The UK government plans to limit price‐based and location‐based promotions for products high in saturated fat, salt and sugars. The 2004/2005 UK Nutrient Profiling Model (NPM) is the proposed legislative basis, but may be superseded by the draft 2018 NPM. This study develops an algorithm to apply both NPMs to a large food composition database (FCDB), and assesses implementation challenges. UK NPMs were applied algorithmically to the myfood24 FCDB, representing ~45 000 retail products. Pass rates – indicating free or restricted promotions – and micronutrient compositions were compared. Challenges were assessed, and recommendations addressed the legislation’s public consultation questions. For products in scope (75% of total), 6% fewer passed the 2018 NPM (36%, P < 0.001) compared with the 2004/2005 NPM (42%). Beverages showed the greatest reduction in pass rate (75%). Under both models, micronutrient contents (per 100 g of product) were generally lower for products that passed; except folate, vitamin C and vitamin D were no different for passed and failed products. Compared with products passing the 2004/2005 NPM, products passing the 2018 NPM on average had marginally higher amounts of iron (0.05 mg, 95% CI: 0.02, 0.08, P < 0.001) and magnesium (1.00 mg, 95% CI: 0.00, 1.17, P = 0.029), but marginally lower levels of calcium (−0.42 mg, 95% CI: −2.00, −0.40, P = 0.025). Missing ingredient information and heterogeneous product categories were challenges for both NPMs. Free sugars calculation further complicated 2018 NPM application. To balance feasibility and public health benefit, the proposed legislative basis may not be appropriate.
Frequent consumption of takeaway meals has been found to be negatively associated with the diet quality of British adolescents (1) . The Food Environment Policy Index believes that strengthening planning policies to discourage unhealthy fast food is a priority and will have a significant influence on mitigating diet-related diseases and obesity (2) . Planning policies for limiting the clustering of hot food takeaways (HFTs) around schools exists in the UK. However, long-term effectiveness and their impact on health should be studied and explored as few studies have investigated the longitudinal associations with health outcomes among adolescents attending secondary school particularly in the UK (3,4) . Therefore, this study aimed to investigate the relationships between the density, proximity and accessibility of takeaway outlets and the BMI and body fat percentage of UK adolescents from the Avon Longitudinal Study of Parents and Children study conducted between 2005 and 2011.In total, 52 state-funded schools with 1382 participants (44.5% male) were included in this study. A Geographical Information System was used to locate all schools and takeaways in the region and to measure the density within 800-and 1000-metres and proximity scores, applying the road network method. In addition, the Hansen Index was used to measure the accessibility score of each school to all takeaways in the region. The statistical analysis tests, including linear and logistic regression tests, were conducted to explore the associations between availability, proximity, and accessibility of HFTs at baseline ( 2007) when the adolescents were 13 years and BMI z-score and body fat percentage status at 15 and17 years in 2009 and 2011; using Stata software, Version 15.0.Adjusted linear regression showed non-significant associations between availability of HFTs and BMI z-score and body fat percentage at 15 or 17 years when using either an 800-or 1000-metre buffer. An adjusted logistic regression showed non-significant associations between availability of HFTs within 800-and 1000-metres and risk of being obese at 15 years. However, the adjusted logistic analysis showed protective effects between the availability of HFTs within 800-and 1000-metres and the risk of being obese, particularly at 17 years, which was the opposite effect expected. For example, the odds of being obese and attending schools with HFTs was 0.56 (95% CI; 0.41, 0.76) at age 17 years. The proximity of HFTs showed no associations with BMI z-score. Accessibility of HFTs showed small negative but significant associations with BMI z-score and attenuated results with body fatness.Overall results showed conflicting findings, and further exploration is still needed. An intensive understanding of the effect of the food environment, particularly around secondary schools, is needed, especially using more recent data for both the exposure and health outcomes.
BackgroundObesity prevalence of epidemic proportions continues to be a major public health problem globally. Better understanding of the spatial and social variation in obesity is essential in order to support changes to policy or our environment to reduce obesity prevalence. This paper uses a geodemographic classification – which combines demographic characteristics with a small area geographic unit – to profile weight status and estimate small-area obesity prevalence in Australia and the US.MethodsThis study is a cross sectional analysis of two large studies; the Australian Longitudinal Study on Women’s Health (ALSWH) and the Seattle Obesity Study (SOS1). Descriptive statistics, chi2 and Kruskal Wallis test for difference, linear and multinomial logistic regression were carried out using Stata 12 statistical software. ArcMap10 was used to: (1) match the study participants to a CAMEO geodemographic identifier, using the longitude and latitude of their home address and (2) to visualise the obesity estimates for Newcastle (Australia) and Seattle (US). CAMEO is a commercially available geodemographic classification.ResultsBoth ALSWH and SOS1 had under and over-representation in certain CAMEO groups compared to national representation, but each group contains high numbers of individuals suggesting results are robust. Demographic characteristics of the study participants are in line with those expected in the corresponding CAMEO groups. In both studies significant differences in body mass index across CAMEO groups exists (p < 0.001). In Australia, the Diverse Low Income Urban Communities had twice the odds of being obese than the Affluent Urban Professionals (OR = 2.24 (95% CI 1.55 to 3.23)). In the US, compared to the American Aristocracy, the Enterprising Households (OR 1.97 (95% CI 1.25 to 3.09)), Comfortable Communities (OR 2.01 (95% CI 1.25 to 3.22)) and Dynamic Neighbourhoods (2.09 (1.30 to 3.36)) also had twice the odds of being obese. Comprehensive obesity maps for Newcastle and Seattle at a small area geographical resolution were produced, identifying neighbourhoods with likely high prevalence of obesity.ConclusionGeodemographic classifications, such as CAMEO, combined with survey data offer promising solutions for profiling obesity outcomes worldwide which could facilitate effective targeting of potential public health interventions at a neighbourhood geography scale.
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