PRIMEtime: an epidemiological model for informing diet and obesity policy

Background Mathematical modelling can play a vital role in guiding public health action. In this paper, we provide an overview of the revised and updated PRIMEtime model, a tool for evaluating health and economic impacts of policies impacting on diet and obesity. We provide guidance on populating PRIMEtime with country-specific data; and illustrate its validation and implementation in evaluating a combination of interventions in the UK: a sugar-sweetened beverage (SSB) tax; a ban on television advertising of unhealthy foods; and a weight loss program. Methods PRIMEtime uses routinely available epidemiological data to simulate the effects of changes in diet and obesity on 19 non-communicable diseases, in open- or closed-population cohorts, over time horizons from 1 year to a lifetime. From these simulations, the model can estimate impact of a policy on population health (obesity prevalence, cases of disease averted, quality-adjusted life years), health and social care costs, and economic measures (net monetary benefit, cost-effectiveness ratios). We populated PRIMEtime with UK data and validated epidemiological predictions against two published data collections. We then evaluated three current obesity intervention policies based on estimates of effectiveness from published evaluation studies. Results There was considerable variation in the modelled impact of interventions on prevalence of obesity and subsequent changes in health and the need for health care: restrictions on TV advertising of unhealthy foods to children led to the largest reductions in obesity prevalence; but the SSB tax, which also targeted adults, had the biggest benefits in reducing obesity-related disease; and the weight loss program, while having very small impact on obesity prevalence at the population scale, had large and immediate benefits in improving health and reducing health sector spending. From a health sector perspective, the combination of interventions produced a favourable net monetary benefit of £31,400 (12,200 to 50,700) million. But the combined effect in reducing prevalence of overweight and obesity, was not estimated to reach more than 0.81 percentage points (95% uncertainty interval: 0.21 to 1.4) for males and 0.95 percentage points (0.24 to 1.7) for females by 2050. Conclusions Diet and obesity interventions have the potential to improve population health and reduce health sector spending both immediately and in the long-term. Models such as PRIMEtime can be used to evaluate the economic merits of intervention strategies and determine how best to combine interventions to achieve maximum population benefit. But with almost a third of children and two-thirds of adults currently overweight or obese, we need to broaden the application of public health models to evaluating the structural and systemic changes that are needed in our society to address the underlying drivers of the obesity epidemic. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was supported by the NIHR Biomedical Research Centre at Oxford (IS-BRC-1215-20008) and an NIHR project grant for an evaluation of the UK Soft Drinks Industry Levy (16/130/01). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used to support the PRIMEtime model and the analyses conducted in this paper are available in the public domain, sometimes with licensed agreements from relevant data archives (e.g. Health Survey for England data).