Pattern of hospital admissions and costs associated with acute rheumatic fever and rheumatic heart disease in Australia, 2012–2017
Ingrid Stacey A B * , Judith Katzenellenbogen A , Joseph Hung C , Rebecca Seth D , Carl Francia E F , Bradley MacDonald A G , James Marangou H I J , Kevin Murray A and Jeffrey Cannon KA
B
C
D
E
F
G
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I
J
K
Abstract
This study aims to describe the pattern and trends in acute rheumatic fever (ARF)/rheumatic heart disease (RHD)-related hospitalisations and costs for Australians aged <65 years.
This retrospective linked data study measured trends in hospitalisations and costs for ARF, RHD and complications of ARF/RHD in Northern Territory, South Australia, Western Australia, Queensland and New South Wales between 1 July 2012 and 30 June 2017. Persons with ARF/RHD were identified from RHD registers and/or hospital records.
Over the 5-year study period, 791 children, aged <16 years (86.3% Indigenous), and 2761 adults, aged 16–64 years (44.8% Indigenous), were hospitalised for ARF, RHD or associated complications. On average there were 296 paediatric admissions per year, increasing 6.1% annually (95% CI: 2.4–9.6%, P = 0.001) and 1442 adult admissions per year, increasing 1.7% annually (95% CI: 0.1–3.4%, P = 0.03). Total 5-year costs were AU$130.6 m (AU$17.6 m paediatric, AU$113.0 m adult). Paediatric costs were mostly for ARF-related admissions whereas adult costs mostly involved valvular surgery. Emergency admissions and air ambulance transfers were common, particularly for non-metropolitan residents.
Successful ARF/RHD prevention would deliver significant hospital cost savings. Investment in primary and specialist health care in regional areas may reduce emergency admissions and regional transfers, further reducing hospital burden.
Keywords: Aboriginal and Torres Strait Islander health, acute rheumatic fever, diagnosis related groups, End RHD in Australia: Study of Epidemiology (ERASE), hospitalisations, linked data, rheumatic heart disease.
Introduction
Acute rheumatic fever (ARF) and rheumatic heart disease (RHD) are caused by untreated Streptococcal A (Strep A) bacterial infections. Aboriginal and Torres Strait Islander peoples (hereafter, Indigenous) experience 61-times higher RHD rates than non-Indigenous Australians.1 As an entirely preventable condition, the premature loss of health and human life associated with RHD causes significant trauma and sadness for affected people, families and communities. The number of new ARF/RHD diagnoses notified to RHD registers is increasing annually, with progression to complications and surgery common, even for young patients with initially uncomplicated illness.2–5
All levels of the Australian healthcare system are impacted by ARF and RHD. The Cost of Inaction report on RHD in Australia projected costs of AU$317 million to treat ARF and RHD in Australia between 2016 and 2031, with 60% of costs contributed by hospitalisations.6,7 Projected costs of RHD are uncertain though, as they were modelled on multiple sources of unlinked data and assumptions. There are no comprehensive, Australia-wide analyses of hospitalisations and costs based on real-world data from people with ARF/RHD.
Hospitalisation patterns and treatment costs for ARF/RHD are needed to inform health system planning and investment priorities, and for evaluating cost savings of primordial and primary prevention strategies.8 Since 2009, the Commonwealth-funded Rheumatic Fever Strategy (RFS) has targeted ARF/RHD by focusing on penicillin delivery and specialist care and/or surgery. More recently, Australian efforts have pivoted to preventing ARF/RHD by transitioning to an Indigenous-led implementation and coordination model for the RFS; Strep A vaccine development (ASAVI: Australian Strep A Vaccine Initiative); environmental health research (STARFISH project: STopping Acute Rheumatic Fever Infections to Strengthen Health) and routine screening strategies (NEARER SCAN study: Non-Expert Acquisition and Remote Expert Review of Screening echocardiography images from Child health and AnteNatal clinics).9–11 Objective evidence of hospitalisation patterns and costs associated with treating ARF/RHD is critical for economic evaluation of preventative strategies and for service planning.
The primary study aim was to examine patterns and trends in ARF/RHD-related hospitalisations and associated costs for paediatric patients aged <16 years and adults aged 16–64 years with ARF or RHD in Australia from 1 July 2012 to 30 June 2017. The secondary aim was to compare hospital admission patterns for metropolitan and non-metropolitan residents.
Methods
Study design
This was a retrospective linked-data analysis of hospitalisations and costs for ARF, RHD and related complications between 1 July 2012 and 30 June 2017 (the study period).
Data from the End-RHD in Australia: Study of Epidemiology (ERASE) was used, which included RHD register, adult cardiac surgery, emergency presentation, inpatient admission and death records for people with ARF/RHD.12 Data were available from Northern Territory (NT), Queensland (Qld), Western Australia (WA), South Australia (SA) and New South Wales (NSW) for 1 January 2001 to 31 December 2017. Victoria, Tasmania and Australian Capital Territory were excluded due to lack of RHD registers.
Study sample
People aged <65 years at 1 July 2012 with any register/hospital record of ARF/RHD in the previous 11.5 years, or newly diagnosed during the study period were included. Individuals with congenital abnormalities were excluded (Supplementary material file S1).
Earliest ARF/RHD diagnosis date across all records was determined per person. For register records, diagnosis date was symptom onset date; for hospital records, it was earliest admission date for diagnosis of ARF/RHD using International Classification of Disease 10 Australian Modification (ICD-10 AM) codes I00–I02 (ARF, principal diagnosis) and I05–I09 (RHD, all diagnoses). For RHD hospitalisations, an ERASE algorithm with high positive predictive rate for RHD reduced misclassification.13,14
Hospitalisation for ARF, RHD or complication during the study period (on or after ARF/RHD diagnosis date) were analysed for the cohort. Hospitalisations were included for a given financial year if individuals who had ARF/RHD were alive at the midpoint. For analyses by residential location, inter-hospital transfers were considered part of a single episode of care. Only residents of the same state where data were captured were included to ensure complete data coverage, except for SA and NT which had inter-jurisdictional linkage.
Demographics and admission characteristics
Study variables included financial year of hospitalisation, diagnosis (ARF, RHD or complication), emergency admission status (inpatient admissions commencing with an emergency presentation), inpatient surgical procedure, sex, population group (Indigenous, immigrant from low or middle income country, other Australian), state of residence, geographical remoteness (based on Accessibility/Remoteness Index of Australia, ARIA+), socioeconomic status (index of relative socioeconomic disadvantage quintiles, IRDS) and RHD register notification status.
Person-level Indigenous status was assigned using the algorithm developed by the WA-based Getting our Story Right project and applied across all jurisdictions and data sets to avoid over or under-counting persons identifying as Indigenous.15
Admissions coded to ARF/RHD were identified as described in ‘Study sample’. Complications including atrial fibrillation (I48), heart failure (I50), stroke (I60–I64, I69) and infective endocarditis (I33) were identified from the principal diagnosis field. Admissions involving valvular surgeries were identified via ICD-10 AM procedure code in any field (Supplementary material file S1).
Hospitalisation patterns
Hospitalisations were aggregated by financial year of admission and stratified into paediatric (age <16 years) and adult (age 16–64 years) groups. Total persons represented by these admissions, diagnosis, emergency admission status, RHD-related surgery within admission and hospital bed days were calculated. For location-based analyses, grouping of admissions into episodes allowed for investigation of treatment outside of residential region and air transfers. Air transfers were identified using mode of admission variables from both emergency and hospitalisation datasets.
Hospitalisation costs
Hospitalisation costs were generated using ‘National Weighted Activity Unit’ calculators obtained from the Independent Health and Aged Care Pricing Authority, which estimate costs based on Australian Refined Diagnosis Related Group (AR-DRG) codes (Supplementary material file S2).16 Costs were indexed to 2016/17 Australian Dollars (AU$) and presented by financial year of admission, diagnosis, emergency admission status and jurisdiction. Sensitivity analyses were conducted to assess the implications of missing interstate surgeries (Supplementary material file S3).
Statistical analyses
Linear regression models with financial year as the predictor variable were applied to total cases, admissions, surgeries, hospital bed days and costs. Regression coefficients and 95% confidence intervals (CIs) around the financial year variable were interpreted as annual average change during the study. Z-tests were used to compare hospitalisation patterns between metropolitan and non-metropolitan residents.
Data management and cost calculations were conducted in SAS 9.4; modelling and graphics used R 4.1.1.
Ethics
Aboriginal Ethics Committees approved ERASE in WA (No. 717), SA (No. 04-16-700) and NSW (No. 1363/18). Human Research Ethics Committee approvals were also obtained from Menzies School of Health Research (No. 2016–2705), WA Health (No. 2016/29), SA Health (No. HREC/16/SAH/120), Queensland Health (No. HREC/15/QPCH/289) and NSW Health (No. HREC/18/CIPHS/15).
Results
Baseline descriptive statistics
During the study, 36.9% (n = 791) of 2142 paediatric patients had one or more ARF/RHD-related hospitalisations, this was true for 24.2% (n = 2761) of 11,387 adult patients (Table 1). Hospitalised children were predominantly Indigenous (85.5%), >60% resided in remote/very-remote regions and >60% were from the most disadvantaged SEIFA category. Most hospitalisations were new cases, with 55.2% not on the RHD register. Over one-half of adults hospitalised were non-Indigenous and resided in major cities/inner regional areas, and only 7.6% were registered.
Paediatric (<16 years) | Adult (16–64 years) | ||||||||
---|---|---|---|---|---|---|---|---|---|
All | % | Hosp. | % | All | % | Hosp. | % | ||
Total people | 2142 | 791 | 11387 | 2761 | |||||
ARF only | 1139 | 53.2 | 424 | 53.6 | 1170 | 10.3 | 203 | 7.4 | |
RHD only | 229 | 10.7 | 27 | 3.4 | 9093 | 79.9 | 2237 | 81.0 | |
Both ARF and RHD | 774 | 36.1 | 340 | 43.0 | 1124 | 9.9 | 321 | 11.6 | |
Average midyear age (years) | 11.9 | 11.2 | 48.9 | 48.2 | |||||
Sex A | |||||||||
Male | 1097 | 51.2 | 408 | 51.6 | 4031 | 35.4 | 1041 | 37.7 | |
Female | 1044 | 48.8 | 383 | 48.4 | 7356 | 64.6 | 1720 | 62.3 | |
Population group B | |||||||||
Indigenous | 1847 | 86.3 | 676 | 85.5 | 5056 | 44.8 | 1144 | 41.5 | |
Immigrant (ILIC) | 130 | 6.1 | 61 | 7.7 | 2276 | 20.2 | 631 | 22.9 | |
Other Australian | 164 | 7.7 | 54 | 6.8 | 3961 | 35.1 | 985 | 35.6 | |
State of residence | |||||||||
New South Wales | 136 | 6.4 | 53 | 6.7 | 3245 | 28.5 | 813 | 29.5 | |
Queensland | 773 | 36.1 | 224 | 28.3 | 3588 | 31.5 | 797 | 28.9 | |
South Australia | 52 | 2.4 | 15 | 1.9 | 655 | 5.8 | 190 | 6.9 | |
Western Australia | 315 | 14.7 | 139 | 17.6 | 1478 | 13.0 | 408 | 14.8 | |
Northern Territory | 866 | 40.4 | 360 | 45.5 | 2421 | 21.3 | 553 | 20.0 | |
Geographical remoteness | |||||||||
Major cities | 237 | 11.1 | 115 | 14.5 | 4762 | 41.8 | 1274 | 46.1 | |
Inner regional | 70 | 3.3 | 23 | 2.9 | 1208 | 10.6 | 313 | 11.3 | |
Outer regional | 385 | 18.0 | 139 | 17.6 | 1563 | 13.7 | 349 | 12.6 | |
Remote | 366 | 17.1 | 146 | 18.5 | 1098 | 9.6 | 288 | 10.4 | |
Very remote | 943 | 44.0 | 360 | 45.5 | 2321 | 20.4 | 507 | 18.4 | |
Other/missing | 141 | 6.5 | 8 | 1.0 | 435 | 3.8 | 30 | 1.1 | |
Index of relative socio-economic disadvantage quintile | |||||||||
1 (most disadvantaged) | 1385 | 64.7 | 551 | 69.7 | 4999 | 43.9 | 1280 | 46.4 | |
2 | 252 | 11.8 | 102 | 12.9 | 1947 | 17.1 | 514 | 18.6 | |
3 | 184 | 8.6 | 74 | 9.4 | 1473 | 12.9 | 399 | 14.5 | |
4 | 95 | 4.4 | 33 | 4.2 | 1168 | 10.3 | 275 | 10.0 | |
5 (least disadvantaged) | 42 | 2.0 | 16 | 2.0 | 862 | 7.6 | 200 | 7.2 | |
Missing | 184 | 8.6 | 15 | 1.9 | 938 | 8.2 | 93 | 3.4 | |
Registered with RHD control program | |||||||||
On the register | 1073 | 50.1 | 354 | 44.8 | 691 | 6.1 | 552 | 7.6 |
Hospitalisation patterns and temporal trends
The number of children hospitalised for ARF/RHD-related treatment increased 9.3% annually (95% CI: 4.7–13.0%, P < 0.001), from 191 in 2012/13 to 262 in 2016/17 (Fig. 1). On average there were 296 paediatric hospitalisations per year, this increased 6.2% annually (95% CI: 2.4–9.6, P = 0.001) and average bed days/child increased 5.1% (95% CI: 3.7–6.3, P < 0.001, Fig. 1, Supplementary material Table S1). Each admitted child was hospitalised an average 1.2 times for 10.6 bed days per year (Table S1).
Paediatric and adult inpatient hospital utilisation for ARF, RHD and related complications between 2012/13 and 2016/17.

The mean number of adults hospitalised was unchanged at 1442 annually; however, admission numbers increased by 1.7% annually (95% CI: 0.1–3.4%, P = 0.03) and bed days increased by 3.5% (95% CI: 2.9–4.0%, P < 0.001, Fig. 1, Table S1). Each admitted adult was hospitalised an average 1.5 times for 13.4 bed days per year (Table S1).
Frequencies and rates of RHD-related surgery were unchanged over the study period (Fig. 1, Table S1). For adults, 154 valvular surgeries were conducted interstate during the study period, with 147 NT residents having surgery in SA.
Differences by residential location
Almost 90% of paediatric and 70% of adult episodes were publicly funded in metropolitan areas; this proportion was higher in non-metropolitan areas (97.7% paediatric, 88.6% adult, Table 2). Episodes initiated via emergency admission were more frequent for non-metropolitan than metropolitan residents (79.2% vs 50.0% paediatric; 56.7% vs 40.7% adults, both P < 0.001). Non-metropolitan residents received proportionally fewer surgeries than metropolitan residents (1.8% vs 15.8% paediatric; 15.0% vs 25.3% adults; both P < 0.001). Among non-metropolitan residents, one-third or more were hospitalised outside of their residential region. Almost one-fifth of paediatric episodes involved air ambulance transfers; among adults this was recorded for 8.0% of episodes.
Paediatric (<16 years) | Adult (≥16–64 years) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Non-metropolitan residents | Metropolitan residents | P-value A | Non-metropolitan residents | Metropolitan residents | P-value A | |||||||
Total episodes of care (2012/13 to 2016/17) | 1093 | 228 | 3538 | 2777 | ||||||||
Paid from government budget B | 1068 | 97.7% | 205 | 89.9% | <0.001 | 3135 | 88.6% | 1936 | 69.7% | <0.001 | ||
Initiated via emergency admission | 866 | 79.2% | 114 | 50.0% | <0.001 | 2006 | 56.7% | 1129 | 40.7% | <0.001 | ||
Surgery/procedure within episode | 20 | 1.8% | 36 | 15.8% | <0.001 | 529 | 15.0% | 702 | 25.3% | <0.001 | ||
Admission outside of residential region | 443 | 40.5% | 1190 | 33.6% | ||||||||
Air ambulance transfer during episode | 197 | 18.0% | 284 | 8.0% |
Pattern and trends in hospital costs
Costs of treating ARF/RHD totalled AU$130.6 m over 5 years, with AU$17.6 m for paediatric and AU$113.0 m for adult hospitalisations, and 72.2% of all hospitalisation costs (AU$81.6 m) were associated with emergency admissions (Fig. 2, Table S1). The average cost per admission was AU$11,872 per child and AU$15,670 per adult (Fig. 2, Table S1).
Total paediatric and adult inpatient costs for ARF, RHD and associated complications for 2012/13 to 2016/17 (a) paediatric patients <16 years and (b) adult patients aged 16–64 years. Subset of admission costs by diagnosis, procedure and emergency admission status are shown (non-additive).

Emergency admissions accounted for 79.0% of paediatric hospitalisation costs (AU$13.9 m of AU$17.6 m); admissions principally for ARF were 71.6% of costs (AU$12.6 m, Fig. 2, Table S1). Total paediatric costs were unchanged annually, however, the subset of costs for emergency admissions increased 11.8% annually (95% CI: 0.7–24.0%, P = 0.04). Costs for RHD admissions decreased 16.0% annually (95% CI: 2.9–27.4%, P = 0.03) (Table S1). Sensitivity analysis identified 17 valvular surgeries among NT children that were excluded from the primary analysis, representing AU$1.3 m additional costs (Supplementary material file S4). Highest paediatric ARF/RHD-related costs were incurred in NT (AU$7.6 m) and Qld (AU$5.2 m). Adult admissions for ARF/RHD incurred costs of AU$113.0 m; 60.0% (AU$67.7 m) of these costs were via emergency presentation and 69.8% (AU$78.9 m) involved valvular surgery (Fig. 2, Table S1). Adult hospitalisation costs did not change annually. The highest adult ARF/RHD-related costs were incurred in NSW (AU$33.3 m) and Qld (AU$30.7 m, Table S1).
Discussion
In Australia, ARF and RHD disproportionately affect Aboriginal and Torres Strait Islander peoples, their families and communities.17–20 Our study quantifies the high hospital burden of ARF/RHD, finding that one-third of children and one-quarter of adults with ARF/RHD required hospitalisation during the study. Admissions and total bed days for ARF/RHD increased annually between 2012/13 and 2016/17. Among non-metropolitan residents, at least one-third of episodes required inter-regional transfer and almost one-fifth of paediatric episodes involved air ambulance transfer. Total costs were AU$130 m (equivalent to AU$31.4 m/year in 2023); most admissions were emergencies. Paediatric admissions cost AU$17.6 m, with most admissions for ARF; adult admissions incurred AU$113.0 m, with most involving valvular surgery. Annual costs were stable over the study period.
No studies have previously estimated ‘real-world’ ARF/RHD hospitalisation costs in Australia. High cardiac surgery costs associated with remote-residing Aboriginal cardiac patients have previously been described.21 Farnsworth et al. identified the intersection of Aboriginality, remoteness and RHD as a special high-cost population, with the present study describing these costs in detail. Our findings are not comparable to the Cost of Inaction report, which modelled costs from unlinked sources and incorporated secondary prophylaxis expenses, pre-operative medical/dental management, specialist follow-up, travel and accommodation.6,7 Rather, this study contributes real-world cost details regarding hospitalisations within these large, projected costs.
Frequent inter-regional transfers among non-metropolitan ARF/RHD patients quantifies anecdotes of significant travel and dislocation involved in seeking treatment.20,22 Treatment guidelines recommend all people with suspected ARF be hospitalised, with hospitalisation also essential for RHD complications and surgical procedures. In this study, non-metropolitan residents with ARF/RHD received fewer surgeries than metropolitan residents, possibly due to limited access to care in remote locations.23,24 The high proportion of emergency admissions, especially for non-metropolitan patients, may reflect inadequate access to primary and specialist health care and/or surgical delays.25,26 Additionally, the high volume of air ambulance transfers reported, although not costed by this study, involves considerable expenses (≈AU$5135 per engine hour), and are potentially avoided by earlier elective, non-emergency services.27
Successful ARF/RHD prevention offers large hospital cost-saving opportunities. Challenges coordinating prevention activities exist, due to the siloed nature of government funding, but can be guided by the Endgame Strategy.8,22 Current investment in prevention activities that improve housing and environments is unknown, but would have benefits beyond ARF/RHD.8 At the primary and secondary prevention level, Commonwealth funding is in place to support the National Aboriginal Controlled Community Health Organisation (AU$12 m for 2021–2025)9,11 and RHD registers (AU$12 m for 2022–2025);28 however, this funding is greatly exceeded by the 2012–2017 costs of treatment. Increased investment in RHD prevention costing less than RHD-related hospitalisations would deliver long-term cost savings, especially given the increasing paediatric admission trend over time reported herein. Even those costing more might still be cost-effective via their prevention of non-RHD illnesses.
Limitations of this study include lack of inter-jurisdictional linkage (except SA and NT), which precludes interstate follow-up. The ERASE datasets do not include Victorian hospitalisation records, where cardiac surgeries for NT children are usually undertaken, resulting in exclusion of ≈17 paediatric surgeries that were included in the sensitivity analysis (Supplementary material file S3). This study generated government costs of hospitalisation and excludes other costs (to insurers, individuals etc.) and therefore underestimates full costs to health services. Additionally, primary healthcare costs (including penicillin delivery) and personal costs of ARF/RHD (travel, loss of income, etc.) were not measured by this study. Our study was limited to known cases of ARF/RHD, making it possible that costs were underestimated via the exclusion of undiagnosed cases (up to 4.6 cases per 1000 high risk children).29 The lack of complete ERASE data post-2017 limits our ability to draw more contemporary conclusions; in addition, the COVID-19 pandemic may have altered patterns reported herein.
This study quantifies the high costs of ARF and RHD hospitalisations. Frequent inter-regional transfers likely reflect lack of suitable service availability where need is highest. Increasing annual bed days indicate the potential for ongoing high hospitalisation costs if ARF/RHD are not adequately prevented, diagnosed and managed. Future analyses will reveal whether increased prevention activities (ASAVI, STARFISH, NEARER SCAN) will reduce ARF/RHD-related hospitalisations. Increased investment in strategies that prevent ARF/RHD would deliver significant future hospital cost savings.
Data availability
The data underlying this article are based on sensitive health records, obtained from Australian health services by permission from data custodians. Data will only be shared on reasonable request to the corresponding author subject to rigorous conditions, including additional ethical and data custodian approvals.
Declaration of funding
This work was supported by funding from the National Health and Medical Research Council through project grant no. 1146525 and seed funds from the End Rheumatic Heart Disease Centre for Research Excellence and HeartKids. IS was supported by a National Health and Medical Research Council Postgraduate Scholarship (grant no. 2005398) and an ad hoc postgraduate scholarship from The University of Western Australia. JK was supported by a National Heart Foundation Future Leader Fellowship (no. 102043).
Acknowledgements
The authors thank the staff of the data linkage units of the state and territory governments (Western Australia, South Australia–Northern Territories, New South Wales, Queensland) for linkage of the ERASE project data. They thank the State and Territory Registries of Births, Deaths and Marriages, the State and Territory Coroners, and the National Coronial Information System and the Victorian Department of Justice for enabling Cause of Death Unit Record File data to be used for this project. Furthermore, the authors thank the data custodians and data managers for providing inpatient hospital and emergency department data (5 states and territories), RHD registers (5 states and territories), the Australian and New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database (single registry covering 5 states and territories), the Royal Melbourne Children’s Hospital Paediatric Cardiac Surgery database (single data source for RHD pediatric patients from South Australia and Northern Territory receiving surgical intervention in Melbourne) and the Northern Territory Department of Health primary healthcare data.
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