What is the current evidence on the cost-effectiveness of fiscal policies to prevent obesity? (#182)
Internationally, there is a growing interest in fiscal policy approaches to address the risk factors for obesity. A review of recently published economic evaluation literature on the use of fiscal measures to prevent obesity was undertaken to assess contemporary evidence of their value-for-money.
A literature search was undertaken for the period 2010-2013 using Medline Complete. A combination of key search terms was used to identify papers relating to the effectiveness and cost-effectiveness of fiscal policy.
Ten recent reviews of the effectiveness of fiscal measures highlighted the variability in both the types of measures examined and the methodologies, thereby ruling out pooling of results. Whilst there is some promising evidence, uncertainty and gaps in the evidence base preclude the drawing of definitive conclusions about the most effective fiscal strategies to prevent obesity.
Whilst eight economic studies were identified, only three entailed a full economic evaluation. Five studies evaluated a tax to discourage the consumption of unhealthy foods or sugar-sweetened beverages, whilst four assessed a discount /subsidy to promote healthy food consumption. Variation in the magnitude of the tax or subsidy assessed, the methods used and the limited number of studies reviewed precluded results pooling. Discussion of associated equity issues, potential side effects and the feasibility of implementing such fiscal measures for obesity prevention was limited.
Whilst the effectiveness literature around fiscal measures for obesity prevention is vast, it has inherent limitations which impact on the quality of the small body of existing cost-effectiveness literature. Definitive conclusions about the value for money of fiscal policies cannot yet be drawn. Uncertainty and gaps in the effectiveness evidence base need to be addressed by collecting ‘real-world’ empirical data in larger studies with more robust designs and longer follow-up time frames.