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      Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

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          Abstract

          Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10–15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector–pathogen systems.

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          Most cited references198

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          Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

          Measuring disease and injury burden in populations requires a composite metric that captures both premature mortality and the prevalence and severity of ill-health. The 1990 Global Burden of Disease study proposed disability-adjusted life years (DALYs) to measure disease burden. No comprehensive update of disease burden worldwide incorporating a systematic reassessment of disease and injury-specific epidemiology has been done since the 1990 study. We aimed to calculate disease burden worldwide and for 21 regions for 1990, 2005, and 2010 with methods to enable meaningful comparisons over time. We calculated DALYs as the sum of years of life lost (YLLs) and years lived with disability (YLDs). DALYs were calculated for 291 causes, 20 age groups, both sexes, and for 187 countries, and aggregated to regional and global estimates of disease burden for three points in time with strictly comparable definitions and methods. YLLs were calculated from age-sex-country-time-specific estimates of mortality by cause, with death by standardised lost life expectancy at each age. YLDs were calculated as prevalence of 1160 disabling sequelae, by age, sex, and cause, and weighted by new disability weights for each health state. Neither YLLs nor YLDs were age-weighted or discounted. Uncertainty around cause-specific DALYs was calculated incorporating uncertainty in levels of all-cause mortality, cause-specific mortality, prevalence, and disability weights. Global DALYs remained stable from 1990 (2·503 billion) to 2010 (2·490 billion). Crude DALYs per 1000 decreased by 23% (472 per 1000 to 361 per 1000). An important shift has occurred in DALY composition with the contribution of deaths and disability among children (younger than 5 years of age) declining from 41% of global DALYs in 1990 to 25% in 2010. YLLs typically account for about half of disease burden in more developed regions (high-income Asia Pacific, western Europe, high-income North America, and Australasia), rising to over 80% of DALYs in sub-Saharan Africa. In 1990, 47% of DALYs worldwide were from communicable, maternal, neonatal, and nutritional disorders, 43% from non-communicable diseases, and 10% from injuries. By 2010, this had shifted to 35%, 54%, and 11%, respectively. Ischaemic heart disease was the leading cause of DALYs worldwide in 2010 (up from fourth rank in 1990, increasing by 29%), followed by lower respiratory infections (top rank in 1990; 44% decline in DALYs), stroke (fifth in 1990; 19% increase), diarrhoeal diseases (second in 1990; 51% decrease), and HIV/AIDS (33rd in 1990; 351% increase). Major depressive disorder increased from 15th to 11th rank (37% increase) and road injury from 12th to 10th rank (34% increase). Substantial heterogeneity exists in rankings of leading causes of disease burden among regions. Global disease burden has continued to shift away from communicable to non-communicable diseases and from premature death to years lived with disability. In sub-Saharan Africa, however, many communicable, maternal, neonatal, and nutritional disorders remain the dominant causes of disease burden. The rising burden from mental and behavioural disorders, musculoskeletal disorders, and diabetes will impose new challenges on health systems. Regional heterogeneity highlights the importance of understanding local burden of disease and setting goals and targets for the post-2015 agenda taking such patterns into account. Because of improved definitions, methods, and data, these results for 1990 and 2010 supersede all previously published Global Burden of Disease results. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            The representative concentration pathways: an overview

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              Vulnerability

              W Adger (2006)
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                Author and article information

                Journal
                Philosophical Transactions of the Royal Society B: Biological Sciences
                Phil. Trans. R. Soc. B
                The Royal Society
                0962-8436
                1471-2970
                April 05 2015
                April 05 2015
                April 05 2015
                : 370
                : 1665
                : 20130551
                Affiliations
                [1 ]Department of Public Health and Policy, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 3GL, UK
                [2 ]Grantham Institute for Climate Change, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, St Mary's Campus, London W2 1PG, UK
                [3 ]The Cyprus Institute, Nicosia, Cyprus
                [4 ]Imperial College London, London SW7 2AZ, UK
                [5 ]Meteorological Office Hadley Centre, UK Meteorological Office, Fitzroy Road, Exeter, EX1 3PB, UK
                [6 ]Department of Mathematics, Austin Peay State University, Clarksville, TN 37044, USA
                [7 ]Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831, USA
                [8 ]Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, NJ 08901, USA
                [9 ]Department of Biological Sciences, Old Dominium University, Norfolk, VA 23529, USA
                [10 ]Simon A. Levin Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe, AZ 85287-1904, USA
                [11 ]School of Mathematical and Natural Sciences, Arizona State University, Phoenix, AZ 85069-7100, USA
                [12 ]Cary Institute of Ecosystem Studies, PO Box AB, Millbrook, NY 12545-0129, USA
                [13 ]Department of Mathematics, University of Tennessee, Knoxville, TN 37996-1300, USA
                [14 ]Department of Physics, Clark Atlanta University, PO Box 172, Atlanta, GA 30314, USA
                [15 ]Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
                [16 ]Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
                [17 ]Department of Entomology, Pennsylvania State University, University Park, PA 16802, USA
                [18 ]Universidad Nacional Autnoma de Mexico Institute of Mathematics Mexico City, Distrito Federal, Mexico
                [19 ]Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556-0369, USA
                Article
                10.1098/rstb.2013.0551
                25688012
                f9a9bc8d-58bb-49d8-b583-9535ea11c5b7
                © 2015

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