Myalgic encephalomyelitis / chronic fatigue syndrome in Australia

Myalgic encephalomyelitis /chronic fatigue syndrome (ME/CFS), is a condition involving a persistent and disabling fatigue with a primary symptom called post-exertional malaise or PEM (Healthdirect 2024). PEM is where the fatigue symptoms worsen either immediately or with a delay of up to 3 days after mental or physical exertion, and where the symptoms do not improve even after rest. 

There is no single test to diagnose ME/CFS (Healthdirect 2024). A diagnosis of ME/CFS requires a patient to have PEM as the primary sign. Secondary signs for a diagnosis include neurocognitive impairment (problems with thinking, memory, attention and language), pain and sleep disturbance. Other symptoms include problems with the cardiovascular system, respiratory functions (breathing problems), gastro-intestinal disturbances and immune problems (vulnerability to viral infections with prolonged recovery periods) (Bridges & Pathways n.d.).

ME/CFS currently has no cure. The primary aim of treatment is to manage symptoms and improve people’s ability to function in their life.

This report presents both crude and age-standardised rates, where available.

Crude rates are based on unadjusted data and indicate whether there is a difference between populations without accounting for differences in the age structures of the populations.

Age-standardised rates are based on data that are adjusted to account for differences in the age structures of the populations.

Therefore, a difference between 2 populations in crude rates indicates that the rate differs between them. The differences may be due to any number of factors, including different age structures of the populations. In contrast, a difference between 2 populations in age-standardised rates indicates that the rate differs between them but that this difference is not due to different age-structures.

For example, a difference in the prevalence rate between males and females indicates that prevalence differs between them, and this difference in prevalence could be due to any number of risk factors, including age. A difference in the age-standardised prevalence rate between males and females indicates that prevalence differs between them and, furthermore, that this difference in prevalence is not due to age.

For more details about rate calculations see the Technical notes in the Neurological conditions in Australia report.

Reporting and analysis of ME/CFS is not available for all data sources or for certain population groups due to the following limitations.

Hospitalisations

ME/CFS does not have an ICD-10-AM (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification) classification code. There is a classification code for post-viral fatigue syndrome and a classification code for fatigue or malaise. Post-viral fatigue syndrome is a narrow category that is unlikely to capture all cases of ME/CFS and will therefore result in undercounting the number of hospitalisations for ME/CFS. Fatigue or malaise is very broad and seems to be used for classifying fatigue symptoms, which can co-occur with other conditions such as Parkinson’s disease or pregnancy. The classification code for fatigue or malaise would therefore capture cases that are not of people living with ME/CFS.

This article reports statistics from hospitals data using the classification code for post-viral fatigue syndrome. The statistics will therefore underestimate how many hospitalisations occur due to ME/CFS.

Emergency department presentations

The National Non-admitted Patient Emergency Department Care Database (NAPEDC) uses the Principal Diagnosis Short List (EPD Short List), a set of codes and medical terms derived from ICD-10-AM classifications.

Post-viral fatigue syndrome emergency department presentations are not recorded separately but are included within the broader “encephalopathy and other brain disorder” category. As a result, it is not possible to determine or report the specific number of presentations for post-viral fatigue syndrome.

Health-system costs

Estimates for health system costs relating to ME/CFS cannot be reported from the Health system spending on disease and injury in Australia 2023–24 report as this study did not report on ME/CFS as a separate category on its own.

Burden of disease

Estimates for burden of disease relating to ME/CFS cannot be reported from the Australian Burden of Disease Study as this study did not report on ME/CFS as a separate category on its own due to data limitations. However, analyses are being undertaken to assess its inclusion in future Australian Burden of Disease Studies.

NDIS

There were no National Disability Insurance Scheme (NDIS) data for ME/CFS specifically or for post-viral fatigue syndrome.

Mortality

ME/CFS does not have an ICD-10 (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision) classification code. There is a classification code for post-viral fatigue syndrome and a classification code for fatigue or malaise. Post-viral fatigue syndrome is a narrow category that is unlikely to capture all cases of ME/CFS and will therefore result in undercounting the number of deaths due to ME/CFS. Fatigue or malaise is very broad and seems to be used for classifying fatigue symptoms, which can co-occur with other conditions such as Parkinson’s disease or pregnancy. The classification code for fatigue or malaise would therefore capture cases that are not of people living with ME/CFS.

This article reports statistics from mortality data using the classification code for post-viral fatigue syndrome. The statistics will therefore underestimate how many deaths occur due to ME/CFS.

Lim et al. (2020) conducted a meta-analysis of a total of 46 studies from 13 countries around the world, with 23 of these studies (mostly with adult populations) estimating prevalence using interviews combined with a medical test and 6 studies estimating prevalence using physician (or GP) diagnosis. However, only one study (from 1990) used Australian data.

Nonetheless, Lim et al.’s (2020) meta-analysis estimate based on physician diagnoses (90 per 100,000) closely matched the Orji et al. (2022) estimate, noted in the dropdown box below, based on GP diagnoses in Australia (95 per 100,000). Both the international meta-analysis and the study on GP diagnoses in Australia suggest that almost 0.1% of people receive a ME/CFS diagnosis. This convergence of results makes the meta-analysis results of Lim et al. (2020) appear reliable.

Importantly, Lim et al. (2020) also had a meta-analysis estimate based on interviews combined with a medical test, which are reported in the main body of this article. Estimates based on interviews combined with a medical test avoid the limitations of estimates based on GP diagnoses from primary (see ‘Prevalence based on primary care data’ dropdown box below). Although the estimates reported in this article from Lim et al. (2020) do not include any data from Australia, they are the current best estimate of prevalence for ME/CFS for the reasons provided above.

Orji et al. (2022) analysed primary care data for patients aged 13 years and older from the national MedicineInsight program. These are patient data from general practices (GP) across Australia. A patient was identified as having ME/CFS based on whether they received a diagnosis from the GP. 

Orji et al. (2022) reported that, in 2019:

• almost 0.1% (95 per 100,000 population) of Australians who visited GPs were diagnosed with ME/CFS
• females were almost 2.6 as likely as males to be diagnosed with ME/CFS (130 per 100,000 females and 51 per 100,000 males)
• ME/CFS was most common in people aged between 40 and 60 years
• people living in the highest socioeconomic areas had the highest rate of ME/CFS (110 per 100,000), people living in the lowest socioeconomic areas had the lowest rate (85 per 100,000), but this varied from previous years
• regarding remoteness areas, people living in Inner regional areas had the highest rate of ME/CFS (115 per 100,000) and people living in remote and very remote areas had the lowest rate (66 per 100,000), and this has been consistent since 2015.
• from 2015 to 2019, the rate of people diagnosed with ME/CFS decreased slightly, from about 100 per 100,000 to 95 per 100,000, and there was little change in the sex difference, with females being more than twice as likely as males to be diagnosed with ME/CFS since 2015 (135 per 100,000 females and 58 per 100,000 males).

However, estimates for the rate of GP diagnoses of ME/CFS based on primary care data will tend to underestimate the true prevalence of the condition in the Australian population. Orji et al. (2022) noted several reasons why GP diagnoses from primary care data would underestimate prevalence:

• some people with ME/CFS may not go to the GP for their symptoms, and these cases would be missed in analyses of primary care data
• GPs may misdiagnose ME/CFS or not diagnose it at all.

Self-reported data from the National Health Survey (NHS) 2022 (ABS 2023) can also be used to give an estimate for how common ME/CFS is in Australia. Due to low counts, reporting prevalence disaggregated by sex and age is not possible.

Based on the NHS 2022, there were an estimated 36,000 Australians living with ME/CFS, equivalent to 0.14% of the population (or a rate of 140 per 100,000).

However, the NHS is likely to underestimate the true prevalence of ME/CFS because the condition was not asked about directly, nor was it included on the prompt cards used in the interviews. Respondents may therefore not have reported having the condition if they had not experienced the symptoms in recent memory or they may not be aware of having the condition, as it may never have been diagnosed.

Information on procedures in the National Hospital Morbidity Database (NHMD) is reported using the Australian Classification of Health Interventions (ACHI) which classifies surgical operations, procedures and other types of interventions performed for the purpose of investigating and/or remedying health state. 

In 2023–24, there were 380 procedures for hospitalisations with a principal diagnosis of post-viral fatigue syndrome, equating to around 2.1 procedures per hospitalisation. Over two thirds (68%) of these were generalised allied health interventions, 6.9% were administration of pharmacotherapy and 5.8% were therapies using agents not elsewhere classified.

For more information on surgeries and intervention types, see Surgery and other interventions.

Zhao et al. (2023) estimated the annual costs of ME/CFS based on participants completing cost diaries and records in the Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS).

PBS records provided information about prescription items, including costs and the amount paid by government.

MBS records provided information about health service items, including costs and the amount paid by government.

For participants who did not consent for their PBS and MBS records to be accessed, cost diaries provided information about use of prescription medication, health care professional visits, medical investigations, hospital admissions and emergency department and outpatient attendances. PBS/MBS information was then used to apply costs incurred by government due to prescription medication and health care professional visits and investigations identified in the cost diaries. Costs for hospital admissions were estimated based on the national average hospital inpatient admission costs.

There are major limitations to the estimates of health-system costs from Zhao et al. (2023):

1. participants with self-reported ME/CFS or their carers were recruited through a convenience sampling procedure (advertisements on Emerge Australia’s website, radio, newspapers and Facebook)
2. only 175 participants completed the cost diaries
3. only 48 participants consented to have their PBS/MBS records accessed.

The relatively small number of participants (point 2) combined the convenience sampling procedure (point 1) may mean that the participants are not representative of Australians living with ME/CFS. The sampling procedure may have attracted carers and people who are more severely impacted by ME/CFS who would be seeking out sources for help with the condition, resulting in fewer people with milder cases of ME/CFS in the sample. This would result in an overestimation in the health system costs associated with ME/CFS.

The estimated costs for prescription medications covered by PBS and health services and investigations covered by MBS were accurately assessed only for a small number of participants (point 3), with the costs for the remaining participants being estimated based on cost diaries which rely on memory.

Notwithstanding the above caveats, Zhao et al. (2023) provide the current best estimates of health-system costs for ME/CFS in Australia.

Zhao et al. (2023) estimated the annual costs occurring from loss in productivity due to being absent from work, being present at work but being less productive, and due to lost wages, for both the patient and their informal carer(s). A total of 175 patients and 23 carers, all aged 18 years and over, completed the surveys. Given the low number of respondents, particularly of carers, the estimates below should be used cautiously.

Absenteeism

Lost productivity from absenteeism was based on the number of days patients and their carers missed work in the past 4 weeks and their annual personal income.

Per patient, the estimated annual loss in productivity due to absenteeism was about $2,900 (about $1,600 and about $1,300 for the patient and carer respectively).

Presenteeism

Lost productivity from presenteeism was based on the number of days patients and their carers went to work in the past 4 weeks while suffering from problems related to ME/CFS, what percentage of these times they were just as productive as usual and their annual personal income.

Per patient, the estimated annual loss in productivity due to presenteeism was about $5,600 (about $2,900 and about $2,700 for the patient and carer respectively).

Lost wages

Lost productivity due to lost wages was determined for those currently too unwell to work by using the annual average income for the occupation the patient had prior to living with ME/CFS. If patients had never worked, the national average annual income was used. Patients currently working were asked if their occupation category had changed due to ME/CFS and, if so, the difference in income between the occupation categories was calculated as an estimate for lost wages.

Per patient, the estimated annual loss in productivity due to lost wages was about $45,100 (about $42,200 and about $2,900 for the patient and carer respectively).

Total

Applying the prevalence rate of 950 per 100,000 population to the Australian estimated resident population (ERP) (ABS 2025) for people aged 18 years and older in June 2024, an estimated 203,000 adult Australians were living with ME/CFS in 2024. With an annual cost of around $53,600 per patient, due to loss of productivity from absenteeism, presenteeism and lost wages, the total loss in productivity due to ME/CFS is estimated to be over $10.9 billion.

Note: the total societal costs reported by Zhao et al. (2023) differ from the estimates reported in this article because they used a different prevalence estimate.

Close et al. (2020) also estimate the health-system costs and loss in income (for both patient and carers) associated with ME/CFS based on data from 85 participants living with the condition. They estimated the annual health-system cost to government to be just under $4,500 per patient. With an estimated 219,000 Australians living with ME/CFS in 2024, the total health-system cost is estimated at about $985.5 million.

Close et al. (2020) estimated a loss in income of about $48,800 for each patient and just over $3,900 for each carer, for a total of around $52,700 per patient. With an estimated 203,000 adult Australians living with ME/CFS in 2024 (see “Loss in productivity based on estimates from Zhao et al. (2023)” dropdown box above), the total loss in income is estimated at around $10.7 billion.

Note: the total costs reported by Close et al. (2020) differ from the estimates reported in this article because they used a different prevalence estimate.

A main limitation of the estimates from Close et al. (2020) is that they are based on a sample of only 85 people living with ME/CFS. Similar to Zhao et al. (2023), Close et al.’s estimates for loss in income were based on self-report of the difference in income prior to living with ME/CFS and income at the time of participating in the study.

The Neurological conditions in Australia 2025 project was undertaken by members of the Chronic Conditions Unit of the Australian Institute of Health and Welfare (AIHW).

The AIHW acknowledges the ongoing contributions and consultation provided by the Department of Health, Disability and Ageing and the Neurological Conditions Expert Advisory Group. A special thanks is extended to an anonymous expert for their advice on this article. For further information on contributions to the project, see Notes in the Neurological conditions in Australia report.

ABS (Australian Bureau of Statistics) (2023) Microdata: National Health Survey, 2022, AIHW analysis of detailed microdata, accessed March 2025.

ABS (2025) Population Australia: Population at 30 June, by sex and single year of age, Aust., from 1971 onwards [data set], National, state and territory population, September 2024, ABS website, accessed 6 June 2025.

AIHW (Australian Institute of Health and Welfare) (2025) Health system spending on disease and injury in Australia 2023–24 AIHW website, accessed 29 October 2025.

Bridges & Pathways (n.d.) Diagnosis – Bridges & Pathways [website], accessed 21 October 2025.

Close S, Marshall-Gradisnik S, Byrnes J, Smith P, Nghiem S, Staines D (2020) ‘The Economic Impacts of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in an Australian Cohort’ Frontiers in Public Health, 420, doi: 10.3389/fpubh.2020.00420.

Healthdirect (2024) Chronic fatigue syndrome (Myalgic Encephalomyelitis), Healthdirect Australia website, accessed 19 May 2025.

Lim EJL, Ahn YC, Jang ES, Lee SW, Lee SW and Son CG (2020) ‘Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis (ME/CFS)’, Journal of Translational Medicine, 18: Article number 100, doi:10.1186/s12967-020-02269-0.

Orji N, Campbell JA, Wills K, Hensher M, Palmer AJ, Rogerson M, Kelly R and de Graaff B, (2022) ‘Prevalence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in Australian primary care patients: only part of the story?’ BMC Public Health, 22:Article number 1516, doi:10.1186/s12889-022-13929-9.

Zhao T, Cox IA, Ahmad H, Campbell JA, Hensher M, Palmer AJ, Kelly RM, Rogerson MJ, Wills K and de Graaff B (2023) ‘The economic burden of myalgic encephalomyelitis/chronic fatigue syndrome in Australia’, Australian Health Review, 47(6):707–715, doi:10.1071/AH23106.