How Is Nutrition Related to Obesity Peer Reviewed Articles

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Improving food environments and tackling obesity: A realist systematic review of the policy success of regulatory interventions targeting population diet

  • Jackie M. Street ,
  • Tracy Merlin

Improving food environments and tackling obesity: A realist systematic review of the policy success of regulatory interventions targeting population diet

  • Jana Sisnowski,
  • Jackie M. Street,
  • Tracy Merlin

PLOS

x

  • Published: August 4, 2017
  • https://doi.org/x.1371/journal.pone.0182581

Abstract

Background

This systematic review (PROSPERO: CRD42015025276) employs a realist arroyo to investigate the consequence of "real-world" policies targeting dissimilar aspects of the food environment that shape individual and commonage nutrition.

Objectives

We were interested in assessing intermediate outcomes along the assumed causal pathway to "policy success", in addition to the final result of changed consumption patterns.

Information sources

We performed a search of 16 databases through October 2015, with no initial restriction by language.

Study eligibility criteria

We included all publications that reported the issue of statutory provisions aimed at reducing the consumption of energy-dense foods and beverages in the full general population. We immune all methodological approaches that contained some measure of comparison, including studies of implementation progress.

Study appraisal and synthesis methods

Nosotros reviewed included studies using the appraisal tools for pre-post and observational studies adult by the National Heart, Lung, and Claret Constitute. Given the considerable heterogeneity in interventions assessed, study designs employed, and result measures reported, nosotros opted for a narrative synthesis of results.

Results and implications

Results drawn from 36 peer-reviewed articles and grayness literature reports demonstrated that isolated regulatory interventions can amend intermediate outcomes, merely fail to affect consumption at clinically pregnant levels. The included literature covered six different types of interventions, with 19 studies reporting on calorie posting on chain restaurant menus. The big majority of the identified interventions were conducted in the United states of america. Early results from recent taxation measures were published after the review cut-off date just these suggested more favorable effects on consumption levels. All the same, the evidence assessed in this review suggests that electric current policies are generally falling short of predictable health impacts.

Introduction

Regulatory measures that aim to improve population nutrition have get an increasingly popular public health strategy against obesity. As a growing number of approaches are being field tested, a new dimension of evidence has become available to inform futurity policy-making more realistically [1] than modeling exercises and researcher-manipulated studies in controlled settings [two, 3]. Even so, evaluations of early policy efforts accept not been systematically and comprehensively examined. Although one recent systematic review [4] analyzed natural experiments in the areas of physical activeness and nutrition, information technology relied on a search of PubMed but and excluded outcomes measured direct in the food surround. Information technology reported mostly cipher results across the categories of interest to this study and did not place any studies on fiscal policies or food supply measures [four].

Evaluations of policy interventions are methodologically challenging as they are necessarily observational and involve long and often indirect cause-and-effect chains that occur in parallel with a myriad of other changes in the population and environment [four–6]. Preventive interventions that target environments rather than individual behaviors present the additional difficulty that the desired bear upon might emerge only gradually or cumulatively in conjunction with other interventions [seven]. These considerations suggest that just measuring ultimate outcomes of interest, such as changes to nutritional patterns or torso weight, is not an adequate indication of policy success or failure. Instead, the impact of real-life public health interventions may be more appropriately assessed by substantiating a logical pathway connecting intervention and outcome, and by demonstrating realization of firsthand program goals or the presence of more distal jurisdiction-broad trends in average weight or nutritional intake. [6–eight].

In this paper, we reviewed electric current inquiry evaluating existent-life policy interventions addressing obesity. We used a realist review approach [9] which focused on program mechanisms to provide a more than nuanced assessment of policy success or failure. Specifically, we investigated the issue of statutory provisions of a regulatory nature that aim to reduce the consumption of free energy-dumbo foods and beverages in the full general population. Outcomes included both directly (east.m. BMI) and indirect (east.g. calorie count of food choices) measures.

Methods

Realist approaches review interventions along a continuum of indicators of successful implementation [9]. Based on a theory of the mechanisms by which an intervention is intended to bring about an intended issue, the aim of a realist review is to provide a nuanced assessment of the extent to which interventions work and at which signal of the implementation at that place is a failure to produce the desired finish result [9]. This approach stands in contrast to the traditional review that focuses on 1 cumulative outcome as a maker of intervention success or failure; for instance the ability of a regulation providing data to consumers to result in changes in average BMI. Following the realist arroyo, the outcomes of interest for this systematic review align with the assumed plan logic of interventions designed to reduce the consumption of unhealthy food and beverages and ultimately curb the prevalence of overweight and obesity. To this end, we collected data regarding (i) the effect of these interventions on average BMI or weight and on calorie intake and related proxy measures and (2) indicators measuring parameters on the causeless causal pathway to inverse consumption patterns, including measures of the degree of program implementation and not-behavioral consumer responses such as awareness and knowledge. In recognition that new policies may be evaluated on the basis of process indicators alone, we allowed all methodological approaches that included some measure of comparison, including studies of implementation progress with an assumed baseline of null. A review protocol was developed and registered on the International Prospective Register of Systematic Reviews (PROSPERO) prior to commencement of this written report (registration number CRD42015025276). As summarized in Fig one and in line with a realist review approach, our search and selection methods were informed by the likely program logic of interventions in the principal areas identified in the literature every bit possible regulatory levers [x,eleven].

Data sources

We systematically searched sixteen databases that span bookish enquiry as well as enquiry undertaken by public agencies and other public or private organizations. In addition, we mitt-searched the reference lists of all articles that met the study eligibility criteria detailed below. A full overview of the search strategies used in the following databases is available in the certificate fastened to the review protocol in PROSPERO. (Also see S2 File).

Report selection

We considered all studies published betwixt 2004, the year WHO member states first acknowledged a part for market-related regulatory interventions for obesity prevention in the Global Strategy on Nutrition, Physical Action [12] and October 31st, 2015 for inclusion in this review, with no initial restriction on the language of publication.

We included all total-scale policy interventions designed to meliorate population nutrition, regardless of whether the outcome(s) reported was related to the food environment or to behavioral patterns. Eligible studies (i) examined an enacted statutory intervention (ii) practical to the unabridged population of its jurisdiction and (iii) targeted the consumption of free energy-dense foods and beverages. All interventions that are not office of a full-calibration, jurisdiction-wide policy were excluded, such equally airplane pilot programs and private sector or NGO actions without a change of primary or secondary legislation. Differential sales taxes and low-level soda taxes, usually enacted solely as means to raise revenue [thirteen], were excluded due to the lack of public health policy intent. Accordingly, eight studies reporting the effects of differential sales taxes were excluded (also see Fig 2), regardless of the presence or direction of any effect on sales levels, consumption patterns, or weight and wellness outcomes. In addition, we excluded all interventions aimed only at children or other defined or implicit sub-groups (east.k. school-based programs or the U.s. Special Supplemental Nutrition Plan for Women, Infants, and Children), but retained those that provide a social safety internet open to anybody in demonstrated demand (eastward.m. the U.s.a. Supplemental Diet Assistance Programme (SNAP)/ food stamp plan).

After removal of duplicates, we screened 25,323 items for relevance according to the inclusion criteria. The starting time reviewer (JS) initially assessed each title and, where available, abstruse. A subset of 10% of the initial search results was again reviewed for eligibility according to the inclusion criteria past a second reviewer (JMS). Where written report eligibility was disputed, the co-authors reached a consensus decision. The outset reviewer so retrieved and assessed the full text of 302 manufactures that had been adamant to perchance run into the inclusion criteria in the first round of screening. In addition to studies reporting on the evaluation of one specific intervention matching the inclusion criteria, we as well retained 11 systematic reviews whose inclusion criteria overlapped at least partially with ours. We reviewed the reference lists of these reviews for boosted eligible studies before excluding the reviews themselves from farther analysis. Together with the paw-searching of the reference lists of all included studies, this process yielded an additional vii eligible articles. The same two reviewers independently assessed the 48 selected studies for methodological quality prior to inclusion in the review using the appraisement tools for Quality Assessment of Earlier-Afterward (Pre-Post) Studies With No Control Group [14] and for Quality Assessment of Observational Cohort and Cross-Sectional Studies [15] developed by the National Eye, Lung, and Blood Plant. At this stage, we excluded a further three studies which reported evaluation outcomes, just did not particular or reference the underlying methodology. An additional 2 studies assessing health department-led initiatives aimed at improving the availability of healthier nutrient in corner stores in New York City and Philadelphia were excluded due to these interventions beingness programmatic in nature without a change in legislation or regulation. By contrast, studies reporting on the event of local subsidies for salubrious purchases made under the US Supplemental Nutrition Assistance Program (SNAP) are considered inside scope of the inclusion criteria given that incentive programs targeting SNAP recipients are subject to the laws and The states Section of Agriculture (USDA) regulations governing the SNAP scheme and, at the time the interventions under evaluation were implemented, required jurisdictions to obtain formal approval from the USDA Food and Diet Service. The catamenia diagram in Fig 2 beneath summarizes the database search and study pick process. Also see Prisma Checklist, S3 File)

Data extraction

We grouped studies according to blazon of intervention reported and extracted the following details for each reference: setting, study blueprint, time post-implementation, primary population and, where applicable, sub-populations, results for the principal outcome, and, where applicable, results for whatever secondary outcomes reported.

Results

The 36 studies [xvi–53] span six unlike types of interventions: a majority (n = 19) report on calorie posting on chain eating house menus, followed past changes to food infrastructure (northward = five), subsidies for good for you food purchases (northward = 5), taxation of unhealthy foods and beverages (n = 5), government food standards (n = 1), and nutrition labeling of products (n = one). Approximately 80% of included studies (n = xxx) assessed interventions implemented in the Us. Evaluation strategies varied and resulted in unlike endpoints, oftentimes with multiple strategies used in one report to quantify the success of programme implementation and the effects on behavior. Methodological quality against the Quality Cess of Before-After (Pre-Post) Studies With No Control Grouping [xiv] and the Quality Assessment of Observational Cohort and Cross-Sectional Studies tools varied between studies. The majority of studies were judged to be of medium quality. Recurring limitations were related to express sampling frames and overly descriptive approaches. Methodological limitations are considered as part of the narrative synthesis below and further discussed in the discussion and conclusion department. The results tabular array in the annex (S1 File) provides a detailed overview of the included studies, including quality appraisal. In the post-obit, nosotros summarize the results in a narrative meta-synthesis.

Bill of fare labeling

Calorie posting on menus at chain restaurants has been the well-nigh comprehensively examined intervention of the approaches identified in this study. Our review identified xix private studies with predominantly pre-mail service designs or repeat cantankerous-sectional surveys with a control group, with sample sizes ranging from a few hundred participants [sixteen,23,29,32] to over 100 million transactions [17]. In the following, nosotros synthesize the study results by the type of effect examined, starting with the outcomes most distal from the intervention in accordance with the programme logic outlined in Fig one in the methods department (Fig one). Note that several studies reported more than ane outcome measure.

(ane) Changes in calorie value of purchases.

Studies measuring average calorie intake, based on verified purchases or self-reported consumption and not restricted to one restaurant concatenation, suggest that carte du jour labelling using calorie per particular does not impact on consumer purchasing behavior [21,23–27,29,31–33]. However, 2 studies reported a differential post-implementation drop in average calories ordered. Both took place at only one regulated chain. The first used ii outlets of a non-identified fast food chain in Philadelphia and control locations in neighboring states, resulting in a total sample of 648 verified purchases. This study reported a nine% drop, equivalent to 151 food calories less purchased on average compared to non-regulated jurisdictions [16]. The 2nd study was limited to the Starbucks chain in New York Metropolis, with Boston and Baltimore as control locations [17]. Information technology was one of two evaluations [17,26] in which a concatenation agreed to share its sales data. Starbucks sales data showed a drop of vi% to an boilerplate order of 232 calories postal service-implementation [17]. However, the caloric value of average purchases at Starbucks were much lower than at other regulated chains both pre- and post-implementation. For example, in the other single concatenation study, customers at regulated outlets purchased an average of i,556 calories [sixteen] and the average entrée in Rex County contained 777 calories at 18 months mail-labelling before adding whatever side orders [xviii]. This suggests that the Starbucks study may not be representative of the regulation'south impact in the broader fast food sector. Interestingly, the Starbucks written report also observed that the company's aggregate sales revenue remained stable mail service-implementation and even increased by 3% at stores located virtually a Dunkin Donuts [17]. Assuming that the increase in sales most rival outlets indeed represents a shift of customers rather than new customers, it seems that the chain attracts more than health-conscious consumers away from every bit regulated competitors. Taken together, these two observations call into question the external validity of the Starbucks study.

(2) Changes in frequency of visits to fast food restaurants.

The idea that an effect might occur outside the eatery setting and therefore be undetectable in cross-sectional studies was investigated 4 months afterwards Philadelphia'south introduction of carte labeling [25]. Program logic indicates that potential fast food consumers might reply to the new labeling past reducing the number of eating house visits without irresolute the amount of calories at each visit. Still, the study establish no reduction in the number of fast food restaurant visits past either consumers intercepted at a fast nutrient restaurant or by those questioned in a random-digit dialing phone survey [25]. While there was no statistically significant association in either direction, trends across several sub-groups propose that if there was an effect, it would more than likely trend towards an increased number of average visits mail service implementation [25].

(3) Changes in consumer knowledge.

Intermediate outcomes on the logical pathway to consumption were oft measured every bit the sole endpoint [19,20,22,31] or as a secondary event [16,21,23–25,27,29,32,33]. Changes in measures such equally self-reported noticing of calorie labels and self-reported usage in ordering varied past location and study: for instance, the average share of consumers reporting having noticed calorie labeling at the end of the corresponding postal service-implementation observation period ranged from 38% to 76% in Philadelphia [25,16], from 58% to 59% in Washington Land [nineteen,27], and from 54% to 64% in NYC [24,20]. Similarly, 57% of adolescents in NYC [23] and 87% of parents ordering for their children in Washington State reported noticing calorie labels later their introduction [32].

Beyond all studies, the share of customers who reported using the calorie information in purchasing decisions was far below the share noticing information technology. Among those making utilise of the labeling, uptake tended to vary past sub-population, but showed few consistent trends across studies. For example, in a Washington State study women, loftier income earners, and whites had greater odds of using card labeling [xix] and in a 2d Washington State study usage differences were constitute between women and men, but non between races or ethnicities [27]. In NYC, men were more probable than women to study using the information [twenty], with the opposite finding reported in another study too conducted there [21].

In addition to cocky-reported usage of information, a more objective measure of the successful translation of information provision to nutrition knowledge was reported by one study: in New York Urban center, the proportion of respondents correctly estimating the caloric value of their buy rose from fifteen% to 24%, while declining from baseline in the control urban center of Newark, resulting in a statistically significant differential change during the postal service-implementation period [22]. Nonetheless, no statistically significant differential change in correct estimates of recommended daily calorie intake was reported post-implementation [22], suggesting that customers even so lacked a reference against which to judge the calorie content of their meals. In Philadelphia, differential changes in the accuracy of estimates of calories purchased were statistically significant only in customers with at least some higher education and in those ordering pocket-sized meals, perhaps a sign of greater health consciousness in those customers [31].

(4) Reformulation by regulated chain restaurants.

It appears that at least in Washington State, where King County implemented a new menu labeling regulation, chain restaurants responded to the change through small reformulation of their menus, thus bypassing consumer decision-making on the pathway to reduced calorie intake [18]. On average, entrées contained 41 calories less at 18 months after enactment of the new dominion compared to at six months, a 5% drop to 777 calories per entrée [eighteen]. A comparison of menus between chains operating in regulated jurisdictions and chains operating only in not-regulated jurisdictions showed that the availability of healthier food options increased by viii% at regulated chains, but remained constant at control chains [28]. No divergence in average caloric content was found between regulated and control menus [28]. Another Rex County study looked at the wider restaurant environment post-implementation and institute few qualitative changes to the nutrient surroundings other than compliance with the use of regulation compared to a control jurisdiction [30]. Overall, these studies imply wide compliance with the regulations, but show simply minor spillover effects into other aspects of the eating place nutrient environment.

(5) Policy diffusion through convergence of practise.

1 study from Australia provides insights into the possible event of policy innovation across jurisdictional borders. Conducted one year before and 11 months after New Due south Wales (NSW) became the first land to introduce mandatory menu labeling, the report reports on nationwide trends for the five fast food chains with the largest numbers of outlets in Commonwealth of australia [34]. The report design neglected to compare NSW with the not-regulated states and is therefore of little utilise to appraise the implementation of the regulation in NSW. However, the study reported that the boilerplate total nutrition information available in stores rose significantly beyond the nation while the number of outlets with no nutrition information available dropped past 31% to just 2 stores in the sample. This finding attests to the power of policy diffusion through convergence of practice in nationally and internationally operating nutrient businesses.

Improvement of food infrastructure

Studies reporting on the success of changes to the food infrastructure are limited to the two US jurisdictions of New York City and South Los Angeles in Los Angeles County.

New York City'south Light-green Carts program made available upwardly to one,000 permits for mobile vendors of fresh produce in specified disadvantaged neighborhoods. The program did not result in whatsoever statistically pregnant increase in reported fruit and vegetable consumption [36]. Vendors tended to cluster forth public transport, commercial, and other hubs inside their designated zones and thereby largely bypassed the almost disadvantaged neighborhoods [37,38]. In add-on, not all 1,000 permits were taken upwards: two evaluations found approximately 50% of permits active on paper [35,36], but when attempting to locate all vendors, just 166 carts could be located [35].

Meanwhile, S Los Angeles' ban on new free-standing fast food chain outlets also showed limited effectiveness in improving the food environment. Four and a one-half years afterwards implementation, only 10% of food outlets operating at the time of the written report had opened nether the new dominion [39]. This indicates express reach of a law applying only to new businesses in a adequately stable nutrient environment. Not surprisingly, afterward controlling for private and collective characteristics, the study found no statistically significant differences in nutrition and BMI changes in comparison to control jurisdictions [39].

Subsidies for healthy foods

These studies examined the use of subsidies centered on the US Supplemental Nutrition Help Program (SNAP), formerly known as food stamps, and local efforts to incentivize their use for the purchase of healthy foods. At the aforementioned Green Carts in NYC, the utilise of SNAP benefits was associated with an average of $3.86 more spent compared to cash payment [41]. The Health Bucks program in NYC and the Philly Food Bucks plan in Philadelphia both offered $2 vouchers per $five in SNAP benefits spent at farmers' markets. Both programs resulted in increased SNAP sales at farmers' markets (forty,44). In addition, vendors in NYC reported a high degree of satisfaction with the impact of the program on their business [43]. Although voucher users in Philadelphia were ii.6 times more probable to study increased fruit and vegetable consumption since becoming market customers than non-voucher users [44], health survey data in NYC showed no differential increase in self-reported fruit and vegetable consumption after introduction of the program compared to control neighborhoods [42].

Taxation of unhealthy foods and beverages

Taxation of unhealthy foods and beverages expressly for public health purposes represented the only category not dominated by Usa evidence. All five studies in this category investigated European approaches. The French beverage revenue enhancement of 7.sixteen euros per hectoliter (0.076 euros per liter) was passed through fully to retail prices for soda and partially for other taxed beverage categories at half-dozen months mail-implementation [45], thereby validating the first step on the logical pathway to reduced consumption of sweetened beverages.

Three studies [47–49] quantified the effects of the now abolished Danish tax on saturated fatty content and concluded that there was an consequence on consumption levels as measured by proxy sales and purchasing data. A report based on a panel of 2,000 households constitute that purchases of butter, butter blends, margarine and oils decreased past x%-15% in the offset nine months mail service-implementation [48]. However, this was at least partially attributed to hoarding prior to the entry into force of the new taxation [48]. A study of sales data nerveless from unlike retail chains owned by Coop Denmark showed dissimilar price developments for the iii production groups (minced beef, cream, and sour cream) up to one yr mail-implementation [49]. Prices of minced beef and cream were higher post-implementation, but no consistent pattern was observed for sour cream prices. In addition, cost changes were stronger for the medium-fatty and weakest for the low-fatty varieties of minced beef and cream [49]. Matching the toll changes, sales changes suggested in that location was a decrease of 4–six% in the intake of saturated fat from minced beef and cream, while no significant result was found for sour cream [49]. Another study examined sales information for twelve taxed foodstuffs over the entire 15 months of the tax's being [47]. Information technology reported a total decrease in sales across production categories by 0.9%, merely an increase past 1.3% pre-implementation and post-abolition of the taxation. 1 modelling approach estimated that sales changes could translate into a population-wide increase in the incidence of ischemic centre illness by 0.ii% due to a decrease of both harmful saturated fat and benign unsaturated fat intake [47]. Although this is only i of two possible estimates (the other one forecasting a total decrease of 0.3%) and a very small-scale event size, this result illustrates that targeting specific nutrients in a wide range of foodstuffs may entail unintended changes in consumption patterns that mitigate or negate the intended effects.

In Hungary, a broad-based junk food tax was estimated to have reduced purchases of processed foods, which were by and large taxed, past 3.iv%, while purchased quantities of unprocessed foods increased by a statistically insignificant 1.1% at 16 months post-implementation [46].

Procurement standards for public institutions

The only full-scale evaluation of a jurisdiction setting standards for the nutritional quality of items available to its employees and the full general public is that of the Healthy Drinkable Executive Society enacted by the city of Boston [50]. 2 years mail-implementation, unhealthy beverage availability and average caloric content per beverage declined considerably compared to the pre-implementation catamenia and compared to control sites. Command sites were owned by the city and the state of Massachusetts and not covered past the club. Compared to the pre-implementation flow, access to carmine-coded, unhealthy beverages decreased past 27.8% (P<0.001) overall; red drink access in vending machines decreased by 28.9% (P<0.001) and in cafes/cafeterias by twenty.4% (P = 0.02). In addition, average calories per beverage sold within access points decreased by 48.6 kcal from 88.ane kcal to 39.5 kcal post-implementation [50]. However, positive trends of a lesser magnitude were also observed at the comparison sites, particularly the ones owned by the city rather than the state, indicating that a larger trend or a signaling effect across the direct intervention may have contributed to the changes.

Diet labeling of products

The only study identified that assessed nutrition labeling on products originated in New South Wales, an Australian country [51]. Reporting that only 7% of 350 product samples matched the exact nutritional information given on the label in a laboratory test [51], this study is narrowly focused on compliance. However, equally interpretive labeling approaches are increasingly considered, it does heighten the question to what extent nutrition labeling can be enforced beyond adherence to design and presentation rules and what constitutes an acceptable margin of error for consumer information.

Discussion and conclusion

These findings signal that isolated regulatory interventions ofttimes result in improvements of the most proximal outcomes, measured in the food surroundings and situated at the very beginning of the logic model. However, the interventions assessed here fail to achieve an effect on consumption that could plausibly be considered as clinically significant, i.e. as having an upshot on individuals' nutritional intake to the extent that information technology would reduce the incidence of overweight, obesity, and related chronic diseases. This is a differentiation betwixt different levels of policy success and failure that have not been highlighted previously [4].

When compared to just a few examples of outcome estimates put forward during policy development and decision-making processes, information technology is clear that current interventions are falling curt of the public health impact hoped for by policy-makers and predicted by many researchers. For instance, in New York Metropolis, the Section of Health estimated that the new calorie posting rule would lead to "at least 150,000 fewer New Yorkers [becoming] obese, [and] at to the lowest degree thirty,000 fewer cases of diabetes" [52] over v years. Yet, with the exception of the written report focused on Starbucks, the New York City-based studies profiled hither failed to detect statistically and clinically significant calorie reductions [21–24,33]. Moreover, a recent await at the sustained touch on of the intervention, published just afterward the cutoff appointment for this review, concluded that even minimal improvements in consumer sensation appear to have diminished over time [53]. Meanwhile, the Danish forecast that a fatty taxation would eventually add together 5.5 days to the average Dane'south lifespan [54] will remain unrefuted given the quick abolition of the measure out, only appears tenuous given the early evaluation results. Similarly, one of the evaluations of New York City'southward Green Cart program reported that the metropolis originally estimated that the generated increased intake of fruits and vegetables would measurably amend the health condition of 75,000 individuals and avert loss of at to the lowest degree fifty lives a year [35]. Even so, programme administrators concluded that the direct impact of the intervention on morbidity and bloodshed would be likewise hard to quantify and the program evaluators observed that direct health-enhancing arguments for Dark-green Carts subsequently faded [35].

This is not to say that these interventions may not evangelize cumulative behavioral and wellness effects in the long-term, peculiarly where they act in parallel with complementary interventions and alter social and political perceptions of diet. In this context, information technology is notable that recent studies at national level and in hotspots of obesity prevention activities such equally New York Urban center have found both a shift in the attitudes of consumers towards sugary drinks and an actual reduction in average soft drink consumption [55–57].

Some of the interventions discussed above may likewise be inconsequential and not have a meaningful impact on consumption: a recent review of regulation targeting sugar-sweetened beverages argued that policies are squandering the potential for more than pronounced behavioral touch considering of their restrained pattern, perchance to appease industry and political opponents [58]. Indeed, very few of the above interventions match the designs identified in the literature equally the more than effective public health approaches, exist it displays of physical activeness equivalents instead of evidently calorie counts [59] or excise taxes amounting to price increases of at to the lowest degree 15–25%, equivalent to the long advocated penny per ounce tax [60–62]. In 2015, Berkeley, California, passed a tax on sugar-sweetened beverages that matched the magnitude suggested past public health experts and in 2014, Mexico implemented a tax of one peso per liter which, if passed on to consumers, comes close to the recommended level with a 10% increase in price. Two early evaluations, published just after the cut-off date for this review, reported that in both locations, the taxes were generally passed on, with college price increases relative to tax levels reported for Mexico compared to Berkeley [63,64]. The report in Berkeley employed comparing cities for control of pre-post trends and reported pass-through rates of 69% for soda and 47% for all taxed products [63]. Neither jurisdiction reported deleterious effects on the prices of non-taxed beverages such as bottled water, with the exception of slight price increases for diet soda in Berkeley [63,64]. A 3rd evaluation of the Mexican tax published in 2017 indicated that purchases of taxed beverages fell by seven.half-dozen% in the two years afterward implementation of the taxation compared with a 2.one% drib in untaxed beverage purchases over the same period [65]. These two fiscal interventions warrant close attention from the experts and policy-makers every bit they correspond rare examples of electric current policy recommendations being put into practice. Most recently, boosted jurisdictions have canonical soda taxes, among these decisions by the legislative bodies of Philadelphia City and Cook Country, which includes Chicago, every bit well as pop votes in San Francisco, Oakland, and Albany in California and in Boulder in Colorado [66]. Both the British and Irish governments have announced plans for the introduction of soda taxes in 2018 [66].

Some limitations must exist taken into account when interpreting the results of this systematic review. Firstly, despite an expansive search of a diversity of databases and broad inclusion criteria, it is likely that some evaluations of real-life interventions are non available through academic and greyness literature repositories. It is reasonable to suppose that in many instances, peculiarly in lower level jurisdictions and in center and depression-income countries, no formal evaluation of relevant policies would have been undertaken and/or reported. As a result, only a small number of studies from outside the OECD were identified, with several articles describing interventions in Ghana [67] and the Pacific island region [68,69] excluded at the appraisal stage due to the unavailability of detailed evaluation processes and results. Similarly, it is possible that unsuccessful policy interventions remain underreported and those that are published put a greater accent on intermediate program outcomes that bear witness greater progress than the actual policy end goal. These limitations underline the need to brand methodically audio evaluations a routine component of policy implementation and highlight the usefulness of some form of centralized repository for comprehensive evaluation reporting that is attainable globally.

Secondly, our study purposely used appraisal tools tailored to observational study designs that are amenable to evaluating real-life policies. As outlined in the introduction, existent-life policy experiments practice not always fully align with the methodological expectations of prove-based health sciences, particularly when compared to targeted prevention delivered in wellness care settings. This issue was raised in relation to the written report by Dumanovsky and colleagues who employed a uncomplicated pre-post study design to quantify the event of carte du jour labeling in New York City [21]. Criticized for the chosen study pattern [70], the authors responded that the methodology needs to match both the reality of a policy in progress and the limited resources of a public bureau carrying out its own evaluation while stymied by the refusal of manufacture to share its sales data [71]. Despite some reservations virtually these written report designs expressed in the literature, studies using conventional cross-sectional designs or uncomplicated pre-postal service designs posed little difficulty for appraisal and information extraction. Conversely, studies that evaluated implementation processes were problematic to appraise and summarize. Aspects of design, mutual in the evaluation of nutrient infrastructure interventions such every bit the SNAP subsidies in Philadelphia and NYC and NYC's Dark-green Cart programme, complicated the assessment of studies examining food infrastructure improvements and food subsidization programs. These aspects included the utilise of descriptive approaches and a mix of different report designs of varying quality within single reports. The difficulties that we encountered suggest that scholarly assessment of written report quality and the reality of policy-making in perennially resource-constrained wellness departments occasionally collide. As a issue, even more differentiated appraisal tools need to exist used for evaluation in recognition that studies with descriptive approaches can be useful for charting implementation progress by ensuring that plan logic is in identify.

To conclude, our review underlines that the immediate expectations associated with the examined types of regulatory interventions need tempering. At this point in time, the policy examples discussed above primarily deliver proof of feasibility: the fact that they survived the policy-making process and have been mostly successful in reaching immediate program goals should raise the political palatability of such approaches even if, at the fourth dimension of exam, at that place has been little demonstrated impact on risk factors and health outcomes. Policy-makers should therefore not dismiss such recent policy experiments as failures, simply pursue the example of these jurisdictions as necessary edifice blocks for more stringent and comprehensive nutrition policy and obesity prevention regimes.

Supporting information

Acknowledgments

The authors gratefully acknowledge the back up of the HealthyLaws research squad and advisory committee. This project was conducted as part of a study funded by an Australian National Preventive Health Bureau Grant, project ID: 182BRA2011. JMS was also supported by an NHMRC Chapters Building Grant (565501) and an Australian National Preventive Wellness Bureau Fellowship (20STR2013F).

References

  1. ane. Shemilt I, Marteau TM, Smith RD, Ogilvie D. Utilise and cumulation of evidence from modelling studies t inform policy on nutrient taxes and subsidies: biting off more than we can chew? BMC Public Health. 2015;15(ane):297.
  2. 2. Eyles H, Mhurchu CN, Nghiem Northward, Blakely T. Nutrient pricing strategies, population diets, and non-communicable disease: a systematic review of simulation studies. PLoS Medicine. 2012; 9(12):e1001353. pmid:23239943
  3. 3. Powell LM, Chriqui JF, Khan T, Wada R, Chaloupka FJ. Assessing the potential effectiveness of food and drink taxes and subsidies for improving public health: a systematic review of prices, demand and trunk weight outcomes. Obesity Reviews. 2013;14(2):110–128. pmid:23174017
  4. 4. Mayne S, Auchincloss A, Michael Y. Affect of policy and congenital environment changes on obesity‐related outcomes: a systematic review of naturally occurring experiments. Obesity Reviews. 2015;sixteen(5):362–375. pmid:25753170
  5. 5. Reeve B, Ashe Thousand, Farias R, Gostin Fifty. State and municipal innovations in obesity policy: why localities remain a necessary laboratory for innovation. American Journal of Public Health. 2015;105(three):442–450. pmid:25602886
  6. half dozen. Victora CG, Habicht J-P, Bryce J. Testify-based public health: moving across randomized trials. American Journal of Public Wellness. 2004;94(3):400–405. pmid:14998803
  7. 7. Swinburn B, Gill T, Kumanyika S. Obesity prevention: a proposed framework for translating evidence into activity. Obesity reviews. 2005;6(1):23–33.Swinburn B, Gill T, Kumanyika S. Obesity prevention: a proposed framework for translating testify into action. Obesity Reviews. 2005;half dozen(one):23–33. pmid:15655036
  8. eight. Habicht J-P, Victora C, Vaughan JP. Evaluation designs for adequacy, plausibility and probability of public wellness programme performance and impact. International Journal of Epidemiology. 1999;28(1):10–18. pmid:10195658
  9. nine. Pawson R, Greenhalgh T, Harvey K, Walshe K. Realist review–a new method of systematic review designed for complex policy interventions. Journal of Wellness Services Research & Policy. 2005;10(suppl 1):21–34.
  10. 10. Magnusson R. What's police got to do with it? Part 2: Legal strategies for healthier nutrition and obesity prevention. Australia and New Zealand Health Policy. 2008;5(1):xi.
  11. 11. Gostin LO. Police force as a tool to facilitate healthier lifestyles and forestall obesity. Journal of the American Medical Clan. 2007;297(i):87–90. pmid:17200479
  12. 12. Earth Health Organization. Global Strategy on Diet, Physical Activity and Wellness. 2004. Available from: http://www.who.int/dietphysicalactivity/strategy/eb11344/strategy_english_web.pdf [last accessed 2 Oct 2015].
  13. 13. Kim D, Kawachi I. Food Revenue enhancement and Pricing Strategies to Thin Out the Obesity Epidemic. American Journal of Preventive Medicine. 2006;30(5):430–437. pmid:16627131
  14. 14. National Heart, Lung, and Claret Institute. Quality Assessment Tool for Before-After (Pre-Mail service) Studies With No Control Group. Available from: http://world wide web.nhlbi.nih.gov/health-pro/guidelines/in-develop/cardiovascular-hazard-reduction/tools/before-afterwards [last accessed 12 January 2016].
  15. xv. National Heart, Lung, and Blood Institute. Quality Cess Tool for Observational Accomplice and Cross-Exclusive Studies. Available from: http://world wide web.nhlbi.nih.gov/health-pro/guidelines/in-develop/cardiovascular-risk-reduction/tools/accomplice [last accessed 12 January 2016].
  16. xvi. Auchincloss AH, Mallya GG, Leonberg BL, Ricchezza A, Glanz K, Schwarz DF. Client responses to mandatory menu labeling at full-service restaurants. American Journal of Preventive Medicine. 2013;45(6):710–719. pmid:24237912
  17. 17. Bollinger B, Leslie P, Sorensen A. Calorie Posting in Concatenation Restaurants. American Economic Journal: Economic Policy. 2011;iii(1):91–128.
  18. 18. Bruemmer B, Krieger J, Saelens BE, Chan N. Energy, saturated fat, and sodium were lower in entrees at chain restaurants at 18 months compared with 6 months following the implementation of mandatory menu labeling regulation in King County, Washington. Journal of the University of Nutrition and Dietetics. 2012;112(8):1169–1176. pmid:22704898
  19. 19. Chen R, Smyser M, Chan N, Ta M, Saelens Exist, Krieger J. Changes in sensation and use of calorie data afterwards mandatory menu labeling in restaurants in King County, Washington. American Journal of Public Health. 2015;105(3):546–553. pmid:25602868
  20. 20. Dumanovsky T, Huang CY, Bassett MT, Silver LD. Consumer awareness of fast-food calorie information in New York Urban center later implementation of a menu labeling regulation. American Periodical of Public Health. 2010;100(12):2520. pmid:20966367
  21. 21. Dumanovsky T, Huang CY, Nonas CA, Matte TD, Bassett MT, Silver LD. Changes in energy content of lunchtime purchases from fast food restaurants later on introduction of calorie labelling: cantankerous sectional client surveys. BMJ. 2011;343:d4464. pmid:21791497
  22. 22. Elbel B. Consumer estimation of recommended and actual calories at fast nutrient restaurants. Obesity (Silver Leap). 2011;19(10):1971–1978.
  23. 23. Elbel B, Gyamfi J, Kersh R. Child and boyish fast-nutrient option and the influence of calorie labeling: a natural experiment. International Journal of Obesity. 2011;35(four):493–500. pmid:21326209
  24. 24. Elbel B, Kersh R, Brescoll VL, Dixon LB. Calorie labeling and food choices: a starting time expect at the effects on low-income people in New York Metropolis. Health Diplomacy. 2009;28(6):w1110–w21. pmid:19808705
  25. 25. Elbel B, Mijanovich T, Dixon LB, Abrams C, Weitzman B, Kersh R, et al. Calorie labeling, fast food purchasing and restaurant visits. Obesity (Silverish Jump). 2013;21(11):2172–2179.
  26. 26. Finkelstein EA, Strombotne KL, Chan NL, Krieger J. Mandatory menu labeling in one fast-food chain in Rex County, Washington. American Journal of Preventive Medicine. 2011;40(ii):122–127. pmid:21238859
  27. 27. Krieger JW, Chan NL, Saelens Exist, Ta ML, Solet D, Fleming DW. Carte labeling regulations and calories purchased at chain restaurants. American Journal of Preventive Medicine. 2013;44(6):595–604. pmid:23683977
  28. 28. Namba A, Auchincloss A, Leonberg BL, Wootan MG. Exploratory analysis of fast-food chain eatery menus earlier and later on implementation of local calorie-labeling policies, 2005–2011. Preventing Chronic Disease. 2013;10:E101. pmid:23786908
  29. 29. Rendell SL, Swencionis C. Point-of-Buy Calorie Labeling Has Picayune Influence on Calories Ordered Regardless of Trunk Mass Index. Current Obesity Reports. 2014;3(3):368–375. pmid:26626769
  30. 30. Saelens BE, Chan NL, Krieger J, Nelson Y, Boles Chiliad, Colburn TA, et al. Nutrition-labeling regulation impacts on restaurant environments. American Periodical of Preventive Medicine. 2012;43(five):505–511. pmid:23079173
  31. 31. Taksler GB, Elbel B. Calorie labeling and consumer interpretation of calories purchased. International Journal of Behavioral Nutrition and Physical Activity. 2014;11:91. pmid:25015547
  32. 32. Tandon PS, Zhou C, Chan NL, Lozano P, Couch SC, Glanz Chiliad, et al. The affect of menu labeling on fast-food purchases for children and parents. American Journal of Preventive Medicine. 2011;41(4):434–438. pmid:21961472
  33. 33. Vadiveloo MK, Dixon LB, Elbel B. Consumer purchasing patterns in response to calorie labeling legislation in New York Urban center. International Journal of Behavioral Nutrition and Physical Activity. 2011;8:51. pmid:21619632
  34. 34. Wellard L, Havill M, Hughes C, Watson WL, Chapman K. The availability and accessibility of nutrition information in fast food outlets in v states mail service‐menu labelling legislation in New South Wales. Australian and New Zealand Journal of Public Wellness. 2015;39(6):546–549. pmid:26259855
  35. 35. Fuchs ER, Holloway SM, Bayer K, Feathers A. Innovative partnership for public health: an evaluation of the New York City Green Cart initiative to aggrandize admission to salubrious produce in low-income neighborhoods. New York (NY): Columbia University School of International and Public Diplomacy; 2014.
  36. 36. Kerker P., Farley Southward., Johns 1000., Leggat P., Nonas C., Parton H., Green Cart Evaluation 2008–2011. EPI Information Brief 2014;48.
  37. 37. Li KY, Cromley EK, Trick AM, Horowitz CR. Evaluation of the Placement of Mobile Fruit and Vegetable Vendors to Convalesce Nutrient Deserts in New York City. Preventing Chronic Illness. 2014;xi:E158. pmid:25211506
  38. 38. Lucan SC, Maroko A, Shanker R, Jordan WB. Dark-green Carts (Mobile Produce Vendors) in the Bronx- Optimally Positioned to Meet Neighborhood Fruit-and-Vegetable Needs? Journal of Urban Health. 2011;88(five):977–981. pmid:21691925
  39. 39. Sturm R, Hattori A. Nutrition and obesity in Los Angeles County 2007–2012: Is there a measurable effect of the 2008 "Fast-Nutrient Ban"? Social Science & Medicine. 2015;133:205–211.
  40. 40. Baronberg Southward, Dunn L, Nonas C, Dannefer R, Sacks R. Peer Reviewed: The Impact of New York City's Health Bucks Program on Electronic Benefit Transfer Spending at Farmers Markets, 2006–2009. Preventing Chronic Disease. 2013;10: E163. pmid:24070035
  41. 41. Breck A, Kiszko KM, Abrams C, Elbel B. Spending at Mobile Fruit and Vegetable Carts and Using SNAP Benefits to Pay, Bronx, New York, 2013 and 2014. Preventing Chronic Illness. 2015;12:E87. pmid:26043302
  42. 42. Olsho LE, Payne GH, Walker DK, Baronberg Southward, Jernigan J, Abrami A. Impacts of a farmers' market place incentive programme on fruit and vegetable admission, buy and consumption. Public Health Diet. 2015;eighteen(xv):2712–2721. pmid:25919225
  43. 43. Payne GH, Wethington H, Olsho Fifty, Jernigan J, Farris R, Walker DK. Implementing a Farmers' Market Incentive Program: Perspectives on the New York Urban center Health Bucks Program. Preventing Chronic Affliction. 2013;10:E145. pmid:23987251
  44. 44. Immature CR, Aquilante JL, Solomon S, Colby Fifty, Kawinzi MA, Uy Due north, et al. Improving fruit and vegetable consumption among low-income customers at farmers markets: Philly Food Bucks, Philadelphia, Pennsylvania, 2011. Preventing Chronic Disease. 2013;10:E166. pmid:24135390
  45. 45. Berardi Due north, Sevestre P, Tepaut M, Vigneron A. The impact of a'soda tax'on prices: evidence from French micro data. Banque de France Working Paper. 2012;415.
  46. 46. Bíró A. Did the junk food tax make the Hungarians eat healthier? Food Policy. 2015;54:107–115.
  47. 47. Bødker Grand, Pisinger C, Toft U, Jørgensen T. The Danish fat revenue enhancement-Effects on consumption patterns and risk of ischaemic centre disease. Preventive Medicine. 2015;77:200–203. pmid:25982852
  48. 48. Jensen JD, Smed Southward. The Danish taxation on saturated fat-Short run effects on consumption, substitution patterns and consumer prices of fats. Nutrient Policy. 2013;42:xviii–31.
  49. 49. Jensen JD, Smed S, Aarup L, Nielsen Eastward. Furnishings of the Danish saturated fat tax on the demand for meat and dairy products. Public Health Diet. 2015;26:i–10.
  50. 50. Cradock AL, Kenney EL, McHugh A, Conley 50, Mozaffarian RS, Reiner JF. Evaluating the Impact of the Salubrious Drinkable Executive Club for City Agencies in Boston, Massachusetts, 2011–2013. Preventing Chronic Affliction. 2015;12:E147. pmid:26355828
  51. 51. Fabiansson SU. Precision in nutritional data declarations on food labels in Australia. Asia Pacific Journal of Clinical Nutrition. 2006;xv(4):451–458. pmid:17077059
  52. 52. Section of Health and Mental Hygiene. Notice of Adoption of a Resolution to Repeal and Reenact §81.50 of the New York City Wellness Lawmaking. 2008. www.nyc.gov/html/doh/downloads/pdf/public/notice-adoption-hc-art81-l-0108.pdf [last accessed xviii January 2016].
  53. 53. Cantor J, Torres A, Abrams C, Elbel B. Five years afterward: Awareness Of New York Metropolis'southward calorie labels declined, with no changes in calories purchased. Health Diplomacy. 2015;34(xi):1893–1900. pmid:26526247
  54. 54. Bødker Grand, Pisinger C, Toft U, Jørgensen T. The rise and fall of the world'south offset fat tax. Health Policy. 2015;119(half-dozen):737–742. pmid:25840733
  55. 55. Ng SW, Slining MM, Popkin BM. Turning point for Us diets? Recessionary effects or behavioral shifts in foods purchased and consumed. American Journal of Clinical Nutrition. 2014;99(3):609–616. pmid:24429538
  56. 56. Kansagra SM, Kennelly MO, Nonas CA, Curtis CJ, Van Wye One thousand, Goodman A et al. Reducing sugary drink consumption: New York Urban center'southward arroyo. American Journal of Public Wellness. 2015;105(iv):e61–64. pmid:25713971
  57. 57. McCarthy J. Americans More Likely to Avert Drinking Soda Than Before. Gallup. 2014. Available from: http://www.gallup.com/poll/174137/americans-likely-avoid-drinking-soda.aspx [last accessed 21 January 2016].
  58. 58. Studdert DM, Flanders J, Mello MM. Searching for Public Health Law's Sweet Spot: The Regulation of Carbohydrate-Sweetened Beverages. PLoS Medicine. 2015;12(7):e1001848 pmid:26151360
  59. 59. Bleich SN, Rutkow L. Improving obesity prevention at the local level-Emerging opportunities. New England Journal of Medicine. 2013;368(nineteen):1761–1763. pmid:23656641
  60. lx. Brownell KD, Farley T, Willett WC, Popkin BM, Chaloupka FJ,Thompson JW, et al. The public wellness and economic benefits of taxing sugar-sweetened beverages. New England Journal of Medicine. 2009;361(sixteen):1599–1605. pmid:19759377
  61. 61. Powell LM, Chaloupka FJ. Food prices and obesity: evidence and policy implications for taxes and subsidies. Milbank Quarterly. 2009;87(ane):229–257. pmid:19298422
  62. 62. Wang YC, Coxson P, Shen YM, Goldman L, Bibbins-Domingo K. A penny-per-ounce tax on saccharide-sweetened beverages would cut health and price burdens of diabetes. Wellness Affairs. 2012;31(1):199–207. pmid:22232111
  63. 63. Falbe J, Rojas N, Grummon AH, Madsen KA. College Retail Prices of Sugar-Sweetened Beverages 3 Months Later Implementation of an Excise Tax in Berkeley, California. American Periodical of Public Health. 2015;105(xi):2194–2201. pmid:26444622
  64. 64. Colchero M, Salgado J, Unar-Munguía M, Molina M, Ng S, Rivera-Dommarco J. Changes in Prices Later on an Excise Tax to Sweetened Saccharide Beverages Was Implemented in Mexico: Evidence from Urban Areas. PloS 1. 2015;10(12):e0144408. pmid:26675166
  65. 65. Colchero MA, Popkin BM, Rivera JA, Ng SW.Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ. 2016;352:h6704. pmid:26738745
  66. 66. Duckett S, Swerissen H, Wiltshire T. A sugary drinks tax: recovering the community costs of obesity. 2016. Grattan Institute. Victoria, Australia.
  67. 67. Thow AM, Annan R, Mensah L, Chowdhury SN. Evolution, implementation and consequence of standards to restrict fatty meat in the food supply and prevent NCDs: learning from an innovative merchandise/nutrient policy in Ghana. BMC Public Health. 2014;fourteen:249. pmid:24625217
  68. 68. Thow AM, Quested C, Juventin L, Kun R, Khan AN, Swinburn B. Taxing soft drinks in the Pacific: implementation lessons for improving health. Health Promotion International. 2011;26(one):55–64. pmid:20739326
  69. 69. Snowdon W, Thow AM. Trade policy and obesity prevention: challenges and innovation in the Pacific Islands. Obesity Reviews. 2013;fourteen(S2):150–158.
  70. 70. Pande AH, Soumerai SB. Serious methodological flaws compromise study findings. BMJ. 2011;343:d5797. pmid:21914749
  71. 71. Silverish L, Bassett M. Response: The challenge of public policy evaluation in the existent earth. BMJ. 2011;343:d4464. pmid:21791497

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