Feasibility of a health-climate-economic decision model for depression care

The interaction between climate change and health is gaining increasing attention. This concerns not only the adaption to global warming, but also the impact of the health system itself, which is between 4 and 8% of total carbon emissions in Europe. In this context, health systems are currently preparing to include the carbon footprint of treatment options into their decision making, both at the provider and the governance level. Our objective is to develop and test a decision making tool that takes into account carbon emissions to the same extent as the currently established evaluation criteria, i.e. clinical benefit and economic cost, for depression care.

To this end, we have chosen to build and adapt a Markov decision model, which is one of the most frequently used tools to assess the value of new and existing treatment options. The model simulates, over the course of five years, the clinical, economic and climate effects of three treatment options for depression: pharmacotherapy, psychotherapy, and the combination of both.

Preliminary results indicate that the model is feasible, but that several assumptions that have to be made – for example, on patients’ distance to services and mode of transport – heavily impact the outcomes. Overall, the work undertaken so far suggests that the lessons from a bottom-up analysis for a single disease can contradict conclusions inferred from country-level, top-down carbon footprint assessments. The latter generally distinguish sectors (hospital, ambulatory) or functions (logistics, care) only. These differences raise important questions about the priority setting for decarbonizing health systems, as well as the data, tools and expertise needed.