Advertisement

BMC Nutrition

, 5:2 | Cite as

The impact of food reformulation on nutrient intakes and health, a systematic review of modelling studies

  • Carlo FedericiEmail author
  • Patrick Detzel
  • Francesco Petracca
  • Livia Dainelli
  • Giovanni Fattore
Open Access
Research article
Part of the following topical collections:
  1. Nutritional interventions, policies and public health

Abstract

Background

Unhealthy diet is a risk factor for adverse health outcomes. Reformulation of processed foods has the potential to improve population diet, but evidence of its impact is limited. The purpose of this review was to explore the impact of reformulation on nutrient intakes, health outcomes and quality of life; and to evaluate the quality of modelling studies on reformulation interventions.

Methods

A systematic review of peer-reviewed articles published between January 2000 and December 2017 was performed using MEDLINE, ScienceDirect, Embase, Scopus, Cochrane, and the Centre for Reviews and Dissemination of the University of York. Additional studies were identified through informal searches on Google and specialized websites. Only simulation studies modelling the impact of food reformulation on nutrient intakes and health outcomes were included. Included articles were independently extracted by 2 reviewers using a standardized, pre-piloted data form, including a self-developed tool to assess study quality.

Results

A total of 33 studies met the selected inclusion criteria, with 20, 5 and 3 studies addressing sodium, sugar and fats reformulation respectively, and 5 studies addressing multiple nutrients. Evidence on the positive effects of reformulation on consumption and health was stronger for sodium interventions, less conclusive for sugar and fats. Study features were highly heterogeneous including differences in methods, the type of policy implemented, the extent of the reformulation, and the spectrum of targeted foods and nutrients. Nonetheless, partial between-study comparisons show a consistent relationship between percentages reformulated and reductions in individual consumption. Positive results are also shown for health outcomes and quality of life measures, although comparisons across studies are limited by the heterogeneity in model features and reporting. Study quality was often compromised by short time-horizons, disregard of uncertainty and time dependencies, and lack of model validation.

Conclusions

Reformulation models highlight relevant improvements in diets and population health. While models are valuable tools to evaluate reformulation interventions, comparisons are limited by non-homogeneous designs and assumptions. The use of validated models and extensive scenario analyses would improve models’ credibility, providing useful insights for policy-makers.

Review Registration

A research protocol was registered within the PROSPERO database (ID number CRD42017057341).

Keywords

Food reformulation Modelling Nutrition policies Public health Decision-analytical models Sodium intake Sugar intake Fat intake 

Abbreviations

AMI

Acute myocardial infarction

BMI

Body mass index

CHF

Congestive heart failure

CVD

Cardiovascular diseases

DALY

Disability adjusted life years

LYG

Life-years gained

QALY

Quality adjusted life years

SFA

Saturated fatty acids

SSB

sugar sweetened beverage

T2D

Type 2 diabetes

TFA

Trans-fatty acids

UI

Uncertainty interval

Background

The growing evidence on the detrimental effects of poor diet on health, including cardiovascular diseases, diabetes and some type of cancers [1, 2, 3, 4, 5], has stimulated national and international nutrition programs and policies to strive to reduce the intake of sodium, sugar and fat to the recommended limits [6, 7]. Such initiatives largely focus on changing individuals’ behavior by promoting social marketing campaigns, community-based interventions and/or primary healthcare programs, whereas other possible strategies target the dietary environment by affecting availability and prices of unhealthy foods. More recently, increasing attention has been given to interventions favoring the reformulation of processed foods towards healthier alternatives [8, 9]. Reformulation is defined as the process of altering a food or beverage product’s recipe or composition to improve the product’s health profile [10]. These strategies are consistent with the World Health Assembly 2004 report that called for initiatives by the food industry to reduce the content of harmful nutrients in processed foods and increase the introduction of innovative, healthy and nutritious choices. However, the current policy agenda of reformulation strategies exhibits a prevailing focus on the former objective, thus aiming to remove or reduce public health sensitive nutrients from foods, while maintaining the same appearance, use and characteristics such as flavor, texture and shelf life [11]. According to a recent review, 59 out of 83 countries have on-going programs of work with the industry to reduce sodium in processed foods [12]. To a minor extent, similar efforts are now in place to reduce excess intakes of sugar, particularly from sugar sweetened beverages, and fat [13].

However, despite the growing emphasis given to reformulation, evidence of its impact on both nutrient intakes and health outcomes is limited. Several design characteristics may affect the policy impact. These include i) the voluntary or mandatory nature of the program, and its consequences on the adherence from the industry; ii) the breadth of the reformulation in terms of products targeted; iii) the amount and type of nutrients reformulated; and iv) the pace of implementation, including how reduction targets are set scheduled and reached.

Additionally, how consumers might react to reformulated products and how this will affect the overall policy effectiveness remains largely unexplored, especially for sugar and fat reformulations. Indeed, consumers might react to changes in taste and energy density by shifting to other, non-reformulated products, eating more, or increasing the use of additional ingredients (e.g. discretionary salt and sugar).

Due to these features, estimating the effectiveness of current and future reformulation policies is challenging, and likely to be both policy and context specific.

Given to the obvious difficulties of performing experimental designs to measure the impact of nutrition policies on population health [14], the majority of published studies use mathematical models to predict the effects of reformulation on intakes and clinical outcomes [15]. Nonetheless, the extreme flexibility of modelling, and the required assumptions needed to simplify complex nutrition interventions may introduce a considerable variability, thus limiting between-study comparability and even challenging the plausibility of models results for population health [16]. To the authors’ best knowledge, only one previous review collected and critically appraised analytical models predicting the effects of several nutrition interventions, including reformulation, on intake and health [14]. In the study, reformulation was found to have positive effects on intake and health outcomes; however, due to the broader scope of the authors’ work, detailed considerations of reformulation-specific characteristics were only partially addressed.

Therefore, the objectives of this review are to focus on reformulation studies aimed at reducing the content of harmful nutrients to: i) further explore the impact of reformulation on the intake of target nutrients, as well as on health outcomes and quality of life measures; and ii) critically evaluate the quality of reformulation models, with a focus on the key elements that are specifically relevant to their appraisal.

Methods

A research protocol was previously defined following the PRISMA-P guidelines [17, 18], and registered within the PROSPERO database (ID number CRD42017057341). Studies were reviewed according to the PRISMA guidelines [19] (Additional file 1).

Search strategy

Records published between January 2000 and December 2017 were searched in electronic bibliographic databases including Medline (via Web of Science), ScienceDirect, Embase, Scopus, the Cochrane and Cochrane Public Health Group Specialized Register, and the Centre for Reviews and Dissemination of the University of York. The full search strategy was first defined on Web of Science (Additional file 2), and then adapted to the other databases. Grey literature was retrieved through informal searches on Google, and on the following websites: Opengrey (http://www.opengrey.eu/), WHO, CDC, and the FAO websites.

Selection of studies

Studies were considered eligible if they addressed mandatory or voluntary food reformulation strategies aimed at reducing individual intake of sodium, saturated fatty acids (SFA), trans-fatty acids (TFA), and sugar. For studies addressing sugar intake, all definitions were accepted, including added sugars, total sugars and free sugars. Included reformulation strategies were required to be aimed at healthy individuals without any age restriction, and to target foods commonly available in retail stores. On the contrary, initiatives limited to restaurants or specific settings such as hospitals, schools, and workplace, or interventions targeting fortification and improved intake of “healthy” nutrients were considered out of scope.

The primary outcomes of interest were i) changes in individual intakes of target nutrients; ii) effects on health outcomes, including obesity, incidence of cardiovascular diseases (CVD), type 2 diabetes (T2D), life-years gained or reduced mortality; and iii) changes in health-related quality of life measures, i.e. Quality Adjusted Life Years (QALYs) and Disability Adjusted Life Years (DALYs).

Although the types of studies to be included were not set in advance in the research protocol, a focus was later given on mathematical and statistical models, since a first scoping of the literature showed that modelling studies were by far the most frequent study design to assess the effects of reformulation policies on intake and health outcomes. Focusing on model-specific issues for reformulation interventions was then deemed more relevant than reporting results from a broader set of study designs.

Cost-effectiveness models were included as well. However, cost data were not reported, since such policies are usually cost-saving [20], and the main focus of the present review was to assess the magnitude of the effects on intake and health outcomes, rather than cost-effectiveness. Finally, only studies in English were considered for review.

Data extraction

One reviewer (CF) defined the search strategy and imported the retrieved records in Endnote (ver. X6), whereas two reviewers (CF and FP) independently performed title and abstract screening, full-text analysis and data extraction. At each stage, discrepancies were resolved by discussion.

A standardized, pre-piloted form was used for data extraction including data on: model type; target population; time horizon of the analysis; target country; mandatory/voluntary nature of the policy; target nutrients; data sources used in the models and funding sources.

Even if not explicitly stated, interventions where all products consumed/marketed are reformulated were assumed to be likewise mandatory interventions, as it was assumed that no voluntary policy would achieve such degree of pervasiveness across manufacturers.

The effects of the interventions were collected and reported for each causal step from reduction in intake, through its effects on risk factors levels (e.g. blood pressure), to the impact on clinical outcomes and health related quality of life measures. When reported, both absolute and percentage reductions were collected. To improve between-study comparability, estimates of QALYs or DALYs were reported per 100,000 individuals whenever the total size of the population was reported by the original studies. In addition, salt amounts were converted to sodium amounts using a 1 g/400 mg conversion rate.

In addition, when available, further data were extracted on the technical feasibility of the modelled interventions, and on whether potential behavioral changes among consumers had been considered in the studies.

If modelling results were provided separately by population sub-groups (e.g. age or sex), the weighted mean was calculated across estimates.

Quality assessment of studies

There are no specific tools to assess the quality of modelling studies evaluating population-wide nutrition interventions. Therefore, a self-developed evaluation tool was used, mainly drawing from relevant criteria recommended by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Practice Task Force [21]. The proposed tool identifies 9 criteria to assess both the scientific quality of the studies (model validation, credible data inputs, uncertainty analysis, transparency and reporting quality), and the pertinence to the research question (objective, scope and relevance). The full list of criteria and the evaluation guide are provided in the Additional file 3.

Results

After removing duplicates, the literature search identified 22,907 records from bibliographic databases and other sources. After abstract and title screening, 49 records were analyzed full-text and 33 studies were finally included in the review. Reasons for full-text exclusion were documented and are reported in the flow diagram (Fig. 1).
Fig. 1

flow chart of the literature search process

Study characteristics

Tables 1, 2 and 3 Fig. 2 and the Additional file 4 summarize the main findings and characteristics of the included studies. Overall, studies were heterogeneous in the way interventions and outcomes were modelled and reported, limiting between-study comparisons. As regards the type of nutrient reformulated, 20 studies focused exclusively on sodium reduction (60.6%) [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], followed by 5 studies considering at least 2 different nutrients (15.1%) [8, 42, 43, 44, 45], 5 studies addressing sugar (15.1%) [46, 47, 48, 49, 50] and 3 study addressing fat (9%) [11, 51, 52].
Table 1

Interventions targeting sodium consumption

Author (year)

Study Characteristics

Study Outcomes

Model type

Time horizon

Target foods

Type of intervention(s)

Voluntary or mandatory

Reduction in intake

Reduction in blood pressure (BP)

Life years gained and reduction in the incidence/prevalence of health outcomes

Results on QOL measures

Cogswell et al. (2017) [29]

Mathematical/Statistical

Not modelled

All processed food

↓28% in Na content (Health Canada Benchmarks)

Mandatory

0.61 Na g/day (22%, UI = 0.59–0.63)

Choi et al. (2016) [30]

Micro-simulation

10y

All processed foods

Product reformulated to meet product-specific NSRI criteria extended to all food producers in the US

Mandatory

0.365 (SE = 0.9) Na g/day (10.9%)

Hypertension: 0.97%

All AMI: 2.59%

All strokes: 2.67%

Fatal AMI: 0.36%

Fatal Strokes: 0.23%

Food and Drink Industry Ireland (2016) [26]

Mathematical/Statistical

Not modelled

10 Food macrocategories

Reformulation based on actual FDII voluntary programme

Mandatory extension of existing programme

0.57 Na g/day (17.8%) in adults

Voluntary

0.06 Na g/day (2.3%) in adults

Leroy et al. (2016) [32]

Epidemiological

1y

F&V, bread, meat, fish, sandwiches, sauces

Strong reformulation based on the Choices Programme criteria

Mandatory

12.7% daily Na intake

Fatal CVD/Strokes deaths averted: 422

Cancer deaths averted: 187

Mild reformulation based on the Choices Programme criteria

Mandatory

9.3% daily Na intake

CVD/Strokes and cancer deaths averted: 2408 (3.7%) - due to total reductions in Na, SFA and sugar consumption combined

Masset et al. (2016) [25]

Mathematical/Statistical

Not modelled

Pizza

Reformulation to meet Nestlè Nutrient Profiling targets

Mandatory

0.14 Na g/day (4%)

Nghiem et al. (2016) [42]

Markov

Cohort life-time

All processed foods

59% substitution of NaCl with other salts (K, Mg)

Mandatory

1.82 Na g/day (51.5%)

12,783 QALYs gained/100000 pop. (UI = 10,348–15,609)

25% substitution of NaCl with other salts (K, Mg)

Mandatory

0.77 Na g/day (21.8%)

5261 QALYs gained/100000 pop. (UI = 4230–6391)

Bread

↓38,5% in NaCl content

Mandatory

0.28 Na g/day (7.9%)

1891 QALYs gained/100000 pop. (UI = 1509–2296)

↓11,1% in NaCl content

Mandatory

0.08 Na g/day (2.3%)

678 QALYs gained/100000 pop. (UI = 548–822)

Wilson et al. (2016) [43]

Markov

Cohort life-time

All processed foods (bread, processed meats, sauces, snack food, bakery, cheese)

↓36% in NaCl content across product types

Mandatory

0.628 Na g/day

5304 QALYs gained/100000 pop. (UI = 4270–6478)

Voluntary

Same efficacy with higher uncertainty

5000 QALYs gained/100000 pop. (UI = 3709–6391)

Bread

↓12–37% in NaCl content across bread types

Mandatory

0.043 Na g/day

387 QALYs gained/100000 pop.(UI = 309–470)

Voluntary

Same efficacy with higher uncertainty

365 QALYs gained/100000 pop. (UI = 270–461)

Processed meats

↓35–55% in NaCl content overall

Mandatory

0.069 Na g/day

583 QALYs gained/100000 pop. (UI = 470–704)

Voluntary

Same efficacy with higher uncertainty

552 QALYs gained/100000 pop. (UI = 417–696)

Sauces

↓30–63% in NaCl content across sauces types

Mandatory

0.104 Na g/day

870 QALYs gained/100000 pop. (UI = 700–1057)

Voluntary

Same efficacy with higher uncertainty

822 QALYs gained/100000 pop. (UI = 626–1039)

Combination of bread, processed meats and sauces

Mandatory

0.217 Na g/day

1843 QALYs gained/100000 pop. (UI = 1487–2239)

Voluntary

Same efficacy with higher uncertainty

1743 QALYs gained/100000 pop. (UI = 1326–2204)

Snack food

↓34–48% in NaCl content across snacks types

Mandatory

0.032 Na g/day

265 QALYs gained/100000 pop. (UI = 217–322)

Voluntary

Same efficacy with higher uncertainty

252 QALYs gained/100000 pop. (UI = 191–317)

Bread and bakery products

↓12–37% in NaCl content across bread types; ↓54–63% in NaCl content across other bakery products

Mandatory

0.107 Na g/day

887 QALYs gained/100000 pop. (UI = 722–1078)

Voluntary

Same efficacy with higher uncertainty

843 QALYs gained/100000 pop. (UI = 639–1061)

Cheese

↓27–42% in NaCl content across cheese types

Mandatory

0.045 Na g/day

383 QALYs gained/100000 pop. (UI = 309–461)

Voluntary

Same efficacy with higher uncertainty

361 QALYs gained/100000 pop. (UI = 274–457)

Bruins et al. (2015) [45]

Mathematical/Statistical

Cohort life-time

Soups

↓25% in Na content

Mandatory

0.05 Na g/day

0.11 mmHg

Strokes: 0.49%

6.45 DALYs averted/100000 pop

AMI: 0.34%

Angina: 0.34%

CHF: 0.24%

Dötsch-Klerk et al. (2015) [23]

Mathematical/Statistical

Not modelled

All processed foods

Products reformulated to meet the 6 g/day NaCl consumption target

Mandatory

US: 1.8 Na g/day (23%)

UK: 1.8 Na g/day (27%)

NL: 1.3 Na g/day (19%)

Products reformulated to meet the 5 g/day NaCl consumption target

Mandatory

US: 2.2 Na g/day (28%)

UK: 2.1 Na g/day (32%)

NL: 1.8 Na g/day (26%)

Gillespie et al. (2015) [31]

Epidemiological

10y

All processed foods

↓30% in NaCl

Mandatory

0.58 Na g/day (UI = 0.56–0.60)

0.81 mmHg (UI = 0.53–1.10)

CHD deaths averted or postponed: 4467 (UI = 2854–6147)

↓10% in NaCl

Mandatory

0.19 Na g/day (UI = 0.18–0.20)

0.27 mmHg (UI = 0.18–0.37)

CHD deaths averted or postponed: 1502 (UI = 953–2068)

↓24% in NaCl

Voluntary (applied to 39% of products)

0.19 Na g/day (UI = 0.03–0.63)

0.27 mmHg (UI = 0.04–0.92)

CHD deaths averted or postponed: 1474 (UI = 220–4995)

Hendriksen et al. (2015) [22]

Mathematical/Statistical

Not modelled

Selected foods contributing to high intakes of NaCl

↓50% in NaCl content on average

Mandatory

0.9 Na g/day (37%)

Nghiem et al. (2015) [46]

Markov

Cohort life-time

All processed foods

↓25% in NaCl

Mandatory

0.525 Na g/day (15%)

4783 QALYs gained/100000 pop (UI = 3804–7174)

Breads, processed meats and sauces

↓25% in NaCl

Mandatory

0.296 Na g/day (9%)

2683 QALYs gained/100000 pop (UI = 2161–3256)

Wilcox et al. (2015) [34]

Epidemiological

10y

Not modelled

Not modelled

Mandatory

0.005 Na g/day (10%) (UI = 0.003–0.021)

1.15 mmHg (UI = 0.57–4.58)

CHD Deaths averted: 497 (UI = 130–3032)

LYG: 11192 (UI = 5679–41,039)

Collins et al. (2014) [36]

Epidemiological

10y

Not modelled

↓15% in NaCl content overall

Voluntary

1.21 Na g/day (UI = 0.32–1.94)

LYG: 14593 (UI = 9000–21,049)

↓20% in NaCl content overall

Mandatory

1.62 Na g/day (UI = 0.65–3.11)

LYG: 19365 (UI = 11,967–27,887)

Hendriksen et al. (2014) [47]

Markov

20y (clinical outcomes); cohort life-time (DALYs)

All processed foods

↓50% in NaCl content on average

Mandatory

2.3 Na g/day (28%)

1.5 mmHg (1.2%)

4.4% AMI (UI = 3.1–5.6%)

0.5% DALYs averted in the population (UI = 0.37–0.68%)

CHF: 1.8% (UI = 1.3–2.3%)

Strokes: 6% (UI = 4.1–7.8%)

Increase in life expectancy: 0.7% (UI = 0.5–0.9%)

Mason et al. (2014) [53]

Epidemiological

10y

Not modelled

Not modelled

Mandatory

10% daily Na intake (UI = 5–40%)

Tunisia: LYG 2272 (UI = 1151–3361)

Syria: LYG 11192 (UI = 5679–41,039)

Palestine: LYG 945 (UI = 479–3479)

Turkey: LYG 135221 (UI = 68,816–487,712)

Konfino et al. (2013) [37]

Markov

10y

All processed foods

↓8% in NaCl intake (stepped reduction by 4% for the first 2y)

Mandatory (80% of sodium from processed foods)

0.353 Na g/day

1.00–2.00 mmHg

Total Deaths: 0.61%

Fatal CHD: 0.98%

AMI: 1.48%

Strokes: 0.99%

↓40% in NaCl intake (4% per year for 10y)

Mandatory (80% of sodium from processed foods)

1.763 Na g/day

5.00–9.00 mmHg

Total Deaths: 1.77%

Fatal CHD: 2.63%

AMI: 4.27%

Strokes: 2.79%

Bertram et al. (2012) [38]

Epidemiological

1y

Bread, margarine, gravy, soups

↓54% in NaCl content on average

Mandatory

0.85 Na g/day

Strokes: 8%

CHD: 6.5%

Hypertensive heart disease: 11%

Cobiac et al. (2012) [48]

Markov

Cohort life-time

Bread, margarine, breakfast cereals

Based on Heart Foundation Tick Programme: ↓26% in NaCl content in bread; 11% in margarine and 61% in breakfast cereals

Mandatory

0.009 Na g/day

1451 DALYs averted/100000 pop (UI = 1088–1813)

Combris et al. (2011) [8]

Mathematical/Statistical

Not modelled

Breakfast cereals

Mild to strong reformulation based on food nutrient distribution

Mandatory

0.001–0.013 Na g/day (1.4–13.5%)

Biscuits/ pastries

0.0003–0.002 Na g/day (1.70–10.81%)

Bread-based products

0.0023–0.013 Na g/day (1.60–8.8%)

Cobiac et al. (2010) [49]

Epidemiological

Cohort life-time

Bread, margarine, breakfast cereals

Based on Heart Foundation Tick Programme: ↓26% in NaCl content in bread; 11% in margarine and 61% in breakfast cereals

Voluntary

0.009 Na g/day

5300 DALYs averted (UI = 2600–9200)

Mandatory extension of actual program to all products

110,000 DALYs averted (UI = 53,000–180,000)

Smith-Spangler et al. (2010) [50]

Markov

Cohort life-time

Not modelled

Not modelled

Voluntary

9.5% daily Na intake (UI = 5–40%)

1.25 mmHg

Strokes averted: 513885

2,060,790 DALYs averted

AMI averted: 480538

Roodenburg et al. (2009) [27]

Mathematical/Statistical

Not modelled

All processed foods

Reformulation set to meet Choices Programme criteria

Mandatory

23% daily Na intake (10% adjusting for energy compensation)

Rubinstein et al. (2009) [51]

Markov

Cohort life-time

Bread

↓ to 1 g of NaCl per 100 g of bread

Voluntary

1.33 mmHg

18.7 DALYs averted/100000 pop

Murray et al. (2003) [52]

Markov

Cohort life-time

Not modelled

Not modelled

Mandatory

Assumed 30% Na Intake

AmrB: 3.11% on average

600,000 DALYs averted

EurA: 3.49% on average

1,300,000 DALYs averted

SearD: 3.49% on average

1,000,000 DALYs averted

Voluntary

Assumed 15% Na Intake

AmrB: 1.56% on average

300,000 DALYs averted in the population

EurA: 1.74% on average

700,000 DALYs averted in the population

SearD: 1.75% on average

500,000 DALYs averted in the population

(Abbreviations: AMI – Acute Myocardial Infarction, AmrB – Region of the Americas group B, CHD – Coronary Heart Disease, CHF – Coronary Heart Failure, CVD – Cardiovascular diseases, DALY – Disability Adjusted Life Years, EurA – European Region group A, FDII – Food and Drink Industry Ireland, F&V – fruit and vegetables, K – potassium, LYG – Life Years Gained, Mg – magnesium, Na – sodium, NaCl – Sodium Chloride, NL – Netherlands, NSRI – National Salt Reduction Initiative, QALY – Quality Adjusted Life Year, SearD – Southeast Asian Region group D, UI – Uncertainty Interval, UK – United Kingdom, US – United States of America)

Table 2

– Interventions targeting sugar consumption

Author (year)

Study Characteristics

Study Outcomes

Model type

Time Horizon

Target foods

Type of intervention(s)

Voluntary/mandatory

Reduction in individual intake

Reduction in weight

Reduction in the incidence of clinical outcomes

Briggs et al. (2017) [39]

Epidemiological

1y

High and mid-sugar drinks

↓30% in sugar for high-sugar drinks; 15% for mid-sugar drinks

Mandatory

5.38 sugar g/day (27.5%, UI = 4.19–5.76) - i.e. about 21 Kcal/day

T2D incidence: 31.1 per 100,000 persons (UI = 11–53)

Obesity prevalence: 0.9% (UI = 0.3–19%)

↓5% in sugar content for both high and mid-sugar drinks

Mandatory

0.98 sugar g/day (5%, UI = 0.92–1.05) - i.e. about 3.92 Kcal/day

T2D incidence: 5.8 per 100,000 persons (UI = 2–10)

Obesity prevalence: 0.2% (UI = 0.09–4%)

Yeung et al. (2017) [28]

Mathematical/statistical

Not modelled

Selected foods with at least 5 g of added sugars/100 g

↓10% in added sugar

Mandatory

10.75 Kcal/day (SE = 36, 0.52%) in 2-16y

4.62 added sugar g/day (SE = 2.9, 7.69%) in 2-16y

↓15% in added sugar

Mandatory

16.25 Kcal/day (SE = 55, 0.79%) in 2-16y

6.97 added sugar g/day (SE = 4.47, 11.59%) in 2-16y

↓25% in added sugar

Mandatory

27.24 Kcal/day (SE = 92, 1.34%) in 2-16y

11.73 added sugar g/day (SE = 7.5, 19.5%) in 2-16y

Food and Drink Industry Ireland (2016) [26]

Mathematical/statistical

Not modelled

10 Food macrocategories

Reformulation based on actual FDII voluntary programme

Mandatory extension of existing programme

1.02 sugar g/day (1.12%) in adults

Voluntary

0.27 sugar g/day (0.30%) in adults

Leroy et al. (2016) [32]

Epidemiological

1y

F&V, bread, meat, fish, sandwiches, sauces

Strong reformulation based on the Choices Programme criteria

Mandatory

14.4% daily sugar intake

Fatal CVD/Strokes deaths averted: 421

Cancer deaths averted: 324

Mild reformulation based on the Choices Programme criteria

Mandatory

4.6% daily sugar intake

CVD/Strokes and Cancer deaths averted: 2408 (3.7%) - due to total reductions in Na, SFA and sugar consumption combined

Ma et al. (2016) [40]

Mathematical/statistical

5y

Sugar sweetened beverages (with juices)

↓40% in added sugar content (9.7% per year over 5 years)

Mandatory

38.4 Kcal/day (UI = 36.3–40.7)

1.2 kg (UI = 1.12–1.28)

BMI reduction: 0.42 kg/m2 (1.5%)

Overweight prevalence: 1%

Obesity prevalence: 2.1%

T2D incidence: 274000–309,000 cases averted

Sugar sweetened beverages (without juices)

31.0 Kcal/day (UI = 28.6–33.7)

0.96 kg (UI = 0.88–1.04)

BMI reduction: 0.34 kg/m2(1.2%)

Overweight prevalence: 0.7%

Obesity prevalence: 1.7%

T2D incidence 221,000–250,000 cases averted

Masset et al. (2016) [25]

Mathematical/statistical

Not modelled

Pizza

Reformulation to meet Nestlè Nutrient Profiling targets

Mandatory

0.1 sugar g/day (0.1%)

Combris et al. (2011) [8]

Mathematical/statistical

Not modelled

Breakfast cereals

Mild to strong reformulation based on food nutrient distribution

Mandatory

0.125–0.278 sugar g/day (1.80–4%)

Biscuits/ pastries

0.006–0.068 sugar g/day (0.30–3.5%)

Bread-based products

0.058–0.288 sugar g/day (2.80–13.9%)

Hendriksen et al. (2011) [41]

Mathematical/statistical

Not modelled

Carbonated soft drinks

100% substitution of sugar with intense sweeteners

Mandatory

80.5 Kcal/day

3.55 kg

Obesity prevalence: 4%

BMI reduction: 1.5 kg/m2

Roodenburg et al. (2009) [27]

Mathematical/statistical

Not modelled

All processed foods

Reformulation set to meet Choices Programme criteria

Mandatory

37% daily sugar intake (29% adjusting for energy compensation)

Husøy et al. (2008) [24]

Mathematical/statistical

Not modelled

Carbonated soft drinks

100% substitution of sugar with intense sweeteners

Mandatory

36.5% energy intake

Abbreviations: BMI – Body Mass Index; CVD – Cardiovascular diseases; FDII – Food and Drink Industry Ireland; F&V – fruit and vegetables; SE – Standard Error; T2D – Type 2 diabetes; UI – Uncertainty Interval

Table 3

– Interventions targeting fat consumption

Author (year)

Study Characteristics

Study Outcomes

Model type

Time horizon

Target foods

Type of intervention(s)

Voluntary/mandatory

Reduction in individual intake

Reduction/increase in the incidence of clinical outcomes

Results on QOL measures

Food and Drink Industry Ireland (2016) [26]

Mathematical/statistical

Not modelled

10 Food macrocategories

Reformulation based on actual FDII voluntary programme

Mandatory extension of existing programme

1.67 SFA g/day (5.5%) in adults

Voluntary

0.47 SFA g/day(1.50%) in adults

Leroy et al. (2016) [32]

Epidemiological

1y

F&V, bread, meat, fish, sandwiches, sauces

Strong reformulation based on the Choices Programme criteria

Mandatory

14.8% daily SFA intake

Fatal CVD/Strokes deaths averted: 1339

Cancer deaths averted: 558

Mild reformulation based on the Choices Programme criteria

Mandatory

11.7% daily SFA intake

CVD/Strokes and Cancer: 2408 deaths averted (3.7%) - due to total reductions in Na, SFA and sugar consumption combined

Masset et al. (2016) [25]

Mathematical/statistical

Not modelled

Pizza

Reformulation to meet Nestlè Nutrient Profiling targets

Mandatory

0.3 SFA g/day (1.1%)

Pearson-Stuttard et al. (2016) [33]

Epidemiological

10y

All processed food

↓100% (Total ban) on industrial TFA

Mandatory

↓100% industrial TFA ➔ approx. 0.4% of daily energy intake from ruminant TFA

CHD deaths averted or postponed: 1700 (UI = 1619–1825)

LYG: 15000 (UI: 13952–16,934)

Allen et al. (2015) [44]

Epidemiological

6y

All processed foods

↓100% (Total ban) on industrial TFA

Mandatory

↓100% industrial TFA ➔ approx. 0.4% of daily energy intake from ruminant TFA

CHD deaths averted or postponed: 7200 (UI = 3200–12,500; 2.6%)

7900 QALYs gained (UI = 3000–13,900)

Combris et al. (2011) [8]

Mathematical/statistical

Not modelled

Breakfast cereals

Mild to strong reformulation based on food nutrient distribution

Mandatory

0.032–0.172 fat g/day (1.40–7.5%)

Biscuits/ pastries

0.0162–0.061 fat g/day (1.40–5.30%)

Bread-based products

0.009–0.229 fat g/day (0.40–9.90%)

Temme et al. (2011) [11]

Mathematical/statistical

Not modelled

Potato-products, bread, pastry, cakes and biscuits (excluding foods made with butter); (meat) snacks and salads, fats and margarines

↓ 300% TFA in potato products for frying; 33% in bread; 75% in pastry, cakes and biscuits; 67% for meat snacks and salads

Mandatory

0.4 TFA g/day (21.1%, UI = 0.35–0.45)

Roodenburg et al. (2009) [27]

Mathematical/statistical

Not modelled

All packaged foods

Reformulation set to meet Choices Programme criteria

Mandatory

40% SFA daily intake (32% adjusting for energy compensation)

63% TFA daily intake (58% adjusting for energy compensation)

Abbreviations: CHD – Coronary Heart Disease, CVD – Cardiovascular diseases, FDII – Food and Drink Industry Ireland, F&V – fruit and vegetables, LYG – Life Years Gained, Na – sodium, QALY – Quality Adjusted Life Year, SFA – Saturated Fatty Acids, TFA – Trans Fatty Acids: UI – Uncertainty Interval

Fig. 2

Characteristics of the included studies

Target foods in the included studies were all processed foods (n = 9) [27, 31, 33, 37, 38, 41, 45, 51, 52], selected groups of nutrient-dense foods (n = 8) [11, 26, 34, 35, 36, 43, 44, 50], or single food products (n = 8) [23, 24, 42, 46, 47, 48, 49]. Four studies provided estimates for more than one item [8, 28, 29, 30], whereas in 4 studies target foods were not specified since reformulation was modelled directly through its assumed effect on intake [22, 25, 32, 39]. Ten studies simply estimated changes in intake following reformulation (30.3%) [8, 11, 26, 33, 42, 43, 45, 49, 50, 53], whereas other studies estimated effects on health outcomes (n = 12, 36.4%) [25, 31, 35, 37, 39, 40, 41, 44, 46, 47, 48, 51] or health related quality of life measures (n = 11, 33.3%) [22, 23, 24, 28, 29, 30, 32, 34, 36, 38, 52].

Ten studies used epidemiological models linking changes in nutrients intake to changes in disease incidence and prevalence at an established time [25, 34, 35, 39, 40, 41, 44, 47, 52]. These studies include comparative risk assessment models (e.g. WHO Comparative Risk Assessment, or PRIME) [35, 47], potential impact fraction models [34] and other validated models, including DIETRON [44], and IMPACT models [25, 39, 40, 41, 51, 52]. Ten studies used cohort Markov models [22, 23, 28, 29, 30, 32, 36, 37, 38], or individual level micro-simulations [31]. Finally, 13 records, accounting for almost 40% of the included studies, used mathematical-statistical models to estimate the potential change in intake by linking cross-sectional, individual-level consumption data to the nutrient-density of foods before and after reformulation [8, 11, 24, 26, 33, 42, 43, 45, 46, 48, 49, 50, 53]. In the majority of this last group, time was not explicitly considered, since the analysis was limited to a re-assessment of past individual intakes after reformulating all or a specific set of products. In the remaining studies, the time horizon of the analysis was highly variable: 5 studies considered a time ranging from 1 to 6 years [35, 44, 46, 47, 52], 8 studies between 10 and 20 years [25, 31, 37, 38, 39, 40, 41, 51] and 9 studies considered the average lifetime of the modelled population [22, 23, 24, 28, 29, 30, 32, 34, 36].

Strikingly, 30% of the studies were not clear about what informed the amounts of nutrient reformulated, and whether considerations were made on technical feasibility, or other aspects such as shelf life and palatability.

Most of the studies explored the impact of likewise mandatory policies, i.e. they assumed that all target foods would be reformulated, while only 5 studies performed scenario analysis modelling both voluntary and mandatory reformulation. This was usually done by assuming smaller proportions of reformulated products in the voluntary scenario [34, 41, 43], or by assuming longer implementation times and/or more uncertain effects on intake [22, 29].

Only 2 studies performed scenario analysis to explore the interplay between reformulation policies and consumers’ reactions. Roodenburg et al. tested the results of the model against the possibility that consumers maintained the same caloric intake by eating more food [45], whereas Choi et al. considered whether results were robust to the possibility that consumers might add discretionary salt, or switch to more salty alternatives after introducing low-salt reformulations [31].

Interventions aiming to reduce sodium intake

Reported percentages of sodium reductions in the models varied between 11 and 63% across different products, focusing particularly on bread, sauces and processed meats (Table 1).

Studies reporting absolute reductions in sodium intake showed effects ranging from 0.009 to 1.82 g/day per person, mainly depending on the amount of nutrients reformulated, the spectrum of targeted foods and scenario studied [30, 36]. Overall, a certain degree of consistency was found across studies, with higher percentages of sodium reformulated leading to higher reductions in sodium intake, which in addition tend to be more marked for interventions addressing all processed foods compared to narrower sets of products (Fig. 3, panel A).
Fig. 3

Relationship between amounts reformulated and individual intakes of sodium and sugar. Scatter plot of studies reporting the effects of % reductions in the nutrient content of food on sodium intake (g/day, panel a), and energy intake for sugar reformulations (Kcal/day, panel b)

Studies estimating the effects on health outcomes showed a percentage reduction in CVD-related mortality in a range between 0.6 and 1.7% [31, 37], stroke incidence between 0.5 and 8% [24, 31, 35, 37, 38], and Acute Myocardial infarction (AMI) incidence between 0.3 and 4.4% [24, 31, 37, 38].

Most studies reported the number of deaths averted, life year gains and quality of life measures (QALYs or DALYs) in absolute values. This limited between-study comparisons, since results were sensitive to the time horizon of the analysis, the size of the modelled population, and baseline risk factors, which varied across studies. In the studies for which it was possible to calculate gains per 100,000 population, reductions in the sodium content of foods resulted in an increase between 265 QALYs and 12,783 QALYs [28, 29, 30], or in a reduction between 6.35 and 1452 DALYs [23, 24, 36]. Although based on less studies, results seem to be consistent across models, showing a positive association between sodium reductions and QALYs (DALYs) gained (lost).

Interventions to reduce intake of sugar

Reformulation to reduce sugar intake targeted Sugar Sweetened Beverages (SSBs) alone [46, 47, 48, 49], or other sets of foods ranging from pizza to all processed foods [8, 42, 43, 44, 45, 50] (Tables 2).

Similarly to sodium strategies, although based on fewer studies, reductions in energy intake (Kcal per day) for SSBs are consistent across studies and proportional to the amount of sugar reformulated (Fig. 3, panel B). For example, Briggs et al. estimate that a reduction in the sugar content of SSBs by 5 and 23% would reduce calorie intake by about 4 Kcal and 21 Kcal per day [47]. Likewise, when cutting 10 to 25% of sugar content in selected sugar-dense foods, Yeung et al. estimated a reduction in energy intake from 11 to 27 Kcal per day [50].

Few studies modelled the impact of reformulating sugar on health outcomes. Prevalence of obesity is estimated to be reduced in a range between 0.2 and 4% [46, 47, 48], whereas one study estimated a reduction in type 2 diabetes between 5.8 and 31.1 incident cases per 100,000 persons [47]. A further study estimated that a broader reformulation policy, designed to comply with the Dutch Choices Program for selected processed foods, would yield a reduction in mortality caused by chronic disease between 3.7 and 5.5% [44]. Lastly, no studies were found that estimated the impact of reformulation on health related quality of life measures.

Interventions to reduce intake of fat

Studies addressing reformulation of SFA or TFA are limited. Temme et al., estimated that a broad intervention cutting TFA on a set of products would lower trans-fat consumption by 0.4 g/day (21.1%, UI = 0.35, 0.45) (Table 3) [11]. In addition, two studies estimated that banning all industrial TFA from processed foods would avert 1700 to 7200 deaths and generate a gain of 7900 QALYs [51, 52].

Other interventions modelling broader multi-nutrient reformulations for different target products found percentage reductions in SFA consumption to be in a range between 1.1 and 40% [43, 44, 45] (Table 2). One single study estimated the effect of fat reformulation on mortality, showing that if all producers in the food industry complied with the International Choices Programme [27], SFA consumption would reduce by 15%, which in turn would reduce deaths by 3% (1339 deaths from cardiovascular diseases and 558 deaths from cancer) [44].

Quality of studies

Most of the studies (64%) scored high in at least 50% of the considered assessment elements, whereas 9% of studies had mainly moderate scores and 27% received low scores. Study quality was generally satisfactory in the criteria relating to the description of the problem, the research objective and the scope (91% of the included studies); the transparency and provision of technical documentation (79%); the type of outcomes included (67%). However, studies generally scored poorly when assessing the adequacy of the time horizon (42% low and 27% moderate scores), the management and reporting of uncertainty in the model parameters (30% low, 33% moderate) and the internal and external validation (38% low, 17% moderate). Lastly, only 45% of the studies scored high in face validity that is their results were considered credible and realistic (Fig. 4). Particularly, studies were considered to have high face validity if they duly and credibly took into account all aspects of the decision problem, including the technical feasibility of the reformulation scenarios, and all the causal steps linking the intervention to the outcomes of interests (e.g. the reactions of the consumers and manufacturers to the intervention).
Fig. 4

Quality assessment of the included studies

Discussion

The present systematic review collected and synthetized the results of simulation models estimating the effect of reformulation policies to improve population diets. Most of the studies focused on sodium, which is not unexpected since sodium reduction strategies have been a policy priority for much longer compared to those for sugar and fats. All models predicted positive outcomes; however, between-study comparability was limited, especially for health outcomes and health related quality of life measures. In addition, no studies estimated long-term consequences on QALYs or DALYs for sugar, TFA, SFA, or broader multi-nutrient reformulations. More research is needed to evaluate the potential impact of reformulation strategies in achieving national and international consumption targets, and ultimately improving public health.

In addition, more evidence is required on the relative effectiveness and cost-effectiveness of reformulation compared to alternative public health nutrition interventions (e.g. food taxes, public media campaigns or food labelling).

The effectiveness of reformulation strategies on public health is the result of a complex causal chain that, besides biological factors linking intake to health outcomes, it also includes technical/industrial aspects, marketplace dynamics and consumer reactions. The incorporation of these aspects relates to the face validity of the model, that is defined as the extent to which a model plausibly represents the diseases, settings, populations, interventions, and outcomes it is intended to analyze [21].

For 30% of the included studies, strategies were not clearly informed by aspects related to the technical feasibility of reformulation, including the essential requirements to preserve shelf-life, volume and palatability. However, since the amounts of nutrients substituted in the scenarios directly affect the estimated impact on intake and health, the nature and the intensity of reformulation strategies should always be defined based on real observed reductions from on-going initiatives, or informed by reliable evidence or expert opinions.

In addition, reformulation can alter the sensory attributes of food products and influence consumer liking [54, 55]. This in turn may trigger unattended behaviors including the consumption of more public health sensitive nutrients, or simply more calories. Nonetheless, only two of the included studies explicitly modelled how consumer reactions might affect the policy effects. Choi et al. estimated that the beneficial effects of a mandatory reformulation policy would remain even if the use of discretionary salt increased by 15% [31]. However, such threshold percentage may be lower for less pervasive or voluntary reformulation policies. Roodenburg et al. tested the results of the model against the possibility that consumers maintained the same caloric intake by eating more food [45]. They found that, after this adjustment, differences in intakes for energy, SFA, TFA, sodium and sugar would be smaller. In addition, while there is evidence that consumers gradually adapt to changes in the salt content of foods [56, 57] and that small salt reductions in certain products cannot be detected and do not affect acceptability or consumption [34, 58, 59], this is more controversial for fat [60] and sugar [61].

With respect to marketplace dynamics, unsurprisingly, in the few studies where both mandatory and voluntary interventions were modelled, mandatory scenarios were always found to be more effective. Nevertheless, such differences may partly reflect various assumptions around intervention phase-in periods [29], or the considered rate of adherence from the industry. For example, when allowing for the possibility that consumers switched to non-reformulated products, Choi et al. estimate that significant reductions in CVD mortality would occur only if more than 65% of products in the market met the reduction criteria [31].

Anticipating how food manufacturers and consumers will react to a reformulation initiative are core components of models, so that lack of consideration for these aspects may undermine the credibility of the model results. Since there may be little data on these parameters at the time of the assessment, studies should clearly report and justify the underlying assumptions made in their models, and conduct extensive sensitivity analysis to test the robustness of results at different levels of industry uptake and consumers reactions. In addition, in some cases, real world sources can be used to model more realistic scenarios. A study by Temme et al. assessing the impact on intake of foods reporting a health logo estimated the expected consumption rate of healthy and unhealthy products, by looking at real market shares of products with a healthy logo over the total purchases in each food category [62].

The time horizon considered in reformulation models should be long enough to account for all relevant consequences of the interventions, including the long-term health effects of improved dietary patterns. In addition, many aspects of the reformulation models can vary over time including the industry uptake of reformulated products, consumption habits and preferences, and secular epidemiological trends in non-communicable diseases. In this review, 40% of the studies did not explicitly model the effects of the interventions over time and focused on simulating how cross-sectional nutrient intake data would change if foods were reformulated. Although consistent with their declared research questions, these studies do not estimate how the modelled interventions will dynamically affect food purchases, intake, and ultimately health outcomes. The dynamic nature of public health interventions and the presence of complex, interdependent factors have already been pointed out in the literature [63, 64, 65] calling for more methodological developments, such as the use of system dynamics modelling to incorporate time dependencies [66].

Besides face validity, other standard steps of validation apply to reformulation models [21]. Internal validity should be verified by demonstrating that the model behaves as intended and has been implemented correctly. In addition, whenever possible, the simulated model outcomes should be confronted with real-world event data. External validation involves that the entire model or any of its components are verified by confronting the forecasted estimates with actual event data. For example, epidemiological studies or trial data could be used to verify the correctness of the simulated incidence of non-communicable diseases in the absence of reformulation. In addition, models should prove to be able to correctly predict future outcomes for the specific setting, population and intervention of interest. Therefore, when possible, the results of the model should be compared ex-post with the real-world outcomes, should the modelled intervention be implemented as planned. However, assessing the predictive ability of a model for medium and long term effects such as health and quality of life is challenging, as it would require longitudinal data with a rigorous counterfactual scenario [67]. While this type of evidence is often missing, partial validation could be achieved by assessing the goodness of models in predicting intermediary effects such as intakes or surrogate health outcomes (e.g. hypertension).

External and predictive validation are critical for simulation models as their main purpose is primarily to help decision makers anticipate what will occur after introducing a certain policy [68]. Therefore, models should be transparent on how validation is verified. However, only one third of the studies used previously validated models or reported methods used to validate their own models. A more careful consideration of validation would improve study quality and increase trust and confidence in models to inform policy-making.

This review reported the potential effects of reformulation policies on intake and health and proposed an ad-hoc tool to assess the quality of modelling studies on reformulation. .

Nonetheless, a number of limitations are outlined: studies were very different in the way policy effects were modelled and reported so that the provided ranges of effectiveness should be considered with caution; this heterogeneity is a reflection of the lack of guidelines and standardized methods for the evaluation of public health interventions in general. Methods for the evaluation of healthcare interventions have indeed existed for several years [63, 68, 69, 70, 71, 72], but these have mainly been applied to more narrowly-defined ‘clinical’ interventions, such as drugs, devices and medical procedures [64]. In contrast, approaches in the field of nutrition and public health are not framed by common objectives, shared methods and/or a strong regulatory environment. Therefore, the establishment of an agreed framework specifying best modelling practices is needed to improve the methodological and reporting quality, as well as the comparability of studies evaluating public health interventions in general and nutrition interventions in particular.

Finally, the number of available studies and the described heterogeneity did not allow to perform subgroup analyses to verify whether specific model features or study quality impact their results. Particularly, in this study, we did not assess existing sources of variations in the effectiveness of reformulation and other nutrition interventions between and within countries, including biological, cultural, socio-economic and institutional factors. Future work may explore the contribution of these aspects on each link of the causal chain from intervention to public health outcomes. Explaining cross-country variations may help to understand which factors are favoring or hindering the effectiveness of nutrition policies at the global level, and their role in reducing the burden of non-communicable diseases. Explaining within-country variations may contribute to incorporate equity considerations in public decision making about nutrition policies at the national level.

Conclusions

Reformulation policies have the potential to improve diets and population health. Evidence is stronger for sodium interventions, but far less conclusive for sugar and fat reformulations. Mathematical models are valuable tools to predict policy effects, although comparability is often limited by different study designs, assumptions and reporting quality. More homogeneous designs and assumptions, combined with the validation of model results and extensive scenario analysis to evaluate the relevance of specific policy features, would improve model credibility and provide policy-makers with useful insights to design evidence-based nutrition policies.

Notes

Acknowledgments

Authors thank Dr. Zsuzsa Hutton (Nestlé Research Center, Lausanne, Switzerland) for her support in the organization of the project and for the revision of the final manuscript.

Funding

These findings are the results of work supported by Nestec SA. The views expressed in this paper are those of the authors, and no official endorsement by Nestec SA is intended or should be inferred.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Authors’ contributions

The authors responsibilities were as follows: CF, PD, and GF designed research. CF and FP conducted research and analyzed data; CF wrote the paper; CF, PD, GF and LD had primary responsibility for the final content. All authors have read and approved the final manuscript

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

the authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

40795_2018_263_MOESM1_ESM.docx (21 kb)
Additional file 1: PRISMA checklist. (DOCX 21 kb)
40795_2018_263_MOESM2_ESM.docx (17 kb)
Additional file 2: Search strategy on Medline (via Web of Science). (DOCX 16 kb)
40795_2018_263_MOESM3_ESM.docx (22 kb)
Additional file 3: Quality assessment tool. (DOCX 22 kb)
40795_2018_263_MOESM4_ESM.doc (104 kb)
Additional file 4: Additional study data. (DOC 104 kb)

References

  1. 1.
    Mozaffarian D, Clarke R. Quantitative effects on cardiovascular risk factors and coronary heart disease risk of replacing partially hydrogenated vegetable oils with other fats and oils. Eur J Clin Nutr. 2009;63(Supplement 2):S22–33.  https://doi.org/10.1038/sj.ejcn.1602976.CrossRefGoogle Scholar
  2. 2.
    Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans fatty acids and cardiovascular disease. NEJM. 2006;15:1601–13.  https://doi.org/10.1056/NEJMra054035.CrossRefGoogle Scholar
  3. 3.
    World Health Organisation. Diet, Nutrition and the prevention of chronic diseases. 2003. http://www.who.int/dietphysicalactivity/publications/trs916/en/. Accessed 18 Apr 2016.
  4. 4.
    World Cancer Research Fund / American Institute for Cancer Research. Food, nutrition and the prevention of cancer: a global perspective. Washington, D.C.: AICR; 2007.  https://doi.org/10.1007/s12082-007-0105-4.CrossRefGoogle Scholar
  5. 5.
    Key TJ, Schatzkin A, Willett WC, Allen NE, Spencer EA, Travis RC. Diet, nutrition and the prevention of type 2 diabetes. Public Health Nutr. 2004;7:147–65.  https://doi.org/10.1079/PHN2003586.CrossRefGoogle Scholar
  6. 6.
    He FJ, MacGregor GA, Boon C. A comprehensive review on salt and health and current experience of worldwide salt reduction programmes. J Hum Hypertens. 2009;23:363–84.CrossRefGoogle Scholar
  7. 7.
    Public Health England. Sugar reduction: Achieving the 20% - GOV.UK. London; 2017. https://www.gov.uk/government/publications/sugar-reduction-achieving-the-20. Accessed 24 May 2017.
  8. 8.
    Combris P, Goglia R, Henini M, Soler LG, Spiteri M. Improvement of the nutritional quality of foods as a public health tool. Public Health. 2011;125:717–24.  https://doi.org/10.1016/j.puhe.2011.07.004.CrossRefGoogle Scholar
  9. 9.
    Griffith R, O’Connell M, Smith K. The importance of product reformulation versus consumer choice in improving diet quality. Economica. 2017;84:34–53.  https://doi.org/10.1111/ecca.12192.CrossRefGoogle Scholar
  10. 10.
    Scott C, Hawkins B. Knai C. Soc Sci Med: Food and beverage product reformulation as a corporate political strategy; 2017.Google Scholar
  11. 11.
    Temme EHM, Millenaar IL, Van Donkersgoed G, Westenbrink S. Impact of fatty acid food reformulations on intake of Dutch young adults. Acta Cardiol. 2011;66:721–8.  https://doi.org/10.2143/AC.66.6.2136955.CrossRefGoogle Scholar
  12. 12.
    Webster J, Trieu K, Dunford E, Hawkes C. Target salt 2025: a global overview of national programs to encourage the food industry to reduce salt in foods. Nutrients. 2014;6:3274–87.  https://doi.org/10.3390/nu6083274.CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Public Health England. Guidelines on reducing sugar in food published for industry - GOV.UK. 2017. https://www.gov.uk/government/news/guidelines-on-reducing-sugar-in-food-published-for-industry. Accessed 28 May 2017.
  14. 14.
    Grieger JA, Johnson BJ, Wycherley TP, Golley RK. Evaluation of simulation models that estimate the effect of dietary strategies on nutritional intake: a systematic review. J Nutr. 2017;147:908–31.  https://doi.org/10.3945/jn.116.245027.CrossRefGoogle Scholar
  15. 15.
    Mcdaid D, Sassi F, Merkur S, Sassi F, Mcdaid D. Promoting health, preventing disease: is there an economic case? Policy summary 6. 2013. http://www.euro.who.int/__data/assets/pdf_file/0004/235966/e96956.pdf. Accessed 5 Sep 2017.
  16. 16.
    Buxton MJ, Drummond MF, Van Hout BA, Prince RL, Sheldon TA, Szucs T, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ. 1997;6:217–27.  https://doi.org/10.1002/(SICI)1099-1050(199705)6:3<217::AID-HEC267>3.0.CO;2-W.CrossRefGoogle Scholar
  17. 17.
    Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647.CrossRefGoogle Scholar
  18. 18.
    Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.  https://doi.org/10.1186/2046-4053-4-1.CrossRefPubMedCentralPubMedGoogle Scholar
  19. 19.
    Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement J Clin Epidemiol. 2009;62:1006–12.Google Scholar
  20. 20.
    Hope SF, Webster J, Trieu K, Pillay A, Ieremia M, Bell C, et al. A systematic review of economic evaluations of population-based sodium reduction interventions. PLoS One. 2017;12:e0173600.  https://doi.org/10.1371/journal.pone.0173600.CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Jaime Caro J, Eddy DM, Kan H, Kaltz C, Patel B, Eldessouki R, et al. Questionnaire to assess relevance and credibility of modeling studies for informing health care decision making: an ISPOR-AMCP-NPC good practice task force report. Value Heal. 2014;17:174–82.  https://doi.org/10.1016/j.jval.2014.01.003.CrossRefGoogle Scholar
  22. 22.
    Murray CJ, Lauer JA, Hutubessy RC, Niessen L, Tomijima N, Rodgers A, et al. Effectiveness and costs of interventions to lower systolic blood pressure and cholesterol: a global and regional analysis on reduction of cardiovascular-disease risk. Lancet. 2003;361:717–25.  https://doi.org/10.1016/S0140-6736(03)12655-4.CrossRefGoogle Scholar
  23. 23.
    Rubinstein A, García Martí S, Souto A, Ferrante D, Augustovski F. Generalized cost-effectiveness analysis of a package of interventions to reduce cardiovascular disease in Buenos Aires. Argentina Cost Eff Resour Alloc. 2009;7:10.  https://doi.org/10.1186/1478-7547-7-10.CrossRefGoogle Scholar
  24. 24.
    Bruins MJ, Dötsch-Klerk M, Matthee J, Kearney M, van Elk K, Weber P, et al. A modelling approach to estimate the impact of sodium reduction in soups on cardiovascular health in the Netherlands. Nutrients. 2015;7:8010–9.  https://doi.org/10.3390/nu7095375.CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Wilcox ML, Mason H, Fouad FM, Rastam S, al Ali R, Page TF, et al. Cost-effectiveness analysis of salt reduction policies to reduce coronary heart disease in Syria, 2010–2020. Int J Public Health. 2015;60(Suppl 1):S23–30.  https://doi.org/10.1007/s00038-014-0577-3.CrossRefGoogle Scholar
  26. 26.
    Hendriksen MAH, Verkaik-Kloosterman J, Noort MW, van Raaij JMA. Nutritional impact of sodium reduction strategies on sodium intake from processed foods. Eur J Clin Nutr. 2015;69:805–10.  https://doi.org/10.1038/ejcn.2015.15.CrossRefPubMedCentralPubMedGoogle Scholar
  27. 27.
    Dötsch-Klerk M, Jansen L. The choices programme: a simple, front-of-pack stamp making healthy choices easy. Asia Pac J Clin Nutr. 2008;17(SUPPL. 1):383–6.Google Scholar
  28. 28.
    Nghiem N, Blakely T, Cobiac LJ, Pearson AL, Wilson N. Health and economic impacts of eight different dietary salt reduction interventions. PLoS One. 2015;10:e0123915.  https://doi.org/10.1371/journal.pone.0123915.CrossRefPubMedCentralPubMedGoogle Scholar
  29. 29.
    Wilson N, Nghiem N, Eyles H, Mhurchu CN, Shields E, Cobiac LJ, et al. Modeling health gains and cost savings for ten dietary salt reduction targets. Nutr J. 2016;15:44.  https://doi.org/10.1186/s12937-016-0161-1.CrossRefPubMedCentralPubMedGoogle Scholar
  30. 30.
    Nghiem N, Blakely T, Cobiac LJ, Cleghorn CL, Wilson N. The health gains and cost savings of dietary salt reduction interventions, with equity and age distributional aspects. BMC Public Health. 2016;16:423.  https://doi.org/10.1186/s12889-016-3102-1.CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Choi SE, Brandeau ML, Basu S. Expansion of the National Salt Reduction Initiative: a mathematical model of benefits and risks of population-level sodium reduction. Med Decis Mak. 2016;36:72–85.  https://doi.org/10.1177/0272989X15583846.CrossRefGoogle Scholar
  32. 32.
    Smith-Spangler CM, Juusola JL, Enns EA, Owens DK, Garber AM. Population strategies to decrease sodium intake and the burden of cardiovascular disease. Ann Intern Med. 2010;152:481.  https://doi.org/10.7326/0003-4819-152-8-201004200-00212.CrossRefGoogle Scholar
  33. 33.
    Cogswell ME, Patel SM, Yuan K, Gillespie C, Juan W, Curtis CJ, et al. Modeled changes in US sodium intake from reducing sodium concentrations of commercially processed and prepared foods to meet voluntary standards established in North America: NHANES. Am J Clin Nutr. 2017;106:530–40.CrossRefGoogle Scholar
  34. 34.
    Cobiac LJ, Vos T, Veerman JL. Cost-effectiveness of interventions to reduce dietary salt intake. Heart. 2010;96:1920-1925.  https://doi.org/10.1136/hrt.2010.199240
  35. 35.
    Bertram, MY, Steyn, K, Wentzel-Viljoen, E, Tollman, S, Hofman, KJ. Reducing the sodium content of high-salt foods: effect on cardiovascular disease in South Africa. South African Medical Journal. 2012;102(9):743-745.Google Scholar
  36. 36.
    Cobiac LJ, Magnus A, Lim S, Barendregt JJ, Carter R, Vos T. Which interventions offer best value for money in primary prevention of cardiovascular disease? PLoS One. 2012;7:e41842.  https://doi.org/10.1371/journal.pone.0041842.CrossRefPubMedCentralPubMedGoogle Scholar
  37. 37.
    Konfino J, Mekonnen TA, Coxson PG, Ferrante D, Bibbins-Domingo K. Projected impact of a sodium consumption reduction initiative in Argentina: an analysis from the CVD policy model – Argentina. PLoS One. 2013;8:e73824.  https://doi.org/10.1371/journal.pone.0073824.CrossRefPubMedCentralPubMedGoogle Scholar
  38. 38.
    Hendriksen MA, Hoogenveen RT, Hoekstra J, Geleijnse JM, Boshuizen HC, van Raaij JM. Potential effect of salt reduction in processed foods on health. Am J Clin Nutr. 2014;99:446–53.  https://doi.org/10.3945/ajcn.113.062018.CrossRefGoogle Scholar
  39. 39.
    Mason H, Shoaibi A, Ghandour R, O’Flaherty M, Capewell S, Khatib R, et al. A cost effectiveness analysis of salt reduction policies to reduce coronary heart disease in four eastern Mediterranean countries. PLoS One. 2014;9:e84445.  https://doi.org/10.1371/journal.pone.0084445.CrossRefPubMedCentralPubMedGoogle Scholar
  40. 40.
    Collins M, Mason H, O’Flaherty M, Guzman-Castillo M, Critchley J, Capewell S. An economic evaluation of salt reduction policies to reduce coronary heart disease in England: a policy modeling study. Value Health. 2014;17:517–24.  https://doi.org/10.1016/j.jval.2014.03.1722.CrossRefGoogle Scholar
  41. 41.
    Gillespie DOS, Allen K, Guzman-Castillo M, Bandosz P, Moreira P, McGill R, et al. The health equity and effectiveness of policy options to reduce dietary salt intake in England: policy forecast. PLoS One. 2015;10:e0127927.  https://doi.org/10.1371/journal.pone.0127927.CrossRefPubMedCentralPubMedGoogle Scholar
  42. 42.
    Masset G, Mathias KC, Vlassopoulos A, Mölenberg F, Lehmann U, Gibney M, et al. Modeled dietary impact of pizza reformulations in US children and adolescents. PLoS One. 2016;11:e0164197.  https://doi.org/10.1371/journal.pone.0164197.CrossRefPubMedCentralPubMedGoogle Scholar
  43. 43.
  44. 44.
    Leroy P, Réquillart V, Soler L-G, Enderli G. An assessment of the potential health impacts of food reformulation. Eur J Clin Nutr. 2016;70:694–9.  https://doi.org/10.1038/ejcn.2015.201.CrossRefGoogle Scholar
  45. 45.
    Roodenburg AJC, Temme EHM, Davies OH, Seidell JC. Potential impact of the choices Programme on nutrient intakes in the Dutch population. Nutr Bull. 2009;34:318–23.  https://doi.org/10.1111/j.1467-3010.2009.01767.x.CrossRefGoogle Scholar
  46. 46.
    Ma Y, He FJ, Yin Y, Hashem KM, MacGregor GA. Gradual reduction of sugar in soft drinks without substitution as a strategy to reduce overweight, obesity, and type 2 diabetes: a modelling study. lancet Diabetes Endocrinol. 2016;4:105–14.  https://doi.org/10.1016/S2213-8587(15)00477-5.CrossRefGoogle Scholar
  47. 47.
    Briggs ADM, Mytton OT, Kehlbacher A, Tiffin R, Elhussein A, Rayner M, et al. Health impact assessment of the UK soft drinks industry levy: a comparative risk assessment modelling study. Lancet Public Heal. 2017;2:e15–22.  https://doi.org/10.1016/S2468-2667(16)30037-8.CrossRefGoogle Scholar
  48. 48.
    Hendriksen MA, Tijhuis MJ, Fransen HP, Verhagen H, Hoekstra J. Impact of substituting added sugar in carbonated soft drinks by intense sweeteners in young adults in the Netherlands: example of a benefit-risk approach. Eur J Nutr. 2011;50:41–51.  https://doi.org/10.1007/s00394-010-0113-z.CrossRefGoogle Scholar
  49. 49.
    Husøy T, Mangschou B, Fotland TØ, Kolset SO, Nøtvik Jakobsen H, Tømmerberg I, et al. Reducing added sugar intake in Norway by replacing sugar sweetened beverages with beverages containing intense sweeteners - a risk benefit assessment. Food Chem Toxicol. 2008;46:3099–105.  https://doi.org/10.1016/j.fct.2008.06.013.CrossRefGoogle Scholar
  50. 50.
    Yeung CHC, Gohil P, Rangan AM, Flood VM, Arcot J, Gill TP, et al. Modelling of the impact of universal added sugar reduction through food reformulation. Sci Rep. 2017;7:17392.CrossRefPubMedGoogle Scholar
  51. 51.
    Pearson-Stuttard J, Hooton W, Critchley J, Capewell S, Collins M, Mason H, et al. Cost-effectiveness analysis of eliminating industrial and all trans fats in England and Wales: modelling study. J Public Health (Bangkok). 2016;39:574–82.Google Scholar
  52. 52.
    Allen K, Pearson-Stuttard J, Hooton W, Diggle P, Capewell S, O’Flaherty M. Potential of trans fats policies to reduce socioeconomic inequalities in mortality from coronary heart disease in England: cost effectiveness modelling study. BMJ. 2015;351:h4583.CrossRefPubMedGoogle Scholar
  53. 53.
    Dötsch-Klerk M, Goossens WP, Meijer GW, van het Hof KH. Reducing salt in food; setting product-specific criteria aiming at a salt intake of 5 g per day. Eur J Clin Nutr. 2015;69:799–804.  https://doi.org/10.1038/ejcn.2015.5.CrossRefPubMedCentralPubMedGoogle Scholar
  54. 54.
    Chollet M, Gille D, Schmid A, Walther B, Piccinali P. Acceptance of sugar reduction in flavored yogurt. J Dairy Sci. 2013;96:5501–11.  https://doi.org/10.3168/jds.2013-6610.CrossRefGoogle Scholar
  55. 55.
    Hoppu U, Hopia A, Pohjanheimo T, Rotola-Pukkila M, Mäkinen S, Pihlanto A, et al. Effect of salt reduction on consumer acceptance and sensory quality of food. Foods. 2017;6:103.  https://doi.org/10.3390/foods6120103.CrossRefPubMedCentralPubMedGoogle Scholar
  56. 56.
    Bertino M, Beauchamp GK, Engelman K. Long-term reduction in dietary sodium alters the taste of salt. The American journal of clinical nutrition. 1982;36(6):1134-1144.  https://doi.org/10.1093/ajcn/36.6.1134
  57. 57.
    Blais CA, Pangborn RM, Borhani NO, Ferrell MF, Prineas RJ, Laing B. Effect of dietary sodium restriction on taste responses to sodium chloride: a longitudinal study. The American journal of clinical nutrition. 1986;44(2):232-243.  https://doi.org/10.1093/ajcn/44.2.232
  58. 58.
    Girgis S, Neal B, Prescott J, Prendergast J, Dumbrell S, Turner C, et al. A one-quarter reduction in the salt content of bread can be made without detection. Eur J Clin Nutr. 2003;57:616–20.  https://doi.org/10.1038/sj.ejcn.1601583.CrossRefGoogle Scholar
  59. 59.
    Rodgers A, Neal B, Engelman K, Wills R. Less salt does not necessarily mean less taste. Lancet (London, England). 1999;353:1332. doi: https://doi.org/10.1016/S0140-6736(99)01198-8.
  60. 60.
    Mytton O, Gray A, Rayner M, Rutter H. Could targeted food taxes improve health? J Epidemiol Community Health. 2007;61:689–94.  https://doi.org/10.1136/jech.2006.047746.CrossRefPubMedCentralPubMedGoogle Scholar
  61. 61.
    Markey O, Lovegrove JA, Methven L. Sensory profiles and consumer acceptability of a range of sugar-reduced products on the UK market. Food Res Int. 2015;72:133–9.  https://doi.org/10.1016/j.foodres.2015.03.012.CrossRefGoogle Scholar
  62. 62.
    Temme EH, van der Voet H, Roodenburg AJ, Bulder A, van Donkersgoed G, van Klaveren J. Impact of foods with health logo on saturated fat, sodium and sugar intake of young Dutch adults. Public health nutrition. 2011;14(4):635-644.  https://doi.org/10.1017/S1368980010002089
  63. 63.
    Jaime Caro J, Briggs AH, Siebert U, Kuntz KM. Modeling Good Research Practices—Overview: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1 on Behalf of the ISPOR-SMDM Modeling Good Research Practices Task Force.  https://doi.org/10.1016/j.jval.2012.06.012.
  64. 64.
    Weatherly H, Drummond M, Claxton K, Cookson R, Ferguson B, Godfrey C, et al. Methods for assessing the cost-effectiveness of public health interventions: Key challenges and recommendations. Health Policy (New York). 2009;93:85–92.  https://doi.org/10.1016/j.healthpol.2009.07.012.CrossRefGoogle Scholar
  65. 65.
    Kelly AMP, Mcdaid D, Ludbrook A, Powell J. Economic appraisal of public health interventions. Development. 2004:1–8. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.3820&rep=rep1&type=pdf.
  66. 66.
    Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. Am J Public Health. 2006.Google Scholar
  67. 67.
    Mazzocchi M. Ex-post evidence on the effectiveness of policies targeted at promoting healthier diets. 2017. http://www.fao.org/3/a-i8191e.pdf.Google Scholar
  68. 68.
    Eddy DM, Hollingwoth W, Jaime Caro J, Tsevat J, McDonald KM, Wong JB. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task Force-7. Value Heal. 2012;15:843–50.CrossRefGoogle Scholar
  69. 69.
    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D. Consolidated health economic evaluation reporting standards (CHEERS)--explanation and elaboration: a report of the ISPOR health economic evaluations publication guidelines task force. Value Heal. 2013;16:231–50.CrossRefGoogle Scholar
  70. 70.
    Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ: British Medical Journal. 1996;313(7052):275.Google Scholar
  71. 71.
    Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th ed. Oxford: Oxford University Press; 2015.Google Scholar
  72. 72.
    Evers S, Goossens M, de Vet H, van Tulder M, Ament A. Criteria list for assessment of methodological quality of economic evaluations: Consensus on Health Economic Criteria. Int J Technol Assess Health Care. 2005;21:240–5.  https://doi.org/10.1017/S0266462305050324.CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019
corrected publication 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.CeRGAS (Centre for Research on Health and Social Care Management), SDA Bocconi School of ManagementMilanItaly
  2. 2.Nestlé Research Center, Nestec SALausanneSwitzerland
  3. 3.Department of Policy Analysis and Public ManagementBocconi UniversityMilanItaly

Personalised recommendations