on Primary Care (SIDIAP)
6. United Kingdom: Clinical Practice Research Datalink GOLD (CPRD GOLD)
7. United Kingdom: UK BioBank (UKBB)
Study size
No sample size has been calculated, as this is an exploratory study which will not test a specific hypothesis; instead, the study specifically aims to estimate the number of and describe the variables under study.
Statistical analysis
The period prevalence of obesity and obesity-related measurements will be estimated in all individuals in the data sources, overall and stratified by sex and age categories. Characteristics will be described by means of median age, sex, and the covariates of interest, which will be reported as counts and proportions. The statistical analyses will be performed based on OMOP common data model mapped data using the IncidencePrevalence and CohortCharacterisation R packages. A minimum cell counts of 5 will be used when reporting results.

Rationale and background
Pregnancy episodes and outcomes are often missing or inconsistently derived across individuals and data sources in routinely collected healthcare data. When structured pregnancy information is missing, pregnancy algorithms are required to infer structured pregnancy information using available input variables. Specifically, the extent to which OMOP-based pregnancy algorithms perform across a wide range of data types and settings is uncertain, including whether further development is needed.
Research question and objectives
Research question
Can algorithm-based inference of pregnancy episodes and outcomes be reliably applied across different populations and healthcare settings within the DARWIN EU® Data Network? Objectives This study aims to evaluate the reliability of inferring pregnancy episodes and their outcomes using algorithms applied within the DARWIN EU® Data Network.
The specific objectives of this study are:
2.1 To describe existing published pregnancy algorithms that have been applied in data sources mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
2.2 To assess the availability of the required input variables for the identified pregnancy algorithms across the selected DARWIN EU® data sources.
2.3 To implement the identified pregnancy algorithm(s) in the selected DARWIN EU® data sources and adapt for initial execution, accounting for data availability.
2.4 To iteratively evaluate the internal consistency and plausibility of the performance of the selected pregnancy algorithms.
2.5 To optimise the pregnancy algorithms in the selected DARWIN EU® data sources (based on results from Objective 2.4).
2.6 To validate the algorithm’s performance at the episode-level and population-level within the DARWIN EU® Data Network.
Methods
Study design
This study will follow a stepwise methodological approach to identify, implement, adapt, and validate a pregnancy algorithm in real-world data mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). First, we will conduct a scoping review of existing published algorithms that have been developed or adapted for use in the OMOP CDM. The algorithm chosen to be implemented will be selected based on its ability to infer pregnancy start and end dates and outcomes, its prior application to comparable data sources, and the availability of reproducible and well-documented code. When related algorithms exist, preference will be given to refinements of earlier logic. For the selected algorithm, we will document the input variables required for implementation. Next, we will assess the availability of these variables across selected data sources. The selected algorithm, identified in Objective 2.1, will be implemented on the data sources. Internal consistency and plausibility checks will be used to iteratively evaluate and optimise the algorithm. Finally, we will validate the algorithm’s output through comparisons at both the episode and population levels.
Population
The study population will include all individuals registered as female sex (at birth) present in the database during the study period, from 01/01/2010 (or database start, if later) to the end of available data. Individuals require at least 365 days of continuous observation time. At least one record indicative of a pregnancy episode needs to occur up to one year before the database end date, to ensure full-term pregnancies can be identified.
Variables
Outcome:
The primary outcome will be the occurrence and timing of the algorithm-derived variables:
• Pregnancy Start Date
• Pregnancy End Date
• Pregnancy Outcome
Relevant covariates:
Age, Sex, Year.
Statistical analysis
We will perform a scoping review to identify and summarise existing pregnancy algorithms used in OMOP CDM. The algorithm chosen to be implemented will be selected based on prespecified criteria. Descriptive analyses will be performed to assess the availability and completeness of required input variables across data sources. The selected algorithm’s performance will be evaluated using internal consistency and plausibility metrics (intermediate checks). Results obtained from these intermediate checks will guide algorithm optimisation in an iterative manner. The final algorithm’s performance will be validated at both the episode and population levels. Validation results will be reported independently for each data source.

This study examines the low uptake of influenza vaccination among pregnant women in Barcelona, where coverage during the 2023-2024 campaign was only 26%. Using the 5C model of vaccine hesitancy, researchers aim to explore the barriers influencing vaccine acceptance through a qualitative, phenomenological approach. Pregnant women who did not receive the influenza vaccine despite eligibility will be recruited using intentional sampling through healthcare centers, social media, and snowballing. Semi-structured interviews will be conducted to gather in-depth insights into participants’ experiences, with data collection
continuing until saturation is reached. Thematic content analysis will be applied to identify key barriers such as distrust in vaccine safety and efficacy, low perceived risk of influenza, and insufficient medical recommendations. Findings will inform tailored interventions, including awareness campaigns and healthcare provider training, to enhance vaccination rates and
improve maternal and child health outcomes.

El cáncer es responsable de una proporción importante de la carga total de morbilidad en la población mundial. Sin embargo, no afecta a todas las subpoblaciones por igual. Para la mayoría de los tipos de cáncer, los países desarrollados tienen tasas de incidencia más altas, aunque las tasas aumentan desproporcionadamente en los países en desarrollo. En España, la incidencia del cáncer varía en función de una serie de factores. Actualmente existe un vacío en el conocimiento sobre las tendencias temporales de la incidencia del cáncer en España y, más concretamente, en Catalunya. Nuestro objetivo es describir las tasas de incidencia anual de cáncer de los 5 tipos de cáncer sólido más incidentes en España (cáncer de mama, próstata, colorrectal, pulmón y vejiga) desde 2009 hasta 2018 utilizando la base de datos SIDIAP. SIDIAP es una recopilación de historias clínicas longitudinales pseudoanonimizadas de casi seis millones de pacientes de Catalunya (80% de la población) registrados en 370 centros de atención primaria, y es representativa del total de la población catalana por sexo, edad y distribución geográfica. Las tasas de incidencia del cáncer se estratificarán por sexo y niveles de privación del índice MEDEA para investigar las subpoblaciones que son más vulnerables a los tipos de cáncer especificados. Esto ayudará a planificar futuras intervenciones que puedan ser más específicas y efectivas para las poblaciones con un mayor riesgo.

We aim to estimate the risks of long-term COVID-19 outcomes among individuals with COVID-19 or exposed to COVID-19 during pregnancy, as well as the effect of vaccines on the development of long COVID and long-term COVID-19 outcomes.
Data will be obtained from SIDIAP, which includes primary care records for approximately 6 million people in Catalonia. To estimate long-term COVID-19 outcomes (Objective 1), we will conduct three population-based matched cohort studies using a target trial emulation design from September 2020 to December 2023. We will match COVID-19 infections to uninfected controls in a 1:5 ratio using propensity score (PS) matching. Cases/controls matched cohorts will include: Objective 1.1: people not vaccinated against COVID-19; Objective 1.2: people vaccinated against COVID-19; and Objective 1.3: newborns (cases: exposed to COVID-19 infection during pregnancy, controls: unexposed). Cohorts will be followed for 2 years. Our outcomes will include autoimmune, cardiovascular, mental, neurological, renal and early-life complications. We will estimate cause-specific Hazard Ratios (HR) for each outcome.To estimate vaccine effectiveness on the development of long COVID, we will first characterise long COVID based on persistent symptoms for >28 days (Objective 2.1). We will then estimate the effect of vaccines on the development of long COVID (Objective 2.2) using a staggered cohort study design. Vaccinated and unvaccinated cohorts will be compared using different PS techniques. We will then calculate HR for long COVID and long-term outcomes.
Our findings will inform preventive strategies and post-acute care pathways, thus contribute to prevent long COVID and improve COVID-19 survivors’ health.

Background: The overwhelming numbers of COVID-19 infections have led to an unprecedented crisis for healthcare systems worldwide. Understanding the impact of this pandemic on health is an urgent priority.
Objectives: UNCOVER aims to unravel the mid- and long-term effects of the pandemic on COVID-19 and non-COVID-19 morbidity and mortality through the use of large real-world databases from Catalonia and the UK.
Methods: UNCOVER will conduct a cohort study using longitudinal electronic health records from Catalonia (SIDIAP) and the UK (CPRD). These databases have been transformed to an international common data model (CDM), and when possible, the study will be replicated in other databases around the world. COVID-19 testing, diagnosis in primary care, hospitalisations, and deaths will be identified from March 1st 2020. Study periods will be defined as pre-COVID-19, COVID-19-pre-vaccine, and COVID-19-vaccination. Different periods will be defined for the study of non-pharmaceutical interventions by country. Mid- and long-term symptoms and outcomes will be identified. Time-series and multi-state models will be used for data analyses.
Expected results: UNCOVER will provide novel scientific knowledge aimed at helping decision-makers and clinicians in the control and management of the pandemic on a national and international level, whilst improving populations’ health and quality of life.

Antecedentes: las enfermeras pediátricas y los pediatras (PN&P) pueden necesitar habilidades y recursos para abordar las dudas de las familias sobre las vacunas.
Objetivos: 1) Diseñar e implementar una intervención para aumentar los conocimientos, actitudes favorables y habilidades de comunicación de la PN&P hacia la vacunación; 2) Evaluar su efectividad sobre los determinantes psicosociales de P&PN; 3) Evaluar su impacto en la cobertura vacunal infantil.
Métodos: Estudio de métodos mixtos.
1) Desarrollo de una intervención en línea basada en evidencia dirigida a PN&P.
2) Ensayo aleatorizado por grupos. Se emparejarán y aleatorizarán los Centros de Atención Primaria de Barcelona (N=41) y Cataluña Central (N=38). PN&P que trabaja en ellos (N=500) se ofrecerá a participar. Muestra: 270 (135 por grupo) participantes permiten detectar un 18% de diferencia en los resultados. Las variables sociodemográficas y psicosociales de PN&P se obtendrán con cuestionario al inicio y a los 6 meses. Resultados: conocimientos, comportamientos, actitudes, autoeficacia y normas sociales de PN&P relacionados con la vacilación de vacunas. Análisis por intención de tratar y por protocolo. Se modelarán regresiones multinivel ajustadas. Un estudio cualitativo con 8 grupos focales (4 en cada área) evaluará las percepciones de PN&P sobre resultados y procesos. Análisis temático y triangulación de datos.
3) El impacto en la cobertura de vacunación infantil se estudiará con otro ensayo aleatorizado por conglomerados. La fuente de datos será el sistema de información de atención primaria. Una muestra de 4.527 niños en cada grupo y cohorte detectará una diferencia del 1,5% entre los grupos. Variables de resultado: Porcentaje de niños vacunados correctamente para la primovacunación y la vacunación de refuerzo a los 0-1, 1-2 y 4-6 años. Compararemos las coberturas de vacunación por áreas de intervención con modelos multivariantes.

El projecte inclou dos estudis:
El primer estudi, ja fet fa una setmana durant 4 dies amb diverses bases de dades internacionals online, ha estudiat el comportament d’infeccions víriques similars que hi ha hagut en el passat: s’han descrit les característiques de les persones amb complicacions d’infeccions víriques com la grip, s’han valorat els predictors de resultats adversos entre els pacients hospitalitzats amb pneumònies virals, s’han generat algoritmes per identificar els pacients amb més risc de complicacions i/o morbimortalitat, i s’ha avaluat la seguretat dels tractaments utilitzats per a un ús potencial en Covid-19.
El segon estudi vol descriure les característiques de les persones amb Covid-19 a Catalunya, així com desenvolupar models predictius de les complicacions de la Covid-19 fent servir els mètodes i resultats obtinguts en el primer estudi. Per això, es farà la integració de la informació dels pacients infectats amb Covid-19 a Catalunya, la transformació de la nova informació a un model de dades internacional, i després de contrastar les dades amb altres bases de dades d’altres països, s’elaboraran uns criteris pronòstic aplicables a les polítiques de control de pandèmia de Covid-19 al nostre país.
Els models predictius de casos greus d’infecció vírica permetran classificar els pacients amb Covid-19 per gestionar-los adequadament, avaluant la necessitat de que el malalt vagi a l’hospital o es quedi a casa, i les condicions necessàries per al seu tractament.