Additional file 1: of A systematic review, and meta-analyses, of the impact of health-related claims on dietary choices

Definitions and taxonomy used for the classification of health-related claims. Column headings used for data extraction. Search strategies used for MEDLINE, EMBASE, PsychINFO, CAB abstracts, Business Source Complete, and Web of Science/Science Citation Index & Social Science Citation Index. Data extracted for the risk of bias assessment. Completed PRISMA systematic review checklist. (ZIP 90 kb

Categorisation of English wards (n = 7,932) by the Office for National Statistics (ONS) area classification variable and the urbanicity variable used for this paper.

<p>Categorisation of English wards (n = 7,932) by the Office for National Statistics (ONS) area classification variable and the urbanicity variable used for this paper.</p

Summary of included studies.

<p>^a Roman numerals (e.g. i, ii, iii) indicate participants were assigned to each condition in a randomised order. Alphabet characters indicate that participants were randomised to only one of the conditions presented. Numbers indicate where the order of conditions was pre-specified.</p><p>^b HED: High energy density</p><p>^c LED: Low energy density</p><p>^d CFN: Calorie for Nutrient</p><p>Summary of included studies.</p

Summary statistics, correlation co-efficient matrix of the continuous exposure variables, and mean of exposure variables by urbanicity category (wards, n = 7,929).

<p> <i>SDs = Standard Deviations.</i></p

PubMed search strategy.

<p>PubMed search strategy.</p

Beta coefficients for multi-level regression models for physical environment exposure variables in univariate (MODELS A–C) and multivariate (MODEL D) analyses, and after further adjustement for confounding variables (MODELS E–F).

<p> <i>SDs – Standard Deviations;</i></p>†<p> <i>in comparison to coastal and countryside wards;</i></p>*<p> <i>significant at p<0.05;</i></p>**<p> <i>significant at p<0.01.</i></p

Residual variance at ward-level (n = 7,929) and local authority-level (n = 354) for baseline (no exposure variables) and final models (MODEL L).

<p>Residual variance at ward-level (n = 7,929) and local authority-level (n = 354) for baseline (no exposure variables) and final models (MODEL L).</p

Differential Responses to Food Price Changes by Personal Characteristic: A Systematic Review of Experimental Studies

<div><p>Background</p><p>Fiscal interventions to improve population diet have been recommended for consideration by many organisations including the World Health Organisation and the United Nations and policies such as sugar-sweetened beverage taxes have been implemented at national and sub-national levels. However, concerns have been raised with respect to the differential impact of fiscal interventions on population sub-groups and this remains a barrier to implementation.</p><p>Objective</p><p>To examine how personal characteristics (such as socioeconomic status, sex, impulsivity, and income) moderate changes in purchases of targeted foods in response to food and beverage price changes in experimental settings.</p><p>Design</p><p>Systematic review</p><p>Data Sources</p><p>Online databases (PubMed, EMBASE, Web of Science, EconLit and PsycInfo), reference lists of previous reviews, and additional data from study authors.</p><p>Study Selection</p><p>We included randomised controlled trials where food and beverage prices were manipulated and reported differential effects of the intervention on participant sub-groups defined according to personal characteristics.</p><p>Data Analysis</p><p>Where possible, we extracted data to enable the calculation of price elasticities for the target foods by personal characteristic.</p><p>Results</p><p>8 studies were included in the review. Across studies, the difference in price elasticity varied from 0.02 to 2.43 between groups within the same study. 11 out of the total of 18 comparisons of own-price elasticity estimates by personal characteristic differed by more than 0.2 between groups. Income related factors were the most commonly considered and there was an indication that own-price elasticity estimates do vary by income but the direction of this effect was not clear.</p><p>Conclusion</p><p>Experimental studies provide an opportunity to examine the differential effects of fiscal measures to improve population diets. Patterns in price sensitivity by personal characteristics are complex. General conclusions pertaining to the effects of personal characteristics on price sensitivity are not supported by the evidence, which shows heterogeneity between studies and populations.</p><p>Trial Registration</p><p>PROSPERO <a href="http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014009705#.VYAbNPlViko" target="_blank">CRD42014009705</a></p></div

Current and proposed recommendations used as constraints in the optimisation modelling (after Scarborough et al [4]).

<p>Current and proposed recommendations used as constraints in the optimisation modelling (after Scarborough et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167859#pone.0167859.ref004" target="_blank">4</a>]).</p

The contribution of modelled risk factors to the net gain in health when: (a) total energy intake is constrained; and (b) energy intake is not constrained.

<p>(NB. each value reflects the change in total DALYs if the risk factor is eliminated from the analyses).</p