Existe controversia con respecto al uso de la vitamina D y el riesgo de fracturas osteoporóticas. Actualmente el 80% de los suplementos de vitamina D está prescritos a personas con bajo riesgo de fractura o sin déficit de vitamina D por lo que es necesario identificar la población que se beneficiarían más de estos los suplementos en relación al riesgo de fracturas osteoporóticas.

Objetivos: Determinar la incidencia anual por cada 100.000 personas-año de las fracturas osteoporóticas. Determinar la asociación entre los niveles de vitamina D en sangre, el uso de suplementos con vitamina D y las fracturas osteoporóticas en
1.Sujetos mayores de 45 años, 2. Sujetos con osteoporosis y en 3. Sujetos con tratamiento para la osteoporosis (bifosfonatos, denosumab, teriparatida, moduladores selectivos de la recaptación de estrógenos)

Metodología:
1. Estudio de cohortes para el análisis de la incidencia de fracturas osteoporóticas
2. Estudio de casos-control anidado para analizar la asociación entre las fracturas osteoporóticas, los niveles de vitamina D en sangre y la dispensación de vitamina D. Los casos serán aquellos con una primera fractura osteoporótica, emparejados con hasta 5 controles/caso por edad, sexo, índice de masa corporal e índice de deprivación socioeconómica. Los datos se extraerán de la base de datos SIDIAP (Sistema de Información para el Desarrollo de la Investigación en Atención Primaria y de las altas hospitalarias de la base de datos CMBD (Conjunto Mínimo Básico de Datos). Periodo estudio: 2007-2023. Variables de Exposición: niveles de vitamina D en sangre (25(OH)D) y dispensaciones de vitamina D (con o sin calcio). Variables de resultado: Fracturas osteoporóticas (todas excepto, cara, cabeza y dedos). Otras variables: sociodemográficas, tabaco, alcohol, valores de densitrometría ósea, función renal, comorbilidades y medicaciones. Análisis estadístico: Incidencia acumuladas por 100.000 personas-año. modelos de regresión logística (95% IC).

Rationale and Background
Shortage of essential medicines has a harmful impact on patient’s health by increasing mortality, adverse events, and therapy errors.
Since 2016, the European Medicines Agency (EMA) has published a public catalogue of medicines under surveillance due to shortage in more than one European country. The list includes information such as the reason for the shortage, and recommendations for patients, healthcare professionals and other stakeholders. The work of the task force that publish the catalogue of drug shortages was temporarily suspended between March 2020 and December 2021 due to the COVID-19 pandemic.
This study shall improve our understanding of drug shortages in routine health care delivery by showing trends over time as well as patient characteristics, including indication, treatment duration, and dose. The results will contribute to the European efforts to monitor use of critical medicines as part of the global fight against medicine shortage.
Research question and Objectives
The objectives of this study are:
i To investigate the incidence and prevalence of use of medicines with (suggested) shortage, including those listed as ongoing in the EMA shortages catalogue for =1 year and their alternatives by calendar year, stratified by healthcare setting for each database during the study period 2010-2023.

i To describe the incident and prevalent patients (including but not limited to duration of usage of the medicines of interest, the potential indication for prescribing/dispensing it, and dosage) stratified by calendar year), in order to observe changes in patient’s profiles

Research Methods
Study design
• Population level cohort study (Objective 1, Population-level drug utilisation study on medicines with [suggested] shortages and their alternatives)
• Patient level cohort study (Objective 2, Patient-level drug utilisation analysis describing patient characteristics, including duration of treatment, potential indication of the medicines of interest, and dose)

Population
Population-level drug utilisation study: All individuals present in the database in the period between 2010 and 2023 will be included. For this population, incidence and prevalence of use of medicines with (suggested) shortages and their alternatives will be explored.
Patient-level drug utilisation study: All new and prevalent users of medicines with (suggested) shortages and their alternatives in the period between 2010 and 2023. Patients can be eligible more than once during the study period.
Variables
Drug of interest: Drugs with (suggested) shortage, including ongoing shortages listed in the EMA shortages catalogue for =1 year, and their alternatives between 2010 and 2023 (see also section 9.3.1 – exposure)
Data sources
Open to any database from EHDEN data partners who finished their mapping to the OMOP CDM, this includes EHDEN consortium members (i.e. EFPIA).
Sample size
No sample size has been calculated.
Data analyses
Population-level drug utilisation study: annual period prevalence of the medicine use and annual incidence proportion, as described in section 9.7.5.1 – Population-level drug utilisation study.
Patient-level drug utilisation study: Large-scale patient-level characterisation will be conducted in incident and in prevalent users, defined as follows: I) in incident users, the index date will be the date of a prescription qualifying as a “new prescription” (i.e. after at least 30 days of non-use) of the specific medicine of interest for each person; II) the index date in prevalent users will be the earliest date of a prescription in the calendar year. Frequency of potential indication at index date will be assessed. Treatment duration will be estimated and the minimum, p25, median, p75, and maximum will be provided. Furthermore, daily dosage will be estimated and the minimum, p25, median, p75, and maximum will be provided. See for further description section 9.7.3 and 9.7.5.2 – Patient level drug utilisation study.
For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts being masked.

Objective
To assess the incidence, prevalence, and characteristics of macrolide users within individuals diagnosed with asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap syndrome (ACOS).

Methodology
Design
On one hand, it is a retrospective cohort study based on the purpose of assessing incidence of macrolide use. On the other hand, the cross-sectional study design is applicable as also period prevalence will be assessed.
Reference population
The entire SIDIAP population meeting the inclusion criteria (as seen in Figure 1).
Study population, and inclusion criteria
Individuals will be considered from the moment of database enrollment up to 31st of December 2023 (as seen in Figure 1).
First, individuals will be selected for the “general population” cohort, which serves as a reference cohort. An individual will enroll based on 3 requirements:
– Individual must have a database history of at least 365 days.
– After previous requirement, the individual will enroll in the cohort at the moment whichever disease is diagnosed first (i.e. index date, alias “start of observation” date)
– Lastly; a follow-up time is required of at least 90 days after index date. An individual exits the cohort (“end of observation” date) for whichever reason comes first (e.g. geographical movement, death).
Also, 3 target cohorts will be created based on the 3 previous criteria, but instead of “any disease”, the focus diagnosis per cohort is:
i) a COPD diagnosis (with no history of asthma. In case of a following asthma diagnosis, cohort exit applies)
ii) an asthma diagnosis (with no history of COPD. In case of a following COPD diagnosis, cohort exit applies) and
iii) a diagnosis of both asthma and COPD, i.e. the “Asthma & COPD overlap syndrome” (ACOS). Start of observation is at the moment whichever of the asthma and COPD diagnosis comes second.
Selection of these cohorts will be done by selecting the appropriate SNOMED codes (or other standard codes within OMOP-CDM) and their descendants related to asthma and COPD. In Annex 1, code details are provided.

Variables, most important
Demographics: age, sex
Life style factors: smoking, alcohol use
Measurements: spirometry data, blood eosinophilia
Procedures: electrocardiogram
History of underlying chronic comorbidities; e.g. pneumonia, cardiovascular conditions, cerebrovascular conditions, atopy, gastro-esophageal reflux disease, diabetes mellitus, obesity
Concomitant medication; respiratory agents, systemic steroids

Statistical data analysis
Baseline characteristics
Pearson’s chi-square test will be used to assess statistical difference between drug (macrolide) users and non-drug users for each cohort for the categorical variables. A t-test will be performed to determine statistically significant differences for continuous variables between drug users and non-drug users for each cohort. Descriptive statistics will be also performed for the duration of prescribed macrolides.
Incidence and prevalence
The incidence rate will be expressed as the number of prescription per 1,000 PY. The prevalence rates will be expressed as the proportion of users of the drug of interest. The prevalence and incidence rates will be stratified by age groups, sex, calendar year, and length of macrolide use (chronic versus incidental).
Other analyses
A sensitivity analysis will be performed based on the exclusion of individuals with a history of arrhythmia, an increased follow-up time (being =90, =180 and =365 days) and descriptive statistics will be performed on the reporting of an electrocardiogram procedure up to 7, 14, and 30 days before prescription of a chronic macrolide.
Software
All analyses will be performed mainly utilizing R version 4.2.1, RStudio version 2022.07.1 build 554, R-packages DrugUtilisation version 0.4.0, IncidencePrevalence version 0.5.1, CDMConnector version 1.2.0., and ggplot2 version 3.4.4. It is preferred to receive/work on data in OMOP-CDM tables suitable to be imported in a PostgreSQL database.

Expected results, applicability, relevance, and limitations
Use of macrolides will have increased in the years before the global pandemic of the coronavirus disease 2019 (COVID-19), decreased in the years 2020-2022, and increased in the year 2023. We expect that azithromycin is prescribed the most, followed by clarithromycin, and then followed by erythromycin. We expect to see an overall higher proportion of incidental use of macrolides rather than chronic use, but we also expect to see an increase in chronic use over time.

It is of high importance to understand what the real-world use is of macrolides as it contributes to i) showcasing actual real-world use of macrolides, ii) understanding user characteristics iii) making science-based public health decisions, iv) foundations for follow-up studies on efficacy- and safety studies on the topic.

Chronic use of macrolides might be underestimated, as it might be initiated by the specialist and not repeated by the general practitioner (GP). Data is not pro-actively collected for our specific research question implying that we might not obtain all covariates of interest. Hence, important effect modifiers or confounders might not be taken in to account. Disease misclassification might occur as it is based on disease codes with no further validation.

Keywords
“Asthma”, “Pulmonary Disease, Chronic Obstructive”, “Asthma-Chronic Obstructive Pulmonary Disease Overlap Syndrome”, “Macrolides”, “Drug Utilization”, “Incidence”, “Prevalence”

Background and current status of the subject
Macrolides (i.e. erythromycin, azithromycin and clarithromycin) are antibiotic agents, which also have anti-inflammatory effects.[1] Results from clinical trials showed that macrolide treatment improves health outcomes in a range of obstructive airways diseases. Chronic use of macrolides is being assessed as a treatment option for asthma and chronic obstructive pulmonary disease (COPD) as data from randomized clinical trials (RCTs) has shown that chronic use is associated with a reduced risk of exacerbations.[2,3] Data on the real-world-use of macrolides in patients with asthma and/or COPD is scarce. A recent cohort study investigating administrative data from Ontario (Canada) reported an increase in the use of macrolides in patients with COPD with a prevalence of 0.8 per 1000 people in 2004 to 13.8 per 1000 people in 2018.[4] In 2018, we reported the findings of antibiotic use in children with asthma based on data from the Dutch Integrated Primary Care Information (IPCI) database and the Health Improvement Network® (THIN®); macrolides accounted for 22% of all antibiotic prescriptions in IPCI and 15% in THIN. [5] In 2020, the British Thoracic Society published guidelines for the use of long-term macrolides in adults with respiratory disease, stating that an electrocardiogram (ECG) should be performed prior to initiation of macrolide therapy. [6] It is in our interest to assess whether ECGs are performed (or rather registered in the database). To our knowledge, further information on real-world drug utilization patterns of macrolides in patients with asthma and/or COPD is lacking. The main objective of this research is to assess the incidence, prevalence, and characteristics of macrolide users within individuals diagnosed with asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap syndrome (ACOS).

Rationale and Background:
HPV vaccination programmes have been shown to reduce not only HPV infection but also the incidence of cervical cancer. However, uncertainty remains on the real-world effectiveness of different brands, and dose schedules.

Research Questions and Objectives:
To generate evidence from real-world data on the effectiveness of HPV vaccination in preventing severe disease outcomes in women, including invasive cervical cancer and CIN2+, for the different licensed HPV vaccines in Europe. This study will include data sources from UK, Spain and Germany.
More specifically the study objectives are:
Main objectives:
• To assess the effectiveness of HPV vaccination in prevention of invasive cervical cancer stratified by licenced vaccine brand
• To assess the effectiveness of HPV vaccination in prevention of CIN2+, stratified by licenced vaccine brand
• To assess the effectiveness of HPV vaccination in prevention of conization, stratified by licenced vaccine brand

Secondary objectives:
• To assess the effectiveness of HPV vaccination overall for the three outcomes (i.e. invasive cervical cancer, CIN2+ and conization)
• To assess the effectiveness of HPV vaccination in prevention of invasive cervical cancer, CIN2+ and conization in subgroups defined by number of doses, within each brand.

Results in both main and explanatory analyses will be further stratified by age group.

Research Methods
The target trial emulation approach will be used for this non-interventional study. The summary of the Target Trial is as follows:
• Study primary objective: to investigate the effectiveness of HPV vaccines to prevent cervix cancer.
• Estimand:
• Population: Women eligible for vaccination according to eligibility criteria in each country. More generally, females 9 years old or older any date after the launch of the vaccination programme in the corresponding country.
• Treatments:
• Placebo (unvaccinated).
• Vaccinated with Gardasil/Silgard.
• Vaccinated with Cervarix
• Vaccinated with Gardasil-9.
Patients are randomised to one of the above groups in a 1:1:1:1 ratio.
• Variable/outcome: incidence of invasive cervical cancer within 5, 10 and 15 years of vaccination.
• Summary measure: incidence rate. Comparison of interest is incidence rate ratio between every vaccinated group and the unvaccinated group.
• Intercurrent events:
• For the unvaccinated: vaccination, dealt with a hypothetical strategy. To implement this, data from women in the unvaccinated group that at some point get vaccinated will be included in the analysis up to the time of vaccination. (This means in the analysis these women will be censored at the time of vaccination.)
• For the vaccinated, any group: treatment discontinuation (i.e. not receiving all scheduled doses), dealt with a treatment policy strategy. To implement this strategy, all available data from these women will be included in the analysis regardless of treatment discontinuation.
f. Statistical methods: The incidence rate ratio between each vaccinated group and the unvaccinated group will be estimated.

Study Design:
New user matched cohort study

Population:
All females aged 9 years or older on any date after the launch of the vaccination programme in any of the contributing datasets and with at least 365 days of prior data availability at the beginning of vaccination programme launch date in their country of residence will be eligible. The analysis will be further restricted to matched cohorts of vaccinated and unvaccinated participants with similar baseline characteristics (see ‘Data Analysis’).

Data Sources:
– Primary care records from the UK (Clinical Practice Research Datalink (CPRD) GOLD) and Catalonia, Spain (Information System for Research in Primary Care (SIDIAP)); outpatient primary care and specialist data from Germany (IQVIA Disease Analyzer (DA) Germany)

Study Period:
HPV national vaccination programs have different start dates. For CPRD, the study period will begin on 1/1/2008. For SIDIAP and IQVIA DA Germany, the study period will begin on 1/1/2007.
For all databases, the end year of the study period is the most recent data available, sometime in 2023.

Eligibility criteria
Eligibility for vaccination in each country. More generally, females >9yo any date after the launch of the vaccination programme in the corresponding country.

Exposure
Assignment procedures: Vaccination status (brand and number of doses) is assigned as seen in the data at 15 years old. Unvaccinated will be assigned as not being vaccinated at 15 years old and censored when (and if) they get vaccinated later on.
Brand: For those vaccinated, brand will be primarily assigned as brand of all the doses administered before 15. Women with heterologous brand (not the same brand for each dose) schedules will be excluded. If this information is not available, it will be inferred, when possible, using each country’s vaccination schedules.
Schedules: Unvaccinated, vaccinated with 1 dose, vaccinated with 2 doses , and vaccinated with 3 doses.

Outcome
The main outcome of interest is invasive cervical cancer. Two secondary outcomes are also considered: CIN2+ and Conization. These outcomes will be phenotyped and diagnostics will be carried out.

Follow-up
Follow up will start at the moment of the administration of first dose before 15 years old. For unvaccinated, the follow up will start at the same date as their vaccinated matched counterpart. Follow-up will extend until another vaccine dose or outcome event, end of available follow-up, or death of any individual of the matched pair, whichever comes first.

Other variables
Year of birth, calendar year, age at vaccination, cytology results from smear test prior to the first dose of vaccine if available. For LASSO regression, all recorded features recorded in the database, including socio-demographics, geographic location, healthcare resource use, comorbidity, medicine/s use, previous smear testing, and previous vaccination/s.

Data Analysis
All analyses will be conducted separately for each database, and carried out in a federated manner, with effectiveness estimates meta-analysed and the I2 heterogeneity coefficient reported.
We will conduct a propensity score (PS) matched cohort design, where target and comparator cohort participants will be matched 1:5. Matching will be done based on PS, year of birth, year of first dose (for analyses not involving dose number) and geographic region using nearest neighbour matching, with calliper width 0.2 standard deviations as is standard for propensity score matching. Large-scale PS will be estimated using lasso regression to estimate the probability of being in the target cohorts, potentially including any of the covariates mentioned above.
The following matched cohorts will be compared:

Main comparisons:
Vaccinated vs unvaccinated per brand:
• Vaccinated with Gardasil/Silgard (target) (1 or more dose) vs unvaccinated (comparator)
• Vaccinated with Cervarix (target) (1 or more dose) vs unvaccinated (comparator)
• Vaccinated with Gardasil-9 (target) (1 or more dose) vs unvaccinated (comparator)

Secondary comparisons:
Vaccinated (target) (1 or more dose) (any brand) vs unvaccinated (comparator) overall.
Dose comparisons:
• Vaccinated with 2 or more doses (target) vs 1 dose (comparator) of the same brand.
• Vaccinated with 3 or more doses (target) vs 2 doses (comparator) of the same brand.

Vaccine effectiveness analyses
Incidence rates and incidence rate ratios (IRR) will be calculated for the matched cohorts and outcomes at 5, 10 and 15 years (if enough follow-up is available). Cox proportional hazard models will be used to calculate hazard ratios (HR) for time-to-event analyses.

Rationale and Background:
Frailty, polypharmacy, and comorbidities are common and important factors which usually coexist in older patients aged = 65. Assessment of frailty and polypharmacy is difficult, due to lack of standardized definitions. However, accounting for them is relevant, especially among older adults with cancer, due to their adverse impact on cancer outcomes and treatment. Despite this, studies reporting on the prevalence of frailty and polypharmacy specifically in older adults with cancer remain sparse.
This study intends to investigate the ability to characterise frailty and polypharmacy in EU real-world data sources, to estimate the prevalence of frailty and polypharmacy in people aged 18 and above with selected cancers at the point of diagnosis and to describe their characteristics. This will provide a benchmark to aid comparisons with evidence submitted for assessment by applicants to EMA.

Research question and Objectives:
The aim of this study is to estimate the prevalence of frailty and polypharmacy at the point of diagnosis of selected cancers in people aged 18 and above and to describe their characteristics.
The specific objectives of the study are:
1. To estimate the prevalence of frailty and polypharmacy in people aged 18 and above diagnosed with selected cancers at the point of cancer diagnosis.
2. To describe the characteristics of people aged 18 and above diagnosed with selected cancers among different frailty and polypharmacy categories at the point of cancer diagnosis.
All results will be reported by database and selected cancer type, overall and stratified by age and sex when possible.

Research Methods:
-Study design:
Population-based cohort study.

-Population:
The study population will include all individuals aged 18 years and above with a primary diagnosis of cancer (lung, breast, ovary, endometrium, prostate, pancreas, colorectal cancer, lymphoma, leukemia and myeloma) between 01/01/2017 and 31/12/2022, with at least one year of prior history available before cancer diagnosis. Individuals with a diagnosis of cancer (any, excluding non-melanoma skin cancer) any time prior to the diagnosis of one of the selected cancers will be excluded.
Additional eligibility of a minimum of 1 year of potential follow-up time will be imposed for the estimation of one-year hospitalisation and mortality rates.

-Data sources:
1. Clinical Practice Research Datalink (CPRD) GOLD, United Kingdom
2. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
3. IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), Belgium
4. Integrated Primary Care Information Project (IPCI), The Netherlands
5. Estonian Biobank (EBB), Estonia
6. Sistema d’Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP), Spain

-Variables of interest:
Polypharmacy will be defined as the concomitant prescription of >= 5 or >= 10 medications (therapeutic groups) anytime during the year prior to cancer diagnosis.
A score will be created based on the presence of polypharmacy (defined as >=5 prescriptions of medications as mentioned above) and the following conditions included in frailty indexes: Mobility and transfer problems; Housebound; Activity limitation; Visual impairment; Hearing impairment; Requirement for care; Social vulnerability; Falls; Urinary incontinence; Weight loss and anorexia; Memory and cognitive problems; Dizziness; Dyspnoea; Sleep disturbance; Anaemia and haematinic deficiency; Hypertension;
Ischaemic heart disease; Heart failure; Cerebrovascular disease; Peripheral vascular disease; Atrial fibrillation; Heart valve disease; Hypotension/syncope; Diabetes; Foot problems; Arthritis; Respiratory disease; Peptic ulcer; Thyroid disease; Chronic kidney disease; Osteoporosis; Fragility fracture; Parkinsonism and tremor; Urinary system disease; Skin ulcer. Their definition will be based on diagnosis codes recorded anytime prior or at cancer diagnosis. The score will be calculated based on the number of present conditions and polypharmacy divided by the total number of conditions/polypharmacy mentioned above (35 conditions and 1 for polypharmacy prevalence), and will be further categorised accordingly into the following levels of severity: fit: 0–0.12; mild frailty: >0.12–0.24; moderate frailty= >0.24–0.36; severely frail: >=0.36.
Additionally, the Charlson Comorbidity Index at index date will be estimated also based in data recorded prior or at cancer diagnosis.
All co-morbidities and co-medications will be used for large-scale patient characterisation, identified as concept/code and descendants.
Other variables of interest will include number of hospitalisation and date of death during the year after date of cancer diagnosis.
-Sample size:
No sample size will be calculated as this is a descriptive Disease Epidemiology Study where we were interested in the characteristics of all incident cases of selected cancers.

-Data analyses:
The prevalence of an approximate score for frailty (based on the above mentioned score) and polypharmacy (objective 1) will be estimated at the time of cancer diagnosis.
Large-scale patient-level characterisation (objective 2) will be conducted for individuals with different frailty and polypharmacy categories as follows: Age and sex at time of cancer diagnosis will be described;
Medical history will assessed for anytime –and up to 365 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 history will be reported for 365 to 31 days before index date, for 30 to 1 day before index date, and at index date; hospitalization and mortality rates will be calculated for up to 365 days after index date.
For all analyses n and % will be reported. A minimum cell count of 5 will be used when reporting results,
with any smaller counts reported as “<5” or “0”. Overall analyses will be done separately for each database and selected cancer. Further stratification by age category (18-44, 45-64, 65-74, 75-84 and 85+) and sex will be conducted when possible (minimum cell count reached).

Rationale and Background:

Disease prevalence depends on the rate of incidence of the disease in the population as well as on the average duration of the disease. Under the assumption that both the incidence of the disease and its average duration are stable over time, a well-known mathematical relationship between prevalence (P), incidence (I) and average duration (D) is:
P/(1-P)=I·D
When P is low,(1-P)˜1 and the equation reduces to the following expression for the indirect estimated prevalence:
P=I·D
In this study direct and indirect estimated prevalence will be compared using real-world datasources.

Research question and objective:

The objective of this study is to compare direct and indirect estimations of prevalence of some rare, chronic diseases.
This will be done considering all patients with the disease as well as separately for patients with pediatric diagnosis (age 0-17 years) and for patients with adult diagnosis (age 18 and older).

Research Methods:

-Study design:
Population-level disease epidemiology to estimate point prevalence and incidence rate using the analytical pipelines from the DARWIN EU® Complete Catalogue of Standard Data Analyses.
A retrospective cohort design to estimate median disease duration using the R survival package.
Data from three databases with routinely-collected electronic healthcare records of general pratices will be used.

-Population:
All individuals present in one of the databases during the study period 01/01/2010 to 31/12/2022 will be used to estimate incidence and prevalence.
All patients with disease will be used to estimate median disease duration.

-Variables:
Presence of a diagnosis of:
•Cystic fibrosis
•Haemophilia
•Pulmonary arterial hypertension
•Pancreatic cancer
•Sickle cell disease
Age at first diagnosis.
Time from first diagnosis to death.

-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.Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom

-Sample size:
No sample size has been calculated.

-Data analyses:
Population-level disease epidemiology: for each disease of interest, the point prevalence at the middle of the study period, i.e. 01/01/2016, will be calculated as well as incidence rate over the total study period.
For each patient only the first diagnosis of a disease will be considered. The date of this diagnosis can be before the observation period of the patient. The disease is considered to stay present until the end of patient’s observation period. For the point prevalence, all persons with a diagnosis before 01/01/2016 and in observation in the database at this date contribute to the numerator. Denominator is the total number of persons in observation at this date. For the calculation of the incidence rate, only newly diagnosed patients (diagnosed within observation time and within the study period) contribute to the numerator. Denominator is the total number of person-years at risk, i.e. observation time of a patient within the study period or until a diagnosis occurs.
Survival estimation: Kaplan Meier estimates for survival probabilities, time axis is time since first diagnosis. Median disease duration is time where the survival probability decreases to below 50%.
For all analyses a minimum cell count of 5 will be used when reporting results, with any smaller counts obscured.
From the incidence rate and median disease duration, the indirect prevalence is calculated.
Results from the databases will be combined using random effects meta-analysis.

Rationale and background:
Atopic dermatitis (AD) is a highly prevalent, chronic, systemic inflammatory disease causing significant physical and psychological burden, as well as significant economic impact. Upadacitinib is an oral selective and reversible inhibitor of Janus Kinase (JAK)
that was approved by the European Medicines Agency (EMA) on 16 December 2019 for the treatment of moderate to severe active rheumatoid arthritis in adults and 23 August 2021 for the treatment of moderate to severe AD in patients 12 years of age
and older, who are candidates for systemic therapy.
As with other JAK inhibitors also marketed in Europe, important safety risks have been identified with upadacitinib that require additional risk minimization measures (aRMMs) such as a health care provider (HCP) educational guide and a patient card as detailed in the European Union risk management plan for RINVOQ. Following the procedure under Article 20 of Regulation (EC) No 726/2004 (concluded 10 March 2023), upadacitinib warning and precautions for use have been changed and the posology has been updated in the summary of product characteristics (SmPC). In addition, gastrointestinal (GI) perforation was added as an adverse drug reaction in upadacitinib extension indication variation procedure for Crohn’s disease (dated 17 February 2023). The HCP guide has been updated accordingly after these procedures and is focused on the targeted risks: malignancy, serious and opportunistic infections including tuberculosis (TB) and herpes zoster (HZ), major cardiovascular events (MACE), GI perforation, venous thromboembolic events (VTE), and fetal malformation following exposure in utero (pregnancy risk). The HCP educational guide also contains information on the upadacitinib use in patients 65 years of age or older and in atopic dermatitis with doses
higher than 15 mg once daily. Using electronic healthcare data from Denmark, Germany, Spain, and Sweden this drug
utilisation study will describe baseline characteristics of individuals with AD exposed to upadacitinib, and evaluate healthcare utilization in routine clinical care as an indicator of physician adherence to the aRMMs in the AD population by providing insights regarding how clinical practice patterns correspond to the listed recommendations in the SmPC and the HCP guide when using upadacitinib (RINVOQ®) for AD in routine clinical care.
Research question and objectives: The study aims to evaluate the use of upadacitinib (RINVOQ®) in individuals with AD in routine clinical care in Denmark, Germany, Spain, and Sweden. The study objectives are:
1. To describe the baseline characteristics of individuals with AD who are new users of upadacitinib
2. To the extent measurable, evaluate healthcare utilization in routine clinical care as an indicator of physician adherence to the aRMMs among individuals with AD who are new users of upadacitinib, by: a. Quantifying the compliance to recommendations for posology (average daily dose) and by describing the duration of use
b. Quantifying the compliance to recommendations for the use among individuals who have risk factors for GI perforation, serious infections, malignancy, MACE, and VTE
c. Quantifying the compliance to the recommendations for the use among patients aged 65 years and older
d. Quantifying the compliance to the recommendations for contraindicated use including pregnancy, and active TB
e. Quantifying the compliance to recommendations for patient screening and laboratory monitoring prior to and during upadacitinib treatment (Denmark, Germany, and Spain only)
3. To describe the changes in the utilization of upadacitinib following the implementation of revised aRMMs from the Article 20 referral procedure, specifically:
a. Describe the use of upadacitinib among patients with risk factors for VTE,
MACE, malignancy, and serious infections b. Describe the use of upadacitinib among patients aged 65 years and older
c. Describe the use of upadacitinib 30 mg

Study design:

The study is a drug utilization, descriptive, non-interventional, population-based, cohort
study of new users of upadacitinib (RINVOQ®) for the treatment of AD identified in
electronic healthcare data from four European countries: Denmark, Germany, Spain, and
Sweden.

Population:

The study population consists of all individuals with AD registered in the databases in the
four countries who are treated with upadacitinib. Each individual will be followed from the
initiation of upadacitinib to the end of the study period (i.e., 31 December 2024), study
withdrawal, or death.

Variables:

The exposure of interest will be the use of upadacitinib.
To describe the baseline characteristics of new users of upadacitinib the study will include
the baseline variables: demographics, socioeconomic factors, number of
prescribed/administered AD medications in the year prior to upadacitinib initiation,
number of hospitalizations and outpatient visits in the 5 years prior to upadacitinib
initiation, type of prescriber of upadacitinib, lifestyle risk factors, medical history,
comorbidities, as well as prior and concomitant medications.
To evaluate healthcare utilization in routine clinical care as an indicator of physician
adherence to the aRMMs, the study will include outcome variables related to:
malignancy, MACE, GI perforation, VTE, serious and opportunistic infections including HZ,
contraindications (pregnancy, and active TB) and posology.

Data sources:
Electronic healthcare data from Denmark, Germany, Spain, and Sweden.

Study size:

All initiators of upadacitinib during the study period, in the AD population, will be
included.

Data Analysis:

This will be a descriptive study. Upon upadacitinib initiation, baseline characteristics of
individuals will be assessed. Proportions of the aRMM outcome variables will be assessed
prior to upadacitinib initiation, at upadacitinib initiation and during continuous treatment
of upadacitinib, depending on the outcome variable being reported. The proportion of the
outcome variables will be calculated as the number of individuals for each specific
outcome variable over the total number of individuals considered for that specific
outcome. Utilization of upadacitinib will be stratified by the time period before and after
the implementation of the revised aRMMs from the Article 20 referral procedure as well
as by Coronavirus disease 2019 (COVID-19) pandemic time periods (COVID-19 pandemic
and non-COVID-19 pandemic).

Rationale and Background
A shortage of medicines containing Glucagon-Like Peptide-1 receptor agonists (GLP-1 RA) is affecting EU Member States since 2022 and will continue throughout 2024. The medicines belong to the class of GLP-1 RA are either authorised for the treatment of diabetes or authorised for weight management, with the exception of Mounjaro (tirzepatide), a glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 RA that is authorised for both indications.
The shortage is based on an increased demand for these medicines in addition to other causes, e.g. capacity constraints. Of concern has been the off-label use of GLP-1 RAs for weight management which has been mentioned frequently in the news and social media and is worsening shortages.
This study aims to provide an overview of the characteristics of patients prescribed with a GLP-1 RA medicinal product and how these have changed over the past ten years. This will help contextualise what determinants might be driving the demand for GLP-1 RA vis-à-vis the observed shortage of medicines, including exploring comparative trends of prescription of other medicinal products used in diabetes and for weight management as well as patterns of off-label use.
Research question and objectives
This study aims to provide an overview of the characteristics of patients prescribed with a GLP-1 RA medicinal product and how these have changed over the past ten years.
The specific objectives of this study are:
1) To determine the incidence and prevalence of prescriptions of GLP-1 RA medicinal products (overall and stratified by age, sex, database, indications (only for incidence), and calendar time (per month), for the past 10 years. Additionally, new drug users of GLP-1 RA with no diagnosis of diabetes mellitus II and no diagnosis of obesity will be characterised by age, sex and a list of prespecified indications.

2) To determine the incidence and prevalence of prescriptions of medicines that help contextualise exposure to GLP-1 RA, including orlistat, naltrexone/bupropion, phentermine/topiramate, phentermine and metformin (overall and stratified by age, sex, database, and calendar time (per month), for the past 10 years.
Research Methods
Study design
• Population level cohort study (Objective 1.1, Population-level drug utilisation study on GLP-1 RA)
• Patient level cohort study (Objective 1.2, Patient-level characterisation on GLP-1 RA users with no diabetes mellitus II nor obesity)
• Population level cohort study (Objective 2, Population-level drug utilisation study on medicines that help contextualise exposure to GLP-1 RA)
Population
Population-level drug utilisation of GLP-1 RA and medicines that help contextualise exposure to GLP-1 RA: All individuals present in the database in the last 10 years of available data will be included in the analysis. For this population, incidence and prevalence of use of GLP-1 RA and medicines that help contextualise exposure to GLP-1 RA will be explored.
Patient level cohort study on GLP-1 RA users with no diabetes mellitus II nor obesity: new drug users of GLP-1 RA with no diagnosis of diabetes mellitus II and no diagnosis of obesity will be characterised by age, sex and a list of prespecified indications.
Variables
Drugs of interest: list of 8 GLP-1 RA medicines (Objective 1) and 5 medicines that help contextualise exposure to GLP-1 RA (Objective 2).
Calendar month, age, sex and indications (diabetes mellitus II and obesity, and only for incidence) will be used for stratification.
Age, sex and a list of prespecified indications will be used for the characterisation.
Data sources
1) CPRD Gold (UK, Primary Care Database)
2) SIDIAP (Spain, Primary Care Database)
3) IPCI (Netherlands, Primary Care Database)
4) IQVIA DA Germany (combination of primary and secondary care (outpatient visits) database)
5) IQVIA LPD Belgium (combination of primary and secondary care (outpatient visits) database)
Sample size
No sample size has been calculated. Based on a preliminary study feasibility assessment the expected number of prescriptions in the period investigated is expected to be between <1,000 and 760,000 across the five data sources considered. Data analyses Population-level drug utilisation study on GLP-1 RA and medicines that help contextualise exposure to GLP-1 RA: monthly incidence rates of use of medicines of interest per 100,000 person-year, and monthly point prevalence rates of medicines of interest. Incidence will be stratified by age, sex and indications cohorts in each database of the study. Prevalence will be stratified by age and sex in each database of the study. Patient-level drug utilisation study: new drug users of GLP-1 RA with no diagnosis of diabetes mellitus II and no diagnosis of obesity will be characterised by age, sex and a list of prespecified indications.

Objetivos: Analizar la asociación entre las medicaciones potencialmente inadecuadas (MPI) de la herramienta STOPPFall (Screening Tool of Older Persons Prescriptions in older adults with high fall risk) y los eventos adversos (hospitalizaciones, mortalidad general, caídas, fracturas y demencia) en atención primaria.
Material y métodos: Estudio de cohortes retrospectivo y de casos-control anidado del 2010 al 2024 usando datos anonimizados de la base de datos SIDIAP (www.sidiap.org) que recoge información médica de atención primaria de >5.8 millones de sujetos (75% de los centros de salud de Cataluña). Los = 65 años, con = 365 días de seguimiento previo y sin ninguno de los eventos adversos a estudio, serán incluidos en las cohortes de 1. Expuestos a MPI (=1 dispensación durante el periodo de estudio) o 2. No expuestos a MPI (en ningún momento dado). Para los estudios casos-control, los casos serán aquellos con un primer evento adverso (hospitalización, mortalidad general, caídas, fracturas o demencia) durante el periodo de estudio. Se emparejarán con controles mediante el procedimiento “optmatch”, por edad, sexo, índice de comorbilidad de Charlson (ICC) y número de medicaciones (excepto MPI). Seguimiento: desde su entrada en la cohorte, inicio del estudio o primera dispensación de MPI (cohorte de expuestos), hasta el evento adverso, fin del estudio, traslado a otro centro o 1 año de seguimiento.Variables de exposición: MPI del listado STOPPFall (benzodiacepinas e hipnóticos, antipsicóticos, opioides, antidepresivos, anticolinérgicos, antiepilépticos, diuréticos, bloqueadores alfa, antihipertensivos centrales, antihistamínicos, vasodilatadores usados en enfermedad cardiaca, medicaciones para la incontinencia urinaria). Variables de resultado: primer registro de hospitalización, mortalidad general, caída, fractura o demencia. Otras variables: datos sociodemográficos, hábitos tóxicos (alcohol y tabaco), índice de masa corporal, número/tipo de MPI, índice de deprivación socioeconómica (MEDEA), visitas en atención primaria (año previo), comorbilidades y medicaciones identificadas como confusoras. Se calculará el porcentaje de exposición a MPI: baja (<25%), media (25-74%) o alta (=75%). Análisis estadístico: Se calculará la incidencia anual total y para cada MPI de los eventos adversos por cada 100 000 personas-año, con intervalo de confianza 95% (IC 95%). Se determinará (regresión logística) el riesgo del evento adverso entre expuestos y no expuestos y en función del porcentaje de exposición a MPI comparado con sujetos con una exposición baja (<25%) (referencia). Las ODDs Ratio, con IC 95%, se ajustarán por edad, sexo, estatus socioeconómico y factores confusores. Los resultados se estratificarán por edad, sexo y estatus socioeconómico. La significación estadística se establecerá en p-valor<0,05. El análisis estadístico se realizará con el programa STATA MP/18 (StataCorp, Texas, USA) y el programa R 4.3.3. Aplicabilidad de los resultados esperados: Los resultados permitirán caracterizar e identificar la población con más riesgo de sufrir eventos adversos debidos a la MPI y determinará si existen diferencias por género en la incidencia de estos eventos adversos.