Effect of calorie labelling in the out-of-home food sector on adult obesity prevalence, cardiovascular mortality, and social inequalities in England: a modelling study.

BACKGROUND: England implemented a menu calorie labelling policy in large, out-of-home food businesses in 2022. We aimed to model the likely policy impact on population-level obesity and cardiovascular disease mortality, as well as the socioeconomic equity of estimated effects, in the adult population in England. METHODS: For this modelling analysis, we built a comparative assessment model using two scenarios: the current implementation scenario refers to actual deployment only in large (≥250 employees), out-of-home food businesses, whereas the full implementation scenario refers to deployment in every out-of-home food business. We compared each scenario with a counterfactual: the scenario in which no intervention is implemented (ie, baseline). For both scenarios, we modelled the impact of the policy through assumed changes in energy intake due to either consumer response or product reformulation by retailers. We used data from the Office for National Statistics and the National Diet and Nutrition Survey 2009-19, and modelled the effect over 20 years (ie, 2022-41) to capture the long-term impact of the policy and provided mid-period results after 10 years. We used the Monte Carlo approach (2500 iterations) to estimate the uncertainty of model parameters. For each scenario, the model generated the change in obesity prevalence and the total number of deaths prevented or postponed. FINDINGS: The current implementation scenario was estimated to reduce obesity prevalence by 0·31 percentage points (absolute; 95% uncertainty interval [UI] 0·10-0·35), which would prevent or postpone 730 cardiovascular disease deaths (UI 430-1300) of the 830 000 deaths (UI 600 000-1 200 000) expected over 20 years. However, the health benefits would be increased if calorie labelling was implemented in all out-of-home food businesses (2·65 percentage points reduction in obesity prevalence [UI 1·97-3·24] and 9200 cardiovascular disease deaths prevented or postponed [UI 5500-16 000]). Results were similar in the most and the least deprived socioeconomic groups. INTERPRETATION: This study offers the first modelled estimation of the impact of the menu calorie labelling regulation on the adult population in England, although we did not include a cost-effectiveness analysis. Calorie labelling might result in a reduction in obesity prevalence and cardiovascular disease mortality without widening health inequalities. However, our results emphasise the need for the government to be more ambitious by applying this policy to all out-of-home food businesses to maximise impact. FUNDING: European Research Council.

Socioeconomic position and the effect of energy labelling on consumer behaviour: a systematic review and meta-analysis.

BACKGROUND: There are well documented socioeconomic disparities in diet quality and obesity. Menu energy labelling is a public health policy designed to improve diet and reduce obesity. However, it is unclear whether the impact energy labelling has on consumer behaviour is socially equitable or differs based on socioeconomic position (SEP). METHODS: Systematic review and meta-analysis of experimental (between-subjects) and pre-post implementation field studies examining the impact of menu energy labelling on energy content of food and/or drink selections in higher vs. lower SEP groups. RESULTS: Seventeen studies were eligible for inclusion. Meta-analyses of 13 experimental studies that predominantly examined hypothetical food and drink choices showed that energy labelling tended to be associated with a small reduction in energy content of selections that did not differ based on participant SEP (X2(1) = 0.26, p = .610). Effect estimates for higher SEP SMD = 0.067 [95% CI: -0.092 to 0.226] and lower SEP SMD = 0.115 [95% CI: -0.006 to 0.237] were similar. A meta-analysis of 3 pre-post implementation studies of energy labelling in the real world showed that the effect energy labelling had on consumer behaviour did not significantly differ based on SEP (X2(1) = 0.22, p = .636). In higher SEP the effect was SMD = 0.032 [95% CI: -0.053 to 0.117] and in lower SEP the effect was SMD = -0.005 [95% CI: -0.051 to 0.041]. CONCLUSIONS: Overall there was no convincing evidence that the effect energy labelling has on consumer behaviour significantly differs based on SEP. Further research examining multiple indicators of SEP and quantifying the long-term effects of energy labelling on consumer behaviour in real-world settings is now required. REVIEW REGISTRATION: Registered on PROSPERO (CRD42022312532) and OSF ( https://doi.org/10.17605/OSF.IO/W7RDB ).

Demographic and socio-economic shifts partly explain the Martinican nutrition transition: an analysis of 10-year health and dietary changes (2003-2013) using decomposition models.

OBJECTIVE: The Caribbean has seen a dramatic shift in the obesity and chronic disease prevalence over the past decades, suggesting a nutrition transition. Simultaneously, Martinique has faced a demographic transition marked by significant population ageing. We aimed to differentiate the contribution of changes in health status and dietary intakes due to shifts in demographic and socio-economic characteristics (DSEC) from that due to unobserved factors. DESIGN: Two cross-sectional surveys conducted in 2003 (n 743) and 2013 (n 573) on representative samples were used. Dietary intakes were estimated by 24-h recalls. The contribution of changes in health status and dietary intakes due to shifts in observed DSEC was differentiated from that due to unobserved factors over a 10-year interval, using Oaxaca-Blinder decomposition models. SETTING: Martinique, French region in the Caribbean. PARTICIPANTS: Martinican adults (≥16 years). RESULTS: Over the study period, health status deteriorated, partly owing to shifts in DSEC, explaining 62 % of the change in the prevalence of hypertension (+13 percentage points (pp)) and 48 % of waist circumference change (+3 cm). Diet quality decreased (mean adequacy ratio -2pp and mean excess ratio + 2 pp) and energy supplied by ultra-processed food increased (+4 pp). Shifts in DSEC marginally explained some changes in dietary intakes (e.g. increased diet quality), while the changes that remained unexplained were of opposite sign, with decreased diet quality, lower fruits, tubers and fish intakes and higher energy provided by ultra-processed foods. CONCLUSION: Explained dietary changes were of opposite sign to nutrition transition conceptual framework, probably because unobserved drivers are in play, such as food price trends or supermarkets spread.

Socioeconomic inequalities in metabolic syndrome in the French West Indies.

BACKGROUND: Obesity and metabolic diseases represent a major health burden in the Caribbean, particularly since a large part of the population is disadvantaged. However, socioeconomic inequalities in chronic diseases are poorly explored in this region. We investigated the association between socioeconomic position and metabolic syndrome (MetS) prevalence and explored the contribution of diet quality to explain this association, among adults in the French West Indies. METHODS: This cross-sectional analysis included 1144 subjects (≥16 y) from a multistage sampling survey conducted in 2013-2014 on a representative sample of the Guadeloupean and Martinican population. MetS prevalence was assessed using the Joint Interim Statement. Dietary intakes were estimated from 24 h-dietary recalls, and diet quality was assessed through the Diet Quality Index-International (DQI-I). Associations between socioeconomic indicators (education, employment, social assistance benefits) and MetS prevalence, and the potential contribution of diet quality in this association were assessed using multivariable logistic regression models, adjusted for sociodemographic characteristics. RESULTS: MetS prevalence adjusted for age and sex was 21 and 30% among Guadeloupean and Martinican, respectively. Compared to high-educated participants, low-educated subjects were more likely to be at risk of MetS (OR = 2.4; 95%CI = [1.3-4.4], respectively), as were recipients of social assistance benefits compared to non-recipients (OR = 2.0; 95%CI = [1.0-4.0]). The DQI-I explained 10.5% of the overall variation in MetS due to education. CONCLUSIONS: Socioeconomic inequalities in MetS prevalence, reflected by education and social assistance benefits, were found. However, diet quality contributed only to socioeconomic inequalities due to education underlining that education may impact health through the ability to generate overall dietary behavior, long-term beneficial. Our work identified subgroups with higher risk of MetS, which is needed when implementing public health measures, particularly in this Caribbean population with of high poverty rates. Further prospective studies are needed to improve our understanding of the mechanisms of social inequalities in MetS in a high poverty rates context.