Introducció
Consisteix en una eina basada en unes ulleres 3D de realitat virtual immersiva basada en entorns de la ciutat, sessions de mindfulness, entorns blaus o espais naturals per tal de millorar el benestar i la qualitat de vida de persones que viuen en residències geriàtriques. Així doncs, l’objectiu principal és demostrar l’efectivitat de l’eina mitjançant la millora de la qualitat de vida, depressió i estat mental, en pacients majors de >=65 anys que viuen en residències.
Metodologia
Estudi quasi experimental sense grup control, estudi pre-post.
S’inclouen 20 pacients =65 anys en conepcte de prova pilot.
La intervenció consisteix en portar les ulleres de realitat virtual durant 10 minuts més la preperació inicial. Es farien 2 sessions per setmana durant 2 mesos, resultant 16 sessions per persona. Un dia abans d’iniciar la intervenció es passa el qüestionari que inclou varialbes sociodemogràfiques, consum de fàrmacs i patologies associades. El qüestionari general també inclou les escales sobre salut mental, estat emocional, depressió i qualitat de vida. Després de cada ús es fan unes preguntes sobre la satisfacció inmediata. Al mes i al final, quan acaba tot el periode d’intervenció es torna a administrar el qüestionari inicial més unes preguntes adicionals sobre l’experiència d’ús final, a adminstrar dins del primers 3 dies posteriors a la intervenció. Es preveu una altra adiministració del qëstionari passat uns 3 mesos per avaluar l’impacte a llarg plaç. Es faran anàlisis descriptius del pre i post i es farà la comparació entre abans i després amb test de dades aparellades, prèvia comparació de la normalitat. Finalment s’anlitzarà l’impacte econòmic.
Resultats esperats
S’espera una millora del benestar emocional, l’estat mental, l’estat físic i de la qualitat de vida al final de la intervenció, tant a curt com a llarg termini.

La cicatrització de ferides és un procés complex i regulat que integra diverses fases, i que pot veure’s alterat per factors metabòlics, especialment en persones d’edat avançada, que presenten una major prevalença de ferides complexes. L’estudi dels processos metabòlics que modulen la cicatrització és clau per entendre els mecanismes subjacents que influeixen en la regeneració tissular. Estudis recents en models animals han demostrat que en presència de dany tissular, els teixits augmenten l’absorció de vitamina B12 i altres metabòlits del cicle de la metionina, fet que provoca una disminució dels nivells sèrics. A més, la suplementació amb vitamina B12 va accelerar el procés de cicatrització en aquests models.
Hipòtesis:
Els canvis multiòmics associats a l’envelliment influeixen en el retard del procés de cicatrització de les ferides en persones d’edat avançada, alterant les respostes metabòliques i regeneratives necessàries per a una curació òptima
Objectiu: Identificar les alteracions moleculars associades a l’edat que influeixen en la transició de la cicatrització normal de les ferides a la cronicitat, mitjançant la recollida prospectiva de mostres biològiques (plasma i exsudat) i variables clíniques de pacients amb dehiscències de ferides quirúrgiques de la Regió Sanitària de Lleida

Interactuar con la naturaleza tiene beneficios significativos para nuestra salud física y psicosocial. Sin embargo, la contaminación ambiental está relacionada con un aumento de diversas patologías como obesidad, asma, trastornos del neurodesarrollo y problemas de aprendizaje. Además, el uso excesivo de pantallas se vincula con problemas de salud en niños y adolescentes, tales como obesidad, problemas de salud mental (ansiedad, depresión) y trastornos del sueño. La falta de contacto con la naturaleza desde edades tempranas, combinada con un uso excesivo de pantallas, contribuye a un estilo de vida poco saludable y está asociado con el desarrollo de estas patologías.
Este estudio tiene como objetivo analizar la relación entre el grado de conexión con la naturaleza y el tiempo dedicado a las pantallas con la incidencia de patologías comunes en consultas pediátricas de Atención Primaria. Estas patologías incluyen asma, obesidad, trastornos del neurodesarrollo (como el TEA y el TDAH), trastornos emocionales y psiquiátricos (ansiedad, depresión), así como trastornos del sueño y la calidad de vida percibida.
Se llevará a cabo un estudio transversal descriptivo a través del grupo de investigación PAPenRed durante un período de 12 meses (mayo de 2025 a abril 2026). En las consultas de salud para niños de 7 a 14 años, se realizarán tres encuestas simultáneas: una dirigida al niño/a, otra a los padres y una más al pediatra.

Queremos realizar grupos focales con profesionales de la salud como parte del estudio presentado en esta solicitud: Estrés térmico ocupacional en España: percepciones de los trabajadores agrícolas inmigrantes. (sección 4.III del protocolo adjunto).

Debido al calentamiento global los veranos son cada vez más calurosos y se esperan más olas de calor en el futuro. Las personas que trabajan al aire libre y realizan tareas físicas pesadas tienen un mayor riesgo de enfermarse por el sol. Queremos investigar cómo los trabajadores agrícolas migrantes experimentan el trabajo al aire libre durante los calurosos meses de verano y cómo el calor afecta su salud. Esa información puede ayudar a mejorar los programas de prevención, las condiciones laborales y el acceso a los servicios de atención médica.

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.

La detección temprana de diabetes en niños puede mejorar significativamente su calidad de vida de varias maneras: 1. Mejores resultados de salud: el cribado permite la detección temprana de la diabetes, lo que permite un tratamiento oportuno y reduce el riesgo de complicaciones graves como la diabetes cetoacidosis (CAD) y la enfermedad renal u ocular a largo plazo38. También permite un mejor control glucémico, que es crucial para prevenir complicaciones y mejorar los resultados de salud a largo plazo38,. 2. Beneficios psicológicos y de estilo de vida: el diagnóstico temprano puede reducir el shock y la angustia asociados con un diagnóstico repentino, lo que permite a las familias prepararse y manejar la afección de manera más efectiva38. 3. Intervenciones en el estilo de vida: la detección temprana puede provocar cambios en el estilo de vida, como dieta y ejercicio39, 4. Beneficios económicos y sociales: la detección y el tratamiento tempranos pueden reducir los costos de atención médica al minimizar la necesidad de medicamentos costosos y hospitalizaciones (aproximadamente una semana) asociadas con la diabetes no controlada40. Además, el diagnóstico temprano permite a los niños y sus familias tomar control de su salud, tomando decisiones informadas sobre el estilo de vida y las opciones de tratamiento40. En general, la detección temprana de diabetes en niños puede conducir a mejores resultados de salud, una mejor calidad de vida y un mejor manejo de la afección.
Hipótesis
El diagnóstico de diabetes tipo 1 (DT1) en etapa temprana en niños de dos grupos de edad, niños pequeños (de dos a seis años) y niños mayores (de ocho a diez años) a través de exámenes de detección específicos genera importantes beneficios clínicos para los padres al permitir una intervención oportuna para prevenir complicaciones y mejores resultados metabólicos y psicosociales.
Objetivos
Identificar la diabetes tipo 1 (T1D) en etapas tempranas en niños de 2-6 y 8-10 años mediante cribado de autoanticuerpos (Ab), análisis genéticos e inmunológicos, y evaluar la efectividad de intervenciones educativas, así como la viabilidad y aceptabilidad de su implementación.
Diseño
Se trata de un estudio de cohortes prospectivo en 2169 niños atendidos en centros de salud primaria del Área del Barcelonés. Se incluirán los atendidos en el programa de actividades preventivas pediátricas y cuenten con consentimiento informado.
Población: niños de 2-6 y 8-10 años
Número total de sujetos: 2169
Variables
Cribado de autoanticuerpos (Ab), análisis genéticos e inmunológicos, y evaluar la efectividad de intervenciones educativas, así como la viabilidad y aceptabilidad de su implementación.El cribado consta de tres visitas: Visita 1: Extracción capilar para analizar cribado de 3 Ab relacionados con T1D (3-screen ELISA).Visita 2: Confirmación de resultados positivos mediante nueva muestra venosa para detección de Ab relacionados con T1D individuales y pruebas metabólicas (glucosa basal, HbA1c, C-péptido), HLA, estudio celular, Visita 3: Estratificación de riesgo según la presencia de Ab: Grupo A (negativo), Grupo B (un Ab positivo, riesgo de T1D) y Grupo C (dos Ab positivos, diagnóstico en etapas 1 o 2).
Fuente de los datos: NA
Medicamento o producto sanitario objeto de evaluación (cuando proceda): NA
Análisis de los datos: Se analizarán cambios inmunológicos y metabólicos, así como la efectividad del cribado (sensibilidad, especificidad, tasas de falsos positivos/negativos). Se estudiará la asociación entre el genotipo HLA y la positividad de Ab mediante regresión logística. Se realizará un análisis coste-efectividad y una evaluación cualitativa de la percepción de padres y actores clave sobre el proceso de cribado.

CRIB-MI is a clinical decision support system, integrated through a software tool in the electronic health record system of primary care, that aimed to improve the screening performance on infectious diseases, mental disorders and female genital mutilation in migrants. The aim of this study is to evaluate the potential long-term cost savings to the health system associated with the implementation of the digital tool CRIB-MI. We also aim to describe the HBV cascade of care among migrants attending primary care as a tracer condition to illustrate the clinical and economic impact of the tool. A pilot cluster controlled randomised trial was conducted in eight primary care centres of Catalonia from March to December of 2018. Four centres had CRIB-MI implemented in the electronic patient record system to support clinicians with the decision to screen migrant patients for the studied conditions, while the other centres followed routine clinical practice. Survival analyses, estimations of the healthcare resource utilization and related costs, and budget impact analyses will be performed to assess the potential savings to the healthcare system arising from implementing CRIB-MI. Data on serological and the molecular tests will be used for the analysis of the HBV cascade of care.

Rationale and background
Terbinafine is a well-established antifungal agent indicated for the treatment of superficial mycoses, with widespread use across Europe in both oral and topical formulations. Monitoring its utilisation is essential to informing regulatory decision-making, particularly in relation to emerging safety concerns, such as antifungal resistance. This study aims to characterise the incidence and patterns of terbinafine use, describe the clinical profiles of treated patients, and examine treatment pathways.
Research question and objectives What are the patterns of use of terbinafine-containing products in Europe, 2015–2024?
1. To calculate the monthly and annual incidence of terbinafine use, overall and stratified by age and sex.
2. To characterise patients at the time of each terbinafine treatment initiation in terms of i) demographics, ii) indication for use, iii) comorbidities and co-administered medicines, iv) other antifungal and antibiotic treatments (6, 3, and 1 month prior to and 6 months following treatment initiation), and v) disease code for resistance (6 months before and 6 months after treatment initiation).
3. To report the number of treatment initiations, dose (initial, cumulative), and duration of terbinafine use, overall and stratified by indication of use.
4. To explore the treatment pattern following new terbinafine treatment initiation, overall and by type of dermatophytosis, including transitions between topical to systemic and combination therapies.
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.
Methods
Study design
This retrospective cohort study aims to estimate population-level drug utilisation of terbinafine-containing products (objective 1), characterise individuals being treated with terbinafine (objective 2), evaluate treatment utilisation at the patient-level by assessing dose and treatment duration (objective 3), and describe patient-level treatment patterns (objective 4).Population
Population-level cohort (objective 1): The study population will include all individuals present in the data source during the study period between 1 January 2015 and 31 December 2024 (or latest date available), and with at least 1 year of data visibility prior to the index date. Children <1 year of age will be excluded. New terbinafine user cohort (objectives 2, 3, and 4): All individuals with a new record of a terbinafine containing product in the period between 1 January 2015 and 31 December 2024 (or latest date available). Eligible individuals must have at least 1 year of data visibility prior to becoming eligible and no use of the terbinafine products in the previous 1 year. To ensure sufficient follow-up, only individual who initiate terbinafine treatment at least 1 year prior the end of data availability in each data source will be included. Children <1 year of age will be excluded. For objectives 2 and 3, the cohort will include all new terbinafine treatment episodes meeting the washout criteria. Individuals may contribute multiple treatment episodes, provided each is preceded by 1 year washout period. For objective 4, only the first recorded terbinafine prescription per individual during the study period will be considered. Statistical analysis Population-level utilisation of terbinafine-containing products (objective 1): Monthly and annual incidence rates of terbinafine use will be estimated and expressed as the number of terbinafine treatment initiations per 1,000 person-years among individuals fulfilling the inclusion and exclusion criteria. Incidence rates will be calculated overall and stratified by age group (=18 years and >18 years) and sex. Estimates will be given together with 95% Poisson confidence intervals. The statistical analyses will be performed based on OMOP CDM mapped data using the IncidencePrevalence R package. Patient-level utilisation of terbinafine-containing products (objectives 2 and 3): Patient demographics (age, sex) will be assessed at the date of terbinafine prescription for each new treatment episode (after one year washout). Indication of use will be estimated at treatment initiation (index date), and across predefined time windows at 6, 3, and 1 month prior to terbinafine treatment initiation. Comorbidities and co-administered medication will be evaluated at the start of each treatment episode (index date) and within one year prior to the terbinafine treatment episode. Comorbidities and comedication will also be evaluated any time prior to the treatment initiation. Pre-specified antifungal and antibiotic treatments will be evaluated 6, 3, and 1 month prior to and 6 months following treatment initiation. The proportion of patients with a disease code for resistance will be evaluated 6 months before and 6 months after. Number of treatment initiations will be reported. Initial and cumulative dose, as well as treatment duration, will be estimated, and the minimum, p25, median, p75, and maximum will be provided. These analyses will be conducted using CohortCharacteristics and DrugUtilisation R packages based on OMOP CDM mapped data. Patient-level characterisation (objective 4): The treatment pattern following the first recorded terbinafine prescription during the study period will be presented by Sunburst and Sankey diagrams, which will provide information on sequences of terbinafine-containing products and other antifungal products over time. These analyses will be stratified by indication. The statistical analysis will be performed based on OMOP CDM mapped data using the TreatmentPatterns R package. A minimum cell counts of 5 will be used when reporting results, with any smaller count reported as “<5”.

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.