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Familienformen, Verwandtschaftsnetzwerke. Allgemeine Lebensumstände und Muster der gegenseitigen Unterstützung. Einkommen; Innerfamiliäre Transferleistungen. Praktische Unterstützung von staatlichen und offiziel anerkannten Versicherungen.
Themen: Erfassung von genealogischen Verbindungen von allen Verwandten durch Abstammung oder Heirat, darunter nicht mehr lebende Vorfahren und entfernte Verbindungen durch Abstammung oder Heirat. Für jedes Mitglied in diesem Netzwerk wurde erfragt: Geburtsort und derzeitiger Wohnort, wirtschaftliche Lage, Bildungsniveau, allgemeiner Gesundheitszustand, Indikator des Lebensstandards. Ähnliche Informationen über die Befragten selbst, einschließlich der eigenen wirtschaftlichen und gesundheitlichen Umstände, Informationen über die Häufigkeit und Art der sozialen Kontakte mit jedem Mitglied des Netzes der bekannten Verwandten (darunter rituelle Beziehungen wie Patenschaften).
Informationen über Umfang und Geflecht helfender Beziehungen, Hilfe für Dritte oder selbst empfangene Hilfe von Mitgliedern des Netzwerks von Bekannten und Verwandten; konkrete Angabe der Arten von Hilfe, z.B. Hilfe beim Einkaufen, Kinderbetreuung, Hinterlassen eines Vermächtnisses, die Zahlung von Gesundheitskosten oder Bildungskosten. Vergleichbare Informationen wurden erfragt über Nachbarn und Freunde, mit denen der Befragte helfende Beziehungen hat. Bei wesentlichen Unterstützungsleistungen wurde das Muster der Hilfe über das ganze Leben erfasst. Die Rolle der Eltern und von Verwandten und Freunden bei Entscheidungen über die Auswahl der Partner und die Planung der Familiengröße.
Background A key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016. Methods Drawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0-100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0-100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita. Findings In 2016, HAQ Index performance spanned from a high of 97.1 (95% UI 95.8-98.1) in Iceland, followed by 96.6 (94.9-97.9) in Norway and 96.1 (94.5-97.3) in the Netherlands, to values as low as 18.6 (13.1-24.4) in the Central African Republic, 19.0 (14.3-23.7) in Somalia, and 23.4 (20.2-26.8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China, performance ranged from 91.5 (89.1-936) in Beijing to 48.0 (43.4-53.2) in Tibet (a 43.5-point difference), while India saw a 30.8-point disparity, from 64.8 (59.6-68.8) in Goa to 34.0 (30.3-38.1) in Assam. Japan recorded the smallest range in subnational HAQ performance in 2016 (a 4.8-point difference), whereas differences between subnational locations with the highest and lowest HAQ Index values were more than two times as high for the USA and three times as high for England. State-level gaps in the HAQ Index in Mexico somewhat narrowed from 1990 to 2016 (from a 20.9-point to 17.0-point difference), whereas in Brazil, disparities slightly increased across states during this time (a 17.2-point to 20.4-point difference). Performance on the HAQ Index showed strong linkages to overall development, with high and high-middle SDI countries generally having higher scores and faster gains for non-communicable diseases. Nonetheless, countries across the development spectrum saw substantial gains in some key health service areas from 2000 to 2016, most notably vaccine-preventable diseases. Overall, national performance on the HAQ Index was positively associated with higher levels of total health spending per capita, as well as health systems inputs, but these relationships were quite heterogeneous, particularly among low-to-middle SDI countries. Interpretation GBD 2016 provides a more detailed understanding of past success and current challenges in improving personal health-care access and quality worldwide. Despite substantial gains since 2000, many low-SDI and middle-SDI countries face considerable challenges unless heightened policy action and investments focus on advancing access to and quality of health care across key health services, especially non-communicable diseases. Stagnating or minimal improvements experienced by several low-middle to high-middle SDI countries could reflect the complexities of re-orienting both primary and secondary health-care services beyond the more limited foci of the Millennium Development Goals. Alongside initiatives to strengthen public health programmes, the pursuit of universal health coverage upon improving both access and quality worldwide, and thus requires adopting a more comprehensive view and subsequent provision of quality health care for all populations. ; Bill & Melinda Gates Foundation. Barbora de Courten is supported by a National Heart Foundation Future Leader Fellowship (100864). Ai Koyanagi's work is supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII —General Branch Evaluation and Promotion of Health Research—and the European Regional Development Fund (ERDF-FEDER). Alberto Ortiz was supported by Spanish Government (Instituto de Salud Carlos III RETIC REDINREN RD16/0019 FEDER funds). Ashish Awasthi acknowledges funding support from Department of Science and Technology, Government of India through INSPIRE Faculty scheme Boris Bikbov has received funding from the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 703226. Boris Bikbov acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Panniyammakal Jeemon acknowledges support from the clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance (2015–20). Job F M van Boven was supported by the Department of Clinical Pharmacy & Pharmacology of the University Medical Center Groningen, University of Groningen, Netherlands. Olanrewaju Oladimeji is an African Research Fellow hosted by Human Sciences Research Council (HSRC), South Africa and he also has honorary affiliations with Walter Sisulu University (WSU), Eastern Cape, South Africa and School of Public Health, University of Namibia (UNAM), Namibia. He is indeed grateful for support from HSRC, WSU and UNAM. EUI is supported in part by the South African National Research Foundation (NRF UID: 86003). Ulrich Mueller acknowledges funding by the German National Cohort Study grant No 01ER1511/D, Gabrielle B Britton is supported by Secretaría Nacional de Ciencia, Tecnología e Innovación and Sistema Nacional de Investigación de Panamá. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Behzad Heibati would like to acknowledge Air pollution Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran. Syed Aljunid acknowledges the National University of Malaysia for providing the approval to participate in this GBD Project. Azeem Majeed and Imperial College London are grateful for support from the Northwest London National Insititute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research & Care. Tambe Ayuk acknowledges the Institute of Medical Research and Medicinal Plant Studies for office space provided. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). João Fernandes gratefully acknowledges funding from FCT–Fundação para a Ciência e a Tecnologia (grant number UID/Multi/50016/2013). Jan-Walter De Neve was supported by the Alexander von Humboldt Foundation. Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (201900). Kazem Rahimi was supported by grants from the Oxford Martin School, the NIHR Oxford BRC and the RCUK Global Challenges Research Fund. Laith J Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008) who provided the main funding for generating the data provided to the GBD-IHME effort. Liesl Zuhlke is funded by the national research foundation of South Africa and the Medical Research Council of South Africa. Monica Cortinovis acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Chuanhua Yu acknowleges support from the National Natural Science Foundation of China (grant number 81773552 and grant number 81273179) Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group. Charles Shey Wiysonge's work is supported by the South African Medical Research Council and the National Research Foundation of South Africa (grant numbers 106035 and 108571). John J McGrath is supported by grant APP1056929 from the John Cade Fellowship from the National Health and Medical Research Council and the Danish National Research Foundation (Niels Bohr Professorship). Quique Bassat is an ICREA (Catalan Institution for Research and Advanced Studies) research professor at ISGlobal. Richard G White is funded by the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement that is also part of the EDCTP2 programme supported by the European Union (MR/P002404/1), the Bill & Melinda Gates Foundation (TB Modelling and Analysis Consortium: OPP1084276/OPP1135288, CORTIS: OPP1137034/OPP1151915, Vaccines: OPP1160830), and UNITAID (4214-LSHTM-Sept15; PO 8477-0-600). Rafael Tabarés-Seisdedos was supported in part by grant number PROMETEOII/2015/021 from Generalitat Valenciana and the national grant PI17/00719 from ISCIII-FEDER. Mihajlo Jakovljevic acknowleges contribution from the Serbian Ministry of Education Science and Technological Development of the Republic of Serbia (grant OI 175 014). Shariful Islam is funded by a Senior Fellowship from Institute for Physical Activity and Nutrition, Deakin University and received career transition grants from High Blood Pressure Research Council of Australia. Sonia Saxena is funded by various grants from the NIHR. Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral program (reference number CD15/00019 from the Instituto de Salud Carlos III (ISCIII–Spain) and the Fondos Europeo de Desarrollo Regional. Stefanos was awarded with a 6 months visiting fellowship funding at IHME from M-AES (reference no. MV16/00035 from the Instituto de Salud Carlos III). S Vittal Katikreddi was funded by a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the MRC (MC_UU_12017/13 & MC_ UU_12017/15) and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). Traolach S Brugha has received funding from NHS Digital UK to collect data used in this study. The work of Hamid Badali was financially supported by Mazandaran University of Medical Sciences, Sari, Iran. The work of Stefan Lorkowski is funded by the German Federal Ministry of Education and Research (nutriCARD, Grant agreement number 01EA1411A). Mariam Molokhia's research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We also thank the countless individuals who have contributed to GBD 2016 in various capacities. ; Peer reviewed
Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation. ; The authors would like to thank the many colleagues who contributed to collection and phenotypic characterisation of the clinical samples, as well as genotyping and analysis of the GWA data. Special mentions are as follows: CGSB participating cohorts: Some of the data utilised in this study were provided by the Understanding Society: The UK Household Longitudinal Study, which is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The data were collected by NatCen and the genome wide scan data were analysed by the Wellcome Trust Sanger Institute. The Understanding Society DAC have an application system for genetics data and all use of the data should be approved by them. The application form is at: https://www.understandingsociety.ac.uk/about/health/data. The Airwave Health Monitoring Study is funded by the UK Home Office, (Grant number 780-TETRA) with additional support from the National Institute for Health Research Imperial College Health Care NHS Trust and Imperial College Biomedical Research Centre. We thank all participants in the Airwave Health Monitoring Study. This work used computing resources provided by the MRC- funded UK MEDical Bioinformatics partnership programme (UK MED-BIO) (MR/L01632X/1). Paul Elliott wishes to acknowledge the Medical Research Council and Public Health England (MR/L01341X/1) for the MRC-PHE Centre for Environment and Health; and the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141). Paul Elliott is supported by the UK Dementia Research Institute which receives its funding from UK DRI Ltd funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. Paul Elliott is associate director of the Health Data Research UK London funded by a consortium led by the UK Medical Research Council. SHIP (Study of Health in Pomerania) and SHIP-TREND both represent population-based studies. SHIP is supported by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung (BMBF); grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG); grant GR 1912/5-1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the BMBF (grant 03Z1CN22). LifeLines authors thank Behrooz Alizadeh, Annemieke Boesjes, Marcel Bruinenberg, Noortje Festen, Ilja Nolte, Lude Franke, Mitra Valimohammadi for their help in creating the GWAS database, and Rob Bieringa, Joost Keers, René Oostergo, Rosalie Visser, Judith Vonk for their work related to data-collection and validation. The authors are grateful to the study participants, the staff from the LifeLines Cohort Study and Medical Biobank Northern Netherlands, and the participating general practitioners and pharmacists. LifeLines Scientific Protocol Preparation: Rudolf de Boer, Hans Hillege, Melanie van der Klauw, Gerjan Navis, Hans Ormel, Dirkje Postma, Judith Rosmalen, Joris Slaets, Ronald Stolk, Bruce Wolffenbuttel; LifeLines GWAS Working Group: Behrooz Alizadeh, Marike Boezen, Marcel Bruinenberg, Noortje Festen, Lude Franke, Pim van der Harst, Gerjan Navis, Dirkje Postma, Harold Snieder, Cisca Wijmenga, Bruce Wolffenbuttel. The authors wish to acknowledge the services of the LifeLines Cohort Study, the contributing research centres delivering data to LifeLines, and all the study participants. Niek Verweij was supported by NWO VENI (016.186.125). Fenland authors thank Fenland Study volunteers for their time and help, Fenland Study general Practitioners and practice staff for assistance with recruitment, and Fenland Study Investigators, Co-ordination team and the Epidemiology Field, Data and Laboratory teams for study design, sample/data collection and genotyping. We thank all ASCOT trial participants, physicians, nurses, and practices in the participating countries for their important contribution to the study. In particular we thank Clare Muckian and David Toomey for their help in DNA extraction, storage, and handling. We would also like to acknowledge the Barts and The London Genome Centre staff for genotyping the Exome Chip array. The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. We would also like to thank the Barts Genome Centre staff for their assistance with this project. Patricia B. Munroe, Mark J. Caulfield, and Helen R. Warren wish to acknowledge the NIHR Cardiovascular Biomedical Research Unit at Barts and The London, Queen Mary University of London, UK for support. Mark J. Caulfield are Senior National Institute for Health Research Investigators. EMBRACE Collaborating Centres are: Coordinating Centre, Cambridge: Daniel Barrowdale, Debra Frost, Jo Perkins. North of Scotland Regional Genetics Service, Aberdeen: Zosia Miedzybrodzka, Helen Gregory. Northern Ireland Regional Genetics Service, Belfast: Patrick Morrison, Lisa Jeffers. West Midlands Regional Clinical Genetics Service, Birmingham: Kai-ren Ong, Jonathan Hoffman. South West Regional Genetics Service, Bristol: Alan Donaldson, Margaret James. East Anglian Regional Genetics Service, Cambridge: Joan Paterson, Marc Tischkowitz, Sarah Downing, Amy Taylor. Medical Genetics Services for Wales, Cardiff: Alexandra Murray, Mark T. Rogers, Emma McCann. St James's Hospital, Dublin & National Centre for Medical Genetics, Dublin: M. John Kennedy, David Barton. South East of Scotland Regional Genetics Service, Edinburgh: Mary Porteous, Sarah Drummond. Peninsula Clinical Genetics Service, Exeter: Carole Brewer, Emma Kivuva, Anne Searle, Selina Goodman, Kathryn Hill. West of Scotland Regional Genetics Service, Glasgow: Rosemarie Davidson, Victoria Murday, Nicola Bradshaw, Lesley Snadden, Mark Longmuir, Catherine Watt, Sarah Gibson, Eshika Haque, Ed Tobias, Alexis Duncan. South East Thames Regional Genetics Service, Guy's Hospital London: Louise Izatt, Chris Jacobs, Caroline Langman. North West Thames Regional Genetics Service, Harrow: Huw Dorkins. Leicestershire Clinical Genetics Service, Leicester: Julian Barwell. Yorkshire Regional Genetics Service, Leeds: Julian Adlard, Gemma Serra-Feliu. Cheshire & Merseyside Clinical Genetics Service, Liverpool: Ian Ellis, Claire Foo. Manchester Regional Genetics Service, Manchester: D Gareth Evans, Fiona Lalloo, Jane Taylor. North East Thames Regional Genetics Service, NE Thames, London: Lucy Side, Alison Male, Cheryl Berlin. Nottingham Centre for Medical Genetics, Nottingham: Jacqueline Eason, Rebecca Collier. Northern Clinical Genetics Service, Newcastle: Alex Henderson, Oonagh Claber, Irene Jobson. Oxford Regional Genetics Service, Oxford: Lisa Walker, Diane McLeod, Dorothy Halliday, Sarah Durell, Barbara Stayner. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust: Ros Eeles, Nazneen Rahman, Elizabeth Bancroft, Elizabeth Page, Audrey Ardern-Jones, Kelly Kohut, Jennifer Wiggins, Jenny Pope, Sibel Saya, Natalie Taylor, Zoe Kemp and Angela George. North Trent Clinical Genetics Service, Sheffield: Jackie Cook, Oliver Quarrell, Cathryn Bardsley. South West Thames Regional Genetics Service, London: Shirley Hodgson, Sheila Goff, Glen Brice, Lizzie Winchester, Charlotte Eddy, Vishakha Tripathi, Virginia Attard. Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton: Diana Eccles, Anneke Lucassen, Gillian Crawford, Donna McBride, Sarah Smalley. Understanding Society Scientific Group is funded by the Economic and Social Research Council (ES/H029745/1) and the Wellcome Trust (WT098051). Paul D.P. Pharoah is funded by Cancer Research UK (C490/A16561). SHIP is funded by the German Federal Ministry of Education and Research (BMBF) and the German Research Foundation (DFG); see acknowledgements for details. F.W. Asselbergs is funded by the Netherlands Heart Foundation (2014T001) and supported by UCL Hospitals NIHR Biomedical Research Centre. The LifeLines Cohort Study, and generation and management of GWAS genotype data for the LifeLines Cohort Study is supported by the Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. Niek Verweij is supported by Horizon 2020, Marie Sklodowska-Curie (661395) and ICIN-NHI. Phenotype collection in the Lothian Birth Cohort 1921 was supported by the UK's Biotechnology and Biological Sciences Research Council (BBSRC), The Royal Society and The Chief Scientist Office of the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was supported by Age UK (The Disconnected Mind project). Genotyping was supported by Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, and the Royal Society of Edinburgh. The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the BBSRC and Medical Research Council (MRC) is gratefully acknowledged. Paul W. Franks is supported by Novo Nordisk, the Swedish Research Council, Påhlssons Foundation, Swedish Heart Lung Foundation (2020389), and Skåne Regional Health Authority. Nicholas J Wareham, Claudia Langenberg, Robert A Sacott, and Jian'an Luan are supported by the MRC (MC_U106179471 and MC_UU_12015/1). The BRIGHT study was supported by the Medical Research Council of Great Britain (Grant Number G9521010D); and by the British Heart Foundation (Grant Number PG/02/128). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. The Exome Chip genotyping was funded by Wellcome Trust Strategic Awards (083948 and 085475). We would also like to thank the Barts Genome Centre staff for their assistance with this project. The ASCOT study and the collection of the ASCOT DNA repository was supported by Pfizer, New York, NY, USA, Servier Research Group, Paris, France; and by Leo Laboratories, Copenhagen, Denmark. Genotyping of the Exome Chip in ASCOT-SC and ASCOT-UK was funded by the National Institutes of Health Research (NIHR). Anna F. Dominiczak was supported by the British Heart Foundation (Grant Numbers RG/07/005/23633, SP/08/005/25115); and by the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant number LSHM-C7-2006-037093). Nilesh J. Samani is supported by the British Heart Foundation and is a Senior National Institute for Health Research Investigator. Panos Deloukas is supported by the British Heart Foundation (RG/14/5/30893), and NIHR, where his work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Centre which is funded by the National Institute for Health Research (NIHR). The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation (SP/04/002), the Medical Research Council (G0601966, G0700931), the Wellcome Trust (084723/Z/08/Z, 090532 & 098381) the NIHR (RP-PG-0407-10371), the NIHR Official Development Assistance (ODA, award 16/136/68), the European Union FP7 (EpiMigrant, 279143) and H2020 programs (iHealth-T2D, 643774). We acknowledge support of the MRC-PHE Centre for Environment and Health, and the NIHR Health Protection Research Unit on Health Impact of Environmental Hazards. The work was carried out in part at the NIHR/Wellcome Trust Imperial Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the Imperial College Healthcare NHS Trust, the NHS, the NIHR or the Department of Health. We thank the participants and research staff who made the study possible. JC is supported by the Singapore Ministry of Health's National Medical Research Council under its Singapore Translational Research Investigator (STaR) Award (NMRC/STaR/0028/2017). The research was supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility and ERC grant 323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC to T.M. Frayling. Hanieh Yaghootkar is funded by Diabetes UK RD Lawrence fellowship (grant:17/0005594) Anna Dominiczak was funded by a BHF Centre of Research Excellence Award (RE/13/5/30177) GSCAN participating cohorts: The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators: B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut. The study includes eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman, J. Kramer); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield, A. Brooks); Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA (L. Almasy), Virginia Commonwealth University (D. Dick), Icahn School of Medicine at Mount Sinai (A. Goate), and Howard University (R. Taylor). Other COGA collaborators include: L. Bauer (University of Connecticut); J. McClintick, L. Wetherill, X. Xuei, Y. Liu, D. Lai, S. O'Connor, M. Plawecki, S. Lourens (Indiana University); G. Chan (University of Iowa; University of Connecticut); J. Meyers, D. Chorlian, C. Kamarajan, A. Pandey, J. Zhang (SUNY Downstate); J.-C. Wang, M. Kapoor, S. Bertelsen (Icahn School of Medicine at Mount Sinai); A. Anokhin, V. McCutcheon, S. Saccone (Washington University); J. Salvatore, F. Aliev, B. Cho (Virginia Commonwealth University); and Mark Kos (University of Texas Rio Grande Valley). A. Parsian and M. Reilly are the NIAAA Staff Collaborators. COGA investigators continue to be inspired by their memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting-Kai Li, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. COGA investigators are very grateful to Dr. Bruno Buecher without whom this project would not have existed. The authors also thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. ASTERISK, a GECCO sub-study, also thanks all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. As part of the GECCO sub-studies, CPS-II authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. Another GECCO sub-study, HPFS and NHS investigators would like to acknowledge Patrice Soule and Hardeep Ranu of the Dana Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS, HPFS under the supervision of Dr. Immaculata Devivo and Dr. David Hunter, Qin (Carolyn) Guo and Lixue Zhu who assisted in programming for NHS and HPFS. HPFS and NHS investigators also thank the participants and staff of the Nurses' Health Study and the Health Professionals Follow-Up Study, for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. PLCO, a substudy within GECCO, was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, and additionally supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Additionally, a subset of control samples were genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS1, CGEMS pancreatic cancer scan (PanScan)2, 3, and the Lung Cancer and Smoking study4. The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems.cancer.gov/data/) accession numbers phs000207.v1.p1 and phs000206.v3.p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www.ncbi.nlm.nih.gov/gap) through accession number phs000093.v2.p2. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. The authors thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the Screening Center investigators and staff or the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Mr. Tom Riley and staff, Information Management Services, Inc., Ms. Barbara O'Brien and staff, Westat, Inc., and Drs. Bill Kopp and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions to making this study possible. We also thank all participants and staff of the André and France Desmarais Montreal Heart Institute's (MHI) Biobank. The genotyping of the MHI Biobank was done at the MHI Pharmacogenomic Centre and funded by the MHI Foundation. HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was funded separately by the National Institute on Aging (RC2 AG036495, RC4 AG039029). Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation of the data were performed by the University of Michigan School of Public Health. CHDExome+ participating cohorts: BRAVE: The BRAVE study genetic epidemiology working group is a collaboration between the Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, UK, the Centre for Control of Chronic Diseases, icddr,b, Dhaka, Bangladesh and the National Institute of Cardiovascular Diseases, Dhaka, Bangladesh. CCHS, CIHDS, and CGPS collaborators thank participants and staff of the Copenhagen City Heart Study, Copenhagen Ischemic Heart Disease Study, and the Copenhagen General Population Study for their important contributions. EPIC-CVD: CHD case ascertainment and validation, genotyping, and clinical chemistry assays in EPIC-CVD were principally supported by grants awarded to the University of Cambridge from the EU Framework Programme 7 (HEALTH-F2-2012-279233), the UK Medical Research Council (G0800270) and British Heart Foundation (SP/09/002), and the European Research Council (268834). We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management, and the team at the EPIC-CVD Coordinating Centre for study coordination and administration. MORGAM: The work by MORGAM collaborators has been sustained by the MORGAM Project's recent funding: European Union FP 7 projects ENGAGE (HEALTH-F4-2007-201413), CHANCES (HEALTH-F3-2010-242244) and BiomarCaRE (278913). This has supported central coordination, workshops and part of the activities of the The MORGAM Data Centre, at THL in Helsinki, Finland. MORGAM Participating Centres are funded by regional and national governments, research councils, charities, and other local sources. PROSPER: collaborators have received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement n° HEALTH-F2-2009-223004 PROMIS: The PROMIS collaborators are are thankful to all the study participants in Pakistan. Recruitment in PROMIS was funded through grants available to investigators at the Center for Non-Communicable Diseases, Pakistan (Danish Saleheen and Philippe Frossard) and investigators at the University of Cambridge, UK (Danish Saleheen and John Danesh). Field-work, genotyping, and standard clinical chemistry assays in PROMIS were principally supported by grants awarded to the University of Cambridge from the British Heart Foundation, UK Medical Research Council, Wellcome Trust, EU Framework 6-funded Bloodomics Integrated Project, Pfizer. We would like to acknowledge the contributions made by the following individuals who were involved in the field work and other administrative aspects of the study: Mohammad Zeeshan Ozair, Usman Ahmed, Abdul Hakeem, Hamza Khalid, Kamran Shahid, Fahad Shuja, Ali Kazmi, Mustafa Qadir Hameed, Naeem Khan, Sadiq Khan, Ayaz Ali, Madad Ali, Saeed Ahmed, Muhammad Waqar Khan, Muhammad Razaq Khan, Abdul Ghafoor, Mir Alam, Riazuddin, Muhammad Irshad Javed, Abdul Ghaffar, Tanveer Baig Mirza, Muhammad Shahid, Jabir Furqan, Muhammad Iqbal Abbasi, Tanveer Abbas, Rana Zulfiqar, Muhammad Wajid, Irfan Ali, Muhammad Ikhlaq, Danish Sheikh and Muhammad Imran. INTERVAL: Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. DNA extraction and genotyping was funded by the National Institute of Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk/) and the NIHR Cambridge Biomedical Research Centre (www.cambridge-brc.org.uk). The academic coordinating centre for INTERVAL was supported by core funding from: NIHR Blood and Transplant Research Unit in Donor Health and Genomics, UK Medical Research Council (MR/L003120/1), British Heart Foundation (RG/13/13/30194), and NIHR Research Cambridge Biomedical Research Centre. A complete list of the investigators and contributors to the INTERVAL trial is provided in reference.
WOS: 000471758500010 ; PubMed ID: 31209238 ; The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZenecaAstraZeneca; European Union Horizon 2020 research [668858 PrECISE]; Joint Research Center for Computational Biomedicine (Bayer AG); National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome TrustWellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZeneca ; European Union Horizon 2020 research [668858 PrECISE] ; Joint Research Center for Computational Biomedicine (Bayer AG) ; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences ; Wellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The threat posed by invasive non-native species worldwide requires a global approach to identify which introduced species are likely to pose an elevated risk of impact to native species and ecosystems. To inform policy, stakeholders and management decisions on global threats to aquatic ecosystems, 195 assessors representing 120 risk assessment areas across all six inhabited continents screened 819 non-native species from 15 groups of aquatic organisms (freshwater, brackish, marine plants and animals) using the Aquatic Species Invasiveness Screening Kit. This multi-lingual decision-support tool for the risk screening of aquatic organisms provides assessors with risk scores for a species under current and future climate change conditions that, following a statistically based calibration, permits the accurate classification of species into high-, medium-and low-risk categories under current and predicted climate conditions. The 1730 screenings undertaken encompassed wide geographical areas (regions, political entities, parts thereof, water bodies, river basins, lake drainage basins, and marine regions), which permitted thresholds to be identified for almost all aquatic organismal groups screened as well as for tropical, temperate and continental climate classes, and for tropical and temperate marine ecoregions. In total, 33 species were identified as posing a & lsquo;very high risk & rsquo; of being or becoming invasive, and the scores of several of these species under current climate increased under future climate conditions, primarily due to their wide thermal tolerances. The risk thresholds determined for taxonomic groups and climate zones provide a basis against which area-specific or climate-based calibrated thresholds may be interpreted. In turn, the risk rankings help decision-makers identify which species require an immediate & lsquo;rapid & rsquo; management action (e.g. eradication, control) to avoid or mitigate adverse impacts, which require a full risk assessment, and which are to be restricted or banned with regard to importation and/or sale as ornamental or aquarium/fishery enhancement.(c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
This major reference is an overview of the current state of theoretical ecology through a series of topical entries centered on both ecological and statistical themes. Coverage ranges across scales—from the physiological, to populations, landscapes, and ecosystems. Entries provide an introduction to broad fields such as Applied Ecology, Behavioral Ecology, Computational Ecology, Ecosystem Ecology, Epidemiology and Epidemic Modeling, Population Ecology, Spatial Ecology and Statistics in Ecology. Others provide greater specificity and depth, including discussions on the Allee effect, ordinary differential equations, and ecosystem services. Descriptions of modern statistical and modeling approaches and how they contributed to advances in theoretical ecology are also included. Succinct, uncompromising, and authoritative—a "must have" for those interested in the use of theory in the ecological sciences
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Background In all societies, the burden and cost of allergic and chronic respiratory diseases are increasing rapidly. Most economies are struggling to deliver modern health care effectively. There is a need to support the transformation of the health care system into integrated care with organizational health literacy. Main body As an example for chronic disease care, MASK (Mobile Airways Sentinel NetworK), a new project of the ARIA (Allergic Rhinitis and its Impact on Asthma) initiative, and POLLAR (Impact of Air POLLution on Asthma and Rhinitis, EIT Health), in collaboration with professional and patient organizations in the field of allergy and airway diseases, are proposing real-life ICPs centred around the patient with rhinitis, and using mHealth to monitor environmental exposure. Three aspects of care pathways are being developed: (i) Patient participation, health literacy and self-care through technology-assisted patient activation, (ii) Implementation of care pathways by pharmacists and (iii) Next-generation guidelines assessing the recommendations of GRADE guidelines in rhinitis and asthma using real-world evidence (RWE) obtained through mobile technology. The EU and global political agendas are of great importance in supporting the digital transformation of health and care, and MASK has been recognized by DG Sante as a Good Practice in the field of digitally-enabled, integrated, person-centred care. Conclusion In 20 years, ARIA has considerably evolved from the first multimorbidity guideline in respiratory diseases to the digital transformation of health and care with a strong political involvement.
Publisher's version (útgefin grein) ; Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10-24 years were also in the top ten in the 25-49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50-74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd. ; Research reported in this publication was supported by the Bill & Melinda Gates Foundation; the University of Melbourne; Queensland Department of Health, Australia; the National Health and Medical Research Council, Australia; Public Health England; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; the Cardiovascular Medical Research and Education Fund; the National Institute on Ageing of the National Institutes of Health (award P30AG047845); and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The authors alone are responsible for the views expressed in this Article and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated, the National Health Service (NHS), the National Institute for Health Research (NIHR), the UK Department of Health and Social Care, or Public Health England; the United States Agency for International Development (USAID), the US Government, or MEASURE Evaluation; or the European Centre for Disease Prevention and Control (ECDC). This research used data from the Chile National Health Survey 2003, 2009-10, and 2016-17. The authors are grateful to the Ministry of Health, the survey copyright owner, for allowing them to have the database. All results of the study are those of the authors and in no way committed to the Ministry. The Costa Rican Longevity and Healthy Aging Study project is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Poblacion and Instituto de Investigaciones en Salud, in collaboration with the University of California at Berkeley. The original pre-1945 cohort was funded by the Wellcome Trust (grant 072406), and the 1945-55 Retirement Cohort was funded by the US National Institute on Aging (grant R01AG031716). The principal investigators are Luis Rosero-Bixby and William H Dow and co-principal investigators are Xinia Fernandez and Gilbert Brenes. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. The Health Behaviour in School-Aged Children (HBSC) study is an international study carried out in collaboration with WHO/EURO. The international coordinator of the 1997-98, 2001-02, 2005-06, and 2009-10 surveys was Candace Currie and the databank manager for the 1997-98 survey was Bente Wold, whereas for the following surveys Oddrun Samdal was the databank manager. A list of principal investigators in each country can be found on the HBSC website. Data used in this paper come from the 2009-10 Ghana Socioeconomic Panel Study Survey, which is a nationally representative survey of more than 5000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana and the Economic Growth Centre (EGC) at Yale University. It was funded by EGC. ISSER and the EGC are not responsible for the estimations reported by the analysts. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license number SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. Data for this research was provided by MEASURE Evaluation, funded by USAID. The authors thank the Russia Longitudinal Monitoring Survey, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology, Russia Academy of Sciences for making data available. This paper uses data from the Bhutan 2014 STEPS survey, implemented by the Ministry of Health with the support of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the Ministry of Health with the support of WHO; the Libya 2009 STEPS survey, implemented by the Secretariat of Health and Environment with the support of WHO; the Malawi 2009 STEPS survey, implemented by Ministry of Health with the support of WHO; and the Moldova 2013 STEPS survey, implemented by the Ministry of Health, the National Bureau of Statistics, and the National Center of Public Health with the support of WHO. This paper uses data from Survey of Health, Ageing and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE. w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); see Borsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N degrees 211909, SHARE-LEAP: GA N degrees 227822, SHARE M4: GA N degrees 261982) and Horizon 2020 (SHARE-DEV3: GA N degrees 676536, SERISS: GA N degrees 654221) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C), and from various national funding sources is gratefully acknowledged. This study has been realised using the data collected by the Swiss Household Panel, which is based at the Swiss Centre of Expertise in the Social Sciences. The project is financed by the Swiss National Science Foundation. The United States Aging, Demographics, and Memory Study is a supplement to the Health and Retirement Study (HRS), which is sponsored by the National Institute of Aging (grant number NIA U01AG009740). It was conducted jointly by Duke University and the University of Michigan. The HRS is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This paper uses data from Add Health, a program project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from grant P01-HD31921 for this analysis. The data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government. Collection of data for the Mozambique National Survey on the Causes of Death 2007-08 was made possible by USAID under the terms of cooperative agreement GPO-A-00-08-000_D3-00. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study IVI conducted. L G Abreu acknowledges support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Brazil; finance code 001) and Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, a Brazilian funding agency). I N Ackerman was supported by a Victorian Health and Medical Research Fellowship awarded by the Victorian Government. O O Adetokunboh acknowledges the South African Department of Science and Innovation and the National Research Foundation. A Agrawal acknowledges the Wellcome Trust DBT India Alliance Senior Fellowship. S M Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. M Ausloos, C Herteliu, and A Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. A Badawi is supported by the Public Health Agency of Canada. D A Bennett was supported by the NIHR Oxford Biomedical Research Centre. R Bourne acknowledges the Brien Holden Vision Institute, University of Heidelberg, Sightsavers, Fred Hollows Foundation, and Thea Foundation. G B Britton and I Moreno Velasquez were supported by the Sistema Nacional de Investigacion, SNI-SENACYT, Panama. R Buchbinder was supported by an Australian National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship. J J Carrero was supported by the Swedish Research Council (2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. A R Chang was supported by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases grant K23 DK106515. V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundacao para a Ciencia e Tecnologia, IP, under the Norma Transitaria DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care South London at King's College Hospital NHS Foundation Trust and the Royal College of Physicians, and support from the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. B B Duncan acknowledges grants from the Foundation for the Support of Research of the State of Rio Grande do Sul (IATS and PrInt) and the Brazilian Ministry of Health. H E Erskine is the recipient of an Australian NHMRC Early Career Fellowship grant (APP1137969). A J Ferrari was supported by a NHMRC Early Career Fellowship grant (APP1121516). H E Erskine and A J Ferrari are employed by and A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. M L Ferreira holds an NHMRC Research Fellowship. C Flohr was supported by the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas acknowledges financial support from the EU (European Regional Development Fund [FEDER] funds through COMPETE POCI-01-0145-FEDER-029248) and National Funds (Fundacao para a Ciencia e Tecnologia) through project PTDC/NAN-MAT/29248/2017. A L S Guimaraes acknowledges support from CNPq. C Herteliu was partially supported by a grant co-funded by FEDER through Operational Competitiveness Program (project ID P_40_382). P Hoogar acknowledges Centre for Bio Cultural Studies, Directorate of Research, Manipal Academy of Higher Education and Centre for Holistic Development and Research, Kalaghatagi. F N Hugo acknowledges the Visiting Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang was supported by China Medical University (CMU107-Z-04), Taichung, Taiwan. S M S Islam was funded by a National Heart Foundation Senior Research Fellowship and supported by Deakin University. R Q Ivers was supported by a research fellowship from the National Health and Medical Research Council of Australia. M Jakovljevic acknowledges the Serbian part of this GBD-related contribution was co-funded through Grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. P Jeemon was supported by a Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome Trust-Department of Biotechnology, India Alliance (2015-20). O John is a recipient of UIPA scholarship from University of New South Wales, Sydney. S V Katikireddi acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_12017/13, MC_UU_12017/15), and the Scottish Government Chief Scientist Office (SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was supported by Research Management Office, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC Centre of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar was supported by K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. J V Lazarus was supported by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). K J Looker thanks the NIHR Health Protection Research Unit in Evaluation of Interventions at the University of Bristol, in partnership with Public Health England, for research support. S Lorkowski was funded by the German Federal Ministry of Education and Research (nutriCARD, grant agreement number 01EA1808A). R A Lyons is supported by Health Data Research UK (HDR-9006), which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, NIHR (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust. J J McGrath is supported by the Danish National Research Foundation (Niels Bohr Professorship), and the Queensland Health Department (via West Moreton HHS). P T N Memiah acknowledges support from CODESRIA. U O Mueller gratefully acknowledges funding by the German National Cohort Study BMBF grant number 01ER1801D. S Nomura acknowledges the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz was supported by ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM. These funding sources had no role in the writing of the manuscript or the decision to submit it for publication. S B Patten was supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health at the University of Calgary. G C Patton was supported by an aNHMRC Senior Principal Research Fellowship. M R Phillips was supported in part by the National Natural Science Foundation of China (NSFC, number 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were supported by grants from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4-Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and B Unnikrishnan acknowledge Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. A L P Ribeiro was supported by Brazilian National Research Council, CNPq, and the Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was supported by The Sir Charles Hercus Health Research Fellowship (#18/111) Health Research Council of New Zealand. D Ribeiro acknowledges financial support from the EU (FEDER funds through the Operational Competitiveness Program; POCI-01-0145-FEDER-029253). P S Sachdev acknowledges funding from the NHMRC of Australia Program Grant. A M Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). R Sarmiento-Suarez received institutional support from Applied and Environmental Sciences University (Bogota, Colombia) and ISCIII (Madrid, Spain). A E Schutte received support from the South African National Research Foundation SARChI Initiative (GUN 86895) and Medical Research Council. S T S Skou is currently funded by a grant from Region Zealand (Exercise First) and a grant from the European Research Council under the EU's Horizon 2020 research and innovation program (grant agreement number 801790). J B Soriano is funded by Centro de Investigacion en Red de Enfermedades Respiratorias, ISCIII. R Tabares-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII-FEDER. N Taveira was partially supported by the European & Developing Countries Clinical Trials Partnership, the EU (LIFE project, reference RIA2016MC-1615). S Tyrovolas was supported by the Foundation for Education and European Culture, the Sara Borrell postdoctoral programme (reference number CD15/00019 from ISCIII-FEDER). S B Zaman received a scholarship from the Australian Government research training programme in support of his academic career. ; "Peer Reviewed"
Il presente elaborato ha ad oggetto l'analisi dei più significativi aspetti di rilevanza costituzionale connessi alla disciplina delle intercettazioni di conversazioni e comunicazioni: uno strumento investigativo che, negli ultimi anni, è stato al centro di accese polemiche e prospettive di riforma. Non si deve dimenticare, infatti, che lo sviluppo dei mezzi di comunicazione e l'utilizzo di tecniche di captazione sempre più avanzate hanno determinato, al contempo, nuove opportunità di confronto interpersonale, ma anche più penetranti forme di restrizione della libertà e segretezza delle comunicazioni ponendo, sul piano dei limiti delle garanzie costituzionali, innumerevoli problematiche specie in ordine ai rapporti tra l'art. 15 della Costituzione e l'utilizzo di mezzi di ricerca della prova fortemente limitativi dei diritti e delle libertà del singolo. In particolare, le intercettazioni di conversazioni e comunicazioni, pur rappresentando un efficace mezzo di ricerca della prova, comportano, allo stesso tempo, un'evidente invasione nella sfera di riservatezza del cittadino: da qui la necessità di bilanciare, da un lato, le esigenze di prevenzione e repressione dei reati a fini di sicurezza e controllo sociale, dall'altro, la tutela dei soggetti privati in relazione alla loro privacy. La prima parte del lavoro è, per questo, dedicata all'esame di quelle che sono le più significative problematiche in ordine alla compatibilità con il dettato costituzionale della disciplina contenuta nel codice di procedura penale del 1988 in materia di intercettazioni di conversazioni o comunicazioni, e quindi le questioni relative al bilanciamento di interessi tra inviolabilità della libertà e della segretezza della corrispondenza e di ogni altra forma di comunicazione ed esigenze di prevenzione e repressione dei reati, nonché tra privacy e pubblicità dei processi penali. Sarà, dunque, necessario, preliminarmente, ricostruire la portata della garanzia costituzionale delle comunicazioni riservate, al fine di stabilire quale sia il confine, in ordine alle restrizioni da essa ammissibili, tra legittime esigenze di repressione penale e utilizzo strumentale o arbitrario delle stesse; soltanto alla luce della stessa, infatti, sarà possibile cogliere i più significativi punti critici della disciplina codicistica in materia di intercettazioni, tra i quali quello relativo alla pubblicità degli atti d'indagine è sicuramente il più importante e il più discusso. E' proprio attorno a questo aspetto, infatti, che ruotano i più recenti progetti di riforma, dal d.d.l Mastella, al d.d.l Alfano, sino ad arrivare a quelli presentati nel corso di questa legislatura e attualmente all'esame delle Camere. Tutti mossi da un comune intento: quello di limitare la possibilità che gli atti d'indagine, tra cui i verbali delle intercettazioni, possano fuoriuscire dal circuito procedimentale ed essere pubblicati indiscriminatamente dagli organi di stampa. Esigenza particolarmente sentita, se si considera che la prassi degli ultimi anni ha fatto registrare un incremento notevole dei casi in cui il contenuto delle conversazioni captate è stato divulgato a mezzo stampa, con conseguente violazione del segreto istruttorio e della riservatezza dei soggetti coinvolti, talora addirittura estranei alle vicende giudiziarie. Solo dopo aver analizzato questi aspetti connessi alla disciplina delle intercettazioni prevista, in generale, nei confronti dei quivis de populo e contenuta nel codice di procedura penale del 1988, l'attenzione sarà posta su un tema che, negli ultimi anni ha fatto molto parlare di sé: quello relativo all'utilizzo di strumenti d'indagine particolarmente invasivi, quali le intercettazioni, nei confronti di soggetti titolari di prerogative costituzionali. Da questo punto di vista verranno esaminate le discipline previste dalla Carta costituzionale (e dalle rispettive norme di attuazione) nei confronti dei più importanti "attori" istituzionali dell'ordinamento italiano: dai parlamentari, ai membri del Governo, al Presidente della Repubblica, sino ad arrivare ai giudici della Corte costituzionale. Il secondo capitolo del presente elaborato è, infatti, interamente dedicato all'analisi del delicato rapporto tra uso delle intercettazioni e immunità parlamentari: tema che si presenta particolarmente interessante se si considera che, a seguito della riforma costituzionale dell'art. 68 (attuata con la legge cost. n. 3 del 1993), l'uso di tale strumento investigativo viene subordinato ad un'espressa autorizzazione della Camera di appartenenza, in modo da preservare la funzione parlamentare da indebite interferenze o condizionamenti. Si tratta di una previsione che, sin dalla sua entrata in vigore, è stata oggetto di critiche provenienti sia dal mondo istituzionale che accademico, anche se l'attenzione dei commentatori si è posta soprattutto sulla relativa normativa di attuazione (la legge n. 140 del 2003), la quale opera un'ingiustificata (e a tratti incostituzionale) distinzione tra intercettazioni "dirette" e "indirette". Da questo punto di vista, sarà, dunque, significativo l'esame della successiva prassi parlamentare instauratasi e delle pronunce della Corte costituzionale rese in materia, le quali condurranno ad un progressivo "smantellamento" della configurazione originaria della legge in questione. Al fine di garantire una più adeguata ed esauriente disamina del tema in esame, saranno, altresì, analizzati i più significativi orientamenti della "giurisprudenza" parlamentare dal 1993 (anno della riforma costituzionale) ad oggi, in modo da individuare, da una parte, i criteri seguiti dalle Camere nelle decisioni sulla concessione o meno dell'autorizzazione, e il loro perfezionamento a distanza di vent'anni dall'introduzione della prerogativa; dall'altra, le principali differenze tra le strade percorse dalle due Camere, senza dubbio espressione dei c.d. Interna Corporis. Il lavoro proseguirà, come anticipato, con la disamina della disciplina prevista in materia di intercettazioni nei confronti dei Ministri e del Presidente del Consiglio dei Ministri, nell'ambito dei procedimenti per i reati indicati dall'art. 96 della Costituzione, contenuta nell'art. 10 della legge cost. 16 gennaio 1989, n.1; il quale prevede espressamente che gli stessi non possano essere sottoposti a tali strumenti investigativi se non previa autorizzazione della Camera competente. Per comprendere appieno la portata della disposizione e, con essa, le principali differenze rispetto all'autorizzazione richiesta nei confronti dei parlamentari, sarà necessario compiere un breve excursus storico, in modo da cogliere le ragioni che hanno condotto ad abbandonare il sistema della c.d. "giustizia politica" e a ricondurre l'accertamento dei reati compiuti dai membri dell'Esecutivo nell'ambito della giurisdizione ordinaria; nonché le ragioni che hanno indotto a mantenere nei loro confronti una peculiare tutela che si estrinseca, per l'appunto, nella necessità di un'autorizzazione da parte del Parlamento sia ai fini del procedere, sia allo scopo di compiere taluni atti investigativi, tra i quali le intercettazioni. Solo successivamente saranno analizzate le speciali prerogative riconosciute al Capo dello Stato, le loro origini storiche e la ratio posta a fondamento delle stesse, con particolare attenzione alla disciplina sul procedimento d'accusa e ai limiti in essa contemplati in ordine alla possibilità di sottoporre ad intercettazione le sue utenze. Tuttavia, sia le prerogative riconosciute in materia nei confronti dei membri del Governo che nei confronti del Presidente della Repubblica rivestono, ad oggi, valore puramente teorico, dato che non hanno mai trovato concreta applicazione. Rispetto a questi organi di rilevanza costituzionale, infatti, le più accese polemiche hanno riguardato casi di diffusione a mezzo stampa del contenuto di intercettazioni disposte nell'ambito di procedimenti a carico di terzi nelle quali essi comparivano come casuali interlocutori. Ne sono un esempio eclatante l'affaire Napolitano e il "caso Ligresti" che ha coinvolto, più di recente, il ministro della giustizia Anna Maria Cancellieri, ai quali sarà dedicata particolare attenzione nel corso della trattazione. Per completezza d'indagine, non si possono trascurare, inoltre, le speciali prerogative di ordine procedurale riconosciute ai membri della Consulta dalla legge costituzionale n. 1 del 1948, le quali saranno analizzate per verificare se anche nei loro confronti, pur in assenza di una specifica disposizione sul punto, sussistano o meno dei limiti all'utilizzo di mezzi di ricerca della prova particolarmente invasivi, quali le intercettazioni di conversazioni o comunicazioni. Si tratta, com'è evidente, di discipline che differiscono tra loro in maniera profonda e ciò è dovuto principalmente alla diversa origine e ratio delle immunità riconosciute agli organi costituzionali di vertice. Per quel che strettamente riguarda l'inviolabilità dei membri del Parlamento, alla quale è stata dedicata particolare attenzione nell'ambito di questo elaborato (anche, e soprattutto, alla luce delle più recenti pronunce della Corte costituzionale), è parso opportuno allargare l'analisi al panorama sovranazionale e comparato. Da un lato, infatti, sarà presa in esame l'immunità parlamentare europea, in ragione dell'accresciuta importanza del Parlamento, non solo come produttore di normativa ma anche come motore politico dell'Unione, guardando, non soltanto ai principali aspetti di iterazione tra normativa interna e comunitaria, ma, altresì, ad alcuni casi concretamente affrontati dalla competente Commissione. Dall'altra, invece, si prenderà in esame l'esperienza di alcuni tra i più importanti ordinamenti democratici contemporanei, al fine di analizzare le rispettive soluzioni adottate in materia di intercettazioni a carico dei membri del Parlamento e cogliere, dunque, le principali differenze rispetto a quanto previsto dalla nostra Carta costituzionale.
Publisher's version (útgefin grein). ; Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes. ; Funding This work was funded by a grant to YJ from the British Heart Foundation (PG/12/38/29615). AGES: This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00–063. The researchers are indebted to the participants for their willingness to participate in the study. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Funding support for "Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium" was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). BRIGHT: The Exome Chip genotyping was funded by Wellcome Trust Strategic Awards (083948 and 085475). This work was also supported by the Medical Research Council of Great Britain (Grant no. G9521010D); and by the British Heart Foundation (Grant no. PG/02/128). AFD was supported by the British Heart Foundation (Grant nos. RG/07/005/23633 and SP/08/005/25115); and by the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant no. LSHM-C7–2006-037093). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. We would also like to thank the Barts Genome Centre staff for their assistance with this project. CHS: This Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201800001C, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants R01HL068986, U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ERF: The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4–2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). We are grateful to all study participants and their relatives, general practitioners, and neurologists for their contributions to the ERF study and to P Veraart for her help in genealogy, J Vergeer for the supervision of the laboratory work, and P Snijders for his help in data collection. FHS: The Framingham Heart Study (FHS) research reported in this article was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI), HL120393. Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping of the Generation Scotland and Scottish Family Health Study samples was carried out by the Genetics Core Laboratory at the Clinical Research Facility, Edinburgh, Scotland and was funded by the UK's Medical Research Council. GOCHA: The Genetics of Cerebral Hemorrhage with Anticoagulation was carried out as a collaborative study supported by grants R01NS073344, R01NS059727, and 5K23NS059774 from the NIH–National Institute of Neurological Disorders and Stroke (NIH-NINDS). GRAPHIC: The GRAPHIC Study was funded by the British Heart Foundation (BHF/RG/2000004). NJS and CPN are supported by the British Heart Foundation and is a NIHR Senior Investigator. This work falls under the portfolio of research supported by the NIHR Leicester Cardiovascular Biomedical Research. INGI-FVG: This study has been funded by Regione FVG (L.26.2008). INTER99: The Inter99 was initiated by Torben Jørgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises the former two and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). JHS: We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General Medical Sciences. KORA: The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. Korcula: This work was funded by the Medical Research Council UK, The Croatian Ministry of Science, Education and Sports (grant 216–1080315-0302), the Croatian Science Foundation (grant 8875), the Centre of Excellence in Personalized health care, and the Centre of Competencies for Integrative Treatment, Prevention and Rehabilitation using TMS. LifeLines: The LifeLines Cohort Study and generation and management of GWAS genotype data for the LifeLines Cohort Study are supported by The Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation, and Dutch Diabetes Research Foundation. Niek Verweij is supported by NWO-VENI (016.186.125) and Marie Sklodowska-Curie GF (call: H2020-MSCA-IF-2014, Project ID: 661395). UHP: Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Ilonca Vaartjes is supported by a Dutch Heart Foundation grant DHF project "Facts and Figures." MGH-CAMP: Dr. Patrick Ellinor is funded by NIH grants (2R01HL092577, 1R01HL128914, R01HL104156, and K24HL105780) and American Heart Association Established Investigator Award 13EIA14220013 (Ellinor). Dr. Steve Lubitz is funded by NIH grants K23HL114724 and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105. NEO: The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants, and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk, and Ingeborg de Jonge for the coordination, lab, and data management of the NEO study. We also thank Arie Maan for the analyses of the electrocardiograms. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). RS-I: The generation and management of the Illumina Exome Chip v1.0 array data for the Rotterdam Study (RS-I) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The Exome chip array dataset was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO)-sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050–060-810); the Netherlands Organization for Scientific Research (NWO; project number 184021007); and by the Rainbow Project (RP10; Netherlands Exome Chip Project) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl). We thank Ms. Mila Jhamai, Ms. Sarah Higgins, and Mr. Marijn Verkerk for their help in creating the exome chip database, and Carolina Medina-Gomez, MSc, Lennard Karsten, MSc, and Linda Broer PhD for QC and variant calling. Variants were called using the best practice protocol developed by Grove et al. as part of the CHARGE consortium exome chip central calling effort. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The work of Bruno H. Stricker is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). The work of Mark Eijgelsheim is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). SHIP: SHIP is supported by the BMBF (grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]; grant GR 1912/5–1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. The EMAU is a member of the Center of Knowledge Interchange (CKI) program of the Siemens AG. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the BMBF (grant 03Z1CN22). We thank all SHIP and SHIP-TREND participants and staff members as well as the genotyping staff involved in the generation of the SNP data. TWINSUK: TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. UKBB: This research has been conducted using the UK Biobank Resource (application 8256 - Understanding genetic influences in the response of the cardiac electrical system to exercise) and is supported by Medical Research Council grant MR/N025083/1. We also wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Unit at Barts and Queen Mary University of London, UK. PD Lambiase acknowledges support from the UCLH Biomedicine NIHR. MO is supported by an IEF 2013 Marie Curie fellowship. JR acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. 608765. YFS: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. The expert technical assistance in the statistical analyses by Irina Lisinen is gratefully acknowledged. Cell culture and biochemistry: Funding was provided by the National Institutes of Health (Program of Excellence in Glycoscience award HL107147 to SSA and F32AR063548 to TJM) and the David and Lindsay Morgenthaler Postdoctoral Fellowship (to TJM) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). Mutant mouse model: Adamts6 mutant mice were generated and further propagated and analyzed by funding provided by NIH grants HL098180 and HL132024 (to CWL) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). ; Peer Reviewed
PERSEUS project aims to identify the most relevant pressures exerted on the ecosystems of the Southern European Seas (SES), highlighting knowledge and data gaps that endanger the achievement of SES Good Environmental Status (GES) as mandated by the Marine Strategy Framework Directive (MSFD). A complementary approach has been adopted, by a meta-analysis of existing literature on pressure/impact/knowledge gaps summarized in tables related to the MSFD descriptors, discriminating open waters from coastal areas. A comparative assessment of the Initial Assessments (IAs) for five SES countries has been also independently performed. The comparison between meta-analysis results and IAs shows similarities for coastal areas only. Major knowledge gaps have been detected for the biodiversity, marine food web, marine litter and underwater noise descriptors. The meta-analysis also allowed the identification of additional research themes targeting research topics that are requested to the achievement of GES. 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. ; peer-reviewed
The threat posed by invasive non-native species worldwide requires a global approach to identify which introduced species are likely to pose an elevated risk of impact to native species and ecosystems. To inform policy, stakeholders and management decisions on global threats to aquatic ecosystems, 195 assessors representing 120 risk assessment areas across all six inhabited continents screened 819 non-native species from 15 groups of aquatic organisms (freshwater, brackish, marine plants and animals) using the Aquatic Species Invasiveness Screening Kit. This multi-lingual decision-support tool for the risk screening of aquatic organisms provides assessors with risk scores for a species under current and future climate change conditions that, following a statistically based calibration, permits the accurate classification of species into high-, medium- and low-risk categories under current and predicted climate conditions. The 1730 screenings undertaken encompassed wide geographical areas (regions, political entities, parts thereof, water bodies, river basins, lake drainage basins, and marine regions), which permitted thresholds to be identified for almost all aquatic organismal groups screened as well as for tropical, temperate and continental climate classes, and for tropical and temperate marine ecoregions. In total, 33 species were identified as posing a 'very high risk' of being or becoming invasive, and the scores of several of these species under current climate increased under future climate conditions, primarily due to their wide thermal tolerances. The risk thresholds determined for taxonomic groups and climate zones provide a basis against which area-specific or climate-based calibrated thresholds may be interpreted. In turn, the risk rankings help decision-makers identify which species require an immediate 'rapid' management action (e.g. eradication, control) to avoid or mitigate adverse impacts, which require a full risk assessment, and which are to be restricted or banned with regard to importation and/or sale as ornamental or aquarium/fishery enhancement
