The development of AUS-OA - a population health economic model of osteoarthritis in Australia
- Department of Surgery, The University of Melbourne, St Vincent’s Hospital, Australia
- Simulation modelling and health economics of chronic disease unit, The University of Melbourne, Australia
Cite this article
as: C. Schilling, Y. Zhou, S. Rele, C. Shadbolt, S. Thuraisingam, P. O’Brien, J. Knight, N. Zakariyya, M. Dowsey, P. Choong; 2025; The development of AUS-OA - a population health economic model of osteoarthritis in Australia; International Journal of Microsimulation; 18(1); 84-94.
doi: 10.34196/ijm.00317
Figures
Figure 1
AUS-OA (simulated) versus National Health Survey (observed) rates of overweight and obese, by age and sex categories, 2013 to 2022.
Figure 2
AUS-OA (simulated) versus National Health Survey (observed) prevalence of osteoarthritis, by age and sex categories, 2013 to 2022.
Figure 3
AUS-OA (simulated) versus AOANJRR (observed) rates of TKR by 100,000 population, by age and sex categories, 2013 to 2022.
Tables
Table 1
Key variables tracked within the model.
| Domain | Variable |
|---|---|
| Socio-demographics | Age, gender, socio-economic position, private health insurance, state |
| Risk factors | BMI, mental illness, comorbidities |
| Outcomes | Osteoarthritis, health-related quality of life, costs |
| Treatments | Osteoarthritis medications, primary care health, joint replacement and revisions |
Table 2
Key dynamics within AUS-OA.
| Variable | Model | Key covariates | Data | Source |
|---|---|---|---|---|
| BMI | Multivariate regression | Age, sex, SEP BMI | NHS | Hayes et al., 2019 |
| OA incidence | Logistic regression | Age, sex, SEP BMI | HILDA | Author’s own – see Supplementary materials |
| OA progression | Logistic regression | Age, sex, BMI | OAI | Author’s own |
| Mental health | Calibration | Sex | HILDA, NHS | Author’s own |
| Other comorbidities | Calibration | Age, sex | HILDA, NHS | Author’s own |
| TKR incidence | Fine and Gray competing risks | Ae, sex, BMI, mental health, previous TKR, KL score | Linked primary care and AOANJRR | Thuraisingam et al., 2022, modified for AUS-OA |
| TKR complication | Logistic regression | Age, sex, BMI, mental health, comorbidities, HRQOL | SMART Registry | Author’s own; |
| Inpatient rehabilitation | Logistic regression | Age, sex, BMI, mental health, comorbidities, HRQOL, complications | SMART Registry | Author’s own; |
| TKR revision | Weibull parametric regression | Age, sex, BMI | AOANJRR | AOA bespoke request 2024 |
| Death | Life tables; standardised mortality ratios | Age, sex, BMI, SEP, TKR | NZJR, NHS | Zhou et al., 2023; Hayes et al., 2019 |
| HRQOL decrements | Various | Age, sex, BMI, KL score, comorbidities | HILDA, OAI | Author’s own; Carrello et al. (2021), Wilson and Abbott (2018) |
| HRQOL after surgery | Multivariate regression | Age, sex, BMI, comorbidities | SMART | Author’s own, based on Schilling et al. (2017) (Schilling et al., 2017) |
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AOA = Australian Orthopaedic Association; AOANJRR = Australian Orthopaedic Association National Joint Replacement Registry; BMI = Body mass index; HILDA = Household Income and Labour Dynamics of Australia; HRQOL = Health-related quality of life; KL = Kellgren-Lawrence; NZJR = New Zealand Joint Register; NHS = National Health Survey; OA = osteoarthritis; OAI = Osteoarthritis Initiative; SEP = socio-economic position as proxied by completion of year 12; TKR = Total knee replacement
Table 3
Cost effectiveness of SMART Choice
| Use of SMART Choice in surgical population | Increment costs | Incremental QALYs | Incremental Cost/QALY |
|---|---|---|---|
| Baseline – 3% | $707,918 | 2,910 | $243 |
| 1% | $1,008,146 | 970 | $1,039 |
| 5% | $407,691 | 4,850 | $84 |
Data and code availability
The model code and sample dataset are available on GitHub: https://github.com/microsimulation/ijm-supplements/tree/ijm-00317. Supplementary material is available from the Journal’s GitHub repository: https://github.com/UnimelbHealthEconomics/AUS_OA_public.
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