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A model was developed to assess the impact of possible moves towards universal coverage in Tanzania over a 15-year time frame. Three scenarios were considered: maintaining the current situation ('the status quo'); expanded health insurance coverage (the estimated maximum achievable coverage in the absence of premium subsidies, coverage restricted to those who can pay); universal coverage to all (government revenues used to pay the premiums for the poor). The model estimated the costs of delivering public health services and all health services to the population as a proportion of Gross Domestic Product (GDP), and forecast revenue from user fees and insurance premiums. Under the status quo, financial protection is provided to 10% of the population through health insurance schemes, with the remaining population benefiting from subsidized user charges in public facilities. Seventy-six per cent of the population would benefit from financial protection through health insurance under the expanded coverage scenario, and 100% of the population would receive such protection through a mix of insurance cover and government funding under the universal coverage scenario. The expanded and universal coverage scenarios have a significant effect on utilization levels, especially for public outpatient care. Universal coverage would require an initial doubling in the proportion of GDP going to the public health system. Government health expenditure would increase to 18% of total government expenditure. The results are sensitive to the cost of health system strengthening, the level of real GDP growth, provider reimbursement rates and administrative costs. Promoting greater cross-subsidization between insurance schemes would provide sufficient resources to finance universal coverage. Alternately, greater tax funding for health could be generated through an increase in the rate of Value-Added Tax (VAT) or expanding the income tax base. The feasibility and sustainability of efforts to promote universal coverage will depend on the ability of the system to contain costs.

Modelling the implications of moving towards universal coverage in Tanzania