The 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. We 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. In 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). In total, risk-attributable DALYs declined by 4·9% (3·3-6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD...

The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6-58·8) of global deaths and 41·2% (39·8-42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Bill & Melinda Gates Foundation.

Quiz Ref ID Previous studies have reported on health disparities in US states and counties.^1,2 These studies showed that health disparities have increased with time. Recent attention has focused on increased mortality in some age groups and a decline in life expectancy.³ In addition, the performance of the US health care system does not match its level of spending on health and lags behind countries with similar financial resources.⁴ For example, in 2014, US life expectancy ranked 43rd in the world, although the United States spent the most ($3.0 trillion) on health care, exceeding the median amount spent by Organisation for Economic Co-operation and Development countries by 35%.⁵

Detailed assessments of mortality patterns, particularly age-specific mortality, represent a crucial input that enables health systems to target interventions to specific populations. Understanding how all-cause mortality has changed with respect to development status can identify exemplars for best practice. To accomplish this, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated age-specific and sex-specific all-cause mortality between 1970 and 2016 for 195 countries and territories and at the subnational level for the five countries with a population greater than 200 million in 2016. We have evaluated how well civil registration systems captured deaths using a set of demographic methods called death distribution methods for adults and from consideration of survey and census data for children younger than 5 years. We generated an overall assessment of completeness of registration of deaths by dividing registered deaths in each location-year by our estimate of all-age deaths generated from our overall estimation process. For 163 locations, including subnational units in countries with a population greater than 200 million with complete vital registration (VR) systems, our estimates were largely driven by the observed data, with corrections for small fluctuations in numbers and estimation for recent years where there were lags in data reporting (lags were variable by location, generally between 1 year and 6 years). For other locations, we took advantage of different data sources available to measure under-5 mortality rates (U5MR) using complete birth histories, summary birth histories, and incomplete VR with adjustments; we measured adult mortality rate (the probability of death in individuals aged 15-60 years) using adjusted incomplete VR, sibling histories, and household death recall. We used the U5MR and adult mortality rate, together with crude death rate due to HIV in the GBD model life table system, to estimate age-specific and sex-specific death rates for each location-year. Using various international databases, we identified fatal discontinuities, which we defined as increases in the death rate of more than one death per million, resulting from conflict and terrorism, natural disasters, major transport or technological accidents, and a subset of epidemic infectious diseases; these were added to estimates in the relevant years. In 47 countries with an identified peak adult prevalence for HIV/AIDS of more than 0·5% and where VR systems were less than 65% complete, we informed our estimates of age-sex-specific mortality using the Estimation and Projection Package (EPP)-Spectrum model fitted to national HIV/AIDS prevalence surveys and antenatal clinic serosurveillance systems. We estimated stillbirths, early neonatal, late neonatal, and childhood mortality using both survey and VR data in spatiotemporal Gaussian process regression models. We estimated abridged life tables for all location-years using age-specific death rates. We grouped locations into development quintiles based on the Socio-demographic Index (SDI) and analysed mortality trends by quintile. Using spline regression, we estimated the expected mortality rate for each age-sex group as a function of SDI. We identified countries with higher life expectancy than expected by comparing observed life expectancy to anticipated life expectancy on the basis of development status alone. Completeness in the registration of deaths increased from 28% in 1970 to a peak of 45% in 2013; completeness was lower after 2013 because of lags in reporting. Total deaths in children younger than 5 years decreased from 1970 to 2016, and slower decreases occurred at ages 5-24 years. By contrast, numbers of adult deaths increased in each 5-year age bracket above the age of 25 years. The distribution of annualised rates of change in age-specific mortality rate differed over the period 2000 to 2016 compared with earlier decades: increasing annualised rates of change were less frequent, although rising annualised rates of change still occurred in some locations, particularly for adolescent and younger adult age groups. Rates of stillbirths and under-5 mortality both decreased globally from 1970. Evidence for global convergence of death rates was mixed; although the absolute difference between age-standardised death rates narrowed between countries at the lowest and highest levels of SDI, the ratio of these death rates-a measure of relative inequality-increased slightly. There was a strong shift between 1970 and 2016 toward higher life expectancy, most noticeably at higher levels of SDI. Among countries with populations greater than 1 million in 2016, life expectancy at birth was highest for women in Japan, at 86·9 years (95% UI 86·7-87·2), and for men in Singapore, at 81·3 years (78·8-83·7) in 2016. Male life expectancy was generally lower than female life expectancy between 1970 and 2016, and the gap between male and female life expectancy increased with progression to higher levels of SDI. Some countries with exceptional health performance in 1990 in terms of the difference in observed to expected life expectancy at birth had slower progress on the same measure in 2016. Globally, mortality rates have decreased across all age groups over the past five decades, with the largest improvements occurring among children younger than 5 years. However, at the national level, considerable heterogeneity remains in terms of both level and rate of changes in age-specific mortality; increases in mortality for certain age groups occurred in some locations. We found evidence that the absolute gap between countries in age-specific death rates has declined, although the relative gap for some age-sex groups increased. Countries that now lead in terms of having higher observed life expectancy than that expected on the basis of development alone, or locations that have either...

Current dietary guidelines recommend eating a variety of fruits and vegetables. However, based on nutrient composition, some particular fruits and vegetables may be more or less beneficial for maintaining or achieving a healthy weight. We hypothesized that greater consumption of fruits and vegetables with a higher fiber content or lower glycemic load would be more strongly associated with a healthy weight. We examined the association between change in intake of specific fruits and vegetables and change in weight in three large, prospective cohorts of 133,468 United States men and women. From 1986 to 2010, these associations were examined within multiple 4-y time intervals, adjusting for simultaneous changes in other lifestyle factors, including other aspects of diet, smoking status, and physical activity. Results were combined using a random effects meta-analysis. Increased intake of fruits was inversely associated with 4-y weight change: total fruits -0.53 lb per daily serving (95% CI -0.61, -0.44), berries -1.11 lb (95% CI -1.45, -0.78), and apples/pears -1.24 lb (95% CI -1.62, -0.86). Increased intake of several vegetables was also inversely associated with weight change: total vegetables -0.25 lb per daily serving (95% CI -0.35, -0.14), tofu/soy -2.47 lb (95% CI, -3.09 to -1.85 lb) and cauliflower -1.37 lb (95% CI -2.27, -0.47). On the other hand, increased intake of starchy vegetables, including corn, peas, and potatoes, was associated with weight gain. Vegetables having both higher fiber and lower glycemic load were more strongly inversely associated with weight change compared with lower-fiber, higher-glycemic-load vegetables (p < 0.0001). Despite the measurement of key confounders in our analyses, the potential for residual confounding cannot be ruled out, and although our food frequency questionnaire specified portion size, the assessment of diet using any method will have measurement error. Increased consumption of fruits and non-starchy vegetables is inversely associated with weight change, with important differences by type suggesting that other characteristics of these foods influence the magnitude of their association with weight change. Obesity—having an unhealthy amount of body fat—is increasing worldwide. In the United States, for example, more than a third of adults are obese and another third are overweight. Obesity is defined as having a body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) of more than 30 kg/m²; overweight individuals have a BMI of 25.0–29.9 kg/m². Compared to people with a healthy weight, overweight and obese individuals have an increased risk of developing diabetes and cardiovascular diseases (conditions that affect the heart and/or the blood vessels), and tend to die younger. People gain too much fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So, people can avoid becoming obese or reduce their BMI by eating a healthy diet that contains fewer calories and by exercising more. The 2010 Dietary Guidelines for Americans recommend that adults and children should eat a variety of fruits and vegetables to help them achieve and maintain a healthy weight. But are all fruits and vegetables equally good at controlling weight? Fruits and vegetables differ in their dietary fiber content and their glycemic load. High fiber foods increase satiety (feeling full after eating), which can reduce total energy intake. Foods with a low glycemic load produce smaller and fewer blood sugar spikes after they are consumed, which may reduce hunger later on. In this study, the researchers investigate whether consumption of fruits and vegetable with a higher fiber content or lower glycemic load is more strongly associated with a healthy weight than consumption of fruits and vegetables with a lower fiber content or higher glycemic load by analyzing data on weight and diet changes among US men and women enrolled in three large prospective cohort studies set up to examine risk factors for major chronic diseases. The researchers examined associations between changes in the intake of specific fruits and vegetables recorded in dietary questionnaires completed every 4 y and self-reported weight changes in 133,468 US men and women followed for up to 24 y. After adjusting for self-reported changes in other lifestyle factors likely to affect weight, such as smoking status and physical activity, an increased intake of fruits and of several vegetables was inversely associated with 4-y weight change. Thus, an increase in total fruit intake was associated with a change in weight over a 4-y interval of -0.53 lb (a weight loss of 0.24 kg) for each extra daily serving, and an increase in total vegetable intake was associated with a weight change of -0.25 lb (-0.11 kg) for each extra daily serving. However, increased intake of starchy vegetables such as corn, peas, and potatoes was associated with weight gain. Notably, higher-fiber, lower-glycemic load vegetables (for example, broccoli and Brussels sprouts) were more strongly inversely associated with weight change than lower-fiber, higher-glycemic load vegetables (for example, carrots and cabbage). These findings suggest that increased consumption of fruits and non-starchy vegetables is inversely associated with weight change and that different fruits and vegetables have different effects on weight. The benefits of increased consumption were greater for fruits than for vegetables and strongest for berries, apples/pears, tofu/soy, cauliflower, and cruciferous and green leafy vegetables. Increased satiety with fewer calories could be partly responsible for the beneficial effects of increasing fruit and vegetable intake. These findings may not be generalizable—nearly all the participants were well-educated white adults. Moreover, the use of dietary questionnaires and self-reported weight measurement may have introduced measurement errors into this study and, although the researchers accounted for some key lifestyle factors that are likely to affect weight, individuals who increased their fruit and vegetable intake and lost weight may have shared other unknown characteristics that were actually responsible for their weight loss. Overall, however, these findings provide new food-specific guidance for the prevention of obesity, a primary risk factor for many life-shortening health conditions. This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001878.

Background—: Among adults, skipping meals is associated with excess body weight, hypertension, insulin resistance, and elevated fasting lipid concentrations. However, it remains unknown whether specific eating habits regardless of dietary composition influence coronary heart disease (CHD) risk. The objective of this study was to prospectively examine eating habits and risk of CHD. Methods and Results—: Eating habits, including breakfast eating, were assessed in 1992 in 26 902 American men 45 to 82 years of age from the Health Professionals Follow-up Study who were free of cardiovascular disease and cancer. During 16 years of follow-up, 1527 incident CHD cases were diagnosed. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals for CHD, adjusted for demographic, diet, lifestyle, and other CHD risk factors. Men who skipped breakfast had a 27% higher risk of CHD compared with men who did not (relative risk, 1.27; 95% confidence interval, 1.06–1.53). Compared with men who did not eat late at night, those who ate late at night had a 55% higher CHD risk (relative risk, 1.55; 95% confidence interval, 1.05–2.29). These associations were mediated by body mass index, hypertension, hypercholesterolemia, and diabetes mellitus. No association was observed between eating frequency (times per day) and risk of CHD. Conclusions—: Eating breakfast was associated with significantly lower CHD risk in this cohort of male health professionals.