title: The indirect costs of back problems (dorsopathies) in Australians aged 45 to 64 years from 2015 to 2030: results from a microsimulation model, Health&WealthMOD2030.
abstract: This study projected the indirect costs of back problems through lost productive life years (PLYs) from the individual's perspective (lost disposable income), the governmental perspective (reduced taxation revenue, greater welfare spending), and the societal perspective (lost gross domestic product, GDP) from 2015 to 2030, using Health&WealthMOD2030-Australia's first microsimulation model on the long-term impacts of ill-health. Quantile regression analysis was used to examine differences in median weekly income, welfare payments, and taxes of people unable to work due to back problems with working full-time without back problems as comparator. National costs and lost GDP resulting from missing workers due to back problems were also projected. We projected that 90,000 people have lost PLYs due to back problems in 2015, increasing to 104,600 in 2030 (16.2% increase). People with lost PLYs due to back problems are projected to receive AU$340.91 less in total income and AU$339.77 more in welfare payments per week than full-time workers without back problems in 2030 and pay no income tax on average. National costs consisted of a loss of AU$2931 million in annual income in 2015, increasing to AU$4660 million in 2030 (60% increase). For government, extra annual welfare payments are projected to increase from AU$1462 million in 2015 to AU$1709 million in 2030 (16.9% increase), and lost annual taxation revenue to increase from AU$671 million in 2015 to $961 million in 2030 (43.2% increase). We projected losses in GDP of AU$10,543 million in 2015, increasing to AU$14,522 million in 2030 due to back problems.
authors.affiliation: Faculty of Pharmacy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.|Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.|Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
authors.forename: Deborah
authors.initials: D
authors.lastname: Schofield
authors.affiliation: Faculty of Pharmacy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
authors.forename: Michelle M
authors.initials: MM
authors.lastname: Cunich
authors.affiliation: Faculty of Pharmacy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
authors.forename: Rupendra N
authors.initials: RN
authors.lastname: Shrestha
authors.affiliation: National Centre for Social and Economic Modelling, University of Canberra, Canberra, ACT, Australia.
authors.forename: Robert
authors.initials: R
authors.lastname: Tanton
authors.affiliation: School of Public Health, Faculty of Medicine and Biomedical Sciences, University of Queensland, Brisbane, Queensland, Australia.
authors.forename: Lennert
authors.initials: L
authors.lastname: Veerman
authors.affiliation: National Centre for Social and Economic Modelling, University of Canberra, Canberra, ACT, Australia.
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Kelly
authors.affiliation: University Centre for Rural Health, School of Public Health, The University of Sydney, Lismore, New South Wales, Australia.
authors.forename: Megan E
authors.initials: ME
authors.lastname: Passey
mesh_terms: D000375:Aging; D001315:Australia / Q000453:epidemiology; D019567:Back Injuries / Q000191:economics* / Q000453:epidemiology*; D003198:Computer Simulation; D017281:Cost of Illness; D004185:Disability Evaluation; D005260:Female; D059066:Gross Domestic Product; D062489:Health Impact Assessment / Q000191:economics; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical