The economic burden of myalgic encephalomyelitis/chronic fatigue syndrome in Australia
Ting Zhao A , Ingrid A. Cox
A , Hasnat Ahmad A , Julie A. Campbell A , Martin Hensher A , Andrew J Palmer A , Ryan M. Kelly B , Melissa J. Rogerson B , Karen Wills A and Barbara de Graaff A *
A
B
Abstract
This study aimed to estimate costs of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) to patients, government and Australian society.
Australian ME/CFS patients and their carers were recruited using convenience sampling. Patients completed an online retrospective cost diary, providing ME/CFS-related direct medical, non-medical and indirect costs. Informal care costs were collected directly from carers. Data from the Pharmaceutical Benefits Scheme and Medicare Benefits Schedule were linked to participant survey data. Annual per patient and total societal costs were estimated, broken down by category and presented in 2021 AUD. Factors associated with higher costs were investigated using generalised linear models.
One hundred and seventy five patients (mean age 49 years s.d. 14, 79.4% female) completed the cost diary. Estimated total annual societal costs of ME/CFS in Australia ranged between $1.38 and $10.09 billion, with average annual total costs of $63 400/patient. Three-quarters of these costs were due to indirect costs ($46 731). Disability severity was the key factor associated with higher costs, particularly for indirect costs (being 2.27-fold higher for severe disability than no/mild disability).
ME/CFS poses a significant economic burden in Australia, owing mainly to high indirect and informal care costs.
Keywords: administrative data linkage, Australia, cost diary, cost of illness, economic burden, health resource utilisation, myalgic encephalomyelitis/chronic fatigue syndrome.
References
1 Clayton EW. Beyond myalgic encephalomyelitis/chronic fatigue syndrome: an IOM report on redefining an illness. JAMA 2015; 313: 1101-1102.
| Crossref | Google Scholar | PubMed |
2 Lim E-J, Ahn Y-C, Jang E-S, et al. Systematic review and meta-analysis of the prevalence of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). J Transl Med 2020; 18: 100.
| Crossref | Google Scholar |
3 Johnston S, Brenu EW, Staines D, et al. The prevalence of chronic fatigue syndrome/myalgic encephalomyelitis: a meta-analysis. Clin Epidemiol 2013; 5: 105-110.
| Crossref | Google Scholar |
4 Orji N, Campbell JA, Wills K, et al. Prevalence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in Australian primary care patients: only part of the story? BMC Public Health 2022; 22: 1516.
| Crossref | Google Scholar |
5 Cortes Rivera M, Mastronardi C, Silva-Aldana CT, et al. Myalgic encephalomyelitis/chronic fatigue syndrome: a comprehensive review. Diagnostics 2019; 9: 91.
| Crossref | Google Scholar | PubMed |
6 Jason LA, Mirin AA. Updating the National Academy of Medicine ME/CFS prevalence and economic impact figures to account for population growth and inflation. Fatigue 2021; 9: 9-13.
| Crossref | Google Scholar |
7 Close S, Marshall-Gradisnik S, Byrnes J, et al. The Economic Impacts of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in an Australian Cohort. Front Public Health 2020; 8: 420.
| Crossref | Google Scholar | PubMed |
8 Bhandari A, Wagner T. Self-reported utilization of health care services: improving measurement and accuracy. Med Care Res Rev 2006; 63: 217-235.
| Crossref | Google Scholar | PubMed |
9 Reserve Bank of Australia. Inflation Calculator. 2023. Available at https://www.rba.gov.au/calculator/
10 Medicine.com.au. 6 Big Australian Pharmacy Groups By Revenue. 2019. Available at https://www.medicine.com.au/the-big-6-australian-pharmacy-groups-by-revenue/
11 Australian Institute of Health and Welfare. Admitted patients. 2023. Available at https://www.aihw.gov.au/reports-data/myhospitals/sectors/admitted-patients
12 Independent Health and Aged Care Pricing Authority. National Efficient Price Determination 2022-23. 2022. Available at https://www.ihacpa.gov.au/resources/national-efficient-price-determination-2022-23
13 Australian Bureau of Statistics. Classifications used in labour statistics. 2023. Available at https://www.abs.gov.au/statistics/detailed-methodology-information/concepts-sources-methods/labour-statistics-concepts-sources-and-methods/2021/concepts-and-sources/classifications-used-labour-statistics
14 Australian Bureau of Statistics. Employee Earning and Hours, Australia. 2022. Available at https://www.abs.gov.au/statistics/labour/earnings-and-working-conditions/employee-earnings-and-hours-australia/may-2021
15 Barber J, Thompson S. Multiple regression of cost data: use of generalised linear models. J Health Serv Res Policy 2004; 9: 197-204.
| Crossref | Google Scholar | PubMed |
16 Hotelling H. New light on the correlation coefficient and its transforms. J Roy Stat Soc Ser B (Method) 1953; 15(2): 193-232.
| Google Scholar |
17 Australian Bureau of Statistics. Regional population by age and sex. 2022. Available at https://www.abs.gov.au/statistics/people/population/regional-population-age-and-sex/2021
18 Sabes-Figuera R, McCrone P, Hurley M, et al. The hidden cost of chronic fatigue to patients and their families. BMC Health Serv Res 2010; 10: 56.
| Crossref | Google Scholar | PubMed |
19 Collin SM, Crawley E, May MT, et al. The impact of CFS/ME on employment and productivity in the UK: a cross-sectional study based on the CFS/ME national outcomes database. BMC Health Serv Res 2011; 11: 217.
| Crossref | Google Scholar | PubMed |
20 Ahmad H, Campbell JA, van der Mei I, et al. The increasing economic burden of multiple sclerosis by disability severity in Australia in 2017: results from updated and detailed data on types of costs. Mult Scler Relat Disord 2020; 44: 102247.
| Crossref | Google Scholar |
