Volume 215 Issue 2

The Olympic Games raise many infection control, health security and ethical challenges

Craig B Dalton · Joanne Taylor

Developing a pathway to prioritise clinical research and prepare for future pandemics remains an urgent need

Asha C Bowen · Steven YC Tong · Joshua S Davis

The research response in Australia has been rapid, but better coordination is imperative

Anna Lene Seidler · Mason Aberoumand · Jonathan G Williams · Aidan Tan · Kylie E Hunter · Angela Webster

Australia’s narcolepsy management is inadequate by international standards and should be aligned with that of other first class health services

Sheila Sivam · Ksenia Chamula · John Swieca · Simon Frenkel · Bandana Saini

Influenza vaccine uptake in children has grown in response to increased awareness and progressive expansion of funding Over the past decade, multiple initiatives have been implemented to strengthen influenza vaccination programs in Australia, with an increasing focus on children. In this article, we review these changes, the events that prompted them, and how they have influenced influenza vaccine uptake in Australia. Burden of influenza Before the coronavirus disease 2019 (COVID‐19) pandemic, influenza was responsible for a higher disease burden and overall health impact than any other vaccine‐preventable disease in Australia.1 Historically, Australian influenza notification rates have been highest in children, particularly in those aged less than 2 years.2 The highest annual hospitalisation rates for influenza overall have been recorded in children aged less than 6 months (192 per 100 000 per year), followed by children aged 6–23 months (109 per 100 000 per year).2 Although paediatric hospitalisation rates are high, annual rates of influenza‐associated deaths in children are low compared with adults: 0.20–0.39 per 100 000 children aged under 5 years compared with 0.65 per 100 000 in people aged 65–74 years and 3.66 per 100 000 in people aged 75 years or more.2 While it appears that influenza may have become more burdensome for children in recent years due to an increase in disease notifications (Box 1), the notification rates also reflect an increase in influenza testing rates. For example, in New South Wales, there was a seven‐fold increase in tests done in 2019 compared with 2009.3 However, influenza notifications dramatically declined in 2020 in all age groups (Box 1), most likely due to increased hygiene and physical distancing measures and the implementation of border closures to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) — the virus that causes COVID‐19. Influenza‐associated morbidity and mortality rates also likely underestimate the true influenza‐associated burden related to underascertainment bias and other factors. Influenza vaccination recommendations and funding All people in Australia aged 6 months or more are recommended to receive annual influenza vaccination, with free influenza vaccines for the highest risk groups provided by the National Immunisation Program (NIP).4 Vaccination is only contraindicated for people who have experienced anaphylaxis in association with a previous dose or any component of an influenza vaccine.4 Children aged 6 months to 9 years receiving the vaccine for the first time require two doses at least 4 weeks apart; those aged 9 years or more require only one dose in their first year of receipt.4 Until recently, there was limited funding for, and promotion of, influenza vaccination in children. In 2018, following the large 2017 influenza season (Box 1), and underpinned by evidence of paediatric disease burden, vaccine safety and efficacy,2,6,7 all Australian states and territories, except the Northern Territory, followed Western Australia’s 2008 initiative in funding influenza vaccination for all children aged 6–59 months; the NT followed in 2019 (Box 2). The NIP expanded in 2019 to include Aboriginal and Torres Strait Islander peoples of all ages (closing the funding gap for those aged 5 to < 15 years), and in 2020, the influenza vaccine was added to the NIP for all children aged 6–59 months.5 Influenza vaccine effectiveness Influenza vaccine effectiveness is usually measured against either all laboratory‐confirmed influenza (using disease notification data) or influenza‐associated hospitalisation (a proxy for severe disease) and varies each year. In 2015, influenza vaccine effectiveness in children aged under 18 years estimated from data collected from sentinel general practitioner networks was 54%,8 indicating that influenza‐associated primary care visits more than halved in vaccinated children compared with unvaccinated children. In 2017, a year dominated by the influenza A subtype H3N2, for which the vaccine typically performs less well, the influenza vaccine effectiveness against hospitalisation for influenza was estimated to be 30% in children;6 however, in 2018, an influenza A subtype H1N1 predominant year, vaccine effectiveness against paediatric influenza hospitalisation was 78%.9 Despite moderate effectiveness, at an individual and population level, influenza vaccination still prevents significant morbidity and mortality. For example, with 55% of population coverage and an adjusted vaccine effectiveness of only 32% (95% CI, 16–44%) for children aged 5–17 years during the 2017–2018 influenza season in the United States, vaccination was still estimated to have prevented 1.4 million illnesses, 711 000 medical visits, 3700 hospitalisations, and 89 deaths of children aged 5–17 years.10 Influenza vaccine safety In April 2010, early in the Australian influenza vaccination season, the Australian Government’s Chief Medical Officer suspended the use of influenza vaccine in children aged 5 years or less due to an unexpectedly high rate of fever and febrile seizures in the 4–24 hours following influenza vaccine administration.11 Influenza vaccination in children aged 5 years or less continued with non‐CSL influenza vaccines from August 2010 onwards,12 given they had no safety issues. The program suspension had negative effects on influenza vaccine attitudes, confidence and coverage in children in the following years.13 However, recent evidence suggests that influenza vaccine safety concerns may no longer be a significant barrier to influenza vaccination of children in Australia. Rather, significant barriers include a lack of recommendation from a health care provider, difficulties in either remembering to make or getting an appointment for vaccination, a general lack of support for influenza vaccination, or a lack of history of influenza vaccine uptake by the child or their parent.14 An independent review into the national response to the Fluvax (CSL) safety incident identified ways to strengthen the safe delivery of influenza (and other) vaccines in Australia.15 In response to these recommendations, a national sentinel vaccine active safety surveillance system, known as AusVaxSafety (www.ausvaxsafety.org.au) was established in 2014. In this system, people of all ages who receive an influenza vaccine (or their carers) at more than 350 participating sentinel clinics (as at March 2021) are sent a short message service (SMS) text message and/or email in the days after vaccination with questions on whether they or their child experienced an adverse event following immunisation.7 Overall, data from this system have shown a safety profile consistent with that expected from clinical trials for all vaccine brands: approximately 10% of children’s carers report an adverse event following immunisation in their child within 3 days of influenza vaccination, the most common being fever or pain, swelling or redness at the injection site.7 Data from this ever‐expanding vaccine safety monitoring system have consistently shown low and expected reporting rates of mild transient adverse events known to be associated with the influenza vaccine. Recorded influenza vaccine uptake Since 2007, the number of influenza vaccine doses distributed and the recorded population coverage have increased in Australia, but with fluctuating uptake in children. Following the rapid attainment of high coverage in Western Australia in both Aboriginal and Torres Strait Islander and non‐Aboriginal children aged 6–59 months from 2008, coverage decreased substantially after the 2010 safety incident (Box 3 and Box 4). Coverage in Aboriginal and Torres Strait Islander children increased after the NIP funding in 2015, with highest rates in the NT (55.8%) in 2015 (Box 3). Coverage also increased dramatically in non‐Aboriginal children in 2018 (Box 4) following the introduction of state‐ and territory‐based programs for all children aged 6–59 months. In 2020, the first year of NIP‐funding for children aged 6–59 months, the reported uptake was 43.9%.16 This estimate may be higher given the uptake was calculated using doses recorded between March and August 2020 (rather than a full 12‐month period),16 and overall, actual coverage is likely higher due to issues of under‐reporting to the Australian Immunisation Register.17 The number of influenza vaccine doses available around Australia for all ages has also increased, with 8.3 million distributed in 2017, to 18 million in 2020.18 While a 43.9% uptake in children aged 6–59 months in 2020 in Australia represents an improvement from past low vaccination rates, Australia needs strategies to improve and sustain high coverage. These could include personalised vaccination reminders19 and provision of greater access to influenza vaccination services.20 Furthermore, given the influence of a recommendation from a health care provider on vaccine uptake,14 implementing a combination of education, communication training, electronic prompts and standing order protocols21 may assist health care providers in recommending influenza vaccination to all patients. Mandatory reporting of vaccination data to the Australian Immunisation Register, recently implemented in the context of the COVID‐19 vaccine roll‐out in Australia and extended to include other vaccines,22 should also assist in ensuring more accurate vaccine coverage estimations of influenza and all vaccines. Conclusion Influenza vaccine uptake in young children in Australia has increased in response to the progressive expansion of funding and is now delivered under the NIP. Further gains in uptake should ensure that protection against influenza disease in children is optimised during the ongoing COVID‐19 pandemic and in years to come. Box 1 – Notification rates of laboratory‐confirmed influenza in children aged less than 5 years in Australia, 2007–2020* * Influenza testing rates also increased over this time period.3 Source: National Notifiable Diseases Surveillance System, as at 18 February 2021. Box 2 – Significant events in influenza disease and vaccination policy in Australia ACT = Australian Capital Territory; NSW = New South Wales; NT = Northern Territory; QLD = Queensland; SA = South Australia; TAS = Tasmania; VIC = Victoria; WA = Western Australia; QIV = quadrivalent influenza vaccine. * Vaccine funded for Aboriginal and Torres Strait Islander people aged 15 years or more since 1999 (for all Aboriginal and Torres Strait Islander people aged ≥ 50 years, and Aboriginal and Torres Strait Islander people aged 15–49 years who have at least one of a range of underlying medical conditions that increase their risk of influenza or complications). Source: National Centre for Immunisation Research and Surveillance.5 Box 3 – Trends in recorded coverage of any dose of seasonal influenza vaccine among Aboriginal and Torres Strait Islander children aged 6 months to less than 5 years, by jurisdiction, 2007–2019 ACT = Australian Capital Territory; NSW = New South Wales; NT = Northern Territory; QLD = Queensland; SA = South Australia; TAS = Tasmania; VIC = Victoria; WA = Western Australia. Source: Australian Immunisation Register, data as at 31 March 2020. Box 4 – Trends in recorded coverage of any dose of seasonal influenza vaccine among non‐Aboriginal children aged 6 months to less than 5 years, by jurisdiction, 2007–2019 ACT = Australian Capital Territory; NSW = New South Wales; NT = Northern Territory; QLD = Queensland; SA = South Australia; TAS = Tasmania; VIC = Victoria; WA = Western Australia. Source: Australian Immunisation Register, data as at 31 March 2020.

Samantha J Carlson · Christopher C Blyth · Frank H Beard · Alexandra J Hendry · Allen C Cheng · Helen E Quinn · Julie Leask · Kristine Macartney

A 42-year-old man presented with acute onset substernal chest pain

Naim Mridha · Eloise Ward · Samual Hayman · Arun Dahiya · Sandhir Prasad

A 75-year-old man presented with right hemiparesis secondary to a left thalamic haemorrhage

Julia Lim · Yi Chao Foong

A lack of clear protocols elevates risks for clinicians for the consequences of decisions that they have a professional duty to make in the interests of their community Clinicians, ethicists and lawyers have long debated the parameters of triage in response to the inevitable disasters that sporadically overwhelm the health care system. Almost universally, they have advocated for open, transparent and consultative triage protocols, guidelines and legislation to combat biases and to support clinicians making unavoidable decisions in the interests of the community as a whole. The coronavirus disease 2019 (COVID‐19) pandemic has highlighted the importance of transparent triage. While there is considerable debate about ethical aspects of triage protocols, including concerns that the traditional focus on utilitarianism is discriminatory, largely missing from this discussion in Australia is that triage protocols are also important from a legal perspective — as a mechanism to promote lawful decision‐making processes and as a justification or defence to support clinicians’ decisions if a matter is litigated. The purpose of this article is twofold. First, after providing an overview of current COVID‐19 triage policies in Australia, we assess their legal status. Second, we argue that beyond ethics, transparent policies are needed so their compliance with law can be tested, and to enable practitioners to better understand their obligations before making sometimes “impossible” decisions. Australian COVID‐19 triage policies Australian clinicians have seen numerous ethical and professional guidance documents addressing COVID‐19 triage.1,2,3 These documents anticipate that if Australia’s health care system is overwhelmed as in other countries, clinicians will need guidelines to allocate limited resources, including ventilators, beds and highly trained personnel. The umbrella term “triage policy” denotes: (i) broad ethical or operational guidelines with suggested decision‐making principles;1,2,3 and (ii) more specific triage protocols,4 with set inclusion and exclusion criteria, and a process to prioritise individual patients when the system is overwhelmed. Many Australian COVID‐19 triage policies are ethical guidelines, but some Australian hospitals have also developed triage protocols.5 Internationally, the availability and content of such protocols varies widely. In a study from the United States, over half of responding institutions lacked a COVID‐19 triage protocol.6 In 2020, Mitchell and colleagues exposed insufficient transparency and significant variation in Victorian protocols.5 In Australia, primary responsibility for the administration of hospital services lies with the states, which have the power to promote a statewide approach to triage. Although every Australian state and territory has disaster management plans,7 publicly available COVID‐19 triage protocols are lacking. From March 2020 to 27 November 2020, the lead author (EC) regularly searched health department websites for COVID‐19 triage policies, examining both the websites’ content dedicated to COVID‐19 and searching keywords alone and in various combinations (COVID; intensive care; critical care; ICU; triage; framework; guidelines; policy; ethical). These searches revealed few relevant documents (Box 1). New South Wales is the only state to mention a triage guideline, but its COVID‐19 framework does not link to it.8 Queensland Health released an extensive ethical framework for COVID‐19 in April 2020,5 which has since been removed.9 Western Australia has a four‐page ethical framework but no publicly accessible protocol.10 The Commonwealth Government’s COVID‐19 strategy indicates the Commonwealth will work with state and territory governments to “agree on novel coronavirus triage criteria (if required)”,11 but there are no such criteria to date. Given constitutional arrangements, there is no expectation that the Commonwealth Government would provide these. The National Health and Medical Research Council has conducted consultation on an ethics framework for pandemics, but this is limited to ethical guidance. Legal status of COVID‐19 triage policies The prospect of deciding between patients who would benefit from life‐sustaining treatment is distressing. Compounding this is the potential for legal liability. Many of the legal issues that arise in pandemic triage are untested, and various areas of law may be engaged and applied in complex, fact‐specific ways. As other work has detailed, health authorities have wide discretion in making resource allocation decisions, which are generally respected by the courts.12,13 However, in some circumstances, clinicians (and institutions) may be found liable, and decisions may also be challenged on public law grounds (Box 2).13,14,15 These concerns are not merely academic; after Hurricane Katrina one doctor faced possible murder charges and civil lawsuits after several patients died during a hospital evacuation.16 Overseas, some governments have enacted immunity or indemnity laws to protect clinicians making COVID‐19 triage decisions.14,15 No such laws exist in Australia, and they do not appear to have been considered. Absent such laws, triage protocols may provide the next strongest legal defence. Under civil liability legislation, a clinician will generally not be negligent if acting in a manner widely accepted in Australia by peer professional opinion as competent medical practice (professional practice defence).12,13 Concrete advice on the legal significance of triage policies is difficult because the relationship between law and professional guidance is complex and each case is evaluated according to its unique facts. Whether the professional practice defence applies generally depends on the guideline’s nature, author and purported authority.17,18 A policy may create additional obligations beyond those imposed by law (eg, a specific hospital COVID‐19 triage protocol that must be followed by its clinicians), which may inform the legal standard of care.18 However, policy is not necessarily determinative of the standard of care, especially when couched as broad guidance (eg, COVID‐19 ethical guidelines from a professional college).18 Rigid adherence to policy can also be problematic; to meet the standard of care (and broader public decision‐making standards), clinicians must use judgment appropriate to the circumstances.17 Moreover, while policy can establish obligations in addition to the law, law may also impose more onerous obligations than a policy.18 When this occurs the legal standard will prevail. In other words, COVID‐19 triage policies can shape a regulatory response but only within the boundaries of the law. COVID‐19 triage policies may infringe laws in various nuanced ways.14 Liddell and colleagues note that the utilitarian “save the most lives possible” principle underlying most triage policies can infringe patients’ legal rights, many of which are unchanged in a disaster.14 In the United Kingdom, a legal challenge to the National Institute for Health and Care Excellence (NICE) COVID‐19 critical care protocol was initiated on the basis that its heavy reliance on the Clinical Frailty Scale constituted unlawful discrimination.19 In response, NICE revised the protocol to reduce reliance on the Clinical Frailty Scale for some patients. These issues have significant implications for clinicians: Absent a COVID‐19 triage policy, not providing beneficial life‐sustaining treatment is potentially risky because it may be harder to establish the professional practice defence in a negligence action. An institution’s failure to promulgate a policy could also result in claims. Additionally, a triage protocol (with its greater degree of specificity) would generally provide more legal protection than ethical guidelines. While it is lawful for governments and professional bodies to issue COVID‐19 triage policies, these policies should rely on appropriate evidence and must comply with specific jurisdictional laws, such as guardianship and human rights legislation (Box 2). Triage policies promote quality and consistency in decision making and guide clinicians to consider appropriate factors. However, clinicians must still exercise judgment which is reasonable and responsive to individual circumstances. Policies should provide guidance for when an individual is denied life‐sustaining treatment, since the duty to exercise reasonable care remains. Where reasonably possible, this may include communicating to the patient (or family) the reasons for the decision, providing appropriate palliative care, and information about complaints or dispute resolution processes. Transparency — not just about ethics From an ethical perspective, legitimate triage decisions require “accountability for reasonableness” — a fair process based on relevant criteria, a publicly accessible rationale, and (to the extent possible given the urgency of decisions) mechanisms for appeal, review and enforcement.20 Transparency is also important from a legal perspective because it subjects triage policies to public scrutiny before public health emergencies reach crisis levels. While internal legal advice on triage policies may have been sought, the NICE example illustrates that public scrutiny, consultation and litigation play an important role in testing legal boundaries. In addition to protecting individual patients, this promotes rigorous policy development and evaluation, and also benefits clinicians who are then not relying on policy later found to be deficient.17 It may also alleviate stress caused by uncertainty about protocols. Disclosure of triage policies also delivers a measure of natural justice by providing notice to patients and their families of decision‐making criteria and processes. Conclusion So far, Australia has avoided the scale of pandemic that has overwhelmed health systems elsewhere. While in this context, governments’ reluctance to develop and/or release triage protocols until a crisis has arrived is politically understandable, such a course of action carries significant risks. Public confidence is enhanced when governments have the political courage to embark on these difficult public debates in advance of need. Prioritising some individuals over others when the demand for resources exceeds supply is confronting for clinicians and the community alike, and challenges us to reflect on our deeply held values as a society. When clinicians are allocating scarce resources, they need standards to support their decisions which have been subject to public consultation and rigorous legal review. Australia’s successful management of the COVID‐19 pandemic is offering us the luxury of time to consult and reflect. [Corrections added on 9 June 2021 after first online publication: an additional row was added to Box 1.] Box 1 – Australian triage protocols and ethical guidelines for resource allocation during the coronavirus disease 2019 (COVID‐19) pandemic Jurisdiction COVID‐19 triage protocol or ethical guidelines Type of guidance Publicly available Commonwealth Australian Health Ethics Committee of the National Health and Medical Research Council: An ethics framework for pandemics (in development). Ethical guidelines Anticipated Australian Capital Territory None located on ACT Health website (https://health.act.gov.au). New South Wales NSW Health provides a COVID‐19 framework entitled “NSW adult intensive care services pandemic response planning”.8 The framework indicates that the NSW guideline for resource‐based decision making includes the “use of allocation frameworks and tools” with a reference (but no link to) a document entitled the “NSW Health COVID‐19 intensive care guidance drawn from principles in the NSW Health Influenza Pandemic Plan (PD2016_016). Sydney: NSW Health; 2020”. This 2020 document is based on the NSW Health Influenza Pandemic Plan (PD2016_016), which references the NSW Health policy “Influenza Pandemic – Providing Critical Care (PD2010_028)”. PD2010_028 contains a triage tool (https://www1.health.nsw.gov.au/pds/Pages/a-z.aspx). However, as the updated COVID‐19 intensive care guidance is not publicly available, we cannot confirm that it contains the same guidance as PD2016_016 or the PD2010_028 triage tool. Triage protocol and ethical and operational guidelines No Northern Territory None located on the NT Health Department website (https://health.nt.gov.au). Queensland On 20 April 2020, Queensland Health released a comprehensive ethical framework (developed in consultation with numerous stakeholders) but this has since been removed from its website.9 Ethical guidelines No (initially available but subsequently recalled) South Australia None located on the SA Health website (https://www.sahealth.sa.gov.au). Tasmania None located on the Tasmanian Department of Health website (https://www.health.tas.gov.au). Victoria None located on the Victorian Department of Health and Human Services website (https://www.dhhs.vic.gov.au/clinical-guidance-and-resources-covid-19). Western Australia The WA Health Department website includes a framework to guide decision making on the appropriateness of intensive care management during the COVID‐19 pandemic (last updated 26 June 2020) in its section on COVID‐19 guidance for health professionals.10 Ethical guidelines Yes Box 2 – Examples of potential areas of legal risk in response to pandemic triage decisions* Civil law Withholding or withdrawing beneficial life‐sustaining treatment from one patient to provide it to a patient with a better prognosis could amount to a breach of the duty of care and liability in negligence (subject to the peer professional practice defence for clinicians and the resource allocation defence in the case of hospitals). Criminal law Withdrawing a ventilator from one patient who is stable to provide it to another patient with a greater chance of survival could lead to charges of murder or manslaughter if the first patient dies as a result (charges would be subject to prosecutorial discretion and jurisdiction‐specific defences such as necessity). Commonwealth and state antidiscrimination laws A triage protocol could violate state and territory antidiscrimination legislation if the decision was made on the basis of a protected attribute such as age, disability or race (although specific protections may apply under the legislation for decision makers). Guardianship legislation This applies to patients who lack decision‐making capacity; for example, because they are unconscious, sedated or have cognitive impairment. At common law, medical practitioners have no legal duty to provide treatment that is non‐beneficial. However, the Guardianship and Administration Act 2000 (Qld) makes it an offence to withhold or withdraw life‐sustaining treatment from patients who lack capacity without the consent of an appropriate decision maker, even if providing that treatment would be “inconsistent with good medical practice” (ie, even if that treatment is non‐beneficial). This may preclude some triage decisions in Queensland. A decision to withhold or withdraw beneficial life‐sustaining treatment from a patient who lacks capacity to provide it to someone with a better prognosis may violate state or territory guardianship legislation, which requires health care decisions to be made in a person’s best interests. (This could also result in an emergency application to the Supreme Court to intervene in its parens patriae jurisdiction to protect the patient’s best interests.) * This is a non‐exhaustive list of examples. For an expanded discussion of legal challenges in Australia, see Close et al.13 See further Liddell et al14 for the UK context, which has some similarities to Australia.

Eliana Close · Lindy Willmott · Tina Cockburn · Simon Young · Will Cairns · Ben P White

In Australia, there is a paucity of coordinated strategies for preventing, detecting, and managing NAFLD

Lucy Gracen · Elizabeth E Powell

Objectives: To investigate the prevalence of non‐alcoholic fatty liver disease (NAFLD) and its risk factors in regional Victoria. Design: Prospective cross‐sectional observational study (sub‐study to CrossRoads II health study in Shepparton and Mooroopna). Setting: Four towns (populations, 6300‒49 800) in the Goulburn Valley of Victoria. Participants: Randomly selected from households selected from residential address lists provided by local government organisations for participation in the CrossRoads II study. Main outcome measures: Age‐ and sex‐adjusted estimates of NAFLD prevalence, defined by a fatty liver index score of 60 or more in people without excessive alcohol intake or viral hepatitis. Results: A total of 705 invited adults completed all required clinical, laboratory and questionnaire evaluations of alcohol use (participation rate, 37%); 392 were women (56%), and their mean age was 59.1 years (SD, 16.1 years). Of the 705 participants, 274 met the fatty liver index criterion for NAFLD (crude prevalence, 38.9%; age‐ and sex‐standardised prevalence, 35.7%). The mean age of participants with NAFLD (61 years; SD, 15 years) was higher than for those without NAFLD (58 years; SD, 16 years); a larger proportion of people with NAFLD were men (50% v 41%). Metabolic risk factors more frequent among participants with NAFLD included obesity (69% v 15%), hypertension (66% v 48%), diabetes (19% v 8%), and dyslipidaemia (63% v 33%). Mean serum alanine aminotransferase levels were higher (29 U/L; SD, 17 U/L v 24 U/L; SD, 14 U/L) and mean median liver stiffness greater (6.5 kPa; SD, 5.6 kPa v 5.3kPa; SD, 2.0 kPa) in participants with NAFLD. Conclusion: The prevalence of NAFLD among adults in regional Victoria is high. Metabolic risk factors are more common among people with NAFLD, as are elevated markers of liver injury.

Stuart K Roberts · Ammar Majeed · Kristen Glenister · Dianna Magliano · John S Lubel · Lisa Bourke · David Simmons · William W Kemp

Vaccination delivery was generally resilient in a period of unprecedented social and health care disruption

Brynley P Hull · Alexandra J Hendry · Aditi Dey · Kerin Bryant · Catherine Radkowski · Stephen Pellissier · Kristine Macartney · Frank H Beard

Intensive metabolic control reduces the incidence and progression of diabetes‐related micro‐ and macrovascular complications.1,2 Nevertheless, the risk of developing cardiovascular disease is higher for people with diabetes,3 although cardiovascular disease incidence rates are generally declining more rapidly for people with diabetes than for other people.4,5 We analysed hospital discharge data from the Victorian Admitted Episode Dataset6 for 1 January 1999 – 31 December 2016. We identified incident cases of three cardiovascular disease complications (acute myocardial infarction [AMI], stroke, and heart failure) by International Statistical Classification of Diseases, tenth revision, Australian modification (ICD‐10‐AM) codes. Data for 1999‒2003 were examined to ensure that admissions during the observation period (2004‒2016) were index admissions for the specific complication, but were not included in our main analysis. Admission rates were separately calculated for people with type 1 or type 2 diabetes (numbers of people with diagnosed diabetes, by year, were obtained from the National Diabetes Services Scheme, which captures 80–90% of diabetes diagnoses7) and for people without diabetes (derived from Australian Bureau of Statistics census data8). We analysed changes in admission rates by Joinpoint regression (https://surveillance.cancer.gov/joinpoint); points at which changes in the direction or magnitude of linear trends were statistically significant (P < 0.05) were determined with permutation tests. Each trend segment was described by an annual percentage change (APC), and the change for the entire study period as the mean APC (further details: online Supporting Information). The study was approved by the St Vincent’s Hospital Melbourne Human Research Ethics Committee (HREC/18/SVHM/146). A total of 382 107 patients were admitted to Victorian hospitals during 2004–2016 with cardiovascular complications: 278 991 without diabetes (73%), 3645 with type 1 diabetes (1%), and 99 471 with type 2 diabetes (26%). AMI admission rates declined during this period for people with type 1 (mean APC, –7.7%; 95% confidence interval [CI], –13.4% to –1.5%) or type 2 diabetes (mean APC, –11.4%; 95% CI, –13.0% to –9.9%), as well as for people without diabetes (mean APC, –5.0%; 95% CI, –6.7% to –3.4%) (Box 1, Box 2). Stroke admission rates declined significantly during 2004–2016 for people with type 1 diabetes (mean APC, –7.2%; 95% CI, –12.2% to –1.9%); for people with type 2 diabetes, rates declined during 2005–2011 and 2014–2016, but not during 2011–2014 (overall change: –11.9%; 95% CI, –17.0% to –6.5%). For patients without diabetes, the decline during 2005–2014 was significant (mean APC, –4.1%; 95% CI, –5.8% to –2.3%), but not during 2015–2016 (Box 1, Box 2). Admissions for heart failure declined during 2004–2016 for people with type 1 diabetes (mean APC, –10.3%; 95% CI, –14.1% to –6.4%) or type 2 diabetes (mean APC, –9.2%; 95% CI, –11.0% to –7.3%), and also for people without diabetes (mean APC, –2.8%; 95% CI, –4.1% to –1.5%) (Box 1, Box 2). As hospital discharge coding data do not provide information on metabolic control or medication use, we could not assess whether cardiovascular risk factor modification and use of specific medications were associated with changes in admission rates. We also lacked information on disease duration for patients with hospital‐coded diabetes. Further, we have counted admissions of any patients who had presented with complications before 1998 (ie, outside our 5‐year clearance period) as incident admissions; these patients would be at very high risk of further admissions, and their inclusion may have inflated the admission rates we report for the observation period of our study. Few recent studies have assessed outcomes for all three cardiovascular complications in a single investigation. Cardiovascular complication‐related admissions to Victorian hospitals declined during 2004–2016 more rapidly for people with diabetes than for those without diabetes. The relatively greater absolute decline in the numbers of admissions of people with diabetes may be related to the fact that they are considered to be at high risk for cardiovascular disease and are therefore treated more aggressively; the scope for reducing risk with multifactorial target‐driven interventions is greater in these patients. Nevertheless, admission rates for cardiovascular complications of people with diabetes remain relatively high. Box 1 – Age‐ and sex‐adjusted admission rates for cardiovascular complications (with 95% confidence intervals), Victoria, 2004–2016, by diabetes status of patients Box 2 – Annual percentage change (APC) in admissions for cardiovascular complications, Victoria, 2004–2016, by diabetes status Change in event rate, 2004–2016* Change in event rate, by period* Cardiovascular complication and diabetes status Admissions Overall change (95% CI) Mean APC (95% CI%) Mean APC (95% CI) Acute myocardial infarction No diabetes 114 965 –24.8% (–24.9% to –24.7%) –5.0% (–6.7% to –3.4%) — Type 1 diabetes 1272 –7.7% (–8.8% to –6.7%) –7.7% (–13.4% to –1.5%) 1. 2005–2009: +7.0% (–9.7% to +22.8%) 2. 2009–2016: –15.1% (–21.3% to –8.7%) Type 2 diabetes 15 278 –69.0% (–69.0% to –68.8%) –11.4% (–13.0% to –9.9%) — Stroke No diabetes 52 320 –10.9% (–13.6% to –10.6%) –1.7% (–4.9% to +1.5%) 1. 2005–2014: –4.1% (–5.8% to –2.3%) 2. 2014–2016: +9.6% (–10.2% to +33.8%) Type 1 diabetes 504 –44.4% (–50.0% to –41.4%) –7.2% (–12.2% to –1.9%) — Type 2 diabetes 17 440 –68.0% (–68.0% to –67.9%) –11.9% (–17.0% to –6.5%) 1. 2005–2011: –14.7% (–17.6% to –11.7%) 2. 2011–2014: +5.8% (–19.0% to +38.2%) 3. 2014–2016: –26.1% (–39.8% to –9.2%) Heart failure No diabetes 135 524 –22.2% (–22.3% to –22.2%) –2.8% (–4.1% to –1.5%) — Type 1 diabetes 1393 –55.1% (–58.6% to –52.4%) –10.3% (–14.1% to –6.4%) — Type 2 diabetes 52 831 –67.3% (–67.4% to –67.3%) –9.2% (–11.0% to –7.3%) — * Adjusted for age and sex. Event rates for 2004 and 2016 are included in the expanded version of this table in the online Supporting Information.

Katerina V Kiburg · Andrew I MacIsaac · Andrew Wilson · Vijaya Sundararajan · Richard J MacIsaac

Australians have around 19 000 hip fractures each year,1 and the estimated cost to the health care system was $445 million in 2015–16.2 Surgery within 48 hours of initial presentation to hospital is widely accepted as a clinically meaningful indicator of best practice care, and is supported by the Australian Hip Fracture Care Clinical Care Standard when there are no clinical contraindications.3 However, timely access to emergency orthopaedic hip fracture surgery is difficult in a country as large and geographically diverse as Australia; patients admitted to remote or regional hospitals that do not provide orthopaedic surgery must be transferred to larger regional centres. In a retrospective population study, we evaluated the impact of pre‐surgery hospital transfer and time to surgery on 30‐day mortality for people aged 65 years or more who underwent surgical interventions for fall‐related hip fractures in NSW public hospitals during 1 January 2011 – 31 December 2018. Hospitalisation data from the NSW Admitted Patient Data Collection and deaths data from the NSW Registry of Births, Deaths and Marriages were linked to provide person‐level records. Time to surgery (in calendar days) was estimated from the date of admission for the first episode of care to the date of surgery. Comorbid conditions during the preceding year were identified with the Charlson Comorbidity Index (CCI). Multilevel multivariable logistic regression models were fitted to assess the influence of patient‐level factors (age, sex, comorbidity) and process factors (transfer status, time to surgery) on 30‐day mortality. Operating hospitals were included as a random effect to account for variation between hospitals. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) were calculated and residual variation (variance partition coefficient) assessed. All analyses were performed in SAS Enterprise Guide 7.1 and MLwiN 3.02 (http://www.bristol.ac.uk/cmm/software/mlwin). The NSW Population and Health Services Research Ethics Committee approved the study (HREC/17/CIPHS/45). Of 36 956 patients who underwent hip fracture repair procedures in 36 hospitals, 3916 (10.6%) were transferred from peripheral hospitals to operating hospitals for surgery; 1579 were transferred on the day of presentation (40.3%), 1875 the following day (47.9%), and 462 patients (11.8%) spent at least two days at the admitting hospital before being transferred. Larger proportions of transferred patients than of patients admitted directly to operating hospitals were men (29.4% v 27.8%), under 85 years of age (50.9% v 48.4%), or had CCI scores of 1 or more (60.2% v 56.3%). The proportion of transferred patients who underwent surgery within 48 hours of presentation was smaller than for directly admitted patients (53.9% v 72.4%) (Box). In multilevel models adjusted for inter‐hospital variation, transfer was associated with higher risk of 30‐day mortality than direct admission (aOR, 1.15; 95% CI, 1.01–1.32), but after adjusting for age, sex, and comorbidity, neither transfer (aOR, 1.10; 95% CI, 0.95–1.28) nor delayed surgery (> 2 days v ≤ 2 days: aOR, 0.99; 95% CI, 0.89–1.11) significantly influenced mortality. The most influential factor was comorbidity (CCI ≥ 3 v CCI < 3: aOR, 4.89; 95% CI, 4.32–5.54). The discrimination of our fully adjusted model was adequate (area under the curve, 0.73), and 1.8% of residual variation in 30‐day mortality was attributable to differences between hospitals. In our large study of NSW people with hip fractures, we found that transfer from non‐operating to operating hospitals, after adjusting for patient and hospital characteristics, was not associated with higher 30‐day mortality, despite increasing the time between initial presentation and surgery. This is contrary to the findings of earlier, single centre studies in Australia.4,5,6 However, our study was the first to control for several key person‐level factors that increase the risk of death, and our findings suggest that time to surgery may be less important for health outcomes than these factors when other dimensions of care quality are equal. More research is required to understand the interplay between the effects of patient demographic characteristics, pre‐injury health status, and the quality of hip fracture care on 30‐day mortality for patients. Box – Characteristics of patients with hip fractures, by pre‐surgery transfer, New South Wales, 2011–2018* table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Not transferred Transferred Number of people 33 040 (89.4%) 3916 (10.6%) Sex Women 23 866 (72.2%) 2766 (70.6%) Men 9174 (27.8%) 1150 (29.4%) Age at admission (years) 65–74 4684 (14.2%) 535 (13.7%) 75–84 11 311 (34.2%) 1458 (37.2%) ≥ 85 17 045 (51.6%) 1923 (49.1%) Weighted Charlson Comorbidity Index score 0 14 437 (43.7%) 1556 (39.7%) 1–2 12 667 (38.3%) 1595 (40.7%) ≥ 3 5936 (18.0%) 765 (19.5%) Time to transfer (days) 0 1579 (40.3%) 1 1875 (47.9%) ≥ 2 462 (11.8%) Time to surgery (days) 0 12 991 (39.3%) 739 (18.9%) 1 10 939 (33.1%) 1370 (35.0%) ≥ 2 9110 (27.6%) 1807 (46.1%) Length of stay (days), mean (SD) Total 27.5 (21.9) 26.8 (20.5) Acute care 11.9 (8.5) 12.8 (9.0) 30‐day deaths 2172 (6.6%) 288 (7.4%) SD = standard deviation. * Linked hospitalisation and deaths data.

Lara A Harvey · Ian A Harris · Rebecca J Mitchell · Adrian Webster · Ian D Cameron · Louisa R Jorm · Hannah Seymour · Pooria Sarrami · Jacqueline CT Close

A patent foramen ovale (PFO) is an interatrial shunt, with a prevalence of 20–34% in the general population. While most people do not have secondary manifestations of a PFO, some reported sequelae include ischaemic stroke, migraine, platypnoea–orthodeoxia syndrome and decompression illness. Furthermore, in some cases, PFO closure should be considered for patients before neurosurgery and for patients with concomitant carcinoid syndrome. Recent trials support PFO closure for ischaemic stroke patients with high risk PFOs and absence of other identified stroke mechanisms. While PFOs can be associated with migraine with auras, with some patients reporting symptomatic improvement after closure, the evidence from randomised controlled trials is less clear in supporting the use of PFO closure for migraine treatment. PFO closure for other indications such as platypnoea–orthodeoxia syndrome, decompression illness and paradoxical embolism are based largely on case series with good clinical outcomes. PFO closure can be performed as a day surgical intervention with high procedural success and low risk of complications.

Kenneth K Cho · Shaun Khanna · Phillip Lo · Daniel Cheng · David Roy

To the Editor: The broadly accepted definition of a cancer survivor recognises that the survivorship begins at diagnosis.1 However, survivorship care pathways conventionally begin at completion of active treatment, presenting a challenge for addressing survivorship needs at diagnosis and for people living with incurable cancer.2 A revision of the concept of cancer survivorship is needed, placing the survivor at the centre of a dynamic experience of life after a cancer diagnosis and opening up the survivorship experience to persons at any stage of cancer and at any phase of their disease trajectory. Until now, clinical care guidelines and models of survivorship have typically not included consumer input, but rather have been developed principally through health professional expert consensus.3,4 In a novel approach from 2019–2020, a panel of 47 experts and consumers across Australia and New Zealand came together to define six key domains of survivorship care in a Prostate Cancer Survivorship Essentials Framework:5 health promotion and advocacy, shared management, vigilance, personal agency, care coordination, and evidence‐based survivorship interventions. These six domains reached high consensus as being essential, with the 26 elements within domains all rated as high importance. Almost one‐third of the 47‐member panel were cancer survivors working collaboratively with medical, allied health and nursing expert representatives. The degree of consensus in such a broad coalition is remarkable, underscoring the validity of the approach that reflects the lived experience driven by survivors’ preferences. Importantly, the central domain related to personal agency of a survivor as a key element that linked all others (Box) and all domains were framed around outcomes that mattered for the patient (eg, empowerment, information, shared decision making, care coordination, symptom management). While the framework was developed for prostate cancer survivorship, none of the elements were unique to prostate cancer, highlighting the potential relevance of the framework to other cancers. More broadly, this approach aligns with existing models of chronic disease management and frameworks of consumer engagement in care that are fundamental to the delivery of health care in Australia and New Zealand. We believe the essentials framework is applicable to other adult cancer patient cohorts and presents an opportunity to move forward on cancer survivorship in Australia, taking forward a unique consumer–practitioner model where the survivor is not just the passive object of care but an actor in their own health and an empowered and supported agent of change. Box – Prostate Cancer Survivorship Essentials Framework

Jeff Dunn · Bogda Koczwara · Suzanne Chambers

To the Editor: The toll of group A Streptococcus is dramatically unappreciated, despite increasing evidence of its burden.1 In Australia and New Zealand, we recently demonstrated that group A streptococcal throat and skin infections cause a sizable burden at the population level — cellulitis is the main contributor to the total burden of all group A streptococcal diseases and acute rheumatic fever and rheumatic heart disease contribute disproportionately relative to their frequency of occurrence.2,3 At a global level, the burden of group A Streptococcus is not abating. Global Burden of Disease data suggest that incident cases and deaths due to rheumatic heart disease alone have surpassed those of meningitis (Box). In 2019, more than 85% of rheumatic heart disease cases occurred among people aged under 35 years.4 No other group A streptococcal‐specific endpoints are available from the Global Burden of Disease data, yet all‐cause cellulitis was ranked the 24th most frequently occurring condition in high income countries in 2019.4 Group A Streptococcus causes outbreaks of poststreptococcal glomerulonephritis, contributing to the burden of chronic renal disease, and it is estimated to be the fifth most lethal pathogen on the planet, behind the human immunodeficiency virus (HIV), Mycobacterium tuberculosis, Plasmodium falciparum and S. pneumoniae, yet expenditure on vaccine development is only 0.17% of that spent on vaccines for HIV infection, malaria and tuberculosis.5 The divergence in numbers of cases and deaths due to group A Streptococcus compared with meningitis partially demonstrates the value of vaccination. Another major benefit of vaccination is a substantial reduction in antibiotic consumption. Indeed, pharyngitis is a major driver of antibiotic consumption globally, and an estimated 17% of antibiotic prescriptions for pharyngitis among children in the United States could be prevented by a group A Streptococcus vaccine.6 Two major initiatives aim to progress vaccine development. The Australian Strep A Vaccine Initiative (ASAVI) and the Strep A Vaccine Global Consortium (SAVAC) are addressing technical and investment barriers and leading at least one of the current vaccine candidates to an efficacy trial for pharyngitis prevention by 2024.5 An effective vaccine may prevent health and economic burdens due to the full range of group A streptococcal diseases and associated antibiotic consumption. Box – Estimated number of new cases (left) and deaths (right) due to meningitis and rheumatic heart disease globally* * Data obtained from the Global Burden of Disease study 2019.4

Jeffrey W Cannon · Julie Bennett · Michael G Baker · Jonathan R Carapetis

To the Editor: In their article, Blakely and colleagues1 describe an infectious disease model for simulating the effect of a lockdown on the transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Although we cannot say this work determined pandemic policy, two of the authors have described their close collaboration with the Victorian Government, culminating in the release of a road map to reopening2 based directly on, and released alongside, their modelling.3 The model is stochastic and agent‐based, with 2500 individuals moving around a model space. When both an infected and a susceptible person land on the same patch, there is a probability of transmission. Some individuals are marked as being essential workers; population homogeneity is otherwise assumed.4 Models are necessarily abstractions from reality; it is neither possible nor relevant to include every population group. The question is whether the model effectively captures the dynamics of infection. The combination of model type and population structure has a surprising result. People in the model can only be infected by moving around, and a lockdown is simulated by a reduction in the pace and frequency of movement. At a technical level, the model’s mechanics guarantee the effectiveness of a population‐wide lockdown because it most extensively reduces movement. It is hardly surprising that Blakely and colleagues refer to a lockdown as an “opportunity”.1 The assumption of population homogeneity is robust to exceptions, but only to a point. Using official data, we estimate that, in Victoria, the odds of an aged care worker becoming infected were almost 12 times that of the general population (odds ratio [OR], 11.81; 95% CI, 11.76–11.87). For health care workers, the odds were more than three times higher (OR, 3.19; 95% CI, 3.14–3.23).5 At this level of contact and risk heterogeneity, the model cannot reflect the true virus dynamics. Throughout the period covered by the model predictions, interventions targeted at health care settings were implemented. These interventions, such as closing hospital tea rooms and changing aged care working conditions, cannot be factored into the model predictions because health and aged care workers are not included in the model. By failing to specifically consider the populations that drove the epidemic or the interventions targeted at those populations, any ultimate concurrence between the actual and predicted numbers can only be attributable to chance.

Bradley R Crammond · Vishaal Kishore

In reply: In response to the letter by Crammond and Kishore, we would like to make a few points. Firstly, the authors overly conflate two pieces of work. The MJA article1 was prepared before any engagement with the Victorian Department of Health and Human Services. Secondly, Crammond and Kishore incorrectly assert that we assume population homogeneity in the model. The heterogeneity in our model included variance in the over 60s population and individual‐level variables, outlined in the Overview, Design concepts and Details (ODD) protocol.2 For example, the model explicitly defines essential workers as a subpopulation (ie, health care workers, cleaners, carers). Like the real world, infection rates are much higher among essential workers in the model (around three times higher) than the general population. Similarly, the model also identifies students and adjusts the likely asymptomatic status of people by age ranges, as well as the risk of infection, school attendance, transmission, and symptomatic illness. The example Crammond and Kishore offer of tea‐room changes in hospitals being ignored and therefore rendering the work invalid is erroneous. A population‐level policy model representing 6.4 million people could not and should not hope to include detailed interactions within hospital tea rooms any more than it would include interactions in abattoir bathrooms. Rather, a model should describe generic locations where reducing frequency of contacts can result in transmission reduction, wherever and however that is translated and achieved at the local level. The authors’ consequent assertion that the “global transmissibility” variable is undefined or cannot be correct is wrong. To quote the ODD protocol, “a [global transmissibility] setting that controls the likelihood of transmission between an infectious person and a susceptible person per close contact. This can be altered in conjunction with the number of contacts per day to calibrate the [reproduction number (R0)] in the early stages of the model”.2 A transmissibility rate of 0.30 (or 0.016 as used in the Burnet example; or any other number between 0 and 1)3 could be used under circumstances where the definition of close contacts per day varied or the transmissibility of a strain (eg, Alpha variant) altered. In his 1976 essay, George Box4 said that “all models are wrong”. He then went on to say that because models are wrong, the scientist cannot obtain a correct model by overparameterisation — “this is the mark of mediocrity”. He remarked that in modelling it is essential to be alert to what is importantly wrong — “it is inappropriate to be concerned about mice when there are tigers abroad”. We have tried to focus on tigers, not mice. We finish on agreement with Crammond and Kishore that any concurrence between the actual model and reality is attributable to chance. However, on three occasions we have used the base model representation to accurately project severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection trends in Australia, New Zealand and Victoria. We remain satisfied with its performance to date while welcoming constructive ideas for improvement.

Jason Thompson · Natalie Carvalho · Tony Blakely