Research

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

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

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

Objective: To assess long term survival and patient characteristics associated with survival following acute myocardial infarction (AMI) in Australia and New Zealand. Design: Cohort study. Setting, participants: All patients admitted with AMI (ICD‐10‐AM codes I21.0‒I21.4) to all public and most private hospitals in Australia and New Zealand during 2009‒2015. Main outcome measure: All‐cause mortality up to seven years after an AMI. Results: 239 402 initial admissions with AMI were identified; the mean age of the patients was 69.3 years (SD, 14.3 years), 154 287 were men (64.5%), and 64 335 had ST‐elevation myocardial infarction (STEMI; 26.9%). 7‐year survival after AMI was 62.3% (STEMI, 70.8%; non‐ST‐elevation myocardial infarction [NSTEMI], 59.2%); survival exceeded 85% for people under 65 years of age, but was 17.4% for those aged 85 years or more. 120 155 patients (50.2%) underwent revascularisation (STEMI, 72.2%; NSTEMI, 42.1%); 7‐year survival exceeded 80% for patients in each group who underwent revascularisation, and was lower than 45% for those who did not. Being older (85 years or older v 18–54 years: adjusted hazard ratio [aHR], 10.6; 95% CI, 10.1–11.1) or a woman (aHR, 1.15; 95% CI, 1.13–1.17) were each associated with greater long term mortality during the study period, as was prior heart failure (aHR, 1.79; 95% CI, 1.76‒1.83). Several non‐cardiac conditions and geriatric syndromes common in these patients were independently associated with lower long term survival, including major and metastatic cancer, cirrhosis and end‐stage liver disease, and dementia. Conclusion: AMI care in Australia and New Zealand is associated with high rates of long term survival; 7‐year rates exceed 80% for patients under 65 years of age and for those who undergo revascularisation. Efforts to further improve survival should target patients with NSTEMI, who are often older and have several comorbid conditions, for whom revascularisation rates are low and survival after AMI poor.

Bora Nadlacki · Dennis Horton · Sadia Hossain · Saranya Hariharaputhiran · Linh Ngo · Anna Ali · Bernadette Aliprandi‐Costa · Chris J Ellis · Robert JT Adams · Renuka Visvanathan · Isuru Ranasinghe

Objectives: To examine associations between community cultural connectedness indicators and suicide mortality rates for young Aboriginal and Torres Strait Islander people. Study design: Retrospective mortality study. Setting, participants: Suicide deaths of people aged 10‒19 years recorded by the Queensland Suicide Register, 2001‒2015. Main outcome measures: Age‐standardised suicide death rates, by Indigenous status, sex, and age group; age‐standardised suicide death rates for young First Nations people by area level remoteness and Index of Relative Socioeconomic Advantage and Disadvantage, and by cultural connectedness indicators (at statistical area level 2): cultural social capital index score, community Indigenous language use, and reported discrimination. Results: The age‐specific suicide rate was 21.1 deaths per 100 000 persons/year for First Nations young people and 5.0 deaths per 100 000 persons/year for non‐Indigenous young people (rate ratio [RR], 4.3; 95% CI, 3.5‒5.1). The rate for Aboriginal and Torres Strait Islander young people was higher in areas with low levels of cultural social capital (greater participation of community members in cultural events, ceremonies, organisations, and community activities) than in areas classified as having high levels (RR, 1.8; 95% CI, 1.2‒2.7), and also in communities with high levels of reported discrimination (RR, 2.7; 95% CI, 1.7‒4.3). Associations with proportions of Indigenous language speakers and area level socio‐economic resource levels were not statistically significant. Conclusion: We found that suicide mortality rates for Aboriginal and Torres Strait Islander young people in Queensland were influenced by community level culturally specific risk and protective factors. Our findings suggest that strategies for increasing community cultural connectedness at the community level and reducing institutional and personal discrimination could reduce suicide rates.

Mandy Gibson · Jaimee Stuart · Stuart Leske · Raelene Ward · Robert Tanton

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

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

Objectives: To assess the quality of care for patients with diabetes in Queensland hospitals, including blood glucose control, rates of hospital‐acquired harm, the incidence of insulin prescription and management errors, and appropriate foot and peri‐operative care. Design, setting: Cross‐sectional audit of 27 public hospitals in Queensland: four of five tertiary/quaternary referral centres, four of seven large regional or outer metropolitan hospitals, seven of 13 smaller outer metropolitan or small regional hospitals, and 12 of 88 hospitals in rural or remote locations. Participants: 850 adult inpatients with diabetes mellitus in medical, surgical, mental health, high dependency, or intensive care wards. Results: Twenty‐seven of 115 public hospitals that admit acute inpatients participated in the audit, including 4175 of 6652 eligible acute hospital beds in Queensland. A total of 1003 patients had diabetes (24%), and data were collected for 850 (85%). Their mean age was 65.9 years (SD, 15.1 years), 357 were women (42%), and their mean HbA1c level was 66 mmol/mol (SD, 26 mmol/mol). Rates of good diabetes days (appropriate monitoring, no more than one blood glucose measurement greater than 10 mmol/L, and none below 5 mmol/L) were low in patients with type 1 diabetes (22.1 per 100 patient‐days) or type 2 diabetes treated with insulin (40.1 per 100 patient‐days); hypoglycaemia rates were high for patients with type 1 diabetes mellitus (24.1 episodes per 100 patient‐days). One or more medication errors were identified for 201 patients (32%), including insulin prescribing errors for 127 patients (39%). Four patients with type 1 diabetes experienced diabetic ketoacidosis in hospital (8%); 121 patients (14%) met the criteria for review by a specialist diabetes team but were not reviewed by any diabetes specialist (medical, nursing, allied health). Conclusions: We identified several deficits in inpatient diabetes management in Queensland, including high rates of medication error and hospital‐acquired harm and low rates of appropriate glycaemic control, particularly for patients treated with insulin. These deficits require attention, and ongoing evaluation of outcomes is necessary.

Peter Donovan · Jade Eccles-Smith · Nicola Hinton · Clare Cutmore · Kerry Porter · Jennifer Abel · Lee Allam · Alexis Dermedgoglou · Gaurav Puri

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

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

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

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

Objectives: To determine whether a multifaceted primary health care intervention better controlled cardiovascular disease (CVD) risk factors in patients with high risk of CVD than usual care. Design, setting: Parallel arm, cluster randomised trial in 71 Australian general practices, 5 December 2016 – 13 September 2019. Participants: General practices that predominantly used an electronic medical record system compatible with the HealthTracker electronic decision support tool, and willing to implement all components of the INTEGRATE intervention. Intervention: Electronic point‐of‐care decision support for general practices; combination cardiovascular medications (polypills); and a pharmacy‐based medication adherence program. Main outcome measures: Proportion of patients with high CVD risk not on an optimal preventive medication regimen at baseline who had achieved both blood pressure and low‐density lipoprotein (LDL) cholesterol goals at study end. Results: After a median 15 months’ follow‐up, primary outcome data were available for 4477 of 7165 patients in the primary outcome cohort (62%). The proportion of patients who achieved both treatment targets was similar in the intervention (423 of 2156; 19.6%) and control groups (466 of 2321; 20.1%; relative risk, 1.06; 95% CI, 0.85–1.32). Further, no statistically significant differences were found for a number of secondary outcomes, including risk factor screening, preventive medication prescribing, and risk factor levels. Use of intervention components was low; it was highest for HealthTracker, used at least once for 347 of 3236 undertreated patients with high CVD risk (10.7%). Conclusions: Despite evidence for the efficacy of its individual components, the INTEGRATE intervention was not broadly implemented and did not improve CVD risk management in participating Australian general practices. Trial registration: Australian New Zealand Clinical Trials Registry, ACTRN12616000233426 (prospective).

Ruth Webster · Tim Usherwood · Rohina Joshi · Bandana Saini · Carol Armour · Sue Critchley · Gian Luca Di Tanna · Shane Galgey · Charlotte M Hespe · Stephen Jan · Ajay Karia · Baldeep Kaur · Ines Krass · Tracey‐Lea Laba · Qiang Li · Serigne Lo · David P Peiris · Christopher Reid · Anthony Rodgers · Louise Shiel · Jessica Strathdee · Nuria Zamora · Anushka Patel

Coronavirus disease 2019 (COVID‐19) and subsequent containment measures affected consumer behaviour in Australia, including the stockpiling of essential items. Increased demand for prescription medications caused concern about potential medication shortages, and a range of policies were implemented in March 2020 to protect supplies.1 We used interrupted time series modelling to quantify the impact of the COVID‐19 pandemic on medication dispensing. The Pharmaceutical Benefits Scheme (PBS) subsidises public medication costs in Australia. We analysed Section 85 date of supply data2 to model dispensing during January 2016 – December 2019, by month, separately for all PBS prescriptions, the ten medications most frequently dispensed during the 2018–19 financial year, hydroxychloroquine, and dexamethasone. These models, which accounted for long term trends and seasonal changes, were used to predict expected dispensing during January – June 2020 (with 95% confidence intervals [CIs]), which we compared with actual dispensing rates during this period (online Supporting Information). Ethics approval was not required for our analysis of publicly available data. The number of prescriptions dispensed during March 2020 was significantly higher than predicted (4.80 million more prescriptions, +18.5%; 95% CI, +14.0% to +23.3%), but significantly lower in April (2.28 million fewer prescriptions, –9.2%; 95% CI, –5.3% to –12.8%) and May (2.08 million fewer prescriptions; –8.1%; 95% CI, –4.3% to –11.5%); there was no significant difference in June 2020 (988 778 fewer prescriptions, –3.8%; 95% CI, –7.5% to +0.1%) (Box). A similar pattern applied to the ten most dispensed medications; the increase in the number of hydroxychloroquine prescriptions dispensed in March was particularly large (24 286 more prescriptions, +95.5%; 95% CI, +89.1 to +102%) (Supporting Information). Increased dispensing of prescription medications in March 2020 was consistent with the general panic buying reported early in the COVID‐19 pandemic.3 Pharmacies also received increased requests for prescription and over‐the‐counter medications at this time, in some cases causing local shortfalls1 and concern that continued high dispensing might interrupt medication supply at the national level. This applied in particular to drugs considered early in the pandemic as potential treatments for COVID‐19, such as hydroxychloroquine. In response to increased dispensing in March, the Australian government rapidly implemented a range of policies for protecting medication supplies. Dispensing limits of one month’s supply were applied to medications if shortages would have serious health consequences.1 These policies reduced the total number of medications dispensed in April and May 2020, followed by the return to normal levels of prescription dispensing in June. Other factors likely to have been important were stockpiles amassed by people during March, public adjustment to the pandemic, and the early suppression of COVID‐19 in Australia. Restrictions on prescription dispensing were balanced by services to assist susceptible patients to isolate themselves; for example, the COVID‐19 home medicines service funded home delivery of prescription medications by community pharmacies and Australia Post,4 and funding for telehealth was increased to facilitate remote prescribing.5 Our findings indicate that medication supply can be safeguarded from panic dispensing by a range of regulatory policies combined with medication services for vulnerable people. This may be particularly important for ensuring equitable access to medications for treating COVID‐19. The risk of further COVID‐19 outbreaks underscores the importance of maintaining these policies and services. Box – Total number of prescriptions dispensed in Australia, January 2016 – June 2020, and numbers of COVID‐19 diagnoses in Australia, January 2020 – June 2020 CI = confidence interval. * Source: Australian Department of Health.2

Mustafa Mian · Subhashaan Sreedharan · Sarah Giles

Practitioners who prescribe opioid medications for people with chronic non‐cancer pain must navigate increasingly stringent policy requirements,1 research findings questioning the benefit of opioids for such patients,2 and patients who fear uncontrolled pain if opioids are withdrawn.3 In Australia and New Zealand, people with chronic non‐cancer pain may be referred to specialist pain management services, most of which participate in the electronic Persistent Pain Outcomes Collaboration (ePPOC; https://www.uow.edu.au/ahsri/eppoc), an initiative for collecting standardised information about their patients, the services they provide, and the outcomes of treatment. This information is used at point of care, and for reporting, benchmarking, and research. To explore the impact of changes in opioid use on outcomes for patients, we analysed ePPOC data collected at 67 pain services (online Supporting Information) during January 2015 – June 2020. We extracted data for all patients with completed episodes of care and who had answered questions about opioid use at referral and episode end. We summarised their characteristics and outcomes as means with standard deviations (SDs). All analyses were conducted in SAS 9.4. Our study was approved by the University of Wollongong and Illawarra and Shoalhaven Local Health District health and medical human research ethics committee; reference, 2019/ETH03804). The mean age of the 10 302 patients who provided information at both referral and at the end of their treatment episodes was 49.5 years (SD, 14.4 years); 5807 were women (56.4%), and 3490 had experienced their pain for more than five years (33.9%). The most frequent site of their main pain was the back (3936 patients; 38.2%). A total of 6340 patients (61.5%) were using opioid medications at referral (Box 1); their mean oral morphine equivalent daily dose4 was 56.3 mg (SD, 75.3 mg), the median daily dose was 31.0 mg (interquartile range [IQR], 15–75 mg). They reported higher mean pain scores than patients not using opioids at referral (6.2 [SD, 1.6] v 5.8 [SD, 1.7]) and greater interference in daily activities (7.2 [SD, 1.8] v 6.5 [SD, 2.0]; each measured with the Brief Pain Inventory5). Mean values for depression, anxiety, stress, pain catastrophising, and pain self‐efficacy were also worse for people using opioid medications (data not shown). The most frequent service events were individual appointments with medical and allied health staff (35 678 of 55 012 events, 65%) and group pain programs (18 841 events, 34%); there were 493 procedural interventions (1%). The median episode length was 175 days (IQR, 99–322 days). Opioid prescribing varies between pain services, including direct prescribing by the pain specialist and recommendations to patients’ general practitioners. However, a major focus of multidisciplinary care is supporting patients to reduce their opioid use, which typically involves collaboration between the patient, their GP, and the pain service. By the end of their treatment episodes, 1724 patients who reported using opioids at referral (27.2%) had stopped doing so, 1234 patients (19.5%) had reduced their dose by at least 50% and 3382 patients (53.3%) had either not changed, increased, or reduced opioid use by less than 50%. For each group, scores had improved in each clinical domain, and the changes were greatest for patients who had ceased opioid use, as were the proportions experiencing clinically significant improvement. Scores for measures specifically related to pain experience (pain severity, interference, catastrophising and self‐efficacy) at the end of treatment were similar to or better than those of patients who had not been using opioids at referral, despite greater initial pain severity. Conversely, the smallest mean improvements were for the patients who had not reduced opioid use by at least 50% (Box 2). Although our study was limited by its retrospective nature, the lack of follow‐up of patients who did not complete treatment, and its restriction to specialist pain practices, our findings are encouraging. We found that significant clinical improvements are possible for people with chronic non‐cancer pain attending multidisciplinary pain management services in Australia and New Zealand, even as they discontinue opioid medications. The challenge is to extend these services and supported self‐management skills to primary and community care. Box 1 – Opioid use by patients at referral and at the end of treatment in specialist pain clinics * Opioid therapy was initiated for 536 of patients who had not being using opioid medications at referral (13.5%). † Opioid use had been reduced by less than 50% for 1025 patients (30.3%), not changed for 878 patients (26.0%), and increased for 1479 patients (43.7%). Box 2 – Mean pain and psychometric scores, and changes in scores between referral and end of treatment (with standard deviations), by opioid use at the two time points table#t2 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Clinical domain Patients not using opioids at referral Patients who were using opioids at referral Ceased taking opioids Reduced opioid use by at least 50% Other* Total number of patients 3962 1724 1234 3382 Pain severity (BPI5) 3787 1646 1174 3215 Referral 5.8 (1.7) 6.1 (1.7) 6.3 (1.6) 6.3 (1.6) Episode end 4.9 (2.0) 4.9 (2.0) 5.5 (1.8) 5.8 (1.7) Change in score –0.9 (1.7) –1.2 (1.8) –0.8 (1.6) –0.5 (1.5) Clinically significant improvement† 817/2997 (27%) 459/1410 (33%) 231/1035 (22%) 436/2827 (15%) Pain interference (BPI5) 3905 1702 1219 3316 Referral 6.5 (2.0) 7.1 (1.8) 7.3 (1.7) 7.2 (1.9) Episode end 4.9 (2.4) 5.0 (2.4) 5.7 (2.3) 6.2 (2.2) Change in score –1.6 (2.2) –2.1 (2.3) –1.6 (2.1) –1.0 (2.0) Clinically significant improvement† 2050/3279 (63%) 1062/1546 (69%) 679/1133 (60%) 1481/3003 (49%) Depression (DASS‐216) 3827 1673 1201 3240 Referral 17.8 (12.1) 20.2 (12.4) 20.9 (12.6) 20.7 (12.4) Episode end 12.8 (11.1) 13.8 (11.6) 15.6 (12.0) 16.6 (11.9) Change in score –5.0 (10.1) –6.4 (11.0) –5.3 (10.7) –4.0 (10.2) Clinically significant improvement† 1308/2231 (59%) 662/1100 (60%) 434/810 (54%) 1042/2190 (48%) Anxiety (DASS‐216) 3821 1676 1191 3233 Referral 12.1 (10.2) 13.3 (10.4) 14.1 (10.4) 13.7 (10.3) Episode end 10.1 (9.5) 10.9 (9.7) 11.9 (9.7) 12.5 (10.1) Change in score –2.0 (8.2) –2.4 (8.7) –2.2 (8.4) –1.2 (8.0) Clinically significant improvement† 858/1972 (44%) 438/962 (46%) 288/716 (40%) 662/1904 (35%) Stress (DASS‐216) 3818 1660 1191 3226 Referral 19.8 (11.0) 21.1 (10.8) 21.9 (10.9) 21.2 (11.1) Episode end 15.6 (10.6) 16.6 (10.7) 17.9 (10.5) 18.6 (10.8) Change in score –4.1 (9.6) –4.5 (10.2) –4.0 (9.3) –2.6 (9.2) Clinically significant improvement† 1154/1936 (60%) 553/905 (61%) 387/710 (55%) 898/1828 (49%) Pain catastrophising (PCS7) 3796 1649 1174 3204 Referral 26.3 (13.3) 28.1 (13.4) 28.5 (13.4) 28.3 (13.3) Episode end 18.3 (13.3) 17.9 (13.5) 20.9 (13.6) 22.1 (13.6) Change in score –8.0 (11.6) –10.2 (12.2) –7.6 (11.1) –6.2 (11.2) Clinically significant improvement† 1425/2513 (57%) 714/1164 (61%) 435/841 (52%) 1056/2308 (46%) Pain self‐efficacy (PSEQ8) 3860 1685 1210 3262 Referral 24.0 (12.6) 20.6 (12.0) 18.6 (11.0) 18.9 (11.9) Episode end 32.1 (14.4) 32.0 (14.4) 26.9 (13.1) 24.0 (13.1) Change in score +8.1 (12.7) +11.5 (14.0) +8.3 (12.7) +5.1 (12.1) Clinically significant improvement† 1433/2796 (51%) 827/1382 (60%) 504/1058 (48%) 1015/2773 (37%) BPI = Brief Pain Inventory (range, 0–10); DASS‐21 = Depression Anxiety and Stress Scale (range, 0–42); PCS = Pain Catastrophising Scale (range, 0–52); PSEQ = Pain Self‐Efficacy Questionnaire (range, 0–60; higher scores indicate greater self‐efficacy). * Opioid use by patients had been reduced by less than 50%, not changed, or increased. † For patients who reported at least moderate symptom severity at referral (see Supporting Information for definitions of clinically significant improvement).

Hilarie Tardif · Christopher Hayes · Samuel F Allingham

Objective: To assess the analgesic efficacy and safety of single‐dose oral cannabidiol (CBD) as an adjunct to standard care for patients presenting to an emergency department with acute low back pain. Design: Randomised, double blinded, placebo‐controlled clinical trial. Setting: The tertiary emergency department of Austin Hospital, Melbourne. Participants: Patients who presented with acute, non‐traumatic low back pain between 21 May 2018 and 13 June 2019. Intervention: One hundred eligible patients were randomised to receiving 400 mg CBD or placebo in addition to standard emergency department analgesic medication. Main outcome measures: Pain score two hours after administration of study agent, on a verbal numerical pain scale (range, 0‒10). Secondary outcomes were length of stay, need for rescue analgesia, and adverse events. Results: The median age of the 100 participants was 47 years (IQR, 34‒60 years); 44 were women. Mean pain scores at two hours were similar for the CBD (6.2 points; 95% CI, 5.5–6.9 points) and placebo groups (5.8 points; 95% CI, 5.1–6.6 points; absolute difference, –0.3 points; 95% CI, –1.3 to 0.6 points). The median length of stay was 9.0 hours (IQR, 7.4‒12 hours) for the CBD group and 8.5 hours (IQR, 6.5‒21 hours) for the placebo group. Oxycodone use during the four hours preceding and the four hours after receiving CBD or placebo was similar for the two groups, as were reported side effects. Conclusion: CBD was not superior to placebo as an adjunct medication for relieving acute non‐traumatic low back pain in the emergency department. Trial registration: Australian New Zealand Clinical Trials Registry, ACTRN12618000487213 (prospective).

Bronwyn Bebee · David M Taylor · Elyssia Bourke · Kimberley Pollack · Lian Foster · Michael Ching · Anselm Wong

In Australia, an estimated 12.7% of patients with ST‐elevation myocardial infarction (STEMI) die or have recurrent myocardial infarctions within 30 days of diagnosis.1 Prompt reperfusion reduces morbidity and mortality, and guidelines consequently aim to minimise the time between symptom onset and reperfusion.1,2,3 Patients with chest pain may arrange their own transport to an emergency department or travel by ambulance. The risk period is shorter for patients without access to a defibrillator when they travel by ambulance, and they receive initial management more promptly. In Australia, only one in two patients with STEMI calls an ambulance.4 Characterising patients less likely to call an ambulance would inform targeted public health efforts to improve this situation. We analysed data contributed by 43 hospitals across Australia to the Cooperative National Registry of Acute Coronary Care, Guideline Adherence and Clinical Events (CONCORDANCE)5 for patients with confirmed STEMI who presented to these hospitals during 23 February 2009 – 31 December 2017. We excluded patients who experienced out‐of‐hospital cardiac arrest or cardiogenic shock. We compared the clinical characteristics, time to reperfusion, and hospital outcomes, including death and major adverse cardiovascular events (MACE) — cardiac death, myocardial infarction, heart failure, or shock — for patients who arrived by ambulance or otherwise, after adjusting for Global Registry of Acute Coronary Events (GRACE) risk score6 at baseline. The statistical significance of differences in categorical variables was assessed in Rao–Scott χ2 tests and that of continuous variables in Wilcoxon rank‐sum tests. For adjusted analyses, we used multivariable logistic regression models in a generalised estimating equation (GEE) framework, adjusted for clustering by hospital. Analyses were conducted in SAS 9.4. Ethics approval for the study was granted by the Concord Repatriation General Hospital Human Research Ethics Committee (reference, HREC/08/CRGH/180). Of 2765 patients who presented with STEMI to CONCORDANCE hospitals during 2009–2017, 1616 (58.4%) arrived by ambulance and 1149 (41.6%) by other means. The median age of patients arriving by ambulance (64 years; interquartile range [IQR], 54–74 years) was higher than for the other patients (59 years; IQR, 51–67 years), and the proportions with hypertension, a family history of coronary heart disease, or prior myocardial infarction, atrial fibrillation, or stroke/transient ischaemic attack were larger (Box). Time between arrival at hospital and reperfusion (primary percutaneous intervention or fibrinolysis) was significantly shorter for patients who arrived by ambulance than for other patients (Box). After adjusting for GRACE risk score, the odds of death (adjusted odds ratio [aOR], 1.16; 95% confidence interval [CI], 0.65–2.08) and MACE (aOR, 0.89; 95% CI, 0.72–1.10) were similar for the two patient groups (Supporting Information). Our analysis of data from a large Australian registry indicates that fewer than 60% of patients with STEMI arrive at hospital by ambulance; those who do have a higher median age and larger proportions have histories of cardiovascular disease. Importantly, their median time to reperfusion is shorter than for those not arriving by ambulance, probably because STEMI is diagnosed by electrocardiography during their journey to the hospital, which facilitates priming of emergency departments (for fibrinolysis) and catheterisation laboratories (for percutaneous coronary intervention). Despite the less favourable risk profiles of patients who arrive by ambulance, their hospital outcomes are comparable with those of patients who present directly to hospital, presumably because of their more rapid access to reperfusion. Our finding that patients with STEMI who are older and have more comorbid conditions are more likely to call an ambulance is not novel,7 but does indicate that this has not changed in recent years. This underscores the value of calling an ambulance when chest pain develops, and suggest that this public health message should be more actively promoted. Box – Baseline characteristics and times to reperfusion of 2765 patients who presented with STEMI to CONCORDANCE hospitals, 2009–2017 table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Transport to hospital Characteristic Ambulance Other means P Number of patients 1616 (58.4%) 1149 (41.6%) Age (years), median (IQR) 64 (54‒74) 59 (51‒67) < 0.001 Sex (men) 1140 (71%) 933 (81%) < 0.001 English as first language 1383 (86%) 959 (83%) 0.44 Prior myocardial infarction 252 (16%) 151 (13%) 0.046 Prior heart failure 49 (3%) 27 (2%) 0.27 Prior percutaneous coronary intervention 177 (11%) 116 (10%) 0.46 Prior coronary artery bypass graft 52 (3%) 26 (2%) 0.10 Prior atrial fibrillation 96 (6%) 30 (3%) < 0.001 Prior bleeding 17 (1%) 14 (1%) 0.63 Chronic renal failure 73 (5%) 42 (4%) 0.17 Prior stroke/transient ischaemic attack 94 (6%) 32 (3%) < 0.001 Diabetes 321 (20%) 232 (20%) 0.80 Hypertension 853 (53%) 534 (47%) < 0.001 Dyslipidaemia 696 (43%) 473 (41%) 0.21 Family history of coronary heart disease 514 (32%) 477 (42%) < 0.001 Grace risk score (Fox), median (IQR) 114 (95‒135) 102 (85‒119) < 0.001 Reperfusion modality Primary percutaneous coronary intervention 919 (57%) 486 (42%) < 0.001 Fibrinolysis 434 (27%) 442 (38%) < 0.001 None 320 (20%) 273 (24%) 0.010 Hospital arrival to reperfusion (h), median (IQR) Primary percutaneous coronary intervention 1.2 (0.7‒2.1) 2.1 (1.4‒6.1) < 0.001 Fibrinolysis 0.6 (0.3‒1.3) 0.8 (0.5‒1.3) 0.002 IQR = interquartile range; STEMI = ST‐elevation myocardial infarction.

Eleanor Redwood · Karice Hyun · John K French · Leonard Kritharides · Mark Ryan · Derek P Chew · Mario D'Souza · David B Brieger

Many patients had persistent symptoms two months after diagnosis, including fatigue, chest pain, and breathlessness

David R Darley · Gregory J Dore · Lucette Cysique · Kay A Wilhelm · David Andresen · Katrina Tonga · Emily Stone · Anthony Byrne · Marshall Plit · Jeffrey Masters · Helen Tang · Bruce Brew · Philip Cunningham · Anthony Kelleher · Gail V Matthews

In June 2015, 41% of Australian medical specialists were women,1 but only 28% of those in senior or leadership positions.2 Academic research is important for obtaining tenure and promotion in medicine. First authorship on publications is typically granted to junior authors and last authorship to directing senior authors. The proportion of women among first authors in six prominent American medical journals increased from 5.9% in 1970 to 29.3% in 2004, and for last authorship from 3.7% to 19.3%.3 However, a 2016 study found that the proportion of authors who were women in high impact medical journals had plateaued or declined since 2009.4 Examining Australian patterns of authorship could help identify barriers to the academic advancement of women in medicine. We identified in PubMed all journal articles published during 2005–2018 by the eight journals associated with peak bodies of Australian medical practitioners, and used the validated genderize. R tool to determine the probable gender of authors’ first names.5 We used Poisson regression to analyse first and last authorship (male = 0, female = 1) by year; we report the statistical significance of the deviation of the regression slope (B‐value) from zero. The relationship between number of authors and gender were assessed by linear regression, including an interaction term between gender and time. Formal ethics approval was not required for this analysis of publicly available data. Gender could be determined with at least 50% probability for the first authors of 26 621 of 27 804 articles (96%) and the last authors of 26 972 (97%). Between 2005 and 2018, the proportion of women among first authors in the eight journals increased from 522 of 1600 (32.6%) to 899 of 2391 (37.6%; P < 0.001); the proportion among last authors did not change (28.0%). The proportions of women among both first and last authors increased significantly in the Journal of Paediatrics and Child Health, the Australian and New Zealand Journal of Obstetrics and Gynaecology, and the Medical Journal of Australia, as did those of first authors (but not last authors) in the Australian and New Zealand Journal of Public Health, Emergency Medicine Australasia, the Australian and New Zealand Journal of Psychiatry, and the Australian and New Zealand Journal of Surgery; the proportions of neither changed significantly in Australian Family Physician (Box; Supporting Information, table 1). The mean number of authors on publications with women as first authors (3.8; standard deviation [SD], 2.4) was higher than for those with men as first authors (3.3; SD, 2.4; P < 0.001). The difference between author numbers was smaller, but statistically significant, with respect to last author gender (women: mean number of authors, 3.6; SD, 2.4; men: 3.5; SD, 2.4; P = 0.045) (Supporting Information, tables 2, 3). Our study did not distinguish between research, review, and other journal article types. While our findings may reflect overall involvement of women in research, they do not specifically define gender proportions among leaders of high impact academic research programs. The increase in the proportion of first authors of Australian medical journal articles who are women may reflect the rise in the proportion of female doctors from 33% to 43% between January 2006 and December 2018.1 It is also possible that women, under‐represented in their specialties, feel greater pressure than men to publish as first authors for purposes of career progression.2 Our data indicate that the proportion of women as first authors has increased, but that of last authorship has grown only in some specialities. Box – Proportions of women as first and last authors of articles in selected Australian medical journals, 2005–2018* * The raw data are included in the online Supporting Information, tables 4 and 5. † From 2018: the Australian Journal of General Practice.

Matthew J Lennon · Rose Kennedy · Hannah Ryan · Dennis R Neuen · Melissa Godwin

Objective: To determine whether the availability of invasive coronary angiography at the hospital of presentation influences catheterisation rates for patients with acute coronary syndrome (ACS), and whether presenting to a catheterisation‐capable hospital is associated with better outcomes for patients with ACS. Design, setting: Retrospective cohort study; analysis of Cooperative National Registry of Acute Coronary Events (CONCORDANCE) data. Setting, participants: Adults admitted with ACS to 43 Australian hospitals (including 31 catheterisation‐capable hospitals), February 2009 – October 2018. Main outcome measures: Major adverse cardiovascular events (myocardial infarction, stroke, congestive heart failure, cardiogenic shock, cardiovascular death) and all‐cause deaths in hospital and by six and 12‐ or 24‐month follow‐up. Results: The proportion of women among the 5637 patients who presented to catheterisation‐capable hospitals was smaller than for the 2608 patients who presented to hospitals without catheterisation facilities (28% v 33%); the proportion of patients diagnosed with ST elevation myocardial infarction was larger (32% v 20%). The proportions of patients who underwent catheterisation (81% v 70%) or percutaneous coronary intervention (49% v 35%) were larger for those who presented to catheterisation‐capable hospitals. The baseline characteristics of patients who underwent catheterisation were similar for both presentation hospital categories, as were rates of major adverse cardiovascular events and all‐cause death in hospital and by 6‐ and 12‐ or 24‐month follow‐up. Conclusions: Although a larger proportion of patients who presented to catheterisation‐capable hospitals underwent catheterisation, patients with similar characteristics were selected for the procedure, independent of the hospital of presentation. Major outcomes for patients were also similar, suggesting equitable management of patients with ACS across Australia.

Michael Ayad · Karice Hyun · Mario D’Souza · Julie Redfern · Janice Gullick · Mark Ryan · David B Brieger

Objectives: To describe patterns of care in New South Wales for men with prostate cancer, and to ascertain factors associated with receiving different types of treatment. Design: Individual patient data record linkage study. Setting, participants: 4003 New South Wales men aged 45 years or more enrolled in the population‐based 45 and Up Study in whom prostate cancer was first diagnosed during 2006–2013. Main outcome measures: Prostate cancer treatment type received; factors statistically associated with treatment received; proportions of patients who consulted radiation oncologists prior to treatment. Results: In total, 1619 of 4003 patients underwent radical prostatectomy (40%), 893 external beam radiotherapy (EBRT) (22%), 183 brachytherapy (5%), 87 chemotherapy (2%), 373 androgen deprivation therapy alone (9%), and 848 no active treatment (21%). 205 of 1628 patients who had radical prostatectomies (13%) had radiation oncology consultations prior to surgery. Radical prostatectomy was more likely for patients aged 45–59 years, with regional stage disease, living 100 km or more from the nearest radiotherapy centre, having partners, or having private health insurance, while lower physical functioning, obesity, and living in areas of greater socio‐economic disadvantage reduced the likelihood. EBRT was more likely for patients aged 70–79 years, with non‐localised or unknown stage disease, living less than 100 km from the nearest radiotherapy centre, or not having private health insurance, while the likelihood was lower for patients aged 45–59 years or more than 80 years and for those who had several comorbid conditions. Conclusions: Men with prostate cancer were twice as likely to have radical prostatectomy as to receive EBRT, and fewer than one in seven had consulted radiation oncologists prior to prostatectomy. The treatment received was influenced by several socio‐demographic factors. Given the treatment‐specific side effects and costs, policies that affect access to different treatments for prostate cancer should be reviewed.

Mei Ling Yap · Dianne L O'Connell · David E Goldsbury · Marianne F Weber · David P Smith · Michael B Barton

Objectives: To assess the efficacy of a pro‐active, absolute cardiovascular risk‐guided approach to opportunistically modifying cardiovascular risk factors in patients without coronary ischaemia attending a chest pain clinic. Design: Prospective, randomised, open label, blinded endpoint study. Setting: The rapid access chest pain clinic of Royal Hobart Hospital, a tertiary hospital. Participants: Patients who presented to the chest pain clinic between 1 July 2014 and 31 December 2017 who had intermediate to high absolute cardiovascular risk scores (5‐year risk ≥ 8%). Patients with known cardiac disease or from groups with clinically determined high risk of cardiovascular disease were excluded. Main outcome measures: The primary endpoint was change in 5‐year absolute risk score (Australian absolute risk calculator) at follow‐up (at least 12 months after baseline assessment). Secondary endpoints were changes in lipid profile, blood pressure, smoking status, and body mass index, and major adverse cardiovascular events. Results: The mean change in risk at follow‐up was +0.4 percentage points (95% CI, –0.8 to 1.5 percentage points) for the 98 control group patients and –2.4 percentage points (95% CI, –1.5 to –3.4 percentage points) for the 91 intervention group patients; the between‐group difference in change was 2.7 percentage points (95% CI, 1.2–4.1 percentage points). Mean changes in lipid profile, systolic blood pressure, and smoking status were larger for the intervention group, but not statistically different from those for the control group. Conclusions: An absolute cardiovascular risk‐guided, pro‐active risk factor management strategy employed opportunistically in a chest pain clinic significantly improved 5‐year absolute cardiovascular risk scores. Trial registration: Australia New Zealand Clinical Trial Registry, ACTRN12617000615381 (retrospective).

J Andrew Black · Julie A Campbell · Serena Parker · James E Sharman · Mark R Nelson · Petr Otahal · Garry Hamilton · Thomas H Marwick

Early control of transmission was successful, but efforts to reduce further transmission remain important

Heather F Gidding · Dorothy A Machalek · Alexandra J Hendry · Helen E Quinn · Kaitlyn Vette · Frank H Beard · Hannah S Shilling · Rena Hirani · Iain B Gosbell · David O Irving · Linda Hueston · Marnie Downes · John B Carlin · Matthew VN O'Sullivan · Dominic E Dwyer · John M Kaldor · Kristine Macartney

Medicare Benefits Schedule (MBS) data indicated that there were 37% fewer screening procedures for breast cancers and 55% fewer for colorectal cancers in April than in March 2020.1 We examined the temporal relationship between coronavirus disease 2019 (COVID‐19)‐related restrictions in Victoria during 1 April – 15 October 2020 and cancer pathology notifications to the Victorian Cancer Registry (VCR), to estimate their impact on cancer diagnoses. Victorian legislation requires pathology services to notify reportable cancer diagnoses to the VCR.2 The E‐Path system, installed in all Victorian pathology services during 2013–2018,3 automatically transmits notifications to the VCR together with pathologist report authorisations. During 2019, 97 313 of 104 025 cancer pathology notifications to the VCR (94%) were received via E‐Path (data supplied by author LB). Changes to the E‐Path system during 2019 meant that we were unable to directly compare notification numbers for 2019 and 2020. We therefore modelled cancer incidence during 2014–2018 by Poisson regression. A spline function was fitted to VCR cancer incidence data for weeks 1–52, adjusted for day type (working or non‐working day/public holiday) and year, and the fitted curve used to predict daily incidence during 7 January – 15 October 2020. Predicted incidence was re‐scaled to estimate expected notification numbers; the scale factor was the number of notifications during the baseline period — 1 February – 16 March 2020, allowing a two‐week washout period before restrictions were formally announced — divided by the predicted incidence during this period. Observed and predicted notification numbers were compared using Poisson regression, with the expected number as an offset term, enabling estimation of relative reductions with 95% confidence intervals (CIs). Differences between predicted and actual notification numbers were estimated, both overall and for specific groups (eg, by tumour or age group), based on the pertinent incidence data. As a single cancer diagnosis can be associated with several pathology notifications, the number of undiagnosed cancers was estimated by multiplying the difference in notification numbers by the ratio of newly diagnosed tumours to pathology notifications in 2018 (Supporting Information, table 1). The confidence interval for the number of undiagnosed cases was based on the Poisson model, keeping the ratio of newly diagnosed tumours to pathology notifications constant. In sensitivity analyses, data were fitted to polynomial models, different baseline periods were used, or data were restricted to reportable cancer diagnoses. The study was exempted from formal ethics review by the human research ethics committee of Cancer Council Victoria. During 1 April – 15 October 2020, there were 5446 fewer notifications of new cancer diagnoses than predicted by our primary model (predicted, 54 609 v observed, 49 163; relative reduction, –10.0%; 95% CI, –10.8% to –9.2%) (Supporting Information, figure 1); we estimated that there were 2530 undiagnosed cancers (95% CI, 2327–2731). The relative reduction was greatest during 1 April – 4 May 2020 (Box 1). By tumour group, the relative reductions were most marked for prostate cancer, head and neck tumours, melanoma, and breast cancer; they were greater for men, people aged 50 years or more, and for people in areas of higher socio‐economic position (Box 2). The pattern of difference in notifications varied between tumour groups (Supporting Information, figure 2). The 6.5‐month period of COVID‐19‐related restrictions in Victoria was accompanied by a 10% reduction in cancer pathology notifications; we estimated that about 2530 cancer diagnoses were either delayed or missed. The impact of delayed diagnosis is greatest for patients with aggressive cancers. Changes in care delivery during the restrictions, including suspension of screening services and outpatient clinics and postponed surveillance of existing cancers, may have affected notification numbers for some tumour groups and consequently the estimated number of delayed diagnoses. Planning for a possible surge in cancer diagnoses over the coming 6–12 months, and media campaigns encouraging people to not further delay seeking medical attention, may ameliorate any negative impact of delayed cancer diagnosis. Box 1 – Cancer pathology notifications to the Victorian Cancer Registry, January–October 2020: observed (red) and predicted numbers (green), by day type LOESS = locally estimated scatterplot smoothing. The grey area marks the baseline period, the vertical dotted lines the analysis period for predicted notifications. A state of emergency was declared in Victoria on 16 March 2020. Stage 3 movement restrictions were applied from 30 March, eased on 13 May, and re‐applied from 8 July. The state of emergency was renewed on 2 August, together with application of stage 4 restrictions to metropolitan Melbourne until their easing from 19 October. For further details, see the footnote to figure 2 in the online Supporting Information. Box 2 – Cancer pathology notifications and estimated numbers of undiagnosed reportable cancers, 1 April – 15 October 2020* table#t2 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Notifications Relative difference (95% CI) Absolute difference (a) Tumour to notification ratio (b) Estimated number of undiagnosed tumours (a*b) Characteristic Predicted Observed All notifications 54 609 49 163 –10.0% (–10.8% to –9.2%) –5446 0.465 2530 Sex† Males 15 458 14 190 –8.2% (–9.7% to –6.7%) –1268 0.427 541 Females 10 408 10 367 –0.4% (–2.3% to 1.5%) –41 0.434 18 Age at diagnosis (years) < 50 9981 9674 –3.1% (–5.0% to –1.1%) –307 0.454 139 50–74 30 949 27 555 –11.0% (–12.0% to –9.9%) –3394 0.447 1516 ≥ 75 13 697 11 934 –12.9% (–14.4% to –11.3%) –1763 0.514 906 Tumour group Breast 7923 7130 –10.0% (–12.1% to –7.9%) –793 0.380 301 Colorectal 5063 4838 –4.4% (–7.1% to –1.7%) –225 0.501 113 Haematologic 10 011 9321 –6.9% (–8.8% to –5.0%) –690 0.234 162 Melanoma 7168 6217 –13.3% (–15.4% to –11.1%) –951 0.538 511 Lung 2967 3062 3.2% (–0.4% to 6.9%) 95 0.483 –46 Head and neck 1363 1155 –15.3% (–20.0% to –10.3%) –208 0.504 105 Bladder 2159 2009 –6.9% (–10.9% to –2.8%) –150 0.370 56 Prostate 6417 4770 –25.7% (–27.8% to –23.5%) –1647 0.560 922 All other 11 931 10 661 –10.6% (–12.3% to –8.9%) –1270 0.546 693 Socio‐economic position (quintile)‡ 1 (most disadvantaged) 10 334 9789 –5.3% (–7.1% to –3.4%) –545 0.453 247 2 10 378 9447 –9.0% (–10.8% to –7.1%) –931 0.456 425 3 10 192 9624 –5.6% (–7.4% to –3.7%) –568 0.488 277 4 10 925 9463 –13.4% (–15.1% to –11.6%) –1462 0.455 665 5 (least disadvantaged) 11 385 9714 –14.7% (–16.4% to –13.0%) –1671 0.460 769 Remoteness¶ Major cities 37 506 33 753 –10.0% (–11.0% to –9.0%) –3753 0.461 1731 Inner regional 13 414 12 031 –10.3% (–11.9% to –8.7%) –1383 0.472 652 Outer regional/remote 2553 2457 –3.8% (–7.5% to 0.1%) –96 0.472 45 CI = confidence interval. * Poisson regression (spline function, adjusted for day type [working day or non‐working day/public holiday] and year; baseline period: 1 February – 16 March 2020). † For cancers common in both sexes (melanoma, colorectal cancer, lung, head and neck cancers, haematological malignancies). ‡ Based on residential address, using the Google Geocoding API (https://developers.google.com/maps/documentation/geocoding/overview), spatially joined to Australian Bureau of Statistics Statistical Area 1 (SA1) polygons.4 Area‐based socio‐economic quintiles were based on 2016 Australian Bureau of Statistics census data.5 ¶ Accessibility and Remoteness Index of Australia.6

Luc te Marvelde · Rory Wolfe · Grant McArthur · Louis A Blake · Sue M Evans

Objective: To describe the clinical and procedural outcomes of per‐oral endoscopic myotomy (POEM) for achalasia in Australia. Design, setting: Prospective observational study in three Australian tertiary referral centres, 5 May 2014 – 27 October 2019 (66 months). Participants: Patients who had undergone POEM for achalasia. Major outcome measures: Eckardt scores calculated prior to POEM and six months, one year, and two years after POEM. The primary outcome was clinical success, defined as an Eckardt score of 3 or less without a second intervention. Results: 142 patients underwent POEM for achalasia; their mean age was 52 years (SD, 18 years), 83 were men (58%), and the median length of hospital stay two days (IQR, 1–3 days). Their mean Eckardt score before POEM was 8.0 (SD, 2.4) and 1.1 (SD, 1.6) six months after POEM; it did not change significantly between six months and two years after POEM (mean monthly increase, 0.014 points; 95% CI, –0.001 to 0.029). A total of 127 patients (89%) improved clinically after POEM. Intra‐procedural capnoperitoneum was the only risk factor associated with treatment failure (adjusted hazard ratio, 2.85; 95% CI, 1.08–7.51). Previous treatments — botulinum toxin injection (25 patients, 18%), endoscopic balloon dilatation (69, 49%), and Heller myotomy (14, 10%) — did not affect POEM outcomes. Five patients (4%) experienced major adverse events, including pneumonia, oesophageal leak, empyema and melaena, that were managed during admission and without sequelae. Conclusions: POEM is an effective treatment for achalasia. Significant reductions in Eckardt scores achieved by six months are sustained at two years. POEM can be both a first line definitive therapy and a salvage therapy for patients not helped by other treatments.

Sunil Gupta · Mayenaaz Sidhu · Xuan Banh · Joseph Bradbear · Karen Byth · Luke F Hourigan · Spiro Raftopoulos · Michael J Bourke

Objective: To investigate whether integrating pharmacists into general practices reduces the number of unplanned re‐admissions of patients recently discharged from hospital. Design, setting: Stepped wedge, cluster randomised trial in 14 general practices in southeast Queensland. Participants: Adults discharged from one of seven study hospitals during the seven days preceding recruitment (22 May 2017 ‒ 14 March 2018) and prescribed five or more long term medicines, or having a primary discharge diagnosis of congestive heart failure or exacerbation of chronic obstructive pulmonary disease. Intervention: Comprehensive face‐to‐face medicine management consultation with an integrated practice pharmacist within seven days of discharge, followed by a consultation with their general practitioner and further pharmacist consultations as needed. Major outcomes: Rates of unplanned, all‐cause hospital re‐admissions and emergency department (ED) presentations 12 months after hospital discharge; incremental net difference in overall costs. Results: By 12 months, there had been 282 re‐admissions among 177 control patients (incidence rate [IR], 1.65 per person‐year) and 136 among 129 intervention patients (IR, 1.09 per person‐year; fully adjusted IR ratio [IRR], 0.79; 95% CI, 0.52‒1.18). ED presentation incidence (fully adjusted IRR, 0.46; 95% CI, 0.22‒0.94) and combined re‐admission and ED presentation incidence (fully adjusted IRR, 0.69; 95% CI, 0.48‒0.99) were significantly lower for intervention patients. The estimated incremental net cost benefit of the intervention was $5072 per patient, with a benefit‒cost ratio of 31:1. Conclusion: A collaborative pharmacist‒GP model of post‐hospital discharge medicines management can reduce the incidence of hospital re‐admissions and ED presentations, achieving substantial cost savings to the health system. Trial registration: Australian New Zealand Clinical Trials Registry, ACTRN12616001627448 (prospective).

Christopher R Freeman · Ian A Scott · Karla Hemming · Luke B Connelly · Carl M Kirkpatrick · Ian Coombes · Jennifer Whitty · James Martin · Neil Cottrell · Nancy Sturman · Grant M Russell · Ian Williams · Caroline Nicholson · Sue Kirsa · Holly Foot

Objectives: To determine the age‐standardised prevalence of inflammatory bowel disease (IBD) in a metropolitan area of Sydney, with a focus on its prevalence among older people. Design, setting: Population‐based epidemiological study of people with IBD in the City of Canada Bay, a local government area in the inner west of Sydney, during 1 March 2016 – 10 November 2016. Participants: Patients diagnosed with confirmed IBD according to the Copenhagen or revised Porto criteria. Main outcome measures: Crude prevalence of IBD, including Crohn disease and ulcerative colitis; age‐standardised prevalence of IBD, based on the World Health Organization standard population; prevalence rates among people aged 65 years or more. Results: The median age of 364 people with IBD was 47 years (IQR, 34–62 years); 185 were women (50.8%). The crude IBD prevalence rate was 414 cases (95% CI, 371–456 cases) per 100 000 population; the age‐standardised rate was 348 cases (95% CI, 312–385 cases) per 100 000 population. The age‐standardised rate for Crohn disease was 166 cases (95% CI, 141–192 cases) per 100 000 population; for ulcerative colitis, 148 cases (95% CI, 124–171 cases) per 100 000 population. The IBD prevalence rate in people aged 65 years or more was 612 cases (95% CI, 564–660 cases) per 100 000, and for those aged 85 years or more, 891 cases (95% CI, 833–949 cases) per 100 000; for people under 65, the rate was 380 cases (95% CI, 342–418 cases) per 100 000. Conclusions: We found that the prevalence of confirmed IBD in a metropolitan sample was highest among older people. Challenges for managing older patients with IBD include higher rates of comorbid conditions, polypharmacy, and cognitive decline, and the immunosuppressive nature of standard therapies for IBD.

Aviv Pudipeddi · Jeffrey Liu · Viraj Kariyawasam · Thomas J Borody · James L Cowlishaw · Charles McDonald · Peter Katelaris · Grace Chapman · Crispin Corte · Daniel A Lemberg · Cheng H Lee · Anil Keshava · John Napoli · Robert Clancy · Webber Chan · Sudarshan Paramsothy · Rupert Leong

The Royal Commission into Aged Care Quality and Safety has highlighted the high rates of polypharmacy and potential medication‐related harm in residential aged care facilities (RACFs) in Australia.1 Residential medication management review (RMMR) is a government‐funded service for facilitating quality use of medicines in RACFs.2 Previous studies have found that RMMRs by accredited pharmacists and general practitioners identify a mean of 2.7–3.9 medication‐related problems per resident, and 45–84% of pharmacists’ recommendations were accepted by GPs.3 Guidelines recommend that residents should generally receive an RMMR on entering an RACF and when their clinical circumstances change,4 but annual claims data5,6 and recent research indicate that not all residents receive RMMRs.7 We examined time to first RMMR after RACF entry by analysing data for the national historical cohort of the Registry of Senior Australians (ROSA).7 In ROSA, de‐identified data collected during aged care eligibility assessments are linked to information about government‐subsidised aged care services, general practice and allied health services subsidised under the Medicare Benefits Schedule (MBS), medicines subsidised under the Pharmaceutical Benefits Scheme (PBS), and the Australian Institute of Health and Welfare National Death Index.8 Non‐Indigenous people aged 65 years or more who first entered permanent residential care during 1 January 2012 – 31 December 2015, had received an entry‐into‐care assessment within 100 days, and had received at least one PBS‐subsidised medication during the preceding year were included. Recipients of Department of Veterans’ Affairs‐funded services and people who had previously undergone RMMRs (eg, during transition care) were excluded. The cumulative incidence function was used to determine time to first MBS claim lodged by GPs for RMMRs (item code 903) or Home Medicines Reviews (HMRs) (item code 900) after entry to permanent residential care, adjusted for competing events (death, or permanent departure from the first RACF for another reason) using the Fine–Gray method,9 with follow‐up to 31 December 2016. Statistical analyses were undertaken in SAS 9.4. The University of South Australia (reference, 200489) and Australian Institute of Health and Welfare (reference, E02018/1/418) Human Research Ethics Committees provided ethics approval for the study. A total of 176 390 residents in 2799 RACFs were followed for a median 479 days (interquartile range [IQR], 149–858 days). Median age at entry was 84 years (IQR, 79–88 years), 108 908 were women (61.7%), and 84 864 were living with dementia (48.1%). In the year preceding entry, residents received a median of 11 unique prescription medications (IQR, 8–16 medications); 109 765 (62.2%) had received at least one high risk medication (as defined by the United States Institute for Safe Medication Practices10), and 7912 (4.5%) had received HMRs in the 12 months prior to RACF entry. By three months after RACF entry, 19.1% of residents (Wald 95% confidence interval [CI], 18.9–19.3%) had received RMMRs, 11.8% (95% CI, 11.6–11.9%) had died without RMMRs, and 5.7% (95% CI, 5.6–5.8%) had left their RACF for other reasons without RMMRs. At 12 months, 43.1% (95% CI, 42.8–43.3%) had received RMMRs, 20.6% (95% CI, 20.5–20.8%) had died without RMMRs, and 9.0% (95% CI, 8.8–9.1%) had left without receiving RMMRs. By 24 months, 49.7% (95% CI, 49.5–50.0%) had received RMMRs, 25.8% (95% CI, 25.6–26.0%) had died without RMMRs, and 10.2% (95% CI, 10.1–10.4%) had left their first RACF for other reasons without receiving RMMRs (Box). The high burden of medication use at the time of RACF entry suggests that most residents could have benefited from RMMRs, but MBS claims for RMMRs were lodged for fewer than one in five residents within three months of RACF entry, and fewer than one in two within two years. Our findings are generalisable to all older Australians entering RACFs, as ROSA captures data for all people aged 65 years or more who access government‐subsidised permanent residential aged care in Australia. We could not determine why residents were not referred for RMMRs, nor the impact of recent program changes2 on RMMR uptake and resident outcomes. In 2014–15, fewer GP medication review claims were reimbursed under the MBS (54 803 RMMRs, 63 872 HMRs) than pharmacist claims (93 517 RMMRs, 72 607 HMRs).5,6 Analysing GP claims may underestimate the number of RMMR reports prepared by pharmacists because GP claims are submitted after the medication management plan is discussed with the resident or family, while pharmacist claims are submitted after the report is sent to the GP.7 MBS claims may not be lodged if the full RMMR process cannot be completed (eg, because the resident died, their clinical circumstances had changed, or the RMMR report was not received or followed up), or claiming may be overlooked. Linkage with pharmacist claims data at the individual resident level could facilitate investigation of these limitations. Despite RMMRs being a key means for minimising medication‐related harm, MBS claims for RMMRs are lodged for only a fraction of residents who enter RACFs. The potential underuse of the program may be a missed opportunity for identifying and resolving medication‐related problems in Australian RACFs. Box – Stacked cumulative incidence function for time to first residential medication management review (RMMR), for first two years of permanent residential care* RACF = residential aged care facility. * For 176 390 residents (in 2799 residential aged facilities) included in the Registry of Senior Australians.8

Janet K Sluggett · J Simon Bell · Catherine Lang · Megan Corlis · Craig Whitehead · Steven L Wesselingh · Maria C Inacio

The consequences of removing some restrictions may not be apparent for more than two months

Nick Scott · Anna Palmer · Dominic Delport · Romesh Abeysuriya · Robyn M Stuart · Cliff C Kerr · Dina Mistry · Daniel J Klein · Rachel Sacks‐Davis · Katie Heath · Samuel W Hainsworth · Alisa Pedrana · Mark Stoove · David Wilson · Margaret E Hellard