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.

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.

Rationale and Background:
Vaproic acid/valproate-containing medicine (VPA) are first-line treatment for generalised tonic-clonic seizures (epilepsy) and adjunctive therapies in other types of seizures. They are also used as second-line treatments or adjuncts for the treatment of bipolar disorder, and for migraine prevention. Valproic acid is a teratogen, with prenatal exposure carrying a substantial risk of neurodevelopmental impairment and congenital malformations in the child. Therefore, its use in women of childbearing age is restricted to prevent valproate exposure during conception and pregnancy.
The European Medicines Agency (EMA) has issued risk minimisation measures in 2014 and 2018 including a compulsory pregnancy prevention program. Timely information on the use of VPA in young women across Europe is important.

Research question and Objectives:
The objectives of this study are
1. To estimate the population-level use (incidence rate and prevalence) of VPA in women between 12 and 55 years of age
2. To characterise patient-level VPA use in women between 12 and 55 years of age initiating treatment with VPA.

Research Methods:
Study design
• Population level cohort study (Objective 1, Population-level VPA utilisation)
• New user cohort study (Objective 2, Patient-level VPA utilisation)
Population:
Population-level utilisation of VPA and alternative treatments: All women aged between 12 years and =55 years between 01/01/2010 and 31/12/2022, with at least 365 days of prior history before the day they become eligible for study inclusion (study period: 2009-2022 including 1 year of previous data). For incidence, anyone with prior use of VPA will be excluded from the analysis.
Patient-level VPA utilisation: New users of VPA in the period between 01/01/2010 and 31/12/2021 (or latest date available), with at least 365 days of visibility prior to the date of their first VPA prescription, and no used VPA in the previous 365 days.
Variables
Drug of interest: Valproic acid, Sodium valproate, Magnesium valproate, Valproate semisodium and Valpromide
Alternative treatments: Carbamazepine, Phenobarbital, Phenytoin, Primidone, Clobazam, Clonazepam, Eslicarbazepine acetate, Lamotrigine, Oxcarbazepine, Perampanel, Rufinamide, Topiramate, Zonisamide, Brivaracetam, Ethosuximide, Gabapentin, Lacosamide, Levetiracetam, Pregabalin, Tiagabine, Vigabatrin, Lithium, Quetiapine, Olanzapine, Lamotrigine, Propranolol, Metoprolol, Atenolol, Nadolol, Timolol, Bisprolol, Topiramate, Amitriptyline, Flunarizine, Pizotifen, Clonidine
Data sources
1. Integrated Primary Care Information Project (IPCI), The Netherlands
2. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
3. IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), Belgium
4. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
5. Hospital District of Helsinki and Uusimaa (HUS), Finland
6. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom
Sample size
No sample size has been calculated. Feasibility counts have been generated in the general population in each databases.
Data analyses
For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts obscured.
Population-level VPA utilisation: Annual period prevalence of VPA use and alternative treatments will be estimated, as will annual incidence rates per 100,000 person years.
Patient-level VPA utilisation: Large-scale patient-level characterisation will be conducted. Medical History will be assessed for anytime – 366 days before index date, for 365 to 31 days before index date, for 30 to 1 day before index date, and at index date. Medication use will be reported for 365 to 31 days before index date , for 30 to 1 day before index date, and at index date. Frequency of indication, namely epilepsy, bipolar disorder and migraine at index date will be assessed. Initial dose/strength and treatment duration will be estimated and the minimum, p25, median, p75, and maximum will be provided.

Las enfermedades reumáticas inflamatorias (IRMD) incluyendo la artritis reumatoide (AR), el lupus eritematoso sistémico (LES), la artritis psoriásica (APs), la esclerosis sistémica (SSc) y las espondiloartropatías (Spa) entre otras están asociadas con un aumento significativo de la morbilidad y mortalidad. La carga de enfermedad de dichas patologías en nuestro entorno no es bien conocida. Nuestros objetivos son:
1) Evaluar la carga de enfermedad de IRMD en Cataluña, estimando su prevalencia e incidencia,
2) determinar los factores independientes asociados con las complicaciones de las IRMD en comparación a la población sana, incluyendo la enfermedad coronaria, la enfermedad tromboembólica, la diabetes mellitus, las fracturas osteoporóticas, las infecciones graves, el cáncer y la insuficiencia renal crónica y
3) Estimar el impacto de las IRMD sobre la mortalidad en comparación a la población general.
Para ello proponemos un estudio poblacional retrospectivo que incluirá todos los casos identificados mediante códigos ICD-10 del programa del Sistema de Información para el Desarrollo de la Investigación en Atención Primaria (SIDIAP) del 2006 al 2021. El SIDIAP cuenta con información de mas de 5.7 millones de habitantes e incluye datos demográficos, diagnósticos, prescripciones/dispensaciones y fecha de muerte.
Se utilizará una cohorte de control sin IRMD apareada por el año de nacimiento, género, y estado socio-económico como grupo control.

There is a lack of scientific literature on the characteristics of patients who have subsequent osteoporotic fractures and on their associated burden compared to single osteoporotic fractures.

OBJECTIVES:
1. To describe the characteristics of postmenopausal women with an imminent subsequent fracture
2. To describe the incidence of imminent subsequent fractures (fractures within 2 years after an index fracture) amongst postmenopausal women
3. To estimate the impact of an imminent subsequent fracture on healthcare resource utilisation and direct costs amongst postmenopausal women.

Data sources
This study will be done with the SIDIAP-OMOP data (Obervational and Medical Outcome Partnership Common Data Model) as well as in other 5 countries as a Network stuty cohort (UK, Frances, Germany, Italy, The Netherlands). No data extraction is required from SIDIAP just the Data Access to SIDIAP-OMOP. The Health Care Resource Utilization for Objective 3 will be calculated based on the registries of visits (primary care and hospitalizations) and procedures (referals) as well as drug use.

METHODS:
Study design and population: Retrospective cohort study of all women aged =50 years with a require observation time of at least 730 days pre-index date and will be followed-up for a maximum of 730 days.

Study period: 01 April 2008 to 31 March 2020 (included the pre-index and follow-up required observation period)

Exclusion: Patients with <730 days observation time prior to the index date, those with cancer (except non-melanoma skin cancer), Paget's disease of the bone, or other metabolic bone diseases (e.g., osteomalacia, hyperparathyroidism, osteogenesis imperfecta) at any time prior to and including index date will also be excluded. Three cohorts are of interest: (1) Patients with an index fracture and at least one previous fracture (of any type except facial, skull or digit fracture) in the prior two years, making the index fracture the imminent subsequent fracture (2) comparator cohort 1 includes patients with an index fracture and no history of fracture in the two years prior (3) comparator cohort 2 includes patients with no history of fracture at any time. Follow-up: from the index date until the first date of occurrence of the censoring events: cancer (except non-melanoma skin cancer); Paget's disease of the bone; or other metabolic bone diseases; 730 days after index date; death; end of data collection period; ensuing fracture after the imminent subsequent fracture in the target cohort; ensuing fracture after the index fracture in the comparator cohort 1; or incident fracture in the comparator cohort 2. For the first and second objectives, comparator cohort 1 is of interest. The date of the index fracture for this cohort will be the index date. For the third objective, the healthcare resource utilisation and direct costs amongst (i) patients in cohort 1(target cohort) will be compared with patients with in the comparator cohort 1 and, (ii) patients in the comparator cohort 1 will be compared with patients in the comparator cohort 2. Age and propensity score matching will be performed on an iterative, rolling basis over calendar time. The propensity score will be constructed on the index date. For the target cohort, the index date will be the date of imminent subsequent fracture. For the comparator cohort 1, the index date will be the date of fracture. For the comparator cohort 2, the index date will be a random date chosen in the specific 6-month period as long as the patient is alive at that date. VARIABLES: Demographics (Age), Charlso Comorbidity Index, Comorbid conditions (Fractures, Cardiovascular disease, DM, Chronic Kidney disease, Hiperlipidemia, Vitamin D deficiency, hypocalcemia, hypothyroidism, hypoparathyroidism), laboratory measurements, procedures, and medications (exposures to medications that incluence the risk of fracture) as well as number of GP and hospital contacts and drugs prescribed. We will define baseline covariates according to three time-windows prior to the index date: any time, 720 days and 180 days. OUTCOMES: 1-Imminent subsequent fracture occurring within two years of the previous fracture (objective 2), 2-Healthcare resource utilisation and direct costs measured from the index date until the end of follow-up (objective 3). DATA ANALISIS For Objective 1: the baseline patient characteristics of comparator cohort 1 will be described. For Objective 2: the incidence rate for an imminent subsequent fracture amongst postmenopausal women with an index fracture will be calculated (i.e within comparator cohort 1). Next, the cumulative incidence of imminent subsequent fractures will be calculated using the Fine and Gray model whilst accounting for the competing risk of death. Results will be stratified by the number of times the patient has entered the cohort. For Objective 3: large scale L1 regularised regression will be used to identify confounders and covariates predictive of outcome from a large set of candidate covariates. The propensity score, the probability of exposure assignment conditional on observed baseline patient characteristics, will be estimated using logistic regression including covariates identified from L1 regularized regression. Age- and propensity score matching would then be performed. Baseline patient characteristics and absolute standardised mean differences will be described before and matching for the two comparisons (target cohort vs. comparator cohort 1, comparator cohort 1 vs. comparator cohort 2). The expected incremental healthcare resource utilisation and direct costs amongst (i) patients with an imminent subsequent fracture (target cohort) compared with patients with a single fracture (comparator cohort 1) and, (ii) patients with first fracture (comparator cohort 1) compared with patients with no fracture (comparator cohort 2) will be estimated using generalised linear models. These comparisons are made as our hypothesis is that added burden due to subsequent imminent fracture increases exponentially rather than linearly.

Rationale and Background
Substantial uncertainty surrounds the prevalence of rare blood cancers. Using real-world data, brought together as part of DARWIN EU®, we aim to estimate the prevalence of rare blood cancers in order to see if they still meet the condition to be classified as a rare disease.
Research question and Objectives
Research question
What is the prevalence of rare blood cancers in Europe?
Study objectives
Objective 1: To estimate the prevalence of follicular lymphoma between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Objective 2: To estimate the prevalence of diffuse Large B-Cell Lymphoma between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Objective 3: To estimate the prevalence of multiple myeloma between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Objective 4: To estimate the prevalence of chronic lymphocytic leukaemia between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Objective 5: To estimate the prevalence of acute myeloid leukaemia between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Objective 6: To estimate the prevalence of acute lymphocytic leukaemia between 1st January 2010 and the end of available data in data sources from across Europe, stratified by age and sex
Research Methods
Study design
Population-based cohort
Population
All people in a database will be eligible for inclusion in the study. Included study participants will need to have some observation time during the study period and, for the primary analysis, have a year of prior history available. In sensitivity analyses the requirement for prior history will first be removed, and then increased to three years.
Variables
Two age groupings will be used in the study: 1) 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90-99; 100+, and 2) 0-44; 45-64; 65 and over. The sex (male/ female) of study participants will also be identified.
Study outcomes will be identified based on the presence of a relevant diagnosis or observation. For the primary analysis, 5-year partial prevalence will be estimated and so individuals will be considered as a prevalent case if they have had a such a record in the prior 5 years. In sensitivity analyses, 2-year partial prevalence and complete prevalence will be estimated. For the latter, once identified as a case, an individual will remain so until their exit from the study (i.e. considering people diagnosed with malignancies to always be affected by the condition).
Data sources
1. Integrated Primary Care Information Project (IPCI), The Netherlands
2. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain
3. The Clinical Practice Research Datalink (CPRD) GOLD database
4. IQVIA LPD Belgium
5. IQVIA DA Germany
Sample size
No sample size has been calculated as this is a Disease Epidemiology Study where we are interested in the prevalence of haematological cancers in as large and representative a denominator population as possible.
Data analyses
In line with EMA guidelines for the estimation of the prevalence of rare disease, point prevalence will be used for the primary analysis. The prevalence of each outcome of interest calculated on an annual basis as of the 1st January for each year, estimated overall and stratified by age and sex. As a sensitivity analysis annual period prevalence will also be estimated. A minimum cell count of 5 will be used when reporting results, with any smaller counts obscured.