AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: the RAVEN cohort study

Objectives Despite being prioritized during initial COVID-19 vaccine rollout, vulnerable individuals at high risk of severe COVID-19 (hospitalization, intensive care unit admission, or death) remain underrepresented in vaccine effectiveness (VE) studies. The RAVEN cohort study (NCT05047822) assessed AZD1222 (ChAdOx1 nCov-19) two-dose primary series VE in vulnerable populations. Methods Using the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub, linked to secondary care, death registration, and COVID-19 datasets in England, COVID-19 outcomes in 2021 were compared in vaccinated and unvaccinated individuals matched on age, sex, region, and multimorbidity. Results Over 4.5 million AZD1222 recipients were matched (mean follow-up ∼5 months); 68% were ≥50 years, 57% had high multimorbidity. Overall, high VE against severe COVID-19 was demonstrated, with lower VE observed in vulnerable populations. VE against hospitalization was higher in the lowest multimorbidity quartile (91.1%; 95% CI: 90.1, 92.0) than the highest quartile (80.4%; 79.7, 81.1), and among individuals ≥65 years, higher in the ‘fit’ (86.2%; 84.5, 87.6) than the frailest (71.8%; 69.3, 74.2). VE against hospitalization was lowest in immunosuppressed individuals (64.6%; 60.7, 68.1). Conclusions Based on integrated and comprehensive UK health data, overall population-level VE with AZD1222 was high. VEs were notably lower in vulnerable groups, particularly the immunosuppressed

Women prescribed antibiotics versus never prescribed: time to cerebral palsy and/or epilepsy in childhood.

<p><i>*p<0.01</i></p><p><i>† Cox regression model adjusted for propensity score which included: hypertension, smoking/tobacco use, diabetes, age at delivery, year of delivery, Townsend quintile, treatment of chronic medical condition in pregnancy, alcohol problems, illicit drug use, obesity and potentially neurologically-damaging infection during pregnancy</i>.</p><p><i>‡ Analysis cohort included women with a single respiratory tract infection who either received or did not receive a single antibiotic prescription within plus or minus three days of the recorded date of respiratory tract infection</i>.</p><p>Women prescribed antibiotics versus never prescribed: time to cerebral palsy and/or epilepsy in childhood.</p

Extending the data collection from a clinical trial: The Extended Salford Lung Study research cohort

Abstract The Extended Salford Lung Study (Ext-SLS) is an extension of the Salford Lung Studies (SLS) in asthma and chronic obstructive pulmonary disease (COPD) through retrospective and prospective collection of patient-level electronic health record (EHR) data. We compared the Ext-SLS cohort with the SLS intention-to-treat populations using descriptive analyses to determine if the strengths (e.g. randomization) of the clinical trial were maintained in the new cohort. Historical and patient-reported outcome data were captured from asthma-/COPD-specific questionnaires (e.g., Asthma Control Test [ACT]/COPD Assessment Test [CAT]). The Ext-SLS included 1147 participants (n = 798, SLS asthma; n = 349, SLS COPD). Of participants answering the ACT, 39% scored <20, suggesting poorly controlled asthma. For COPD, 61% of participants answering the CAT scored ≥21, demonstrating a high disease burden. Demographic/clinical characteristics of the cohorts were similar at SLS baseline. EHR data provided a long-term view of participants’ disease, and questionnaires provided information not typically captured. The Ext-SLS cohort is a valuable resource for respiratory research, and ongoing prospective data collection will add further value and ensure the Ext-SLS is an important source of patient-level information on obstructive airways disease

Primary data, claims data, and linked data in observational research: the case of COPD in Germany

Abstract Background Real-world evidence (RWE) can inform patient management decisions, but RWE studies are associated with limitations. Linkage of different RWE data types could address such limitations by enriching data and improving scientific quality. Using the example of chronic obstructive pulmonary disease (COPD) in Germany, this study assessed the value of data linkage between primary and secondary data sources for RWE. Methods Post hoc analysis of data from an observational RWE study, which used prospectively collected data and data from an insurance claims database to assess treatment adherence and persistence in patients with COPD in Germany. Patient-level primary data were collected from the prospective observational study (primary dataset, N = 636), and claims data from the sickness fund AOK Nordost (claims dataset, N = 74,916). Primary and claims data were linked at a patient level using insurance numbers (linked dataset). Patients in the linked dataset were indexed at date of study inclusion for primary data and matched calendar date for claims data. Agreement between primary and claims data was examined for patients in the linked dataset based on comparisons between recorded sociodemographic data at index, comorbidities (primary: any recorded; claims: pre-index), prescriptions for COPD therapies (type and date) and exacerbations in the 12-month post-index period. Results The linked dataset included primary and claims data for 536 patients. Fewer comorbid patients were reported in primary data compared with claims data (p < 0.001), with overall agreement between 63.6% (hypertension) and 90.5% (osteoporosis). Number of prescriptions for COPD therapies per patient was lower in primary versus claims data (3.7 vs 10.3 prescriptions, respectively), with only 24.5% of prescriptions recorded in both datasets. Only 11.5% of exacerbations (moderate or severe) were recorded in both datasets, with 15.5% recorded only in primary data and 73.0% recorded only in claims data. Conclusion Our study highlighted discrepancies between primary and claims data capture for this population of German patients with COPD, with lower reporting of comorbidities, COPD therapy prescriptions and exacerbations in primary versus claims data. Study findings suggest that data linkage of primary and claims data could provide enrichment and be useful in fully describing COPD endpoints

Suboptimally controlled asthma in patients treated with inhaled ICS/LABA: prevalence, risk factors, and outcomes

Abstract This observational claims-linked survey study assessed the prevalence of and risk factors for suboptimal asthma control and healthcare utilization in adults with asthma receiving fixed-dose combination (FDC) inhaled corticosteroid/long-acting β2-agonist (ICS/LABA). Commercially insured adults from the Optum Research Database were invited to complete the Asthma Control Test (ACT) and Asthma Control Questionnaire-6 (ACQ-6). Among participants (N = 428), 36.4% (ACT-assessed) and 55.6% (ACQ-6-assessed) had inadequately controlled asthma. Asthma-related quality of life was worse and asthma-related healthcare resource utilization was higher in poorly controlled asthma. Factors associated with ACT-defined suboptimal asthma control in multivariate analysis included: frequent short-acting β2-agonist (SABA) use, asthma-related outpatient visits, lower treatment adherence, and lower education levels. During follow-up, factors associated with asthma exacerbations and/or high SABA use included: inadequately controlled asthma (ACT-assessed), body mass index ≥30 kg/m2, and high-dose ICS/LABA. Approximately 35–55% of adults with asthma were inadequately controlled despite FDC ICS/LABA; poor control was associated with worse disease outcomes

Identification of an Optimal COVID-19 Booster Allocation Strategy to Minimize Hospital Bed-Days with a Fixed Healthcare Budget

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B1, costing US $1 per dose, B2, costing US$2, and no booster (NB), costing US $0. B1 and B2 were assumed to be 55$2.10 per person; the minimum expected expense using B1, B2, or NB for all. Brazil was the base-case country. The model demonstrated that B1 for those aged &lt;70 years and B2 for those &ge;70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts

Identification of an Optimal COVID-19 Booster Allocation Strategy to Minimize Hospital Bed-Days with a Fixed Healthcare Budget

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B1, costing US $1 per dose, B2, costing US$2, and no booster (NB), costing US $0. B1 and B2 were assumed to be 55$2.10 per person; the minimum expected expense using B1, B2, or NB for all. Brazil was the base-case country. The model demonstrated that B1 for those aged 2 for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts