Perspectives

A multilayered action plan is needed for a substantial, timely and sustained investment in dementia prevention In 2012, the Australian Government declared dementia as the ninth National Health Priority Area. Eight years later, dementia is the greatest cause of disability in Australians aged over 65 years, the second leading cause of mortality, and the highest in women.1 Today, more than 459 000 Australians live with dementia, and this number is expected to exceed one million by 2056.2 The societal, economic and health care burden of dementia is unprecedented, with significant impacts on individuals, caregivers and families. In addition to therapeutic advances, improved and timely diagnosis and coordinated person‐centred care, dementia prevention and risk‐factor management are our best chance to make a difference.3 How do we tackle dementia prevention cost‐effectively in the post‐pandemic era? Between 40% and 48% of dementia risk is considered modifiable.4,5 In Australia, the population‐attributable risk of dementia risk factors, in descending order, are physical inactivity (17.9%), mid‐life obesity (17.0%), low educational attainment in early life (14.7%), mid‐life hypertension (13.7%), depression (8.0%), smoking (4.3%), and diabetes mellitus (2.4%).5 In addition, the 2020 Lancet Commission report on dementia prevention, intervention and care4 includes hearing loss, traumatic brain injury, alcohol use, social isolation, and air pollution as risk factors. Emerging research suggests that a suboptimal diet,6 cognitive inactivity7 and sleep–wake disturbance8 also influence the modifiable dementia risk. We urge substantial, timely, and sustained investment in dementia prevention via a multilayered action plan with eight recommendations (Box). 1. Create public health and clinical practice guidelines for dementia prevention across the lifespan for the Australian setting. In 2019, the World Health Organization released dementia risk‐reduction guidelines stating that “the existence of potentially modifiable risk factors means that prevention of dementia is possible through a public health approach”.9 These guidelines focus on “interventions that delay or slow cognitive decline or dementia,” with the strongest recommendations being applied to addressing physical inactivity, tobacco cessation, hypertension and diabetes mellitus.9 Yet, in Australia, we do not have dementia prevention guidelines, with the clinical practice guidelines for dementia from the National Health and Medical Research Council (NHMRC) and the Australian Cognitive Decline Partnership Centre (CDPC) focusing on diagnosis and management.10 Since then, Australia has made significant progress by including dementia prevention guidelines for general practitioners in the CDPC’s Care guide for general practice.11 We recommend extending guidelines beyond primary care, including secondary prevention in memory clinics, prioritising educational attainment in early life, and developing occupational and environmental policy to reduce hearing loss, traumatic brain injury, and air pollution. 2. Equip and resource primary care providers to be the clinical spearheads for dementia prevention throughout life. Primary care is the usual entry point and key coordinator of care within the health care system and is well positioned to spearhead dementia prevention throughout life. The Medicare Benefits Schedule should increase focus on dementia prevention, enabling primary care, specialists, and allied health professionals more time, resources and team care. This could be achieved through new Medicare Benefits Schedule item numbers and modification of existing items, such as the 45–49‐year‐old health check for individuals at risk of chronic conditions. Private health insurers could complement this by expanding the scope of preventive health services to target dementia risk factors and rewarding individuals who participate with lower insurance premiums or greater rebates for health services. 3. Support multidisciplinary memory clinics and specialists to implement secondary prevention programs for those at high risk. Memory clinics and specialists should focus on secondary prevention for people at higher risk, such as those with mild cognitive impairment.12 The Australian Dementia Network (ADNeT) aims to unite and build the network of memory clinics, establish practice guidelines, harmonise assessments, and implement dementia prevention tools and strategies. ADNeT will also facilitate access to clinical trials, improve diagnostic accuracy to aid secondary prevention approaches and introduce a Clinical Quality Registry. 4. Fund research for evidence‐based interventions for modifiable risk factors for dementia across the life cycle to reduce the evidence‐to‐practice gap. While there has been increasing funding for dementia prevention research and the establishment of the International Research Network on Dementia Prevention as part of the Australian Government’s commitment to the World Dementia Council,13 urgent funding is still required to address critical evidence‐to‐practice gaps. The current evidence base includes observational studies and intervention trials that have generally focused on cognitive outcomes, rather than dementia incidence, given the long time frames needed. We need to strengthen the evidence base on managing risk factors across different phases of the lifespan, such as the most effective doses and forms of interventions in large‐scale trials. Rigorously evaluated multidomain prevention trials that simultaneously target multiple risk factors may present the best value for money if shown to be effective and sustainable, particularly as they address risk factors that have an established evidence base for preventing other conditions. A number of these trials are already underway in Australia. 5. Implement findings from dementia risk reduction and implementation research through translation into health promotion programs. Implementation research will be key to translating the increasing evidence base for dementia risk reduction interventions into effective health promotion programs. The science of behaviour change will be critical given the evidence‐to‐practice gap. This emphasises the importance of co‐design to empower individuals to modify their risk. For health professionals, education and training on dementia risk factors and skills in motivational interviewing and behaviour change principles should be prioritised. 6. Strengthen dementia prevention public health campaigns embracing Australians’ diversity, particularly Aboriginal and Torres Strait Islander Australians. Australian‐specific dementia prevention guidelines that inform public health campaigns need to appeal to all Australians, embracing geographic, socio‐economic, cultural, linguistic, social, ethnic, age, gender, and sexual diversity. This is particularly important for Aboriginal and Torres Strait Islander people, for whom dementia prevalence is three to five times higher than the general population. These measures need to be equitable and not disadvantage vulnerable groups that may already have reduced access to resources. 7. Resource and coordinate a whole‐of-community approach including government, public and private health care, community services and education sectors to operationalise guidelines and multifaceted dementia prevention programs throughout life. Dementia prevention is everyone’s business. Successful public health and disease prevention campaigns have required a coordinated effort across all levels of the health sector, government, policy makers, non‐government organisations, research, education, industry and the community. Yet, many Australians do not believe that dementia risk can be reduced.14 Dementia prevention is complex due to stigma, literacy, and multifactor risks throughout life. The success of widely known public health campaigns in Australia (eg, Quit for Life and Slip, Slop, Slap) is attributable to their focus on behaviour change using a single behaviour or risk factor, informed by knowledge of barriers and enablers. The Dementia Australia Your Brain Matters campaign was targeted at raising public awareness for dementia, but this was not sustained beyond the funding period (2012–2015). The report from the Lancet Commission identifies educational attainment in early life as an impactful risk factor4 and this should be prioritised given its broader socio‐economic benefits. There are specific mid‐life (hearing loss, traumatic brain injury, hypertension, alcohol intake, obesity) and late‐life factors (smoking, depression, social isolation, physical inactivity, diabetes, air pollution) which offer opportunities for risk reduction across the lifespan.4 From a practical perspective, as many dementia risk factors are shared with other chronic conditions, particularly vascular risk factors, these may present the best opportunity for greatest impact. 8. Mobilise peak health advocacy bodies to promote and coordinate public health messaging on dementia risk factors that cut across chronic conditions. How do we ensure value for money and sustainability of dementia prevention public health campaigns? A unified approach with clear messaging communicated through media, community organisations, and health professionals promoting shared responsibility is crucial. An initial focus on risk factors with the highest population‐attributable risk (physical inactivity and midlife obesity) is recommended to improve wellbeing and reduce risk for multiple chronic conditions. They are also ideal for integrated programs given their overlap with vascular risk factors and successful campaigns (eg, smoking cessation). A key step is the coordination and pooling of resources between peak advocacy bodies such as Dementia Australia, Diabetes Australia, and the Heart Foundation, with clear messaging focusing on single risk factors that have multiple benefits. In clinical practice, this facilitates approaches that are tailored to an individual’s experiences and motivation. For example, motivation for increasing physical activity for one individual may arise from receiving a result of impaired glucose tolerance, while for another it may be the experience of having a family member living with heart disease or dementia. Australia has excellent health infrastructure and an international reputation for dementia prevention due to our depth of clinical, research, and knowledge translation expertise. If we are committed to achieving the ambitious targets of reduced dementia prevalence and incidence, we must shine a spotlight on dementia prevention across all levels of society. To achieve this, the National Health and Medical Research Council National Institute for Dementia Research (NNIDR) Dementia Prevention Special Interest Group proposes this Dementia Prevention Action Plan for Australia. It is time for a call to action in the fight against dementia: dementia prevention needs to be the next international public health area of focus, with Australia playing a leading role. Box – Dementia Prevention Action Plan

For the NHMRC National Institute for Dementia Research, Dementia Prevention Special Interest Group*

The current mechanism for setting air quality thresholds in Australia does not adequately protect community health The current air quality framework to mitigate against the health effects of exposure to air pollution within Australia relies on national environmental protection standards — set out under the National Environmental Protection (Ambient Air Quality) Measure (the ambient air quality NEPM) — and the jurisdictional requirements for monitoring and reporting exceedances.1,2 The ambient air quality NEPM sets reportable limits for key criteria air pollutants.1 Criteria air pollutants are those that are legislated internationally as measures of air quality and include particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide, ozone, sulfur dioxide (SO2) and lead1 (Box). Air toxics are non‐criteria air pollutants that are considered to pose a hazard to human health.7 Air toxics are legislated under a separate NEPM which has the goal of generating baseline data for later development of standards for five compounds: benzene, benzo(a)pyrene, formaldehyde, toluene and xylenes.7 The air toxics standards, based on the gathered baseline data, were due to be set in 20127 but are yet to be reviewed. In 2011, the National Environment Protection Council published guidelines for setting air quality standards.8 These guidelines outline a method that balances risk assessment (health effects based on the exposure–response relationship) with the costs of abatement strategies to achieve the required targets. The process for updating the ambient air quality NEPM based on new evidence about the health effects of criteria air pollutants is slow. Since the publication of these guidelines,8 there has only been one formal change to the NEPM, which was approved in 2016.1 This variation focused on modifications to the measures related to PM10 and PM2.5 (PM ≤ 10 µm and ≤ 2.5 µm in aerodynamic diameter, respectively), as it was thought that the potential benefits to human health, and the available abatement strategies, were greater than those for other criteria pollutants.9 The variation included, among other measures, the introduction of an annual average for PM10 and progress towards the introduction of a PM2.5 standard. The national standards for gaseous pollutants are currently under review.10 Moreover, the catastrophic 2019–20 bushfires have highlighted the importance of air quality for many Australians. It is therefore timely to consider the current ambient air quality standards and whether they are fit for purpose. We focus on the criteria air pollutants as these are the only air pollutants currently covered by legislation that attempts to enforce maximum exposure limits. Criteria air pollutants — are there safe limits? Particulate pollutants Air pollution is composed of a complex mixture of solid, liquid and gaseous molecules. Airborne PM is comprised of solid and liquid particles suspended in the air that vary in size and chemical composition. PM is generated from a range of sources including combustion, plant materials, sea salt and earth‐derived inorganic compounds. PM10 is small enough to bypass the upper airways and lodge in the conducting airways, but is usually too large to reach the alveoli. Acute exposure to PM10 is associated with hospitalisations and mortality for cardiorespiratory conditions,11 while long term exposure is linked to chronic cardiorespiratory conditions and metabolic disorders.12 No safe threshold for PM10 exposure has been identified.5,6 PM2.5 can penetrate deeper into the lungs and is one of the leading causes of global mortality and morbidity.13 PM2.5 has been linked to cardiovascular disease, respiratory disease, pre‐term birth, metabolic disorders and neurological health problems.4 Like PM10, there is no evidence for a safe threshold for PM2.5 exposure. This has been highlighted in Australian studies, where PM2.5 is typically low, showing associations between exposure to PM2.5 and mortality.2 Consistent with this, there is evidence that the exposure–response relationship is steeper at lower PM2.5 concentrations.14 Gaseous pollutants Of the gaseous air pollutants, data are most extensive for NO2, a combustion by‐product. Studies on the health effects of low concentrations of NO2 have shown associations between NO2 and childhood pneumonia and otitis media15 and impaired lung function.16 While data on the exposure–response relationship suggest that there is an effect threshold,2 it is three to five times lower than the current NO2 standard (Box).1 Data on the magnitude of the health effects of SO2, a combustion product primarily related to sulfur‐containing fuels, are less extensive. While there is an established relationship between exposure to SO2 and cardiorespiratory mortality,3 data are not robust enough to determine whether there is a health effect threshold. Similarly, while carbon monoxide has a range of physiological effects on the body,17 the co‐existence of carbon monoxide with other criteria pollutants makes it difficult to disentangle the contribution of this pollutant to the health effects of pollution in general. Acute exposure to ozone, a by‐product of interactions between combustion emissions and sunlight, is strongly linked to respiratory hospitalisations; however, consensus regarding a threshold for these health effects is contentious.18 Lead pollutants The teratogenic and neurological health effects of lead are well established and there is no safe level of exposure.4 While overall community exposure to lead has decreased with the elimination of tetraethyl lead from fuels, there are still some communities in Australia exposed to anthropogenic sources of lead. Summary Collectively, there is sufficient evidence to conclude that there is no safe threshold for exposure to PM10, PM2.5 or lead. For NO2, there is a threshold, but the current NEPM standard is well above this level.1 On this basis, the current standards are not sufficient to adequately protect the health of the Australian community (Box). Principles for setting air quality guidelines in Australia In Australia, the background concentrations of air pollution in most areas are relatively low compared with other countries around the world.19 To a certain extent, it is likely that this observation influences current policies regarding ambient air quality standard setting, which aim to identify a threshold concentration where the health risks are balanced against the feasibility of achieving these thresholds. Unfortunately, this puts regulators in a position of balancing the costs of expanding infrastructure against the benefits to human health, as the existing monitoring network, which assesses adherence to the standards, does not have sufficient coverage to generate data with enough accuracy to monitor exceedances.20 The adverse health effects of the NEPM criteria pollutants are well established. For many (eg, PM2.5), there is sufficient evidence, both from our review and expert consensus, that it is not possible to set a threshold as health effects can be detected even at low exposure doses, whereas for others, the threshold is well below the current NEPM standard (eg, NO2). The current approach to regulation of air pollution implies a causal model that is inconsistent with the available evidence. It provides no incentive for reducing exposure and allows increases in exposure to harmful pollutants, as long as the levels remain below the thresholds. This provides only partial health protection and adversely impacts community perceptions by implying that the current standards represent a “safe” level of exposure. It also relies on an accurate and comprehensive network for monitoring exceedances, which is lacking in many Australian jurisdictions, and appropriate mechanisms to ensure implementation of the measures, including appropriate penalties if standards are not met. We believe this approach must be replaced by regulation focused on harm minimisation using the principle of continual improvement; similar to the approach recently adopted by the European Union where targets for PM2.5 are set for percentage reductions in levels within a given time frame.21 This would drive better practice in air quality management and encourage implementation of strategies that improve ambient air quality and health for all Australians by reducing existing levels of exposure and discouraging new increases in exposure, regardless of the current levels. The concept of “no safe limit” was raised in independent commissioned reports20,22 provided before the most recent NEPM variation23 to guide the decision‐making process. The concept of an exposure reduction framework was also raised at that time and included in the impact statement prepared for the National Environment Protection Council outlining the case for the NEPM variation.9 It was argued that the introduction of an exposure reduction framework was necessary because there was no evidence for a threshold for the health effects of exposure to PM and, in contrast to the existing NEPM approach, it would maximise the community level health benefits.20 Unfortunately, it seems that this approach was dismissed because of concerns regarding the ability to monitor overall reductions in PM, due to inadequate monitoring infrastructure across the country, and whether reductions could actually be achieved.23 This seems to ignore the intent of such a framework — it is not about setting targets, it is about driving behaviour and promoting best practice. Reassuringly, the most recent impact statement prepared for the National Environment Protection Council for the revision of the standards for gaseous pollutants24 recommends changing the NEPM to make reference to minimising the health effects of exposures and “incorporation of exposure–reduction targets”. We endorse this approach. However, the recommended measures for gaseous pollutants still seem to rely on specifying a standard in the future,25 albeit a lower one, as part of an exposure–reduction framework, rather than proposing goals for continual reduction. In the absence of a mechanism to promote continual improvement and best practice by regulators and industry, we are failing to adequately protect the Australian community from the health impacts of air pollution. Box – Current National Environmental Protection Measures (NEPMs) for criteria air pollutants,1 the current evidence for the dose threshold for detectable health effects in humans, and whether the NEPMs are above these thresholds Pollutant Average maximum concentration Measurement period Allowable exceedances Dose threshold for health effects NEPM above health effect threshold Carbon monoxide 9 ppm 8 hours 1 day/year Unknown NA Nitrogen dioxide 0.12 ppm 1 hour 1 day/year Unknown NA 0.03 ppm 1 year None ~ 6–11 ppb2 Yes Ozone 0.10 ppm 1 hour 1 day/year Unknown NA 0.08 ppm 4 hours 1 day/year Unknown NA Sulfur dioxide 0.20 ppm 1 hour 1 day/year 0.2–0.4 ppm3 No 0.08 ppm 1 day 1 day/year Unknown NA 0.02 ppm 1 year None Unknown NA Lead 0.5 µg/m3 1 year None None4 Yes PM10 50 µg/m3 1 day None None5,6 Yes 25 µg/m3 1 year None None5,6 Yes PM2.5 25 µg/m3 1 day None None2 Yes 8 µg/m3 1 year None None2 Yes NA = not applicable; PM10 and PM2.5 = particulate matter ≤ 10 µm and ≤ 2.5 µm in aerodynamic diameter, respectively.

Graeme R Zosky · Stephen Vander Hoorn · Michael J Abramson · Sophie Dwyer · Donna Green · Jane Heyworth · Bin B Jalaludin · Jennifer McCrindle-Fuchs · Rachel Tham · Guy B Marks

Challenges with data infrastructure are affecting medical workforce research and access to medical care Access to high quality medical care can save lives and help reduce the consequences of the growing burden of chronic disease. However, the delivery of this care relies on a well trained health and medical workforce organised to optimally respond to community need, working in supportive work environments within models of care that are fit for purpose, with minimal geographic or financial barriers to access for all communities. There has been a long term need in Australia for coordinated, evidence-informed workforce policies. However, for many years the development of the medical workforce has been shaped by self‐regulation and market forces. Short term and uncoordinated workforce planning has generated cycles of contraction and expansion of training places, sporadic regulation, and recent policy dilemmas.1,2 Most recently, a dramatic increase in numbers of graduates from Australian medical schools has occurred in the absence of clear plans as to how to use these additional doctors to optimally meet community need. Early data suggest that flooding the market with more graduates has not addressed persistent rural shortages, with insufficient numbers willing or able to navigate a career pathway to work in areas of need.3,4 Oversupply continues to be an issue in some specialties (eg, emergency medicine or cardiothoracic surgery) while shortages persist in others such as general practice and psychiatry.5 Over‐reliance on international medical graduates continues in many rural communities,1 while the fierce competition for accredited training places in some specialties leaves many junior doctors caught in the middle.6 Furthermore, Australian doctors are increasingly reporting burnout and mental health problems,7 with significant negative effects on productivity and patient safety.8 With these problems seeming to defy solutions,9 it is not surprising that there have been calls to add the work–life balance of clinicians to the Institute for Healthcare Improvement’s set of principles to guide optimising health system performance (optimal patient experience, improved population health and reducing costs).10 In light of these issues, the development of Australia’s new National Medical Workforce Strategy (NMWS) scoping framework and consultation process for the final strategy is welcome. The NMWS is being designed to frame the development and coordination of national medical workforce policies to address our pervasive workforce challenges: geographic maldistribution; specialty over‐ and undersupply; the balance of generalists and specialists; Indigenous and culturally safe workplaces; doctor work readiness; and changing models of care.11 One of the six principles of the NMWS is to “[a]pply an evidence‐based approach wherever possible, drawing on data and information from all stakeholders”.11 Data on the medical workforce Achieving an evidence‐based approach to workforce policy requires more high quality longitudinal and linkable data that is both broad across different doctor groups and rich in doctor characteristics, compared with what is currently available (Box). Institutional bias, fragmentation, inconsistent definitions and restricted access provide substantial barriers to our ability to use those data for the social good. Few available sources offer a long term, holistic and objective view of the medical workforce: professional training bodies can only use data sourced from relatively brief periods of postgraduate training; the Department of Health relies on Medicare billing data and raw counts of medical practitioners through the Australian Health Practitioner Regulation Agency; and the states are limited to poor data on salaried, generally hospital‐based practitioners. Data that are made available to researchers are overly aggregated, especially geographically, often preventing useful evidence from emerging about medical workforce behaviours, training outcomes, career choices and treatment patterns. Many sources remain closely guarded by training and service providers and governments, such as surveys regularly completed by doctors on registration with the Australian Health Practitioner Regulation Agency (including the new national medical training survey12) or with individual colleges. Where data are controlled by individual agencies, there is minimal potential for multipurpose use and no process for linkage to other sources. Hence, it is impossible to understand and track career pathways of doctors even though these are a key element of policy. The analysis of workforce data to generate evidence from these multiple sources has been relatively unsophisticated and preoccupied with the simple modelling of supply and demand — ignoring how practitioner behaviours, and the drivers of those behaviours, influence workforce numbers and practitioner quality. Although these data can be used to count and describe trends, they mostly cannot be used to understand why decisions are being made and how services are driven, which are essential for designing policy. The Australian community deserves a broader understanding as to how different policies and programs are addressing their needs. Lack of this understanding has been a major contributor to the decisions that have led to the current situation of workforce oversupply.16 Neither are health workforce data linked to patient‐level data — a factor overlooked in the NMWS scoping framework — that is, data on inputs are not linked to data on activities, outputs and health outcomes, making it impossible to determine how workforce and policy changes affect community needs and population health. Any policies aimed at the medical workforce should at least examine their effects on patients. Finally, the availability of administrative medical workforce data to researchers is at an all‐time low. There was a reduction in funding of the Medical Schools Outcomes Database in 2015 and the withdrawal of funding (from 2016) for the Australian Institute of Health and Welfare to produce health workforce statistics. The Bettering the Evaluation and Care of Health (BEACH) study14 was also discontinued as the only data on the clinical activities of general practitioners. Adding to the challenge, the internationally unique Medicine in Australia: Balancing Employment and Life (MABEL) panel survey of 9–10 000 doctors per year ceased in 2019 after 11 annual waves of data collection.13 Moreover, researchers skilled in using health workforce data will be difficult to sustain without addressing the availability of data, and this expertise will soon dissipate, adding to severe reductions of health workforce analytical staff at the Commonwealth level when Health Workforce Australia ceased in 2014. It is notable that the new National Health Information Strategy makes no mention of health workforce data.17 Despite its ongoing reliance on competitive grant funding, MABEL data have played a key role in national medical workforce policy over the past 11 years. It was a World Bank exemplar of health workforce data collection internationally,18 and continues to guide the distribution of over $1 billion funding to regional health care through its use in the design of the Modified Monash Model (used to classify which geographical areas are eligible to receive increased funding), as well as supporting the design of rural health workforce programs. Unlike other datasets (Box), MABEL data transcended traditional divides of salaried and private practice, different doctor types, career stages and career trajectories as the basis for supporting policy and program decision making at a national scale. The future for medical workforce research The medical workforce represents the backbone of the health care system and a major public investment, yet despite the large gap between supply and community need, the scope of available data does not support evidence-informed decision making. While existing administrative and registration minimum data support national medical workforce planning, they are unable to give insights into doctors’ career and clinical decisions. With the pressures on the health care system and medical workforce at an all‐time high, we believe that the Australian community deserves better insights into how different medical workforce policies and programs are promoting access to equitable, high quality care. We need to know more about the doctors being produced from long and expensive taxpayer‐funded training programs, as well as why they choose disciplines, practice locations and practice patterns. Moreover, there is a growing awareness of the importance of maintaining the health and wellbeing of this workforce, but available national data to underpin key policies to prevent poor mental health are missing. We propose that any reforms to the Australian health care workforce must be informed by robust evidence. The collection and availability of this evidence needs to be at the forefront of policy and planning, embedded within objectives of key national strategies such as the NMWS and National Health Information Strategy. Future medical workforce data strategies need to be institutionally neutral, guided by a research strategy including agreed priority research questions with resources to conduct the research, and underpinned by openness and data sharing. Healthy national medical workforce data are fundamental to achieving healthy doctors and communities. Box – Available national datasets on the medical workforce* table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Data source Unit record data available to external researchers Unique identifier to enable linkage over time Data linked to patients Rich data on doctor characteristics Doctors grouped by organisation (practice, hospital) Doctor group Medicare provider file/MBS With consent Yes Yes No No Private practice Medical college surveys No Yes (but some surveys anonymous) No No No Vocational trainees and Fellows National medical training survey12 No No (anonymous) No No No Pre‐vocational and vocational MABEL13 With consent Yes No Yes Yes All medical practitioners BEACH14 No NA (random sample of GPs each year) Yes Yes Yes GPs National Health Workforce Dataset15 AHPRA registration data No Yes No No No All medical practitioners AHPRA registration survey No (table builder available) No No No No All medical practitioners Medical Education and Training dataset No No No No No Pre‐vocational and vocational BEACH = Bettering the Evaluation and Care of Health; GP = general practitioner; MABEL = Medicine in Australia: Balancing Employment and Life; MBS = Medicare Benefits Schedule; NA = not applicable. * States and territories also have their own data collections for the public hospital workforce, but these vary in what is collected and are not available to external researchers. Many hospitals in recent years also conduct surveys of health and wellbeing. Many clinical registries, epidemiological datasets, hospital separation data, and electronic medical record data focus on patients and do not include doctor identifiers or characteristics.

Grant M Russell · Matthew R McGrail · Belinda O’Sullivan · Anthony Scott

We must consider how we can support communities hosting vaccine efficacy trials

George S Heriot · Euzebiusz Jamrozik

Technology is changing the way that people with type 1 diabetes are monitoring and managing their blood glucose levels Diabetes technologies have transformed management options in type 1 diabetes. The most notable innovations include the commercialisation of insulin pumps, advancements in glucose monitoring and the capacity for these technologies to interact. New technologies offer enhanced flexibility in insulin delivery and opportunities to improve glucose levels and enhance quality of life. Recognising these benefits, the uptake of advanced technologies in Australians with type 1 diabetes has increased. In 2018–2019, 41% of children and 26% of adults attending hospital diabetes clinics managed their type 1 diabetes with insulin pumps, and 55% of children and 13% of adults newly commenced continuous glucose monitoring (CGM).1 In this article, we provide a guide to current diabetes technologies available in Australia, describe their benefits and discuss important factors in assessing an individual’s suitability. Continuous glucose monitoring Accurate and accessible glucose monitoring is key to effective diabetes management. Finger‐prick testing of capillary blood for self‐monitoring of blood glucose (SMBG) became commercially available in the 1980s, and CGM since 1999. CGM is distinguished from SMBG by the measurement of glucose concentration within interstitial fluid by a small subcutaneous glucose‐sensing electrode. Data are transmitted to a receiving device (eg, insulin pump or smartphone) and converted into a continuous graphic display. Interstitial glucose concentrations correlate with plasma glucose, albeit with an average 7–8 minute time lag for equilibration of glucose between blood and the interstitial compartment. This delay is exaggerated at times of rapidly changing glucose. CGM systems come in different forms, with the main distinguishing features being the capacity to alert users and/or carers to high and/or low blood glucose levels set to individual preference. Various systems are available (Box 1).2,3 Product selection depends on the desirability of alarm functions, ease of sensor application, need for finger‐prick calibration, cost, and connectivity of the device to existing technologies (insulin pumps, Apple v Android systems). Modern CGM systems are reliable. Their performance is assessed by the mean absolute relative difference, an accuracy metric expressed as the percentage difference relative to a reference glucose concentration. CGM devices with a mean absolute relative difference < 10% are appropriate for treatment decisions.2,4 To optimise sensor performance, SMBG testing is still required to calibrate a number of real‐time CGM devices, yet devices are increasingly reliable such that newer factory calibrated devices no longer require user calibrations5 — a welcome feature for convenience and reduced finger‐prick burden. CGM offers several benefits to users, including on‐demand glucose testing, trend arrows, alarm functions and easy detection of out‐of‐range glucose levels. For clinicians, CGM offers additional data beyond glycated haemoglobin (HbA1c) measurements. HbA1c measurements are point estimates of haemoglobin glycation over 3 months, whereas CGM displays day‐to‐day glucose variability that often challenges people with type 1 diabetes. Consensus statements have attempted to harmonise the various commercially available CGM systems into a common reporting method to aid comparison between devices and also provide clinicians and users with more clinically meaningful data and targets (Box 2).2,6,7 Clinical trials provide evidence that CGM use may improve glycaemia in type 1 diabetes. A recent meta‐analysis of 15 randomised controlled trials comparing CGM with standard care (usually SMBG) in people with either type 1 or type 2 diabetes over 12–36 weeks found that CGM was associated with a slight reduction in HbA1c levels (weighted mean difference, − 0.17%), and increased time in range (TIR; 71 minutes/day).8 Added up over a year the benefit equates to an additional 18 days of TIR. The effect on TIR was independent of diabetes type, or method of insulin delivery (insulin pump v needle injections). Overall, studies favour CGM to improve glucose variability (optimal coefficient of variation in diabetes management, 34%; coefficient of variation reduced by 3.0–6.7%) and reduce hypoglycaemia (0.4–1.2 hours reduction in time spent with glucose levels < 3.9 mmol/L) compared with SMBG.2 The benefits of CGM and flash glucose monitoring for type 1 diabetes management have been recognised by the Australian Government, which first pledged $54 million in 2016 to fully subsidise CGM in people under 21 years of age. In 2019, a further $100 million in subsidies was added. Eligibility criteria were again expanded in March 2020 (Box 3).9 Insulin pumps An insulin pump delivers short‐acting insulin continuously via a cannula self‐inserted into subcutaneous tissue. In the 1970s, the first insulin pumps were large and bulky and delivered a single basal rate of insulin. Modern pumps are more discrete, the size of a pager. An insulin pump set‐up includes two major components (Box 4): Insulin pump — case with display, battery and an insulin reservoir connected to a plunger that controls the passage of insulin into the line tubing. The insulin pump is programmed to deliver continuous quick‐acting insulin in equal aliquots (0.01–0.025 mL) across an hour depending on the pre‐set rate to replicate basal insulin. Programmed rates can be customised to vary across a 24‐hour period, distinguishing delivery from long‐acting insulin delivered at an inflexible basal rate. The user must initiate bolus doses for meals or for correction of an elevated glucose reading, but pre‐programmed settings provide dose calculations (insulin‐to-carbohydrate ratio for meals, and insulin sensitivity factor for correction doses). Line tubing and infusion set — up to 60 cm of thin plastic tubing connects the insulin reservoir to a subcutaneous teflon cannula (tubeless insulin patch pumps with variable hourly rates are not currently available in Australia). Potential benefits of insulin pumps include: ► Flexibility in dosing — useful for extreme insulin sensitivity, erratic schedules, more convenient and frequent bolusing, to accommodate exercise, or to manage the dawn phenomenon (increased insulin requirements in the early morning period due to counter‐regulatory hormone secretion). ► Bolus calculation capacity. ► Less frequent insertion events (about every 3 days) — favourable for individuals with needle phobia. ► Insulin delivery and glucose data can be generated electronically and remotely for review. As only quick‐acting insulin is used in insulin pumps, insulin deficiency (leading to possible diabetic ketoacidosis) may occur within 2–3 hours of discontinuation of the insulin pump, or in the event of set occlusion. Set occlusion is one of the leading causes of ketoacidosis in insulin pump‐treated individuals but is rapidly corrected with recommencement of insulin in the absence of intercurrent infection (Box 5). However, insulin pump use has not resulted in the increased diabetic ketoacidosis events anticipated when first introduced, aided by appropriate education.10 There are out‐of‐pocket costs, especially for individuals without private health insurance, and running costs are higher than with insulin injection. Wearing an externally attached device to the body 24 hours a day is a deterrent to some, but a convenience for others who dislike carrying needle tips and insulin pens. Insulin pumps can be used either as a stand‐alone device or in conjunction with CGM sensors (Box 6). Sensor‐augmented insulin pumps have the added benefits of suspended insulin delivery for predicted low glucose (predictive low glucose suspend), or at the threshold of hypoglycaemia (low glucose suspend) to reduce the frequency and duration of hypoglycaemia. In a study of individuals with documented nocturnal hypoglycaemia, those randomised to insulin pumps with low glucose suspend function for 3 months had 32% less frequent hypoglycaemia than without suspend function.11 Other trials have also demonstrated reduced time in hypoglycaemia without increase in time in hyperglycaemia.12 The latest insulin pump systems (hybrid closed loop; HCL) can provide a further degree of automation of insulin delivery. HCL pumps provide real‐time adjustment of insulin delivery in response to ambient glucose levels detected by a CGM sensor, via an inbuilt control algorithm. The user is still required to manually deliver boluses for meals or adjust insulin for exercise. A recent study comparing HCL to sensor‐augmented insulin pump therapy reported improved TIR during daytime hours as well as overnight, and a small reduction in time in hypoglycaemia over 6 months.13 There is currently only one registered HCL insulin pump in Australia. Future technologies may provide further integration of CGM and insulin pump devices via phone‐based applications. Tailoring treatments to individual needs The optimal approach for the management of type 1 diabetes depends on individual and practical considerations. Initiation of insulin pump therapy requires extended consultation to discuss device selection and cannula insertion technique, and review carbohydrate counting and troubleshooting (including diabetic ketoacidosis risk mitigation). It also requires a multidisciplinary approach involving an endocrinologist, credentialled diabetes educator and dietitian.14 In concert with the individual with diabetes, factors to discuss include: the need for alerts and alarms: presence of hypoglycaemia unawareness and susceptibility to alarm fatigue; affordability and eligibility for CGM supplied under the National Diabetes Services Scheme (Box 3); access to training and education; customisation of glucose targets for pregnancy, age and comorbidities; ability to use software to upload data and share reports with health professionals; and allergies to cannula or CGM site adhesives. Conclusion Diabetes technologies are being increasingly adopted by people with type 1 diabetes, and clinicians should familiarise themselves with the spectrum of devices. These advancements offer potential benefits for people with diabetes, although prescribing these devices requires evaluation of cost and benefit for the individual. Human factors are the main determinant of success and satisfaction, highlighting the importance of consideration of the needs of the individual. Box 1 – Types of continuous glucose monitoring (CGM) systems2,3 Professional (retrospective): professional CGMs were the first CGM systems approved by the United States Food and Drug Administration in 1999. They provide blinded glucose data for review by a health care provider. The iPro (Medtronic) and Freestyle Libre Pro (Abbott) are currently available systems in Australia. Real‐time CGM: patient‐inserted systems include Guardian (Medtronic), Guardian Connect (Medtronic) and G6 (Dexcom). The Eversense (Senseonics) CGM implantable system is inserted subcutaneously by a physician and worn for 90–180 days with a transmitter adherent to the overlying skin with alert capacity (not currently available in Australia). Intermittently viewed CGM or flash glucose monitoring: Freestyle Libre for continuous glucose measurements shown retrospectively at the time of physical scanning of the sensor does not have alert capacity. Freestyle Libre 2 will have optional alerts but is not yet available in Australia. Box 2 – Internationally accepted continuous glucose monitoring (CGM) metrics for clinical use and comparison between devices (adapted from guidelines)2,6,7 Percentage sensor wear and data captured — to gauge completeness of data capture (optimal wear time assessed as > 70% capture across a 14‐day time period) Mean glucose — the sum of all glucose levels, divided by number of measurements; a surrogate of overall glucose control, with reasonable correlation with glycated haemoglobin Glucose variability — standard deviation of glucose/mean glucose × 100 = coefficient of variation (CV); goal is CV < 36% in type 1 diabetes Time in range (3.9–10.0 mmol/L) — aim > 70%; the ranges can be tailored to the individual depending on their age and comorbidities (eg, older individuals or pregnancy) and provide an estimate of level of current glycaemic control otherwise not reflected in a glycated haemoglobin measurement Time in hypoglycaemia: ► < 3.9 mmol/L — goal < 4% (includes proportion values < 3.0 mmol/L) ► < 3.0 mmol/L — goal < 1% ► number of CGM events < 3.0 mmol/L for 15 minutes or more in previous 2 weeks — focuses on the importance of moderate hypoglycaemia Time in hyperglycaemia: ► 10 mmol/L — goal < 25% (including time > 13.9 mmol/L) ► 13.9 mmol/L — goal < 5%. Other reportable data (from insulin pump downloads): ► total daily insulin, % basal — a summary of current total insulin delivery, split into dose delivered as basal and bolus insulin; this allows for comparison between visits Standardisation of CGM reporting improves comparisons between devices and treatments and enhances decisions in diabetes care for both clinicians and people with diabetes Box 3 – Access to subsidised continuous glucose monitoring (CGM) through the National Diabetes Services Scheme (NDSS)*9 The following groups can access CGM or flash glucose monitoring through the NDSS: Children and young people under 21 years of age with type 1 diabetes Children and young people with conditions very similar to type 1 diabetes, such as cystic fibrosis‐related diabetes or forms of genetic diabetes (including maturity onset diabetes of the young), who require insulin Women with type 1 diabetes who are actively planning pregnancy (up to 12 months before conception), pregnant or immediately post‐pregnancy (pregnancy plus 3 months from expected date of confinement) People with type 1 diabetes aged 21 years or older who have concessional status Applications can be made through the patient’s credentialled diabetes educator or endocrinologist *Criteria valid from 1 March 2020. Box 4 – Major components of insulin pump and continuous glucose monitoring (CGM) set‐up Box 5 – Steps in managing ketosis caused by insulin pump line occlusion (in the absence of vomiting) Insulin pen injection using pump‐advised correction dose for high blood glucose Replace insulin pump cannula set Run increased basal insulin rates (200%) temporarily for 2 hours to restore subcutaneous insulin reservoir and missed insulin At 2 hours, deliver correction insulin dose with insulin pump Monitor blood ketones every 3–4 hours using a ketone meter to ensure ketone levels < 1.5 mmol/L Box 6 – Options for insulin delivery and glucose monitoring CGM = continuous glucose monitoring; HCL = hybrid closed loop; MDI = multiple daily injections; PLGS = predictive low glucose suspend; SMBG = self‐monitoring of blood glucose.

Jennifer R Snaith · D Jane Holmes‐Walker

Evidence to inform conversations on Aboriginal health issues — in response to COVID‐19 and beyond Nearly everyone has been affected in some way by the coronavirus disease 2019 (COVID‐19) pandemic, and it is a public health risk for Aboriginal peoples and communities.1 The impacts of the pandemic are pervasive, wide‐ranging and continue to affect people and communities differently. Concerns about the indirect impacts of COVID‐19, caused by missed, delayed and avoided health care — not as a direct consequence of COVID‐19 infections — are shared internationally.2,3,4 While the prevalence of COVID‐19 in New South Wales remains low,5 local data show significant changes in health utilisation across the state. During the 4‐month period from March to June 2020, compared with the same period in 2019, face‐to‐face primary care consultations decreased by 22.1%, breast screen activity by 51.5%, ambulance incidents by 7.2%, emergency department visits by 13.9%, public hospital inpatient episodes by 14.3%, and public hospital planned surgical activity by 32.6%.6 Such decreases are not unique to NSW.7 Before COVID‐19, Aboriginal people faced health disadvantages and inequitable access to health care. Any decrease in health care access for Aboriginal people through missed, delayed or avoided health care may lead to further adverse health outcomes and inequities.1,4,8 In recent months, we came together as a group of 12 Aboriginal community members from across NSW to share our experiences and perspectives regarding the indirect impacts of COVID‐19. We live and work on Eora, Wilyakali, Bundjalung, Yuin and Gumbaynggirr lands. The discussions occurred over three separate sessions, each held a week apart between 24 August and 1 September 2020. Six members of the group (DF, CP, PO, BO, DL and KB) captured the key messages identified from the talks and synthesised the findings into three main themes: community supporting the community; the social determinants of health; and access to health care. These conversations were hosted and supported by the Critical Intelligence Unit established as part of the NSW Health COVID‐19 response and the Agency for Clinical Innovation (TDB). Illustrative quotes shared by the co‐authors have been selected to demonstrate salient points. The term “mob” has been used throughout to identify who we are and where we are from — our connection to our shared identity as Aboriginal people. Community supporting the community is a real strength — in the pandemic, and always In responding to COVID‐19, we see that Aboriginal organisations are coming together, more than ever, to create a movement that will continue to inform positive change to address Aboriginal health issues. Mob are proud of how they are keeping each other safe. It is a point of pride that has strengthened community. Our mob are concerned about the safety of others and our elders. (CP) Aboriginal leaders and Aboriginal community controlled health services are active in responding to COVID‐19, drawing on experiences from the 2009 HINI influenza pandemic and implementing culturally appropriate resources.9 The pandemic has been disruptive, and community events and gatherings have been cancelled because of important and legitimate public health concerns. However, this does impact our community approach to health care, cultural practices and connection to country.1,10 Our mob aren’t able to connect for sorry business and funerals, marriages and births. The provision of our health care, along with the provision of our social and emotional wellbeing, has changed. And connectivity is the main ingredient for our mob to stay healthy. This is the biggest barrier. (CP) Social determinants of health for Aboriginal people Social determinants are the conditions in which people are born, grow, live and age, and how these factors influence our health and determine health inequalities.11 Cultural determinants of health such as connection to country (land and water), traditional practices and kinship systems promote resilience and support social and emotional wellbeing for Aboriginal peoples and communities.10,12 The COVID‐19 pandemic is likely to amplify the social determinants of health,13,14 and our concern is these determinants will continue to affect access to health care and increase health inequalities. Based on our own lived experiences and anecdotal community feedback, we are hearing that food security has increased for some Aboriginal people in response to COVID‐19. People are fearful of going into large shopping centres — fearful of catching COVID‐19. In some rural and remote areas, local shops are pushing up their prices, and people are left with no choice but to buy cheaper (and often less healthy) options to feed their families. Increase in government payments has resulted in the one and only shop in community providing food jamming their prices up. The price of food and water is beyond compare when you are paying $10 for a loaf of bread. Because of COVID‐19, people don’t want to come into town to do their shopping. (DL) We are concerned that restricted access to health care in response to border closures will impact the health and wellbeing of Aboriginal peoples. Some communities are being hit hard. To give a raw example, people are being refused medical treatment and are driving 600–800 km just to get any sort of medication or treatment around their health. (DL) We are also concerned that a lack of cultural safety displayed during COVID‐19 will lead to Aboriginal people being confronted with racism when trying to access health care.15 COVID‐19 has made accessing health care even more difficult Deciding to seek health care is difficult, and for some Aboriginal people, access to care has become more challenging during COVID‐19 with reduced availability of services. Many doctors and services have temporarily shut their doors to new patients, and this is likely to have a profound impact on people’s health. More generally, there have been efforts to overcome access challenges posed by COVID‐19 through the use of telehealth and virtual care. In our opinion, telehealth for diagnosis and e‐prescribing can be useful; however, there are challenges to using telehealth such as limited access to equipment and internet connection, and reluctance from some people to disclose personal information over a device. When we look at the provision of health care for our mob, one of the biggest barriers is having to sit in front of a computer. And talk to a computer, rather than a human connection. Our mob like to connect and have a yarn. (CP) Our view is that paying attention to the intersections of culture and diversity is essential to understanding the indirect impacts of COVID‐19. Within Aboriginal communities, there are minority groups who are significantly affected by COVID‐19. Minority groups include people with existing chronic conditions, people with disabilities, people experiencing homelessness, people living in rural and remote areas, and people who identify as lesbian, gay, bisexual, transgender, queer, asexual and questioning. Sistergirl and brotherboy are terms used for gender diverse people within some Aboriginal or Torres Strait Islander communities.16 If the mob aren’t receiving health related treatment, how this is feeding into direct or indirect impacts on disabilities. And how we can pick this up through the health system as disability is not in closing the gap. If we aren’t addressing it at a higher level, we are never going to address it at the ground level. (DL) We are also concerned about an increase in risk for our older people living with disability. These risks have been outlined by Aboriginal people with disability and their representative organisations, advocates and allies in international and national calls to action for governments to ensure Aboriginal disability‐inclusive public health, social and economic responses to the pandemic that put our mob at the forefront of any future planning in the health system.17 The recent drought, bushfires and now COVID‐19 are compounding risk factors for mental health issues and suicide. There is concern that some government measures to control the spread of COVID‐19 are triggering for mob — especially for those with trauma histories.18 We know mental health issues and suicide rates are high for our peoples,8,19 and we are concerned this level of disadvantage will worsen in response to COVID‐19. We support the recommendations made by the Centre of Best Practice in Aboriginal and Torres Strait Islander Suicide Prevention at the University of Western Australia to manage COVID‐19 recovery and address adverse impacts.19 The recommendations focus on the right to self‐determination, the health and mental health workforce, social and cultural determinants of health, digital and telehealth inclusion, and evaluation that includes Indigenous data sovereignty. These recommendations directly align with our lived experiences and were running themes throughout our discussions and overall assessment of the indirect impacts of COVID‐19 in our communities across NSW. Where to next? We prepared this article to inform future conversations on Aboriginal health issues in response to the COVID‐19 pandemic and beyond. Our view is that drawing on the lived experience and realities of Aboriginal peoples, taking firm action on the social determinants of health and working collaboratively with Aboriginal peoples and communities is the most effective way to address the indirect impacts of COVID‐19.

David Follent · Cory Paulson · Phillip Orcher · Barbara O'Neill · Debbie Lee · Karl Briscoe · Tara L Dimopoulos‐Bick

Careful planning is required to ensure new resources for mental health lead to better consumer care Both the Productivity Commission into Mental Health1 and the Royal Commission into Victoria’s Mental Health System (RCVMHS)2 acknowledge that mental health services have been in the grip of a protracted resourcing drought. The RCVMHS identified that, until recently, Victoria’s public mental health services have fared particularly badly,3 with the 2015–2016 per capita funding to public mental health services being the lowest of any state. The impacts of chronic under‐resourcing, including a predominant focus on managing risk, underutilisation of evidence‐based therapies, and a lack of individualised care, were all reported by service users in testimonies highlighted in the RCVMHS interim report.2 After years of stagnation, in 2020 Victorian mental health services experienced some funding growth.4 These resources have allowed our team to strengthen existing services; bring on new staff, including expanding our lived experience workforce; and initiate new programs, including the Hospital Outreach Post‐Suicidal Engagement (HOPE) initiative, intensive community packages of care, the pre‐hospital response of mental health and paramedic team (PROMPT), and a mental health, alcohol and other drugs hub in our emergency department. In addition, our service is currently planning new mental health beds and rolling out an innovative hospital‐in‐the‐home program stemming from the recommendations of the RCVMHS interim report. We anticipate the final recommendations, due in February 2021, will bring even more new resources. Like sudden heavy rain on degraded soils after drought, such an inundation is welcome, but not without its own risks and challenges. Our recent implementation efforts have highlighted several challenges in managing rapid funding growth, including issues with human resources, leadership capacity, change management competency and stakeholder engagement, which will need consideration across the system to ensure services can translate funding into better consumer care. Human resources Delivering care requires staff who are difficult to find. There are insufficient mental health nurses5 and psychiatrists6 to fill current roles, particularly in regional and rural areas, and with an ageing workforce,7 problems with staffing are predicted to worsen.6 While international recruitment may assist, these processes come with lengthy delays and high administrative burdens. As a consequence, program implementation may be delayed due to recruitment challenges, or may adversely affect the operations of other service areas when clinicians shift between roles. Significant investment in training and recruitment pathways are required to prevent workforce shortages becoming a barrier to the pace of reform. Diversification of disciplines, grades and programs that encourage qualified staff to enter mental health care will all be required.1 Leadership capacity and competency in change management The presence of effective leadership and competency in change management principles are critical for successful health reform.8,9 Rapid growth can stretch existing leadership capacity. Health leaders already face significant challenges at system (eg, demographic changes, increasing demands, advancing technology), organisational (eg, human resources, changes to organisational structures and processes, intensification of frontline and middle management roles) and individual levels (eg, lack of role clarity, lack of training in managerial and leadership capabilities).10 For leaders already managing the challenge of daily operations, additional responsibilities to enact significant reform quickly carries a risk of overload. A key potential outcome of overload is loss of focus on the consumer.11 Therefore, as the pace of desired reform increases, a focus on developing current and future mental health leaders should be prioritised.2,12 Engaging stakeholders meaningfully Meaningful stakeholder engagement is crucial to successful change.9 As outlined by the RCVMHS, the “necessary changes to the mental health system cannot … be achieved by government alone”.2 The implementation of new programs requires engagement with diverse stakeholders including consumers, carers, staff, hospital executives, government and industrial bodies. Such stakeholders often have different interests, and forging and maintaining alignment is critical to progress. Meaningful engagement is vital to achieving this and subsequent success,9 but it is also resource intensive. Enacting multiple reforms quickly carries the risk that meaningful stakeholder engagement may be sacrificed. For this reason, timelines for delivery need to balance urgency with getting things right. The appropriate urgency for implementation articulated in the initial recommendations from the RCVMHS (eg, operation of an additional 170 acute mental health beds by mid‐2022) will only be achieved if those leading the change are supported and those affected by the change are included. Harnessing a generational opportunity for reform The RCVMHS calls for transformational change2 to our mental health system. The pace of this change and the other challenges involved must be managed carefully by system leaders to ensure that the intended reform occurs and results in provision of better care to the community.

Steven Moylan

Posts can provide valuable feedback during public consultation for health guidelines

Benjamin C Riordan · Daniel T Winter · Paul S Haber · Carolyn A Day · Kirsten C Morley

The disability burden of non-fatal disease is not reflected in allocation of grants

Stephen E Gilbert · Rachelle Buchbinder · Ian A Harris · Christopher G Maher

Investigator‐initiated trials run by clinical trial networks provide net economic benefits to health systems Delivery of optimal health care relies on evidence from randomised clinical trials, among other factors, to inform best practice. While the generation of such evidence requires resources, both national and international assessments of health and economic benefits resulting from medical research indicate large returns on investment.1,2,3 In Australia, during the decade 2006–2015, more than 10 000 clinical trials were conducted through Australian clinical trials networks (CTNs), including more than 5 million participants, ranking Australia in the top tier of clinical trial activity.4 Industry‐funded clinical trials accounted for an estimated $930 million of the total $1.1 billion spent annually on clinical trials, with National Health and Medical Research Council (NHMRC) funding accounting for about $164 million annually.4 While the proportion of funding for non‐industry‐sponsored investigator‐initiated clinical trials (IITs) is relatively small, these studies account for more than half of Australia’s clinical trial activity.4 This study funding balance is similar to what is reported elsewhere.5 In Australia, IITs conducted by Australasian CTNs have had a major impact on the improvement of health care quality and outcomes around the world.6,7 IITs are designed and conducted by independent clinicians and academic researchers to generate clinical evidence to improve health care. Benefits are multilayered and not restricted to the discovery of new therapies. Much of the benefit comes from identifying and addressing uncertainty in existing practices; evaluating a range of treatment options that address key unanswered questions free of commercial imperatives, identifying alternative and potentially more efficient diagnostic strategies; and identifying more effective models of care or expensive interventions that are no more active than the lower cost alternative. Australasia has large, geographically dispersed CTNs across multiple clinical areas,8 with many more having been launched since the original report (personal communication Australian Clinical Trials Alliance [ACTA]). Between one‐quarter and one‐third of all Australian Government‐funded NHMRC support for clinical trials between 2004 and 2014 was awarded to IITs conducted by an established CTN.8 CTNs ensure clinically important, high priority and relevant research questions are appropriately conducted and provide efficiency through established infrastructure. Within Australasia, CTNs are widely regarded as key drivers of innovation and represent good value for public investment.8 Although the Australian Government invests in IITs and the CTNs that coordinate them, their value has not been well characterised. Governments are increasingly looking to systematically integrate activities that generate high quality evidence (such as IITs) with other aspects of the health care system (such as measurement of health outcomes or development of safety and quality policies) to build self‐improving, sustainable systems (Box). Understanding the potential return on investment is therefore paramount. In 2015, ACTA and the NHMRC profiled 37 established CTNs in Australia.8 Subsequently, a cost–benefit analysis for the profiled networks was calculated for those that i) were operational for more than 10 years; ii) had conducted more than five high impact peer‐reviewed IITs where an influence (or potential influence) on clinical practice and/or policy were identified (maturity); iii) received a significant proportion of funding from Australian funders (local investment); and iv) were available to participate (feasibility) in this analysis.9 Three CTNs that had conducted a total of 25 IITs were included in the analysis: the Australasian Stroke Trials Network (ASTN), the Interdisciplinary Maternal Perinatal Australasian Collaborative Trials (IMPACT) Network, and the Australian and New Zealand Intensive Care Society Clinical Trials Group (ANZICS CTG). Gross economic benefits across these CTNs were almost $2 billion, with the majority due to improvements in patient health outcomes ($1.4 billion), and 30% due to avoided health service costs — $453 million from the difference in outcomes and $127 million from differences in service costs. Gross costs, which included the cost of running the CTN, coordinating centre costs and the cost of running the entire IIT program in each CTN, were about $335 million, with most of those costs being for the IIT program itself (accounting for 73% of total costs). The benefit to cost ratio was 5.8:1 if findings from the 25 IITs were implemented in 65% of the eligible Australian population for one year.9 Similar findings have been reported internationally, with studies in the United States reporting a benefit to cost ratio of 4.2:1 over 10 years.3 In the United Kingdom, randomised clinical trials funded under the National Institute for Health Research health technology assessment program were expected to have a net benefit of £3 billion, with just 12% of this benefit required to cover the costs for all research undertaken.10 In the Australian analysis, funding provided to run a portfolio of IITs did not cover the total costs within either a CTN or at an individual IIT level, and in‐kind support was relied upon to make up the shortfall. The NHMRC funding received by all Australasian CTNs between 2004 and 2014 was represented by just 9% of the $2 billion gross benefit.9 The magnitude of avoided health care costs appears large, reflecting the size of health care expenditure. The Australian analysis highlighted the importance of in‐kind support within CTNs not only to sustain the viability of the CTNs but for their ability to conduct individual IITs.9 The total quantum of site level, in‐kind support could not be quantified accurately during the study. However, this support was described as being finite, at capacity in many instances, and at risk of exhaustion. From a sustainability perspective, the reliance on in‐kind support is concerning, and undermines the timeliness, volume and international competitiveness of clinical research in Australasia. Anecdotal evidence from interviews suggested that site level in‐kind support represents up to a 50% increase in trial funding. Late‐phase IITs conducted by CTNs deliver better health outcomes and health service value through a variety of mechanisms. Importantly, IITs play a critical role in addressing clinically significant questions, influencing guidelines, and identifying ways to improve safety and quality and opportunities for more efficient resource use. As stated in a scoping review, IITs “can also yield a substantial knowledge return on investment for hospitals and institutions that actively engage in trials, including the following: more skilled clinicians and increased research capacity, improved patient outcomes, and better health system performance. Also, the difference in cost of care for trial and non‐trial patients can be negligible”.11 Large increases in the benefit to cost ratio could be realised through relatively small increases in implementation rates. Research to identify the barriers and enablers of trial implementation should allow IITs to be translated more effectively into frontline health care delivery. But, intuitively, the conduct of potentially practice‐changing IITs through CTNs is likely to enhance implementation rates, as these virtual, nationwide consortia of clinicians are likely to involve a majority of the relevant clinical community. Hence, the reasonable assumption that clinicians who participate in IITs are more likely to implement trial results in their own practice and to translate new knowledge to their clinical colleagues. What we do not yet know is the extent to which IITs translate into routine practice. This is rarely measured or monitored in Australia. Measures of implementation should be incorporated routinely into IIT design, particularly for randomised clinical trials that are arguably more likely to result in clinically significant and potentially practice‐changing findings. Notwithstanding the clear economic benefit demonstrated for the 25 trials conducted by the selected group of three CTNs, it might be possible to reduce trial costs further. The overall cost of trials is a complex, multilayered issue, particularly as small pilot studies are often required to demonstrate the feasibility of recruitment. But combined with the push to answer key questions more quickly especially for the seriously or critically ill patients, such considerations have been drivers in implementing newer adaptive trial designs, which have flexible sample sizes that might reasonably be expected to reduce clinical trial costs.12 The analysis conducted of the three selected CTNs represents the first such analysis conducted of the role of CTNs in the Australian health sector. Despite the limitations of the analysis, it is clear further investment in existing CTNs, as well as therapeutic areas for which there are no CTNs at present, is warranted. This needs to be done in a manner that seeks operational efficiencies, including consolidation of infrastructure and the means to ensure engagement with geographically dispersed health services to improve patient access to trials across communities.11 In conclusion, there is potentially enormous, and arguably untapped, value in investing in IITs conducted by CTNs, as they provide net benefits to health care systems. However, the exact return on investment is contingent on the level of implementation. Further work in this regard is warranted. So, where to from here? High quality health systems are reliant on a strong clinical trials sector. In particular, the role of IITs run by CTNs is paramount in order to address clinically important questions, especially those that relate to health care variation. Clinical trial infrastructure needs to be strengthened, and we must endeavour to reduce reliance on in‐kind funding to ensure that the sector remains viable. Finally, we must strive to maximise implementation of trial findings to optimise current investment in the sector. Box – A self‐improving, sustainable health care system

The joint ACTA/ACSQHC Working Group

Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine The autonomous detection and triage of eye disease, or even accurate estimations of gender, age, and blood pressure from a simple retinal photo, may sound like the realms of science fiction, but advances in artificial intelligence (AI) have already made this a reality.1 Ophthalmology is at the vanguard of the development and clinical application of AI. Advances in the field may provide useful insights into the application of this technology in health care more broadly. Artificial intelligence Once described as the capacity of intelligent machines to imitate human intelligence and behaviour, AI now describes many theories and practices used to achieve computer intelligence (Box 1).2 Machine learning is an application of AI that uses algorithms or statistical models to make decisions or predictions. Complex patterns and relationships are learned from data to generate an outcome.2 Machine learning traditionally relies on the extraction of features from the data by human operators which then serve as input variables to optimise algorithm performance. The performance of these systems is constrained by the features that are recognised as important by humans. In contrast, artificial neural networks are an advanced method of machine learning able to extract features without explicit programming.2 Deep learning is the construction of multiple layers of artificial neural networks which can identify features in data that are not recognisable by humans. Although deep learning systems may be powerful, they lack human‐crafted inputs, meaning that large quantities of data are typically required to train algorithms. Artificial intelligence in ophthalmology As a discipline, ophthalmology is at the forefront of AI system development and translation in clinical practice. Leading uses of the technology include detecting, classifying and triaging a range of diseases, such as diabetic retinopathy, age‐related macular degeneration (AMD), glaucoma, retinopathy of prematurity, and retinal vein occlusion, from clinical images.3 The increasing global burden of eye diseases, coupled with the development of new therapies for previously untreatable conditions, has served as a major driver for AI innovation in ophthalmology. As a case in point, there are presently over 430 million people living with diabetes, most of whom require annual or biennial screening for retinopathy using retinal photography. This vast demand for diabetic eye screening services has stimulated the development of AI algorithms to identify sight‐threatening disease. Several algorithms have achieved performance that meets or exceeds that of human experts.4,5 Accordingly, in 2018, the United States Food and Drug Administration approved an AI system to detect referable diabetic retinopathy from retinal photographs, the first autonomous diagnostic system to be approved in any field of medicine.6 Advances in deep learning have extended to other imaging modalities that are commonly used in ophthalmology. Ocular coherence tomography is an imaging technology that produces highly detailed, depth‐resolved images of the retina. A recent collaboration between researchers and clinicians at Google DeepMind, Moorfields Eye Hospital and University College London culminated in the development of a deep learning system capable of detecting and triaging more than 50 different retinal conditions at levels equivalent to a panel of experienced ophthalmologists.7 AI systems with the capacity to detect a wide range of diseases, such as this, are likely to be most useful in clinical practice. A highly anticipated innovation is the development of AI systems capable of accurate disease prediction. Such tools could assist in managing patient expectations, improve the quality of care and reduce treatment costs.3 In ophthalmology, prediction models have been trained to personalise re‐treatment intervals for patients with neovascular AMD,8 predict progression from early to late AMD,9 estimate the extent of future visual field defects in patients with glaucoma,10 and predict diabetic retinopathy progression.11 Although these models presently achieve only moderate levels of accuracy, their performance has been shown to be superior to humans in several studies.3,8 Future advances in the accuracy of prediction models will likely come from the use of large longitudinal datasets drawing on multiple data sources, together with the development of more advanced AI systems.3 Despite these significant advances, AI systems are not in widespread clinical use and in some cases real‐world performance has been inferior compared with in silico validation.2,3 Training and validation of deep learning algorithms with large, representative data (eg, data from people of different ethnicities) acquired using multiple devices (eg, different retinal camera models) and data collection protocols (eg, retinal photographs acquired with and without pupil dilation) are key to achieving clinical applicability.4,5 This approach was used in the development of deep learning systems for retinal photographic screening for diabetic retinopathy, AMD and glaucoma which are now being used in large scale screening programs in Singapore and China.4,5 In these programs, AI is used to identify images without evidence of disease, so that human graders can focus their efforts on the images of those with disease, enabling improved efficiency and cost savings.12 Challenges to the clinical adoption of artificial intelligence Several obstacles to the adoption of AI in health care remain. The training of deep learning systems requires access to large amounts of medical data which has significant implications relating to privacy and data protection. In the context of ophthalmology, this is particularly pertinent, as the retinal vasculature may be considered biometric data, making it impossible to completely anonymise retinal photographs.3 Furthermore, characteristics that are not visible to human examiners, such as age and sex, can now be accurately predicted from a single retinal photograph using deep learning.1 Several recent major breaches of data protection laws relating to AI system development have already come to light.13 While individual patient data used to train an algorithm do not remain within the system, incorrect handling and sharing of data may lead to patients withdrawing consent to the use of their data under General Data Protection Regulation laws. It is not certain how data withdrawal requests will be dealt with when an individual’s data have been used in the process of training a deep learning system. Accordingly, developments in AI need to be accompanied by advanced data protection and security measures. Another challenge to the acceptance of deep learning algorithms in medicine is the difficulty in determining the basis for clinical decisions made by these systems, informally described as the “black box” problem. Visualisation tools have been developed to assist clinicians by highlighting the salient image features that contribute to the AI system classification (Box 2).12 This has the potential to create trust in system‐generated decisions, particularly if the features correspond with those used by experienced clinicians for clinical decision making.14 Interpretability is particularly important when considering legal liability in the event of patient harm arising from the use of AI in medicine. In traditional malpractice cases, a physician may be asked to justify the basis for a particular clinical decision and this is then considered in light of conventional medical practice.15 In comparison, challenges in identifying the basis for a given decision made by AI might pose problems for clinicians whose actions were based on that decision. The extent to which the clinician, as opposed to the technology manufacturer, should be held accountable for harm arising from AI use is a subject of intense debate.15 Factors such as the manner in which these AI systems are used and their classification as either products or software are likely to have important bearings on how cases are litigated.15 Further challenges for existing regulatory frameworks come from algorithms that continue to learn and evolve over time.15 Understanding how a given system is trained, its accuracy, and its operational limits is of great importance. Oversampling of a particular population or disease severity during training has the potential to introduce bias.4 Therefore, consideration of performance thresholds will help to inform appropriate use of AI systems. The Australian Government, through the CSIRO and Data61;16 the Australian Council of Learned Academies;17 the Australian Academy of Health and Medical Sciences;18 and specialty groups, such as the Royal Australian and New Zealand College of Radiologists,19 have made significant efforts to develop frameworks and policies for the effective and ethical development of AI. These consultative works have highlighted key priorities, including building a specialist AI workforce, ensuring effective data governance and enabling trust in AI through transparency and appropriate safety standards. Through targeted investment in research and development, Australia is aiming to advance its AI competitiveness. These framework documents provide guidance for developers, clinicians and health care consumers to navigate this rapidly evolving field. Broad dissemination of these documents should form part of a wider public engagement and education campaign to ensure that AI is developed and used in a considered and careful manner in health care. Rapid advances in AI in ophthalmology are a harbinger of things to come for other fields of medicine. While these technologies may eventually lead to more efficient, cost‐effective and safer health care, they are not a panacea in isolation. The successful integration of AI into health systems will need to first consider patient needs, ethical challenges and the performance limits of individual systems. Box 1 – Relationship between artificial intelligence and its subtypes Box 2 – Original retinal photograph of right eye with macular degeneration (A). Heat map of image A showing visualisation of traditional features associated with macular degeneration, such as central scarring (B). Original retinal photograph of left eye with referable diabetic retinopathy (C). Heat map of image C showing visualisation of traditional features, such as micro‐aneurysms and haemorrhages (D)

Jane Scheetz · Mingguang He · Peter Wijngaarden

Is the current genetics and insurance moratorium an effective long term regulatory solution for Australia? Genetic discrimination in life insurance is a longstanding issue in Australia,1,2 and has been the subject of two government inquiries.3,4 The use of genetic test results in underwriting continues to be self‐regulated by the life insurance industry.5 In 2019, following Parliamentary Joint Committee recommendations,4 the industry voluntarily introduced a moratorium restricting the use of genetic test results in life insurance underwriting for polices worth up to AU$500 000. Although the moratorium is an important step, concerns remain around the financial limits, public awareness, lack of government oversight and compliance monitoring. The impact and effectiveness of the moratorium needs evaluation to inform the planned 2022 review. A new research project has been funded by the Australian Government’s Genomic Health Futures Mission to serve that important function. Genomic testing has the potential to improve disease prevention and public health. For example, predictive testing of BRCA1/2 genes can identify women at high risk of developing breast and ovarian cancer, where risk can be mitigated through preventive surgery and/or screening. As genomic testing becomes more widespread, patients, general practitioners and other health professionals will increasingly be required to address issues related to privacy, data security, genetic discrimination and insurance.2,6 Although health insurance is community‐rated in Australia and therefore not subject to genetic discrimination,1 the use of genetic test results in life insurance is allowed under the Disability Discrimination Act 1992 (Cth). This means that life insurance companies can legally refuse coverage or increase premiums based on genetic test results. A number of ethical, social and medical implications arise when genetic test results are permitted to be used in insurance underwriting, especially predictive testing in otherwise healthy people.1,7 Previous studies show that fear of insurance discrimination deters individuals from taking clinically indicated genetic tests and participating in genetic research.1 In a study where predictive genetic testing for Lynch syndrome (which causes an increased risk of colorectal and other cancers) was offered, the proportion of people who declined testing when informed of the insurance implications was more than double the proportion who declined without knowledge of insurance implications.8 There are different concerns from the insurance industry perspective, including the possible actuarial implications of adding genomic information to risk models. Genomic test results can not only reveal risk (positive results), but also indicate reduced risk (negative results), potentially changing the dynamics of actuarial calculations. The notion of adverse selection, whereby individuals at high genetic risk may be more likely to take out insurance policies, is also raised by insurers. It is critical for the optimisation of genomic medicine that individuals can make informed choices about genetic testing and research participation without fear of insurance implications. Further, moral implications regarding the use of genetic information for insurance underwriting extend beyond actuarial fairness to include consideration of public interests such as justice, beneficence, autonomy and public health.7 Several governments internationally have therefore banned or restricted the use of genetic test results in risk‐rated insurance, including Canada, the United Kingdom and Europe, using various legal mechanisms.9 The National Health Genomics Policy Framework and Implementation Plan 2018–20216 is a strategic policy of the Council of Australian Governments, which recognises the potential of genomics for public health while acknowledging the need for ethical mechanisms for its delivery. Developing a national approach to issues including genetic discrimination was listed as a strategic priority for action in the Framework and listed as the first short term national priority in the implementation plan,6 making it one of the most significant ethical, legal and social issues facing genomic medicine in Australia. However, debate remains regarding the most effective mechanism of regulation. Following previous examination of these issues by the Australian Law Reform Commission and Australian Health Ethics Committee,3 a recent inquiry of the Parliamentary Joint Committee on Corporations and Financial Services into the life insurance industry considered the use of genetic test results in life insurance.4 The report expressed strong concerns about insurer access to genetic information and recommended that: a moratorium be implemented to “prohibit any life insurers from using the outcomes of predictive genetic tests at least in the medium term … as a matter of some urgency and [in] a form similar to the United Kingdom’s Moratorium”;4 the Financial Services Council (FSC), together with the Australian Genetic Non‐Discrimination Working Group (of which the authors are members), assess the consumer impact of a moratorium; and the federal government monitor the implementation of, and adherence to, such a moratorium, and if needed, implement legislation on the issue. The Australian Government has not yet responded to the Parliamentary Joint Committee recommendations. However, the FSC, Australia’s peak national body for life insurers, introduced an industry‐led moratorium in July 2019. Under the moratorium, Australian consumers need no longer disclose their genetic test results when applying for policies up to $500 000 for death/total permanent disability, $200 000 for trauma/critical illness, and $4000/month for income protection cover.10 The moratorium applies to all genetic test results, including research results and results obtained from internet‐based direct‐to‐consumer tests, which are increasingly resulting in clinical referrals.11 Above these financial limits (which are cumulative across multiple policies), life insurers can still ask for, and use, any existing genetic test result, which can lead to refused or delayed cover, exclusions or increased premiums. However, insurers must not require applicants to undergo a genetic test. Applicants can choose to disclose a favourable genetic test result (showing that an individual with a family history of a genetic condition does not have the familial genetic variant) to offset the effects on underwriting of an adverse family history. The FSC moratorium is a self‐regulated industry standard which is not legally enforceable — insurance companies’ legal right to discriminate on the basis of genetic test results remains. By contrast, the UK moratorium (which commenced in 2001) is an agreement between the UK government and the Association of British Insurers. It applies to all life insurance policies without any financial limits, with only one exception for Huntington disease, a progressive, neurodegenerative genetic disorder. Predictive genetic test results for Huntington disease must be disclosed by individuals in the UK only when applying for cover worth over £500 000 (about AU$900 000).12 All other individuals can make informed decisions about whether to have genetic testing or participate in genomic research without concerns about insurance implications. The FSC moratorium is an important step towards consumer protection, but concerns remain around its financial limits, interpretation of its terms, and lack of compliance monitoring. The FSC moratorium has no government or independent regulatory oversight, and as recommended by the Parliamentary Joint Committee, there is a critical need to monitor its implementation and effectiveness. The FSC will review the moratorium and its terms in 2022, to consider amendment and/or extension beyond its current 2024 end date.10 Currently, there are no mechanisms in place to collect independent evidence from different stakeholder perspectives to inform this review and the Australian Government has not indicated any intention to do so directly. A new research project, funded by the first competitive round of the Genomic Health Futures Mission, part of the Australian Government’s Medical Research Future Fund,13 has now commenced to serve that critical function until 2023. The A‐GLIMMER (Australian Genetics and Life Insurance Moratorium: Monitoring the Effectiveness and Response) project brings together leading researchers, clinicians, patient groups, and policy experts in Australia to answer the question of whether the FSC moratorium is an adequate and effective long term regulatory solution for Australia. The project aims to address this question by collecting a range of quantitative and qualitative data after the implementation of the moratorium, from different stakeholders including consumers, health care professionals, researchers and the insurance industry. In some cases, the data collected will be directly comparable to similar data collected and published before the moratorium.14,15 The project has widespread support across the community. More than 20 project partners, including the FSC, and other supporting bodies have provided written support and pledged resources towards the study. The project is endorsed by the Victorian Department of Health and Human Services, the Human Genetics Society of Australasia and Australian Genomics, a collaborative national network of clinical, research, academic and community organisations dedicated to implementation of genomics for health and the development of appropriate genomics policy. The overarching aim of A-GLIMMER is to ensure sufficient evidence is collected in the coming years to inform government and the 2022 FSC review, to help determine the effectiveness of the FSC moratorium as a long term regulatory solution in Australia. See the Box for a summary of project aims. A‐GLIMMER is divided into four work streams, which will collect data from consumers, health professionals, research studies and the insurance industry. A final report will be compiled at the conclusion of the project, and will be provided to the federal government to assist with future policy decisions. Although the project will not conclude until 2023, its findings will help inform the proposed FSC review in 2022. Achieving an adequate policy solution to this issue in Australia is essential for ensuring optimal integration of genomics into Australian health care, engendering public trust and consumer participation in genomics, and paving the way to realise the many benefits of genomic medicine for Australia. Box – Aims of A‐GLIMMER (Australian Genetics and Life Insurance Moratorium: Monitoring the Effectiveness and Response) A‐GLIMMER will: assess dissemination and awareness of the Financial Services Council moratorium following its implementation describe the impact of the moratorium on consumers, health care, research and financial services evaluate the effectiveness of the self‐regulated Financial Services Council moratorium as a long term regulatory solution

Jane Tiller · Ingrid Winship · Margaret FA Otlowski · Paul A Lacaze

Although VTE risk in minor gynaecological procedures is low, a systematic approach to prophylaxis is necessary

Esther MC Johns · Alex Ades · Pavitra Nanayakkara

Current Good Clinical Practice guidelines are bureaucratic and should align with less burdensome examples of international trial policy

Tanya Symons · Steve Webb · John R Zalcberg

Australian services for people with dementia are fragmented, challenging to navigate and hard to access The coronavirus disease 2019 (COVID‐19) pandemic has led to reflections around reforming Australia’s health care system.1 In view of future reforms, this article is intended to provoke policy and clinical discussion regarding what an effective, efficient model of service delivery meeting the needs of people with dementia and their families may look like. The opinion presented here belongs to the members of the National Health and Medical Research Council (NHMRC) National Institute for Dementia Research Special Interest Group in Rehabilitation and Dementia. For the purposes of this article, we define a model of service delivery as the systemic framework through which services are organised, accessed, funded and delivered. Services in Australia for people with dementia are inadequate Dementia is the leading cause of disability, the second leading cause of death in Australians aged over 65 years, and the leading cause of death in women in Australia. In 2020, it is estimated that Australia will spend $8.1 billion on health care and $3.8 billion on social services for people with dementia, with a further $6.1 billion in lost productivity and earnings.2 Australian services for people with dementia are often fragmented, challenging to navigate and hard to access.3 It can be difficult for people with dementia to obtain a diagnosis, there are limited health and social services for early dementia, including post‐diagnostic support, and existing services are often poorly coordinated.3,4 Services face workforce shortages and gaps in worker knowledge and skills related to dementia.5 People with dementia and their care partners have called for support and information after diagnosis; flexibly delivered services that support their quality of life, including meaningful activity; and inclusion in decision making.6 A philosophical and societal shift in thinking is required: from provision of care to enablement, where people living with dementia are empowered to continue to direct their own lives.7 We are not meeting the human rights of people with dementia to health care Australia does not currently meet the human rights of people with dementia to timely and accessible health services of appropriate quality or to participation in health care decisions.4,6 The right to quality health care is affected by the variable delivery of best‐practice dementia care by memory clinics,8 acute hospitals,9 primary care,10 and community and residential aged care,11 perhaps because the role of each of these is unclear. Australia’s systems and context Australia has a long‐standing commitment to a universal health system and to long term care for older people. The health and aged care systems were developed largely in isolation from one another and have failed to resolve conflicts around medical and social models of care for older people. Health care systems are slowly adapting to this era of chronic disease and population ageing,12 but person‐centredness and integration within and across acute, primary, community and residential aged care systems remain a challenge.11 Principles underpinning models of service delivery for Australians with dementia Members of our group reviewed principles underpinning services such as the Department of Health Aged Care Sector and the Council of Australian Governments National Disability Insurance Scheme. 13,14 We reached a consensus that the following principles should apply to models of service delivery for dementia that: has an overarching objective to maintain positive health and wellbeing of people with dementia, their care partners and families; recognises dementia as a disability, consistent with the World Health Organization Convention on the Rights of Persons with Disabilities, and promotes autonomy, social participation and rehabilitation; takes into account the cognitive disability of people with dementia in accessing support and being a partner (along with their families) in planning care through supported decision making; is delivered by a multidisciplinary workforce who have knowledge and skills around dementia; is accessible for all people with dementia and care partners; is ongoing, cost‐effective and economically sustainable; is needs‐based, not capped according to central budgets; is integrated for seamless experience for people with dementia and care partners, within and across primary, acute and subacute health care, aged care and social services; and is evidence‐based. Review of possible models of service delivery for dementia We identified models of service delivery for dementia and other chronic conditions based on input from our broad authorship group and searching the peer‐reviewed and grey literature. These models are described in the and considered in terms of fit with the principles above. We included care pathways even though these are not a model because they are often used to improve service access and integration. In addition, we map the models of service delivery to our health and aged care funding systems, illustrating the limited integration across systems (Box). Learnings from these models: The self‐directed approach places the needs of the person with dementia centrally but may require processes to ensure supported decision making. Information is also needed regarding the risks and benefits of self‐management versus budget holding or service provider management, integration with health care, and consideration of costs. Case management improves outcomes for the person with dementia and could be flexible and needs‐based if sufficient workforce and integration across systems could be achieved. However, it would require a significant investment of resources. Strengths of the primary care chronic disease management model include equity and familiarity of access, and care coordination by a trusted health professional or practice team. Weaknesses include the limited amount of treatment (ie, current cap of five subsidised allied health consultations per year), limited dementia management skills in some general practitioners and practice nurses, and often poor integration with aged care. Shared and stepped care models may be able to be adapted to combine the strengths of the primary care chronic disease and specialist approaches, but integration of aged care services would be essential. Stepped care may not be the best fit for people diagnosed with dementia in other settings (eg, hospitals or residential care facilities). A specialist team approach with a skilled workforce is well equipped to provide evidence‐based care, although this is unlikely to be made universally accessible (eg, in regional areas) and may be cost‐prohibitive. Navigator and care pathway approaches may increase access to services, but do not improve the type or amount of supports or treatment available. None of the models of service delivery that we identified in Australia or overseas appear to sufficiently meet the principles above. There is no clear recognition that dementia is both a social and a medical issue. Australia has moved strongly in the direction of recognising the rights of people with disabilities including social participation but there is limited appreciation of this need in respect to most models for dementia. Recognition of dementia as a disability is only apparent in the self‐directed care model. The models also do not sufficiently consider the needs of the person with dementia and care partners together. Barriers to all the current models are the poor dementia knowledge and the tendency to stigmatise people with dementia by many health and aged care professionals.15 Next step: investment in model development We need to combine desirable elements in the primary care chronic disease management, case management, and specialist multidisciplinary care models. Having a system with a point of entry through primary care could maximise accessibility. Having a dementia and aged care specialist (eg, dementia nurse or case manager) working with GPs would bring the required skills and knowledge. A close partnership with a specialist multidisciplinary team (in person or using telehealth) would assist with diagnosis, ongoing support and management of complex cases, with possibly the most complex cases being managed by the specialist team. There needs to be investment to develop a model that is accessible, integrated and effective in meeting the needs of people with dementia. Our service delivery model needs to be co‐designed with people with dementia, their care partners, health, aged care, and state and federal government stakeholders, including treasury departments. Public health, social equity and human rights principles should underpin model design. Research is needed to explore proposed models and their elements with current recipients, service planners and providers. Methodologies may include service mapping; gap, risk and unintended consequence analysis; and economic modelling. Potential models will then need to be tested in a coordinated series of pilots and rigorous health system trials building towards national implementation. History has shown that piecemeal demonstration pilots and practice improvement projects will not bring about large‐scale change. Australia’s last National Framework for Action on Dementia 2015–2019 has just lapsed.16 Our new framework should include the development of a model of service delivery that considers accessible pathways to diagnosis and effective and seamless ongoing support of health and wellbeing throughout the course of dementia. Box – Current service funding structures and service models for Australians with dementia GPs = general practitioners; NDIS = National Disability Insurance Scheme; NGOs = non‐government organisations; PHNs = primary health networks.

NHMRC National Institute for Dementia Research Special Interest Group in Rehabilitation and Dementia

Current calls for primary care‐based screening and brief interventions for alcohol use should be reviewed Alcohol continues to contribute to significant morbidity and mortality in the Australian community. It is responsible for 4.5% of total disease burden,1 and 4186 deaths in 20172 and over 144 000 hospitalisations per year.3 While levels of alcohol consumption are slowly declining, alcohol continues to be a major preventable contributor to disease and death among Australians. Currently, over 25% of Australians report consuming alcohol at moderate or high risk levels.4 Over the past 20 years, there has been considerable research into the value of alcohol screening, brief intervention and referral for treatment (SBIRT) in primary health care as a public health measure to reduce alcohol consumption and related harms. The Alcohol Use Disorder Identification Test (AUDIT)5 was developed to assist with widespread standardised implementation of screening, and brief intervention for alcohol use disorder and has been extensively researched. More recently, the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST)6 was developed to address a broad range of substances. There is good evidence based on numerous randomised controlled trials that brief interventions for alcohol use result in reductions in drinking which are at least sustained for 12 months.7 However, the actual size of the reduction in drinking has been revised down from 2007 when it was estimated that SBIRT would result in a reduction of alcohol intake by 57 g (nearly six standard drinks) per week,8 to 20 g (two standard drinks) per week.7 This reduction in effect size will inevitably affect estimates in cost‐effectiveness models. While overall average consumption has reduced, at least based on self‐report, SBIRT has been found to have little effect on frequency of binge drinking, numbers of drinking days per week, and intensity of drinking.7 It is therefore likely to have little effect on adverse events from intoxication, the major cause of harm for younger people. Despite strong evidence that SBIRT will result in self‐reported reduced drinking (albeit less reduction than previously thought), there have been problems with real‐world translation into practice, both on a large scale multi‐practice level9,10 and a national basis as demonstrated in Scotland.11 In terms of demonstrated effects on alcohol consumption at a population level, the most extensive program implemented so far has been Scotland’s Alcohol Strategy.11 This program aimed to deliver SBIRT across the entire primary care, emergency department and antenatal populations and was part of a suite of measures to address alcohol‐related harms in Scotland. Other measures included prohibition of multi‐buy discounting (eg, “buy five, get one free”), minimum unit pricing (unsuccessfully challenged by the Scottish Whisky Association in the Scottish Supreme Court and now being implemented), tightening of liquor licencing processes, and a tripling of investment in treatment and support services. Subsequent measures of alcohol consumption across Scotland, Wales and England have not demonstrated any significant differences in the trajectories of alcohol consumption between these countries. Consumption has decreased in all three countries.11 Although 43% of hazardous and harmful drinkers were screened in Scotland and received brief interventions, data on exactly who was screened were difficult to collect, and screening among women attending antenatal care was only partially implemented.11 Young people were difficult to access, probably due to lower health service attendance rates. Furthermore, a 2018 Cochrane review7 found that research into the effects of SBIRT on alcohol‐related harms, the end point of most importance, has been very limited, and was unable to reach a conclusion regarding the effect of SBIRT on alcohol‐related harms. The studies that have looked at this important issue found that there was no effect.7 In addition, recent research has cast doubt on the effectiveness of referral to treatment among the higher risk (mostly dependent) drinkers. Frost and colleagues12 reviewed the effects of brief interventions on rates of referral. They found that patients at high risk who had received a brief intervention actually had less contact with specialist addiction services in the year following the brief intervention compared with those who had not received the brief intervention. Despite these concerns regarding effectiveness in real‐world settings, SBIRT has been recommended over the past decade in Australia by the 2009 National Preventative Health Strategy,13 and by the National Alcohol Strategy in 2019.14 Significant investment in structurally supporting SBIRT in primary care or other settings has not been forthcoming from Commonwealth or state governments. Currently in Australia, we have a situation where the Australian National Alcohol Strategy advocates for the adoption of SBIRT. This is despite a lack of evidence that it is effective in reducing harms even in research settings, as well as a lack of evidence for its effect on reducing population levels of drinking, and evidence that it does not result in increased engagement in specialist treatment even in well resourced health systems which have identified this as a target area. However, despite the current evidence that population‐based screening does not seem to have an effect on overall alcohol consumption, there is no denying the clinical value of addressing unhealthy alcohol consumption when identified in primary care. The AUDIT and the ASSIST both explore relevant key areas such as frequency of use, harms and dependence, which are important for the clinician and the patient to understand and address. They enable the clinician and patient to determine the risks associated with the patient’s current drinking patterns, and to start a conversation which then enables an agreed response. They should still be promoted as tools to use when a patient has been identified as drinking excessively through normal clinical processes. Despite current levels of alcohol‐related morbidity, the general practice environment does not support general practitioners responding to the problem. Longer consultations are insufficiently remunerated, skills development has been suboptimal, and secondary and tertiary services are not readily available when and where required. SBIRT alone will not address the current levels of alcohol use in Australia and associated harms. There should be increased emphasis on development of the skills base of the medical workforce at student, general practice and other specialty training levels so that clinicians can respond to hazardous and harmful alcohol and substance use effectively. Tools such as the AUDIT and the ASSIST may well have a role here. Use of current GP Medicare items such as mental health care plans, chronic disease management plans and team care arrangements should be encouraged and facilitated to better support complex care for patients with problems relating to alcohol and substance use. In addition, addiction services should work with general practice to streamline access to advice and referrals and improve communication channels. At the same time, policy changes to reduce alcohol‐related harms should continue to be pursued. Medical bodies including the Australian Medical Association and the Australian colleges representing physicians, GPs, surgeons, psychiatrists and emergency physicians have advocated strongly for such changes regarding alcohol, but despite this advocacy, most of the Australian community has not felt the need for major change. In general, policy change will only occur in response to community concern. The 2019 National Drug Strategy Household Survey indicated that the Australian community continues to identify methamphetamine as the drug of most concern, above alcohol. In addition, support continues to decline for reducing trading hours for pubs and clubs and increasing the minimum drinking age, as well as for all other evidence‐based measures aimed at reducing the harms nominated in the survey.4 It appears that the Australian community currently least supports the harm reduction strategies with the strongest evidence, but on the other hand supports the strategies with the least evidence. If there were more community support, other policy changes could include reviews of pricing of alcohol and packaged liquor outlet density, further regulation of advertising of alcohol, and further changes to drink driving laws. These might include requiring a zero blood alcohol level for broader groups of drivers such as all younger drivers (ie, under 25 years of age) and drivers with previous drink driving convictions. There should be a renewed emphasis on alcohol as a significant driver of morbidity and mortality at three levels: on the clinical level, renewed emphasis on education and training for medical practitioners to enable clinicians to better respond to people drinking harmfully; on the health care structural level, changing remuneration arrangements to better support primary care treatment for people with alcohol‐related problems should be advocated for; and in parallel with these changes, increased advocacy for changes to policies that reduce drinking and related harms on a population level, with particular emphasis on high risk populations. Health professionals are generally not trained as advocates. Bringing about change, even when supported by sound evidence, is difficult and takes time. Vested interests have sophisticated advocacy skills and are well resourced. Opportunities for the development of advocacy skills at medical student and postgraduate levels should be developed and promoted. Australia remains a world leader in tobacco control. The health professions should join forces, building on the lessons from tobacco control, to change the way the Australian community views alcohol, and then lead changes in clinical practice and policy which will reduce alcohol‐related harms.

Chris B Holmwood

Recommendations to guide residential aged care facilities in preparing for and managing infectious disease outbreaks

Georgia E Aitken · Alice L Holmes · Joseph E Ibrahim

Peer support initiatives can help health professionals experiencing mental health and wellbeing challenges during the COVID-19 pandemic and beyond

Tahnee L Bridson · Kym Jenkins · Kieran G Allen · Brett M McDermott

Stage 3 lockdown measures in Victoria reduced COVID-19 transmission, but more was required to control the epidemic

Allan Saul · Nick Scott · Brendan S Crabb · Suman S Majumdar · Benjamin Coghlan · Margaret E Hellard

The dangers of modern football are often scrutinised, but has safety actually evolved over time?

Jacob L Jewson · Peter Brukner · Thomas J Gara · Lauren V Fortington

The MJA–Lancet Countdown on health and climate change was established in 2017, and produced its first Australian national assessment in 2018 and its first annual update in 2019. It examines indicators across five broad domains: climate change impacts, exposures and vulnerability; adaptation, planning and resilience for health; mitigation actions and health co‐benefits; economics and finance; and public and political engagement. In the wake of the unprecedented and catastrophic 2019–20 Australian bushfire season, in this special report we present the 2020 update, with a focus on the relationship between health, climate change and bushfires, highlighting indicators that explore these linkages. In an environment of continuing increases in summer maximum temperatures and heatwave intensity, substantial increases in both fire risk and population exposure to bushfires are having an impact on Australia’s health and economy. As a result of the “Black Summer” bushfires, the monthly airborne particulate matter less than 2.5 μm in diameter (PM2.5) concentrations in New South Wales and the Australian Capital Territory in December 2019 were the highest of any month in any state or territory over the period 2000–2019 at 26.0 μg/m3 and 71.6 μg/m3 respectively, and insured economic losses were $2.2 billion. We also found growing awareness of and engagement with the links between health and climate change, with a 50% increase in scientific publications and a doubling of newspaper articles on the topic in Australia in 2019 compared with 2018. However, despite clear and present need, Australia still lacks a nationwide adaptation plan for health. As Australia recovers from the compounded effects of the bushfires and the coronavirus disease 2019 (COVID‐19) pandemic, the health profession has a pivotal role to play. It is uniquely suited to integrate the response to these short term threats with the longer term public health implications of climate change, and to argue for the economic recovery from COVID‐19 to align with and strengthen Australia’s commitments under the Paris Agreement.

Ying Zhang · Paul J Beggs · Alice McGushin · Hilary Bambrick · Stefan Trueck · Ivan C Hanigan · Geoffrey G Morgan · Helen L Berry · Martina K Linnenluecke · Fay H Johnston · Anthony G Capon · Nick Watts

Rapid dissemination of information should not come at the expense of quality, ethical standards or oversight The coronavirus disease 2019 (COVID‐19) pandemic has resulted in wide‐ranging health, social and economic impacts. By October 2020, global cases exceeded 41 million, with 1.1 million deaths.1 Urgent requirements for information were met with data on epidemiology, clinical features and recommended management being circulated on social media and pre‐publication servers. While this has allowed timely sharing of data, it has also brought risk of misinformation, with consequent changes to medical practice and misdirection of scarce resources based on flawed evidence. Medical publishing uses peer review to provide independent and critical assessment to verify data integrity, validity of interpretations, and confidence in conclusions. This process can take many weeks; however, in a rapidly spreading pandemic, speed is a competing priority. We hypothesised that these considerations may have altered the nature of medical publication. Accordingly, we characterised various aspects of COVID‐19‐related articles published in the five leading general medical journals with the highest impact factors (Web of Science) compared with an equivalent period in the preceding year. Procedures for identifying, classifying and comparing publications were specified a priori. Research ethics approval was not required. Publications were identified in the United States National Library of Medicine PubMed database. All articles published between 1 January and 31 May (inclusive) in 2019 and 2020 in The New England Journal of Medicine, The Lancet, JAMA, The BMJ and Annals of Internal Medicine were included. The sampling timeframe was defined by the first public health notification of COVID‐19 in China on 31 December 2019, ending at the time of the conduct of the literature search (Box 1). Within the 2019 search results, 60 articles were randomly selected using a random number generator in Stata 15.1. Publications without abstracts were excluded. Journal websites for each study period were searched for retracted articles. Three reviewers independently abstracted the variables contained in Box 2 and Box 3. The h‐index (a measure of publication productivity and citation impact) of the first and last author was taken from Web of Science. A fourth investigator reviewed all data, harmonising interpretations and resolving any errors. Data were analysed using Stata 15.1. Skewed continuous data were described using medians with interquartile ranges (IQRs) and compared using the Wilcoxon–Mann–Whitney test. Categorical data were compared using the Fisher exact test or χ2 test as appropriate. Exact P values are reported and those less than 0.05 deemed significant. During January to May 2020, PubMed listed 4001 articles, of which 1120 (28%) were related to COVID‐19. There were 134 articles with PubMed‐coded abstracts which were included for full review (Box 4). One additional COVID‐19 article was identified in the search for retracted articles but excluded from quantitative comparisons because it lacked an abstract. During the same period in 2019, 54 articles were ultimately identified as eligible for comparison (Box 4). Compared with 2019, among the COVID‐19‐related publications in 2020, there were more case reports or case series, cohort studies, editorials and commentaries and fewer randomised controlled trials (7/134 [5.2%] v 19/54 [35.2%]) (Box 2). A similar proportion (37/52 [68.5%] non‐COVID‐19‐related articles v 74/134 [55.2%] COVID‐19‐related articles; P = 0.09) reported primary data. Of the 2019 articles, only two of 54 (3.7%) originated in China, whereas 32 of 134 (23.9%) of the COVID‐19 articles published in 2020 were from China. The proportion of COVID‐19 articles in 2020 for which a correction was published was higher than for non‐COVID‐19 articles published in 2019 (28/124 [20.9%] v 4/54 [7.4%] respectively; P = 0.03). Time to the first publication of a correction was no different (median, 6 days [IQR, 4–14] v 7.5 days [IQR, 5–18] respectively; P = 0.53). Three 2020 COVID‐19 articles,2,3,4 but none of the 2019 articles, were retracted after publication. Only one journal, JAMA, routinely reported when a manuscript was submitted. In this journal, the median time from submission to publication fell from 139 days (IQR, 130–144) in 2019 to 23 days (IQR, 12–30) in 2020 (P < 0.001). The median number of authors and their publication productivity and impact, as quantified by their median h‐indices, were similar. There was no statistically significant difference in the number of studies willing to share data under appropriate circumstances (P = 0.19), or those receiving commercial funding (P = 0.97). The measured characteristics of randomised trials related to COVID‐19 were not statistically different to studies of an equivalent type published in the preceding year; however, numerically fewer subjects (median, 199 [IQR, 127–397] v 424 [IQR, 225–1076]; P = 0.07) and centres (median, 10 [IQR, 1–55] v 30 [IQR, 4–168; P = 0.15) participated (Box 3). Similarly, the observational study sample size was significantly smaller (median, 152.5 [IQR, 15–3481] v 191 972.5 [IQR, 1407.5–756 444]; P < 0.001), and the number of participating centres was numerically lower in the 2020 COVID‐19 group (median, 1 [IQR, 1–7] v 26 [IQR, 1–49]; P = 0.07). While not significantly different between groups due to the low numbers, 11 (16.7%) observational studies among the COVID‐19 publications did not report oversight by an ethics committee or institutional review board, and only nine (56.3%) case reports and case series with ten patients or fewer stated that patient consent had been obtained or that an exemption from this requirement had been granted. In the first 5 months of the COVID‐19 pandemic, the five leading medical journals published a substantial number of articles that differed in many respects from their usual material. The journals examined were the clinically focused general medical journals with the top five Web of Science 2019 impact factors, ranging from 21.3 to 74.6, representing the medical literature with the greatest international influence on health policy and clinical practice. As reasonably expected, there was a seven‐fold reduction in the proportion of articles reporting randomised controlled trials, and a compensatory increase in small case series, opinions and editorials. While there were few (n = 2) articles in the random selection of 2019 papers that were published from China, nearly one‐quarter of the COVID‐19 publications came from this country, as anticipated given the location of the earliest cases. There was no difference in the median h‐indices of authors, suggesting experienced academics pivoted rapidly to COVID‐19 research. In circumstances which usually require consent, just under half of the COVID‐19 studies did not explicitly state consent was obtained, despite clear recommendations by the International Committee of Medical Journal Editors.5 The proportion of articles that referenced appropriate ethics committee or institutional review oversight was statistically unchanged; however, it is still a concern that 11 (16.7%) observational COVID‐19 studies lacked any statement to this effect. In addition, several other articles stated that they had been exempted from the requirement for ethical review due to the nature of the pandemic. Respect for personal autonomy and the value of independent oversight have always imposed additional workload on those seeking broader public health benefits. If COVID‐19 has created challenges in adhering to the usual practices of obtaining ethics approval and consent, consideration should be given to whether these processes could be amended to improve speed and accessibility, particularly during global health emergencies. There was a near three‐fold increase in the proportion of studies that published corrections, perhaps reflecting the observed reduction in time from submission to publication observed in the one journal for which these data were available. It is likely this figure is an underestimation, given that corrections and retractions would be expected to continue over time. Three COVID‐19 studies were retracted. The publication of one of these articles4 had important implications, resulting in the temporary cessation of the World Health Organization's trial of hydroxychloroquine.6 While the corrections and retractions may be an artefact of increased speed to publication, it is also possible that their higher number might be the effect of enhanced focus on research related to COVID‐19. Nonetheless, journals must retain the integrity of review processes if they are to offer value beyond alternative online means of information dissemination. This review has found similar results to bibliometric studies relating to the COVID‐19 pandemic, which have identified higher numbers of case series and reviews and fewer randomised clinical trials.7,8,9 We did not examine other articles from 2020 to understand the effect of COVID‐19 on contemporaneous publications, or to be able to comment on whether observed changes were specific to COVID‐19 or true of all 2020 articles. We note the convenience sampling of two similar periods may overestimate the magnitude of our findings. The cohort of 2019 studies for comparison was selected at random, rather than being matched by study type or size. When identifying h‐indices, we had difficulty identifying some Chinese authors, highlighting a bias against researchers without a name that can be distinctively rendered in the English language alphabet. Further implementation of unique author identifiers, such as the Open Research and Contributor ID (ORCID; www.orcid.org) or ResearcherID (Clarivate Analytics) would address this problem. We did not assess the quality of published studies or adherence to reporting guidelines. As part of their early response to the worldwide problem presented by the COVID‐19 pandemic, there was a significant change in the characteristics of articles published by leading medical journals, with some evidence of a tendency towards publishing articles prematurely and those with lower internal validity. While these unique circumstances no doubt warranted such a change, rapid dissemination of information should not need to come at the expense of quality, ethical standards or oversight. Others have suggested several solutions to this challenge, including a two‐track review process for pandemic and non‐pandemic research, rapid preliminary assessment of research methodology by skilled in‐house reviewers before deciding whether to send for peer review, sharing of peer‐reviews between reviewers and journals, and mentored peer reviewing by research trainees.10 As part of pandemic preparedness, planning to facilitate augmentation of resources available to medical publishers, allowing maintenance of standards of review, should occur. Box 1 – Search strategy ((“JAMA”[Journal]) or (“The New England Journal of Medicine”[Journal]) or (“Annals of Internal Medicine”[Journal]) or (“BMJ”[Journal]) or (“Lancet”[Journal])) and (2020/1/1:2020/5/31[Date — Entry]) or and (2019/1/1:2019/5/31[Date — Entry]) Articles related to COVID‐19 were identified by adding and ((“covid”[All fields]) or (“coronavirus”[MeSH Terms]) or (“coronavirus”[All fields]) or (“coronaviruses”[All fields])) Box 2 – Characteristics of publications 2019 non‐COVID‐19 2020 COVID‐19 P Total number of articles 54 134 Article type Systematic review/meta‐analysis/narrative review 8 (14.8%) 16 (11.9%) < 0.001 Randomised controlled trial 19 (35.2%) 7 (5.2%) Cohort study 11 (20.4%) 25 (18.7%) Cross‐sectional study 5 (9.3%) 8 (6.0%) Case–control study 1 (1.9%) 2 (1.5%) Case series 2 (3.7%) 30 (22.4%) Case report 0 (0.0%) 4 (3.0%) Diagnostic evaluation 0 (0.0%) 1 (0.7%) Opinion 7 (13.0%) 33 (24.6%) Other 1 (1.9%) 8 (6.0%) Reported primary data 37 (68.5%) 74 (55.2%) 0.09 Correction published 4 (7.4%) 28 (20.9%) 0.03 Days from publication to correction, median (IQR) 6 (4–14) 7.5 (5–18) 0.53 Retracted 0 (0.0%) 3 (2.2%) 0.56 h‐index of first author, median (IQR) 13.5 (3–36) 11.5 (6–30) 0.54 h‐index of last author, median (IQR) 26 (14–38) 21 (10–38) 0.14 Associated editorial of eligible articles 21 (38.9%) 44 (32.9%) 0.43 Number of masthead authors, median (IQR) 8 (5–19) 7 (4–18) 0.52 Number of total authors, median (IQR) 8 (5–23) 7 (4–19) 0.23 Region of origin China 2 (3.7%) 32 (23.9%) < 0.001 United States 24 (44.4%) 67 (50.0%) Europe 20 (37.0%) 24 (17.9%) Rest of world (high income countries) 3 (5.6%) 11 (8.2%) Rest of world (low income countries) 5 (9.3%) 0 (0.0%) COVID-19 = coronavirus disease 2019; IQR = interquartile range. Box 3 – Characteristics of studies reported table#t3 tbody td:nth-child(n+2) P. Pleft { text-align: center; } 2019 non‐COVID‐19 2020 COVID‐19 P Randomised controlled trials 19 7 Number of subjects, median (IQR) 424 (225–1076) 199 (127–397) 0.07 Participating centres, median (IQR) 30 (4–168) 10 (1–55) 0.15 Studies that received funding of any type from a commercial source 8 (42.1%) 3 (42.9%) 0.97 Studies in which a commercial entity had influence over any aspect of study conduct or reporting 7 (36.8%) 2 (28.6%) 0.69 Studies stating willingness to share data under appropriate circumstances 15 (78.9%) 7 (100.0%) 0.19 Studies stating individual patient consent or waiver was granted 19 (100.0%) 7 (100.0%) 1.0 Studies noting review by ethics committee 19 (100.0%) 7 (100.0%) 1.0 Observational studies* 19 66 Number of subjects, median (IQR) 191 972.5 (1407.5–756 444) 152.5 (15–3481) < 0.001 Participating centres, median (IQR) 26 (1–49) 1 (1–7) 0.07 Studies that received funding of any type from a commercial source 0 (0.0%) 4 (6.1%) 0.27 Studies in which a commercial entity had influence over any aspect of study conduct or reporting 0 (0.0%) 3 (4.5%) 0.34 Studies stating willingness to share data under appropriate circumstances 8 (42.1%) 15 (22.7%) 0.09 Studies not stating individual patient consent was obtained or a waiver was granted 3 (15.8%) 17 (25.8%) 0.37 Studies not noting review by ethics committee 0 (0.0%) 11 (16.7%) 0.06 Case reports/case series (≤ 10 patients) 1 16 Studies stating individual patient consent was obtained 1 (100.0%) 9 (56.3%) 0.40 COVID-19 = coronavirus disease 2019; IQR = interquartile range. * Observational studies included cross-sectional studies, case–control studies, cohort studies and case series reporting data from one patient or more. Box 4 – Publication identification flow diagram COVID‐19 = coronavirus disease 2019.

Kirsty A Whitmore · Kevin B Laupland · Clare M Vincent · Felicity A Edwards · Michael C Reade

Controlling the exit from work and accumulating multiple resources early predict adjustment to retirement The traditional approach to leaving a career in medicine has been informal. The fact that about 10% of medical practitioners in Australia are aged 65 years or over1 — a seemingly natural consequence of increased life expectancy, improved quality of life and fluctuations in financial markets — highlights the need for a more methodical process for leaving medicine. The final transition in a medical career is one that the profession has largely ignored, thereby risking unplanned departures that affect succession planning for practices, continuity of care for patients, and the wellbeing of the practitioner. The eventual introduction of proposed mandatory health checks for practitioners aged 70 years and over in Australia2 may hasten the retirement of some, which only increases the urgency of retirement planning becoming a routine task for all practitioners. The aim of this article is to describe a framework that examines how this transition may be achieved, so that practitioner wellbeing and adjustment to retirement are enhanced. For all the changes in medical culture that must occur — and to which the colleges, employers and other professional organisations must contribute — the individual practitioner ultimately remains responsible for their own welfare across the career cycle. While this article is aimed mainly at clinicians, its principles remain pertinent to other medical practitioners. Understanding the process of retirement Retirement is not a lone event. It is better understood as a longitudinal process that comprises three phases that may overlap.3 In the “pre‐retirement” phase, the practitioner continues to work but may anticipate and prepare for retirement. In the “transition” phase, decisions are made about how and when the practitioner should approach stopping work. The final phase of “adaptation” may involve some paid work but the practitioner is principally retired. Each phase is considered a critical turning point, in which action or neglect can influence the outcome of subsequent phases. Some practitioners may chart a non‐linear transition, moving in and out of work. Any approach to determining the optimal time to transition out of a career in medicine must consider individual motivation as well as other competing factors. The first is the right of all and the desire of some older practitioners to continue working versus the extrinsic demands of family expectations or life events, such as illness in a loved one. The second is the continued provision of clinical services by senior medical practitioners, usually within well established patient relationships, versus the right of patients to receive the highest level of care possible. In this regard, older practitioners are at increased risk of physical and cognitive changes that may potentially affect practice, such as poorer patient outcomes,4 and may lead to being the subject of a complaint to a regulatory authority.5 Why retirement planning may be hard When the transition away from work should start is an individual decision. Yet a cross‐sectional survey found that more than one‐third of older practitioners working in Australia had failed to even reach the pre‐retirement phase, as they reported no intention of retiring or were unsure about doing so.6 Moreover, not intending to retire was an occupational factor that predicted practitioners’ perceptions of ageing successfully.7 This suggests that even considering leaving work may be viewed as a sign of personal weakness. Financial factors related to inadequate superannuation funds, continuing debt, or other commitments have been found to prevent retirement planning.8 Several other reasons for continued practice and delayed retirement, however, reflect more intrinsic difficulties in detaching from medicine. These include a feeling of responsibility for patients, a lack of interests outside of medicine, and a fear of potential changes in their relationship with a spouse.8 These factors may be the result of a lifetime of work centrality whereby medicine takes precedence over other life roles.6 For many doctors, self‐identity is bound up in their work and the drive to further their careers. A study of academics suggested that work–life balance was more nebulous because outside interests, including family, were considered an inconvenient distraction.9 Conversely, emotional connections towards a workplace or institution may strengthen. Prioritising work limits social connections and creative pursuits, thereby perpetuating a reluctance to retire. A structured transition to retirement plan The purpose of adequate retirement planning is to enhance wellbeing after ceasing work. Pre‐retirement planning is a long term goal‐oriented behaviour that has been associated with retirement satisfaction.10 In addition, retirement adjustment is predicted by the conditions of exit — namely, control over how and when one leaves work11 — and resource acquisition in multiple domains.12 While the elements of planning should occur throughout the career cycle, we recognise that it is not a compelling consideration for many practitioners. We would still propose that all practitioners formally write an initial transition to retirement plan by the age of 55 at the latest, review it regularly, and the intervals between reviews should become more frequent with time (Box). The proposed introduction of the mandatory health check for practitioners aged 70 years or over should be an important incentive for self‐care. Traditional pre‐retirement planning has tended to consist only of financial advice such as wealth creation, tax optimisation, and estate planning. This is an essential task as people tend to underestimate how much money will be required in retirement, but should not be used as the sole criterion of fitness for retirement. The more pertinent questions are how time in retirement will be spent and how much it will cost to support, rather than a pre‐determined goal of wealth accumulation. Resource accumulation While adequate financial resources do contribute to retirement adjustment, so do adequate physical health, social engagement and emotional resources.12 This means a much broader spectrum of planning that uses advice from multiple professionals is required. Methods for optimising financial, physical and leisure resources are relatively easily sourced. What may be more difficult to manage, however, are the emotional resources needed to navigate the transition to retirement. In particular, the inevitable loss of self‐identity may lead to anticipatory grief and bereavement.13 That intending to retire is viewed as a mark of ageing less well by practitioners not only poses a considerable challenge for their retirement planning, but highlights the importance of understanding successful ageing in any discussion of transitioning away from full‐time work in medicine. Successful ageing is a concept that has evolved from a biomedical model, requiring an absence of physical disease and good physical functioning, to a more subjective notion that emphasises adaptation and autonomy.14 A sense of engagement, a prominent feature of self‐rated successful ageing, is inherent in the work of medical practitioners and is reflected in the pursuit of continued stimulation and learning, a sense of purpose and utility to society.15 Successful occupational ageing is based on insight into personal strengths, a dynamic process of goal setting, generativity (guiding and mentoring the next generation), and self‐care.14 There are a number of areas that are important for self‐reflection, such as the original motivations for training in medicine, the reasons for continuing to work, the anticipatory grief of the loss of identity and role, and the fear of ageing.14 Not every practitioner will be capable of self‐reflection, so that professional help may be required via a career development counsellor or vocational psychologist. While many practitioners will set a pre‐determined age or personal milestone at which to retire, others may continue to work indefinitely, thereby increasing the risk of practising with an impairment. This may be prevented by incorporating a professional advance care plan16 that outlines a set of premorbid views about ongoing practice in the event that capacity to practise is impaired. Permission would be given to one or more people, such as a spouse, friend or colleague, to monitor fitness to practise and to provide regular feedback. “Red flags” to stop working may include physical illness or concern from a trusted source about deterioration in cognition or procedural or clinical skills. Developing an encore career Developing an encore career is the final aspect of the transition plan that allows the use of skills and experience developed over a career, and helps maintain meaning and engagement. Giving consideration to the encore career while still working enables the practitioner to better position themselves to access greater opportunities. Up until this point, the practitioner may have found integrating different life roles challenging and pursuing outside interests unnecessary. An encore career can lead to feeling purposeful, provide goals to strive towards, and opportunities for intellectual and social pursuits. For example, a general practitioner keen to maintain patient contact but reduce caseload may want to specialise in an area of medicine (eg, mental health). Some may investigate governance roles with accrediting bodies, sit on guardianship or mental health review tribunals, or take up committee membership. Others may wish to provide leadership through directorships or management roles in hospitals or medical services. Others may wish to apply their lifetime of insights to teaching or research pursuits. Examples include teaching medical students, mentoring trainees, writing research grants and articles. It might be worthwhile revisiting those businesses or volunteering opportunities that were set aside before a medical career became the sole focus. Conclusion Retirement should not be viewed as a single endpoint but as an anticipatory process that involves the accumulation of social, emotional, financial and other resources. Active participation in retirement planning is essential to ease the transition, gain a better sense of control and enhance emotional adaptation. Encore careers provide the opportunity to capitalise on a lifetime of accumulated wisdom by integrating training, experience, interests and strengths. Given medicine’s long‐standing neglect of retirement planning, there is also a need for professional bodies to provide education about the transition process and for practitioners themselves to share stories of encore careers and inspire peers to explore avenues for transition. Box – My plan for transitioning to retirement I will accumulate the following resources: Physical resources ► What am I doing to take care of my health (diet, exercise, adherence with medications)? ► How often am I seeking independent health care, including consulting my general practitioner? Financial resources ► How do I optimise my finances (reduce debt, maintain income)? ► Who is my professional adviser? How often do I consult with them? Emotional resources ► Who are the people I can connect with for emotional support? Who do I support in return? ► Who do I know who has aged well and transitioned well? What can they share? ► What types of professional assistance do I need to support the transition? Social resources ► How do I maintain healthy relationships (spouse, children, family and friends)? ► Who can provide professional support to help me manage these relationships now and when I am not working? ► What relationships have lapsed that I want to re‐initiate? ► What interests can I develop or revisit? Cognitive resources ► What intellectual pursuits outside medicine can I follow? ► How do l want to learn, develop and grow? ► What creative pursuits do I want to develop? ► What courses or training might help to reposition me for an encore career? My professional advance care plan I will discuss the timing of transition and retirement with my peers and loved ones I will engage my junior colleagues in a discussion about succession planning I will reduce my hours, stop procedural work etc, at age X or if the following health or practice problems occur … I will stop working at age X or if the following health or practice problems occur … I will set up a peer mentoring system with close colleagues so that we can provide each other with feedback on professional issues and review skill levels. Encore career What are some professional aspirations I can pursue given greater time availability? How do I convert my passions and interests into pursuits? Do I want to focus on areas of expertise, governance, leadership or teaching and research? Was there a business opportunity I considered before my medical career that I want to revisit? What other career options have people who have successfully transitioned considered?

Chanaka Wijeratne · Joanne Earl

Artificial intelligence is being used in several different ways to curb the current pandemic while demonstrating its potential to be even more effective for the next one

Ian A Scott · Enrico W Coiera

Recent proposals to adopt tenecteplase as the recommended thrombolytic agent for stroke reperfusion will reduce its availability for patients with acute myocardial infarction

Mark Parsons · Leonid Churilov · Aletta E Schutte · Christopher Levi