Health policy (Amsterdam, Netherlands) · 2026
Counting costs and carbon: A decision model for greener healthcare policy.
СтатьяНазвание и аннотация: Перевод в очереди
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Brunn Matthias, Padget Michael, Molinie Guilhem, Dariel Odessa, Zarca Kevin
Аннотация
BACKGROUND: Healthcare systems contribute approximately 5-8% of national greenhouse gas emissions in high-income countries, yet current evaluation frameworks seldom include environmental externalities. While many nations have committed to decarbonizing healthcare, operational tools that integrate environmental impacts into decision-making remain scarce.
OBJECTIVE: To test the feasibility and implications of a "health-economic-climate" model that incorporates monetized carbon emissions into conventional cost-utility analysis (CUA), using depression care in France as a case study.
METHODS: A semi-Markov model compared three first-line treatments for depression-antidepressants (AD), psychotherapy (PT), and combination therapy (COMB)-from the French healthcare system perspective over five years. Direct medical costs and carbon emissions were estimated using life-cycle data and monetized via a sector-adjusted social cost of carbon (SCC) of €90/tCO₂e. Incremental cost-effectiveness ratios (ICERs) and sensitivity analyses were conducted.
RESULTS: Annual carbon footprints were 104 kgCO₂e (AD), 177 kgCO₂e (PT), and 225 kgCO₂e (COMB). Over five years, PT was the least costly and equally effective compared with AD, while COMB was more effective but more expensive (ICER ≈ €35,000/QALY). Incorporating carbon costs modestly altered results but improved visibility of environmental impacts.
CONCLUSIONS: Integrating monetized carbon emissions within CUA is feasible, methodologically transparent, and compatible with existing HTA structures. This minimal adaptation supports gradual adoption of climate-aligned decision tools without institutional redesign. Future applications should expand to a societal perspective and explore multicriteria frameworks that make trade-offs between health, cost, and environmental goals explicit.
OBJECTIVE: To test the feasibility and implications of a "health-economic-climate" model that incorporates monetized carbon emissions into conventional cost-utility analysis (CUA), using depression care in France as a case study.
METHODS: A semi-Markov model compared three first-line treatments for depression-antidepressants (AD), psychotherapy (PT), and combination therapy (COMB)-from the French healthcare system perspective over five years. Direct medical costs and carbon emissions were estimated using life-cycle data and monetized via a sector-adjusted social cost of carbon (SCC) of €90/tCO₂e. Incremental cost-effectiveness ratios (ICERs) and sensitivity analyses were conducted.
RESULTS: Annual carbon footprints were 104 kgCO₂e (AD), 177 kgCO₂e (PT), and 225 kgCO₂e (COMB). Over five years, PT was the least costly and equally effective compared with AD, while COMB was more effective but more expensive (ICER ≈ €35,000/QALY). Incorporating carbon costs modestly altered results but improved visibility of environmental impacts.
CONCLUSIONS: Integrating monetized carbon emissions within CUA is feasible, methodologically transparent, and compatible with existing HTA structures. This minimal adaptation supports gradual adoption of climate-aligned decision tools without institutional redesign. Future applications should expand to a societal perspective and explore multicriteria frameworks that make trade-offs between health, cost, and environmental goals explicit.