Objective: To estimate the fiscal consequences of schizophrenia compared to the general US population by applying a “government perspective” fiscal analytical modelling framework to estimate lost tax revenue and broader government costs.

Methods: Schizophrenia progression was modeled from the age of 30 using a cohort-based Markov chain with a 6-week cycles, simulating the effect of 4-lines of antipsychotic treatment on remission and relapse status. The Markov states were defined using efficacy and safety outcomes from short-term and long-term trials. Mortality was modelled using US lifetables and schizophrenia-related excess mortality from suicide and cardiovascular disease. A semi-Markov process with annual cycles was used to model the likelihood of incarceration and homelessness in community-based individuals and their resulting costs. Survival, remission/relapse status, and social state estimates were combined with fiscal costs and tax revenue to predict lifetime incremental net fiscal consequences (INC). Costs and life-years were discounted at 3% annually. Uncertainty was explored in one-way sensitivity analyses.

Results: Individuals with schizophrenia, particularly those in relapse, were more likely to be unemployed, disabled, incarcerated, homeless, and use more healthcare resources than individuals without schizophrenia. Caregivers for individuals with schizophrenia had greater productivity losses than individuals without schizophrenia. Over a 30-year period, the INC of schizophrenia were $561,479 per person, 50% of which were non-healthcare costs. Foregone income from employment was estimated to be $774,261 in individuals with schizophrenia and $50,443 in caregivers. Considering a 1.19% prevalence of schizophrenia, the estimated annual fiscal burden to the US government was $57 billion.

Conclusions: Fiscal consequences among the schizophrenia population are substantial. The presented fiscal framework illustrates how changes in health status influence receipt of taxes and transfer payments by government over time. These findings can be used to augment cost-effectiveness analyses and inform stakeholders of the fiscal impact of schizophrenia to help define priority interventions.