SummaryBackgroundThe Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations.MethodsWe used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017.FindingsIn 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). I...

Background Suboptimal diet is an important preventable risk factor for non-communicable diseases (NCDs); however, its impact on the burden of NCDs has not been systematically evaluated. This study aimed to evaluate the consumption of major foods and nutrients across 195 countries and to quantify the impact of their suboptimal intake on NCD mortality and morbidity. Methods By use of a comparative risk assessment approach, we estimated the proportion of disease-specific burden attributable to each dietary risk factor (also referred to as population attributable fraction) among adults aged 25 years or older. The main inputs to this analysis included the intake of each dietary factor, the effect size of the dietary factor on disease endpoint, and the level of intake associated with the lowest risk of mortality. Then, by use of diseasespecific population attributable fractions, mortality, and disability-adjusted life-years (DALYs), we calculated the number of deaths and DALYs attributable to diet for each disease outcome. Findings In 2017, 11 million (95% uncertainty interval [UI] 10-12) deaths and 255 million (234-274) DALYs were attributable to dietary risk factors. High intake of sodium (3 million [1-5] deaths and 70 million [34-118] DALYs), low intake of whole grains (3 million [2-4] deaths and 82 million [59-109] DALYs), and low intake of fruits (2 million [1-4] deaths and 65 million [41-92] DALYs) were the leading dietary risk factors for deaths and DALYs globally and in many countries. Dietary data were from mixed sources and were not available for all countries, increasing the statistical uncertainty of our estimates. Interpretation This study provides a comprehensive picture of the potential impact of suboptimal diet on NCD mortality and morbidity, highlighting the need for improving diet across nations. Our findings will inform implementation of evidence-based dietary interventions and provide a platform for evaluation of their impact on human health annually. Funding Bill & Melinda Gates Foundation.

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Improving physical access to supermarkets may be one strategy to deal with the obesity epidemic; improving economic access to healthy foods is another.

Background/Objectives Socioeconomic disparities in diet quality are well established. This study tested the hypothesis that such disparities are mediated, in part, by diet cost. Subjects/Methods The Seattle Obesity Study (S.O.S.) was a cross sectional study based on a representative sample of 1266 adults of King County, WA conducted in 2008–09. Demographic and socioeconomic variables were obtained through telephone survey. Income and education were used as indicators of socioeconomic position (SEP). Dietary intake data were obtained using a food frequency questionnaire (FFQ). Diet cost was calculated based on retail prices for FFQ component foods. Energy density (KJ/g) and Mean Adequacy Ratio (MAR) were used as two indices of overall diet quality. Results Higher income and education were each associated with lower energy density and higher MAR scores, adjusting for covariates. Higher income and education were also associated with higher energy adjusted diet cost. Higher quality diets were in turn associated with higher diet costs. All these associations were significant (P<0.0001). In formal mediation analyses, diet cost significantly mediated the pathway between income and diet quality measures, adjusting for covariates (p <0.05 each). Further, income – diet cost – diet quality pathway was found to be moderated by education level. Conclusions The social gradient in diet quality may be explained by diet cost. Strategies to improve diet quality among lower socioeconomic strata may need to take food prices and diet cost along with nutrition education into account.

Background The amount of time spent on food preparation and cooking may have implications for diet quality and health. However, little is known about how food-related time use relates to food consumption and spending, either at restaurants or for food consumed at home. Purpose To quantitatively assess the associations among the amount of time habitually spent on food preparation and patterns of self-reported food consumption, food spending, and frequency of restaurant use. Methods This was a cross-sectional study of 1,319 adults in a population-based survey conducted in 2008–2009. The sample was stratified into those who spent <1 hour/day, 1–2 hours/day, and >2 hours/day on food preparation and clean-up. Descriptive statistics and multivariable regression models examined differences between time-use groups. Analyses were conducted in 2011–2013. Results Individuals who spent the least amount of time on food preparation tended to be working adults who placed a high priority on convenience. Greater amount of time spent on food preparation was associated with indicators of higher diet quality, including significantly more frequent intake of vegetables, salads, fruits, and fruit juices. Spending less than 1 hour/day on food preparation was associated with significantly more money spent on food away from home and more frequent use of fast food restaurants compared to those who spent more time on food preparation. Conclusions The findings indicate that time might be an essential ingredient in the production of healthier eating habits among adults. Further research should investigate the determinants of spending time on food preparation.

CF practices were inappropriate and knowledge inadequate in majority of the children studied.

Objective Individual-level determinants of diets are well known. Recently, physical proximity to neighborhood supermarkets has been proposed as an environmental determinant of access to healthy foods, diets and health. The present study hypothesized that supermarket choice, conceptualized as the proxy for underlying personal factors, would better predict supermarket accessibility and diet quality than mere physical proximity. Methods The Seattle Obesity Study geocoded respondents’ home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low-, medium- and high- cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained through telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups. Results Only one-third of respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates. Conclusions Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.