Policy insights from the nutritional food market transformation model: the case of obesity prevention.

Ann. N.Y. Acad. Sci. ISSN 0077-8923

A N N A L S O F T H E N E W Y O R K A C A D E M Y O F SC I E N C E S Issue: Paths of Convergence for Agriculture, Health, and Wealth

Policy insights from the nutritional food market transformation model: the case of obesity prevention Jeroen Struben, Derek Chan, and Laurette Dube Desautels Faculty of Management, McGill University, Montreal, Quebec, Canada Address for correspondence: Jeroen Struben, Desautels Faculty of Management, McGill University, 1001 Sherbrooke Street W, Montreal, Quebec H3A1G5, Canada. [email protected]

This paper presents a system dynamics policy model of nutritional food market transformation, tracing over-time interactions between the nutritional quality of supply, consumer food choice, population health, and governmental policy. Applied to the Canadian context and with body mass index as the primary outcome, we examine policy portfolios for obesity prevention, including (1) industry self-regulation efforts, (2) health- and nutrition-sensitive governmental policy, and (3) efforts to foster health- and nutrition-sensitive innovation. This work provides novel theoretical and practical insights on drivers of nutritional market transformations, highlighting the importance of integrative policy portfolios to simultaneously shift food demand and supply for successful and self-sustaining nutrition and health sensitivity. We discuss model extensions for deeper and more comprehensive linkages of nutritional food market transformation with supply, demand, and policy in agrifood and health/health care. These aim toward system design and policy that can proactively, and with greater impact, scale, and resilience, address single as well as double malnutrition in varying country settings. Keywords: overnutrition; obesity; market formation; agrifood innovation; system dynamics

Introduction Food lies at the core of the Gordian knot linking agriculture, industry, and health systems, binding many developed countries into states of overnutrition, with obesity, diabetes, and other noncommunicable diseases (NCDs)a having reached epidemic proportions; and inflicting an increasing number of emerging economies with the double burden of under- and overnutrition.1,2 Concerns about the complex, dynamic nature of these challenges have stimulated a proliferation of policies, programs, and investments focused on addressing nutrition burdens.3 In the case of overnutrition and its obesity consequences— the focus of this paper—results fall short of expectations, despite intensifying efforts aimed at their prevention and control by governments, industry, and nongovernmental organizations.3,4

a

The online supplement (Appendix I in Supporting Information) includes a list of acronyms.

Most initiatives to reduce dietary fat, sugar, salt, or caloric content remain fragmented, failing to account for and capitalize on the multiple and pervasive interdependencies and long-term character of food market shifts.3,5–7 High-leverage policies must overcome entrenched modern behavioral and social patterns misaligned with a human biology that evolved under dramatically different environmental conditions.8 These patterns, such as food markets that reward motivational value—taste, affordability, availability, and palatability—at the expense of nutritional value, have been historically produced jointly by distributed actors and by the commercial, social, cultural, and political institutions in which their actions are embedded.7,8 Under such dynamic complexity, decision making and coordinated action for policy need grounding in systems sciences.9,10 Although the problem of overnutrition and its health consequences have been well researched from various angles,11–13 the role of diverse interactions within the food market system are

doi: 10.1111/nyas.12381 C 2014 New York Academy of Sciences. Ann. N.Y. Acad. Sci. xxxx (2014) 1–19

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Nutritional market transformation model

much less understood.10 Following recent calls for systems science and integrative approaches to public health14–19 and an increasing number of applications ranging from NCDs,20 obesity,21 communicable diseases,22,23 healthcare systems,24,25 and undernutrition,26 we report the early-stage development of the nutritional market transformation (NMT) policy model. The NMT policy model is a dynamic computational model developed to examine how, over time, interactions between food value chains, consumer demand, and governmental interventions affect the transformation of nutritious food portfolios, and to identify high-leverage policies to address malnutrition. In this paper, we examine the problem of shifting markets away from overnutrition, and its obesity consequences, applied to the Canadian context, and focused on policy coordination and innovation along the value chain. Innovation efforts, within areas ranging from agriculture to food retailing, may for instance improve natural nutritious agricultural products, such as fruits, vegetables, or pulses, by improved postharvest handling, packaging, processing, or marketing, doing so at an affordable price point. Or they may aim to reduce the content of sugar, fats, and calories in processed food while retaining taste, affordability, and palatability. Since innovation lies at the root of economic growth, aggressive reallocation of resources for mainstreaming nutrition, health, and sustainability may benefit virtually all actors in the long run.27,28 However, shifting food market toward higher nutritional quality requires overcoming logistic, social, economic, and political barriers created by interdependencies among consumers, producers, retailers, and policymakers.6,10,29 For example, absent consumer demand, private sector actors perceive funding of research and development (R&D), services, supplier networks, and communications for transformation of product portfolios as high risk. On the other hand, consumers, sensitive to product characteristics but also subject to social influence, are unlikely to rapidly alter existing consumption patterns without being familiar with alternatives. Our analysis explores conundrums of successfully transitioning to food portfolios of higher nutritional quality, by highlighting how supply, demand, and governmental policy endogenously evolve and collectively shape the motivational and nutritional quality of food portfolios. Comparing the status 2

quo in industry and sectoral governmental practice with scenarios that increasingly involve coordinated initiatives—with and without innovation-fostering efforts—we find that altering nutrition pathways is possible. However, success requires governmental and industry alignment, with the most significant synergies achieved when both government and industry foster health- and nutrition-promoting innovation. Moreover, strongly coordinated action reduces the burden of upfront commitments for all parties, reducing the likelihood of shirking, and facilitating a self-fulfilling path of nutritious transformation. In the remainder, we explain the systems science approach and provide an overview of the NMT policy model. We then discuss the scenarios analyzed and present our results. We conclude by discussing future directions for the NMT model within and beyond the scope of overnutrition and its obesity consequences. The NMT model Aiming to facilitate effective action informed by reliable knowledge that crosses disciplinary boundaries and deployed by multiple stakeholders, we grounded the NMT model in the system dynamics (SD) method.9 In line with this approach, the model places actors’ interdependencies and their mental models and behaviors front and center, and highlights dynamics conditioned by multiple feedbacks, time delays, accumulations, and nonlinearities. First, a large model scope, with actors from different sectors and variables from sociobehavioral, physical/material, and economic realms, permits examination of a variety of specific policies. Second, a diagrammatic representation of the major feedback structures and stocks in the system facilitates exploring and hypothesizing about the dynamics of the system. Further, formalization and calibration consistent with theories and empirical evidence and simulation in computer software allow analysis of the underlying cause-and-effect behavior and a number of policies and their interactions. This SD approach is particularly powerful for developing policy insights into socioeconomic problems of high dynamic complexity and poorly understood underlying structure,30–32 characteristic of public health problems.14,33 Existing SD applications to public policy problems are many,31 and include public health,20,24,33,34 climate change, and energy transitions.35–38

C 2014 New York Academy of Sciences. Ann. N.Y. Acad. Sci. xxxx (2014) 1–19

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Figure 1. Scope and main sectors of the model.

The remainder of this section discusses the model scope, its core feedback structure, and associated dynamic behavior, and highlights the key formalizations and parameterizations.b

Model scope The NMT model comprises four main sectors: demand, population health, supply, and government (Fig. 1). The model is designed to analyze how supply, demand, and governmental policy collectively influence population health and dynamically shape the composition of consumed food portfolios and their nutritional quality (Fig. 1, top center). The model differentiates food portfolios with respect to nutritional quality and motivational quality. Motivational quality derives from three attributes: price,

b

The online supplement defines key system dynamics and simulation terms (Appendix II in Supporting Information), explains central system dynamics concepts used (Appendix III in Supporting Information), and, to allow replication, provides model equations and parameters not discussed in the main document (Appendix IV in Supporting Information).

taste, and availability (including assortment size, product variety, shelf space, presentation, and visibility of packaging).39 To make salient the historical trade-off between nutritional and motivational quality of food, the model represents the distribution of food in the market as two intersubstitutable product categories comprising food portfolios of respectively low and high nutritional quality (LN vs. HN). While the HN category is superior in nutritional quality, the LN category is favorable with respect to the motivational quality–related attributes. The population health sector (Fig. 1, top right) traces the population-level body mass index (BMI) to capture health risk.c,40–42 Health risk is linked to food consumption through the nutritional quality of consumed food, which we operationalize as the (the inverse of) caloric density per serving; the effect of consumers’ caloric intake on weight affects BMI.c,43 Caloric consumption may change

c

By focusing on caloric intake, we assume the simple etiology of obesity solely involving excess stored energy and ignore the potential important role of metabolic


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over time, as caloric density within, and consumers’ choice between, categories endogenously evolves through actions in the demand, supply, and government sectors. The demand sector (Fig. 1, top left) captures changes in consumer choice between products from the LN and HN categories. Consumer choice depends on the state of underlying product attributes, but also on sociobehavioral influences, such as marketing, social norms, peer pressure, and habituation. The supply sector (Fig. 1, bottom left) models the representative firm behavior at the level of competing value chains, embodying the major producers and supermarkets that provide 90% of consumed food.44 In the model, firms allocate resources between categories and improvement of attributes. Firms may therefore also undertake individual or coordinated initiatives to improve the nutritional quality of their product offerings, but do this within a competitive environment. Finally, within the government sector (Fig. 1, bottom right), health- and innovation-oriented policies are undertaken that affect supply-related resource allocation or economic or social factors of consumer choice with respect to HN food products. We apply the model to the Canadian context, calibrating it using multiple data and literature sources (discussed below). To concentrate on the conceptual insights, and avoiding data constraints in market sectors, we capture the food market at the national level, while segmenting the population health sector by gender and age.d

Model feedback structure Figure 2 shows a high-level diagram capturing the major feedbacks (thick lines) and stock-and-flow structures (boxes and valves) of the model. (Appendix II, in Supporting Information, provides an additional explanation of the SD concepts used.)

processes.8,43 By focusing on thermodynamic drivers of overnutrition, we ignore population health risk–related consumption (and nonconsumption) behaviors, including, for example, diversity of nutrition and different forms of activity.8 d The full model allows inclusion of multiple nested food categories as well as population segmentation by region and can therefore be used to examine how heterogeneity affects findings from aggregate treatments (see Discussion section).

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Subscripts c, f, d, and l indicate disaggregation of variables, also shown for the stocks as multilayering. First, a category’s food consumption by population segment d, involving gender ࢠ[female, male] and age ࢠ[0–4, 5–12, 13–18, 19+], depends on its market share and on the number and size of daily servings. Category market share is a function of the utility that consumers derive from related products, but also of the population-level propensity to consider (PtC) the category. Consumers consider a category only when sufficiently familiar with it. Constrained by limited information-processing capacity and aspirations to objectively evaluate all options,45 consumer attention is guided by different channels of influence: by firms’ marketing (efforts to shape the category and brands); by social exposure (word of mouth, media attention, social norms, and peer pressure);46,47 and by individual habituation, sensitization, and brand loyalty.48–50 Switching inertia between categories and brands that result from accumulated exposure has been observed in relevant contexts of food brands,50 and nutrition choice and obesity.51–54 As PtC builds, consumption of the category grows, providing further exposure and increase in PtC (reinforcing feedback R1, social exposure),55 until saturation is reached (balancing feedback, B1). Two other major feedback loops involve interactions between the supply and demand sectors. First, the utility that consumers derive from consuming food is a function of the states of its attributes, l ࢠ {price, taste, availability, nutritional quality}, each improving as firms accumulate attribute-specific capabilities. Such capabilities grow with sales through learning by doing and R&D (R2a, learning and R&D). Similarly, firms improve sales directly by reinvesting in marketing efforts (R2b, marketing). Second, firms monitor the market, aspiring to direct a larger share of their reinvested resources toward categories and attributes they believe yield higher returns on investment. Increased food market shares (and, with that, PtC), resulting from actual within-category improvements further strengthen returns on investment (R3a, market returns). The strength of this feedback depends on the (relative) attribute and food category characteristics, but is also suppressed by a balancing feedback of market saturation (B2a). Finally, while resources are adjusted in proportion to current budgets (R3b), diminishing returns in attribute-level capability improvement exert a balancing force (B3b, diminishing returns). The


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Figure 2. Major feedback loops in the model.

presence of strong positive supply-and-demand feedbacks within the market system highlights the challenge in building alternative categories with low initial PtC and firm capabilities. Such challenges are considerable, given the competitive pressures and inertia. Aggravating this problem are distinct properties of HN and LN food and the historical tradeoff between motivational and nutritional quality attributes. We now discuss how we operationalized these issues into the formal model, discussing main equations and parameterizations. (Appendix IV, in Supporting Information, provides complementary details.)

high and low nutritional value, and involving both snack and meal products.e Category-specific calories per serving were then derived using the 2010 Canadian Nutrient File.56 Market share of category c by firms f within population segment d depends on consumers’ relative affinityAcfd with category-related products ␴cfd = Acfd / c f Ac f d . To capture the influence of individual, social, and category-branding exposure, intrinsic utility-based consumer affinity Aicfd is

e

Formal model We differentiated the HN and LN categories by setting the initial variation of caloric content per serving between them using food items characteristic of

For example, we included a bag of salt-and-pepper– flavored potato chips (low-nutrition snack); a bag of baby carrots with hummus (high-nutrition snack); fried chicken with french fries (low-nutrition meal); and dinner salad with chicken and dressing (high-nutrition meal).


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anchored to the sociobehavioral propensity to consume the category, PtCcfd :55,57 i 1−wc c Acfd = PtC w , cfd Acfd

(1)

with wc being the relative importance of PtC. Note that consumer affinity Acfd attains the intrinsic valuation Aicfd when PtCcfd equals 1, while attaining 0 when PtCcfd is 0 (its lower bound), irrespective of the intrinsic value. Population-level intrinsic affinity follows the classic logit-choice utility structure consistent with random distribution of unobserved consumer preferences.58,59 Following this, Aicfd = e ucfd , where individual-averaged utility ucfd sums over the influence of l attributes, each depending on the effective attribute value acfl, and attributerelated consumer elasticity of demand ␤l :f ucfd = ␤l (acfl − 1). (2) l

Consumer elasticity of demand measures sensitivity of consumer expenditure to changes in the attributes. The parameter values (Table 1, demand) reflect the relative strong sensitivity to motivational quality compared to nutritional quality.60,61 Appendix IV, in Supporting Information, provides the equation details for PtC. Most important, PtCcfd adjusts toward exposure pressure ecfd , which consolidates influences from different information channels I; thus, ecfd = i ecfdi . Here, we collapse all channels into firm marketing and social exposure, i ࢠ {m,s}. Exposure pressure from channel i increases with exposure ε cfdi with diminishing returns to scale 0 ≤ ␩ ≤ 1, attaining reference value eci when exposure equals 1. Finally, depending on the context and channel, exposure is, to a degree, brand-, rather than category-specific, reflected in parameter ␥ i ࢠ ␥ 1−␥ [0,1]. Together, the result is: e cfdi = e ci (εcfii εcdi i )␩i . (See Appendix IV, in Supporting Information, for the formulation of marketing and social exposure.) Table 1 (demand) lists PtC-related parameters, highlighting that social exposure is weak at low market shares; that marketing effectiveness for LN products is greater than for HN;62–65 and

f

In the full model, choice parameters and socialization parameters are region specific, to capture long-term systematic demographic variation and social structures. Since we analyze dynamics here at the national level, we omit this index here.

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that while marketing is mostly value chain/brand related,54 social exposure tends to be more category related. A food category’s effective attribute value acfl improves with the firm’s capabilities CAcfl , with diminishing returns, following standard learning curve theory and empirics,66–68 so that acfl = r acl (CAcfl /CA0 )␩ . The learning curve exponent 0 ≤ r ␩ ≤ 1 captures that each subsequent resource investment has a smaller effect on attribute value than the previous, while the attribute value attains acl when capabilities equal the reference value CA0 . The variation in normal attribute values (Table 1, supply) highlight the relative strength of the HN(LN) category with respect to nutritional (motivational) attributes. The accumulation of capabilities CAcfl depends on productivity, total efforts, and share of resources allocated to improving a particular attribute (for details, see Appendix IV in Supporting Information). In short, firms f, comprising two types, f ࢠ [f1 , f2 ], reinvest and distribute a fixed amount of their profits to improve the market share of their products. They distribute resources according to a hierarchical resource-allocation process,69,70 respectively between categories, between marketing and R&D, and between attributes. At each level, firms increase (decrease) budgets for alternatives that offer higher (lower) marginal returns. (Thus, resource distributions stabilize when marginal returns between allocations are equal.) Population-level BMI is linked to category consumption through a body weight expenditure model, with average population weight Wd , depending on stored energy, with endogenous categorydependent calorie intake and expenditure as inputs. The expenditure model captures two fundamental theoretical underpinnings of the human macronutrient balance. First, the mass-balance follows the first law of thermodynamics (weight change is determined solely by the imbalance between dietary intake and energy expended). Second, biological feedback processes result in body weight–dependent energy expended.71,72 Parameters come from Health Canada;73 information on population changes come through births, deaths, and aging. (For details on the BMI model and parameters, see Appendix IV in Supporting Information). We initialized BMI, weight, caloric consumption, and population size by segment to match current and trend statistics for the Canadian population (Table 1).74–76


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Table 1. Main model parameter settings

Value Parameter Demand Attribute demand elasticitya

HN ␤l b

Weight of PtC (vs. intrinsic affinity) in consumer choice

wc

Normal exposure effectiveness (for marketing/social influence)

em es

Exposure returns-to-scale exponent Category-related exposure effectiveness

␩m ␩e ␥m ␥s

Supply Normal attribute statec

acp act aca acn

Learning curve exponent Population health Initial BMI, kg/m2 , adult (male, female) Initial caloric intake, kcal/day, adult (male, female) Initial HN market share

␩r

LN

␤p = −4 ␤t = 4 ␤a = 4 ␤n = −2 0.5

0.25

French and Stables;114 Hoch et al.;61 Blaylock et al.115

0.5 1 0.4 1 0 0.75

Source/Motivation/Note

Albright et al.116 This parameter allows to explicitly vary the relative importance of PtC (which is implicitly also altered through ␤l and ␩e ) Story et al.;62 Epstein and Leddy;64 Wansink and Chandon65 (Social influence normalized to 1) Diminishing returns to scale in marketing effort; Argote and Epple67 Marketing is brand-related, social exposure mostly category-related: Alessie and Kapteyn;51 Seetharaman et al.;47 for brand-related social exposure: Dube et al.50

(0.75) 1

(0.5) 0.8 Higher attribute state produces higher prices; Christian and Rashad81 1.25 1 Davis et al.;86 Raghunathan et al.83 1.25 1 Raynor and Epstein;84 Cullen et al.82 (0.75) 1 (1.5) 1.6 Captures calorie density per serving so higher values are less nutritious 0.3 Brownell et al.;117 Argote and Epple66 26.75, 26.15 2843, 2086 11%

Health Canada;73 Shields74 (see Appendix IV in Supporting Information) Derived (see Appendix IV in Supporting Information) Derived (see Appendix IV in Supporting Information)

a

Appendix IV, in Supporting Information, shows derivations and numerical examples for effective attribute elasticities that endogenously form as investments and market shares shift. b lࢠ {price, taste, availability, nutrition} = {p, t, a, n}. c Values between brackets reflect minimum value.

Analysis

Model initialization and base run All simulation runs last from 2010 to 2050 (see Appendix II, in Supporting Information, for simulation-related definitions). This period is long enough into the future to capture any long-term effects of interventions, which start in 2015 allow-

ing a “historical” window between 2010 and 2015 for observing existing patterns. We first produce a base run reflecting historical and projected patterns of calorie consumption.76–78 Calibrating the model to 2010 calorie consumption76–78 yields an initial market share of HN products at 11% (see Appendix IV in Supporting Information). Total serving size is


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Figure 3. BMI and market dynamics for the base run and selected policy intervention scenarios.

set to grow at a fixed rate of 0.25%/year. The base run, with adult BMI rising to 29.0 in 2030 and to 32.3 in 2050 (Fig. 3A), reflects projections consistent with an obesity crisis.79,80 At the category consumption level, the base run produces patterns with a low and gradual declining market share for representative products from LN categories (Fig. 3B– E, base run). Taste and price for LN food, superior to those for HN food, improve gradually over time. These patterns are consistent with empirical realities of high and growing perceived motivational

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value and market shares of LN food compared to HN;81–84 of heavy advertising and promotion of processed foods; of widespread food establishments and large affinity with LN products;51,62,64,85–87 and of consumer sensitivities to motivational qualities of LN products.88,89 Breaking from these patterns of historically grown differences between HN and LN is challenging, as endogenous supply and demand behaviors reinforce each other (Fig. 2), while consumers’ different valuation of their intrinsic properties moderates this (Table 1).


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Table 2. Interventions analyzed

Sector

Policy

Industrya

fMAR Aggressive marketing campaigns 50% of total R&D and marketing Story et al.91 for HN product categories budget directed to marketing of HN products fMRE Voluntary reduction of LN LN advertising effectiveness Harris et al.;90 Hawkes92 product category advertising reduced by 80% fNRD Dedicated investments to At least 50% of marketing and Yach et al.93 improve taste, availability of R&D resources dedicated to HN products R&D of HN products gMAR Aggressive promotion campaigns Equivalent to half of the fMAR Story et al.91 for HN product categories impact, but for affecting the whole industry gMRE Regulated reduction of LN LN advertising effectiveness Hawkes92 product category advertising reduced by 40% across all firms gEDU Intensive campaigns to increase During the period of the shock, Finkelstein awareness for importance of et al.95 the nutrition sensitivity ␤n = nutritional quality and provide 0.5 moves to ␤n = 1. After the shock, the sensitivity moves related product information through, for example, labeling back to the original level gTAX Caloric tax imposed, increasing $1 for each 25 exceeding desired Giesen et al.98 with caloric density of servings 400 calories per serving. For example, a 450 caloric serving involves a $2 tax INN Market and government stimuli Normal attribute state of HN Popp101 of nutritious food product cost, taste, availability (aph , ath, aah ) improve with up to 50% innovation (e.g., stimulate of difference with LN values, outsider entry, IP protection, as firms dedicate R&D; normal innovation clusters and attribute state of LN nutrition infrastructure, X-prize (aph ) improves with up to 10% breakthrough) of difference with HN values as firms dedicate R&D

Government

Multiple innovationoriented actors

a

Short description

Operationalization in the model

Source

Only 50% of participating firms, those of type f2 , engage in these efforts.

Policies analyzed To assess policy effectiveness in increasing the nutritional quality of food consumption, we explore policy scenarios from different single-pronged initiatives by industry and government. Industry actors, pressured by global health concerns about the unsustainability of food-consumption patterns, increasingly undertake individual and self-regulatory initiatives. Yet, although industry initiatives may lead to efficient reallocation of resources, they may not suffice, and often fail, for a variety of reasons, in particular related to competitive pressures.90 Gov-

ernmental initiatives are designed to overcome such market failures. We develop policy scenarios focusing on a subset of important potential interventions, drawing from seven different single-pronged initiatives (Table 2): three by industry (Fig. 1, supply sector) and four by government (Fig. 1, government). The first industry initiative involves firms’ active marketing of nutritious products (fMAR).91 Second, firms may undertake self-regulated initiatives to collectively refrain from promoting or marketing unhealthy products (fMRE).90,92,93 Finally, to


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improve the appeal of nutritious food, firms may allocate resources above what they would otherwise do on the basis of their marginal benefits (fNRD). For example, retailers may dedicate shelf space to or develop agrifood products for which limited demand exists.93 The first two governmental initiatives involve external promotion of HN categories and regulation of LN marketing (gMAR and gMRE). Governments increasingly regulate or prohibit food advertising, in particular to children.91,92,94 In addition, we capture efforts to make consumers internalize indirect costs of LN food through education (gEDU), through initiatives comprising product labeling and provision of nutrition information charts.65,95–98 Finally, we capture efforts to internalize the indirect cost of unhealthy food by imposing a tax based on nutritional content (gTAX), reflecting a class of solutions that is increasingly discussed and experimented with.98,99 We also explore collective initiatives intended to develop a platform to foster innovation across the value chain. Researchers and policymakers have begun to rethink how innovation-enabling policies may be used to alter consumption pathways. Directing technical-change policy, by mobilizing innovation brokers and developing clusters, and by stimulating entrepreneurial activity and innovation,27,100,101 may reduce costs of other interventions and increase complementary products that spur further acceptance and innovation. In nutrition markets, recent attempts draw upon such innovation-fostering policies across the value chain to alter consumption pathways, such as those geared to further integrate agrifood into the food supply chain.102 The foster-innovation program (INN, Table 2) captures a collective of initiatives intended to develop an innovation-enabling platform of higher nutritional quality. INN acts to improve the potential productivity of firms’ resources dedicated to improve the nutritional quality of food. By assumption of the scenario, the long-term potential of this initiative is considerably greater than that of the single-pronged initiatives above. We examine whether fostering-innovation initiatives interact with other initiatives, and if so, how. Results We summarize the simulation results for a selection of policy scenarios, including single- and mul10

tipronged within-sector (industry or government) initiatives, cross-sectoral whole-of-society7 (WoS) initiatives, and an aggressive focus on fostering innovation (Table 3). Policies last 20 years, from 2015 to 2035, seizing 15 years before the simulation ends, allowing examination of any sustaining impact of policy interventions. Table 3 reports (health-related) average adult BMI, (supply/ industry-related) profits of participating relative to nonparticipating firms, and (demand-related) adult consumer utility (with ud = cf ucfd ␴cfd ). For BMI and profits, we list the percentage deviation from the base run, averaged during the policy shock (2015– 2035) and at the end of the simulation (2050); for utility we report the absolute deviation during the policy shock.

Industry initiatives Simulations of individual industry initiatives show moderate to low effectiveness. In particular, the self-regulated marketing campaign to promote the HN category (fMAR), while costly, shows negligible long-term benefit to BMI. First, while marketing campaigns may induce some swift substitution toward one’s HN products, once the campaign is over, consumers revert to the more attractive and familiar LN categories (in Fig. 2, loop R2b marketing cannot sufficiently counteract the other feedback loops). Moreover, by investing in marketing campaigns, participating firms must divert resources away from R&D and product improvement. Thus, consumers who eventually revert to LN products are more likely to switch to nonparticipating firms (Fig. 2, R1 and R2). Pledges to reduce LN product marketing (fMRE) lead most consumers accustomed to buying LN products to substitute toward nonparticipating firms. Further, while freeing up some resources, participating firms will nevertheless be little incentivized to improve HN products. By contrast, when firms instead commit to innovating HN products (fNRD), resources allocated during the initiative have lasting impact on BMI. However, participating firms (f2 ), by focusing on HN products, will, in the long run, lose market share for LN products (Table 3: under fNRD f2 ’s relative profits are considerably lower in 2050 than during the policy). Combining the three industry policies does not eliminate these problems and does not significantly shift consumption toward HN products (fMulti1


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Table 3. Policy interventions, ranging from single-pronged sector to whole of society (results, compared to base run, show key health, industry, and consumer indicators)

BMI Approach

Scenario

Interventions included

Avg. 2015– 2035

2050

28.33

32.27

Base run Industry Single

Multi Government Single

Multi Whole of society Multi

Innovation Single Multi Whole of society and innovation Multi

Relative profits (f2 ) Avg. 2015– 2035 1.137

2050 1.369

Utility Avg. 2015– 2035 0.690

fMAR fMRE fNRD fMulti1 fMulti2

fMAR fMRE fNRD fMRE, fNRD fMARa , fMRE, fNRD

−0.64% −0.46% −1.13% −1.55% −2.01%

−0.06% −12.59% −0.80% −0.09% −4.57% −0.88% −1.86% −1.58% −4.38% −1.83% −5.36% −4.82% −1.80% −11.35% −5.19%

−0.01 0.00 −0.02 −0.02 −0.02

gMAR gMRE gEDU gTAX gMulti1 gMulti2

gMAR gMRE gEDU gTAX gMRE, gEDU, gTAX gMulti1 + gMAR

−0.88% −0.60% −1.66% −2.58% −7.17% −8.51%

−0.15% −0.09% −1.02% −1.18% −2.29% −2.51%

−0.01 0.00 −0.23 −0.16 −0.40 −0.41

WoS1 WoS2 WoS3

−9.25% −8.97% −9.60%

−4.74% −0.53% −3.58% −4.31% −11.43% −4.09% −5.05% −5.45% −3.36%

−0.41 −0.41 −0.42

WoS4

fNRD + gMulti1 fMulti2 + gEDU, gTAX fMulti1 + gMAR, gEDU, gTAX fNRD + gMulti2

−10.13%

−5.21%

−0.62% −3.29%

−0.42

INN fINN gINN

iINN iINN + fMulti1 iINN + gMulti1

−2.86% −4.80% −11.37%

−5.89% −8.58% −9.67%

0%b −5.01% −4.09% 0%b

0.00 −0.02 −0.39

WoSINN1 WoSINN2 WoSINN3 WoSINN4

WoS1 + iINN WoS2 + iINN WoS3 + iINN WoS4 + iINN

−13.70% −13.38% −14.05% −14.68%

−14.16% −0.09% −2.05% −13.94% −10.82% −2.48% −14.41% −4.84% −2.41% −14.69% −0.18% −2.34%

−0.40 −0.40 −0.41 −0.41

Whole of society and innovation Multi (vs. single) Ratio WoSINN1 versus single Ratio WoSINN2 versus single Ratio WoSINN3 versus single Ratio WoSINN4 versus single

1.55 1.49 1.47 1.51

1.41 1.38 1.41 1.44

0%b

0.06 0.87 0.79 0.11

0.47 0.44 0.46 0.53

0.98 0.98 0.97 0.98

Note: We do not report percent utility change since that is meaningless; instead we show the absolute difference. The multiplier effect of the WoS approach is calculated by dividing the WoSINN impact on BMI by the sum of the impact of the individual policies under the WoSINN approach (e.g., the effect of WoSINN1 on BMI in 2050, –14.17%, is divided by the sum of the effects of fNRD. a Under this and thereafter following scenarios, marketing intensity of the fMAR intervention is reduced by 50%, as this yielded superior results across all indicators (due to the lower resource commitment by the firms). b The zero change is by construction, because for the purpose of analytical clarity we hold value chain margins constant (and equal across product categories). That is, industry profits are a zero-sum game. C 2014 New York Academy of Sciences. Ann. N.Y. Acad. Sci. xxxx (2014) 1–19

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and fMulti2). The productivity of nutritious innovation remains low, as consumers lack sensitivity to improvements (Fig. 2, loop R3, return on investment). Instead, the competitive edge benefiting nonparticipating firms highlights the collectiveaction dilemma due to both worse-before dynamics as well as free riding, rendering industry initiatives prone to failure.

Governmental initiatives The effectiveness of modeled single-pronged governmental interventions ranges from moderate to low (Table 3). Promotion-related initiatives, gMAR and gMRE, produce results similar to those led by industry, although now these initiatives maintain a level playing field across firms. Internalizing the long-term consequences of caloric intake for consumer choice, through education (gEDU) or taxes (gTAX), yields lasting BMI impact (Fig. 3, BMI remains below that of the base run). This is partly due to industry learning about reducing caloric density, constituting a productive use of resources: as gEDU leads to higher consumer sensitivity to nutritional quality, firms may more effectively compete by improving the nutritional quality of (especially LN) products and the appeal of HN products, and benefits persist after the policy expires. Nevertheless, consumer utility decreases in this scenario because of the initial mismatch between demand and supply regarding products of higher nutritional quality. gTAX yields dynamics similar to that achieved under gEDU. Some of the health benefits from shifts in industry capabilities persist after the policy expires (see also the gTAX scenario in Fig. 3D and E). Implementing multiple governmental policies jointly offers some synergies, but few of the added benefits sustain in the long run (compare the 2015–2035 average with the BMI 2050 column). Combining initiatives Combining policies across industry and governmental sectors, as per the WoS paradigm, provides some lasting synergistic effects (WoS1–WoS4), with BMI declining an additional 1% beyond combined reductions in BMI from individual policies used for each WoS scenario; yet, few of the benefits last. The unwinding of impact can also be noted in the large negative relative profits that participating firms incur. Fostering innovation (INN) forms a highleverage strategy with lasting effects. The direct 12

effect of the INN policy is to increase effectiveness and incentives for firms to invest and improve nutritional quality across categories—the appeal of HN products and nutritional quality of LN products, with productivity benefits further increasing with experience (Fig. 2, R3a and R3b). Moreover, the temporal improvements ratchet up the industry experience with higher nutritional quality (Figs. 2 (R2) and 3). In particular, benefits from nutritional LN improvements are large because of the existing demand as well as limited prior investments (Fig. 3E). Note further that INN, absent other initiatives, does not affect relative firm profits. The key benefit from INN resides in its complementarity to multiple policy initiatives. While a smaller-scale multisectoral policy offers moderate improvement compared with INN (see, for example, fINN in Table 3), WoS interventions combined with INN offer by far the greatest synergies, demonstrating the critical importance of alignment across sectors. Table 3 quantifies the WoS synergies, showing the ratio of WoSINN versus single policies (bottom four rows), defined as the BMI reduction due to WoSINN, relative to combined reductions from its underlying policies when implemented individually. Ratios are around 1.5. To see why, consider WoSINN1 (Fig. 3). First, adding gTAX and gEDU to INN leads consumers to internalize benefits from any innovations, which increases demand and, with that, PtC (Fig. 2, R1), spurring even higher returns on investment and subsequent innovation among producers (Fig. 2, R3). Those feedbacks are further strengthened as some firms commit to improving nutritional quality (fNRD), which increases their market share and induces additional investment (Fig. 2, R2). WoS interventions not only improve BMI but also benefit all participating actors, especially compared to single-pronged initiatives. Participating firms’ efforts are rewarded, despite their much larger commitments, because of the higher and further growing sustained PtC and, in the longer run, productivity (Table 3, profit columns show reduced profit penalties for WoSINN initiatives, with ratios well below 1, especially in 2050). Even consumers’ perceived cost of internalization are limited despite much more aggressive moves away from attributes they value (Table 3, utility). Together, these results show the critical importance of alignment across sectors.


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Table 4. Sensitivity analysis

Results Sensitivity type

Change

Parameter

I

II

III

IV

n.a. ␤ n = ␤’p ; ε m HN = ε m LN

100% −13.6% −8.8% 1.54 88% −20.9% −17.8% 1.17

Equivalent potential of HN aHN,l = aLN,l category Role of value chain Social exposure Value chain ␭e = 0 versus category specific to: Category ␭e = 1 Strength of loop R1 PtC adjustment time Fast ␶ e = 0.25 to exposure: Slow ␶e = 4 PtC decay time: Slow ␶d = 4 Fast ␶ d = 0.5 Strength of loop R2 CA learning curve Weak ␩r = ␩m = 0.15 exponent: Strong ␩r = ␩m = 0.6 Demand elasticity: Small ␤ l = 0.5␤l Large ␤ l = 2␤l Strength of loop B3/R3 Time to adjust Slow ␶b = 4 resource allocation: Fast ␶ b = 0.25 Policy intensity Short policies, WoSINN1 with 10-year duration Weak policies, efforts set to 50% of WoSINN1

88% −17.2% −15.5% 1.11

Base run Parameter (nutrition/ motivation trade-off)

n.a. Equivalent sensitivity to NQ as well as to HN exposure

92% 102% 98% 103% 98% 102% 87% 107% 87% 106% 100%

−14.4% −11.0% −13.8% −6.3% −13.2% −13.6% −20.2% −3.8% −12.7% −5.7% −7.4%

−12.0% −6.9% −10.2% −4.9% −9.7% −7.3% −17.8% −3.4% −11.4% −4.3% −11.4%

1.20 1.61 1.36 1.29 1.36 1.87 1.14 1.14 1.12 1.31 1.54

99% −12.5% −15.7% 1.26 100% −9.9% −6.7% 1.48 100% −4.4% −3.7% 1.20

I. BMI, average between 2015 and 2035, for no-policy scenario (% change compared to base run value, BMI = 28.33). II. Effect of WoSINN1 policy scenario on BMI, compared to no-policy scenario (column I). III. Combined effect of single-pronged policies of WoSINN1 on change in BMI, compared to no-policy scenario (column I). IV. WoS ratios:WoSINN1 compared to combined effect of same single-pronged policies (column II divided by column III)

Sensitivity analysis The system of nutritious food market transformation is complex and many factors in this analysis have been left out scope. Nevertheless, the model captures some main mechanisms underlying nutritious food market transformation dynamics. Key insights—that integrated policy approaches offer important synergies within this dynamically complex market system—are robust under parametric and structural sensitivity analysis (Table 4). First, counterfactually reducing the nutritional/motivational quality trade-off through parameter changes (Table 4, top two rows) increases benefits from individual policies (columns II and III), while reducing WoSINN synergies (column IV, WoS ratios). Beyond such parameter sensitivity, structural sensitivity, aimed at challenging

the model scope and feedback, highlights the importance of an elaborate representation of behavioral feedback mechanisms. Strengthening the major positive feedback loops (Table 4, highlighted rows) tends to suppress effectiveness of all policies, but strengthens the WoSINN synergies. For example, when the PtC decay time is reduced from 2 to 0.5 years (see Appendix IV in Supporting Information for details), the WoS ratio increases from 1.54 to 1.87 because much stronger and more sustained exposure is needed to build PtC for the HN category. Likewise, under relatively strong learning curves and other scale economies (␩r = ␩m = 0.6), achieving any nutritional quality improvements is difficult (column II and III), suggesting a need for even stronger policy commitments. Finally, to benefit from WoS synergies, commitment needs to be


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sufficiently strong across initiatives. When overall commitment to policies is reduced, the WoS ratio decreases. Discussion Growing concerns about population health have led to multiple market interventions by various public and private actors intended to alter food consumption in a nutrition- and health-sensitive direction. Remaining fragmented in their approach, these have failed to simultaneously target shifts in supply and demand. Our analysis highlights both the importance of interdependent dynamics across sectors— that is, agriculture, food industry, consumer, and government—in shaping market dynamics and the need to target drivers of both supply and demand simultaneously. Our results show how single-pronged initiatives, whether designed and deployed under governmental or private sector leadership, are ineffective, failure-prone, and costly. For example, while consumers are sensitive to economic stimuli that alter food cost and convenience, the actual change of food consumption patterns is conditioned by social influence and habits, as well as by food availability, all factors being intricately linked. The system’s resistance to single-pronged interventions is consistent with empirical findings of low and variable consumption responses to product labeling or increasing healthy food availability in food deserts.96,103 Instead, transforming value chains and markets and affecting consumer demand and behavior in nutrition- and health-sensitive directions requires implementation of multiple aligned interventions, such as temporary marketing initiatives, consumer education, and R&D commitments so as to benefit from synergies across sectors. Further, we show how coordinated efforts to foster technological and value-chain innovation complement conventional sectoral and cross-sectoral policy efforts in ways necessary for reaching the scale of transformation needed to curb and reverse the progression of obesity. We highlighted how fostering innovation, the lifeblood of economic development, jointly with integrative policies, facilitates the overcoming of multiple barriers to nutritious food market transformation. Our results are consistent with evidence in other fields, such as environmental economics.27 For example, enabling green technology innovation reduces costs and induces learning 14

and complementary industry action, which in turn facilitate demand-side legitimacy and uptake, thus reducing the long-term need for other governmental investments and incentives.27,28,101 In our context, the government has a particular important role in stimulating the development of a market infrastructure for innovation platforms, such as agrifood. Because interests, capabilities, perceptions, and agency differ across sectors and their interventions vary in effectiveness and robustness, firms need support to overcome various collective-action problems. In other words, one should treat the market infrastructure for nutritious food innovation as a semi-public good. Although current sensitivity analysis, parametric and structural, underscores the key insights regarding the importance of integrative policies, additional analysis should reveal further insights into barriers and leverage for transitions toward healthier food markets. Confidence building in large-scope models, such as the NMT, is an iterative process. Therefore, the current insights also reveal important factors not included and point us toward fruitful further research. Future model expansion can focus on a more elaborate representation of the link between nutritional quality and health consequences, such as serving size (currently exogenous) or population physical activity (currently assumed constant) and their links to food consumption. Future analysis should also expand the scope to include nonthermodynamic health risk factors, such as nutritional diversity and noncaloric contributors to BMI, as well as inclusion of non-BMI risk factors.104 More analysis is also needed on determining how the fosteringinnovation mechanisms, including the type of innovations, and entrepreneurship across sectors and at different levels, from community to global, may affect the nature, strength, and durability of nutrition/health and economic return. For example, much innovative potential, such as for agrifood development, may reside in actors outside the established market system. Further, relaxing our assumption of a zero-sum market may shed light on additional innovation benefits to firms that take part in initiatives. While facilitating comprehensibility and focus, our analysis relied on several strongly simplifying assumptions regarding the level of aggregation. A finer-grained representation of the societal burdens of adverse health consequences and economic costs


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is important, as they often reside in the tail rather than the mean of population distributions. Further, accounting for sociodemographic heterogeneity is important for explaining the persistence of, and for overcoming, low-quality nutrition clusters.105 For example, the availability and characteristics of product portfolios evolve endogenously and differently across sociodemographic strata, affecting local consumer response to interventions. Effects of heterogeneous feedback to those undertaking interventions may be nonlinear, thus also affecting their overall impact (as suggested by in-progress research). Beyond mesolevel sociodemographic effects, the role of heterogeneous peer-to-peer and other network influences at producer and consumer levels is another important factor for consideration. For example, Hammond and Ornstein106 report an agentbased model (ABM) capturing such social network influences in accounting for school clustering in BMI patterns. Fruitful insights may be developed by carefully combining more aggregate with ABM approaches that are sensitive to how macrolevel patterns grow from the bottom up.6 The NMT model, currently tailored to resourcerich industrialized contexts of caloric overnutrition, can be further adapted to different economies covering the full development and malnutrition spectrum. Major national and international development efforts have begun to better link agriculture to nutrition and health in the resource-poor countries of sub-Saharan Africa, South Asia, and South America,7,107 and to manage issues of double malnutrition in emerging economies, such as China, Brazil, and Mexico, and many Arab countries. Yet, despite these rising efforts, results are not likely to reverse current and future trends.108 Addressing these challenges requires bridging efforts with existing undernutrition research, such as the multistakeholder simulation tool developed by the World Bank focused on overcoming nutritional deficiencies in mothers and their children.26 Agriculture may be a key bridge in linking community-focused efforts on undernutrition to value chain and market formation and transformation. The geographyflexible NMT model can help identify economically sustainable levers for proactively addressing, with greater impact, scale, and resilience, single as well as double malnutrition challenges. In addition to agriculture, NMT extensions need to capture deeper and more comprehensive linkages


with policy and financing in the health/healthcare system. Countries around the world devote a minimal share of health/healthcare resources, efforts, and investments to health promotion and disease prevention.109 Emerging innovative efforts suggest the potential of such linkages to help health systems and society strike a better balance between promoting health and curing illness. For example, Wholesome Wave, an organization fostering linkages between local agriculture and underserved communities, in their fruit and vegetable prescription program, besides stimulating increased availability of agrifood to low-income at-risk population segments, examines the health and economic impact of having medical doctors prescribing fruits and vegetables.110 These prescriptions are to be filled at local farm markets, with the bill being paid by insurance companies, and link the health systems to agriculture and food in a novel way. We need to better understand the short- and long-term impacts and mechanisms of such cross-sector innovative interventions. Well-intended policy interventions in interdependent systems of agriculture, food-value chains, and consumption, which underrepresent progressive intercoupling and fragmented perceptions and behaviors of actors, tend to be defeated by unintended reactions of the system to these interventions.9,111,112 Such tendencies are reinforced by existing research on these private and public organizations that predominantly select settings and actions that emphasize one or the other exclusively.113 Approaches grounded in systemsscience theory, combining economic, sociobehavioral, and physical/material factors and distributed actors contribute to understanding the path dependency of nutritious food trajectories and their health consequences. Subsequently, these approaches help research communities and policymakers by developing a deeper understanding of the broader boundaries and long-term consequences of their interventions and in identifying high-leverage policy portfolios. Acknowledgments The authors wish to thanks stakeholders affiliated with the McGill Center for the Convergence of Health and Economics who have participated in workshops that informed model development, seminar participants at the 29th International


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Conference of the System Dynamics Society, and in particular, five anonymous reviewers, as well as Saibal Ray, Yu Ma, and Ruthanne Huising, who have made helpful comments. Financial support for this work was provided by a team grant and a young researcher grant from the Fonds de recherche du Québec sur la Société et Culture (# 137173 and 168483) and by a competitive standing offer of service from the Public Health Agency of Canada (# 4600000162). Conflicts of interest The authors declare no conflicts of interest. Supporting Information Additional supporting information (Appendices I–VI) may be found in the online version of this article. References 1. Bloom, D.E., E.T. Cafiero, E. Jané-Llopis, et al. 2011. The Global Economic Burden of Non-Communicable Diseases. Geneva: World Economic Forum. 2. Floud, R., R.W. Fogel, B. Harris & S.C. Hong. 2011. The Changing Body: Health, Nutrition, and Human Development in the Western World Since 1700. Cambridge, UK: Cambridge University Press. 3. Swinburn, B.A., G. Sacks, K.D. Hall, et al. 2011. The global obesity pandemic: shaped by global drivers and local environments. Lancet 378: 804–814. 4. Prentice, A.M. 2006. The emerging epidemic of obesity in developing countries. Int. J. Epidemiol. 35: 93–99. 5. Appelhans, B.M., M.C. Whited, K.L. Schneider & S.L. Pagoto. 2011. Time to abandon the notion of personal choice in dietary counseling for obesity? J. Am. Diet. Assoc. 111: 1130–1136. 6. Hammond, R.A. & L. Dubé. 2012. A systems science perspective and transdisciplinary models for food and nutrition security. Proc. Natl. Acad. Sci. 109: 12356–12363. 7. Dubé, L., P. Webb & P. Pingali. 2012. Paths of convergence for agriculture, health, and wealth. Introductory article to “Agriculture Development and Nutrition Security Special Feature,” Proc. Natl. Acad. Sci. 109: 12294–12301. 8. Popkin, B. 2009. The World is Fat. The Fads, Trends, Policies, and Products That are Fattening the Human Race. London: Penguin Books. 9. Sterman, J.D. 2012. “Sustaining sustainability: creating a systems science in a fragmented academy and polarized world.” In Sustainability Science: The Emerging Paradigm and the Urban Environment. M.P. Weinstein & R.E. Turner, Eds.: 21–58. New York, NY: Springer New York. 10. Huang, T.T., A. Drewnosksi, S. Kumanyika & T.A. Glass. 2009. A systems-oriented multilevel framework for addressing obesity in the 21st century. Prev. Chronic. Dis. 6: 1–10.

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19

The Norwegian food and nutritional policy.

Disseminating policy and environmental change interventions: insights from obesity prevention and tobacco control.

Perspectives of Fijian Policymakers on the Obesity Prevention Policy Landscape.

Discriminating nutritional quality of foods using the 5-Color nutrition label in the French food market: consistency with nutritional recommendations.

Smart food policies for obesity prevention.

Problems in the regulatory policy of the drug market.

Belief in Food Addiction and Obesity-Related Policy Support.

Food industry perspectives on the mature market.

Modified Policy-Delphi study for exploring obesity prevention priorities.

The Role of Nutritional Aspects in Food Allergy: Prevention and Management.

Food Choices and Consequences for the Nutritional Status: Insights into Nutrition Transition in an Hospital Community.

The Portuguese generic medicines market: a policy analysis.

Evaluation of the nutritional status of workers of transformation industries adherent to the Brazilian Workers' Food Program. A comparative study.

Uncovering the nutritional landscape of food.

Correction: Uncovering the nutritional landscape of food.

Market Performance and Food Safety Compliance for Small Food Businesses: The Case of Fifth Town Artisan Cheese.

Remote degeneration: insights from the hemicerebellectomy model.

Ethical Agreement and Disagreement about Obesity Prevention Policy in the United States.

The "new ferment" in national health policies: the case of Norway's nutrition and food policy.

The oversimplification of policy in prevention.

Practitioner insights on obesity prevention: the voice of South Australian OPAL workers.

A critical discussion of the Community Readiness Model using a case study of childhood obesity prevention in England.

The application of theories of the policy process to obesity prevention: a systematic review and meta-synthesis.

Correction: Eliminating Anti-Nutritional Plant Food Proteins: The Case of Seed Protease Inhibitors in Pea.