Research letters

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

Onsite “buddies” are not always available to monitor the donning and doffing of personal protective equipment (PPE) in hospitals, especially during a pandemic, potentially leading to poor PPE compliance and increased risk of health care infections.1,2 We therefore compared monitoring of PPE donning/doffing procedures in a standard critical care setting3,4 by remote buddies with monitoring by onsite buddies. We designed 30 procedural scenarios (15 donning, 15 doffing) that included random errors in some procedural steps (online Supporting Information). Four buddies (two onsite, two remote), unaware of the number and type of errors in each scenario, concurrently viewed and assessed each step. The remote buddies viewed the procedures via videoconferencing on their computers. The camera of the transmitting laptop computer was positioned so that the entire body of the person donning or doffing PPE could be seen. Procedures were live‐streamed to the remote buddies via the hospital Wi‐Fi network. The buddies were not permitted to communicate with each other or with the person donning or doffing PPE. The study was approved by the Melbourne Health Human Research Ethics Committee (QA2020104). Sensitivity (correctly identifying correct procedure) was 100% for both onsite and remote buddies; specificity (correctly identifying incorrect procedure) was 98.9% for onsite buddies and 94.5% for remote buddies; overall accuracy was respectively 99.7% and 98.7% (Box). Concordance between assessments by onsite and remote buddies (κ = 0.95), by the two onsite buddies (κ = 0.97), and by the two remote buddies (κ = 0.98) was very good. The most frequent error was remote buddies missing chin exposure below the mask, probably because of the two‐dimensional view provided by the camera. Paying specific attention to the mask position when the donner turns side on in front of the camera might prevent this error. Practical considerations for remote buddies include the need for reliable hospital network and internet connections, or a wired hardware system, to avoid disruption of monitoring. As the remote buddy is unable to physically intervene when they identify an error, clear verbal communication is important. The psychological effect of having an onsite buddy was not characterised, but may influence user acceptability of remote buddies. All buddies were very experienced in providing observation feedback, but we did not assess their proficiency. Their accuracy may also have reflected greater vigilance while being observed (the Hawthorne effect). Finally, we did not weight the donning and doffing steps according to their importance for safety. Having a trained observer monitor PPE compliance is important for health care safety. The high level of accuracy and the agreement between onsite and remote buddies were encouraging. Apart from identifying errors, remote buddies could also provide step‐by‐step instruction in donning and doffing procedures, which could improve compliance and minimise contamination.5 Using remote buddies may help preserve PPE supplies and ensure reliable access to monitoring, even when PPE supply or onsite staff numbers are limited, while also reducing the infection exposure risk for the monitoring observers. Box – Personal protective equipment (PPE) monitoring assessment accuracy by onsite and remote buddies Scenario outcome* Buddy outcome* Pass Fail Onsite buddies (390 tests) Pass 298 1 PPV, 99.7% Fail 0 91 NPV, 100% Sensitivity, 100% Specificity, 98.9% Overall accuracy, 99.7% Remote buddies (383 tests†) Pass 292 5 PPV, 98.3% Fail 0 86 NPV, 100% Sensitivity, 100% Specificity, 94.5% Overall accuracy, 98.7% PPV = positive predictive value; NPV = negative predictive value. * For each step of each PPE donning/doffing procedure: pass = correctly performed; fail = not correctly performed. † Seven assessments were missing because of internet interruptions.

Reny Segal · William PL Bradley · Daryl Williams · Romulo Correa de Araujo Nunes · Irene Ng

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

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

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

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

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

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

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

Complementary medicines are used by more than half the people in Australia, incurring out‐of‐pocket health expenses of about $5.2 billion in 2019.1 Information about their use by older adults in Australia is more than a decade old.2 Given subsequent demographic changes and doubling in sales of vitamins and supplements,1 we should update our knowledge in this area. We analysed data from the ASPirin in Reducing Events in the Elderly (ASPREE) Longitudinal Study of Older Persons (ALSOP) to assess self‐reported use (every day, occasionally, never) of complementary medicines (fish oil, glucosamine, ginkgo, coenzyme Q10, calcium, zinc, vitamins B, C, D and E, multivitamins, Chinese or herbal) by healthy people over 70 years of age residing in metropolitan or regional Victoria, South Australia, Tasmania, the Australian Capital Territory or southern New South Wales, recruited through their usual general practitioners.3 We summarised data as descriptive statistics; we assessed differences between groups in χ2 tests (categorical variables). Analyses were conducted in SPSS Statistics 23 (IBM). ALSOP was approved by the Monash University Human Research Ethics Committee (reference, CF11/1100). During January 2012 – July 2015, 14 757 of 16 703 ASPREE participants returned ALSOP Baseline Medical Questionnaires3 with at least partial responses to the questions on complementary medicines (response rate, 88%); their mean age was 75.2 years (standard deviation, 4.3 years), and 8068 (55%) were women). A total of 10 961 respondents (74.3%) reported using them either daily or occasionally; fish oil (6563 of 14 757 respondents, 44.5%), vitamin D (4995, 33.8%), glucosamine (3940, 26.7%), and calcium supplements (3652, 24.7%) were the most frequently reported items (Supporting Information, table 1). Complementary medicines were used by larger proportions of women (6637 of 8068, 82.3%) than of men (4324 of 6689, 64.6%; P < 0.001), and of people with more than 12 years of education (4418 of 5838, 75.7%) than of people with less education (6542 of 8918, 73.3%; P = 0.001). The proportions of complementary medicine users who reported a history of depression (987 of 4053, 24.4%) or osteoarthritis (3060 of 5240, 58.4%) were larger than for non‐users (depression, 264 of 1347, 19.6%; P = 0.002; osteoarthritis, 705 of 1598, 44.1%; P < 0.001); self‐reported diabetes was more common among non‐users (363 of 3790, 9.6%) than among complementary medicine users (815 of 10 944, 7.4%; P < 0.001) (Box; Supporting Information, tables 2 and 3). Almost three‐quarters of people in our sample of community‐dwelling older adults in south‐eastern Australia used complementary medicines, with fish oil the most common product. While proprietary complementary medicines are generally regarded as safe, their widespread use by older people, who generally have a greater burden of disease, higher medical expenses, and low or fixed incomes, raises questions about their marketing and promotion.5 Our study population represents Australians over 70 who regularly visit general practitioners, and we included participants from geographically and socio‐economically diverse backgrounds.3 As we pre‐specified a limited number of products, our use estimates may be conservative. In our study, complementary medicine use was defined differently to some earlier studies; for example, the Australian Health Survey which asked about complementary medicine use in the previous 24‐hour period.6 This difference may account for our estimates being slightly higher. Our findings provide the most comprehensive information to date on complementary medicine use by Australians over 70 years of age. Box – Characteristics of respondents to survey of community‐dwelling Australians over 70 years of age on their use of complementary medicine

for the ALSOP Complementary Medicine Research Group*

Switching trauma specialist services to pandemic management would be unwarranted

Teagan L Way · Seth M Tarrant · Zsolt J Balogh

In Australia, colorectal cancer is the second most frequently diagnosed cancer and one of the most common causes of cancer‐related death.1 Evidence that bowel cancer screening reduces mortality through early detection and treatment2 led to the introduction in 2006 of the Australian National Bowel Cancer Screening Program (NBCSP), offering faecal occult blood testing. The NBCSP has been progressively rolled out, from covering those aged 55 or 65 years in 2006 to screening every two years for all Australians aged 50–74 years by 2020.3 During 2016–17, 41% of people invited to participate in screening did so.4 A recent review of the NBCSP found that the risk of death from colorectal cancer was lower for invitees, and that those who had cancer were diagnosed at an earlier stage of disease.5 In Australia, jurisdictional cancer registries do not collect data on surgery‐related morbidity. However, the Binational Colorectal Cancer Audit (BCCA) (https://www.bowelcanceraudit.com) has collected information since 2007 on the diagnosis, management, and outcomes of surgically managed Australian and New Zealand patients with colorectal cancer, as well as whether patients were identified by the NBCSP. BCCA data are voluntarily collected by 435 registered surgeons at 138 participating hospitals across Australia and New Zealand, covering about 24% of newly diagnosed cases of colorectal cancer in 2019.6 We sought to determine whether patients with surgically managed colorectal cancer diagnosed through the NBCSP have better post‐operative outcomes than those diagnosed in other pathways. We undertook a cross‐sectional analysis of de‐identified BCCA data for patients aged 18 years or over who underwent surgery in Australia for colorectal cancer during January 2007 – December 2018. Outcome measures were inpatient and 30‐day mortality; surgical complications; medical complications; return to theatre; and hospital length of stay. We undertook binary logistic regression to assess associations between screening and binary outcomes. The association with length of stay was assessed in ordinary least squares linear regression models. The Monash University Human Research Ethics Committee (project, 19327) and the BCCA Operations Committee provided ethics approval for our study. Of 23 310 cases of colorectal cancer in the database, we could include 15 630 cases with data on cancer type and screening status in our comparison of demographic and clinical characteristics. A larger proportion of patients identified by the NBSCP than of otherwise identified patients were men (58% v 54%); their mean age (64 years, standard deviation [SD], 7 years v 69 years; SD, 14 years) was lower, and larger proportions had American Society of Anesthesiologists (ASA) scores in the low risk range (77% v 59%), were from lower socio‐economic status areas, had presented for elective surgery (96% v 85%), had less advanced cancer stage disease (stages 0–II: 69% v 63%), and underwent minimally invasive surgery (80% v 66%) (Box 1). Data on adjusting variables and outcomes were available for the 11 366 cases included in our logistic regression models. NBSCP‐detected patients were less likely to have post‐operative surgical (adjusted odds ratio [aOR], 0.83; 95% confidence interval [CI], 0.69–0.99) or medical complications (aOR, 0.75; 95% CI, 0.59–0.94); their length of stay was also briefer (adjusted mean difference, –1.56 days; 95% CI, –2.06 to –1.06 days). Post‐operative mortality and return to theatre rates were similar for screened and other patients (Box 2). Our analysis of BCCA data indicates that, in addition to the lower long term mortality associated with the NBCSP,5 short term post‐operative benefits are also evident that should be taken into account when promoting the program. Our study reinforces calls to improve participation rates in the national screening program by eligible participants to optimise the value of this critically important initiative. Box 1 – Demographic and clinical features of 15 730 patients who underwent surgery for colorectal cancer in Australia, 2007–2018, by diagnostic pathway Identification of patients Characteristic Total NBSCP Other P Number of patients 15 730 1357 14 373 Age at surgery (years) Mean (SD) 69 (13) 64 (7) 69 (14) < 0.001 Range 18–100 50–75 18–100 50 or under* 1556 (10%) 77 (6%) 1479 (10%) 51–60 2433 (15%) 385 (28%) 2048 (14%) 61–70 4192 (27%) 651 (48%) 3541 (25%) 71–80 4473 (28%) 244 (18%) 4229 (29%) over 80 3073 (20%) 0 3073 (21%) Missing data 3 0 3 Sex 0.003 Women 7142 (45%) 563 (42%) 6579 (46%) Men 8586 (55%) 792 (58%) 7794 (54%) Missing data 2 2 0 American Society of Anesthesiologists score < 0.001 1–2 (low risk) 9205 (60%) 1000 (77%) 8205 (59%) 3–5 (high risk) 6033 (40%) 294 (23%) 5739 (41%) Missing data 492 63 429 Socio‐economic status (IRSD quintile) < 0.001 1 (most disadvantaged) 2470 (16%) 224 (17%) 2246 (16%) 2 2385 (16%) 221 (17%) 2164 (16%) 3 2957 (20%) 278 (22%) 2679 (19%) 4 3107 (21%) 288 (22%) 2819 (20%) 5 (least disadvantaged) 4153 (28%) 282 (22%) 3871 (28%) Missing data 658 64 594 Cancer type 0.50 Colon 11 287 (72%) 963 (71%) 10 324 (72%) Rectal 4443 (28%) 394 (29%) 4049 (28%) Operative urgency < 0.001 Elective 13 457 (86%) 1310 (96%) 12 147 (85%) Emergency 999 (6%) 11 (1%) 988 (7%) Urgent 1248 (8%) 36 (2%) 1212 (8%) Missing data 26 0 26 Cancer stage < 0.001 0 (cancer in situ) 699 (5%) 92 (7%) 607 (4%) I (local disease) 3728 (24%) 535 (41%) 3193 (23%) II (local disease) 4689 (31%) 278 (21%) 4411 (32%) III (nodal spread) 4437 (29%) 347 (26%) 4090 (29%) IV (metastatic disease) 1625 (11%) 42 (3%) 1583 (11%) X (not identifiable) 121 (1%) 16 (1%) 105 (1%) Missing data 431 47 384 Operative approach < 0.001 Minimally invasive surgery† 10 498 (67%) 1082 (80%) 9416 (66%) Open 5140 (33%) 269 (20%) 4871 (34%) Missing data 92 6 86 IRSD = Index of Relative Socioeconomic Disadvantage (Australian Bureau of Statistics); NBSCP = National Bowel Cancer Screening Program; SD = standard deviation. * National screening program participants are aged 50 years or more. † Laparoscopic, hybrid, conversion of laparoscopic, robotic and transanal total mesorectal excision. table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; } table#t2 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Box 2 – Logistic and linear regression analysis of the association between screening and outcomes for 11 366 patients with colorectal cancer, Australia, 2007–2018 Identification of patients NBSCP v other Outcome NBSCP Other Univariate regression: OR (95% CI) Multivariate regression: aOR* (95% CI) Number of patients 843 10 523 30‐day mortality† 2 175 0.14 (0.02–0.44) 0.31 (0.05–1.01) Surgical complications‡ 171 2494 0.82 (0.69–0.97) 0.83 (0.69–0.99) Medical complications§ 89 1889 0.54 (0.43–0.67) 0.75 (0.59–0.94) Returned to theatre 52 658 0.99 (0.73–1.31) 1.02 (0.75–1.37) Mean difference (95% CI) Adjusted mean difference* (95% CI) Length of stay (days), mean (SD) 7.27 (6.17) 9.62 (8.02) –2.34 (–2.90 to –1.79) –1.56 (–2.06 to –1.06) aOR = adjusted odds ratio; CI = confidence interval; NBSCP = National Bowel Cancer Screening Program; OR = odds ratio; SD = standard deviation. * Adjusted for age, sex, socio‐economic status, screen category, cancer type, American Society of Anesthesiologists score. † Within 30 days of surgery. ‡ Abdominal/pelvic collection, anastomotic leak, entero‐cutaneous fistula, wound dehiscence, wound infection, sepsis, ileus, small bowel obstruction, urinary retention, ureteric injury, splenectomy, post‐operative haemorrhage. § Including chest infection, cardiac complications, deep vein thrombosis, pulmonary embolus.

Sasha Taylor · Farhad Salimi · Arul Earnest · Alexander G Heriot · John R Zalcberg · Susannah Ahern

The incidence of colorectal cancer in Australia is among the highest in the world.1 About 29% of Australians live in rural or remote areas. We have previously reported that colorectal cancer surgery in rural hospitals is safe and that short term outcomes are good.2 This report is based on prospectively collected data for 311 patients treated for stages 1 to 3 colorectal cancer by four surgeons in rural South Australia (Mount Gambier Hospital, with 110 beds and a six‐bed high dependency unit) during 1 February 2006 – 31 January 2020. The follow‐up parameters, intervals between follow‐up examinations, and data analysis tools have been reported previously.2 Briefly, data were analysed in SigmaStat 3.5 (Systat). Survival was analysed by single‐group and log‐rank testing; survival differences between groups were assessed by pairwise multiple comparison (Holm–Šídák). Group data were compared in t, rank sum, and χ2 tests; correlations of covariates and cancer‐specific survival were assessed by multiple logistic regression. The Central Adelaide Local Health Network Human Research Ethics Committee approved our study (reference, 12041). One hundred of 311 patients (32%) had Union for International Cancer Control (UICC) stage 1, 110 (35%) stage 2, and 101 (33%) stage 3 colorectal cancer. The median age of the patients was 71 years (interquartile range [IQR], 63–78 years); 172 (55%) were men. Of the 311 procedures, 277 were elective (89%); 113 were laparoscopic (36%) and 198 laparotomies (64%). Median hospital length of stay was 7 days (IQR, 4–10 days); 30‐day mortality was 1.3% (four deaths), 90‐day mortality 1.6% (five deaths). The proportion of deaths at 30 days after emergency colorectal cancer surgery (three of 34 patients, 9%) was significantly greater than following elective surgery (one of 277, 0.4%; P = 0.002). Leakage occurred in 13 of 259 procedures with anastomosis (5%). The median number of lymph nodes resected was 14 (IQR, 10–20). Overall 5‐year survival of patients (stages 1–3) was 79%, 10‐year survival was 45%. Cancer‐specific 5‐year survival was 86% and 10‐year survival 79% (Box). Multivariate analysis included patient sex, age, intra‐operative blood loss, laparoscopic surgery, American Society of Anesthesiologists (ASA) score, and UICC stage as covariates. More advanced tumour stage (stages 1/2 v stage 3: odds ratio [OR], 2.01; 95% confidence interval [CI], 1.39–2.90) and higher age (< 70 years v ≥ 70 years: OR, 2.28; 95% CI, 1.11–4.71) were significantly associated with lower overall survival. Cancer‐specific survival was significantly reduced by more advanced tumour stage stages 1/2 v stage 3: OR, 4.76; 95% CI, 2.53–8.94). Our follow‐up program included quarterly blood tests (carcino‐embryogenic antigen, carbohydrate antigen 19.9, full blood cell count) and clinical examination during the first two years, semi‐annual tests during the next three years, and annual blood tests and clinical examinations thereafter. Throughout follow‐up, annual computed tomography and colonoscopy were offered to all patients, and additional investigations initiated in response to changes in clinical or laboratory findings. This intense follow‐up program, based on that used at the University of Munich in Germany, was adopted when the current surgical unit was established in Mount Gambier. It is being reviewed and will be adjusted to current Australian recommendations. Recurrent disease was detected in a total of 52 patients (17%), and 13 patients (4%) underwent curative resection. The primary treatment for colorectal cancer is surgical removal. Surgical care should be provided in an adequately staffed and equipped hospital. We found that such surgery can be provided safely and with good long term oncological outcomes in a rural centre. Overall 5‐year survival in our study exceeded the most recent reported value for Australia (2011–2015: 69.9%),3 and contrasts with a Californian study which found that rural residence was associated with poorer cancer‐specific mortality.4 Published data on outcomes beyond 10 years after colorectal cancer surgery are limited. Our overall 10‐year survival rate of 45% is similar to that reported by an earlier study in Fremantle (44%).5 Our findings confirm that tumour stage and age at diagnosis are significant predictors of death following curative surgery for colorectal cancer. We found that colorectal cancer surgery in a non‐metropolitan surgical centre is safe and associated with low 30‐ and 90‐day mortality rates. Oncological results at 5 and 10 years compare well with the results of other groups. Surgery can be provided close to the patients’ homes and families in adequately staffed and equipped centres and can match outcomes in capital city hospitals. Box – Five‐ and 10‐year survival of patients undergoing curative resection for colorectal cancer at Mount Gambier Hospital, February 2006 – January 2020 5‐year survival 10‐year survival Overall Cancer‐specific Overall Cancer‐specific All 79% 86% 45% 79% Union for International Cancer Control (UICC) stage Stage 1 (pT1/pT2) 91% 99% 58% 99% Stage 2 (pT3/pT4) 82% 87% 51% 85% Stage 3 (any T, node positive) 55% 74% 39% 55% American Society of Anesthesiologists (ASA) physical status classification 1 100% — 100% — 2 84% — 58% — 3 70% — 32% — 4 62% — 0 — Age < 70 years 86% — 72% — 70–79 years 76% — 32% — ≥ 80 years 60% — 7% — pT = primary tumour staging. table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; }

Matthias W Wichmann · Timothy K McCullough · Eben Beukes · Thomas Gunning · Guy J Maddern

Objective: To identify individual and organisational factors associated with the prevalence, type and impact of unprofessional behaviours among hospital employees. Design, setting, participants: Staff in seven metropolitan tertiary hospitals operated by one health care provider in three states were surveyed (Dec 2017 – Nov 2018) about their experience of unprofessional behaviours — 21 classified as incivility or bullying and five as extreme unprofessional behaviour (eg, sexual or physical assault) — and their perceived impact on personal wellbeing, teamwork and care quality, as well as about their speaking‐up skills. Main outcome measures: Frequency of experiencing 26 unprofessional behaviours during the preceding 12 months; factors associated with experiencing unprofessional behaviour and its impact, including self‐reported speaking‐up skills. Results: Valid surveys (more than 60% of questions answered) were submitted by 5178 of an estimated 15 213 staff members (response rate, 34.0%). 4846 respondents (93.6%; 95% CI, 92.9–94.2%) reported experiencing at least one unprofessional behaviour during the preceding year, including 2009 (38.8%; 95% CI, 37.5–40.1%) who reported weekly or more frequent incivility or bullying; 753 (14.5%; 95% CI, 13.6–15.5%) reported extreme unprofessional behaviour. Nurses and non‐clinical staff members aged 25–34 years reported incivility/bullying and extreme behaviour more often than other staff and age groups respectively. Staff with self‐reported speaking‐up skills experienced less incivility/bullying (odds ratio [OR], 0.53; 95% CI, 0.46–0.61) and extreme behaviour (OR, 0.80; 95% CI, 0.67–0.97), and also less frequently an impact on their personal wellbeing (OR, 0.44; 95% CI, 0.38–0.51). Conclusions: Unprofessional behaviour is common among hospital workers. Tolerance for low level poor behaviour may be an enabler for more serious misbehaviour that endangers staff wellbeing and patient safety. Training staff about speaking up is required, together with organisational processes for effectively eliminating unprofessional behaviour.

Johanna Westbrook · Neroli Sunderland · Ling Li · Alain Koyama · Ryan McMullan · Rachel Urwin · Kate Churruca · Melissa T Baysari · Catherine Jones · Erwin Loh · Elizabeth C McInnes · Sandy Middleton · Jeffrey Braithwaite

Social restrictions reduced the spread of COVID-19 within 14 days of measurable changes in public mobility

Tu Hao Tran · Suraj N Sasikumar · Annemarie Hennessy · Aiden O'Loughlin · Lucy Morgan

Hospitals in the Northern Territory often operate beyond capacity and serve a sparsely distributed population with rates of chronic disease and household overcrowding that are higher than in many other parts of Australia. The NT consequently adopted particularly strict public health measures to avert the potentially catastrophic consequences of community transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), including supervised isolation until viral clearance of all people with confirmed SARS‐CoV‐2 infections (Supporting Information 1). This measure provided a unique opportunity to study the duration and trajectory of viral shedding in relation to clinical illness. In this article, we describe epidemiologic, clinical, and virological aspects of the first 28 cases of coronavirus disease 2019 (COVID‐19) in the NT. The Top End and Central Australian Human Research Ethics Committees approved the study (reference, 2020‐3737). Between 4 March and 4 April 2020, 28 cases of COVID‐19 were diagnosed in the NT, all linked to overseas or interstate travel. The median age of patients was 45.0 years (range, 1.5–75 years); 16 were women (Supporting Information 1, table). Two patients required supplemental oxygen, one of whom also required intubation. There were no deaths. Symptoms had been present for a median 3 days (range, 0–16 days) before oro‐nasopharyngeal swab collection and lasted a median 9.5 days (range, 4–18 days). Viral RNA could be detected by multiplex tandem real‐time polymerase chain reaction (PCR) assay (AusDiagnostics; Supporting Information 1) for a median 25 days after symptom onset (range, 14–41 days; interquartile range [IQR], 21–32 days), and in most patients for more than two weeks after symptom resolution (median, 17.5 days; range, 2–31 days; IQR, 14.5–22.5 days) (Box 1). Within‐patient variability in viral target cycle threshold values during follow‐up was considerable (Box 2; Supporting Information 1, figure), despite adequate and consistent amounts of human biologic material in test samples (data not shown). Prolonged compulsory isolation was distressing for several patients. The phylogeny of the 27 available NT viral genomes was consistent with acquisition in locations on all inhabited continents (Box 3). Five genetic clusters were evident (maximum of one single nucleotide polymorphism within each cluster) that were also epidemiologically linked by shared travel or household contact. The SARS‐CoV‐2 genomes from two independent travellers without epidemiologic connections were identical, but matched other publicly available genomes, highlighting the importance of interpreting genomic analyses in their epidemiologic context. The priority of the strict NT isolation requirements for patients with COVID‐19 was viral containment at a time when data on the duration of viral transmissibility were sparse. More recent evidence suggests that viable SARS‐CoV‐2 is rarely isolated more than 10 days after symptom onset,1,2,3 and requirements have consequently been eased, while maintaining supervised isolation with health management during the period of greatest infectivity. The high degree of temporal variability in viral shedding during follow‐up indicates that a single assay is not adequate for excluding infection in patients at epidemiologic risk of COVID‐19. The NT implemented particularly aggressive public health measures to contain SARS‐CoV‐2 transmission. Epidemiologic and genomic analyses suggest that this response has successfully prevented local community transmission of the virus. Box 1 – Time course of 28 cases of coronavirus disease 2019 (COVID‐19) diagnosed in the Northern Territory, 4 March – 4 April 2020 Each line represents a single patient. Day zero is the day of collection of the first SARS‐CoV‐2‐positive specimen; thickened sections indicate the period of COVID‐19 symptoms. Closed circles indicate positive SARS‐CoV‐2 assay results, hollow circles negative assay results. Patients 13 and 15 (lighter marking) required supplemental oxygen. The bottom line summarises the median duration of symptoms prior to diagnosis, the median duration of symptoms, and the median time to viral clearance. Box 2 – Multiplex tandem polymerase chain reaction cycle threshold values for detection of the SARS‐CoV‐2 open reading frame 1a gene (ORF1a) Box 3 – Maximum likelihood phylogenetic tree, depicting SARS‐CoV‐2 genomes from the Northern Territory and elsewhere SARS‐CoV‐2 = severe acute respiratory syndrome coronavirus 2. The phylogenetic tree shows that SARS‐CoV‐2 genomes in the Northern Territory (on the inner side of the outer ring) were drawn from across the range of genomes reported elsewhere (outer ring). NT travel‐related cases with epidemiologic links formed genomic clusters. Two cases without epidemiologic links also comprised a cluster, but the genomes were identical with overseas genomes. The context genomes were obtained from GISAID (https://www.gisaid.org), with region based on location of the submitting laboratory; the Wuhan‐Hu‐1 genome was used as an outgroup, and the scale bar indicates substitutions per site.

for the Northern Territory COVID‐19 Response Group

Overcoming patient, GP, and health system barriers to changes in care delivery are critical to progress

Charlotte M Hespe · Anna Campain · Ruth Webster · Anushka Patel · Lucie Rychetnik · Mark F Harris · David P Peiris

The coronavirus disease 2019 (COVID‑19) pandemic has forced many countries to take extraordinary measures to prevent spread of disease. In New South Wales, public health orders introduced during 18–26 March 2020 required the closure of major industries and prohibited non‐essential gatherings of more than 100 people or allowing less than 4 m2 space per person. On 29 March, further public health orders prohibited people leaving home other than for work, study, shopping, medical care, or exercise.1,2 Changes in patterns of presentations to emergency departments (EDs) have been reported during COVID‐19 lockdowns overseas, including reduced numbers of patients with certain high acuity conditions, such as acute coronary syndrome (ACS) and stroke.3,4,5 Understanding the situation in Australia is important for public health policy during this and future pandemics. The Western Sydney Local Health District is a metropolitan health network in NSW of four hospitals (each with EDs) with a total capacity of 1925 beds, serving a catchment of 950 000 people. We analysed triage, International Classification of Diseases, tenth revision, Australian modification (ICD‐10‐AM) coding, and separations data for ED presentations during 29 March – 31 May in each of 2019 and 2020. Differences in mean daily presentation numbers for each triage category and selected presentation types were assessed in non‐paired Student t test with Bonferroni correction. All data analysis was performed in Excel (Microsoft). As a quality assurance project, the study was exempted from formal ethics approval. The number of ED presentations during 29 March – 31 May was almost 25% lower in 2020 than in 2019 (26 617 v 35 268). Presentation numbers in all triage categories were lower in 2020 (P < 0.001), except for category 1 (resuscitation) (506 v 445, 14% increase; P = 0.40). The proportion of patients discharged from the ED was greater in 2020 (60% v 53%) and that of patients who did not wait for treatment smaller (1% v 5%). The number of patients admitted to hospital was lower in 2020 than 2019 (8047 v 11 838), as were the proportions admitted to hospital (30% v 34%) (Box 1). ED presentations with fourteen selected diagnoses were further examined: common infectious diseases (infectious enteric disease, pneumonia), conditions frequently seen in EDs (wrist or hand fractures, femur fractures, appendicitis, renal calculi), conditions for which fewer ED presentations have been reported during COVID‐19 restrictions overseas (stroke or cerebral haemorrhage, ACS, chest pain, transient ischaemic attacks), and conditions that may be exacerbated or for which follow‐up in routine medical services may be reduced by COVID‐19 and its associated restrictions (mental health problems, substance misuse, malignancy). The numbers of presentations with infectious enteric disease, pneumonia, wrist or hand fractures, stroke or intracerebral haemorrhage, and chest pain not resulting in another diagnosis were lower in 2020 than in 2019. The numbers of presentations with ACS were similar. The number of presentations with mental health problems was higher in 2020 (daily mean, 8.4; standard deviation [SD], 3.1) than in 2019 (daily mean, 6.9; SD, 2.6; difference, +1.5 presentations per day; 95% confidence interval, +0.1–2.9) (Box 2; online Supporting Information). Social distancing may have reduced the spread of infectious enteric diseases and community‐acquired pneumonia, and home isolation may have led to fewer fractures. However, lower numbers of presentations with chest pain or stroke (also reported overseas4) may reflect factors other than lower incidence, such as suspension of outpatient clinics and elective procedures, social distancing measures, and public anxiety. COVID‐19 has profoundly affected health care delivery. We found concerning reductions in ED presentation numbers that may indicate delayed seeking of appropriate medical attention. Public health messages should encourage timely presentation of people with time‐sensitive, potentially life‐threatening conditions, even during pandemics. Equally concerning is the higher number mental health‐related presentations, which may reflect anxiety about COVID‐19, loss of job security, or prolonged isolation. Studies of patients presenting to health care services as they re‐open are required to fully appreciate the health implications of the COVID‐19 epidemic. Box 1 – Emergency department presentations to Western Sydney Local Health District hospitals during corresponding two‐month periods in 2019 and 2020 Triage category Resuscitation Emergency Urgent Semi‐urgent Non‐urgent Total 29 March – 31 May 2019 Total number of presentations 445 8910 12 464 10 726 2723 35 268 Daily presentations, mean (standard deviation) 7.0 (3.2) 139 (15.9) 195 (19.3) 168 (22.3) 42.5 (10.7) 551 (41.8) Admitted to hospital 350 (79%) 4550 (51%) 4524 (36%) 2156 (20%) 258 (9%) 11 838 (34%) Discharged: treatment complete 38 (9%) 3350 (38%) 6155 (49%) 7093 (66%) 2039 (75%) 18 675 (53%) Transferred to another hospital or service 26 (6%) 521 (6%) 577 (5%) 299 (3%) 68 (2%) 1491 (4%) Did not wait 0 65 (1%) 560 (4%) 735 (7%) 239 (9%) 1599 (5%) Discharged against medical advice 7 (2%) 413 (5%) 646 (5%) 442 (4%) 81 (3%) 1589 (5%) Died in emergency department/dead on arrival 24 (5%) 11 (< 1%) 2 (< 1%) 1 (< 1%) 38 (1%) 76 (< 1%) 29 March – 31 May 2020 Total number of presentations 506 7609 9095 7346 2061 26 617 Daily presentations, mean (standard deviation) 7.9 (2.6) 119 (18.4) 142 (17.5) 115 (17.9) 32.2 (8.4) 416 (40.6) Admitted to hospital 370 (73%) 3112 (41%) 3072 (34%) 1279 (17%) 214 (10%) 8047 (30%) Discharged: treatment complete 62 (12%) 3836 (50%) 5146 (57%) 5324 (72%) 1525 (74%) 15 893 (60%) Transferred to another hospital or service 26 (5%) 424 (6%) 461 (5%) 304 (4%) 136 (7%) 1351 (5%) Did not wait 0 22 (< 1%) 84 (1%) 170 (2%) 107 (5%) 383 (1%) Discharged against medical advice 9 (2%) 210 (3%) 328 (4%) 267 (4%) 64 (3%) 878 (3%) Died in emergency department/dead on arrival 39 (8%) 5 (< 1%) 3 (< 1%) 0 15 (1%) 62 (< 1%) Change in presentation numbers, 2020 v 2019 +14% –15% –17% –32% –25% –25% table#t1 tbody td:nth-child(n+2) P. Pleft { text-align: center; } Box 2 – Mean changes (with 95% confidence intervals) for numbers of emergency department presentations with selected diagnoses (ICD‐10‐AM codes), 29 March – 31 May 2020 v 29 March – 31 May 2019 ICD-10-AM = International Classification of Diseases, tenth revision, Australian modification. * Not resulting in another diagnosis. † Excluding cases without mention of obstruction.

Andrew W Kam · Sarah G Chaudhry · Nathan Gunasekaran · Andrew JR White · Matthew Vukasovic · Adrian T Fung

It has been reported that about 60% of patients commencing chemotherapy in Australia with curative intent and 47% of those receiving radiotherapy also use complementary medicine.1,2 Ingestible products are frequently used, but are often not discussed with the medical team, which increases the risk of interactions and other undesirable effects. Opportunity costs are another problem; while complementary medicine is typically used by people with cancer for supportive care and wellbeing, some use it to help treat cancer.2 Given the frequent use of complementary medicine by people with cancer, we surveyed Australian public and private hospitals with dedicated cancer services (1 May – 15 December 2016),3,4 to assess various aspects of cancer service coverage, particularly complementary medicine services. In this report, we describe hospital policies on complementary medicine and the availability of related information for patients. The study was approved by the human research ethics committees of the University of Western Sydney (reference, H11389), the University of Wollongong and Illawarra Shoalhaven Local Health District (reference, HREC/16/WGONG/178), and Calvary Health Care, Adelaide (reference, 16‐CHREC‐E011). One staff member from the cancer service of each participating hospital (262 of 282 invited hospitals, 93%) completed a 52‐item electronic survey (online Supporting Information). Chemotherapy was provided by 207 of the participating services (79%) and supportive and allied health care by 196 (75%), including 66 (25%) that provided at least one type of complementary medicine service. Palliative care was provided by 168 hospitals (64%), surgery by 143 (55%), and radiotherapy by 143 (34%). Ninety‐three responding hospitals (36%) could not provide responses to one or more of the five policy‐related survey questions. This was despite the option to complete the survey across several log‐in sessions and 223 of the respondents (85%) having administrator or management roles. Only 89 respondents (34%) were aware of the Council of Australian Therapeutic Advisory Groups (CATAG) position statement on complementary medicines,5 and only 31 of these respondents (35%) thought that their hospital policies were aligned with this statement. A substantial proportion of hospitals did not have policies regarding complementary medicine practitioners or patient‐initiated complementary medicine use (Box). Most hospitals (229, 87%) had policies for documenting complementary medicines: 76 (33%) documented all complementary medicines (including patient‐initiated products) on medication charts, 88 (38%) documented only complementary medicines approved by medical staff, and 48 (21%) documented complementary medicine use only in the clinical history. The policy at 17 hospitals (6%) was that complementary medicines were never permitted, despite CATAG advice.5 In an adjusted backward multinominal logistic regression analysis, hospitals with cancer services without complementary medicine services were significantly less likely to have policies on complementary medicine practitioners and documenting complementary medicines (Box). Further, only 123 services (47%) provided complementary medicine information for patients, and 23 respondents (9%) did not know whether such information was available. The differences in the awareness of and the availability of hospital policies and patient information about complementary medicine are concerning. Irrespective of whether a cancer service provides complementary medicine, consistent policies across Australian hospitals, and staff and patient awareness of these policies, are important because of the widespread use of complementary medicine. Stronger leadership is needed from peak bodies, such as the Australian Commission on Safety and Quality in Health Care and CATAG, to encourage Australian cancer services and hospitals to update or review their complementary medicine policies. Box – Hospital policies regarding complementary medicine products and visiting practitioners, based on survey responses from 262 hospitals with cancer services Complementary medicine (CM) cancer services available Hospitals without v with CM service: adjusted odds ratio* (95% CI) Policy type Number Yes No Total number of hospitals 262 66 (25%) 196 (75%) Documenting CM product use Hospital policy 229 (87%) 60 (91%) 169 (86%) — No policy 24 (9%) 1 (2%) 23 (12%) 10.4 (1.3–81) Unknown 9 (3%) 5 (8%) 4 (2%) 0.29 (0.07–12) Documenting patient‐initiated CM products Hospital policy 43 (16%) 15 (23%) 28 (14%) — No policy 133 (51%) 30 (45%) 103 (53%) 1.8 (0.84–4.0) Case‐by‐case 43 (16%) 9 (14%) 34 (17%) 1.2 (0.48–3.3) Unknown 43 (16%) 12 (18%) 31 (16%) 1.8 (0.68–5.0) Referrals to CM practitioners outside the hospital Hospital policy 25 (10%) 14 (21%) 11 (6%) — No policy 145 (55%) 27 (41%) 118 (60%) 5.2 (2.1–13) Case‐by‐case 43 (16%) 15 (23%) 28 (14%) 2.8 (0.99–8.0) Unknown 49 (19%) 10 (15%) 39 (20%) 4.4 (1.5–13) Scope of practice for visiting CM practitioners Hospital policy 54 (21%) 20 (30%) 34 (17%) — No policy 113 (43%) 16 (24%) 97 (49%) 3.3 (1.5–7.3) Case‐by‐case 34 (13%) 17 (26%) 17 (9%) 0.65 (0.26–1.6) Unknown 61 (23%) 13 (20%) 48 (24%) 2.1 (0.95–5.0) Credentialing for visiting CM practitioners Hospital policy 72 (28%) 32 (48%) 40 (20%) — No policy 103 (39%) 11 (17%) 92 (47%) 6.2 (2.8–14) Case‐by‐case 28 (11%) 11 (17%) 17 (9%) 1.4 (0.56–3.5) Unknown 59 (22%) 12 (18%) 47 (24%) 2.9 (1.3–6.6) CI = confidence interval. * Reference category: hospital has policy and its cancer service provides complementary medicine services. Derived by backward multinominal logistic regression, adjusted for survey responder's role (administration/management: 46 [18%], health care professional: 70 [27%], dual role: 146 [56%]); hospital ownership (public: 132 [50%], private for‐profit: 74 [28%], private not‐for‐profit: 56 [21%]; and Australian Bureau of Statistics remoteness classification (major cities: 117 [40%], inner/outer regional: 87 [30%], remote/very remote: 91 [31%]).

Jennifer Hunter · Suzanne Grant · Geoff P Delaney · Caroline A Smith · Kate Templeman · Jane Ussher

While mumps outbreaks involving professional rugby league, rugby union, and ice hockey teams have been reported in the media,1,2,3,4,5 there have been few scientific reports. On 30 January 2018, a general practitioner notified the local Public Health Unit of a mumps outbreak in a National Rugby League team, prompting investigation according to the NSW Public Health Act 2010. Four players and two coaching staff had developed fever and parotitis during 21–24 January (Box). Mumps virus was detected by polymerase chain reaction (PCR) in the buccal or throat swabs of two patients; each had detectable mumps IgG but not IgM (Liaison Mumps IgG and IgM, DiaSorin). In the other four patients, who had fever and parotid swelling, mumps was diagnosed clinically. The patients were isolated and their travel restricted; the Public Health Unit recommended measles–mumps–rubella (MMR) vaccination of all asymptomatic players and support staff. A further six cases were diagnosed during 1–10 February, in five players and an intimate contact of one of the earlier PCR‐positive patients; the contact developed symptoms 18 days after symptom onset in the source patient. Mumps virus was detected by PCR in four of the six new patients; two were diagnosed clinically. In one PCR‐positive case, mumps IgG, but not IgM, was detected. In all six PCR‐positive patients, genotype G mumps virus was identified. The offer of vaccination was extended to the partners of players and staff, and to players from four elite clubs who shared facilities with the team; by 19 February, 178 players and support staff and their partners had been vaccinated. No new cases were diagnosed after 10 February, and the outbreak was declared ended on 31 March. None of the 12 patients (median age, 25 years; range, 18–39 years) suffered complications. The nine players were from a pool of 42 elite and junior players, an estimated attack rate of 21%. Significantly, mumps‐specific IgG had been detected in nine patients (all players) at the time of their joining the club; the other three patients (all non‐players) had not previously been tested. Documentation of past vaccination was unavailable. No players or staff who received MMR vaccine during the outbreak developed mumps. The intimate contact who developed mumps was vaccinated at least 10 days after first exposure, at which point they were probably in the incubation phase of infection. This was the first mumps outbreak in NSW for many years, and nine of the twelve patients had pre‐existing mumps IgG, which does not appear to be a reliable marker of protective immunity.6 Patients who underwent both serology and PCR testing had detectable IgG but not detectable IgM. This pattern, generally understood to reflect waning immunity following vaccination — that is, pre‐existing mumps‐specific IgG does not prevent infection but its concentration rapidly increases after infection — was also reported for a community outbreak in Western Australia.7 PCR testing is consequently preferable for detecting infection in vaccinated populations, and outbreak control should include vaccination of contacts, even if they have previously received two doses of mumps vaccine.8 Apart from hockey, mumps outbreaks in elite team sports other than the rugby codes have not been reported. Intensive exposure to saliva may result in greater force of infection; tackling and scrums facilitate frequent contact with saliva from fellow players’ faces and on jerseys contaminated by the wiping of mouthguards. Ensuring at registration that players have received two documented lifetime doses of mumps vaccine may be a more effective preventive measure than relying on IgG screening. Ethics approval All patients and their rugby league club provided written consent for the publication of this report. Box – Timeline of the mumps outbreak in a New South Wales National Rugby League team, 21 January – 10 February 2018 PCR = polymerase chain reaction testing.

Karen Chee · Cassy Workman · Susan Irvine · Mark J Ferson