Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Public Health

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Food, health, and complexity: towards a conceptual understanding to guide collaborative public health action

  • Shannon E. Majowicz1Email author,
  • Samantha B. Meyer1,
  • Sharon I. Kirkpatrick1,
  • Julianne L. Graham1,
  • Arshi Shaikh2,
  • Susan J. Elliott1, 3,
  • Leia M. Minaker4,
  • Steffanie Scott3 and
  • Brian Laird1
BMC Public HealthBMC series – open, inclusive and trusted201616:487

https://doi.org/10.1186/s12889-016-3142-6

Received: 13 January 2016

Accepted: 14 May 2016

Published: 8 June 2016

Abstract

Background

What we eat simultaneously impacts our exposure to pathogens, allergens, and contaminants, our nutritional status and body composition, our risks for and the progression of chronic diseases, and other outcomes. Furthermore, what we eat is influenced by a complex web of drivers, including culture, politics, economics, and our built and natural environments. To date, public health initiatives aimed at improving food-related population health outcomes have primarily been developed within ‘practice silos’, and the potential for complex interactions among such initiatives is not well understood. Therefore, our objective was to develop a conceptual model depicting how infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy can be linked via shared drivers, to illustrate potential complex interactions and support future collaboration across public health practice silos.

Methods

We developed the conceptual model by first conducting a systematic literature search to identify review articles containing schematics that depicted relationships between drivers and the issues of interest. Next, we synthesized drivers into a common model using a modified thematic synthesis approach that combined an inductive thematic analysis and mapping to synthesize findings.

Results

The literature search yielded 83 relevant references containing 101 schematics. The conceptual model contained 49 shared drivers and 227 interconnections. Each of the five issues was connected to all others. Obesity and food insecurity shared the most drivers (n = 28). Obesity shared several drivers with food allergy (n = 11), infectious foodborne illness (n = 7), and dietary contamination (n = 6). Food insecurity shared several drivers with infectious foodborne illness (n = 9) and dietary contamination (n = 9). Infectious foodborne illness shared drivers with dietary contamination (n = 8). Fewer drivers were shared between food allergy and: food insecurity (n = 4); infectious foodborne illness (n = 2); and dietary contamination (n = 1).

Conclusions

Our model explicates potential interrelationships between five population health issues for which public health interventions have historically been siloed, suggesting that interventions targeted towards these issues have the potential to interact and produce unexpected consequences. Public health practitioners working in infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy should actively consider how their seemingly targeted public health actions may produce unintended positive or negative population health impacts.

Keywords

Public healthPublic policyHealth policyPopulation-based planningFoodborne diseasesFood allergyFood insecurityDietary contaminationObesity

Background

Food and health are intimately intertwined: what we eat simultaneously impacts our exposure to foodborne pathogens [14], allergens [59], and environmental contaminants [10, 11], our nutritional status [12, 13], body composition [1416], mental health [1719], risks for and the progression of chronic diseases [2022], and other outcomes. Furthermore, what we eat is influenced by a complex web of drivers, such as socioeconomic status [2325], food security [26, 27], preferences [2830], culture [3134], politics [3537], economics [3840], trade [41, 42], industry [4345], legislation [46, 47], and our built [4850] and natural [5153] environments.

To date, public health initiatives aimed at improving food-related population health outcomes have primarily been developed within ‘practice silos’ (e.g., chronic disease prevention efforts typically developed independently from food safety activities), and their potential to interact or have unintended consequences has been largely ignored. As is the case in any system, ‘solutions’ to one issue may create new problems for another [54]. With respect to food and health, for example, food safety measures to limit microbial growth, such as the addition of salt [55, 56], can pose chronic disease risks [57, 58], and the development of urban gardens to improve food security [5961] can increase exposure to a variety of contaminants [6265]. Therefore, understanding the potential for complex interactions among public health initiatives aimed at improving specific aspects of population health (here, as related to food) is imperative to understanding how such efforts may influence one another, or influence seemingly unrelated population health outcomes, in unexpected ways.

Systems thinking offers public health practitioners a paradigm for framing issues that explicates complexity and interrelationships among parts of a whole, and facilitates identification of less obvious influences and consequences [66, 67]. Systems-informed public health initiatives exist in the realm of food and health; perhaps the most well-known is the Foresight Obesity System Map [68], an in-depth exploration of the complex web of social, economic, biological, psychological, and other drivers of a single food-related population health issue, obesity. In addition to exploring the myriad of drivers for one issue, the bilateral connectivity between issues has been explored, for example between obesity and food security [69]. Such bilateral explorations of food-health issues have also been expanded to involve system actors (i.e., those whose actions influence the parts within the system) from non-health sectors. For example, one local public health agency integrated the economic viability of agriculture with community food security, diet, and nutrition considerations to generate a municipal-level healthy community food system approach [70]. However, there are no conceptual models that support thinking systemically about multiple food-related population health issues in concert, to provide a roadmap for considering how targeted public health initiatives may interact in unexpected ways or have unintended consequences. Therefore, the objective of this study was to develop a conceptual model depicting how multiple food-related population health issues can be linked via shared drivers, illustrating the potential for complex interactions, with the ultimate goal of supporting collaboration across public health practice silos to guide informed interventions.

Methods

We applied a complex adaptive systems (CAS) lens to the concept of population health as related to food and diet. A CAS lens suggests that a given population health outcome or issue is an emergent property of an underlying system of inter-related drivers including political, environmental, social, biological, and other factors [71]. We developed a conceptual model depicting the shared drivers of five population health issues related to food (hereafter called ‘issues’): infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy. We selected these because (a) they are important public health issues for which there is significant investment in prevention and improvement, and (b) we hypothesized that they had shared drivers, and we assembled our research team to provide expert knowledge in these areas. We defined ‘drivers’ broadly, as factors with the potential to impact one or more issues, and included drivers across scales (from individual to societal) and types of associations (e.g., direct causes, higher-level proxies for more complex pathways). The conceptual model was developed by identifying drivers from the peer-reviewed literature and synthesizing shared drivers into a common model, using a modified thematic synthesis approach [72] that combined a systematic search process, inductive thematic analysis [73], and mapping to synthesize findings.

In March 2015, we searched the peer-reviewed literature in MEDLINE (via PubMed) for English-language reviews published in the last five years, using the search terms shown (Table 1). We selected only review articles for inclusion because initial topic-specific searches returned between 800 and 54,000 articles per topic. We designed the search to identify recent, evidence-based descriptions of a range of drivers of the five chosen issues; the search was not intended to produce a comprehensive nor exhaustive list of drivers, but rather to identify a range of drivers, including possible shared drivers. Additional articles were identified via citation cross-referencing and from authors’ areas of expertise. Because we included all types of review articles, including traditional narrative reviews, and because there is lack of consensus on how to assess quality among such heterogeneous types of reviews, we did not assess article quality.
Table 1

Search strings used to identify English-language reviews describing drivers of five food-related population health issues

Food-related population health issue

Search conducted

Obesity

(system OR complexity OR model OR driver OR influence OR determinant OR “risk factor”) AND (weight OR obes* OR (food AND (neighborhood OR neighbourhood)) OR “food environment” OR “nutrition environment” OR “food retail” OR “food desert” OR “food store” OR “food access”)

Food allergy

(system OR complexity OR model OR driver OR influence OR determinant OR “risk factor”) AND ((food AND allerg*) OR (food AND anaphylaxis))

Infectious foodborne illness

(system OR complexity OR model OR driver OR influence OR determinant OR “risk factor”) AND (“food safety” OR “foodborne disease” OR “food-borne disease” OR “foodborne illness” OR “food-borne illness” OR “food poisoning” OR (food AND pathogen) OR (food AND infection))

Food insecurity

(system OR complexity OR model OR driver OR influence OR determinant OR “risk factor”) AND (“food security” OR “food insecurity” OR “food system” OR hunger OR “food deprivation” OR “food affordability” OR “food unaffordability” OR “food accessibility” OR “food inaccessibility” OR “food sufficiency” OR “food insufficiency” OR “food access” OR “food poverty”)

Dietary contaminantsa

(system OR complexity OR model OR driver OR influence OR determinant OR “risk factor”) AND ((food AND toxin) OR (diet AND toxin) OR (food AND toxicant) OR (diet AND toxicant) OR (food AND pollutant) OR (diet AND pollutant) OR (food AND contaminant) OR (diet AND contaminant) OR (food AND metal*) OR (diet AND metal*) OR (food AND chemical*) OR (diet AND chemical*) OR (food AND (PAH OR “polycyclic aromatic hydrocarbon”)) OR (diet AND (PAH OR “polycyclic aromatic hydrocarbon”)) OR (food AND (POP OR “persistent organic pollutant”)) OR (diet AND (POP OR “persistent organic pollutant”)) OR (food AND (EDC OR “endocrine disrupting chemical”)) OR (diet AND (EDC OR “endocrine disrupting chemical”)) OR (food AND mercury) OR (diet AND mercury) OR (food AND cadmium) OR (diet AND cadmium))

*This symbol indicates that the truncation search feature was used in order to capture all variations of this search term

aSearch terms were included to yield a cross-section of key dietary contaminants within environmental public health

Search results were combined in a RefWorks database (2015, ProQuest LLC) and duplicates eliminated.

Two reviewers conducted the first relevance screen (Fig. 1, Stage 1). Both independently reviewed the first 87 articles; given high reviewer agreement (kappa = 0.891 [95 % C.I. 0.786, 0.995]), the remaining articles were screened by one reviewer per reference, using the title, and abstract if available. Articles were considered relevant and were included if they: explicitly identified drivers of one or more of the five population health issues, or depicted the links between two or more of the issues; pertained to human populations (including specific sub-populations); and were relevant to developed country contexts. Developing country contexts were considered out-of-scope for this study because social, cultural and political drivers differ greatly between developing and developed contexts. Articles pertaining solely to the following were excluded: animal or plant populations; animal models; development of laboratory or measurement techniques; methods for screening or surveillance for the population health issue; diagnosis, clinical characteristics, management, treatment, or impacts (including costs) of the issue; or details of chemical, hormonal, microbiological, cellular, or molecular properties or processes (including pathogenesis).
Fig. 1

Search results for English-language reviews (January 2010-March 2015) of five food-related population health issues

Applying a CAS lens, full text articles identified via the first screening were obtained, and screened, by a single reviewer per article (Fig. 1, Stage 2), to identify those containing any schematic representation that depicted drivers of one or more of the issues. The remaining articles were then screened, by a single reviewer per article (Fig. 1, Stage 3), to identify those containing causal diagrams, conceptual models, or similar schematics that included some visual representation of the relationships between the driver(s) and issue(s). Articles without such representations were excluded. Types of visual representations included were: causal loop diagrams; conceptual representations that showed or described either the directionality of relationships, or strength of effect of drivers on outcomes; and socio-ecological frameworks that depicted specific driver(s) and their relative relationships to the issue(s). Schematics were included if they depicted either the broad issue (e.g., obesity), or a specific case of the broad issue (e.g., adipose tissue proliferation). Schematics that named drivers without depicting or describing their relationships to the issue (e.g., a bullet-point list of drivers with no relationships shown or described) were excluded.

To synthesize the final set of schematics into a conceptual model, we extracted those drivers that were common to two or more issues (as well as their associations with the issues) and combined the driver-issue associations into a single model via an inductive thematic analysis, as follows. First, we familiarized ourselves with all schematics, specifically the types of drivers and ways that a given driver (e.g., global warming) might be differentially expressed (e.g., “warming climate”, “permafrost thaw”). From our identified schematics, we created an initial working list of drivers within each of the five issues. Drivers found only within one issue’s literature (e.g., physical activity factors found only in the obesity literature) were omitted from further analysis.

We then created a codebook containing preliminary names and descriptions for the drivers. Specific wording in a given schematic (e.g., “high fat diet and gut microbiota”) relevant to more than one of our drivers (e.g., ‘gut microbiota’ and ‘Western-style diet’) was captured under each relevant driver (see Additional file 1). Two authors then independently reviewed 10 references’ schematics (two from each issue), refined the driver names and descriptions in the codebook accordingly, and compared, discussed, and merged these revisions. The resulting revised codebook was used to code drivers within all schematics, and was iteratively refined during coding, such that the final codebook of drivers (Additional file 1) fully captured the drivers in the identified literature. The codebook (during development and in its final state) was reviewed by research team members from the five issue areas, to ensure that information from the literature was being accurately captured under all relevant drivers, and that names and descriptions developed for each driver adequately reflected the details from the identified literature. Using the final codebook, we identified common drivers from all schematics. Each identified driver was extracted, together with its depicted association with the issue(s) of interest and with any other drivers, and these depictions were merged to produce the conceptual model, in Vensim® PLE Plus for Macintosh (version 6.3; Ventana Systems, Inc.).

Because one issue could be a driver for another issue (e.g., adenovirus infection, under the umbrella of infectious foodborne illness, linked to obesity [74]), these associations were also extracted and included in the conceptual model. Relationships between drivers that were described textually in the schematic instead of visually (e.g., “socio-cultural norms influencing food choice”) were also included in the conceptual model. Because the goal was to depict potential connections between issues, rather than definitively represent causes, we did not assess the strength of association or the type of correlation (i.e., positive [e.g., an ‘increase’ in the driver ‘increases’ the issue], or negative [e.g., an ‘increase’ in the driver ‘decreases’ the issue]) between drivers and issues. Rather, we captured solely whether there was suggestion of a relationship among the five population health issues and their common drivers, that is, that a given driver could lead to, of have influence on, the population health issue(s).

Results

An initial 5145 references were identified from the literature search (Fig. 1). Via screening, we identified 83 relevant references containing 101 schematics; from these, 49 drivers common to two or more of the issues were identified. All drivers, and their specific wording extracted from the literature, are given by issue and reference (Additional file 1). Table 2 shows the 35 drivers shared between only two of the five population health issues [75149]. Figure 2 depicts the 11 drivers common to three issues (climate [75, 124, 138, 145, 146, 149]; consumer food choice and eating behaviours [76, 86, 91, 97, 109, 112, 115, 117119, 123126, 129, 130, 139, 141, 142, 150, 151]; individual food intake [76, 87, 97, 101, 114, 116, 120, 122, 124, 129131, 139, 141, 143, 151, 152]; food availability [76, 100, 101, 104, 112, 113, 117, 123125, 128130, 136, 138, 146]; suppressed/susceptible immune system [97, 124, 136, 146]; socioeconomic status [75, 76, 93, 98, 100, 117, 123, 124, 127, 128, 130, 137, 138, 146]; availability of clean, safe water [75, 124, 130, 148]; urbanization [113, 127, 129, 138, 145]; access to health care services [75, 100, 109, 124, 130]; food production and distribution environment and infrastructure [86, 129131, 153]; and government and industry laws, policies, and regulations [100, 101, 109, 126, 127, 130, 132, 133, 135, 138, 153]), and the three drivers common to four issues (nutrients in diet [83, 93, 97, 129, 134, 148, 154156]; changes in vegetation, habitats, and ecosystems [100, 123, 124, 133, 138, 145148]; presence of contaminants in the environment [75, 79, 80, 89, 123, 124, 137, 138, 147, 148, 153]). No drivers were common to all five issues.
Table 2

The 35 drivers shared between only two of the five food-related population health issues

 

Infectious foodborne illness

Dietary contamination

Food allergy

Food insecurity

Obesity

Population demographics [75, 76]

Diet [7780]

Gut microbiota [7779, 8197]

Food prices and affordability [98, 117, 123128]

Genetics [78, 79, 82, 84, 86, 90, 94, 97104]

Food environments [76, 100, 102, 104, 119, 126128]

Epigenetics [81, 86, 89, 97, 105109]

Social norms [113, 116, 126, 129133]

Western-style diet [83, 84, 8992, 94, 98, 110116]

Types of foods available within schools and daycares [76, 100, 125127, 133135]

Age [76, 84, 93, 97, 100, 108, 109, 117, 118]

Health status [79, 117, 124, 125, 131, 135137]

Caesarean birth [79, 81, 90, 94, 95]

Sex and gender [76, 79, 84, 100, 109, 117, 127, 128]

Use of antibiotics [79, 84, 90, 94, 95]

Ethnicity [76, 109, 118, 128]

Early life feeding [76, 79, 81, 86, 9395, 97, 98, 100, 102, 107, 108, 116, 117, 119121]

Culture [101, 117, 128130, 132, 133, 138, 139]

Maternal-fetal interaction [81, 89, 95, 98, 104106, 122]

Globalization and increasing global trade [113, 129, 130]

The economic environment [113, 123, 124, 129, 132, 133]

Food marketing and advertising [86, 98, 101, 112, 124128, 130]

Inter-personal influences and supports [76, 100, 101, 109, 117, 125, 126, 130, 131, 133, 135, 138, 139]

Food skills and knowledge [76, 86, 93, 101, 116, 117, 123, 125127, 130, 138]

Household/family structure and dynamics [76, 98, 100, 101, 116, 117, 119, 121, 125, 126, 129, 130, 133, 135, 137140]

Built environment [86, 98, 113, 123, 127, 132, 133, 135, 138, 139]

Community dynamics and well-being [76, 100, 123, 133, 137]

Time and resources needed to eat ‘healthy’ [101, 117, 123, 125, 141144]

Infectious foodborne illness

-

Global warming [75, 145147]

Changes in exposure to infectious diseases [75, 94, 96]

(none)

Precipitation [75, 145148]

Spatial co-existence of people with fauna [145, 147, 149]

Agricultural intensification [145, 147]

Dietary contaminants

-

-

(none)

Traditional foods and diet [123, 138, 147]

The food supply [124, 128, 130, 133, 147]

Food allergy

-

-

-

(none)

Fig. 2

The 14 drivers shared between three or more of the five food-related population health issues

The full diagram showing the 49 drivers, their links with the five issues, and their 227 interconnections is given in Additional file 2. Tree diagrams presenting the same links, but by individual issue, are also given (Additional file 3). Of the 49 drivers, 14 were directly associated with dietary contaminants, 14 with food allergy, 15 with infectious foodborne illness, 33 with food security, and 39 with obesity. Obesity and food insecurity shared the most drivers (n = 28). Obesity shared several drivers with food allergy (n = 11), infectious foodborne illness (n = 7), and dietary contamination (n = 6). Food insecurity shared several drivers with infectious foodborne illness (n = 9) and dietary contamination (n = 9). Infectious foodborne illness shared drivers with dietary contamination (n = 8). Fewer drivers were shared between food allergy and: food insecurity (n = 4); infectious foodborne illness (n = 2); and dietary contamination (n = 1).

Discussion

We merged visual schematics extracted from peer-reviewed literature reviews to produce a conceptual model showing how infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy are connected via 49 shared drivers. Although none of the identified drivers were surprising or unexpected in and of themselves, synthesizing the drivers into a common model provided new insights into potential interrelationships between issues for which interventions have historically been siloed in public health practice. Because of these connections, interventions targeted towards individual issues that impact the identified drivers have the potential to ‘ripple’ through the system, interacting and producing unexpected consequences. It therefore behooves individuals working in these areas to actively consider how their seemingly targeted activities (e.g., enforcing municipal sanitation requirements for food premises) may have unintended impacts in the larger system (e.g., emergency food provision suffering due to the need to expend resources on training rather than service delivery). Our conceptual model offers a heuristic for such systemic thinking that can aide future collaboration across public health practice areas to guide informed interventions. Of course, we recognize that complex conceptual models such as ours can produce “despair and retreat” [131], leaving practitioners unclear about how to operationalize findings within their day-to-day activities. To this end, we offer several concrete applications of this model, as follows.

First, this model can be used to more fully understand the range of drivers impacting a single population health issue, particularly within specific contexts. For example, practitioners wishing to identify drivers and points of intervention to improve community food security can use this conceptual model as an evidence-based draft causal loop diagram to start a group model building process [157159], thereby layering tacit knowledge from practitioners, industry, community groups, and other stakeholders and system actors onto literature-based evidence. In doing so, it will be important to ask: ‘what other key drivers and relationships do we need to add to the model, given our specific context?’; ‘what drivers and relationships are irrelevant to our context?’; and, ‘what drivers and relationships are too vague and need to be more detailed?’. Creating a more complete model, however, must be balanced against utility, since increasingly complex conceptualizations become more difficult to understand and apply. Nevertheless, this model is a useful starting point for those wishing to identify driving forces for a given issue, to “[transcend] silos to find solutions” [160].

Second, this model can be used to explore how a single driver can widely impact multiple issues, ultimately revealing high leverage drivers deserving concerted public health effort. For example, in our model, socio-economic status was directly (n = 3) or indirectly (n = 2) related to all five population health issues. Evidence indicates that low-income families of food allergic children may have difficulty managing the child’s allergy because of a lack of affordable medication and the perceived high cost of allergen-free foods [161163], and that low-income individuals are also at risk for obesity [100, 117, 127, 137], contracting foodborne disease [146, 164], and food insecurity [123, 124, 128, 130, 133]. An association between socio-economic status and exposure to dietary contaminants may exist, but is less clear [165]. Therefore, our model highlights the importance of considering the impacts of socio-economic status in addressing any of the food-health issues discussed here, concurrent with prior calls to focus on the underlying socioeconomic determinants of health [166]. Other potential high leverage drivers are those that directly influence multiple issues; here, we identified three (nutrients in diet; changes in vegetation, habitats, and ecosystems; presence of contaminants in the environment) that were directly associated with four of the five population health issues. A more detailed exploration of the specific ways in which these drivers can influence population health (e.g., ‘which nutrients in the diet may help mitigate or reduce food insecurity, allergy, obesity, and dietary contamination?’) may help identify high leverage areas where actions or interventions may yield multiple benefits.

Third, this model can be used to identify relevant systems actors with whom to engage when planning, undertaking, or evaluating public health actions, programs, or policies. Although collaboration is a necessary response to addressing complex problems [167], Trochim et al. [67] identified “supporting dynamic and diverse networks” as a key challenge to effective systems thinking in public health. Our model can be used to identify which diverse, potentially non-traditional individuals to approach, particularly from other areas within public health. For example, individuals developing school food allergy policies can use the model to ask whether actions of public health inspectors, food security advocates, nutritionists, health promotion specialists, environmental or toxicological risk assessors, or other public health actors could impact the planned policies, and therefore whether said individuals should be engaged. At the very least, public health practitioners should invite those with responsibility for other food-health issues to discuss potential impacts of their respective actions. Although public health organizations recognize the importance of engaging other non-health sectors [168], working intra-organizationally across seemingly disparate units (e.g., food safety and obesity) seems less common. Since individuals and dialogue matter within complex systems [131, 169], organizational support for dialogue across public health domain areas is needed, particularly among front line practitioners who understand the contexts and nuances of the food-health issues.

Fourth, this model can be used to explore potential unintended consequences of actions, programs, or policies. Public health practitioners can apply this model to their context-specific practice situations, to assess how their activities might impact the drivers in the model, and thus whether they might inadvertently impact any of the other four issues. For example, food security advocates developing local programs that encourage community gardens and urban agriculture should consider whether these activities have the potential to impact food allergy, infectious foodborne disease, obesity, or dietary contaminants for the population involved. Although there is evidence that community gardens can positively impact food security [61], they can also lead to soil contaminant exposure via increased incidental soil ingestion and accumulation of contaminants within edible plant tissues [6264, 170]. Whether the detrimental impact on contaminant exposure exceeds the beneficial impact on food security will inevitably be context-specific, again underscoring the need for front line practitioners to engage across the issues, to co-develop public health actions that minimize risk while striving to improve population health. In addition to identifying inadvertent impacts of an activity on population health, this model can help identify how diverse public health actions, programs, or policies may act in synergy or antagonistically. For example, food security advocates and food allergy policy makers interested in foods in schools could work together to ensure that programs that improve access to nutritious foods (e.g., school fruit and vegetable programs) and restrictions on allergic foods in classrooms are co-developed, ensuring that food allergic and food insecure children are not negatively impacted; additionally, engaging food safety experts in such plans will help minimize infectious foodborne illness risks that may inadvertently result from shifting the types of foods available and allowable in school environments.

Finally, this model may help reframe how future public health actions are evaluated and issues prioritized. Trochim et al. [67] identified priority setting by “analyzing system-wide issues rather than simply ranking disease burden or attributable risk” as a challenge to effective systems thinking in public health. Reframing successful public health initiatives from ones which reduce disease burden for one issue, to ones which do so without negative consequences for other issues, and ultimately to a suite of initiatives that act together to optimize population health across all issues may be useful. This idealistic goal may mean explicitly accepting less-than-optimal health states for individual issues in order to optimize population health overall. However, it is worth acknowledging that – akin to “health in all policies” [171], which gives non-health sectors the responsibility to consider impacts of their activities on health – public health practitioners bear a responsibility to consider “all health in policies”, that is, to explicitly consider other potential health impacts of their planned or current activities. At the very least, activities that target reducing the burden of a single issue without considering potential impacts on the other issues should be challenged to explain their impact on population health as a whole, whether any unintended negative consequences might arise, and what consultation has occurred to mitigate such consequences.

In addition to the model and its potential applications, we also offer a methodological approach for use by public health researchers faced with synthesizing evidence from different domains. We used a modified thematic synthesis [72] to merge and map evidence across five bodies of literature that varied substantially. We found the evidence varied significantly in scale, scope, and the terminology used to describe the drivers, both between and within the five issues’ bodies of literature. Our inductive thematic mapping allowed evidence about drivers to be synthesized across disciplines, despite differences in terminologies and conceptualizations. We also observed that literature on issues underpinned by disciplines with a historically strong biomedical paradigm, or for which specific single causes are known (here, infectious foodborne illness and dietary contamination) had substantially fewer reviews that collated evidence on known social, economic, political, environmental, and other drivers. In contrast, issues such as obesity and food insecurity, for which discrete causal agents do not exist, had strikingly more review articles covering the range of drivers and their relationships to the issue, and therefore yielded the majority of common drivers in our model. Thus, our model may be differentially biased towards these drivers, and future efforts to illuminate the broad range of drivers of infectious foodborne illness and dietary contamination are warranted. Nevertheless, our methodology provides a practical and systematic means of synthesizing evidence present in the peer-reviewed literature that may otherwise remain in silos.

Our results are subject to several limitations that highlight the challenges in synthesizing the vast and varied evidence encountered when investigating significant public health issues with a systems lens. To manage the volume of literature and support creation of a visual conceptual model, we chose to search for schematic representations from published review articles indexed in the database MEDLINE, meaning that we did not include grey literature, and were constrained to articles predominantly from the natural and health sciences. Thus, our conceptual model likely underrepresents important drivers investigated predominantly within the social sciences, such as factors related to food industry marketing. Even within the scope of the natural and health sciences, we recognize that our strategy captured only a subset of the known drivers of our five issues of interest. For example, relationships between food insecurity and diet [172] were not included in our final set of articles, and although our search identified ‘means of food storage and preservation’ as linked to food insecurity [130], it did not identify known links between this driver and infectious foodborne illness [173]. Thus, our conceptual model contains only a portion of the actual drivers of, and inter-relationships among, the five population health issues included here. Another limitation associated with synthesizing a large amount of literature from different disciplines is the variation in terminology, language, scope, and scale, as described above. Our multidisciplinary team was specifically assembled to overcome variations in terminology and language. However, variation in scope and scale across issues’ bodies of literature necessitated limiting our search to key important concepts (e.g., including only some key dietary contaminants), as well as simplification of complex concepts into higher-level drivers, and precluded any assessment of the types of correlations (i.e., positive, negative) and strengths of identified associations. Thus, the drivers presented in our model each comprise what is, in reality, a multifaceted set of variables and relationships of differing influences and strengths. In future, those applying this conceptual model should consider whether including more detail could help (or hinder) population health efforts.

Conclusions

This model is an evidence-based starting point for researchers and public health practitioners to collaborate across practice areas, specifically infectious foodborne illness, food insecurity, dietary contaminants, obesity, and food allergy. This model has value as a heuristic in approaching these important public health issues systemically, and can help individuals answer questions like: “what unexpected forces may impact my issue?”, “which potentially non-traditional individuals should I be involving in discussions?”, “how might my planned activities have negative impacts on population health?”, and “who should I engage with to minimize unintended consequences?”. In doing so, it is important to recognize that this model is strongly rooted in evidence from the natural and health sciences, that important evidence from the social, economic, and political science realms is likely missing, and that including experts from these missing domains will be important when answering the above questions. This model also suggests that involving other non-health sectors in multi-disciplinary collaborations may be insufficient when addressing complex population health issues, and that collaborations must also include individuals from other seemingly unrelated public health practice areas in order to best optimize policy and program outcomes. In future, research examining the utility of conceptual models such as this one in specific public health practice situations is warranted, particularly as practitioners seek to incorporate systems perspectives into day-to-day activities.

Abbreviation

CAS, complex adaptive systems

Declarations

Acknowledgements

The authors thank Jackie Stapleton (Librarian, University of Waterloo) for her assistance in structuring the literature searches.

Funding

This research was funded by the Chronic Disease Prevention Initiative, Propel Centre for Population Health Impact, University of Waterloo (PI: SE Majowicz). Outside of the participation of Propel-affiliated co-author LMM as described in the “Authors’ Contributions”, the funding body did not participate or have any role in the design of the study, the collection, analysis, and interpretation of data, nor in writing the manuscript.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and additional files.

Authors’ contributions

SEM, SIK, SBM, and SJE conceived the study. All authors designed the overall methods, and SEM and SBM designed the inductive thematic analysis methods. All authors designed the literature search strategy. SEM and JLG conducted the analyses with input from all authors, and all authors participated in interpreting the results. SEM and JLG drafted the initial manuscript, and all authors wrote sections of, or provided substantial input into, revisions. All authors approved the final version.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethics approval was not required as this study used published literature only.

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’ Affiliations

(1)
School of Public Health and Health Systems, University of Waterloo
(2)
Social Development Studies, Renison University College-University of Waterloo
(3)
Department of Geography & Environmental Management, University of Waterloo
(4)
Propel Centre for Population Health Impact, University of Waterloo

References

  1. Lynch MF, Tauxe RV, Hedberg CW. The growing burden of foodborne outbreaks due to contaminated fresh produce: risks and opportunities. Epidemiol Infect. 2009;137(03):307–15.PubMedView ArticleGoogle Scholar
  2. Swaminathan B, Gerner-Smidt P. The epidemiology of human listeriosis. Microbes Infect. 2007;9(10):1236–43.PubMedView ArticleGoogle Scholar
  3. Allos BM, Moore MR, Griffin PM, Tauxe RV. Surveillance for sporadic foodborne disease in the 21st century: the FoodNet perspective. Clin Infect Dis. 2004;38 Suppl 3:S115–20.PubMedView ArticleGoogle Scholar
  4. Adak GK, Meakins SM, Yip H, Lopman BA, O’Brien SJ. Disease risks from foods, England and Wales, 1996–2000. Emerg Infect Dis. 2005;11(3):365–72.PubMedPubMed CentralView ArticleGoogle Scholar
  5. Lack G. Epidemiologic risks for food allergy. J Allergy Clin Immunol. 2008;121(6):1331–6.PubMedView ArticleGoogle Scholar
  6. Sausenthaler S, Koletzko S, Schaaf B, et al. Maternal diet during pregnancy in relation to eczema and allergic sensitization in the offspring at 2 y of age. Am J Clin Nutr. 2007;85(2):530–7.PubMedGoogle Scholar
  7. Devereux G. The increase in the prevalence of asthma and allergy: food for thought. Nat Rev Immunol. 2006;6(11):869–74.PubMedView ArticleGoogle Scholar
  8. Soller L, Ben-Shoshan M, Harrington D, et al. Prevalence and predictors of food allergy in Canada: a focus on vulnerable populations. J Allergy Clin Immunol. 2015;3(1):42–9.View ArticleGoogle Scholar
  9. Ben-Shoshan M, Harrington D, Soller L, et al. Demographic predictors of peanut, tree nut, fish, shellfish and sesame allergy in Canada. J Allergy. 2012;2012(ID 858306):1–6.View ArticleGoogle Scholar
  10. Van Oostdam J, Gilman A, Dewailly E, et al. Human health implications of environmental contaminants in Arctic Canada: a review. Sci Total Environ. 1999;230(1):1–82.PubMedView ArticleGoogle Scholar
  11. Reddy KRN, Salleh B, Saad B, Abbas HK, Abel CA, Shier WT. An overview of mycotoxin contamination in foods and its implications for human health. Toxin Rev. 2010;29(1):3–26.View ArticleGoogle Scholar
  12. Rampersaud GC, Pereira MA, Girard BL, Adams J, Metzl JD. Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. J Am Diet Assoc. 2005;105(5):743–60.PubMedView ArticleGoogle Scholar
  13. Arimond M, Ruel MT. Dietary diversity is associated with child nutritional status: evidence from 11 demographic and health surveys. J Nutr. 2004;134(10):2579–85.PubMedGoogle Scholar
  14. Houston DK, Nicklas BJ, Ding J, et al. Dietary protein intake is associated with lean mass change in older, community-dwelling adults: the Health, Aging, and Body Composition (Health ABC) study. Am J Clin Nutr. 2008;87(1):150–5.PubMedGoogle Scholar
  15. Layman DK, Boileau RA, Erickson DJ, et al. A reduced ratio of dietary carbohydrate to protein improves body composition and blood lipid profiles during weight loss in adult women. J Nutr. 2003;133(2):411–7.PubMedGoogle Scholar
  16. Carruth BR, Skinner JD. The role of dietary calcium and other nutrients in moderating body fat in preschool children. Int J Obes Relat Metab Disord. 2001;25(4):559–66.PubMedView ArticleGoogle Scholar
  17. Gomez-Pinilla F. The influences of diet and exercise on mental health through hormesis. Ageing Res Rev. 2008;7(1):49–62.PubMedView ArticleGoogle Scholar
  18. Simon GE, Von Korff M, Saunders K, et al. Association between obesity and psychiatric disorders in the US adult population. Arch Gen Psychiatry. 2006;63(7):824–30.PubMedPubMed CentralView ArticleGoogle Scholar
  19. Logan AC. Omega-3 fatty acids and major depression: a primer for the mental health professional. Lipids Health Dis. 2004;3(25):1–8.Google Scholar
  20. George SM, Ballard-Barbash R, Manson JE, et al. Comparing indices of diet quality with chronic disease mortality risk in postmenopausal women in the Women’s health initiative observational study: evidence to inform national dietary guidance. Am J Epidemiol. 2014;180(6):616–25.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Hert KA, Fisk PS, Rhee YS, Brunt AR. Decreased consumption of sugar-sweetened beverages improved selected biomarkers of chronic disease risk among US adults: 1999 to 2010. Nutr Res. 2014;34(1):58–65.PubMedView ArticleGoogle Scholar
  22. Fardet A, Boirie Y. Associations between food and beverage groups and major diet-related chronic diseases: an exhaustive review of pooled/meta-analyses and systematic reviews. Nutr Rev. 2014;72(12):741–62.PubMedView ArticleGoogle Scholar
  23. Conklin AI, Forouhi NG, Suhrcke M, Surtees P, Wareham NJ, Monsivais P. Variety more than quantity of fruit and vegetable intake varies by socioeconomic status and financial hardship. Findings from older adults in the EPIC cohort. Appetite. 2014;83:248–55.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Atkins JL, Ramsay SE, Whincup PH, Morris RW, Lennon LT, Wannamethee SG. Diet quality in older age: the influence of childhood and adult socio-economic circumstances. Br J Nutr. 2015;113(09):1441–52.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Fernandez-Alvira JM, Bammann K, Pala V, et al. Country-specific dietary patterns and associations with socioeconomic status in European children: the IDEFICS study. Eur J Clin Nutr. 2014;68(7):811–21.PubMedView ArticleGoogle Scholar
  26. Mark S, Lambert M, O’Loughlin J, Gray-Donald K. Household income, food insecurity and nutrition in Canadian youth. Can J Public Health. 2012;103:94–9.PubMedGoogle Scholar
  27. Walker RE, Kawachi I. Use of concept mapping to explore the influence of food security on food buying practices. J Acad Nutr Diet. 2012;112(5):711–7.PubMedView ArticleGoogle Scholar
  28. Drewnowski A. Taste preferences and food intake. Annu Rev Nutr. 1997;17(1):237–53.PubMedView ArticleGoogle Scholar
  29. Birch LL. Preschool children’s food preferences and consumption patterns. J Nutr Educ. 1979;11(4):189–92.View ArticleGoogle Scholar
  30. Drewnowski A, Hann C. Food preferences and reported frequencies of food consumption as predictors of current diet in young women. Am J Clin Nutr. 1999;70(1):28–36.PubMedGoogle Scholar
  31. Shatenstein B, Ghadirian P. Influences on diet, health behaviours and their outcome in select ethnocultural and religious groups. Nutrition. 1998;14(2):223–30.PubMedView ArticleGoogle Scholar
  32. Blissett J, Bennett C. Cultural differences in parental feeding practices and children’s eating behaviours and their relationships with child BMI: a comparison of Black Afro-Caribbean, White British and White German samples. Eur J Clin Nutr. 2013;67(2):180–4.PubMedView ArticleGoogle Scholar
  33. Anthony D, Baggott R, Tanner J, et al. Health, lifestyle, belief and knowledge differences between two ethnic groups with specific reference to tobacco, diet and physical activity. J Adv Nurs. 2012;68(11):2496–503.PubMedView ArticleGoogle Scholar
  34. Orzech KM, Vivian J, Torres CH, Armin J, Shaw SJ. Diet and exercise adherence and practices among medically underserved patients with chronic disease variation across four ethnic groups. Health Educ Behav. 2013;40(1):56–66.PubMedView ArticleGoogle Scholar
  35. Haddad L. Redirecting the diet transition: What can food policy do? Dev Policy Rev. 2003;21(165):599–614.View ArticleGoogle Scholar
  36. Zizza CA. Policies and politics of the US food supply. J Acad Nutr Diet. 2015;1(115):27–30.View ArticleGoogle Scholar
  37. Nestle M, Wilson T. Food industry and political influences on American nutrition. In: Nutritional health. New York City: Humana Press; 2012. p. 477–90.View ArticleGoogle Scholar
  38. Drewnowski A, Darmon N. The economics of obesity: dietary energy density and energy cost. Am J Clin Nutr. 2005;82(1):265S–73.PubMedGoogle Scholar
  39. Lo YT, Chang YH, Lee MS, Wahlqvist ML. Health and nutrition economics: diet costs are associated with diet quality. Asia Pac J Clin Nutr. 2009;18(4):598.PubMedGoogle Scholar
  40. Drewnowski A, Darmon N. Food choices and diet costs: an economic analysis. J Nutr. 2005;135(4):900–4.PubMedGoogle Scholar
  41. Rayner G, Hawkes C, Lang T, Bello W. Trade liberalization and the diet transition: a public health response. Health Promot Int. 2006;21 Suppl 1:67–74.PubMedView ArticleGoogle Scholar
  42. Hawkes C. Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Glob Health. 2006;2(1):4.View ArticleGoogle Scholar
  43. Nestle M. Food politics: How the food industry influences nutrition and health, vol. 3. Berkeley: Univ of California Press; 2013.Google Scholar
  44. Buttriss JL. Food reformulation: the challenges to the food industry. Proc Nutr Soc. 2013;72(01):61–9.PubMedView ArticleGoogle Scholar
  45. Kraak VI, Story M, Wartella EA, Ginter J. Industry progress to market a healthful diet to American children and adolescents. Am J Prev Med. 2011;41(3):322–33.PubMedView ArticleGoogle Scholar
  46. MacKay S. Legislative solutions to unhealthy eating and obesity in Australia. Public Health. 2011;125(12):896–904.PubMedView ArticleGoogle Scholar
  47. Roberto CA, Schwartz MB, Brownell KD. Rationale and evidence for menu-labeling legislation. Am J Prev Med. 2009;37(6):546–51.PubMedView ArticleGoogle Scholar
  48. Sallis JF, Glanz K. The role of built environments in physical activity, eating, and obesity in childhood. Future Child. 2006;16(1):89–108.PubMedView ArticleGoogle Scholar
  49. Rahmanian E, Gasevic D. The association between the built environment and dietary intake-a systematic review. Asia Pac J Clin Nutr. 2014;23(2):183.PubMedGoogle Scholar
  50. Carroll-Scott A, Gilstad-Hayden K, Rosenthal L, et al. Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: the role of built, socioeconomic, and social environments. Soc Sci Med. 2013;95:106–14.PubMedPubMed CentralView ArticleGoogle Scholar
  51. Tirado MC, Crahay P, Mahy L, Zanev C, Neira M, Msangi S, et al. Climate change and nutrition: creating a climate for nutrition security. Food Nutr Bull. 2013;34(4):533–47.PubMedView ArticleGoogle Scholar
  52. Wheeler T, von Braun J. Climate change impacts on global food security. Science. 2013;341(6145):508–13.PubMedView ArticleGoogle Scholar
  53. Smith P, Gregory PJ. Climate change and sustainable food production. Proc Nutr Soc. 2013;72(1):21–8.PubMedView ArticleGoogle Scholar
  54. Meadows DH, Wright D. Thinking in systems: a primer. White River Junction: Chelsea Green Publishing; 2008.Google Scholar
  55. Doyle ME, Glass KA. Sodium reduction and its effect on food safety, food quality, and human health. Compr Rev Food Sci Food. 2010;9(1):44–56.View ArticleGoogle Scholar
  56. Taylor CL, Henry JE. Preservation and physical property roles of sodium in foods. In: Strategies to reduce sodium intake in the United States. Washington: National Academies Press; 2010. Chpt. 4.Google Scholar
  57. Bibbins-Domingo K, Chertow GM, Coxson PG, et al. Projected effect of dietary salt reductions on future cardiovascular disease. N Engl J Med. 2010;362(7):590–9.PubMedPubMed CentralView ArticleGoogle Scholar
  58. Asaria P, Chisholm D, Mathers C, Ezzati M, Beaglehole R. Chronic disease prevention: health effects and financial costs of strategies to reduce salt intake and control tobacco use. Lancet. 2007;370(9604):2044–53.PubMedView ArticleGoogle Scholar
  59. Barthel S, Isendahl C. Urban gardens, agriculture, and water management: sources of resilience for long-term food security in cities. Ecol Econ. 2013;86:224–34.View ArticleGoogle Scholar
  60. Brown KH, Jameton AL. Public health implications of urban agriculture. J Public Health Policy. 2000;21:20–39.PubMedView ArticleGoogle Scholar
  61. Carney PA, Hamada JL, Rdesinski R, et al. Impact of a community gardening project on vegetable intake, food security and family relationships: a community-based participatory research study. J Community Health. 2012;37(4):874–81.PubMedPubMed CentralView ArticleGoogle Scholar
  62. Hough RL, Breward N, Young SD, et al. Assessing potential risk of heavy metal exposure from consumption of home-produced vegetables by urban populations. Environ Health Perspect. 2004;112(2):215.PubMedPubMed CentralView ArticleGoogle Scholar
  63. Finster ME, Gray KA, Binns HJ. Lead levels of edibles grown in contaminated residential soils: a field survey. Sci Total Environ. 2004;320(2):245–57.PubMedView ArticleGoogle Scholar
  64. Clark HF, Brabander DJ, Erdil RM. Sources, sinks, and exposure pathways of lead in urban garden soil. J Environ Qual. 2006;35(6):2066–74.PubMedView ArticleGoogle Scholar
  65. Bacigalupo C, Hale B. Human health risks of Pb and As exposure via consumption of home garden vegetables and incidental soil and dust ingestion: a probabilistic screening tool. Sci Total Environ. 2012;423:27–38.PubMedView ArticleGoogle Scholar
  66. Leischow S, Best A, Trochim WM, et al. Systems thinking to improve the public’s health. Am J Prev Med. 2008;35(2):S196–203.PubMedPubMed CentralView ArticleGoogle Scholar
  67. Trochim WM, Cabrera DA, Milstein B, Gallagher RS, Leischow SJ. Practical challenges of systems thinking and modeling in public health. Am J Public Health. 2006;96(3):538–46.PubMedPubMed CentralView ArticleGoogle Scholar
  68. Government Office for Science. Reducing obesity: Obesity System Map. 2007. https://www.gov.uk/government/publications/reducing-obesity-future-choices. Accessed 21 Apr 2015.Google Scholar
  69. Olson S, Miller EA, Troy LM, editors. Hunger and obesity: understanding a food insecurity paradigm: workshop summary. Washington: National Academies Press; 2011.Google Scholar
  70. Xuereb M, Desjardins E. Towards a healthy community food system for Waterloo Region. 2005. http://chd.region.waterloo.on.ca/en/researchResourcesPublications/resources/FoodSystems_Report.pdf. Accessed 17 Nov 2015.Google Scholar
  71. Jayasinghe S. Conceptualising population health: from mechanistic thinking to complexity science. Emerg Themes Epidemiol. 2011;8:2.PubMedPubMed CentralView ArticleGoogle Scholar
  72. Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008;8(1):45.PubMedPubMed CentralView ArticleGoogle Scholar
  73. Pope C, Mays N, Popay J. Synthesizing qualitative and quantitative health evidence: a guide to methods. Maidenhead: McGraw Hill Education; 2007.Google Scholar
  74. Hur SJ, Kim DH, Chun SC, Lee SK. Effect of adenovirus and influenza virus infection on obesity. Life Sci. 2013;93(16):531–5.PubMedView ArticleGoogle Scholar
  75. Patz JA, Hahn MB. Climate change and human health: a One Health approach. Curr Top Microbiol Immunol. 2013;366:141–71.PubMedGoogle Scholar
  76. Galvez MP, Pearl M, Yen IH. Childhood obesity and the built environment. Curr Opin Pediatr. 2010;22(2):202–7.PubMedPubMed CentralView ArticleGoogle Scholar
  77. Cox AJ, West NP, Cripps AW. Obesity, inflammation, and the gut microbiota. Lancet Diabetes Endocrinol. 2015;3(3):207–15.PubMedView ArticleGoogle Scholar
  78. Ganu RS, Harris RA, Collins K, Aagaard KM. Maternal diet: a modulator for epigenomic regulation during development in nonhuman primates and humans. Int J Obes Suppl. 2012;2 Suppl 2:S14–8.PubMedPubMed CentralView ArticleGoogle Scholar
  79. Cox LM, Blaser MJ. Antibiotics in early life and obesity. Nat Rev Endocrinol. 2015;11(3):182–90.PubMedView ArticleGoogle Scholar
  80. Lassiter MG, Owens EO, Patel MM, et al. Cross-species coherence in effects and modes of action in support of causality determinations in the U.S. Environmental Protection Agency’s Integrated Science Assessment for Lead. Toxicology. 2015;330:19–40.PubMedView ArticleGoogle Scholar
  81. Paliy O, Piyathilake CJ, Kozyrskyj A, Celep G, Marotta F, Rastmanesh R. Excess body weight during pregnancy and offspring obesity: potential mechanisms. Nutrition. 2014;30(3):245–51.PubMedView ArticleGoogle Scholar
  82. Pataky Z, Bobbioni-Harsch E, Golay A. Obesity: a complex growing challenge. Exp Clin Endocrinol Diabetes. 2010;118(7):427–33.PubMedView ArticleGoogle Scholar
  83. Teixeira TF, Collado MC, Ferreira CL, Bressan J, Peluzio Mdo C. Potential mechanisms for the emerging link between obesity and increased intestinal permeability. Nutr Res. 2012;32(9):637–47.PubMedView ArticleGoogle Scholar
  84. Delzenne NM, Neyrinck AM, Backhed F, Cani PD. Targeting gut microbiota in obesity: effects of prebiotics and probiotics. Nat Rev Endocrinol. 2011;7(11):639–46.PubMedView ArticleGoogle Scholar
  85. Moran CP, Shanahan F. Gut microbiota and obesity: role in aetiology and potential therapeutic target. Best Pract Res Clin Gastroenterol. 2014;28(4):585–97.PubMedView ArticleGoogle Scholar
  86. Palou A, Bonet ML. Challenges in obesity research. Nutr Hosp. 2013;28 Suppl 5:144–53.PubMedGoogle Scholar
  87. Flint HJ. Obesity and the gut microbiota. J Clin Gastroenterol. 2011;45(Suppl):S128–32.PubMedView ArticleGoogle Scholar
  88. Tsai YT, Cheng PC, Pan TM. Anti-obesity effects of gut microbiota are associated with lactic acid bacteria. Appl Microbiol Biotechnol. 2014;98(1):1–10.PubMedView ArticleGoogle Scholar
  89. Holvoet P. Stress in obesity and associated metabolic and cardiovascular disorders. Scientifica. 2012;2012:205027.PubMedPubMed CentralView ArticleGoogle Scholar
  90. Cox LM, Blaser MJ. Pathways in microbe-induced obesity. Cell Metab. 2013;17(6):883–94.PubMedPubMed CentralView ArticleGoogle Scholar
  91. Prescott SL. Early-life environmental determinants of allergic diseases and the wider pandemic of inflammatory noncommunicable diseases. J Allergy Clin Immunol. 2013;131(1):23–30.PubMedView ArticleGoogle Scholar
  92. Burcelin R. Regulation of metabolism: a cross talk between gut microbiota and its human host. Physiology (Bethesda). 2012;27(5):300–7.View ArticleGoogle Scholar
  93. Thompson AL. Developmental origins of obesity: early feeding environments, infant growth, and the intestinal microbiome. Am J Hum Biol. 2012;24(3):350–60.PubMedView ArticleGoogle Scholar
  94. Feehley T, Stefka AT, Cao S, Nagler CR. Microbial regulation of allergic responses to food. Semin Immunopathol. 2012;34(5):671–88.PubMedView ArticleGoogle Scholar
  95. Tsabouri S, Priftis KN, Chaliasos N, Siamopoulou A. Modulation of gut microbiota downregulates the development of food allergy in infancy. Allergol Immunopathol (Madr). 2014;42(1):69–77.View ArticleGoogle Scholar
  96. Vassallo MF, Camargo Jr CA. Potential mechanisms for the hypothesized link between sunshine, vitamin D, and food allergy in children. J Allergy Clin Immunol. 2010;126(2):217–22.PubMedView ArticleGoogle Scholar
  97. Brandtzaeg P. Food allergy: separating the science from the mythology. Nat Rev Gastroenterol Hepatol. 2010;7(7):380–400.PubMedView ArticleGoogle Scholar
  98. Monasta L, Batty GD, Cattaneo A, Lutje V, Ronfani L, Van Lenthe FJ, et al. Early-life determinants of overweight and obesity: a review of systematic reviews. Obes Rev. 2010;11(10):695–708.PubMedView ArticleGoogle Scholar
  99. deShazo RD, Hall JE, Skipworth LB. Obesity bias, medical technology, and the hormonal hypothesis: Should we stop demonizing fat people? Am J Med. 2015;128(5):456–60.PubMedView ArticleGoogle Scholar
  100. Willows ND, Hanley AJ, Delormier T. A socioecological framework to understand weight-related issues in Aboriginal children in Canada. Appl Physiol Nutr Metab. 2012;37(1):1–13.PubMedView ArticleGoogle Scholar
  101. Lunn TE, Nowson CA, Worsley A, Torres SJ. Does personality affect dietary intake? Nutrition. 2014;30(4):403–9.PubMedView ArticleGoogle Scholar
  102. Carnell S, Kim Y, Pryor K. Fat brains, greedy genes, and parent power: a biobehavioural risk model of child and adult obesity. Int Rev Psychiatry. 2012;24(3):189–99.PubMedView ArticleGoogle Scholar
  103. Carnell S, Gibson C, Benson L, Ochner CN, Geliebter A. Neuroimaging and obesity: current knowledge and future directions. Obes Rev. 2012;13(1):43–56.PubMedView ArticleGoogle Scholar
  104. Tounian P. Programming towards childhood obesity. Ann Nutr Metab. 2011;58 Suppl 2:30–41.PubMedView ArticleGoogle Scholar
  105. Galliano D, Bellver J. Female obesity: short- and long-term consequences on the offspring. Gynecol Endocrinol. 2013;29(7):626–31.PubMedView ArticleGoogle Scholar
  106. O’Reilly JR, Reynolds RM. The risk of maternal obesity to the long-term health of the offspring. Clin Endocrinol (Oxf). 2013;78(1):9–16.View ArticleGoogle Scholar
  107. Bourguignon JP, Parent AS. Early homeostatic disturbances of human growth and maturation by endocrine disrupters. Curr Opin Pediatr. 2010;22(4):470–7.PubMedView ArticleGoogle Scholar
  108. Zheng J, Xiao X, Zhang Q, Yu M. DNA methylation: the pivotal interaction between early-life nutrition and glucose metabolism in later life. Br J Nutr. 2014;112(11):1850–7.PubMedView ArticleGoogle Scholar
  109. Wang ML, Peterson KE, McCormick MC, Austin SB. Environmental factors associated with disordered weight-control behaviours among youth: a systematic review. Public Health Nutr. 2014;17(7):1654–67.PubMedView ArticleGoogle Scholar
  110. Davidson TL, Sample CH, Swithers SE. An application of Pavlovian principles to the problems of obesity and cognitive decline. Neurobiol Learn Mem. 2014;108:172–84.PubMedView ArticleGoogle Scholar
  111. Kanoski SE, Davidson TL. Western diet consumption and cognitive impairment: links to Hippocampal dysfunction and obesity. Physiol Behav. 2011;103(1):59–68.PubMedView ArticleGoogle Scholar
  112. Rolls ET. Taste, olfactory and food texture reward processing in the brain and obesity. Int J Obes (Lond). 2011;35(4):550–61.View ArticleGoogle Scholar
  113. Malik VS, Willett WC, Hu FB. Global obesity: trends, risk factors and policy implications. Nat Rev Endocrinol. 2013;9(1):13–27.PubMedView ArticleGoogle Scholar
  114. Wells JC, Siervo M. Obesity and energy balance: Is the tail wagging the dog? Eur J Clin Nutr. 2011;65(11):1173–89.PubMedView ArticleGoogle Scholar
  115. Ryan KK, Woods SC, Seeley RJ. Central nervous system mechanisms linking the consumption of palatable high-fat diets to the defense of greater adiposity. Cell Metab. 2012;15(2):137–49.PubMedPubMed CentralView ArticleGoogle Scholar
  116. Skouteris H, McCabe M, Swinburn B, Newgreen V, Sacher P, Chadwick P. Parental influence and obesity prevention in pre-schoolers: a systematic review of interventions. Obes Rev. 2011;12(5):315–28.PubMedView ArticleGoogle Scholar
  117. El-Behadli AF, Sharp C, Hughes SO, Obasi EM, Nicklas TA. Maternal depression, stress and feeding styles: towards a framework for theory and research in child obesity. Br J Nutr. 2015;113(Suppl):S55–71.PubMedView ArticleGoogle Scholar
  118. Melzer K, Schutz Y. Pre-pregnancy and pregnancy predictors of obesity. Int J Obes (Lond). 2010;34 Suppl 2:S44–52.View ArticleGoogle Scholar
  119. Larsen JK, Hermans RC, Sleddens EF, Engels RC, Fisher JO, Kremers SS. How parental dietary behavior and food parenting practices affect children’s dietary behavior. Interacting sources of influence? Appetite. 2015;89:246–57.PubMedView ArticleGoogle Scholar
  120. DiSantis KI, Hodges EA, Johnson SL, Fisher JO. The role of responsive feeding in overweight during infancy and toddlerhood: a systematic review. Int J Obes (Lond). 2011;35(4):480–92.View ArticleGoogle Scholar
  121. Faith MS, Van Horn L, Appel LJ, Burke LE, Carson JA, Franch HA, et al. Evaluating parents and adult caregivers as “agents of change” for treating obese children: evidence for parent behavior change strategies and research gaps. Circulation. 2012;125(9):1186–207.PubMedView ArticleGoogle Scholar
  122. Calkins K, Devaskar SU. Fetal origins of adult disease. Curr Probl Pediatr Adolesc Health Care. 2011;41(6):158–76.PubMedPubMed CentralView ArticleGoogle Scholar
  123. Skinner K, Hanning RM, Desjardins E, Tsuji L. Giving voice to food insecurity in a remote indigenous community in subarctic Ontario, Canada: traditional ways, ways to cope, ways forward. BMC Public Health. 2013;13:427.PubMedPubMed CentralView ArticleGoogle Scholar
  124. Hammond RA, Dube L. A systems science perspective and transdisciplinary models for food and nutrition security. Proc Natl Acad Sci U S A. 2012;109(31):12356.PubMedPubMed CentralView ArticleGoogle Scholar
  125. Pocock M, Trivedi D, Wills W, Bunn F, Magnusson J. Parental perceptions regarding healthy behaviours for preventing overweight and obesity in young children: a systematic review of qualitative studies. Obes Rev. 2010;11(5):338–53.PubMedView ArticleGoogle Scholar
  126. Minaker LM. Evaluating food environment assessment methodologies: a multi-level examination of associations between food environments and individual outcomes, PhD thesis. Edmonton: University of Alberta; 2013.Google Scholar
  127. Franco M, Sanz B, Otero L, Dominguez-Vila A, Caballero B. Prevention of childhood obesity in Spain: a focus on policies outside the health sector. SESPAS report 2010. Gac Sanit. 2010;24 Suppl 1:49–55.PubMedView ArticleGoogle Scholar
  128. Neff RA, Palmer AM, McKenzie SE, Lawrence RS. Food systems and public health disparities. J Hunger Environ Nutr. 2009;4:282.PubMedPubMed CentralView ArticleGoogle Scholar
  129. Dixon J, Omwega AM, Friel S, Burns C, Donati K, Carlisle R. The health equity dimensions of urban food systems. J Urban Health. 2007;84(S1):118–29.PubMed CentralView ArticleGoogle Scholar
  130. Burchi F, Fanzo J, Frison E. The role of food and nutrition system approaches in tackling hidden hunger. Int J Environ Res Public Health. 2011;8(2):358–73.PubMedPubMed CentralView ArticleGoogle Scholar
  131. Finegood DT, Merth TDN, Rutter H. Implications of the Foresight Obesity System Map for solutions to childhood obesity. Obesity. 2010;18(S1):S13–6.PubMedView ArticleGoogle Scholar
  132. Kremers SP. Theory and practice in the study of influences on energy balance-related behaviors. Patient Educ Couns. 2010;79(3):291–8.PubMedView ArticleGoogle Scholar
  133. Thompson S, Kamal AG. Community development to feed the family in northern Manitoba communities: evaluating food activities on their food sovereignty, food security, and sustainable livelihood outcomes. Can J Nonprofit Soc Econ Res. 2012;3(2):43–66.Google Scholar
  134. Brennan LK, Brownson RC, Orleans CT. Childhood obesity policy research and practice: evidence for policy and environmental strategies. Am J Prev Med. 2014;46(1):e1–16.PubMedPubMed CentralView ArticleGoogle Scholar
  135. Cockrell Skinner A, Foster EM. Systems science and childhood obesity: a systematic review and new directions. J Obes. 2013;2013(ID 129193):1–10.View ArticleGoogle Scholar
  136. Frega R, Duffy F, Rawat R, Grede N. Food insecurity in the context of HIV/AIDS: a framework for a new era of programming. Food Nutr Bull. 2010;31(4):S292–312.View ArticleGoogle Scholar
  137. Schreier HM, Chen E. Socioeconomic status and the health of youth: a multilevel, multidomain approach to conceptualizing pathways. Psychol Bull. 2013;139(3):606–54.PubMedView ArticleGoogle Scholar
  138. Elliott B, Jayatilaka D, Brown C, Varley L, Corbett KK. “We are not being heard”: aboriginal perspectives on traditional foods access and food security. J Environ Public Health. 2012;2012(ID 130945):1–9.View ArticleGoogle Scholar
  139. Perez-Escamilla R, Kac G. Childhood obesity prevention: a life-course framework. Int J Obes Suppl. 2013;3 Suppl 1:S3–5.PubMedPubMed CentralView ArticleGoogle Scholar
  140. Kitzman-Ulrich H, Wilson DK, St George SM, Lawman H, Segal M, Fairchild A. The integration of a family systems approach for understanding youth obesity, physical activity, and dietary programs. Clin Child Fam Psychol Rev. 2010;13(3):231–53.PubMedPubMed CentralView ArticleGoogle Scholar
  141. Hart CN, Cairns A, Jelalian E. Sleep and obesity in children and adolescents. Pediatr Clin North Am. 2011;58(3):715–33.PubMedPubMed CentralView ArticleGoogle Scholar
  142. Shlisky JD, Hartman TJ, Kris-Etherton PM, Rogers CJ, Sharkey NA, Nickols-Richardson SM. Partial sleep deprivation and energy balance in adults: an emerging issue for consideration by dietetics practitioners. J Acad Nutr Diet. 2012;112(11):1785–97.PubMedView ArticleGoogle Scholar
  143. Knutson KL. Does inadequate sleep play a role in vulnerability to obesity? Am J Hum Biol. 2012;24(3):361–71.PubMedPubMed CentralView ArticleGoogle Scholar
  144. Zimberg IZ, Damaso A, Del Re M, et al. Short sleep duration and obesity: mechanisms and future perspectives. Cell Biochem Funct. 2012;30(6):524–9.PubMedView ArticleGoogle Scholar
  145. Atkinson JA, Gray DJ, Clements AC, Barnes TS, McManus DP, Yang YR. Environmental changes impacting Echinococcus transmission: research to support predictive surveillance and control. Glob Chang Biol. 2013;19(3):677–88.PubMedView ArticleGoogle Scholar
  146. Weaver HJ, Hawdon JM, Hoberg EP. Soil-transmitted helminthiases: implications of climate change and human behavior. Trends Parasitol. 2010;26(12):574–81.PubMedView ArticleGoogle Scholar
  147. Armitage JM, Quinn CL, Wania F. Global climate change and contaminants--An overview of opportunities and priorities for modelling the potential implications for long-term human exposure to organic compounds in the Arctic. J Environ Monit. 2011;13(6):1532–46.PubMedView ArticleGoogle Scholar
  148. Brambilla G, D’Hollander W, Oliaei F, Stahl T, Weber R. Pathways and factors for food safety and food security at PFOS contaminated sites within a problem based learning approach. Chemosphere. 2015;129:192–202.PubMedView ArticleGoogle Scholar
  149. Ciaccio CE, Kennedy K, Portnoy JM. A new model for environmental assessment and exposure reduction. Curr Allergy Asthma Rep. 2012;12(6):650–5.PubMedPubMed CentralView ArticleGoogle Scholar
  150. Sinha R, Jastreboff AM. Stress as a common risk factor for obesity and addiction. Biol Psychiatry. 2013;73(9):827–35.PubMedPubMed CentralView ArticleGoogle Scholar
  151. Singh M. Mood, food, and obesity. Front Psychol. 2014;5:925.PubMedPubMed CentralView ArticleGoogle Scholar
  152. Bray GA, Popkin BM. Calorie-sweetened beverages and fructose: What have we learned 10 years later. Pediatr Obes. 2013;8(4):242–8.PubMedView ArticleGoogle Scholar
  153. Duarte SC, Pena A, Lino CM. A review on ochratoxin A occurrence and effects of processing of cereal and cereal derived food products. Food Microbiol. 2010;27(2):187–98.PubMedView ArticleGoogle Scholar
  154. Soares MJ, Pathak K, Calton EK. Calcium and vitamin D in the regulation of energy balance: Where do we stand? Int J Mol Sci. 2014;15(3):4938–45.PubMedPubMed CentralView ArticleGoogle Scholar
  155. Mangge H, Summers K, Almer G, et al. Antioxidant food supplements and obesity-related inflammation. Curr Med Chem. 2013;20(18):2330–7.PubMedView ArticleGoogle Scholar
  156. Fardet A. New hypotheses for the health-protective mechanisms of whole-grain cereals: What is beyond fibre? Nutr Res Rev. 2010;23(1):65–134.PubMedView ArticleGoogle Scholar
  157. Berard C. Group model building using system dynamics: an analysis of methodological frameworks. Electron J Bus Res Methods. 2010;8(1):35–45.Google Scholar
  158. Allender S, Owen B, Kuhlberg J, et al. A community based systems diagram of obesity causes. PLoS One. 2015;10(7):e0129683.PubMedPubMed CentralView ArticleGoogle Scholar
  159. Siokou C, Morgan R, Shiell A. Group model building: a participatory approach to understanding and acting on systems. Public Health Res Pract. 2014;25(1):e2511404.PubMedView ArticleGoogle Scholar
  160. Machalaba C, Romanelli C, Stoett P, et al. Climate change and health: transcending silos to find solutions. Ann Glob Health. 2015;81(3):445–58.PubMedView ArticleGoogle Scholar
  161. Minaker LM, Elliott SJ, Clarke A. Exploring low income families’ financial barriers to food allergy management and treatment. J Allergy. 2014;2014(ID 160363):1–7.View ArticleGoogle Scholar
  162. Minaker LM, Elliott SJ, Clarke A. Low income, high risk: the overlapping stigmas of food allergy and poverty. Crit Public Health. 2015;25(5):599–614.View ArticleGoogle Scholar
  163. Protudjer JLP, Jansson SA, Arnlind MH, et al. Household costs associated with objectively diagnosed allergy to staple foods in children and adolescents. J Allergy Clin Immunol. 2015;3(1):68–75.View ArticleGoogle Scholar
  164. Quinlan JJ. Foodborne illness incidence rates and food safety risks for populations of low socioeconomic status and minority race/ethnicity: a review of the literature. Int J Environ Res Public Health. 2013;10(8):3634–52.PubMedPubMed CentralView ArticleGoogle Scholar
  165. Freire C, Amaya E, Fernandez MF, et al. Relationship between occupational social class and exposure to organochlorine pesticides during pregnancy. Chemosphere. 2011;83(6):831–8.PubMedView ArticleGoogle Scholar
  166. Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health. 2010;100(4):590–5.PubMedPubMed CentralView ArticleGoogle Scholar
  167. Australian Government (Australian Public Service Commission). Tackling wicked problems: a public policy perspective. 2012. http://www.apsc.gov.au/publications-and-media/archive/publications-archive/tackling-wicked-problems. Accessed 17 Nov 2015.Google Scholar
  168. World Health Organization. Health in all policies: training manual. 2015. http://who.int/social_determinants/publications/health-policies-manual/en/. Accessed 3 Dec 2015.Google Scholar
  169. Kreuter MW, De Rosa C, Howze EH, Baldwin GT. Understanding wicked problems: a key to advancing environmental health promotion. Health Educ Behav. 2004;31(4):441–54.PubMedView ArticleGoogle Scholar
  170. Archbold J, Goldacker S. Assessing urban impacted soil of urban gardening: decision support tool. 2011. https://www1.toronto.ca/city_of_toronto/toronto_public_health/healthy_public_policy/lead/files/pdf/urban_gardening_assessment.pdf. Accessed 3 Dec 2015.Google Scholar
  171. World Health Organization, Government of South Australia. Adelaide statement on health in all policies. 2010. http://www.who.int/social_determinants/hiap_statement_who_sa_final.pdf. Accessed 17 Nov 2015.Google Scholar
  172. Hanson KL, Connor LM. Food insecurity and dietary quality in US adults and children: a systematic review. Am J Clin Nutr. 2014;100(2):684–92. doi:10.3945/ajcn.114.084525.PubMedView ArticleGoogle Scholar
  173. U.S. Food and Drug Administration. Analysis and evaluation of preventative control measures for the control and reduction/elimination of microbial hazards on fresh and fresh-cut produce: executive summary. 2015. http://www.fda.gov/Food/FoodScienceResearch/SafePracticesforFoodProcesses/ucm091016.htm. Accessed 3 Dec 2015.Google Scholar

Copyright

© Majowicz et al. 2016

Advertisement