Abstract
Introduction
To address uncertainty regarding the cost-effectiveness of implementing geriatric assessment (GA) in oncology practice, we undertook a synthetic, model-based economic evaluation.
Materials and Methods
A decision-analytic model with embedded Markov chains was developed to simulate a cost-effectiveness analysis of implementing GA within standard oncological care compared to current practice. This was for patients aged 77 years (the mean age in included trials) receiving chemotherapy or surgery as first-line treatment. Assumptions were made about model parameters, based on available literature, to calculate the incremental net health benefit (INHB) of GA, using a data synthesis.
Results
GA has additional costs over standard care alone of between £390 and £576, depending upon implementation configuration. When major assumptions about the effectiveness of GA were modelled, INHB was marginally positive (0.09–0.12) at all cost-effectiveness thresholds (CETs). If no reduction in postoperative complications was assumed, the intervention was shown not to be cost-effective (INHB negative at all CETs). When used before chemotherapy, with minimal healthcare staffing inputs and technological assistance, GA is cost-effective (INHB positive between 0.06 and 0.07 at all CETs).
Discussion
Considering emerging evidence that GA improves outcomes in oncology, GA may not be a cost-effective intervention when used for all older adults with cancer. However, with judicious selection of implementation models, GA has the potential to be cost-effective. Due to significant heterogeneity and centre dependent success in implementation and effectiveness, GA is difficult to study in oncology settings. Stakeholders could take a pragmatic approach towards GA introduction with local evaluation favoured over generalisable research. Because GA tends towards utilitarianism and has no safety issues, it is a suitable intervention for more widespread implementation.
To address uncertainty regarding the cost-effectiveness of implementing geriatric assessment (GA) in oncology practice, we undertook a synthetic, model-based economic evaluation.
Materials and Methods
A decision-analytic model with embedded Markov chains was developed to simulate a cost-effectiveness analysis of implementing GA within standard oncological care compared to current practice. This was for patients aged 77 years (the mean age in included trials) receiving chemotherapy or surgery as first-line treatment. Assumptions were made about model parameters, based on available literature, to calculate the incremental net health benefit (INHB) of GA, using a data synthesis.
Results
GA has additional costs over standard care alone of between £390 and £576, depending upon implementation configuration. When major assumptions about the effectiveness of GA were modelled, INHB was marginally positive (0.09–0.12) at all cost-effectiveness thresholds (CETs). If no reduction in postoperative complications was assumed, the intervention was shown not to be cost-effective (INHB negative at all CETs). When used before chemotherapy, with minimal healthcare staffing inputs and technological assistance, GA is cost-effective (INHB positive between 0.06 and 0.07 at all CETs).
Discussion
Considering emerging evidence that GA improves outcomes in oncology, GA may not be a cost-effective intervention when used for all older adults with cancer. However, with judicious selection of implementation models, GA has the potential to be cost-effective. Due to significant heterogeneity and centre dependent success in implementation and effectiveness, GA is difficult to study in oncology settings. Stakeholders could take a pragmatic approach towards GA introduction with local evaluation favoured over generalisable research. Because GA tends towards utilitarianism and has no safety issues, it is a suitable intervention for more widespread implementation.
| Original language | English |
|---|---|
| Article number | 101504 |
| Journal | Journal of Geriatric Oncology |
| Volume | 14 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 13 Jun 2023 |