ABSTRACT
Objectives: Social determinants of health (SDoH) including income, education, employment, and housing are known to affect health outcomes;while use in real-world database studies are limited. This study assessed socioeconomic differences in burden of disease and utilization of COVID-19 specific medications in a large cohort of patients in the US. Method(s): A total of 17,682,111 patients having a COVID-19 diagnosis between 4/1/2020 and 4/30/2022 were identified in the IQVIA longitudinal medical and pharmacy claims databases of >277 million patients. For SDoH, a 3-digit zip code median Area Deprivation Index (ADI) (v2.0 University of Wisconsin School of Medicine and Public Health 2015) was calculated for each patient, maintaining patient privacy. The ADI is a validated tool ranking neighborhoods by socioeconomic disadvantage. Medical and pharmacy utilization was assessed and stratified by ADI pentiles, where 0-20 was the least disadvantaged, and 81-100 was the most disadvantaged. Result(s): The proportion of patients having a claim with COVID-19 diagnosis was higher in the most disadvantaged (7.75%) compared to the least disadvantaged group (5.94%) (US overall: 6.37%). Medical claims prior to COVID-19 diagnosis were highest in the least disadvantaged, while prior pharmacy utilization was highest in the most-disadvantaged group. There was sparse use of COVID-19 medications overall;the least disadvantaged patients had the lowest use of COVID-19 specific medications. Casirivimab/imdevimab use was highest in the 61-80 (2.01%) and 81-100 (1.79%) ADI groups, and remdesivir use was highest in the moderately disadvantaged (ADI 41-60 and 61-80) groups (both 2.33%). Utilization of hydroxychloroquine (unapproved for COVID-19) increased from 0.91% in the least to 2.13% in the most disadvantaged groups. Conclusion(s): This study shows unequal burden of COVID-19 prevalence by SDoH, with the most disadvantaged having a higher disease burden and utilization of certain approved and unapproved COVID-19 medications, highlighting the need for further study of the reasons for these disparities.Copyright © 2023
ABSTRACT
Objectives: COVID-19 infected over 150 million people and caused over 1 million deaths in the US. This study evaluates several variables thought to be associated with mortality risk in the COVID-19 population. Method(s): The IQVIA longitudinal medical and pharmacy claims databases identified 17,682,111 patients with a COVID-19 diagnosis between 4/1/2020-4/30/2022 from a population of >277 million patients in the US. Patients were linked to Veritas Data Research fact-of-death records (90% complete compared to CDC reporting) and confirmed deaths were flagged. Confirmed mortality rates (CMR) were evaluated by age group, socioeconomic status (SES) using the Area Deprivation Index (v2.0, University of Wisconsin, 2015), co-morbidities and COVID-specific (approved and unapproved) treatments. Result(s): Of the 563,744 patients (3.2%) identified as dead (3.67% in men, 2.85% in women overall), CMR was lowest in patients aged 0-17 (0.08%), highest in age 65-75 (5.92%) and >75 (16.40%). Patients in the lowest 40% of SES had CMR of 4.43% while in the highest 20% was 1.56%. Respiratory failure, pneumonia and sepsis were the most common acute diagnoses accompanying COVID-19 deaths in all SES. In patients with comorbid dementia or Alzheimer's disease, CMR were 21.62% and 23.40% respectively. Additionally, congestive heart failure (15.79%), atrial fibrillation (15.50%), chronic kidney disease (15.30%) and COPD (12.19%) were associated with high CMR. Among patients receiving approved therapies, casirivimab/imdevimab and remdesivir had CMR of 1.41% and 12.63% respectively, while for those receiving unapproved therapies, ivermectin and hydroxychloroquine had CMR of 2.54% and 2.45%. Conclusion(s): Compared to the 1.1% case-mortality rate (Johns Hopkins 2023) among US COVID-19 patients, we found CMR exceeded 3% among those with a medical claim for COVID-19. Advanced age, dementia, and cardio-renal disease were associated with mortality. Patients with the lowest SES had approximately 3 times the confirmed mortality rate compared to those in the highest SES group.Copyright © 2023
ABSTRACT
Objectives: According to the CDC, as of December 2022, almost one in three Americans had confirmed COVID-19 infection;yet only a small portion generated healthcare claims related to COVID-19. Higher burden of COVID-19 cases in Northeastern states compared to other US regions has been documented. This study examined the regional variation in demographic characteristics and treatment patterns among patients with a claim for COVID-19 in a nationwide US claims database. Method(s): Analysis of data from over 277 million patients in IQVIA's longitudinal medical and pharmacy claims databases resulted in a cohort of 17,682,111 patients with COVID-19 diagnosis between 4/1/2020 and 4/30/2022. Demographic characteristics and treatment rates for key approved and un-approved COVID-19 therapies were assessed and stratified by region. Result(s): Among patients in the database, 6.4% had a COVID-19 diagnosis. The proportion was higher in the Northeast (7.1%) and South (6.9%) compared with the West (4.8%). The highest proportion of patients were aged 18-44 years (32.7% in South to 35.2% in West). Over a fifth of the patients were >= 65 years old (US overall= 23.7%;22.5% in Northeast to 25.8% in Midwest). Approximately 57% of the patients nationally and within each region were women. For approved medications, utilization ranged from 1.7% in Northeast to 2.7% in Midwest (overall:2.2%) for remdesivir;0.7% in Northeast to 2.2% in South (overall: 1.5%) for casirivimab/imdevimab. For unapproved medications, utilization ranged from 0.9% in Northeast to 1.6% in South (overall:1.3%) for hydroxychloroquine and 0.4% in Northeast to 1.8% in South (overall:1.1%) for ivermectin. Conclusion(s): Less than one in five US cases of COVID-19 had a claim with diagnosis of COVID-19. Use of COVID-19 specific medications remained low throughout the pandemic. Despite the higher disease burden, proportion of patients with claims and receiving COVID-19 treatment were low nationally, particularly in the northeast US region.Copyright © 2023
ABSTRACT
Biological Field-Effect Transistors (BioFETs) have already demonstrated enormous potential for detecting minute amounts of ions and molecules. The use of two-dimensional (2D) materials has been shown to boost their performance and to enable the design of new applications. This combination deserves special interest in the current pandemic caused by the SARS-CoV-2 virus which demands fast, reliable and cheap detection methods. However, in spite of the experimental advances, there is a lack of a comprehensive and in-depth computational approach to capture the mechanisms underlying the sensor behaviour. Here, we present a multiscale platform that combines detailed atomic models of the molecules with mesoscopic device-level simulations. The fine-level description exploited in this approach accounts for the charge distribution of the receptor, its reconfiguration when the target binds to it, and the consequences in terms of sensitivity on the transduction mechanism. The results encourage the further exploration of improved sensor designs and 2D materials combined with diverse receptors selected to achieve the desired specificity.
ABSTRACT
Introduction: Ofatumumab is a self-administered subcutaneous CD20 monoclonal antibody approved in the United States (US) in August 2020 (in the pandemic era) for the treatment of relapsing multiple sclerosis (MS) in adults. Since US approval, there is a lack of information available on the real-world utilisation of ofatumumab. Objectives: To understand the overall profile of MS patients initiating ofatumumab in the real-world clinical practice using data from a nationally representative claims database in the first 6 months post-approval. Aims: To evaluate patient demographics, treatment status, geographical distribution, premedication use, claims-based disability levels, disease-modifying therapy (DMT) use in the year prior, and corresponding median time to transition (treatment gap) for patients initiating ofatumumab. Methods: This retrospective cohort study used IQVIA opensource claims data. Adults with a diagnosis of MS who initiated ofatumumab from August 2020-February 2020 were included. The index date was defined based on the first prescription fill for ofatumumab, and the baseline period was 1-year prior to the index date. Results: Overall, 1015 patients initiating ofatumumab were included in the study. Mean (± standard deviation [range]) age was 48.2±12.3 (18-85) years, and 72.5% were females. Approximately, 33% of patients were ≥55 years of age. Most patients were from the Southern and Western regions of the US. Ofatumumab was mostly prescribed by neurologists (86.8%) vs PCP/NP/PAs. The proportion of patients with moderate to severe MS disability was 38.2%. Common comorbidities among patients were osteoarthritis (31.3%), hypertension (16.7%), and depression (12.2%). Overall, 54.8% were not on any DMT in the year prior to initiating ofatumumab. Patients commonly switched from ocrelizumab (24.2%), dimethyl fumarate (23.3%), platform injectables (19.3%) and teriflunomide (14.8%). The median transition period for dimethyl fumarate, teriflunomide, and ocrelizumab was 62, 51, and 174 days, respectively. Patients received ofatumumab pre-and post-COVID and influenza vaccine. Steroid and antihistamine use as premedication was minimal (≤2.5% of patients). Conclusions: In the real-world pandemic environment, ofatumumab was prescribed, also beyond the trial population. Most patients newly initiated ofatumumab had no treatment in the prior year. Understanding patient profile, prior DMT use in the realworld may help stakeholders guide treatment decisions.