Patterns and trends in the intake distribution of manufactured and homemade sugar-sweetened beverages in pre-tax Mexico, 1999-2012

Objective To describe trends across the intake distribution of total, manufactured and homemade sugar-sweetened beverages (SSB) from 1999 to 2012, focusing on high SSB consumers and on changes by socio-economic status (SES) subgroup.Design We analysed data from one 24 h dietary recall from two nationally representative surveys. Quantile regression models at the 50th, 75th and 90th percentiles of energy intake distribution of SSB were used.Setting 1999 Mexican National Nutrition Survey and 2012 Mexican National Health and Nutrition Survey.Participants School-aged children (5-11 years) and women (20-49 years) for trend analyses (n 7718). Population aged >1 year for 2012 (n 10 096).Results Over the 1999-2012 period, there were significant increases in the proportion of total and manufactured SSB consumers (5·7 and 10·7 percentage points), along with an increase in per-consumer SSB energy intake, resulting in significant increases in per-capita total SSB energy intake (142, 247 and 397 kJ/d (34, 59 and 95 kcal/d) in school-aged children and 155, 331 and 456 kJ/d (37, 79 and 109 kcal/d) in women at the 50th, 75th and 90th percentile, respectively). Total and manufactured SSB intakes increased sharply among low-SES children but remained similar among high-SES children during this time span.Conclusions Large increases in SSB consumption were seen between 1999 and 2012 during this pre-tax SSB period, particularly for the highest consumers. Trends observed in school-aged children are a clear example of the nutrition transition experienced in Mexico. Policies to discourage high intake of manufactured SSB should continue, joined with strategies to encourage water and low-calorie beverage consumption

Estimating the burden of COVID-19 on mortality, life expectancy and lifespan inequality in England and Wales : a population-level study

Objective: To determine the impact of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in the first half of 2020 (from week 1 to week 26 starting June 22) in England and Wales. Design: Demographic analysis of all-cause mortality from week 1 through week 26 of 2020 using publicly available death registration data from the Office for National Statistics. Setting and population: England and Wales population by age and sex in 2020. Main outcome measure: Age and sex-specific excess mortality risk and deaths above a baseline adjusted for seasonality in the first half of 2020. We additionally provide estimates of life expectancy at birth and lifespan inequality defined as the standard deviation in age at death. Results: We estimate that there have been 53,937 (95% Prediction Interval: 53,092, 54,746) excess deaths in the first half of 2020, 54% of which occurred in men. Excess deaths increased sharply with age and men experienced elevated risks of death in all age groups. Life expectancy at birth dropped 1.7 and 1.9 years for females and males relative to the 2019 levels, respectively. Lifespan inequality also fell over the same period. Conclusions: Quantifying excess deaths and their impact on life expectancy at birth provides a more comprehensive picture of the full COVID-19 burden on mortality. Whether mortality will return to - or even fall below - the baseline level remains to be seen as the pandemic continues to unfold and diverse interventions are put in place

COPD classification models and mortality prediction capacity

Our aim was to assess the impact of comorbidities on existing COPD prognosis scores. Patients and methods: A total of 543 patients with COPD (FEV1 < 80% and FEV1/ FVC <70%) were included between January 2003 and January 2004. Patients were stable for at least 6 weeks before inclusion and were followed for 5 years without any intervention by the research team. Comorbidities and causes of death were established from medical reports or information from primary care medical records. The GOLD system and the body mass index, obstruction, dyspnea and exercise (BODE) index were used for COPD classification. Patients were also classified into four clusters depending on the respiratory disease and comorbidities. Cluster analysis was performed by combining multiple correspondence analyses and automatic classification. Receiver operating characteristic curves and the area under the curve (AUC) were calculated for each model, and the DeLong test was used to evaluate differences between AUCs. Improvement in prediction ability was analyzed by the DeLong test, category-free net reclassification improvement and the integrated discrimination index. Results: Among the 543 patients enrolled, 521 (96%) were male, with a mean age of 68 years, mean body mass index 28.3 and mean FEV1% 55%. A total of 167 patients died during the study follow-up. Comorbidities were prevalent in our cohort, with a mean Charlson index of 2.4. The most prevalent comorbidities were hypertension, diabetes mellitus and cardiovascular diseases. On comparing the BODE index, GOLDABCD, GOLD2017 and cluster analysis for pre-dicting mortality, cluster system was found to be superior compared with GOLD2017 (0.654 vs 0.722, P=0.006), without significant differences between other classification models. When cardiovascular comorbidities and chronic renal failure were added to the existing scores, their prognostic capacity was statistically superior (P<0.001). Conclusion: Comorbidities should be taken into account in COPD management scores due to their prevalence and impact on mortalit

Chronic obstructive pulmonary disease subtypes. transitions over time

Background Although subtypes of chronic obstructive pulmonary disease are recognized, it is unknown what happens to these subtypes over time. Our objectives were to assess the stability of cluster-based subtypes in patients with stable disease and explore changes in clusters over 1 year. Methods Multiple correspondence and cluster analysis were used to evaluate data collected from 543 stable patients included consecutively from 5 respiratory outpatient clinics. Results Four subtypes were identified. Three of them, A, B, and C, had marked respiratory profiles with a continuum in severity of several variables, while the fourth, subtype D, had a more systemic profile with intermediate respiratory disease severity. Subtype A was associated with less dyspnea, better health-related quality of life and lower Charlson comorbidity scores, and subtype C with the most severe dyspnea, and poorer pulmonary function and quality of life, while subtype B was between subtypes A and C. Subtype D had higher rates of hospitalization the previous year, and comorbidities. After 1 year, all clusters remained stable. Generally, patients continued in the same subtype but 28% migrated to another cluster. Together with movement across clusters, patients showed changes in certain characteristics (especially exercise capacity, some variables of pulmonary function and physical activity) and changes in outcomes (quality of life, hospitalization and mortality) depending on the new cluster they belonged to Conclusions Chronic obstructive pulmonary disease clusters remained stable over 1 year. Most patients stayed in their initial subtype cluster, but some moved to another subtype and accordingly had different outcomes

Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis

Background Deaths directly linked to COVID-19 infection may be misclassified, and the pandemic may have indirectly affected other causes of death. To overcome these measurement challenges, we estimate the impact of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality from week 10 of 2020, when the first COVID-19 death was registered, to week 47 ending 20 November 2020 in England and Wales through an analysis of excess mortality. Methods We estimated age and sex-specific excess mortality risk and deaths above a baseline adjusted for seasonality with a systematic comparison of four different models using data from the Office for National Statistics. We additionally provide estimates of life expectancy at birth and lifespan inequality defined as the SD in age at death. Results There have been 57 419 (95% prediction interval: 54 197, 60 752) excess deaths in the first 47 weeks of 2020, 55% of which occurred in men. Excess deaths increased sharply with age and men experienced elevated risks of death in all age groups. Life expectancy at birth dropped 0.9 and 1.2 years for women and men relative to the 2019 levels, respectively. Lifespan inequality also fell over the same period by 5 months for both sexes. Conclusion Quantifying excess deaths and their impact on life expectancy at birth provide a more comprehensive picture of the burden of COVID-19 on mortality. Whether mortality will return to—or even fall below—the baseline level remains to be seen as the pandemic continues to unfold and diverse interventions are put in place

Is a Hypertension Diagnosis Associated With Improved Dietary Outcomes Within 2 to 4 Years? A Fixed-Effects Analysis From the China Health and Nutrition Survey

Background: Evidence shows that dietary factors play an important role in blood pressure. However, there is no clear understanding of whether hypertension diagnosis is associated with dietary modifications. The aim of this study is to estimate the longitudinal association between hypertension diagnosis and subsequent changes (within 2–4 years) in dietary sodium, potassium, and sodium-potassium (Na/K) ratio. Methods and Results: We included adults (18–75 years, n=16 264) from up to 9 waves (1991–2015) of the China Health and Nutrition Survey. Diet data were collected using three 24-hour dietary recalls and a household food inventory. We used fixed-effects models to estimate the association between newly self-reported diagnosed hypertension and subsequent within-individual changes in sodium, potassium, and Na/K ratio. We also examined changes among couples and at the household level. Results suggest that on average, men who were diagnosed with hypertension decreased their sodium intake by 251 mg/d and their Na/K ratio by 0.19 within 2 to 4 years after diagnosis (P<0.005). Among spouse pairs, sodium intake and Na/K ratio of women decreased when their husbands were diagnosed (P<0.05). Household average sodium density and Na/K ratio decreased, and household average potassium density increased after a man was diagnosed. In contrast, changes were not statistically significant when women were diagnosed. Conclusions: Our findings suggest that hypertension diagnosis for a man may result in modest dietary improvements for him, his wife, and other household members. Yet, diagnosis for a woman does not seem to result in dietary changes for her or her household members

Fiabilidad de la velocidad de ejecución en tres modalidades del ejercicio de press de banca: influencia del nivel de experiencia

Premio Congreso SIBB 2019El objetivo del estudio fue comparar la fiabilidad de la velocidad media propulsiva (VMP) entre tres variantes del ejercicio de press de banca (PB). Quince hombres con experiencia y 15 sin experiencia con el ejercicio de PB realizaron en orden aleatorizado tres variantes del ejercicio de PB en diferentes sesiones (sólo-concéntrico, excéntrico-rápido y excéntrico-controlado). La VMP se registró ante tres cargas (≈ 30%1RM, 50%1RM y 75%1RM) con un transductor lineal de velocidad. La fiabilidad fue siempre alta (coeficiente de variación [CV] ≤ 5,76%, coeficiente de correlación intraclase [CCI] ≥ 0,74). La comparación de los CV reveló una mayor fiabilidad para las variantes sólo-concéntrico y excéntrico-rápido en comparación con la variante excéntrico-controlado (CV ratio > 1,15), no existiendo diferencias significativas en fiabilidad entre las variantes sólo-concéntrico y excéntrico-rápido (CV ratio < 1,15). No se observaron diferencias en fiabilidad entre los participantes con (CV ≤ 5,76%; CCI ≥ 0,83) y sin experiencia (CV ≤ 5,21%; CCI ≥ 0,74). Estos resultados apoyan el uso de las modalidades de PB sólo-concéntrico y excéntrico-rápido para evaluar la fuerza de los miembros superiores a través de la medición de la velocidad de ejecución en participantes con y sin  experiencia con el ejercicio de PB.Award-winningPremio Congreso SIBB 2019Peer Reviewe

The Dependence of the Superconducting Transition Temperature of Organic Molecular Crystals on Intrinsically Non-Magnetic Disorder: a Signature of either Unconventional Superconductivity or Novel Local Magnetic Moment Formation

We give a theoretical analysis of published experimental studies of the effects of impurities and disorder on the superconducting transition temperature, T_c, of the organic molecular crystals kappa-ET_2X and beta-ET_2X (where ET is bis(ethylenedithio)tetrathiafulvalene and X is an anion eg I_3). The Abrikosov-Gorkov (AG) formula describes the suppression of T_c both by magnetic impurities in singlet superconductors, including s-wave superconductors and by non-magnetic impurities in a non-s-wave superconductor. We show that various sources of disorder lead to the suppression of T_c as described by the AG formula. This is confirmed by the excellent fit to the data, the fact that these materials are in the clean limit and the excellent agreement between the value of the interlayer hopping integral, t_perp, calculated from this fit and the value of t_perp found from angular-dependant magnetoresistance and quantum oscillation experiments. If the disorder is, as seems most likely, non-magnetic then the pairing state cannot be s-wave. We show that the cooling rate dependence of the magnetisation is inconsistent with paramagnetic impurities. Triplet pairing is ruled out by several experiments. If the disorder is non-magnetic then this implies that l>=2, in which case Occam's razor suggests that d-wave pairing is realised. Given the proximity of these materials to an antiferromagnetic Mott transition, it is possible that the disorder leads to the formation of local magnetic moments via some novel mechanism. Thus we conclude that either kappa-ET_2X and beta-ET_2X are d-wave superconductors or else they display a novel mechanism for the formation of localised moments. We suggest systematic experiments to differentiate between these scenarios.Comment: 18 pages, 5 figure

Mortality forecasting in Colombia from abridged life tables by sex

[EN] BACKGROUND: An adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables. OBJECTIVE: Select a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available. DATA AND METHOD: We used Colombian abridged life tables for the period 1973-2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures. RESULTS: Only three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model's fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children's ages and later ages for both sexes. CONCLUSION: The LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.Support for the research presented in this paper was provided by a grant from the Ministerio de Economía y Competitividad of Spain, project no. MTM2013-45381-P.Diaz-Rojo, G.; Debón Aucejo, AM.; Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus. Journal of Population Sciences (Online). 74(15):1-23. https://doi.org/10.1186/s41118-018-0038-6S1237415Aburto, J.M., & García-Guerrero, V.M. (2015). El modelo aditivo doble multiplicativo. Una aplicacion a la mortalidad mexicaná. Papeles de Población, 21(84), 9–44.Acosta, K., & Romero, J. (2014). Cambios recientes en las principales causas de mortalidad en Colombia. Technical report: Banco de la Banco de la República- Serie Documentos de Trabajo Sobre Economía Regional.Aguilar, E. (2013). Estimación y proyección de la mortalidad para Costa Rica con la aplicación del método Lee-Carter con dos variantes. Población y, Salud en Mesoamérica, 11(1), 3–24.Andreozzi, L (2012). Estimación y pronósticos de la mortalidad de Argentina utilizando el modelo de Lee-Carter. Revista de la Sociedad Argentina de Estadística, 10(1), 21–43.Andreozzi, L, & Blaconá, MT (2011). Estimación y pronóstico de las tasas de mortalidad y la esperanza de vida en la República Argentina. In Proceedings of Anales de las Decimosextas Jornadas Investigaciones en la Facultad de Ciencias Económicas y Estadística. Universidad Nacional de Rosario, Argentina.Andres, V, Millossovich, P, Vladimir, K (2018). StMoMo: Stochastic Mortality Modeling in R. Journal of Statistical Software, 84(3), 1–38.Belliard, M., & Williams, I. (2013). Proyección estocástica de la mortalidad. Una aplicación de Lee-Carter en la Argentina. Revista Latinoamericana de Población, 7(13), 129–148.Bertranou, E. (2008). Tendencias demográficas y protección social en América Latina y el Caribe: CEPAL. http://repositorio.cepal.org/handle/11362/7224 . Accessed 8 Aug 2017.Blaconá, M.T, & Andreozzi, L. (2014). Análisis de la mortalidad por edad y sexo mediante modelos para datos funcionales. Estadística, 66(186–187), 65–89.Blum, A., Kalai, A., Langford, J. (1999). Beating the hold-out: bounds for k-fold and progressive cross-validation. In Proceedings of the twelfth annual Conference on Computational Learning Theory. ACM, (pp. 203–208).Booth, H., Maindonald, J., Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43.Butt, Z., Haberman, S., Shang, H.L. (2014). ilc: Lee-Carter mortality models using iterative fitting algorithms. http://cran.r-project.org/package=ilc .Cairns, A.J., Blake, D., Dowd, K. (2006). A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration. Journal of Risk and Insurance, 73(4), 687–718.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Khalaf-Allah, M. (2011). Mortality density forecasts: an analysis of six stochastic mortality models. Insurance: Mathematics and Economics, 48(3), 355–367.Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A., Balevich, I. (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 1–35.Canudas-Romo, V. (2008). The modal age at death and the shifting mortality hypothesis. Demographic Research, 19(30), 1179–1204.Carfora, M.F., Cutillo, L., Orlando, A. (2017). A quantitative comparison of stochastic mortality models on Italian population data. Computational Statistics & Data Analysis, 112, 198–214.Currie, I.D., Durban, M., Eilers, P.H.C. (2006). Generalized linear array models with applications to multidimensional smoothing. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(2), 259–280.Debón, A., Martínez-Ruiz, F., Montes, F. (2012). Temporal evolution of mortality indicators: application to Spanish data. North American Actuarial Journal, 16(3), 364–377.Debón, A., Montes, F., Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operation Research, 189(3), 624–637.Debón, A., Martínez-Ruiz, F., Montes, F. (2010). A geostatistical approach for dynamic life tables: the effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327–336.Díaz, G., & Debón, A. (2016). Tendencias y comportamiento de la mortalidad en Colombia entre 1973 y 2005. Estadística Española, 58(191), 277–300.García-Guerrero, V.M., & Mellado, M.O. (2012). Proyección estocástica de la mortalidad mexicana por medio del método de Lee-Carter. Estudios Demográficos y Urbanos, 27(2), 409–448.Garfield, R., & Llanten, C. (2004). The public health context of violence in Colombia. Revista Panamericana de Salud Pública, 16(4), 266–271.Haberman, S. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35–55.Holford, T.R. (2006). Approaches to fitting age-period-cohort models with unequal intervals. Statistics in Medicine, 25(6), 977–993.Hunt, A., & Villegas, A.M. (2015). Robustness and convergence in the Lee-Carter model with cohort effects. Insurance: Mathematics and Economics, 64, 186–202.Hyndman, R.J. (2016). Forecast: forecasting functions for time series and linear models. R package version 7.3. https://CRAN.R-project.org/package=forecast .Hyndman, R.J, Booth, H., Tickle, L., Maindonald, J. (2014). Demography: forecasting mortality, fertility, migration and population data. R package version 1.18. https://CRAN.R-project.org/package=demography .Hyndman, R.J., & Ullah, M.S (2007). Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.Kennes, T. (2017). The convergence and robustness of cohort extensions of mortality models. MaRBLe, 1, 36–53.Lee, R., & Carter, L. (1992). Modelling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–671.Lee, R., & Rofman, R. (1994). Modelación y proyección de la mortalidad en Chile. Notas de Poblacion, 6(59), 183–213.Lee, W. (1997). Characterizing exposure-disease association in human populations using the Lorenz curve and Gini index. Statistics in Medicine, 16(7), 729–739.Levitt, S., & Rubio, M. (2000). Understanding crime in Colombia and what can be done about it. Technical Report 20, FEDESARROLLO.Llorca, J., Prieto, M.D., Alvarez, C.F., Delgado-Rodriguez, M. (1998). Age differential mortality in Spain, 1900–1991. Journal of Epidemiology & Community Health, 52, 259–261.Llorca, J, Prieto, M.D, Delgado-Rodriguez, M (2000). Medición de las desigualdades en la edad de muerte: cálculo del índice de Gini a partir de las tablas de mortalidad. Revista Española de Salud Pública, 74(1), 5–12.Lora, E. (2008). Técnicas de medición económica. Metodología y aplicaciones en Colombia, Ed. Siglo Veintiuno XXI y Fedesarrollo, fourth edition. Bogotá D.C.: Alfaomega Colombiana S.A.Ochoa, C.A. (2015). El modelo Lee-Carter para estimar y pronosticar mortalidad: una aplicación para Colombia. Master’s thesis: Universidad Nacional de Colombia-Sede Medellín.O’hare, C., & Li, Y. (2017). Modelling mortality: are we heading in the right direction?Applied Economics, 49(2), 170–187.Ornelas, A. (2015). La mortalidad y la longevidad en la cuantificación del riesgo actuarial para la población de México. PhD thesis: Universitat de Barcelona.Remund, A., Camarda, C.G., Riffe, T., et al. (2017). A cause-of-death decomposition of the young adult mortality hump. Technical report. Rostock, Germany: Max Planck Institute for Demographic Research.Renshaw, A.E., & Haberman, S. (2003). Lee-Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255–272.Renshaw, A.E., & Haberman, S. (2006). A cohort-based extensionto the Lee-Carter model for mortality reduction factors. Insurance: Mathematic and Economics, 38(3), 556–570.Reyes, A.R. (2010). Una aproximación al costo fiscal en pensiones como consecuencia del envejecimiento de la población en Colombia y el efecto de la sobre-mortalidad masculina. Master’s thesis: Universidad Nacional de Colombia.Richards, S. (2008). Detecting year-of-birth mortality patterns with limited data. Journal of the Royal Statistical Society: Series A (Statistics in Society), 171(1), 279–298.Rodríguez, J. (2007). Desigualdades socioeconómicas entre departamentos y su asociación con indicadores de mortalidad en Colombia en 2000. Rev Panam Salud Publica, 21(2/3), 111–124.Shkolnikov, V.M., Andreev, E.M., Begun, A. (2003). Gini coefficient as a life table function: computation from discrete data, decomposition of differences and empirical examples. Demographic Research, 8, 305–358.Singh, A., Shukla, A., Ram, F., Kumar, K. (2017). Trends in inequality in length of life in India: a decomposition analysis by age and causes of death. Genus, 73(1), 5.Tabeau, E. (2001). A review of demographic forecasting models for mortality. In: Tabeau, E., van den Berg Jeths A., Heathcote C. (Eds.) In Forecasting Mortality in Developed Countries. European Studies of Population, vol 9. Springer, Dordrecht, (pp. 1–32).Turner, H., & Firth, D. (2015). gnm: Generalized nonlinear models in R. R package version 1.0-8. https://CRAN.R-project.org/package=gnm .Urdinola, B.P., & Queiroz, B.L. (2017). Latin American Human Mortality Database. http://www.lamortalidad.org . Accessed 21 Nov 2015

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015

Background: 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. Methods: 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). Findings: 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. Interpretation: 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. Funding: Bill & Melinda Gates Foundation