Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C (preprint)

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

The role of medical support workers during the Covid-19 pandemic in the National Health Service in the UK: a qualitative service evaluation at the Oxford University Hospitals Foundation Trust (preprint)

Background During the COVID-19 pandemic, the National Health Service (NHS) England created a short-term position known as a Medical Support Worker (MSW), for International Medical Graduates (IMGs) and qualified doctors who had left medicine, to return to medical practice. We conducted a service evaluation of the MSW role at Oxford University Hospitals NHS Foundation Trust (OUHFT), with the aim of understanding how MSWs were perceived and contributed to the NHS, factors driving MSWs’ career choices, the short- and long-term goals of the position, analysing the perspectives of MSWs, their supervisors and recruiters.Methods A qualitative case study approach was adopted. A total of nine semi-structured interviews and two focus group discussions involving 18 participants were conducted with MSWs, their supervisors and recruiters based in OUHFT. A thematic analysis and narrative synthesis of results were conducted.Results Findings were categorised into micro, meso, and macro levels of the health system. At the micro level, MSWs were identified as a diverse group of highly qualified international medical graduates (IMGs) holding a supernumerary role, who contributed their skills during the pandemic. At the meso level, the importance of a comprehensive induction by the hospital was highlighted by all participants, to clarify the job responsibility and familiarise MSWs with the local health system. At the macro level, the role enabled familiarisation and integration of MSWs within the NHS with the aim of obtaining a license to practice as a doctor in the UK.Conclusions This service evaluation highlighted the importance of the role of MSWs during the pandemic. The MSW scheme could be a pathway for IMGs to integrate into the NHS and fill workforce shortages. This study has the potential to inform the NHS long-term policy on the role of MSWs and the integration of IMGs into the workforce.

Coding Long COVID: Characterizing a new disease through an ICD-10 lens (preprint)

Naming a newly discovered disease is always challenging; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux. The deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Our results include a characterization of common diagnostics, treatment-oriented procedures, and medications associated with U09.9-coded patients, which give us insight into current practice patterns around Long COVID. We also established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into three major categories: cardiopulmonary, neurological, and metabolic. We aim to apply the patterns gleaned from this analysis to flag probable Long COVID cases occurring prior to the existence of U09.9, thus establishing a mechanism to ensure patients with earlier cases of Long-COVID are no less ascertainable for current and future research and treatment opportunities.