Modelling of potential food policy interventions in Fiji and Tonga and their impacts on noncommunicable disease mortality

Type Journal Article - Food Policy
Title Modelling of potential food policy interventions in Fiji and Tonga and their impacts on noncommunicable disease mortality
Author(s)
Volume 36
Issue 5
Publication (Day/Month/Year) 2011
Page numbers 597-605
URL http://dro.deakin.edu.au/eserv/DU:30042721/snowden-modellingofpotential-2011.pdf
Abstract
Background: To compare the likely costs and benefits of a range of potential policy interventions in Fiji and Tonga targeted at diet-related noncommunicable diseases (NCDs), in order to support more evidence-based decision-making.

Method: A relatively simple and quick macro-simulation methodology was developed. Logic models were developed by local stakeholders and used to identify costs and dietary impacts of policy changes. Costs were confined to government costs, and excluded cost offsets. The best available evidence was combined with local data to model impacts on deaths from noncommunicable diseases over the lifetime of the target population. Given that the modelling necessarily entailed assumptions to compensate for gaps in data and evidence, use was made of probabilistic uncertainty analysis.

Results: Costs of implementing policy changes were generally low, with the exception of some requiring additional long-term staffing or construction activities. The most effective policy options in Fiji and Tonga targeted access to local produce and high-fat meats respectively, and were estimated to avert approximately 3% of diet-related NCD deaths in each population. Many policies had substantially lower benefits. Cost-effectiveness was higher for the low-cost policies. Similar policies produced markedly different results in the two countries.

Conclusion: Despite the crudeness of the method, the consistent modelling approach used across all the options, allowed reasonable comparisons to be made between the potential policy costs and impacts. This type of modelling can be used to support more evidence-based and informed decision-making about policy interventions and facilitate greater use of policy to achieve a reduction in NCDs.

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