A multi-centre national study of COVID-19 infection in cancer patients

Background & aims: COVID19 disproportionately affects the immunosuppressed, but its epidemiology over time is incompletely characterised. We describe Australian experiences of COVID19 in a national observational study of patients with malignancy. Methods: An ongoing multisite prospective cohort study of adult COVID19 patients with active cancer was conducted. Clinical and laboratory data over 28 months (1/3/20-22/7/22) was collated from 15 hospitals. Results: There were 491 patients included. Patients were a median of 63(IQR:50-71) years with majority male (254,52%). Solid organ malignancy was most common (296,60%), followed by haematological malignancy (180,37%), then both (15,4%). Most common solid tumour was breast cancer (74/296,25%);most common haematological cancer was lymphoma (102/180,57%). Majority (275,56%) were undergoing cancer treatment at COVID19 diagnosis. From 2020-2022, patients presented less with lower respiratory tract infections (57%,36%,5%) with increasing outpatient management (26%,50%,67%). Improved mortality was seen (27%,19%,11%). Median inpatient length of stay was 8(4-11) days. Intensive care admission was low (21,4%). For patients who had repeated respiratory PCR testing, median time from first to last positive test was 17(7-25, n = 123) days. Cancer treatment modification occurred in 18(4%) and delay in 74(15%). Conclusion: Despite improvements in outcomes, COVID19 still results in morbidity with impacts on cancer treatment. This preliminary data shows that cancer patients remain a vulnerable group and should be prioritised for public health interventions.

The role of telehealth in oncology care: A qualitative analysis of patient and clinician perspectives

Purpose: The COVID-19 pandemic has accelerated the rapid expansion and adoption at scale of telehealth, where previously reserved in use to remote communities. This afforded a novel opportunity to study the impact of telehealth delivery on oncology care. Our study aimed to qualitatively explore current physician and patient perspectives on the utility of telehealth in oncology care. Materials and Methods: Semi-structured interviews of seven physicians and eleven patients, recruited from an oncology department in an Australian tertiary teaching hospital, were conducted. Two authors independently coded the transcripts and emerging themes were identified and refined in an iterative process of thematic analysis. Results : Telehealth offered broadened possibilities by allowing continuity of care in the pandemic and enabling patients and physicians to discover novel advantages. It also brought to the fore often overlooked elements of in-person care unavailable on telehealth. These included the information communicated physically through formal and informal physical examination, the collaboration between patient and physician in shaping outcomes and building rapport, and the confidence in decisions made and physician performance. While patients and physicians envisioned the continuation of telehealth post-pandemic, logistical steps are necessary to address these challenges. Conclusion: This study highlights the unprecedented opportunities that telehealth presents in widening access and diversifying oncology care. However, it also demonstrates that telehealth, particularly in a 'one-size-fits-all' approach, cannot always reach equivalence in quality of care. Further research is required to identify when and for whom telehealth is most acceptable, to maximise its potential benefits in a potential hybrid model of care.

COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing. (Special Issue: COVID-19.)

COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting data to avoid highly misleading conclusions about infection and death rates. We describe how causal (Bayesian network) models can provide such explanations and the need to combine these with more random testing in order to achieve reliable data and predictions for the both policy makers and the public.