Volume 214 Issue 10

A virtual brain bank could maximise the potential of brain donation by extending the core physical bank to include existing repositories of clinical tissues and data Brain banking, whereby post mortem brains are harvested, processed, stored and made available to facilitate health and medical research, provides scientists with an unparalleled resource for macroscopic, microscopic and molecular investigations into many brain conditions. The human brain is seen as the final frontier of scientific research, with many cognitive processes and neurological diseases exclusively manifesting in humans. This uniqueness has been postulated as an explanation for why many brain disease drug leads do not progress past the acknowledged “valley of death” whereby success in animals is not translated to human clinical trials.1 For many brain researchers, human post mortem tissue is therefore preferred or essential for their investigations. The importance and utility of whole brain banking was recently demonstrated by a collection of articles in the Handbook of Clinical Neurology.2 In particular, Zielke and Mash, in a wide ranging review of bank management and operations, posed the question of whether “the value of the brain can be enhanced by collecting other tissues”.3 Here we make the case for the affirmative by describing how brain banking can, by aligning with broader biobanking initiatives, enhance the value of brain tissue for both current and “future patients and society”3. State of play Biobanks that collect tissue other than brains are typically embedded in clinical workflows, whereby collection and characterisation of residual tissue for biobanking takes place in parallel with tissue required for clinical purposes. However, in our experience in Australia, brain removal is not routinely included as part of an autopsy or post mortem examination. Autopsies themselves are now uncommon, even within the forensic setting;4 reasons for this are varied and include advances in ante mortem diagnosis propelled by imaging technologies, a belief that autopsy reports fuel malpractice lawsuits, logistic issues, and poor reimbursement rates for pathologists.5 It is now common in Australia for pathology specialists to complete their training without having conducted a post mortem examination, with the future pathology workforce destined to be demarcated into those who have and have not received training to conduct an autopsy. Today, brain removal is largely confined to the setting of brain donor programs, established to recruit and clinically characterise donors with specific diseases and, more rarely, controls.4 One reason for the decline in clinical and forensic autopsies performed is the increasing quality of modern imaging techniques.6 Similarly, ‐omic approaches, particularly metabolomics, for obtaining brain‐specific information7,8 are being increasingly applied to clinically available tissues such as serum and cerebrospinal fluid. Brain organoids developed from patient‐derived stem cells are also a promising in vitro model.9 At present, neuropathological confirmation of disease provides a “ground truth” but over time, refinement of imaging, peripheral biomarkers and in vitro models could diminish the importance of whole brain banking in isolation. For brain donor programs, brain removal logistics are often complex and costly, with reliance on in‐kind support from funeral directors, clinicians and mortuary staff. After tissue harvesting, brains require specialist processing expertise and large storage areas, resulting in increased labour and space costs. The timing and finality of brain removal can also have an impact on the collection of longitudinal clinical data, which may require medical records departmental input and/or facilitation by family members. The predicted rise in the morbidity and mortality of dementia and reported increases in the prevalence of mental health in Australia provide convincing evidence of the need for research into risk factors and therapies for neurological diseases. Currently, whole brain banks typically characterise and collect in the vicinity of 1000 donors. Cohorts of pathologically confirmed cases and controls tend to be an order of magnitude smaller than that required to efficiently carry out genetic analyses such as genome‐wide association studies. In the future, even larger cohorts will be required to examine the probable gene–environment interactions that confer risk for many sporadic brain diseases.10 We propose a novel brain banking strategy that maximises the potential of brain donation by extending the core physical bank to include existing repositories of clinical tissues and data, creating a virtual brain bank. This would not only benefit brain researchers but also researchers investigating potential interactions between the brain and other vital organs. A next‐generation solution Rather than competing with alternative technologies, a next‐generation (virtual) brain bank could incorporate these technologies into a suite of products offered to researchers. Although brain donor programs already strive to maximise the clinical and demographic information available for each participant (Box 1), an integrated brain bank could extend their involvement to more comprehensive clinical data collection, generation and analysis. This would make samples and derivatives such as serum, DNA, images and genetic/‐omic data available for researchers, in addition to brain tissue.3 We suggest extending this approach beyond tissues from donors themselves to include collaborations with existing brain‐specific clinical tissue banks such as the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD). The NCRAD stores clinical non‐brain tissue samples from over 90 000 participants — in the order of two degrees of magnitude larger than the number of donors in most brain banks (Box 2). These samples have been subjected to multi‐omic analyses, and with associated imaging data have provided key insights into Alzheimer disease.11 Their level of analysis on ante mortem samples would allow an unprecedented depth of clinicopathological correlations if a subset of participants consented to brain autopsy. Extending this scenario, a next‐generation brain bank could be integrated into multipurpose biobanking initiatives. The size and intensive phenotyping within prospective cohort studies such as the UK Biobank (https://www.ukbiobank.ac.uk/), which hosts 500 000 participants, offers data on a rich source of age‐related brain diseases over time. Furthermore, there is already genetic, neuroimaging and neuropsychological testing data available from neurologically normal volunteers, enabling brain bank personnel to use their skills and expertise to provide risk factor insights as well as directing subsequent mechanistic studies in post mortem brain tissue (Box 2). In this scenario, the brain bank could remain responsible for the characterisation and provision of brain‐related tissue and data, but be just one component in an integrated resource that characterises the lifespan of an individual donor. This would not only allow brain banks to contribute to research on brain diseases for living patients, but would also create bi‐directional synergies with researchers of other diseases; that is, “brain and body” biobanking. For example, diabetes has been shown to have a central component,12 dementia and cardiovascular disease share common risk factors,13 and there are fascinating inverse associations between neurodegenerative diseases and cancer.14 In the integrated biobank envisaged, a dynamic consent model could be employed whereby an initial permission to contact could be followed by consent for provision of data and clinical samples, and eventually by consent for post mortem brain donation. A dynamic consent model also encourages deeper participant engagement. Ultimately, only a small proportion of participants are likely to become whole brain donors (Box 2), meaning direct clinicopathological correlations will always be limited. However, the workflow of a more inclusive brain and body banking model would enable complementary resources to be offered to a broader range of scientists. The 2016 National Research Infrastructure Roadmap15 recommended investment into collaborative and effective biobanking in Australia, with the government response recommending a national biobank scoping study. One possible outcome of a biobank scoping study is for the federal government to re‐engage in funding single or multi‐disease initiatives on a state or national basis. For example, the 45 and Up Study that follows approximately 250 000 middle aged community volunteers in New South Wales is a data‐linked cohort study with the potential to underpin such a brain and body biobank.16 Importantly, data linkage with routinely collected clinical and administrative data in the Australian health system gives further credence to the integration of brain banking with state or nation‐wide biobanking initiatives where clinical laboratory test results, medication history and comorbidity data can validate or extend self‐reported information. A multi‐focus bank or any research infrastructure becomes challenging to fund after initial investments. The integration of expertise across diseases and an intramural science program that kick starts traditional collaborations and commercial opportunities should have a favourable impact on the value proposition for current and future investors. Governance will be the key ingredient for success, but as with the multi‐focal nature of the proposed biobank, the board, science advisory committee and management team should look outside traditional professional boundaries for their representation. Certainly, a modern biobank needs buy‐in from state and federal health authorities, but it should also include representatives from the business community, patient advocacy groups and health practitioners to promote bi‐directional communication to known and as yet unrealised stakeholders. It has been suggested that to be most effective, biobanking needs to change its modus operandi from a static operation that banks tissue indefinitely to one that is actively involved in the research process — a so‐called biolibrary. By integrating with wider biobanking initiatives, next‐generation brain banks can contribute to the clinical, pathological and clinicopathological characterisation of a range of tissues and data for researchers of all disease interests. Importantly, a virtual brain bank or brain and body biolibrary will create future research synergies that otherwise would not be achieved. Box 1 – Schematic diagram showing a typical brain bank operating in conjunction with a brain donor program for a specific disease K = 1000. Box 2 – Schematic diagram of an integrated brain biobank with capacity to combine with and leverage wider biobanking endeavours (ideally suited to sporadic brain diseases with multi‐factorial aetiologies) K = 1000.

Amanda Rush · Greg T Sutherland

AI-based tools can help with image acquisition, reconstruction and quality; interpretation, diagnosis and decision support; and assisting with manual tasks

Meng Law · Jarrel Seah · George Shih

A new clinical trials network aims to bridge the gap in cancer outcomes between rural and metropolitan cancer patients in Victoria

Arun Muthusamy · Donna Long · Craig R Underhill

Screen‐detected breast cancer overdiagnosis occurs, but each woman has been diagnosed with cancer that cannot be ignored There are an increasing number of publications estimating the extent of cancer overdiagnosis, which for breast cancer is in the context of population cancer screening programs.1 Researchers investigating overdiagnosis point to a range of related harms, but it is important to view these in the context of screening benefits, such as reductions in risk of breast cancer death.2 Care needs to be taken not to conflate formal screening programs with informal or opportunistic approaches to early detection, such as prostate‐specific antigen (PSA) testing in prostate cancer. This article focuses on the risk of overdiagnosis in the context of population‐based breast screening programs, given that overdiagnosis is often at the heart of calls to cease mammographic breast cancer screening.3,4 Despite the emphasis often given to breast cancer screening in discussions of overdiagnosis, the concept should not be regarded as only applying to breast cancer screening, or to cancer screening more generally, but as an outcome that could apply, to varying degrees, to a wider range of screening and diagnostic practices. Defining overdiagnosis Overdiagnosis of a cancer is not a false positive or misdiagnosis; it is a diagnosis with histological verification of a cancer that would otherwise not have gone on to cause morbidity or death — although it cannot be determined at the time of diagnosis whether the cancer would have progressed to cause morbidity or death.5 An overdiagnosed cancer is in part a consequence of our capacity to diagnose cancers at increasingly earlier stages. It depends on competing causes of death; that is, a cancer will not cause morbidity or death in people who die beforehand from other causes, such as respiratory or cardiac diseases or trauma. The reality that a proportion of cancers will therefore be overdiagnosed is inherent in all screening programs, although the issue is not limited to screening. Major international reviews have concluded, after a careful evaluation of the balance between benefits and harms, that there is a net benefit from inviting women to receive breast screening (ie, benefits outweigh harms).2 The problem of overdiagnosis Concerns about overdiagnosis stem from the potential harms that may be experienced by a person receiving the overdiagnosis. Harms can range from the psychological stress of receiving a diagnosis through to the potential for complications and adverse effects of diagnostic procedures or treatments. However, the challenge is that for any individual, it is not possible at diagnosis to determine whether their cancer is overdiagnosed or not. The cancers that are overdiagnosed are indistinguishable from other cancers histologically. As this is a post mortem classification, cancers can only be classified as overdiagnosed when another cause of death supervenes. Estimating overdiagnosis and mortality benefits Estimates of overdiagnosis within breast cancer screening programs vary widely, and this is in part due to methodological as well as programmatic differences. Recent Australian modelling suggested that the rate of overdiagnosis across five cancers (breast, prostate, renal, thyroid and melanoma) was 18% in women and 24% in men.1 However, of these cancers, only breast cancer is part of population screening in Australia. Other studies of breast cancer overdiagnosis specifically point to much lower levels of overdiagnosis. Based on British and European reviews, Cancer Australia has estimated that for every 1000 Australian women screened for breast cancer every 2 years from age 50 to 74 years, around eight breast cancers (range, 2–21) may be found and treated which would not otherwise have been found in a woman’s lifetime.6 In addition, an equivalent number of breast cancer deaths would be avoided in these women.6 The European Screening Network (EUROSCREEN) Working Group calculated a summary estimate of overdiagnosis as about 6.5% of the expected number of diagnosed breast cancers (range, 1–10%) in screened women, based on data from studies in Europe.2 Overall, data from around the world indicate that breast screening confers an estimated reduction in breast cancer mortality of 23% in women invited for screening and 40% or more among those women who are screened.2 Cancer screening programs need to balance benefits and harms Decisions about whether to implement screening at a national level in any country should follow a comprehensive assessment of likely benefits at a population level (mortality reduction, delivery of more conservative therapy to people diagnosed with cancer via screening), harms (unnecessary treatments, psychological impacts) and costs (health service, individual out‐of‐pocket expenses, societal costs). In Australia, this principle is encapsulated in the Australian Population Based Screening Framework.7 It is well understood that some cancers are slower growing while others are more aggressive, but there are significant limits to our capacity to determine at diagnosis these characteristics at both an individual tumour and patient level. The concern, therefore, is that the potential to discourage women from breast screening through concerns of overdiagnosis would result in harms associated with later diagnosis, including deaths from breast cancer. Based on current international evidence, if left untreated, more than 90% of cancers found through routine screening would progress and become symptomatic and be potentially lethal, depending on modelling assumptions.2 Even drawing from the higher estimates of overdiagnosis, data would support that more than 70% of screen‐detected invasive cancers would progress to become symptomatic without treatment.8 These estimates are indicative of the scale of risk of developing a symptomatic breast cancer which could be detected earlier through screen detection. Screen‐detected cancers are found at an earlier stage and tend to be smaller; treatment guidelines advise less extensive surgery and reduced need for adjuvant treatments, as well as being associated with improved survival.9 Reducing the harms of overdiagnosed cancers The most readily recognised harm of overdiagnosis is overtreatment. However, overtreatment is distinct from overdiagnosis and the effects can be mitigated by promotion of evidence‐based clinical management guidelines. Significant advances have been made in tailoring treatment for breast cancer. These include advances in surgery and radiotherapy and using tumour characteristics such as oestrogen receptor, progesterone receptor, and HER2 status to tailor systemic therapies. Research is also being undertaken to investigate using active monitoring rather than surgery for ductal carcinoma in situ, as there is debate regarding the potential for this type of carcinoma, if left untreated, to progress to invasive cancer. Research is underway to determine if genomic or other molecular signals in tumours will provide clearer indications of which ductal carcinoma in situ and invasive cancers need treatment, including chemotherapy, and at what level of aggressiveness, irrespective of whether found through screening or other means.10 Advances from this research will better support women to make informed decisions about treatment. Population screening programs are offered within a policy framework that carefully considers the target population that stands to benefit from screening, including age criteria, and ongoing monitoring and reporting of sensitivity, specificity and interval cancers.7 There are also ongoing research efforts to improve the effectiveness of breast screening, including evaluation of new approaches to tailor screening to the individual woman’s risk profile to maximise benefit and minimise harms. Informed consent about breast screening needs to balance the potential harms with the demonstrated benefits of the current national screening program.

Vivienne Milch · Sanchia Aranda · Karen Canfell · Megan Varlow · David M Roder · David Currow · Cleola Anderiesz · Dorothy Keefe

A 67-year-old woman was referred to our ear, nose and throat department with confirmed cerebrospinal fluid (CSF) rhinorrhoea

Sandeep G Mistry · Wallace Walker · James Earnshaw · Anders Cervin

A 56-year-old man presented with cough, dyspnoea and urinary symptoms

Michael A Cilento · Christopher M Hocking

People with existing mental health problems or living in difficult circumstances may be at particular risk

Helen Herrman · Christian Kieling

Objectives: To estimate initial levels of symptoms of depression and anxiety, and their changes during the early months of the COVID‐19 pandemic in Australia; to identify trajectories of symptoms of depression and anxiety; to identify factors associated with these trajectories. Design, setting, participants: Longitudinal cohort study; seven fortnightly online surveys of a representative sample of 1296 Australian adults from the beginning of COVID‐19‐related restrictions in late March 2020 to mid‐June 2020. Main outcome measures: Symptoms of depression and anxiety, measured with the Patient Health Questionnaire (PHQ‐9) depression and Generalised Anxiety Disorder (GAD‐7) scales; trajectories of symptom change. Results: Younger age, being female, greater COVID‐19‐related work and social impairment, COVID‐19‐related financial distress, having a neurological or mental illness diagnosis, and recent adversity were each significantly associated with higher baseline depression and anxiety scores. Growth mixture models identified three latent trajectories for depression symptoms (low throughout the study, 81% of participants; moderate throughout the study, 10%; initially severe then declining, 9%) and four for anxiety symptoms (low throughout the study, 77%; initially moderate then increasing, 10%; initially moderate then declining, 5%; initially mild then increasing before again declining, 8%). Factors statistically associated with not having a low symptom trajectory included mental disorder diagnoses, COVID‐19‐related financial distress and social and work impairment, and bushfire exposure. Conclusion: Our longitudinal data enabled identification of distinct symptom trajectories during the first three months of the COVID‐19 pandemic in Australia. Early intervention to ensure that vulnerable people are clinically and socially supported during a pandemic should be a priority.

Philip J Batterham · Alison L Calear · Sonia M McCallum · Alyssa R Morse · Michelle Banfield · Louise M Farrer · Amelia Gulliver · Nicolas Cherbuin · Rachael M Rodney Harris · Yiyun Shou · Amy Dawel

During the coronavirus disease 2019 (COVID‐19) epidemic, the continued presence of viral RNA in the upper airways of infected people has been reported.1 Such persistence does not necessarily signify active infection or that the virus can be transmitted.2 In Queensland, repeat testing for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in people with an initial positive test result was undertaken until June 2020, providing an opportunity to explore patterns of test positivity, apparent rates of clearance of viral RNA, and the extent to which each varied by the age and sex of the infected person. We analysed de‐identified data for people who underwent swab tests for SARS‐CoV‐2 processed in Queensland Health public laboratories between 10 January and 4 June 2020. SARS‐CoV‐2 RNA was detected by polymerase chain reaction (PCR). Testing was initially restricted to people with relevant symptoms who had visited high risk areas (Box 1); from April 2020, anyone with relevant symptoms could be tested. We analysed data on PCR test result, age and sex of the tested person, and postcode of the facility that requested the test; clinical information and reasons for testing were not available. People with positive results who subsequently received two consecutive negative test results at least 24 hours apart were defined as achieving “negative status”. Details of dataset structure, analysis and visualisation methods and code have been reported elsewhere.3,4 Our investigation was exempted from formal ethics review by the Gold Coast Health Human Research Ethics Committee (reference, LNR/2020/QGC/63045). We analysed data for 103 984 swabs from 97 476 people during the 146‐day study period. Time to negative status was examined by Kaplan–Meier analysis. Differences by age (under 65 years, 65 years or over) and sex, with adjustment for both, were calculated by Cox regression. Other associations between variables were quantified as unadjusted odds ratios. The timing of sample collection, particularly of repeat swabs, was not standardised, reflecting the exploratory nature of SARS‐CoV‐2 testing early in the pandemic. The median age of tested people was 41 years (interquartile range [IQR], 27–57 years; range, under one to 105 years); 55 708 (57%) were female. Nine hundred and fifty‐eight people (0.98%) were positive for SARS‐CoV‐2; their median age was 45 years (IQR, 29–61 years; range, under one to 88 years), and 496 were female (52%). Compared with people under 16 years of age, the odds of a positive result were higher for people aged 17–64 years (odds ratio [OR], 5.2; 95% confidence interval [CI], 3.4–8.1) and for those aged 65 years or more (OR, 6.0; 95% CI, 4.0–9.5); the odds of a positive test were lower for females than for males (OR, 0.80; 95% CI, 0.70–0.91). The numbers of people tested and of those positive for SARS‐CoV‐2 both peaked in the second half of March 2020, after which testing rates declined until late April before climbing again, while positivity rates remained low (Box 1). Of the 958 people with positive test results, 317 (33.1%) had repeat tests. Of the 243 people with initial positive results and at least two repeat tests, 147 (60.5%) achieved negative status. The median age of those who achieved negative status was 45 years (IQR, 30–59 years; range 20–84 years); 94 were women, 53 men (OR, 1.7; 95% CI, 1.0–2.9). Of the 243 people who underwent two or more repeat tests, 224 (92.2%) had positive results beyond 10 days and up to 72 days after their initial tests (Box 2). Seven of 147 people who achieved negative status (5%) subsequently had positive test results, including six men. For the 147 positive patients who achieved negative status, median time to clearance was 31 days (IQR, 18–47 days), and was unaffected by sex (women, 30 days; IQR 16–45 days; men: 31 days; IQR 20–49 days; hazard ratio [HR], 0.93; 95% CI, 0.66–1.3). Clearance was more rapid in people under 65 years of age (median, 29 days; IQR, 17–45 days) than in people aged 65 years or more (median, 43 days; IQR, 25–62 days; HR, 1.82; 95% CI, 1.17–2.93) (Box 3). We found that positive PCR test results often persisted for ten or more days after an initial positive result, in one case for 72 days. Such persistence does not indicate continued viral replication.2,5 From 21 March 2020, patients in Queensland, other than workers at high risk, were released from isolation on the basis of their symptoms and illness duration (ie, without further testing), and local transmission declined to zero (Box 1). Our finding of lower infection rates in younger people is consistent with previous reports,6 as is our finding that infection rates were higher for males than females.7 After adjusting for age, the viral clearance rate was similar for males and females. Clearance was greater for people under 65 years of age than for those aged 65 or more, as noted previously.8 This effect may have clinical significance; rates of hospitalisation, admission to intensive care, and death from COVID‐19 are higher among older people. Box 1 – Numbers of SARS‐CoV‐2 tests processed by Queensland Health public laboratories and of people with positive results, 10 January – 4 June 2020, with trend lines and indications for testing* * Repeat tests after first positive result are not included. Test trend line based on a generalised additive model for “all tests”; positive result trend line based on local polynomial regression fitting. Box 2 – Categorical heat map of SARS‐CoV‐2 tests for people with initial positive results who had at least two subsequent tests Box 3 – Kaplan–Meier analyses of virus clearance in 958 people who were initially positive for SARS‐CoV‐2, by age and sex* * Confidence bands generated by Cox proportional hazards regression, with Efron approximation (coxph function in R 3.6.3).

Paulina Stehlik · Kylie Alcorn · Anna Jones · Sanmarie Schlebusch · Andre Wattiaux · David A Henry

Characterising the care requirements of patients with coronavirus disease 2019 (COVID‐19) is essential for resource allocation.1 Knowledge of care needs is based predominantly on experience in regions where health care capacity has been strained, and may not reflect ideal practice.2,3 We therefore examined COVID‐19 testing data for South Australia, the care requirements of patients with confirmed COVID‐19, and the disposition of people with potential COVID‐19 who presented to the designated COVID‐19 hospital for SA, the Royal Adelaide Hospital (RAH), during a period of low COVID‐19 prevalence and limited community transmission (30 January – 26 April 2020). We analysed SA Pathology data on tests for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and other respiratory pathogens, and clinical data from hospital electronic health records (further details: online Supporting Information). The Central Adelaide Local Health Network Human Research Ethics Committee approved the study, and waived the requirement for patient consent (reference, 13091). Of 52 883 people tested in SA for SARS‐CoV‐2, 438 had polymerase chain reaction (PCR)‐confirmed infections (0.8%); their median age was 54 years (interquartile range [IQR], 31–64 years; range, 1–94 years), and 211 were female (48%). The median age of screened people with negative results was 43 years (IQR, 28–60 years; range, 0–104 years), of whom 30 380 were female (58%). Seventeen people with confirmed COVID‐19 (3.9%) and 7588 of those without COVID‐19 (14%) were also positive for another respiratory pathogen (Supporting Information, table 1). There were no cases of COVID‐19 among people in high care nursing facilities or prisons, nor among homeless people; one infection of a health care worker caring for people with COVID‐19 was recorded in Adelaide. The number of patients admitted to the RAH with COVID‐19 broadly paralleled that of new cases in SA, but intensive care unit (ICU) occupancy peaked (6/7 April) and the number of people screened in the RAH emergency department for COVID‐19 declined (from 17 April) after the peak in new cases (21 March) (Box 1). The median time from diagnosis to viral clearance (according to national guidelines4) was 15 days (IQR, 12–19 days); it was lower for people managed in the community (14 days; IQR, 11–17 days) than for those admitted to hospital (17 days; IQR, 13–22 days), and there were no sex‐ or age‐related differences (data not shown). A total of 285 people with confirmed COVID‐19 (227 aged 18–65 years; 58 over 65 years) were managed entirely in the community (Box 2); their age and sex distributions were similar to those of all SARS‐CoV‐2‐positive people (data not shown). Of 18 228 patients who presented to the RAH emergency department, 2327 (12.8%) met screening criteria for potential COVID‐19, of whom 120 (5.2%) proved to be SARS‐CoV‐2‐positive (new diagnoses in 19 people) (Supporting Information, figure). Among people who met the criteria for potential COVID‐19, a larger proportion of people with positive results than of those with negative results arrived by private vehicle (59 [49%] v 797 [36%]), and smaller proportions required resuscitation (one [0.8%] v 72 [3%]) or had conditions deemed imminently life‐threatening (14 [12%]) v 706 [32%]); among people over 65 years, two of 37 SARS‐CoV‐2‐positive people (5%) and 406 of 939 SARS‐CoV‐2‐negative people (43%) required resuscitation or emergency review. Most people with confirmed infections were admitted to the inpatient COVID‐19 unit (90 [75%] v 419 with negative results [19%]), while three SARS‐CoV‐2‐positive (2%) and 79 SARS‐CoV‐2‐negative people (4%) were admitted from the emergency department to the ICU (Supporting Information, table 2). One of 18 228 people who presented to the ED did not meet screening criteria for potential COVID‐19 but subsequently tested positive (screening failure rate, 0.005%). A total of 536 patients were admitted to the inpatient COVID‐19 unit, including 117 who were SARS‐CoV‐2‐positive (22%). The proportion of SARS‐CoV‐2‐positive patients aged 18–65 years was larger than for other patients in the COVID‐19 unit (84 [72%] v 188 patients [45%]); the proportions of women were similar (53 [45%] v 186 patients [44%]). Median length of stay was longer for SARS‐CoV‐2‐positive than for SARS‐CoV‐2‐negative patients over 65 years of age (182 h; IQR, 87–285 h v 96 h; IQR, 48–158 h), but was similar for all patients aged 18–65 years. Six SARS‐CoV‐2‐positive (18%) and five SARS‐CoV‐2‐negative patients over 65 (2%) were transferred from the COVID‐19 unit to the ICU (Supporting Information, table 3). Seventeen patients hospitalised with COVID‐19 (14%) were admitted to the ICU. The median time from hospital to ICU admission was 2.4 days (IQR, 1.8–3.4 days) for the eight patients over 65, and 5.2 days (IQR, 1.0–6.1 days) for the nine aged 18–65 years; the median ICU stay was 17.3 days (IQR, 3.0–29.3 days) for those over 65, and 2.2 days (IQR, 1.6–4.4 days) for those aged 18–65 years. Four patients died (24%), the only COVID‐19‐related deaths in South Australia (overall case fatality, 0.9%; 18–65 years, 0.3%; over 65 years, 3.2%) (Supporting Information, table 4). Over the past 14 years, 15% of RAH patients with viral pneumonia in intensive care died, with a medium length of stay of 6.2 days (18–65 years, 7.4 days; over 65 years, 4.8 days) (unpublished data). More modest, but persistent, prevalence of COVID‐19 is expected to follow the major pandemic wave of 2020. Our data, gathered in an environment of low community transmission and a health care system with considerably greater capacity than demand, reflects the COVID‐19‐related resource burden that might be anticipated as we prepare for living with COVID‐19. Box 1 – New confirmed cases of COVID‐19 in South Australia, numbers of inpatients with COVID‐19 in the Royal Adelaide Hospital, and numbers of people presenting with potential COVID‐19 infection to the Royal Adelaide Hospital emergency department, 30 January – 26 April 2020 COVID‐19 = coronavirus disease 2019. Box 2 – Care requirements of people with confirmed COVID‐19 admitted to the Royal Adelaide Hospital, 30 January – 26 April 2020 COVID‐19 = coronavirus disease 2019; SARS‐CoV‐2 = severe acute respiratory syndrome coronavirus 2. * Includes one patient initially admitted under a non‐COVID‐19 inpatient team. † Fourteen SARS‐CoV‐2‐positive patients admitted under the COVID‐19 inpatient team required transfer to the intensive care unit, 11 of whom returned to the COVID‐19 inpatient team, as did two of three patients admitted to the intensive care unit from the emergency department. These patients are counted in both intensive care unit and COVID‐19 inpatient team numbers. ‡ includes three intensive care unit patients admitted directly from the emergency department then transferred to the inpatient team, and one patient who was still an inpatient at the end of the study.

Daniel Haustead · Dylan J Toh · Benjamin Reddi · Emily Kirkpatrick · Emily Rowe · Pamela Outhwaite · Elizabett Harnack · Michael Cusack · Megan Brooks

People in hotel quarantine need Increased psychological support and other mental health services

Michael Dinh · Owen Hutchings · Kendall Bein · James Shaw · Aditi Raut · Saartje Berendsen Russell · Dianna Jagers · Rajip Thapa · Gregory Fox · James Edwards · Timothy Wand · Warwick Britton

Introduction: Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths both globally and in Australia. Surveillance for HCC in at‐risk populations allows diagnosis at an early stage, when potentially curable. However, most Australians diagnosed with HCC die of the cancer or of liver disease. In the changing landscape of HCC management, unique challenges may lead to clinical practice variation. As a result, there is a need to identify best practice management of HCC in an Australian context. This consensus statement has been developed for health professionals involved in the care of adult patients with HCC in Australia. It is applicable to specialists, general medical practitioners, nurses, health coordinators and hospital administrators. Methods and recommendations: This statement has been developed by specialists in hepatology, radiology, surgery, oncology, palliative care, and primary care, including medical practitioners and nurses. The statement addresses four main areas relevant to HCC management: epidemiology and incidence, diagnosis, treatment, and patient management. A modified Delphi process was used to reach consensus on 31 recommendations. Principal recommendations include the adoption of surveillance strategies, use of multidisciplinary meetings, diagnosis, treatment options and patient management. Changes in management as a result of this statement: This consensus statement will simplify HCC patient management and reduce clinical variation. Ultimately, this should result in better outcomes for patients with HCC.

John S Lubel · Stuart K Roberts · Simone I Strasser · Alexander J Thompson · Jennifer Philip · Mark Goodwin · Stephen Clarke · Darrell HG Crawford · Miriam T Levy · Nick Shackel

Burrell AJC, Pellegrini B, Salimi F, et al. Outcomes for patients with COVID‐19 admitted to Australian intensive care units during the first four months of the pandemic. Med J Aust 2021; 214: 23–30. https://doi.org/10.5694/mja2.50883. In the Results, paragraph 1, the final sentence should read: “The most frequently reported ICU nurse to patient ratios were 1:1 on 1766 of 2270 patient‐days (77.8%) and 2:1 on 171 patient‐days (7.5%).

To the Editor: Doctors’ wellbeing is an important agenda for reducing doctors’ burnout and its consequences. It is often confused with wellbeing related to personal lives that is not controlled by workplaces. My observation is that systems are implementing symbolic solutions, which undermine the efforts of advocacy for system solutions. I see wellness through my experience during teenage years, growing up in the middle of a war. I suffered emotional trauma; more than that, moral injury that was inflicted by the hypocrisy of the system that violated my human rights. Moral injury occurs when we perpetrate, bear witness to, or fail to prevent an act that transgresses our deeply held moral beliefs.1 All I wanted was for someone to stop the war; I was not expecting to be sent to a wellness officer or to wellness and resilience training workshops. In the past 22 years as a doctor, I am seeing the emergence of the term “moral injury” in health care settings and is linked to doctors’ wellbeing.1 I feel that moral injury within health care settings occurs when workers’ rights, expectations of doctors, and the organisational values and purpose are met with contradictions at workplaces.1 The literature is clear that doctors’ wellness is related to the culture and environment of the workplace rather than issues with the individuals’ resilience (Box).2 Of course, training to fine‐tune skills to manage emotionally challenging clinical situations and self‐care is important, but resilience training should not be about how to tolerate situations that cause moral injury. It will be difficult for systems to address workload‐related stress driven by doctors’ own choices. While some of the system’s problems can only be solved through organisational alignment of values and purpose, medical leaders of all levels need to take ownership of the doctors’ wellness agenda. They need to advocate for removing situations that cause moral injury and focus on cultural and structural solutions within their work teams and units, fostering a sense of belonging, cohesion and autonomy among colleagues, promoting self‐care and minimising burnout. This may create psychologically safe and joyful work teams. Box – Examples of contradictions that may cause moral injury at workplaces Expectations Contradictions Accreditation standards call for better workload and fatigue management Vacancies are not filled in a timely manner to manage the workload Front‐line staff are keen to help patients and colleagues Not enough personal protective equipment sourced Clinicians are keen to adopt Choosing Wisely and patient‐centred models Efficiency not rewarded by enhancing clinicians’ capabilities or supporting their initiatives Research as core business of organisations Prohibitive and time‐consuming regulatory processes for research Clinical directors are expected to lead change Clinical directors are not given necessary support or time to drive change Organisational values call for consultation and engagement with staff Decisions are made unilaterally by colleagues and leaders Nurses and doctors ask for help when patients with violent behaviours pose a threat to their safety Nurses and doctors get told to sort it out themselves

Sabe Sabesan

To the Editor: The article by Giuseppin,1 Chair of the Australian Medical Association Council of Rural Doctors, published in MJA InSight+, on ending geographic narcissism, overcoming metro‐based policy making, and instituting health self‐determination by rural practitioners and communities echoes the feedback we have received from health practitioners and consumers attending our workshops throughout northern Australia. The HOT NORTH (Improving Health Outcomes in the Tropical North) program (Box), funded by the National Health and Medical Research Council, aims to address inequitable health coverage across northern Australia through more widespread implementation of locally designed research and practice. Epidemiological and health service data indicate a higher disease burden and risk profile in northern Australia compared with the rest of the country, with health disparity increasing with age and remoteness and Indigenous Australians living in the north having worse health outcomes than the non‐Indigenous population.2 At 15 HOT NORTH forums held over the past 3 years, attended by over 1600 participants in locations from South Hedland to Thursday Island, we provided an opportunity for communities and local health staff to take control over the agenda, presentations and input to discussions. Participation increased, discussions became more interactive, and pride in the achievements of local health practitioners and researchers replaced the deficit data and focus of many previous presentations. The wider benefits of a consultative, locally designed and led health research and capacity‐building program are captured in the recent HOT NORTH impact report.3 While several initiatives have addressed regional and remote health care (eg, the Centre for Research Excellence in Rural and Remote Primary Healthcare, the Advanced Health Research and Translation Centre in Alice Springs, and Centres for Innovation in Regional Health in north Queensland and in regional New South Wales), we agree with Giuseppin that fundamental shifts in the rusted‐on core–periphery relationships are required to address the inequity of health coverage across Australia. However, in Australia (and its universities), this requires recognition of the pervasive dogma of “winner‐takes‐all” urbanism of “superstar cities”4 with their “creative class”,5 which arguably militates against an appetite and capacity for sustainably reshaping the service delivery and research landscape in response to the remoteness, cultures, power relations, social ties and other dynamics in rural and remote settings. Box – HOT NORTH capacity building, collaborations and regional engagement activities 2017–2019

Kevin Williams · Sean Rung · Bart J Currie

To the Editor: Te Marvelde and colleagues1 report that patients with prostate cancer diagnosed in the private health system in Victoria are more likely to undergo radical treatment than patients in the public system. In particular, they report that patients in the private system undergo surgery more often than those in the public system (44% v 28%; odds ratio, 2.28; 95% CI, 2.13–2.44). The authors do not provide an explanation for this, but the inference is that private patients may be more likely to be overtreated in private hospitals. We respectfully point out two more plausible explanations. First, prostate‐specific antigen (PSA), local clinical staging, and cancer grading form the three essential parameters that define the risk groupings of low, intermediate and high risk prostate cancer. This risk categorisation forms the basis upon which evidence‐based clinical guidelines recommend treatment options, which unfortunately has not been accounted for in the article by te Marvelde et al. The suggestion that cancer grade alone is sufficient to inform on treatment choice is without evidence and is a limitation of this article. Much more granular risk stratification is already available to describe patterns of care of prostate cancer in Victoria from the Prostate Cancer Outcomes Registry (PCOR‐Vic), and these data have already reported that patients diagnosed in the private system in Victoria are actually less likely to undergo treatment than those diagnosed in the public system.2 The PCOR‐Vic data are in direct contradiction to this article, but are more robust as they are based on a granular registry across both public and private health systems, with many publications to validate patterns of care in Victoria.3,4,5 Second, patients in the public system are much less likely to access minimally invasive surgery than patients in the private system, which is likely also a deterrent to surgery in the public system. In 2019, 88% of prostatectomies performed in the private sector were performed using a robotic approach, compared with only 28% in the public sector.6 This ongoing inequity likely leads to underutilisation of surgery for patients in the public system. There is also a failure to contextualise major studies mentioned in the discussion to support the authors’ interpretation of their data. For example, the ProTect study is cited to highlight the lack of differences between treatment options for prostate cancer. This study was conceived and commenced well before active surveillance became accepted as the most appropriate treatment for low risk prostate cancer, where 77% of participants were categorised as such. Rates of utilisation of active surveillance in Australia, including in the private sector, are among the highest in the world and are not accounted for by the authors. In addition, the reference to 40% of overdiagnosis rates based on data collected from 1982 to 2012 bears no reflection on current practice.7 Te Marvelde and colleagues have also failed to consider the recent evidence that magnetic resonance imaging reduces the rates of overdiagnosis of low risk prostate cancer while improving the detection of clinically significant cancers.8 The authors assert that treatment of people with cancer should be high quality and evidence‐based. Nobody would disagree with this. Indeed, let us cite high quality randomised controlled trials to support the interpretation of the data we publish, but appropriate contextualisation is everything.

Henry H Woo · Declan G Murphy

In reply

Luc te Marvelde · Roger L Milne · Ian E Haines

To the Editor: In the retrospective study by te Marvelde and colleagues,1 the proportions of men in public and private health services receiving radical prostatectomy and curative external beam radiation therapy were examined in a multivariable logistic regression analysis. However, only age, International Society of Urological Pathology (ISUP) tumour grade, and comorbidity were studied. Prostate‐specific antigen (PSA) level and T stage are two of the strongest determinants of choice of treatment modality in clinical practice and have not been considered or discussed by the authors. We consider this to be a major flaw in this study and a failure of the peer‐review process to highlight this deficiency, which has a significant impact on the results and subsequent conclusions reached by the authors. Furthermore, patient comorbidities have not been adequately accounted for. The authors identify comorbidity as a factor influencing treatment, but they fail to assess and account for this variable in a reliable way. Victorian Admitted Episodes Dataset (VAED) data for the year preceding the prostate cancer diagnosis and up to 30 days after diagnosis were assessed to identify comorbid conditions other than cancer according to the Charlson Comorbidity Index, categorised as 0 or at least 1. This variable provided little discriminatory power (3% v 6%), and yet it was the only surrogate variable that accounted for comorbidity in the study’s key multivariable analyses. Additionally, the odds ratios for this variable in these analyses were not reported. It should be noted that 38% of the study population were men older than 70 years, but only 3.8% scored 1 or more on the VAED‐derived Charlson Comorbidity Index. We believe that the method used in this study to account for comorbidity is not adequately robust to provide an accurate picture of the patients’ general health status. The authors also cite the ProTect trial2 to suggest no major differences between active treatment options exist; however, they did not identify the vast differences in the disease characteristics of men in the ProTect trial compared with those included in their study (77% ISUP 1 and 2% ISUP 4/5 v 35% ISUP 1 and 18% ISUP 4/5). Moreover, te Marvelde and colleagues did not address clinical outcomes and have not presented evidence that the variation in treatment modalities between public and private services has had a negative impact on the final clinical outcome. Outcomes data can be helpful in identifying systematic shortcomings, inequities and barriers to just health care, but the authors missed the opportunity to highlight these issues. They concluded that the treatment of people with cancer should be consistent, safe, of high quality and evidence‐based, but did not provide evidence that the current practice is to the contrary.

Stephen Mark · Prem Rashid · Peter Heathcote · Kamran Zargar Shoshtari

A family history of activism and early death have brought Dr Summer May Finlay to the pointy end of working for better health outcomes for First Nations peoples …

Cate Swannell