Public Health Nutrition

Few previous studies in Latin America (LA) have provided data on dietary intake composition with a standardized methodology. The present study aimed to characterize energy intake (EI) and to describe the main food sources of energy in representative samples of the urban population from eight LA countries from the Latin American Study in Nutrition and Health (ELANS).

Cross-sectional study. Usual dietary intake was assessed with two non-consecutive 24 h dietary recalls.

Urban areas from eight countries (Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, Venezuela), September 2014 to July 2015.

Adolescents and adults aged 15–65 years. Final sample comprised 9218 individuals, of whom 6648 (72·1 %) were considered plausible reporters.

Overall, mean EI was 8196 kJ/d (1959 kcal/d), with a balanced distribution of macronutrients (54 % carbohydrate, 30 % fat, 16 % protein). Main food sources of energy were grains, pasta and bread (28 %), followed by meat and eggs (19 %), oils and fats (10 %), non-alcoholic homemade beverages (6 %) and ready-to-drink beverages (6 %). More than 25 % of EI was provided from food sources rich in sugar and fat, like sugary drinks, pastries, chips and candies. Meanwhile, only 18 % of EI was from food sources rich in fibre and micronutrients, such as whole grains, roots, fruits, vegetables, beans, fish and nuts. No critical differences were observed by gender or age.

Public health efforts oriented to diminish consumption of refined carbohydrates, meats, oils and sugar and to increase nutrient dense-foods are a priority in the region to drive to a healthier diet.

To understand the effects of interviewers on the responses they collect for measures of food security, income and selected survey quality measures (i.e. discrepancy between reported Supplemental Nutrition Assistance Program (SNAP) status and administrative data, length of time between initial and final interview, and missing income data) in the US Department of Agriculture’s National Household Food Acquisition and Purchase Survey (FoodAPS).

Using data from FoodAPS, multilevel models with random interviewer effects were fitted to estimate the variance in each outcome measure arising from effects of the interviewers. Covariates describing each household’s socio-economic status, demographics and experience in taking the survey, and interviewer-level experience were included as fixed effects. The variance components in the outcomes due to interviewers were estimated. Outlier interviewers were profiled.

Non-institutionalized households in the continental USA (April 2012–January 2013).

Individuals (n 14 317) in 4826 households who responded to FoodAPS.

There was a substantial amount of variability in the distributions of the outcomes examined (i.e. time between initial and final interview, reported values for food security, individual income, missing income) among the FoodAPS interviewers, even after accounting for the fixed effects of the household- and interviewer-level covariates and removing extreme outlier interviewers.

Interviewers may introduce error in food acquisition survey data when they are asked to interact with the respondents. Managers of future surveys with similarly complex data collection procedures could consider using multilevel models to adaptively identify and retrain interviewers who have extreme effects on data collection outcomes.

To determine if family childcare homes (FCCH) in Nebraska meet best practices for nutrition and screen time, and if focusing on nutrition and screen time policies and practices improves the FCCH environment.

A pre–post evaluation was conducted using the Go Nutrition and Physical Activity Self-Assessment for Childcare (Go NAP SACC).

FCCH in Nebraska, USA.

FCCH enrolled in the Child and Adult Care Food Program (CACFP; n 208) participated in a pre–post evaluation using Go NAP SACC.

At baseline, all FCCH met the minimum childcare standards for fifty-four of fifty-six practices in nutrition and screen time. After the intervention, FCCH demonstrated significant improvement in fourteen of the forty-four Child Nutrition items and eleven of the twelve Screen Time items. However, FCCH providers did not meet best practices at post-intervention. Lowest scores were found in serving meals family-style, promoting visible support for healthy eating, planned nutrition education and written policy on child nutrition. For screen time, lowest scores were reported on the availability of television, offering families education on screen time and having a written policy on screen time.

FCCH in Nebraska were able to strengthen their policies and practices after utilizing Go NAP SACC. Continued professional development and participation in targeted interventions may assist programmes in sustaining improved practices and policies. Considering the varying standards and policies surrounding FCCH, future studies comparing the current findings with childcare centres and non-CACFP programmes are warranted.

To examine associations between household-level characteristics and underweight in a post-conflict population.

Nutritional status of residents in the Gulu Health and Demographic Surveillance Site was obtained during a community-based cross-sectional study, ~6 years after the civil war. Household-level factors included headship, polygamy, household size, child-to-adult ratio, child crowding, living with a stunted or overweight person, deprived area, distance to health centre and socio-economic status. Multilevel logistic regression models examined associations of household and community factors with underweight, calculating OR, corresponding 95 % CI and intraclass correlation coefficients. Effect modification by gender and age was examined by interaction terms and stratified analyses.

Rural post-conflict area in northern Uganda.

In total, 2799 households and 11 312 individuals were included, representing all age groups.

Living in a female-headed v. male-headed household was associated (OR; 95 % CI) with higher odds for underweight among adult men (2·18; 1·11, 4·27) and girls <5 years (1·51; 0·97, 2·34), but lower odds among adolescent women aged 13–19 years (0·46; 0·22, 0·97). Higher odds was seen for residents living in deprived areas (1·37; 0·97, 1·94), with increasing distance to health services (P-trend <0·05) and among adult men living alone v. living in an average-sized household of seven members (3·23; 1·22, 8·59). Residents living in polygamous households had lower odds for underweight (0·79; 0·65, 0·97).

The gender- and age-specific associations between household-level factors and underweight are likely to reflect local social capital structures. Adapting to these is crucial before implementing health and nutrition interventions.

The present study assessed the nutrition information displayed on ready-to-eat packaged foods and the nutritional quality of those food products in Thailand.

In March 2015, the nutrition information panels and nutrition and health claims on ready-to-eat packaged foods were collected from the biggest store of each of the twelve major retailers, using protocols developed by the International Network for Food and Obesity/Non-communicable Diseases Research, Monitoring and Action Support (INFORMAS). The Thai Nutrient Profile Model was used to classify food products according to their nutritional quality as ‘healthier’ or ‘less healthy’.

In total, information from 7205 food products was collected across five broad food categories. Out of those products, 5707 (79·2 %), 2536 (35·2 %) and 1487 (20·6 %) carried a nutrition facts panel, a Guideline Daily Amount (GDA) label and health-related claims, respectively. Only 4691 (65·1 %) and 2484 (34·5 %) of the products that displayed the nutrition facts or a GDA label, respectively, followed the guidelines of the Thai Food and Drug Administration. In total, 4689 products (65·1 %) could be classified according to the Thai Nutrient Profile Model, of which 432 products (9·2 %) were classified as healthier. Moreover, among the 1487 products carrying health-related claims, 1219 (82·0 %) were classified as less healthy. Allowing less healthy food products to carry claims could mislead consumers and result in overconsumption of ready-to-eat food products.

The findings suggest effective policies should be implemented to increase the relative availability of healthier ready-to-eat packaged foods, as well as to improve the provision of nutrition information on labels in Thailand.

To (i) evaluate the extent to which Coca-Cola’s ‘Transparency Lists’ of 218 researchers that it funds are comprehensive; (ii) map all scientific research acknowledging funding from Coca-Cola; (iii) identify those institutions, authors and research topics funded by Coca-Cola; and (iv) use Coca-Cola’s disclosure to gauge whether its funded researchers acknowledge the source of funding.

Using Web of Science Core Collection database, we retrieved all studies declaring receipt of direct funding from the Coca-Cola brand, published between 2008 and 2016. Using conservative eligibility criteria, we iteratively removed studies and recreated Coca-Cola’s transparency lists using our data. We used network analysis and structural topic modelling to assess the structure, organization and thematic focus of Coca-Cola’s research enterprise, and string matching to evaluate the completeness of Coca-Cola’s transparency lists.

Three hundred and eighty-nine articles, published in 169 different journals, and authored by 907 researchers, cite funding from The Coca-Cola Company. Of these, Coca-Cola acknowledges funding forty-two authors (<5 %). We observed that the funded research focuses mostly on nutrition and emphasizes the importance of physical activity and the concept of ‘energy balance’.

The Coca-Cola Company appears to have failed to declare a comprehensive list of its research activities. Further, several funded authors appear to have failed to declare receipt of funding. Most of Coca-Cola’s research support is directed towards physical activity and disregards the role of diet in obesity. Despite initiatives for greater transparency of research funding, the full scale of Coca-Cola’s involvement is still not known.