521 research outputs found
National athletic trainers\u27 association position statement: lightning safety for athletics and recreation.
OBJECTIVE: To educate athletic trainers and others about the dangers of lightning, provide lightning-safety guidelines, define safe structures and locations, and advocate prehospital care for lightning-strike victims.
BACKGROUND: Lightning may be the most frequently encountered severe-storm hazard endangering physically active people each year. Millions of lightning flashes strike the ground annually in the United States, causing nearly 100 deaths and 400 injuries. Three quarters of all lightning casualties occur between May and September, and nearly four fifths occur between 10:00 AM and 7:00 PM, which coincides with the hours for most athletic or recreational activities. Additionally, lightning casualties from sports and recreational activities have risen alarmingly in recent decades.
RECOMMENDATIONS: The National Athletic Trainers\u27 Association recommends a proactive approach to lightning safety, including the implementation of a lightning-safety policy that identifies safe locations for shelter from the lightning hazard. Further components of this policy are monitoring local weather forecasts, designating a weather watcher, and establishing a chain of command. Additionally, a flash-to-bang count of 30 seconds or more should be used as a minimal determinant of when to suspend activities. Waiting 30 minutes or longer after the last flash of lightning or sound of thunder is recommended before athletic or recreational activities are resumed. Lightning- safety strategies include avoiding shelter under trees, avoiding open fields and spaces, and suspending the use of land-line telephones during thunderstorms. Also outlined in this document are the prehospital care guidelines for triaging and treating lightning-strike victims. It is important to evaluate victims quickly for apnea, asystole, hypothermia, shock, fractures, and burns. Cardiopulmonary resuscitation is effective in resuscitating pulseless victims of lightning strike. Maintenance of cardiopulmonary resuscitation and first-aid certification should be required of all persons involved in sports and recreational activities
The PDZ Protein Canoe/AF-6 Links Ras-MAPK, Notch and Wingless/Wnt Signaling Pathways by Directly Interacting with Ras, Notch and Dishevelled
Over the past few years, it has become increasingly apparent that signal transduction pathways are not merely linear cascades; they are organized into complex signaling networks that require high levels of regulation to generate precise and unique cell responses. However, the underlying regulatory mechanisms by which signaling pathways cross-communicate remain poorly understood. Here we show that the Ras-binding protein Canoe (Cno)/AF-6, a PDZ protein normally associated with cellular junctions, is a key modulator of Wingless (Wg)/Wnt, Ras-Mitogen Activated Protein Kinase (MAPK) and Notch (N) signaling pathways cross-communication. Our data show a repressive effect of Cno/AF-6 on these three signaling pathways through physical interactions with Ras, N and the cytoplasmic protein Dishevelled (Dsh), a key Wg effector. We propose a model in which Cno, through those interactions, actively coordinates, at the membrane level, Ras-MAPK, N and Wg signaling pathways during progenitor specification
Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms
[EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms.
The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS).
Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. In the same vein, they results confirm the presence of the cyclic movement of innovative outcome with the Exploitation.In addition, this research is part of the Project ECO2015-71380-R funded by the Spanish Ministry of Economy, Industry and Competitiveness and the State Research Agency. Co-financed by the European Regional Development Fund (ERDF).Vargas-Mendoza, NY.; Lloria, MB.; Salazar Afanador, A.; Vergara Domínguez, L. (2018). Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms. International Entrepreneurship and Management Journal. 14(4):1053-1069. https://doi.org/10.1007/s11365-018-0496-5S10531069144Alegre, J., & Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: an empirical test. Technovation, 28, 315–326.Amara, N., Landry, R., Becheikh, N., & Ouimet, M. (2008). Learning and novelty of innovation in established manufacturing SMEs. Technovation, 28, 450–463.Aragón-Mendoza, J., Pardo del Val, M., & Roig, S. (2016). The influence of institutions development in venture creation decision: a cognitive view. Journal of Business Research, 69(11), 4941–4946.Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice: motivators, barriers, and enablers. Advances in Developing Human Resources, 10(4), 541–554.Argyris, C., & Schön, D. (1978). Organizational learning: a theory of action perspective. Reading: Addison Wesley.Bagozzi, R. P., Yi, Y., & Singh, S. (1991). On the use of structural equation models in experimental designs: two extensions international. Journal of Research in Marketing, 8, 125–140.Belda, J., Vergara L., Salazar, A., & Safont G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs, accepted for publication in Signal Processing.Boland, R. J. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in communities of knowing. Organization Science, 6(4), 350–372.Bontis, N., (1998). Intellectual capital: an exploratory study that develops measures models. Management Decision, 36, 63–76.Bontis, N. (1999). Managing an organizational learning system by aligning stocks and flows of knowledge: an empirical examination of intellectual capital, knowledge management, and business performance. 1999. Management of Innovation and New Technology Research Centre, McMaster University.Bontis, N., Keow, W., & Richardson, S. (2000). Intellectual capital and the nature of business in Malaysia. Journal of Intellectual Capital, 1(1), 85–100Bontis, N., Hullan, J., & Crossan, M. (2002). Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies, 39, 438–469.Brachos, D., Kostopulos, K., Sodersquist, K. E., & Prastacos, G. (2007). Knowledge effectiveness, social context and innovation. Journal of Knowledge Management, 11(5), 31–44.Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31, 515–524.Chang, T. J., Yeh, S. P., & Yeh, I. J. (2007). The effects of joint rewards system in new product development. International Journal of Manpower, 28(3/4), 276–297.Chin, W. (1998). The partial least square approach to structural equation modeling. In G. A. Marcoulides (Ed.) (pp. 294–336). New Jersey: Lawrence Erlbaum Associates.Cho, N., Li, G., & Su, C. (2007). An empirical study on the effect of individual factors on knowledge sharing by knowledge type. Journal of Global Business and Technology, 3(2), 1–15.Cohen, W. M., & Levin, R. C. (1989). Empirical studies of innovation and market structure. In R. Schmalansee & R. D. Willing (Eds.), Handbook of industrial organization II. New York: Elsevier.Cohen, W. M., & Levinthal, D. A. (1990). Absorptive-capacity – a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.Cooper, R. G. (2000). New product performance: what distinguishes the star products. Austrian Journal of Management, 25, 17–45.Crossan, M., & Berdrow, I. (2003). Organizational learning and strategic renewal. Strategic Management Journal, 24, 1087–1105.Crossan, M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: a systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.Crossan, M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. Academy of Management Review, 24, 522–537.Damanpour, F., & Aravind, D. (2012). Managerial innovation: conceptions, processes, and antecedents. Management and Organization Review, 8(2), 423–454.Damanpour, F., & Shanthi, G. (2001). The dynamics of the adoption of products and process innovations in organizations. Journal of Management Studies, 38(1), 21–65.Decarolis, D. M., & Deeds, D. L. (1999). The impact of stock and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20, 953–968.Demartini, C. (2015). Relationships between social and intellectual capital: empirical Evidence from IC statements. Knowledge and Process Management, 22(2), 99–111.Dupuy, F. (2004). Sharing knowledge: they why and how of organizational change. Hampshire: Palgrave Macmillan.Fornell, C., & Bookstein, F. I. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.Ganter, A., & Hecker, A. (2013). Deciphering antecedents of organizational innovation. Journal of Business Research, 66(5), 575–584.Ganter, A., & Hecker, A. (2014). Configurational paths to organizational innovation: qualitative comparative analyses of antecedents and contingencies. Journal of Business Research, 67, 1285–1292.Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. International Journal of Management Science, 25, 15–28.Hedberg, B. (1981). How organizations learn and unlearn. In P. Nystrom & W. Starbuck (Eds.), Handbook of organizational design. New York: Oxford University.Hedlund, G., & Nonaka, I. (1993). Models of knowledge management in the west and Japan. In: P. Lorange, B. Chacravrarthy, J. Ross, and J. Van de ven (Eds.) Cambridge: Basil Blackwell.Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use the partial least squares path modeling. In: R. Sinkovics and N. Pervez (Eds.) 277–319.Hsu, I. (2006). Enhancing employee tendencies to share knowledge-case studies on nine companies in Taiwan. International Journal of Information Management, 26(4), 326–338.Hsu, I. (2008). Knowledge sharing practices as a facilitating factor for improving organizational performance though human capital: a preliminary test. Expert Systems with Application, 35, 316–1326.Huang, Q., Davison, R., & Gu, J. (2008). Impact of personal and cultural factors on knowledge sharing in China. Asia Pacific Journal Management, 25(3), 451–471.Ibarra, H. (1993). Network centrality, power, and innovation involvement – determinants of technical and administrative roles. Academy of Management Journal, 36(3), 471–501.Iebra, I. L., Zegarra, P. S., & Zegarra, A. S. (2011). Learning for sharing: an empirical analysis of organizational learning and knowledge sharin. International Entrepreneurship Management Journal, 7, 509–518.Ipe, M. (2003). Knowledge sharing in organizations: a conceptual framework. Human Resource Development Review, 2(4), 337–359.Jenkin, T. (2013). Extending the 4I organizational learning model: information sources, foraging processes and tools. Administrative Sciences, 3, 96–109.Jiménez-Jiménez, D., & Sanz-Valle, R. (2011). Innovation, organizational learning, and performance. Journal of Business Research, 64, 408–417.Kane, G. C., & Alavi, M. (2007). Information technology and organizational learning: an investigation of exploration and exploitation processes. Organization Science, 18(5), 796–812.Kleinbaum, D. G., Kupper, N. N., Muller, K. E. (1988). Applied regression analysis and other Multivariable’s methods, PWS KENT.Klomp, L., & Van Leeuwen, G. (2001). Linking innovation and firm performance: a new approach. International Journal of the Economics of Business, 8(3), 343–364.Lansisalmi, H., Kivimaki, M., Aalto, P., & Ruoranen, R. (2006). Innovation in healthcare: a systematic review of recent research. Nursing Science Quarterly, 19(1), 66–72.Laperrière, A., & Spence, M. (2015). Enacting international opportunities: the role of organizational learning in knowledge-intensive business services. Journal of International Entrepreneurship, 13(3), 212–241.Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.Lin, H. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315–332.Lloria, M. B., & Moreno-Luzón, M. D. (2014). Organizational learning: proposal of an integrative scale and research instrument. Journal of Business Research, 67, 692–697.March, J. G. (1991). Exploration and exploitation in organizational learning. Organizational Science, 2, 71–87.Matikainen, M., Terho, H., Parvinen, P., & Juppo, A. (2016). The role and impact of firm’s strategic orientations on launch performance: significance of relationship orientation. Journal of Business & Industrial Marketing, 31(5), 625–639.Mone, M. A., McKinley, W., & Barker, V. L. (1998). Organizational decline and innovation: a contingency framework. Academy of Management Review, 23, 115–132.Moreno-Luzón, M. D., & Lloria, B. (2008). The role of non-structural and informal mechanisms of integration and integration as forces in knowledge creation. British Journal of Management, 19, 250–276.Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: how patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55, 69–75.Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. How Japanese companies create the dynamics of innovation. New York: Oxford University Press.Nonaka, I., & von Krogh, G. (2009). Perspective tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organization Science, 20(3), 635–652.Parida, V., Lahti, T., & Wincent, J. (2016). Exploration and exploitation and firm performance variability: a study of ambidexterity in entrepreneurial firms. International Entrepreneurship Management Journal, 12, 1147–1164.Pew, H., Plowman, D., & Hancock, P. (2008). The involving research on intellectual capital. Journal of Intellectual Capital, 9, 585–608.Potter, R. E., & Balthazard, P. A. (2004). The role of individual memory and attention processes during electronic brainstorming. MIS Quarterly, 28(4), 621–643.Ramadani, V., Hyrije, A. A., Léo-Paul, D., Gadaf, R., & Sadudin, I. (2017). The impact of knowledge spillovers and innovation on firm-performance: findings from the Balkans countries. International Entrepreneurship Management Journal, 13, 299–325.Ren, S., Shu, R., Bao, Y., & Chen, X. (2016). Linking network ties to entrepreneurial opportunity discovery and exploitation: the role of affective and cognitive trust. International Entrepreneurship and Management Journal, 12(2), 465–485.Ringle, C. M., Wende, S., & Will, A. (2005). Smart PLS 2.0 (M3) beta, Hamburg: http://www.smartpls.de .Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM. MIS Quarterly, 36(1), iii–xiv.Sanchez, R., & Heene, A. (1997). A competence perspective on strategic learning and knowledge management. En Sanchez, R. and Heene, A. (eds.) Strategic learning and knowledge management. John Wiley and Sons.Seidler-de Alwis, R., & Hartmann, E. (2008). The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises. Journal of Knowledge Management, 12(1), 133–147.Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20, 7–28.Tansky, J., Ribeiro, D., & Roig, S. (2010). Linking entrepreneurship and human resources in globalization. Human Resource Management, 49(2), 217–223.Teece, D. (2012). Dynamic capabilities: routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401.Tenenhaus, M., Vinzi, V., Chatelin, Y., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 49, 159–205.vande Vrande, V., de Jong, J., Vanhaverbeke, W., & Rochemont, M. (2009). Open innovation in SMEs: trends, motives and management challenges. Technovation, 29, 423–437.Vargas, N., & Lloria, M. B. (2014). Dynamizing intellectual capital through enablers and learning flows. Industrial Management and Data Systems, 114(1), 2–20.Vargas, N., & Lloria, M. B. (2017). Performance and intellectual capital: how enablers drive value creation in organisations. Knowledge and Process Management, 24(2), 114–124.Vargas, N., Lloria, M. B., & Roig-Dobón, S. (2016). Main drivers of human capital, learning and performance. The Journal of Technology Transfer, 41(5), 961–978.Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., & Iglesias, S. (2017). Signal processing on graphs for improving automatic credit card fraud detection. Proceeding of 2017 I.E. 51st international Carnahan Conference on Security Technology (ICCST 2017), https://doi.org/10.1109/CCST.2017.8167820 , 23–26 Oct, 2017, Madrid, Spain.Wallin, M. W., & Von Krogh, G. (2010). Organizing for open innovation: focus o the integration of knowledge. Organizational Dynamics, 39(2), 145–154.Wang, C. L., & Ahmed, P. K. (2004). Linking innovation and firm performance: a new approach. European International Journal of Technology Management, 27, 674–688.Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). Cambridge: Academic Press.Wold, H. (1985). Factors influencing the outcome of economic sanctions. In Sixto Ríos Honorary. Trabajos de Estadística and de Investigación Operativa, 36(3), 325–337
A Novel Role for MAPKAPK2 in Morphogenesis during Zebrafish Development
One of the earliest morphogenetic processes in the development of many animals is epiboly. In the zebrafish, epiboly ensues when the animally localized blastoderm cells spread, thin over, and enclose the vegetally localized yolk. Only a few factors are known to function in this fundamental process. We identified a maternal-effect mutant, betty boop (bbp), which displays a novel defect in epiboly, wherein the blastoderm margin constricts dramatically, precisely when half of the yolk cell is covered by the blastoderm, causing the yolk cell to burst. Whole-blastoderm transplants and mRNA microinjection rescue demonstrate that Bbp functions in the yolk cell to regulate epiboly. We positionally cloned the maternal-effect bbp mutant gene and identified it as the zebrafish homolog of the serine-threonine kinase Mitogen Activated Protein Kinase Activated Protein Kinase 2, or MAPKAPK2, which was not previously known to function in embryonic development. We show that the regulation of MAPKAPK2 is conserved and p38 MAP kinase functions upstream of MAPKAPK2 in regulating epiboly in the zebrafish embryo. Dramatic alterations in calcium dynamics, together with the massive marginal constrictive force observed in bbp mutants, indicate precocious constriction of an F-actin network within the yolk cell, which first forms at 50% epiboly and regulates epiboly progression. We show that MAPKAPK2 activity and its regulator p38 MAPK function in the yolk cell to regulate the process of epiboly, identifying a new pathway regulating this cell movement process. We postulate that a p38 MAPKAPK2 kinase cascade modulates the activity of F-actin at the yolk cell margin circumference allowing the gradual closure of the blastopore as epiboly progresses
A multi-level system quality improvement intervention to reduce racial disparities in hypertension care and control: study protocol
BACKGROUND: Racial disparities in blood pressure control have been well documented in the United States. Research suggests that many factors contribute to this disparity, including barriers to care at patient, clinician, healthcare system, and community levels. To date, few interventions aimed at reducing hypertension disparities have addressed factors at all of these levels. This paper describes the design of Project ReD CHiP (Reducing Disparities and Controlling Hypertension in Primary Care), a multi-level system quality improvement project. By intervening on multiple levels, this project aims to reduce disparities in blood pressure control and improve guideline concordant hypertension care. METHODS: Using a pragmatic trial design, we are implementing three complementary multi-level interventions designed to improve blood pressure measurement, provide patient care management services and offer expanded provider education resources in six primary care clinics in Baltimore, Maryland. We are staggering the introduction of the interventions and will use Statistical Process Control (SPC) charting to determine if there are changes in outcomes at each clinic after implementation of each intervention. The main hypothesis is that each intervention will have an additive effect on improvements in guideline concordant care and reductions in hypertension disparities, but the combination of all three interventions will result in the greatest impact, followed by blood pressure measurement with care management support, blood pressure measurement with provider education, and blood pressure measurement only. This study also examines how organizational functioning and cultural competence affect the success of the interventions. DISCUSSION: As a quality improvement project, Project ReD CHiP employs a novel study design that specifically targets multi-level factors known to contribute to hypertension disparities. To facilitate its implementation and improve its sustainability, we have incorporated stakeholder input and tailored components of the interventions to meet the specific needs of the involved clinics and communities. Results from this study will provide knowledge about how integrated multi-level interventions can improve hypertension care and reduce disparities. TRIAL REGISTRATION: ClinicalTrials.gov NCT0156686
Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021
Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions
Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017
Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030
Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.
BACKGROUND: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of 'leaving no one behind', it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator
Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants
Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks
Rising rural body-mass index is the main driver of the global obesity epidemic in adults
Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe
- …