Berta Raventós Roca

Objetivos: 1. Estimar la exposición a la adiposidad a lo largo de la vida, incluyendo la edad de inicio y los años vividos con sobrepeso/obesidad, usando una base de datos poblacional longitudinal; 2. Investigar la asociación entre la exposición a la adiposidad a lo largo de la vida y el riesgo y la mortalidad por cáncer; 3. Investigar el papel de las comorbilidades y las intervenciones de pérdida de peso (cirugía bariátrica) en la asociación entre la exposición a la adiposidad a lo largo de la vida y el riesgo de cáncer. Fuente de información y métodos: utilizaremos datos de una base de datos prospectiva poblacional de España (SIDIAP). SIDIAP incluye registros electrónicos de atención primaria de salud pseudoanonimizados para >5,8 millones de personas. Los datos son recogidos por los profesionales de la salud durante visitas médicas desde 2006. Incluiremos personas de =2 años durante al menos un año. Implementaremos un enfoque de imputación múltiple de series temporales para tener información completa sobre el IMC para todos los participantes a diferentes edades. La adiposidad a lo largo de la vida se estimará utilizando valores de IMC repetidos a lo largo del tiempo. Los cocientes de riesgos se calcularán utilizando modelos de Cox y la no linealidad se estudiará utilizando splines cúbicos restringidos. Para el objetivo 3, el riesgo/mortalidad por cáncer estará relacionado en presencia/ausencia de comorbilidad o cirugía bariátrica. Impacto: Este proyecto mejorará sustancialmente nuestra comprensión del impacto de la adiposidad a lo largo del curso de la vida sobre el cáncer. Ayudará a identificar períodos críticos de la vida en los que se deben implementar estrategias de prevención y, por lo tanto, ayudará a mitigar los efectos del cáncer por sobrepeso/obesidad en la salud pública.

ANTECEDENTS: Sistemes de salut basats en una forta atenció primària organitzada en base poblacional, milloren l’accés als serveis sanitaris, disminueixen el número d’ingressos hospitalaris evitables i per tant la despesa sanitària global, i milloren la salut de la població. L’any 2017, la Comissió Europea va identificar l’avaluació del rendiment dels sistemes d’atenció primària com una prioritat política. mesurar la qualitat dels serveis oferts és un requisit fonamental per a identificar àrees de millora. L’avaluació del rendiment d’un sistema és especialment necessària quan es produeixen canvis substancials en l’estructura o funcionament, com l’ocorregut amb la pandèmia de la COVID-19. A causa de la complexa naturalesa dels sistemes d’atenció primària, els indicadors utilitzats per avaluar el seu funcionament solen ser específics de cada context. Els Ambulatory Care Sensitive Conditions (ACSC) son un conjunt de diagnòstics potencialment evitables d’ingrés hospitalari o mort. Sovint és utilitzat com un indicador de mesura de l’efectivitat de l’atenció primària.
JUSTIFICACIÓ: L’avantatge d’analitzar agrupacions d’EAP en lloc de l’estudi d’aquests EAP a nivell individual és que permet una perspectiva més àmplia i generalitzable de les característiques estudiades.
L’ús d’algorismes d’intel·ligència artificial per predir el número d’ingressos hospitalaris per diagnòstic evitable (ACSC) pot ajudar a identificar els EAP amb problemes d’efectivitat, la qual cosa afavoriria una millora en la planificació de recursos sanitaris.
OBJECTIUS: 1) Caracteritzar grups similars d’EAP de Catalunya en funció dels resultats en salut, els recursos disponibles i les característiques poblacionals. 2) Predir el número d’ingressos hospitalaris per diagnòstics evitables (ACSC) dels diferents EAP. 3) Analitzar i comparar els resultats en salut dels equips d’atenció primària en relació als períodes de la COVID-19: pre-pandèmia, pandèmia i post- pandèmia.
PERÍODE D’ESTUDI: 2018 – 2022.
ENTORN I POBLACIÓ DE REFERÈNCIA: L’ICS és el principal proveïdor de serveis sanitaris de Catalunya. Gestiona 279 Equips d’Atenció Primària (EAP) adscrits a 5,8 milions de ciutadans.
POBLACIÓ D’ESTUDI: EAP dels centres del ICS de Catalunya.
OUTCOMES: Les variables d’interès son les relatives als resultats dels EAP: 1. Accessibilitat; 2. Longitudinalitat; 3. Abast (comprehensiveness); 4. Coordinació; 5. Orientació comunitària; 6. Orientació familiar; 7. Satisfacció; 8. Gestió de la demanda d’infermeria; 9. Benestar emocional; 10. Qualitat de prescripció farmacèutica; 11. Efectivitat: Número total d’ingressos hospitalaris per diagnòstic evitable; Mortalitat i mortalitat per ACSC.
VARIABLES: Referides a les característiques poblacionals : Edat, Sexe, Indicadors socioeconòmics, Número de visites als equips d’Atenció Primària per tipus de professional i EAP, Número total de malalties, Fragilitat (eFi, Efragicap, Nombre de medicaments (segons ATC) dispensats i data, Variables estils de vida, Nombre d’ingressos hospitalaris totals per cada EAP; Referides als recursos disponibles: Recursos humans disponibles de cada EAP per categoria professional i mes, Infraestructura de les EAP: Edificis i pisos, o en el seu defecte, variables que recullin informació sobre la fragmentació del personal de cada EAP, Població assignada.
FONTS D’INFORMACIÓ: El Sistema d’Informació per al desenvolupament de la Recerca en Atenció Primària (SIDIAP) disposa d’informació agregada a nivell mensual i per EAP de les característiques de la població, dels recursos disponibles dels EAP i dels seus resultats en salut.
ANÀLISI ESTADÍSTICA: 1) Per a la caracterització de grups similars d’EAP en funció dels resultats en salut, els recursos disponibles i les característiques poblacionals s’usaran tècniques de clústers. Els clústers resultants es compararan en funció de la distribució de les variables sociodemogràfiques, clíniques, socioeconòmiques, d’estil de vida, i, principalment, d’utilització d’assistència sanitària primària, utilitzant anàlisi de variància (ANOVA) i proves de ji-quadrat. 2) S’elaboraran un conjunt d’algorismes d’intel·ligència artificial per predir el número d’ingressos hospitalaris per diagnòstics evitables (ACSC) dels diferents EAP i per períodes de 1,3 i 6 mesos en períodes de 1,3 i 6 mesos. S’aplicaran les següents tècniques de regressió: Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), k-nearest neighbors (K-NN), Support Vector Machine (SVM), Artificial Neural Network (ANN) i Recurrent Neural Network (RNN). 3) Per a analitzar i comparar els resultats en salut dels equips d’atenció primària en els tres períodes de temps que comprenen la COVID-19, es realitzaran models ARIMA, suavitzat temporal i ANOVA.
APLICABILITAT I RELLEVÀNCIA: 1) L’agrupació d’EAP pot ajudar a la identificació de patrons de recursos dels EAP que poguessin resultar més efectius per a aconseguir satisfer les necessitats de la població. 2) L’avaluació d’algorismes que realitzin prediccions de resultats en salut permet identificar els EAP que puguin tenir problemes d’efectivitat, la qual cosa afavoreix una millora en la planificació de recursos sanitaris. 3) El tercer estudi podria ajudar a comprendre com la pandèmia ha afectat els resultats en salut durant el seguiment d’aquesta, i si aquests segueixen la mateixa tendència a mesura que es redueix la focalització de l’atenció de problemes relacionats amb la COVID-19 (període post-pandèmia). En global, el present projecte pot ajudar als responsables polítics i als gestors sanitaris a prendre decisions més informades sobre l’assignació de recursos, millorant així la qualitat d’assistència dels EAP i, en conseqüència, la salut de la població.

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

Mental disorders are increasing in the last years, in particular after COVID-19 pandemic, but its prevalence and incidence is still not quantified in particular subgroups of population and comparing different countries. We aim to investigate time trends of mental disorders from 2006 to 2024 using large real-world data. We propose to conduct a descriptive study on the prevalence and incidence of common mental problems (i.e. anxiety and depression), severe mental disorders (i.e. schizophrenia and bipolar disorder) and other mental conditions, such as self-harm, sleep disorders, developmental disorders, substance abuse and eating disorders among people of all ages. Using a network of large real-world data sources of primary healthcare, we aim to determine whether the long-term trends of prevalence and incidence of several mental problems remain stable over the time.
We will conduct a population-based cohort study to investigate the temporal trends of several mental conditions and whether the patterns vary according to sex, age groups, socioeconomic deprivation, and nationality. We will calculate the prevalence and incidence rates of diagnoses, prescriptions, and sick leaves/absences due to above-mentioned mental illnesses. The present study will be conducted in multiple databases in the Observational Health Data Sciences and Informatics (OHDSI) network willing to participate, including the Information System for Research in Primary Care (SIDIAP) which contains primary care records for approximately 6 million people in Catalonia, Spain.
Our study will enable to identify temporal trends in mental disorders in Catalonia, and potentially other countries, and will inform preventive strategies targeted to specific groups that are more vulnerable, helping to address mental health disparities across different demographics.

“Background
The current COVID-19 vaccines include mRNA vaccines, non-replicating viral vector vaccines and traditional inactivated whole virus vaccines. All of them have demonstrated their effectiveness against confirmed COVID-19 infection in previous and ongoing clinical trials However, vulnerable populations, such as pregnant women, children, individuals living in nursing homes, those with cancer, autoimmune diseases, immunodeficiencies or organ transplant recipients were systematically excluded from these trials, limiting the available evidence regarding the efficacy of the COVID-19 vaccines on these patients to retrospective observational studies, and small non-randomized trials with surrogate endpoints (e.g. antibody levels).

Objectives
To estimate coverage and the effectiveness of COVID-19 vaccines against COVID-19-related outcomes in vulnerable populations, overall and by sex, age, baseline comorbidities, and socioeconomic status, using electronic health records from Catalonia, Spain.
1- To provide periodic estimates of vaccine coverage in predefined subgroups of population at risk of being excluded from the general vaccine campaigns (i.e individuals with mental disorder), stratified by vaccine type/brand, vaccine dose, sex, age groups, socioeconomic status, and nationality.
2- To assess the effectiveness of COVID-19 vaccines in vulnerable population against COVID-19 related outcomes (COVID-19 infection, hospitalisation, intensive care admission, or death) in:
2.1- pregnant women and infants up to 12 months of age;
2.2- children and adolescents from the age of 5 to 19 years;
2.3- persons living with immunodeficient conditions, namely cancer, autoimmune conditions, primary immunodeficiencies, people living with HIV infection, organ transplant recipients or people taking immunosuppressive drugs;
2.4- persons living in nursing homes.

Methods
We will conduct a population-based matched cohort study using individual-level routinely-collected electronic health records (EHR) data from the Information System for Research in Primary Care (SIDIAP; www.sidiap.org) database in Catalonia mapped to the OMOP-CDM.

Study population
We will include all women with a pregnancy episode during the study period (mother-child linkage), children and adolescents, as well as those with history of cancer, immunodeficient conditions (HIV, autoimmune diseases, organ transplant recipients) and/or use of immunosuppressive drugs (including high chronic use of corticosteroids, biological and non-biological disease modifying drugs, antineoplastic drugs), persons living in nurse home residencies or persons with mental health conditions of any age, registered for at least 365 days in the SIDIAP database prior to the 27th of December 2020 (date of initiation of the vaccination in Spain) and up to December 2022 and December 2023.

Exposures
Four COVID-19 vaccines will be included: ChAdOx1-S, BNT162b2, mRNA-1273, and Ad26.COV2.S.

Outcomes
The main outcomes to evaluate effectiveness of COVID-19 vaccination will include: 1.SARS-CoV-2 infection will be defined as any confirmed infection identified by diagnostic codes and/or a positive RT-PCR or antigen test result, 2.COVID-19-related hospitalisation will be defined as a hospital admission (at least one night) where the individual had a positive RT-PCR test result or a clinical diagnosis of COVID-19 over the 21 days prior to their admission up to the end of their hospital stay,3. COVID-19-related intensive care unit (ICU) admission in those with COVID-19 related hospitalisation, 4.COVID-19-related death will be defined as death (any cause) registered after a COVID-19-related hospitalisation or 28 days after a SARS-CoV-2 infection.

Covariates
Covariates to be considered in the analyses will include demographics, socioeconomic status, nationality and diagnosis of mental health condition (only for coverage), BMI, pre-existing comorbid conditions, history of COVID-19 infection (6 months previous), period of predominant SARS-CoV-2 variants, number of GP visits per year, pregnancy variables (gestational age at vaccination, parity, breastfeeding, maternal lifestyle behaviour factors (when available).

Follow-up
For individuals from the age of 5 and above follow up will be from their index date to occurrence of an event, end of pregnancy (for pregnancy cohort), death, or loss of visibility in the database (e.g., person leaving the practice in electronic health records data). For cohorts that had received a first vaccine dose, we also censored follow-up if a second dose was observed before 21 days for BNT162b2 and before 27 days for mRNA-1273 and ChAdOx1-S.
The index date for exposed children (up to 12 months of age) will be the date of maternal vaccination. Children will be followed from the index date until occurrence of an event of interest, exit from the database, death, end of pre-defined follow-up time (12 months after birth), or end of data availability, whichever comes first.

Statistical analysis
Vaccination uptake rate will be calculated as the number of persons receiving any COVID-19 vaccines in a certain population subgroup divided by all individuals eligible for vaccination in that group.
We will conduct a new user cohort study, to compare the effectiveness of COVID-19 vaccines against COVID-19-related outcomes. We will use propensity score (PS) matching to minimise the risk of confounding. Vaccinated individuals will be matched in a 1:3 ratio (if sample size allows) with eligible non-vaccinated individuals with no prior history of COVID-19 infection in the past 3 months. Matching will be done taking into account age group, sex, calendar time, baseline comorbidities associated with increased risks of severe COVID-19 (e.g., obesity, diabetes, immunosuppression), and smoking. We will also use a large-scale, data-driven approach to identify additional potential confounders to be included in the PS.

“Objetivo: Analizar la incidencia y los factores asociados al diagnóstico de salud mental durante la adolescencia y la juventud durante el período 2008-2022 en Cataluña según perspectiva de género y mediante un proceso de investigación participativa.

Metodología: Proyecto de metodología mixta con cuatro fases. Fase 0: construcción de un “”consejo de jóvenes”” que cumplirá el rol de asesor durante el proyecto. Fase I: estudio de cohorte mediante la base de datos del “”Sistema de Información para el desarrollo de la investigación en Atención Primaria”” (SIDIAP), que contiene la información de las historias clínicas de atención primaria en Cataluña. Se calcularán las incidencias de seis diagnósticos de salud mental en adolescentes y jóvenes (2008-2022), se determinarán los factores asociados al diagnóstico de salud mental y se analizará el manejo de un diagnóstico de salud mental en atención primaria.
Fase II: estudio cualitativo, se realizarán entrevistas a adolescentes y jóvenes con diagnóstico de salud mental, a sus familiares, a profesionales de la salud dedicados a la asistencia a esta población y, se realizará el método fotovoz a adolescentes sin diagnóstico de salud mental conocido. Fase III: se realizará un vídeo sobre los resultados, conclusiones y recomendaciones del estudio cocreado entre equipo investigador y jóvenes.”

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
To further monitor COVID-19 vaccine safety and complement pharmacovigilance measures, multi-national observational studies have been requested by the EMA: Incidences of patient-reported side effects after COVID-19 vaccination and adverse events of special interest are closely being monitored. The Covid-Vaccine-Monitor project will facilitate the rapid signal assessment of emerging safety concerns.

Hypothesis
These existing initiatives will provide important data on the incidence of adverse outcomes reported after vaccination and on potential risk factors for thromboembolic events in COVID-19 patients.

Objectives:
1-a)To quantify the association between the administration of a COVID-19 vaccine and the occurrence of thrombosis with thrombocytopenia syndrome/s (TTS) within pre-specified risk periods, stratified by vaccine type/brand, age and gender, while controlling for relevant confounding factors.
1b) To quantify the association between different COVID-19 vaccine types/brand (where possible/applicable), while controlling for relevant confounding factors.
2a) To quantify the association between the administration of a COVID-19 vaccine and the occurrence of thromboembolic events (TE) within pre-specified risk periods, stratified by vaccine type/brand, age and gender, while controlling for relevant confounding factors.
2b) To quantify the association between different COVID-19 vaccine types/brands (where possible/applicable), while controlling for relevant confounding factors.
3) To study the association between pre-specified potential risk factors and TTS in people receiving COVID-19 vaccine/s
4) To characterize the treatments used in patients with TTS, including the use of anticoagulants and other therapeutic products
Exploratory objective:
5) To develop a proof-of-concept study to support future genetic and pharmacogenomic analyses of the association between COVID-19 vaccines and thromboembolic events and TTS. The three specific subobjectives are:
Objective 5.1. To identify genetic variations associated with TE based on previous literature and a review of previous GWAS studies
Objective 5.2. To investigate whether the risk of TE following COVID vaccination is modified by genetic susceptibility
Objective 5.3. To assess the feasibility of identifying TTS in UK Biobank using linked hospital and primary care records to inform future pharmacogenetic studies

Methods:
Data will be obtained from five European primary care records, two outpatient records, and one inpatient records databases (UK,Spain,The Netherlands, France, Denmak). In addition, one US claims and one large US hospital records database will be accessed to maximise sample size and exposure to vaccines currently under-represented in European data.All of the data sources are previously mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).

Distributed network cohort studies will be conducted to answer objectives 1-4. Propensity score matching based on large-scale propensity scores will be used to minimise confounding by indication for objectives 1-2 and 5

The study period will cover from Dec 2020 (first vaccine users) until the latest data extraction available in each of the contributing databases. For objectives 1-3, lags in hospital linkage will result in two different study periods for analyses based on primary care vs linked data in both CPRD AURUM, GOLD and SIDIAP.
For other objectives and data sources the study period will be unique and will go from cohort-specific index date to the latest data available.
Cohort-specific index dates are: For vaccinated people (and matched unvaccinated) [Objectives 1a-2a, 3, 5.2 and 5.3]: date of first dose vaccine (and same date for matched unvaccinated);For comparative cohorts [Objective 1b and 2b]: date of first dose of the corresponding vaccine type/Brand; For TTS cohorts [Objective 4]: date of TTS diagnosis.

Source and study population:
Target population: All persons registered in any of the contributing databases within the study period and with at least one year of data visibility before December 2020 will be eligible.
Study population for Objectives 1a-2a, 5.2 and5.3: Of these, people with at least one exposure to any COVID vaccine in the study period will be included in the ‘exposed’ cohort/s, with 1st and 2nd dose vaccine date as time-varying index dates. Unexposed matched groups will be pooled from the Target population.
Study populations for Objective 1b & 2b: Those with at least one exposure to viral vector-based vaccines will be included in the exposed group/s and those with at least one exposure to mRNA COVID-19 vaccines in the active comparator group/s. Similarly, those with Vaxzevria vaccine will be included in the exposed group and those with Comirnaty as the active comparator in the vaccine brand comparative safety analyses based on CPRD AURUM, GOLD, RCGP, and SIDIAP.
Study population for Objective 3: Those with at least one exposure to any COVID vaccine in the study period will be included in this cohort for the analysis of risk factors of post-vaccine TTS.
Study population for Objectives 4: Those with a TTS event in the up to 28 days post vaccination of any dose will be included as ‘TTS cases’ for Objective 4, with TTS date as index date.
Study population for Objective 5: UKBB participants with linkage to primary care and HES data and vaccine exposure identified in the primary care records will be included in the exposed group. Unexposed matched participants will be pooled from those with linked primary care and HES data

Data analysis
All the analyses detailed below will be conducted stratified by database and by age, gender and vaccine type/brand

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.

A goal of Discovery and Translational Sciences is to implement new technology platforms to accelerate research. This grant allows the rapid acquisition and analysis of emerging data from the ongoing global outbreak of Covid-19. The clinical and epidemiological data will inform the foundation’s response to the outbreak including expanding our understanding of risk factors for disease progression and the design of efficient clinical trials.