This Third Australian Atlas of Healthcare Variation explores the extent to which healthcare use in Australia varies depending on where people live. It uses maps of variation in care, derived from information routinely gathered by the health system, to show how healthcare use differs across the country and to raise important questions about why this variation might be occurring. The aim is to prompt further investigation into whether the observed variation reflects differences in people’s healthcare needs, in the informed choices they make about their treatment options, or in other factors. Variation for these reasons is both expected and desirable. But when variation in the use of health services is due to other factors – such as differences in access to care or in appropriateness of care – it is unwarranted variation and represents an opportunity for the health system to improve. This improvement may involve increasing awareness of, or access to, treatment options that produce better outcomes for patients, or reducing the use of investigations or delivery of treatments with little or uncertain benefit.
Exploring variation in care is one way of identifying whether people in different parts of Australia are being offered appropriate care – that is, care that optimises benefits and minimises harms, and is based on the best available evidence. A key requirement for delivering appropriate care is accessible information about the benefits and risks of treatment options, so clinicians and consumers can make fully informed decisions.
The Atlas examines variation in 13 new significant clinical items according to where consumers live, and presents further data for seven items examined in the first Atlas. The Atlas also provides examples of work by a number of groups to improve appropriate care in clinical areas analysed in the first and second Atlases.1,2 Major change in health care often takes longer than the short period since the first Atlas was published in 20151 and the second Atlas in 20172, but swift action from some states and territories, and other organisations shows what can be achieved with focused effort. Although it is too early to expect these interventions to have made a major impact on data for the items that have been re-analysed, in some cases the concerning findings in the first Atlas show no sign of improvement – and even a worsening situation. Concerted and coordinated effort is needed from all levels to ensure the delivery of appropriate health care to Australians.
The interpretation of data in this Atlas, and discussions of what can be done to improve care, have benefited from extensive consultation by the Australian Commission on Safety and Quality in Health Care (the Commission). Clinicians, consumers, policymakers, epidemiologists and researchers have helped us identify the likely drivers of variation and the changes that are needed to address unwarranted variation. The Commission is grateful for their insights.
This Atlas has been produced in partnership with the Australian Institute of Health and Welfare, which has contributed enormous expertise in its analysis and understanding of the data and data sources. The Australian, state and territory health departments have also been pivotal partners in providing data, and in working with the Commission to interpret findings and find potential avenues for improvements in healthcare delivery.
The Atlas data cannot tell us which portion of the observed variation in healthcare use is unwarranted, but there are two recurring patterns that suggest that healthcare delivery is not matching patient need:
When local areas with similar characteristics have markedly different rates of use of an intervention, with no obvious explanation, differences in clinical and consumer decision-making and system factors should be explored. In addition, when rates of an intervention known to be harmful or ineffective are found to be high, urgent action is clearly needed.
This Atlas, the third in a series, has found eight consistent themes, which are discussed below.
This third Atlas includes a report on early planned caesarean section without a medical or obstetric indication. Short-term adverse effects from planned caesarean section before 39 weeks’ gestation are well established, and more recent research has shown concerning long‑term developmental effects for children, such as poorer educational outcomes.3-8 For this reason, the Commission has included the topic in this Atlas, despite data limitations that preclude the more detailed mapping and analysis presented for other items.
Australian states and territories have recently begun routinely collecting data on the main reason for caesarean section; the quality of data was sufficient for publication for four states only. As reported in this Atlas, the percentage of planned caesarean sections without obstetric or medical indication performed at less than 39 weeks’ gestation ranged from 42% to 60% in 2015. Even if these figures are an overestimate due to data characteristics, these rates are a call to action to reduce caesarean section before 39 weeks’ gestation without a medical or obstetric indication. It is crucially important that prospective parents are given clear information about the short-term and long‑term risks that are now known to accompany early planned caesarean section so that they can give fully informed consent for this procedure. Improving data collection on this topic is also a matter of urgency to allow monitoring and a more complete picture of planned early birth in Australia.
The potential for harm, and a lack of benefit, has also been highlighted in previous Atlas topics – for example, lumbar spinal fusion. The second Atlas found that the rate of hospitalisation for lumbar spinal fusion varied about seven-fold between local areas across Australia.2 Guidelines from the United Kingdom National Institute for Health and Care Excellence (NICE) noted that some studies report that approximately 20% of patients who undergo lumbar spinal fusion experience short- to medium‑term complications.9 High-quality evidence is lacking on outcomes from lumbar spinal fusion.10-13 The NICE guidelines recommend against treating patients with low back pain using spinal fusion except within the context of a clinical trial that could help clarify whether, and for whom, this procedure is of benefit.9 The Commission recommended a clinical quality registry for spinal fusion, to promote evidence‑based care.
A disturbing pattern of inequity has emerged from all three Atlases. For example, despite higher rates of bowel cancer, people in areas of lower socioeconomic status (SES) have lower rates of colonoscopy than people living in areas of higher SES. Despite higher rates of cataract, Aboriginal and Torres Strait Islander Australians have lower rates of cataract surgery than other Australians. And, despite higher rates of heart disease, people living in regional areas have lower rates of cardiac stress tests and imaging than people in major cities.14
Conversely, where the patterns in the Atlas do follow known differences in the burden of disease, they underscore the need to do better to prevent chronic disease by addressing risk factors, and to prevent serious complications in people who have developed disease. People in lower SES groups have higher rates of chronic conditions such as diabetes, heart disease and chronic obstructive pulmonary disease (COPD).14 The second Atlas showed that potentially preventable hospitalisations for heart failure and diabetes complications were approximately 1.5 times as high in the lowest compared with the highest SES areas of major cities.2 For COPD, potentially preventable hospitalisations were twice as high in the lowest compared with the highest SES areas of major cities.2 The Atlas has made many recommendations for improving health care for under-served groups with specific conditions, but models of care and prevention need to be rethought to address health inequities in a systematic way.
Although some patterns in Atlas data tell a clear story, in many cases we will need to dig deeper to understand the situation. Often, we do not know what the optimal rate of an intervention is. Even when the use of an intervention appears well matched to need at a population level, the important question is whether the right individuals within the population are receiving the care. Examining variation in patient outcomes using linked data will be vital for gaining a deeper understanding of what is going on in these situations.
For many items in the Atlas, well-informed consumers could be powerful agents for improving the appropriateness of care. For example, patient expectations are known to influence prescribing behaviour.15 Greater knowledge among consumers about the likely risks and benefits of antibiotics for children could significantly improve use of these medicines. Education of consumers about the importance of considering heavy menstrual bleeding as a cause of anaemia in younger women may reduce rates of unnecessary colonoscopy and gastroscopy. Information for prospective parents about the outcomes of elective caesarean section at 39 weeks’ gestation compared with earlier gestation, could be an important part of a comprehensive strategy to reduce rates of unnecessary early-term delivery.
The need for many of the interventions analysed in the Atlas could be reduced by better prevention. For example, addressing lifestyle-related risk factors such as obesity and smoking could prevent a significant proportion of cardiovascular disease and bowel cancer.14 A substantial reduction in lifestyle related risk factors could deliver enormous benefits in reducing burden of disease, as well as reducing expenditure on investigations and treatment for these diseases.
Several Atlas analyses have shown a pattern of markedly higher healthcare use in some local areas than in others, with no clear clinical indication. For example, the first Atlas reported that the rate of dispensing of medicines for attention deficit hyperactivity disorder (ADHD) in children and young people aged 17 years and under was 75 times higher in the local area with the highest rate compared with the area with the lowest rate – the largest variation seen in the Atlas series.1 This finding prompted an in-depth study of ADHD medicines use, led by a paediatrician, which concluded that some children who could benefit were missing out, and some may have been over-treated (see the case study on page 295).
Uncertainty about best care because of a lack of evidence can lead to differences in clinical management when more than one treatment option is available. A lack of agreement on the appropriate use of an intervention due to differing interpretations of evidence may also contribute to variation. For example, differing opinions on the role of surgery for lumbar spinal fusion for people with low back pain, given the lack of high-quality evidence on outcomes, may have contributed to variation in use shown in the first and second Atlases.1,2
The approach to reducing variation based on differences in clinical management will vary according to the underlying reasons for these differences. Even when there is a strong body of evidence about specific interventions or treatments, finding ways to ensure that evidence is used in practice is challenging. For example, despite the known harms of overuse of antibiotics, and multiple interventions in Australia aimed at improving practice, a recent study showed that general practitioners prescribe antibiotics at 4–9 times the rate expected if guidelines were followed.16
Characteristics of the local health system can influence rates of use of particular treatments. For example, high rates of antidepressant use in Tasmania, as reported in the first Atlas1, were thought to be related to a lack of access to psychological services in the state after local investigation. Primary Health Tasmania, the Tasmanian Health Service, and the Department of Health and Human Services worked together to increase access to psychological services where gaps were found (see the case study on page 294).
The Atlas series has highlighted several opportunities to improve current national data so that more meaningful information can be derived about healthcare use and variation, and appropriateness of care.
The lack of a consistent national approach to hospital admission policies means that it is not possible to accurately identify how many procedures that do not require an overnight stay are undertaken in Australia. This affects a number of commonly performed procedures covered in the Atlas series, such as colonoscopy, gastroscopy and cataract surgery. It seems remarkable that it is not possible to know precisely how many of these types of procedures, which can have a profound impact on people’s health and wellbeing, are undertaken in Australia. For services delivered in the community that are requested and reimbursed through the Medicare Benefits Schedule (MBS), clear descriptions about the reason for service provision would enable more focused auditing and review of the services that have been provided. Capturing the reason for service provision would also mean that much more valuable information could be obtained about care provision and how this aligns with community need.
One of the issues with collection of health data in Australia is that information about the health care that patients receive is split across multiple collections, such as hospital statistics, Medicare figures and Pharmaceutical Benefits Scheme datasets. Medicines supplied by Aboriginal health services are not included in these datasets. This issue meant that many important topics proposed for the Atlas series could not be included, and several of the published analyses were limited.
Tracking experiences across these data divides would provide a much more informative picture of healthcare quality. For example, linked data could show whether someone who has a heart attack in a regional area of Australia has the same likelihood of having the recommended investigations and treatment as someone in the city. The data could also show whether, following a heart attack, people have good secondary preventive care, regardless of where they live. Better access to linked data in the future will allow this kind of detailed analysis on a national scale.
Australians are living longer, and living longer free from disability. However, chronic diseases such as cancer, coronary heart disease and diabetes are becoming increasingly common as a result of a population that is increasing and ageing, as well as social and lifestyle changes. In 2014–15, more than 11 million Australians had at least one of eight selected chronic conditions (arthritis, asthma, back problems, cancer, COPD, cardiovascular disease, diabetes mellitus, or a mental or behavioural condition).17 This means that demand for health care is growing. The range of tests, technologies and treatments that can be used to investigate and manage health problems is also
growing. Although such advances can bring great benefit, they can increase the risk of diagnosing and treating people for conditions that would never have caused them harm (over-diagnosis).18
These factors place pressures on our healthcare system. Total spending on health in Australia was $180.7 billion in 2016–17 – more than two‑thirds ($124 billion) of this was government health expenditure.19 Given the growing demands on the health budget, it is imperative that this money is spent wisely. In a number of the topic areas it has examined, the Atlas series has raised questions about the use of health care and the patterns of use across the country. But, in many clinical areas, it is not currently possible to measure or map the extent to which the care provided and paid for is appropriate; expensive special studies would be needed to provide this information.
This Atlas includes clear recommendations about improving the usefulness of MBS data. The MBS costs more than $23 billion per year.20 It is wasteful if the data systems that support the MBS are regarded solely as a means of providing for, and reporting on, reimbursement of services. It also means that it is not possible to assess whether spending on care best matches need. Medicare data should be regarded as, and designed as, a means of monitoring our investment in health care, as well as supporting a system to reimburse services provided.
The Atlas series has highlighted many challenges and inequities in health care. It has also suggested reasons for variation, as well as realistic and specific recommendations for change. And it has shown how analysis and presentation of routinely collected data can promote action by organisations and clinical groups to investigate and improve appropriateness of care, and the value Australians receive from their healthcare system.
The ultimate goal of the Atlas is to ensure that Australians get care that gives them the best health outcomes. In the absence of national data on patient outcomes, the Atlas has used processes of care as a proxy. In the future, this work must be complemented by data on outcomes. Linked data hold the key, and increasing capacity in this area must be a priority for the Australian health system.
The maps and commentary in the three editions of the Australian Atlas of Healthcare Variation show that there are many opportunities to deliver better health care in this country, by investigating and addressing both underuse and overuse, and by doing more to prevent chronic disease. The challenge now is to ensure that the capacity to routinely monitor and report on appropriateness of care is integrated into the health system so that any need for improvement can be quickly recognised and acted on. In this way, Australia will gain maximum benefit from its investment in its healthcare system, and patients will get the care they need and deserve.
Getting the best outcomes for patients and reducing harm are the goals of the Atlas. Where we see substantial variation in use of a particular treatment, it is an alarm bell that should make us stop and investigate whether appropriate care is being delivered.
Variation in itself is not necessarily bad, and it can be good if it reflects health services responding to differences in patient preferences or underlying needs. When a difference in the use of health services does not reflect these factors, it is unwanted variation and represents an opportunity for the health system to improve. Rates of an intervention that are substantially higher or lower in some areas can highlight:
Looking at how healthcare use varies between people living in different areas, between people with and without socioeconomic disadvantage, and between Aboriginal and Torres Strait Islander Australians and other Australians can show who in our community is missing out. Fundamental changes to address the underlying determinants of ill health, as well as better service delivery for those with existing disease, are needed where these inequities are found.
1. Australian Commission on Safety and Quality in Health Care, National Health Performance Authority. Australian Atlas of Healthcare Variation. Sydney:
2. Australian Commission on Safety and Quality in Health Care, Australian Institute of Health and Welfare. The Second Australian Atlas of Healthcare Variation.
Sydney: ACSQHC; 2017.
3. Dong Y, Chen SJ, Yu JL. A systematic review and meta-analysis of long-term development of early term infants. Neonatology 2012;102(3):212–21.
4. Searle AK, Smithers LG, Chittleborough CR, Gregory TA, Lynch JW. Gestational age and school achievement: a population study. Arch Dis Child Fetal
Neonatal Ed 2017;102(5):F409–16.
5. Bentley JP, Roberts CL, Bowen JR, Martin AJ, Morris JM, Nassar N. Planned birth before 39 weeks and child development: a population-based study.
6. Murray SR, Shenkin SD, McIntosh K, Lim J, Grove B, Pell JP, et al. Long term cognitive outcomes of early term (37–38 weeks) and late preterm
(34–36 weeks) births: a systematic review. Wellcome Open Res 2017;2:101.
7. Noble KG, Fifer WP, Rauh VA, Nomura Y, Andrews HF. Academic achievement varies with gestational age among children born at term.
8. MacKay DF, Smith GC, Dobbie R, Pell JP. Gestational age at delivery and special educational need: retrospective cohort study of 407,503 schoolchildren.
PLoS Med 2010;7(6):e1000289.
9. National Institute for Health and Care Excellence. Low back pain and sciatica in over 16s: assessment and management. London: NICE; 2016.
(NICE Guideline NG59.)
10. Mirza S, Deyo R. Systematic review of randomized trials comparing lumbar fusion surgery to nonoperative care for treatment of chronic back pain.
11. Zaina F, Tomkins-Lane C, Carragee E, Negrini S. Surgical versus non-surgical treatment for lumbar spinal stenosis. Cochrane Database Syst Rev
12. Gibson J, Waddell G. Surgery for degenerative lumbar spondylosis: updated Cochrane Review. Spine 2005;30(20):2312–20.
13. Chou R, Baisden J, Carragee E, Resnick D, Shaffer W, Loeser J. Surgery for low back pain. Spine 2009;34:1094–109.
14. Australian Institute of Health and Welfare. Australia’s health 2018. Canberra: AIHW; 2018. (Cat. No. AUS 221; Australia’s Health Series No. 16.)
15. Fletcher-Lartey S, Yee M, Gaarslev C, Khan R. Why do general practitioners prescribe antibiotics for upper respiratory tract infections to meet
patient expectations: a mixed methods study. BMJ Open 2016;6(10):e012244.
16. McCullough AR, Pollack AJ, Plejdrup Hansen M, Glasziou PP, Looke DF, Britt HC, et al. Antibiotics for acute respiratory infections in general practice:
comparison of prescribing rates with guideline recommendations. Med J Aust 2017;207(2):65–9.
17. Australian Institute of Health and Welfare. Australia’s health 2016. Canberra: AIHW; 2016. (Cat. No. AUS 199; Australia’s Health Series No. 15.)
18. Pathirana T, Clark J, Moynihan R. Mapping the drivers of overdiagnosis to potential solutions. BMJ 2017;358:j3879.
19. Australian Institute of Health and Welfare. Health expenditure Australia 2016–17. Canberra: AIHW; 2018. (Cat. No. HWE 74; Health and Welfare Expenditure
Series No. 64.)
20. Medicare Australia. Annual Medicare statistics: financial year 2017–18 [Internet]. Canberra: Australian Government Department of Health; 2018.
Available from: www.health.gov.au/internet/main/publishing.nsf/content/annual-medicare-statistics