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Objective: Multiple treatment options are recommended for Systemic Lupus Erythematosus (SLE) by clinical guidelines. This study aimed to explore SLE treatment patterns as there is limited real-world data of SLE medication utilisation, especially in childhood-onset SLE (cSLE). Methods: We conducted a longitudinal cohort study using five routinely collected healthcare databases from four European countries (United Kingdom, France, Germany, and Spain). We described the characteristics of adult and paediatric patients at time of SLE diagnosis. We calculated the percentage of patients commencing SLE treatments in the first month and year after diagnosis, reported number of prescriptions, starting dose, cumulative dose, and duration of each treatment, and characterised the line of therapy. Results: We characterised 11,255 patients with a first diagnosis of SLE and included 5718 in our medication utilisation analyses. The majority of adult SLE patients were female (range 80–88 %), with median age of 49 to 54 years at diagnosis. In the paediatric cohort (n = 378), 66–83 % of SLE patients were female, with median age of 12 to 16 years at diagnosis. Hydroxychloroquine and glucocorticoids were common first-line treatments in both adults and children, with second-line treatments including mycophenolate mofetil and methotrexate. Few cases of monoclonal antibody use were seen in either cohort. Initial glucocorticoid dosing in paediatric patients was often higher than in adults. Conclusion: Treatment choices for adult SLE patients across four European countries were in line with recent therapeutic consensus guidelines. High glucocorticoid prescriptions in paediatric patients suggests the need for steroid-sparing treatment alternatives and paediatric specific guidelines.

Purpose: To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. Methods: Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19–59 and ≥ 60 years old). Seasonality-adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. Results: Post-RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19–59) and 19.29% ((Formula presented.) 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19–59) and 41.47% ((Formula presented.) 60) decreases. Slope analysis indicated increases for East Scotland (19–59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's (Formula presented.) 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19–59 group. Both models showed no post-modelling autocorrelations across databases, yet Barts Health's residuals were non-normally distributed with non-constant variance. Conclusions: Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non-normal white noise using both methods.

Background: Lung cancer is the leading cause of cancer-associated mortality worldwide. In the United Kingdom (UK), there has been a major reduction in smoking, the leading risk factor for lung cancer. Therefore, an up-to-date assessment of the trends of lung cancer is required in the UK. This study aims to describe lung cancer burden and trends in terms of incidence, prevalence, and survival from 2000–2021, using two UK primary care databases. Methods: We performed a population-based cohort study using the UK primary care Clinical Practice Research Datalink (CPRD) GOLD database, compared with CPRD Aurum. Participants aged 18+ years, with 1-year of prior data availability, were included. We estimated lung cancer incidence rates (IRs), period prevalence (PP), and survival at 1, 5 and 10 years after diagnosis using the Kaplan-Meier (KM) method. Results: Overall, 11,388,117 participants, with 45,563 lung cancer cases were studied. The IR of lung cancer was 52.0 [95% confidence interval (CI): 51.5 to 52.5] per 100,000 person-years, with incidence increasing from 2000 to 2021. Females aged over 50 years of age showed increases in incidence over the study period, ranging from increases of 8 to 123 per 100,000 person-years, with the greatest increase in females aged 80–89 years. Alternatively, for males, only cohorts aged over 80 years showed increases in incidence over the study period. The highest IR was observed in people aged 80–89 years. PP in 2021 was 0.18%, with the largest rise seen in participants aged over 60 years. Median survival post-diagnosis increased from 6.6 months in those diagnosed between 2000–2004 to 10.0 months between 2015–2019. Both short and long-term survival was higher in younger cohorts, with 82.7% 1-year survival in those aged 18–29 years, versus 24.2% in the age 90+ years cohort. Throughout the study period, survival was longer in females, with a larger increase in survival over time than in males. Conclusions: The incidence and prevalence o

Importance: Incidence, prevalence, and survival are pertinent measures to inform the management and provision of prostate cancer care. Objective: To calculate the incidence, prevalence, and survival rates for prostate cancer in the UK from 2000 to 2021. Design, Setting, and Participants: This population-based cohort study uses routinely collected primary care data from the UK. Male patients aged 18 years or older with at least 1 year of history registered in Clinical Practice Research Datalink (CPRD) GOLD or Aurum were included. Data were analyzed from January 2023 to March 2024. Main Outcomes and Measures: Prostate cancer incidence rates (IR), period prevalence (PP), and 1-, 5-, and 10-year survival after diagnosis between 2000 and 2021, stratified by age and calendar years. Results: This study included 64 925 and 133 200 patients with prostate cancer in CPRD GOLD and Aurum, respectively, with a median age of 72 (65-78) years. The overall IR of prostate cancer was 151.7 (95% CI, 150.6 to 152.9) per 100 000 person-years in GOLD to 153.1 (95% CI, 152.3 to 153.9) per 100 000 person-years for Aurum and increased with age. The incidence of prostate cancer increased from 109 per 100 000 person-years in 2000 to 159 per 100 000 person-years in 2021. Peaks of incidence occurred in 2004 and 2018, before a decline in 2020. PP increased 3.5 times over the study period for both databases, from 0.4% in 2000 to 1.4% in 2021. IR and PP were highest in those aged 80 to 89 years. Median (95% CI) survival was similar in both databases (GOLD: 10.9 [95% CI, 10.7-11.1] years and Aurum: 11.1 [95% CI, 11.0-11.2] years). Survival at 1, 5, and 10 years after diagnosis were 93.4% (95% CI, 93.2%-93.6%), 71.8% (95% CI, 71.4%-72.2%), 53.2% (95% CI, 52.6%-53.7%) in GOLD and 93.9% (95% CI, 93.7%-94.0%), 72.7% (95% CI, 72.5%-73.0%), 53.7% (95% CI, 53.3%-54.1%) in AURUM, respectively. Survival increased over time: 1-yea

Nicola L Barclay,1 Theresa Burkard,1 Edward Burn,1 Antonella Delmestri,1 Andrea Miquel Dominguez,2 Asieh Golozar,3 Carlos Guarner-Argente,4 Francesc Xavier Avilés-Jurado,5 Wai Yi Man,1 Àlvar Roselló Serrano,6 Andreas Weinberger Rosen,7 Eng Hooi Tan,1 Ilona Tietzova,8 Daniel Prieto Alhambra,1,9 Danielle Newby1 On behalf of the OPTIMA Consortium1Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom; 2Otorrinolaringology department, Hospital Joan XXIII de Tarragona, Tarragona, Spain; 3Odysseus Data Services, Cambridge, MA, USA; 4Gastroenterology Department, Hospital de la Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Sant Quintí, Barcelona, Spain; 5Head Neck Tumors Unit, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain; 6Institut Català d’Oncologia, Hospital Universitari Dr Josep Trueta, Girona, Spain; 7Centre for Surgical Science, Department of Surgery, Zealand University Hospital, Koege, Denmark; 8First Department of Tuberculosis and Respiratory Diseases, First Faculty of Medicine, Charles University, Prague, Czech Republic; 9Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, the NetherlandsCorrespondence: Daniel Prieto Alhambra, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, United Kingdom, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: The COVID-19 pandemic profoundly affected healthcare systems and patients. There is a need to comprehend the collateral effects of the pandemic on non-communicable diseases. We examined the impact of the pandemic on short-term survival for common solid tumours, including breast, colorectal, head and neck, liver, lung, oesophageal, pancreatic, prostate, and stomach cancer in the UK.Methods: This was a population-based cohort study of electronic he

Evidence on the comparative effectiveness of osteoporosis treatments is heterogeneous. This may be attributed to different populations and clinical practice, but also to differing methodologies ensuring comparability of treatment groups before treatment effect estimation and the amount of residual confounding by indication. This study assessed the comparability of denosumab vs oral bisphosphonate (OBP) groups using propensity score (PS) methods and negative control outcome (NCO) analysis. A total of 280 288 women aged ≥50 yr initiating denosumab or OBP in 2011-2018 were included from the UK Clinical Practice Research Datalink (CPRD) and the Danish National Registries (DNR). Balance of observed covariates was assessed using absolute standardized mean difference (ASMD) before and after PS weighting, matching, and stratification, with ASMD >0.1 indicating imbalance. Residual confounding was assessed using NCOs with ≥100 events. Hazard ratio (HR) and 95%CI between treatment and NCO were estimated using Cox models. Presence of residual confounding was evaluated with 2 approaches (1) >5% of NCOs with 95% CI excluding 1, (2) >5% of NCOs with an upper CI <0.75 or lower CI >1.3. The number of imbalanced covariates before adjustment (CPRD 22/87; DNR 18/83) decreased, with 2%–11% imbalance remaining after weighting, matching, or stratification. Using approach 1, residual confounding was present for all PS methods in both databases (≥8% of NCOs), except for stratification in DNR (3.8%). Using approach 2, residual confounding was present in CPRD with PS matching (5.3%) and stratification (6.4%), but not with weighting (4.3%). Within DNR, no NCOs had HR estimates with upper or lower CI limits beyond the specified bounds indicating residual confounding for any PS method. Achievement of covariate balance and determination of residual bias were dependent upon several factors including the population under study, PS method, prevalence of NCO, and the threshold indic

Objective: Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients’ clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important. Methods: ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0–1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries. Results: The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a

BACKGROUND: We assessed the risk of adverse events-severe acute kidney injury (AKI), falls and fractures-associated with use of antihypertensives in older patients with complex health needs (CHN). SETTING: UK primary care linked to inpatient and mortality records. METHODS: The source population comprised patients aged >65, with ≥1 year of registration and unexposed to antihypertensives in the year before study start. We identified three cohorts of patients with CHN, namely, unplanned hospitalisations, frailty (electronic frailty index deficit count ≥3) and polypharmacy (prescription of ≥10 medicines). Patients in any of these cohorts were included in the CHN cohort. We conducted self-controlled case series for each cohort and outcome (AKI, falls, fractures). Incidence rate ratios (IRRs) were estimated by dividing event rates (i) during overall antihypertensive exposed patient-time over unexposed patient-time; and (ii) in the first 30 days after treatment initiation over unexposed patient-time. RESULTS:Among 42,483 patients in the CHN cohort, 7,240, 5,164 and 450 individuals had falls, fractures or AKI, respectively. We observed an increased risk for AKI associated with exposure to antihypertensives across all cohorts (CHN: IRR 2.36 [95% CI: 1.68-3.31]). In the 30 days post-antihypertensive treatment initiation, a 35-50% increased risk for falls was found across all cohorts and increased fracture risk in the frailty cohort (IRR 1.38 [1.03-1.84]). No increased risk for falls/fractures was associated with continuation of antihypertensive treatment or overall use. CONCLUSION: Treatment with antihypertensives in older patients was associated with increased risk of AKI and transiently elevated risk of falls in the 30 days after starting antihypertensive therapy.

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primà ria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiolo

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three nonmutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: More women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwi

BackgroundWe described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.MethodsWe conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.ResultsWe included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.ConclusionsPatients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.ImpactThis study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

Using real-world data and past vaccination data, we conducted a large-scale experiment to quantify bias, precision and timeliness of different study designs to estimate historical background (expected) compared to post-vaccination (observed) rates of safety events for several vaccines. We used negative (not causally related) and positive control outcomes. The latter were synthetically generated true safety signals with incident rate ratios ranging from 1.5 to 4. Observed vs. expected analysis using within-database historical background rates is a sensitive but unspecific method for the identification of potential vaccine safety signals. Despite good discrimination, most analyses showed a tendency to overestimate risks, with 20%-100% type 1 error, but low (0% to 20%) type 2 error in the large databases included in our study. Efforts to improve the comparability of background and post-vaccine rates, including age-sex adjustment and anchoring background rates around a visit, reduced type 1 error and improved precision but residual systematic error persisted. Additionally, empirical calibration dramatically reduced type 1 to nominal but came at the cost of increasing type 2 error.

OBJECTIVES: To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017–2018. METHODS: International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020. We described baseline demographics, comorbidities, symptoms, 30-day in-hospital treatments, and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome, multisystem inflammatory syndrome in children, and death. RESULTS: A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications (<10%) and adjunctive therapies: systemic corticosteroids (6.8%–7.6%), famotidine (9.0%–28.1%), and antithrombotics such as aspirin (2.0%–21.4%), heparin (2.2%–18.1%), and enoxaparin (2.8%–14.8%). Hospitalization was observed in 0.3% to 1.3% of the cohort diagnosed with COVID-19, with undetectable (n < 5 per database) 30-day fatality. Thirty-day outcomes including pneumonia and hypoxemia were more frequent in COVID-19 than influenza. CONCLUSIONS: Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with inf

Menopausal Hormone Therapy (MHT) use in Australia fell by 55% from 2001 to 2005, following the release of large-scale findings on its risks and benefits. Comprehensive national data, including information on overall prevalence of MHT use as well as information on duration of use in Australia have not been reported since the 2004–5 National Health Survey, when 11% of women aged 45+ years were estimated to be current MHT users. No national data are available on prevalence of use of “bioidentical” hormone therapy (BHT). The objective of this study was to determine recent prevalence of MHT and BHT use. A cross-sectional, national, age-stratified, population survey was conducted in 2013. Eligible women, aged 50–69 years, resident in Australia were randomly sampled in 5-year age groups from the Medicare enrolment database (Australia’s universal health scheme). The response rate was 22% based on return of completed questionnaires, and analyses were restricted to 4,389 women within the specified age range. The estimated population-weighted prevalence of current use of MHT was 13% (95%CI 12–14), which was broadly similar to the previously reported national figures in 2004–5, suggesting that the use of MHT in Australia has largely stabilised over the past decade. A total of 39% and 20% of current-users with an intact uterus reported use of oestrogen-progestagen MHT and oestrogen-only MHT, respectively, whereas 77% of hysterectomised current-users used oestrogen-only MHT. Almost three-quarters of current-users [population-weighted prevalence 9% (95%CI 8–10)] had used MHT for ≥5 years. In regard to BHT, estimated population-weighted prevalence of ever use was 6% (95%CI 6–7) and 2% (95%CI 2–3) for current use. The population-weighted prevalence of MHT and BHT combined, in current users in their fifties and sixties was 15% (95%CI 14–16). These data provide a recent national “snapshot” of Australian women’s use of both conventional MHT and of BHT.