mortality, the risk of non-fatal adverse events for patients with various levels of glucose control is also reported [51], as well as overseas studies [35]. The risk of various other adverse health outcomes could be predicted in a similar way. These could include hypo- or hyperglycaemic events, retinopathy, neuropathy, nephropathy and amputations. The costs of medical resources used will be derived from national price schedules [52-54]. Where possible, the economic evaluation will use Australian studies [55], which quantify the total cost (including direct healthcare costs, direct non-healthcare costs, and government subsidies) associated with T2DM. The clinical data may suggest that shared medical appointments produce more vigilant monitoring, an earlier diagnosis of emerging problems and lower overall rates of these more severe health outcomes. This could be captured in the economic model by a reduced risk of these adverse outcomes for each health state in the treatment arm versus the control arm. The impact of removing this effect should then be tested in a sensitivity analysis. Comprehensive sensitivity analyses will be undertaken to address the uncertainty of the data estimates. The figure below provides a possible Markov structure that could be a starting point from which to undertake the cost-utility analysis. Figure 3: Possible structure of a Markov model to address the economic evaluation of shared medical appointments SMA=shared medical appointments, T2DM = Type 2 diabetes mellitus Appendix Figure A1 presents the current algorithm for T2DM screening and diagnosis. Figure A1: T2DM: Screening and diagnosis algorithm Source: General practice management of type 2 diabetes, Diabetes Australia 2014-2015 Table A1 presents the population specific thresholds for elevated waist circumference.
mortality, the risk of non-fatal adverse events for patients with various levels of glucose control is also reported [51], as well as overseas studies [35]. The risk of various other adverse health outcomes could be predicted in a similar way. These could include hypo- or hyperglycaemic events, retinopathy, neuropathy, nephropathy and amputations. The costs of medical resources used will be derived from national price schedules [52-54]. Where possible, the economic evaluation will use Australian studies [55], which quantify the total cost (including direct healthcare costs, direct non-healthcare costs, and government subsidies) associated with T2DM. The clinical data may suggest that shared medical appointments produce more vigilant monitoring, an earlier diagnosis of emerging problems and lower overall rates of these more severe health outcomes. This could be captured in the economic model by a reduced risk of these adverse outcomes for each health state in the treatment arm versus the control arm. The impact of removing this effect should then be tested in a sensitivity analysis. Comprehensive sensitivity analyses will be undertaken to address the uncertainty of the data estimates. The figure below provides a possible Markov structure that could be a starting point from which to undertake the cost-utility analysis. Figure 3: Possible structure of a Markov model to address the economic evaluation of shared medical appointments SMA=shared medical appointments, T2DM = Type 2 diabetes mellitus Appendix Figure A1 presents the current algorithm for T2DM screening and diagnosis. Figure A1: T2DM: Screening and diagnosis algorithm Source: General practice management of type 2 diabetes, Diabetes Australia 2014-2015 Table A1 presents the population specific thresholds for elevated waist circumference.
1385-SharedApptsDiabetes-RatifiedFinalProtocol-accessible.docx