Markov state transition model to estimate the health impacts and related costs to the public of different trans fatty acids (TFA) intake scenarios at the EU level

The present model is a computer-simulated, Markov, state-transition model built in Excel (Microsoft Office 2010). Based on a certain industrially-produced trans fatty acids (iTFA) intake as a starting point (“today”), measured as a weighted average of data at MS level collected through existing evidence and a survey, and projected future iTFA intakes, such as resulting from alternative policy scenarios, the model calculates the iTFA-intake affected probabilities of the EU population to transit between health states, as follows:

1. probability of keeping well (in this context, staying alive and not having a CAD event);
2. probability of experiencing a CAD event for persons without a previous CAD event;
3. probability of surviving a CAD event;
4. probability of staying alive in the post–acute CAD state;
5. probability of experiencing a new CAD event when in the post–acute CAD state;
6. probability of death from any cause (except from CAD) for persons without a previous CAD event;
7. probability of death from a CAD event; and
8. probability of death from any cause except for CAD for individuals with a history of CAD.

The model simulates how people move in yearly cycles between the four health states and accounts for costs (policy- and health status-related) as well as health effects (CAD incidence and disability-adjusted life years, DALYs). These are calculated for each scenario, such as when comparing policy option, and then compared with one another.  

In order to better illustrate how the model works in detail in the following a brief summary of a public health economic evaluation study using the TFA-POL model is given. N.b., the described study was not part of the study of the contractor ICF to support the impact assessment nor of the impact assessment itself. The policy scenarios in that study analysed are as follows:

1. the reference situation is described by the highest cumulative TFA intake and entails the highest risk of CAD. This scenario assumes a continued decrease in TFA consumption that leads to a removal of iTFAs from the food supply over 10 years due to continuous innovation in the industry and efforts at the national or regional levels. There are no added public costs from implementing this policy option, and therefore all costs result from CAD-associated morbidity and loss of productivity;
2. the implementation of a common framework toward reducing TFAs in foods and diets at the EU level is simulated by assuming that public costs are CAD-associated and are also related to food inspection programs to monitor and evaluate the agreements. It is also assumed a faster reduction in TFA consumption than in the reference scenario, leading to a quicker removal of iTFAs from the food supply due to the additional private-public commitments. For this, the model assume the total removal of iTFAs from the food supply after 5 years, half the time needed in reference situation, albeit acknowledging that the rare use of iTFA-containing raw materials by some producers and imports of iTFA-containing foods from countries in which the iTFA issue has not been addressed cannot be excluded;
3. mandatory TFA labelling at the EU level is simulated by considering CAD-associated and other non–CAD related public costs. The reduction in population TFA intake is faster than in the reference situation but slightly slower than in scenario 2, because in this case there are only incentives toward reducing TFA content in pre-packaged foods. The assumption in the model is that iTFA removal is faster in pre-packaged foods than in options 1 (reference) and 2, but not in non-prepackaged foods, in which iTFA removal proceeds at the same speed as in the reference option 1. The model assumes population TFA intake reductions for the first 2 years until TFA content labelling is available for all pre-packaged foods, as in the reference situation (option 1), then a faster reduction in iTFA intake from pre-packaged foods, which, based on the available information, is assumed to contribute to 50% of population TFA intake at the start and decrease to 0% in 3 years. The model assumes that reductions in iTFAs from non-pre-packaged foods continue at the same speed as in the reference situation albeit acknowledging that, in reality, some spillover effects in the efforts to remove iTFAs from pre-packaged foods might also be expected for non pre-packaged foods
4. a scenario in which there is a legal limit of iTFA content in foods assumes a fast removal of iTFAs in all of the EU food supply and represents therefore the lowest cumulative TFA consumption of all 4 options. The model assumes the total removal of iTFAs in 2 years, and considers CAD-associated public costs as well as other costs not associated with CAD.

As all the costs and effects (obtained from the literature) assume deterministic values, and there are uncertainties related to the current levels of iTFA intakes for the EU population,  the model also performs a probabilistic sensitivity analysis by applying probabilistic distributions to all variables. Each of the distributions used in this analysis were chosen following the current trends and literature recommendations.

The point estimates of the mean costs and effects are used to calculate the incremental cost-effectiveness ratios (ICERs), obtained by dividing the difference in costs between a policy option and the reference situation (no EU-level action) by the respective difference in effects (DALYs). The ICER is then interpreted as the cost for each DALY gained.

The below variables (and their respective sources) were used as inputs in the TFA-POL model:

1. Probability of death from acute CAD, and probability of death from any cause (from the European Health for All Database);
2. Probability of CAD (Hospital discharges by ischemic heart disease; data from European Health for All Database);
3. Reduction in RR of CAD in legal limit strategy, Reduction in RR of CAD in voluntary agreements strategy, Reduction in RR of CAD in mandatory labeling strategy (Mozzafarian et al., O’Flaherty et al. );
4. RR of second and subsequent CAD events after the first event and RR probability of death from second CAD event compared with death from the first CAD event (assumption);
5. Production losses due to mortality, Production losses due to morbidity, Informal care, Primary care, Outpatient care, Accident and emergency, In-patient care, Medication (Nichols et al. );
6. School-based intervention, Worksite intervention, Mass media campaigns, Physician counselling, Program of food inspection (from Cecchini et al., Sassi et al. ).

To account for uncertainty, beyond the deterministic analysis (which uses a single value for costs and for effects in the model calculations), different probabilistic distributions to every variable in the model according to the current trends and literature recommendations were assumed. A log-normal distribution was applied to variables (1) to (4), and a gamma distribution were used for variables (5) to (6). Annual discount rate of 3.5% is applied to both costs and effects following best-practice guidelines.

References to input sources: 

• Mozaffarian D, Aro A, Willett WC. Health effects of trans-fatty acids: experimental and observational evidence. Eur J Clin Nutr. 2009;63 Suppl 2:S5–21
• O’Flaherty M, Flores-Mateo G, Nnoaham K, Lloyd-Williams F, Capewell S. Potential cardiovascular mortality reductions with stricter food policies in the United Kingdom of Great Britain and Northern Ireland. Bull World Health Organ 2012;90:522–31.
• European Health for All Database (HFA-DB) [Internet]. 2013. [cited 2013 June 10] Available from: http://www.euro.who.int/en/what-wedo/data-and-evidence/databases/european-health-for-all-database-hfa-db2.
• Nichols MTN, Luengo-Fernandez R, Leal J, Gray A, Scarborough P, Rayner M. European Cardiovascular Disease Statistics 2012. Brussels (Belgium): European Heart Network, European Society of Cardiology; 2012
• Cecchini M, Sassi F, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D. Tackling of unhealthy diets, physical inactivity, and obesity: health effects and cost-effectiveness. Lancet 2010;376:1775–84.
• Sassi F, Cecchini M, Lauer J, Chisholm D. Improving lifestyles, tackling obesity: the health and economic impact of prevention strategies. OECD Health working paper 48. OECD, Paris; 2009.[Internet] [cited 2013 June 10]. Available from: http://www.who.int/choice/publications/d_OECD_prevention_report.pdf