L'eau: la nouvelle vague
In: Le courrier: Communauté Européenne, Afrique-Carai͏̈bes-Pacifique, Heft 161, S. 49-71
ISSN: 0378-4401, 1013-7343, 1026-2350
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In: Le courrier: Communauté Européenne, Afrique-Carai͏̈bes-Pacifique, Heft 161, S. 49-71
ISSN: 0378-4401, 1013-7343, 1026-2350
World Affairs Online
Resumen: El desarrollo agrícola de la altillanura Colombiana llevado a cabo con apoyo del gobierno es una meta ambiciosa por múltiples razones, empezando con la infertilidad intrínseca de los suelos que ha limitado el desarrollo agrícola hasta la fecha. Es importante diseñar un modelo de desarrollo agrícola que sea ecoeficiente, es decir rentable del punto de vista económico, equitativo del punto de vista social y sostenible del punto de vista ambiental. En este estudio, se diseñaron indicadores para evaluar y monitorear la ecoeficiencia de los cuatro sistemas de producción más usados por los productores que invirtieron en el área: cultivos transitorios, cultivos perenes (palma africana y hevea) y pastizales mejorados y se les comparo con la sabana natural, como sistema de referencia. El objetivo del estudio fue la construcción de indicadores de ecoeficiencia en los sistemas de producción: cultivos transitorios (soya, maíz, y arroz), cultivos permanentes (caucho y palma de aceite), pasturas mejoradas y la sabana natural, a partir de la generación de subindicadores sintéticos con valores entre 0.10 y 1.00, asociados a los grupos de variables de regulación hídrica, fertilidad química de suelo, biodiversidad (macrofauna), regulación climática (gases efecto invernadero y almacenamiento de carbono) y de variables el componente socioeconómico. El indicador de ecoeficiencia es la suma de un indicador de servicios ecosistemicos, de desarrollo social y de eficiencia económica. El estudio socioeconómico se hizo en 120 fincas que representan la diversidad de situaciones encontradas en el área. Análisis iniciales de la base de datos inicial de 227 preguntas organizadas en 13 bloques establecida por el CIAT, se extrajeron 5 variables representativas del entorno social (edad, tenencia de la tierra, trabajadores permanentes hombre y/o mujeres, capacitación recibida) y se usaron para diseñar un indicador de Capital Humano. Otro grupo de 11 variables se usó para describir el entorno económico con un indicador de sistemas de producción. Finalmente, un grupo de 7 variables, que describen la composición de la finca en diferentes coberturas vegetales, se usó para diseñar un indicador de paisaje. De los análisis multivariados (ACP y ACM) de cada grupo se calcularon indicadores variando de 0,1 a 1,0 siguiendo la metodología de Velásquez et al., (2007). Se hicieron también tipologías de las fincas de acuerdo a cada indicador, basadas en análisis de cluster. Se realizaron análisis de coinercias para evaluar la relación entre los juegos de variables, encontrándose una estrecha relación entre el capital humano disponible (número de trabajadores permanentes, edad y capacitación del productor), el sistema de producción, más o menos tecnificado que se usa, y la calidad del paisaje medida por la proporción de sistemas naturales, bosques y cultivos perenes presentes. El impacto ambiental de los sistemas de uso se evaluó en 75 parcelas localizadas en 41 fincas diferentes, distribuidas en el transecto entre Puerto López, Puerto Gaitán, incluyendo CNI Carimagua. Se evaluaron los cuatro sistemas productivos pastura mejorada, cultivos transitorios y cultivos perennes (caucho y palma de aceite) con respecto a la sabana natural. A partir de un extenso diagnóstico agroecológico, se diseñaron 5 sub indicadores: fertilidad química de suelos, funciones hídricas, biodiversidad (macrofauna de suelo), regulación climática (almacenamiento de carbono, emisiones de GEI) y estabilidad estructural del suelo (macroagregación y morfología del suelo). Estos 5 sub indicadores se combinaron después para constituir un indicador de servicios ecosistemicos que mide el impacto ambiental de los sistemas de producción. Cada uno de los indicadores generados separó los sistemas de uso de forma altamente significativa. Mientras que la pastura mejorada en promedio mejora la biodiversidad de la macrofauna (0.73 ±0.05) y la agregación (0.76 ± 0.02) del suelo limitando su erosión en comparación con la sabana natural, la palma de aceite mejora las funciones hídricas del suelo y el almacenamiento de C y los cultivos anuales mejoran la calidad química (0.78 ± 0.03) estos sistemas de producción desmejoran las otras funciones, aunque de formas distintas según el tipo de producción. Se observó así que cada sistema de uso tiene la capacidad de mejorar por lo menos uno de los servicios eco-sistémicos medidos, además de aumentar el indicador económico (máximo para los cultivos transitorios con un valor de 0.91 ± 0.09) y mejorar los índices sociales (valor promedio máximo con los cultivos perenes 0.46 ± 0.28). Existe sin embargo una variabilidad grande entre los sistemas, probablemente debida a la diversidad de los paisajes creados y al valor del capital humano presente. El componente socioeconómico presentó patrones muy interesantes asi (1) La ecoeficiencia aumenta de forma regular con el capital humano, (2) La ecoeficiencia aumenta con la intensificacion del uso de la tierra (indicador económico), hasta un punto de inflexión que corresponde a un valor del indicador de sistemas de producción de 0.9 y parece disminuir de forma abrupta justo después; (3) La ecoeficiencia aumenta con la intensificación del paisaje, pero disminuye fuertemente en los paisajes más intensificados. Estos resultados muestran globalmente una gran variabilidad en los datos de ecoeficiencia. Es clara la influencia de los sistemas de cultivo implementados sobre los parámetros ambientales y el capital humano. Sin embargo, se nota una relación fuerte con el paisaje creado, con una disminución de la ecoeficiencia ya notable en las fincas que tienen menores proporciones de espacios naturales y bosques, aunque los cultivos perenes puedan ayudar a mejorar la condición ambiental hasta cierto punto. Palabras clave: Oxisoles, Servicios ecosistemicos, Gases efecto invernadero, Caracterización socioeconómica y Análisis multivariado ; Abstract: The agricultural development of the Colombian altillanura, carried out with government support, is an ambitious goal for many reasons, starting with the intrinsic infertility of the soils that has limited the agricultural development up to now. It is important to design an ecofficient agricultural development model, that is economically profitable, equitable from a social point of view and environmentally sustainable. In this study, indicators were designed to evaluate and monitor the eco-efficiency of the four production systems most used by the producers who invested in the area: transitional crops, perennial crops (African palm and hevea) and improved pastures compared with Natural savanna as a reference system. The objective of the study was the construction of eco-efficiency indicators in the production systems: transitional crops (soybean, maize, and rice), permanent crops (rubber and oil palm), improved pastures and natural savanna, from generation Of synthetic sub-indicators with values between 0.10 and 1.00, associated to the groups of variables of water regulation, soil chemical fertility, biodiversity (macrofauna), climate regulation (greenhouse gases and carbon storage) and socioeconomic variables. The eco-efficiency indicator is the sum of an indicator of ecosystem services, social development and economic efficiency. The socioeconomic study was done in 120 farms that represent the diversity of situations found in the area. Five initial variables of the social environment (age, land tenure, permanent male and/or female workers, training received) were extracted from the initial database of 227 questions organized in 13 blocks established by CIAT and were used to design a Human Capital indicator. Another group of 11 variables were used to describe the economic environment with an indicator of Production Systems. Finally, a group of 7 variables, which describe the composition of the farm in different vegetation cover, was used to design a landscape indicator. From the multivariate analysis (PCA and ACM) of each group, indicators ranging from 0.1 to 1.0 were calculated following the methodology of Velasquez et al. (2007). Typologies of the farms were also made according to each indicator, based on clusters analysis to assist in the interpretation. Significant covariations were measured showing a close relationship between the available human capital (number of permanent workers, age and training of the producer), the production system, more or less technified, that is used, and the quality of the landscape measured by the relative proportions of natural systems, forests and perennial crops present. Ecosytem services of land use systems was evaluated in 75 plots located in 41 different farms, distributed in the transect between Puerto López, Puerto Gaitán and Carimagua. The four production systems improved pasture, transient crops and perennial crops (rubber and oil palm) were evaluated with respect to the natural savanna. Based on an extensive agroecological diagnosis, 5 sub indicators were designed: chemical soil fertility, water functions, biodiversity (soil macrofauna), climate regulation (carbon storage, GHG emissions) and soil structural stability (macroaggregation and morphology of floor). These 5 sub indicators were added later to constitute an indicator of ecosystem services that measures the environmental impact of production systems. Each of the indicators generated separated the systems of use in a highly significant way. While improved pasture on average improves the biodiversity of macrofauna (0.73 ± 0.05) and aggregation (0.76 ± 0.02) of the soil by limiting its erosion compared to the natural savanna, the oil palm improves the water functions of the soil and the Storage of C and annual crops improve the chemical quality (0.78 ± 0.03) these production systems deteriorate the other functions, but in different ways according to the type of production. It was observed that each system of use has the capacity to improve at least one of the measured ecosystemic services, besides increasing the economic indicator (maximum for transitory crops with a value of 0.91 ± 0.09) and to improve the social indexes (Maximum value with perennial crops 0.46 ± 0.28). There is, however, a large variability between the systems, probably due to the diversity of landscapes created and the value of present human capital. The socio-economic component presents very interesting patterns as well (1) Eco-efficiency increases on a regular basis with human capital, (2) Eco-efficiency increases with the intensification of land use (economic indicator), to an inflection point corresponding to a Value of the indicator of production systems of 0.9 and seems to decrease abruptly just after; (3) Eco-efficiency increases with the intensification of the landscape, but decreases strongly in the intensified landscapes These results globally show great variability in eco-efficiency data. The influence of cropping systems on environmental parameters and human capital is clear. However, there is a strong relationship with the landscape created, with a decrease in ecoefficiency already Key words: Oxisols, Ecosystem services, Greenhouse Gases, Socioeconomic Characterization and Multivariate Analysis. ; Doctorado
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In: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715680/
Only Vanderbilt University affiliated authors are listed on VUIR. For a full list of authors, access the version of record at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715680/ ; Fanconi anemia (FA) is a genetically heterogeneous disorder with 22 disease-causing genes reported to date. In some FA genes, monoallelic mutations have been found to be associated with breast cancer risk, while the risk associations of others remain unknown. The gene for FA type C, FANCC, has been proposed as a breast cancer susceptibility gene based on epidemiological and sequencing studies. We used the Oncoarray project to genotype two truncating FANCC variants (p.R185X and p.R548X) in 64,760 breast cancer cases and 49,793 controls of European descent. FANCC mutations were observed in 25 cases (14 with p.R185X, 11 with p.R548X) and 26 controls (18 with p.R185X, 8 with p.R548X). There was no evidence of an association with the risk of breast cancer, neither overall (odds ratio 0.77, 95% CI 0.44-1.33, p = 0.4) nor by histology, hormone receptor status, age or family history. We conclude that the breast cancer risk association of these two FANCC variants, if any, is much smaller than for BRCA1, BRCA2 or PALB2 mutations. If this applies to all truncating variants in FANCC it would suggest there are differences between FA genes in their roles on breast cancer risk and demonstrates the merit of large consortia for clarifying risk associations of rare variants. ; We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. We acknowledge all contributors to the COGS and OncoArray study design, chip design, genotyping, and genotype analyses. ABCFS thank Maggie Angelakos, Judi Maskiell, Gillian Dite. ABCS thanks the Blood bank Sanquin, The Netherlands. ABCTB Investigators: C.L.C., Rosemary Balleine, Robert Baxter, Stephen Braye, Jane Carpenter, Jane Dahlstrom, John Forbes, Soon Lee, Deborah Marsh, Adrienne Morey, Nirmala Pathmanathan, Rodney Scott, Allan Spigelman, Nicholas Wilcken, Desmond Yip. Samples are made available to researchers on a non-exclusive basis. The ACP study wishes to thank the participants in the Thai Breast Cancer study. Special Thanks also go to the Thai Ministry of Public Health (MOPH), doctors and nurses who helped with the data collection process. Finally, the study would like to thank Dr Prat Boonyawongviroj, the former Permanent Secretary of MOPH and Dr Pornthep Siriwanarungsan, the Department Director-General of Disease Control who have supported the study throughout. BBCS thanks Eileen Williams, Elaine Ryder-Mills, Kara Sargus. BCEES thanks Allyson Thomson, Christobel Saunders, Terry Slevin, BreastScreen Western Australia, Elizabeth Wylie, Rachel Lloyd. The BCINIS study would not have been possible without the contributions of Dr. K. Landsman, Dr. N. Gronich, Dr. A. Flugelman, Dr. W. Saliba, Dr. E. Liani, Dr. I. Cohen, Dr. S. Kalet, Dr. V. Friedman, Dr. O. Barnet of the NICCC in Haifa, and all the contributing family medicine, surgery, pathology and oncology teams in all medical institutes in Northern Israel. The BREOGAN study would not have been possible without the contributions of the following: Jose Esteban Castelao, Angel Carracedo, Victor Munoz Garzon, Alejandro Novo Dominguez, Sara Miranda Ponte, Carmen Redondo Marey, Maite Pena Fernandez, Manuel Enguix Castelo, Maria Torres, Manuel Calaza (BREOGAN), Jose Antunez, Maximo Fraga and the staff of the Department of Pathology and Biobank of the University Hospital Complex of Santiago-CHUS, Instituto de Investigacion Sanitaria de Santiago, IDIS, Xerencia de Xestion Integrada de Santiago-SERGAS; Joaquin Gonzalez-Carrero and the staff of the Department of Pathology and Biobank of University Hospital Complex of Vigo, Instituto de Investigacion Biomedica Galicia Sur, SERGAS, Vigo, Spain. BSUCH thanks Peter Bugert, Medical Faculty Mannheim. The CAMA study would like to recognize CONACyT for the financial support provided for this work and all physicians responsible for the project in the different participating hospitals: Dr. German Castelazo (IMSS, Ciudad de Mexico, DF), Dr. Sinhue Barroso Bravo (IMSS, Ciudad de Mexico, DF), Dr. Fernando Mainero Ratchelous (IMSS, Ciudad de Mexico, DF), Dr. Joaquin Zarco Mendez (ISSSTE, Ciudad de Mexico, DF), Dr. Edelmiro Perez Rodriguez (Hospital Universitario, Monterrey, Nuevo Leon), Dr. Jesus Pablo Esparza Cano (IMSS, Monterrey, Nuevo Leon), Dr. Heriberto Fabela (IMSS, Monterrey, Nuevo Leon), Dr. Fausto Hernandez Morales (ISSSTE, Veracruz, Veracruz), Dr. Pedro Coronel Brizio (CECAN SS, Xalapa, Veracruz) and Dr. Vicente A. Saldana Quiroz (IMSS, Veracruz, Veracruz). CBCS thanks study participants, co-investigators, collaborators and staff of the Canadian Breast Cancer Study, and project coordinators Agnes Lai and Celine Morissette. CCGP thanks Styliani Apostolaki, Anna Margiolaki, Georgios Nintos, Maria Perraki, Georgia Saloustrou, Georgia Sevastaki, Konstantinos Pompodakis. CGPS thanks staff and participants of the Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgard Hansen. The Danish Cancer Biobank is acknowledged for providing infrastructure for the collection of blood samples for the cases. COLBCCC thanks all patients, the physicians Justo G. Olaya, Mauricio Tawil, Lilian Torregrosa, Elias Quintero, Sebastian Quintero, Claudia Ramirez, Jose J. Caicedo, and Jose F. Robledo, the researchers Ignacio Briceno, Fabian Gil, Angela Umana, Angela Beltran and Viviana Ariza, and the technician Michael Gilbert for their contributions and commitment to this study. Investigators from the CPSII cohort thank the participants and Study Management Group for their invaluable contributions to this research. They also acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, as well as cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. CTS Investigators include Leslie Bernstein, S.L.N., James Lacey, Sophia Wang, and Huiyan Ma at the Beckman Research Institute of City of Hope, Jessica Clague DeHart at the School of Community and Global Health Claremont Graduate University, Dennis Deapen, Rich Pinder, and Eunjung Lee at the University of Southern California, Pam Horn-Ross, Christina Clarke Dur and David Nelson at the Cancer Prevention Institute of California, Peggy Reynolds, at the Department of Epidemiology and Biostatistics, University of California San Francisco, H.A-C, A.Z., and Hannah Park at the University of California Irvine, and Fred Schumacher at Case Western University. DIETCOMPLYF thanks the patients, nurses and clinical staff involved in the study. We thank the participants and the investigators of EPIC (European Prospective Investigation into Cancer and Nutrition). ESTHER thanks Hartwig Ziegler, Sonja Wolf, Volker Hermann, Christa Stegmaier, Katja Butterbach. FHRISK thanks NIHR for funding. GC-HBOC thanks Stefanie Engert, Heide Hellebrand, Sandra Krober and LIFE -Leipzig Research Centre for Civilization Diseases (Markus Loeffler, Joachim Thiery, Matthias Nuchter, Ronny Baber). The GENICA Network: Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, and University of Tubingen, Germany [H.B., W-Y.L.], German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) [H. B.], Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2180 -390900677, Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany [Yon-Dschun Ko, Christian Baisch], Institute of Pathology, University of Bonn, Germany [Hans-Peter Fischer], Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany [UH], Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany [Thomas Bruning, Beate Pesch, Sylvia Rabstein, Anne Lotz]; and Institute of Occupational Medicine and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Germany [Volker Harth]. HABCS thanks Michael Bremer and Johann H. Karstens. HEBCS thanks Sofia Khan, Johanna Kiiski, Kristiina Aittomaki, Rainer Fagerholm, Kirsimari Aaltonen, Karl von Smitten, Irja Erkkila. HKBCS thanks Hong Kong Sanatorium and Hospital, Dr Ellen Li Charitable Foundation, The Kerry Group Kuok Foundation, National Institute of Health 1R03CA130065 and the North California Cancer Center for support. HMBCS thanks Johann H. Karstens. HUBCS thanks Shamil Gantsev. KARMA thanks the Swedish Medical Research Counsel. KBCP thanks Eija Myohanen, Helena Kemilainen. We thank all investigators of the KOHBRA (Korean Hereditary Breast Cancer) Study. LMBC thanks Gilian Peuteman, Thomas Van Brussel, EvyVanderheyden and Kathleen Corthouts. MABCS thanks Milena Jakimovska (RCGEB "Georgi D. Efremov), Emilija Lazarova (University Clinic of Radiotherapy and Oncology), Katerina Kubelka-Sabit, Mitko Karadjozov (Adzibadem-Sistina Hospital), Andrej Arsovski and Liljana Stojanovska (Re-Medika Hospital) for their contributions and commitment to this study. MARIE thanks Petra Seibold, Dieter Flesch-Janys, Judith Heinz, Nadia Obi, Alina Vrieling, Sabine Behrens, Ursula Eilber, Muhabbet Celik, Til Olchers and Stefan Nickels. MBCSG (Milan Breast Cancer Study Group): Bernard Peissel, Jacopo Azzollini, Dario Zimbalatti, Daniela Zaffaroni, Bernardo Bonanni, Mariarosaria Calvello, Davide Bondavalli, Aliana Guerrieri Gonzaga, Monica Marabelli, Irene Feroce, and the personnel of the Cogentech Cancer Genetic Test Laboratory. We thank the coordinators, the research staff and especially the MMHS participants for their continued collaboration on research studies in breast cancer. MSKCC thanks Marina Corines, Lauren Jacobs. MTLGEBCS would like to thank Martine Tranchant (CHU de QuebecUniversite Laval Research Center), Marie-France Valois, Annie Turgeon and Lea Heguy (McGill University Health Center, Royal Victoria Hospital; McGill University) for DNA extraction, sample management and skillful technical assistance. J. S. is Chair holder of the Canada Research Chair in Oncogenetics. MYBRCA thanks study participants and research staff (particularly Patsy Ng, Nurhidayu Hassan, Yoon Sook-Yee, Daphne Lee, Lee Sheau Yee, Phuah Sze Yee and Norhashimah Hassan) for their contributions and commitment to this study. The NBCS Collaborators would like to thank the Oslo Breast Cancer Research Consortium, OSBREAC (breastcancerresearch. no/osbreac/), for providing samples and phenotype data. NBHS and SBCGS thank study participants and research staff for their contributions and commitment to the studies. We would like to thank the participants and staff of the Nurses' Health Study and Nurses' Health Study II 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. OFBCR thanks Teresa Selander, Nayana Weerasooriya. ORIGO thanks E. Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical information. The ORIGO survival data were retrieved from the Leiden hospital-based cancer registry system (ONCDOC) with the help of Dr. J. Molenaar. PBCS thanks Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner. The ethical approval for the POSH study is MREC/00/6/69, UKCRN ID: 1137. We thank staff in the Experimental Cancer Medicine Centre (ECMC) supported Faculty of Medicine Tissue Bank and the Faculty of Medicine DNA Banking resource. PREFACE thanks Sonja Oeser and Silke Landrith. PROCAS thanks NIHR for funding. RBCS thanks Petra Bos, Jannet Blom, Ellen Crepin, Elisabeth Huijskens, Anja Kromwijk-Nieuwlaat, Annette Heemskerk, the Erasmus MC Family Cancer Clinic. We thank the SEARCH and EPIC teams. SGBCC thanks the participants and research coordinator Ms Tan Siew Li. SKKDKFZS thanks all study participants, clinicians, family doctors, researchers and technicians for their contributions and commitment to this study. We thank the SUCCESS Study teams in Munich, Duessldorf, Erlangen and Ulm. SZBCS thanks Ewa Putresza. UCIBCS thanks Irene Masunaka. UKBGS thanks Breast Cancer Now and the Institute of Cancer Research for support and funding of the Breakthrough Generations Study, and the study participants, study staff, and the doctors, nurses and other health care providers and health information sources who have contributed to the study. We acknowledge NHS funding to the Royal Marsden/ICR NIHR Biomedical Research Centre. BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community's Seventh Framework Programme under grant agreement number 223175 (Grant Number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministere de l'Economie, Science et Innovation du Quebec through Genome Quebec and the PSR-SIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement No. 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 -the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS), BCFR-NY, BCFR-PA, BCFR-UTAH, the Northern California Breast Cancer Family Registry (NCBCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) was supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. C.L.C is a NHMRC Principal Research Fellow. The ACP study is funded by the Breast Cancer Research Trust, UK and KM and AL are supported by the NIHR Manchester Biomedical Research Centre and by the ICEP ("This work was also supported by CRUK [grant number C18281/A19169]"). The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (J.S.). The BREast Oncology GAlician Network (BREOGAN) is funded by Accion Estrategica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Accion Estrategica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Conselleria de Industria Programa Sectorial de Investigacion Aplicada, PEME I+ D e I + D Suma del Plan Gallego de Investigacion, Desarrollo e Innovacion Tecnologica de la Conselleria de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigacion Clinica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CAMA study was funded by Consejo Nacional de Ciencia y Tecnologia (CONACyT) (SALUD-2002-C01-7462). Sample collection and processing was funded in part by grants from the National Cancer Institute (NCI R01CA120120 and K24CA169004). CBCS is funded by the Canadian Cancer Society (grant #313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Securite Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. COLBCCC is supported by the German Cancer Research Center (DKFZ), Heidelberg, Germany. Diana Torres was in part supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. The American Cancer Society funds the creation, maintenance, and updating of the CPSII cohort. The CTS was supported by the California Breast Cancer Act of 1993, the California Breast Cancer Research Fund (contract 97-10500) and the National Institutes of Health (R01 CA77398, K05 CA136967, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. HAC receives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by the charity Against Breast Cancer (Registered Charity No. 1121258) and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Sante et de la Recherche Medicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Wurttemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). FHRISK is funded from NIHR grant PGfAR 0707-10031. DGE is supported by the all Manchester NIHR Biomedical Research Centre (IS-BRC-1215-20007). The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: R.K.S., Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GEPARSIXTO study was conducted by the German Breast Group GmbH. The GESBC was supported by the Deutsche Krebshilfe e.V. [70492] and the German Cancer Research Center (DKFZ). The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, by the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. The HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, and the Sigrid Juselius Foundation. The HERPACC was supported by MEXT Kakenhi (No. 170150181 and 26253041) from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan, by National Cancer Center Research and Development Fund, and "Practical Research for Innovative Cancer Control (15ck0106177h0001)" from Japan Agency for Medical Research and development, AMED, and Cancer Bio Bank Aichi. The HMBCS and HUBCS were funded by the German Research Foundation (Do761/10-1) and by the Rudolf Bartling Foundation. The HUBCS was further supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), and by the Russian Foundation for Basic Research and the Federal Agency for Scientific Organizations for support the Bioresource collections and RFBR grants 14-04-97088, 17-29-06014 and 17-44-020498. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Marit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. The KOHBRA study was partially supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 1020350; 1420190). LMBC is supported by the 'Stichting tegen Kanker'. DL is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov" and supported by the German Academic Exchange Program, DAAD. The MARIE study was supported by the Deutsche Krebshilfe e. V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects "5 x 1000"). The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The MEC was supported by NIH grants CA63464, CA54281, CA098758, CA132839 and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286 and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the " CIHR Team in Familial Risks of Breast Cancer" program -grant #CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade - grant #PSR-SIIRI-701. MYBRCA is funded by research grants from the Malaysian Ministry of Higher Education (UM. C/HlR/MOHE/06) and Cancer Research Malaysia. MYMAMMO is supported by research grants from Yayasan Sime Darby LPGA Tournament and Malaysian Ministry of Higher Education (RP046B-15HTM). The NBCS has received funding from the K.G. Jebsen Centre for Breast Cancer Research; the Research Council of Norway grant 193387/V50 (to A-L Borresen-Dale and V.N.K.) and grant 193387/H10 (to A-L Borresen-Dale and V. N. K.), South Eastern Norway Health Authority (grant 39346 to A-L Borresen-Dale) and the Norwegian Cancer Society (to A-L Borresen-Dale and V. N. K.). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Carolina Breast Cancer Study (NCBCS) was funded by Komen Foundation, the National Cancer Institute (National Cancer Institute CA058223, U54 CA156733, U01 CA179715), and the North Carolina University Cancer Research Fund. The NGOBCS was supported by the National Cancer Center Research and Development Fund. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956 and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, UMCA182910, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The scientific development and funding of this project were, in part, supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network U19 CA148065. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. SEBCS was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347). SGBCC is funded by the NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant and the NMRC Clinician Scientist Award. Additional controls were recruited by the Singapore Consortium of Cohort StudiesMulti-ethnic cohort (SCCS-MEC), which was funded by the Biomedical Research Council, grant number: 05/1/21/19/425. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. The TNBCC was supported by: a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation and the Ohio State University Comprehensive Cancer Center. The TWBCS is supported by the Taiwan Biobank project of the Institute of Biomedical Sciences, Academia Sinica, Taiwan. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WAABCS study was supported by grants from the National Cancer Institute of the National Institutes of Health (R01 CA89085 and P50 CA125183 and the D43 TW009112 grant), Susan G. Komen (SAC110026), the Dr. Ralph and Marian Falk Medical Research Trust, and the Avon Foundation for Women.
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In this dissertation, we are mainly interested in the interactions between poverty and one of its greatest dimensions1, namely health. More specifically, we will focus on their inequalities: does poverty inequality have more effect on poverty than health level? Does health inequality matter to poverty? Poverty and health are two related concepts that both express human deprivation. Health is said to be one of the most important dimensions of poverty and vice-versa. That is, poverty implies poor health because of a low investment in health, a bad environment and sanitation and other living conditions due to poverty, a poor nutrition (thus a greater risk of illness), a limited access to, and use of, health care, a lower health education and investment in health, etc2. Conversely, poor health leads inevitably to poverty due to high opportunity costs occasioned by ill-health such as unemployment or limited employability (thus a loss of income and revenues), a lower productivity (due to loss of strength, skills and ability), a loss of motivation and energy (which lengthen the duration of job search), high health care expenditures (or catastrophic expenditures), etc3. But what are the degree of correlation and the direction of the causality between these two phenomena? Which causes which? This is a classic problem of simultaneity that has become a great challenge for economists. Worst, each of these phenomena (health and poverty) has many dimensions4. How to reconcile two multidimensional and simultaneous events? 1 Aside the income-related material deprivation. 2 Tenants of the ?Absolute Income? hypothesis for instance show that absolute income level of individual has positive impact on their health status (Preston, 1975; Deaton, 2003). Conversely, lack of income (and the poverty state it implies) leads unambiguously to poor health. For other authors, it is not the absolute level per se, but the relative level (i.e. comparably to others in the society) that impacts most health outcomes. This is the ?Relative Income? hypothesis (see van Doorslaer and Wagstaff, 2000, for a summary). 3 See Sen (1999) and more recently Marmot (2001) for more information. 4 Poverty could be seen as monetary poverty, human poverty, social poverty, etc. Identically, one talks of mental health, physical health, ?positive? and ?negative? health, etc. So a one-on-one causality could not possibly exits between the two, or will be hard to establish. We?ve chosen the first way of causality: that is, poverty (and inequality) causes poor health. As justification, we consider a life-cycle theory approach (Becker, 1962). An individual is born with a given stock of health. This stock is supposed to be adequate enough. During his life, this stock is submitted to depreciation due to health shocks and aging (Becker?s theory, 1962). We could think that the poorer you are, the more difficult is your capacity to invest in your health5. Empirically, many surveys (too numerous to be enumerated here) show that poor people6 do have worse health status (that is, high mortality and morbidity rates, poor access to health services, etc.). It has been established that poor children are less healthy worldwide, independently of the region or country considered. It is generally agreed that the best way to improve the health of the poor is through pro-poor growth policies and redistribution. Empirically, one of the major achievements of these last two decades in developing countries is the improvement in health status of populations (notably the drop in mortality rates and higher life expectations) following periods of (sustained) economic growth. However, is this relation always true? In some countries as we will see later in this thesis, while observing an improvement in the population?s welfare, the converse is observed in its health status, or vice versa. If health and poverty are so closely related, they should theoretically move in the same direction. But the fact that in some countries we observe opposite trends suggests that some dimensions of health and poverty are not or may not be indeed so closely related, as postulated, and that they may depend of other factors. 1. The Purpose of the Study. 5 Another justification is that many authors have studied the problem the other way. Schultz and Tansel (1992, 1997) for instance showed that ill-health causes a loss of revenues in rural Cote d?Ivoire. Audibert, Mathonnat et al. (2003) also showed that malaria caused a loss of earnings of rural cotton producers in Cote d?Ivoire. 6 Usually defined from some income or expenditure-related metric or some assets-based metric. The ultimate goal of our dissertation in its essence is to measure inequality in health7 in developing countries using mainly Demographic and Health Surveys (DHS, henceforth)8. It deals with interactions between poverty and one of its greatest dimensions, putting aside the income-related material deprivation, namely health. It therefore measures inequality in health status and access to health and discusses which policies should be implemented to correct these inequalities. That is, it aims to see how much rich people are better off and benefit from health interventions, as compared to the poor, and how to reduce such an inequality. The present dissertation contains four papers that are related to these questions. Our main hypothesis (that will be tested) is that poverty impacts health through inequality effects9. Graphically, we can lay these simple relationships as: The dashed line in the figure above suggests that income inequality could impact health directly. But we consider that this direct effect is rather small or negligible, as compared to the indirect effect through inequality in health. Therefore, inequality in health is central to our discussion. To measure inequalities in health, we face three challenges: 7 And corollary health sanitation (access to safe water, toilet and electricity). Though electricity is more a measure of economic development that health measure per se, we add it here as a control for sanitation and nutrition: for example women could read more carefully the drugs? notices, or warm more quickly foods; more generally, electricity often improves the mental and material wellbeing of households. It also conditions health facility?s performance. 8 And potentially other surveys. In this case, we mention explicitly the survey(s). 9 The other important factor that could impact health is the performance of the health system. This is discussed in the Chapter 3. Health Assets Inequality Health Inequality Poverty (Assets Index) - measuring welfare (income metric) and subsequently inequality in welfare, - measuring health, - and measuring inequality in health. The measurements can be conducted using two approaches (Sahn, 2003): - Directly by ranking the households or individuals vis-à-vis their performance in the health indicator; we thus have a direct measure of inequality in health. This is suitable when the health indicator is continuous (such as weight, height or body mass index). According to Prof. David E. Sahn, that approach ?which has been referred to as the univariate approach to measuring pure health inequality, involves making comparisons of cardinal or scalar indicators of health inequality and distributions of health, regardless of whether health is correlated with welfare measured along other dimensions?. - Indirectly by finding a scaling measure such as consumption or income or another indicator (assets index for instance)10 that would help ranking the households or individuals (from the poorest to the richest), and see what are their performance in the health variable of interest. We are therefore measuring an indirect health inequality. The indirect method is mostly suitable when the health indicator is dichotomous (for example whether the individual has got diarrhoea last 2 weeks, or ?have the child been vaccinated?, or ?place of delivery?) or is a rate (such as child mortality). Again, quoting Prof. Sahn, ?making comparisons of health across populations with different social and economic characteristics is often referred to in the literature as following the so-called `gradient? or `socioeconomic? approach to health inequality. Much of the motivation for this work on the gradient approach to health inequality arises out of fundamental concerns over social and economic justice. The roots of the gradient will often arise from various types of discrimination, prejudice, and other legal, social, and economic norms that may contribute to stratification and fragmentation, and subsequently inequality in access to material resources and various correlated welfare outcomes?. While the first method would appear quickly limited for dummy or limited categorical health variables because of the small variability in the population, the second approach could also be 10 Or more generally any other socioeconomic gradient such as education, gender or location. impossible when no information is available to scale the units of observation in terms of welfare. We?ll be mostly focusing on the second approach, as did many health economists, and also due to the nature of the DHS datasets in hand and the indicators that we are investigating. 2. Strategy, Methods and Structure. Measuring wealth-related inequality in health implies in the first stage defining and characterizing the poor. Who are indeed the poor? Traditionally, monetary measures (income or consumption) have been used to distinguish households or people into ?rich? and ?poor? classes. Indeed, it is agreed that the ?incomemetric? approach is one of the best ways to measure welfare11. However, it sometimes, if not often, happens that we lack this essential information in household survey datasets. Especially in our case, the DHS datasets do not have income nor consumption information. Then, how to characterize the poor in this situation? For a long time, economists have eluded the question. But soon, it became evident that an alternative measure is needed to strengthen the ?poverty debate?. In the first part of our dissertation, we start by providing a theoretical framework to find a proxy for wellbeing, in the case where consumption or income-related data are missing, namely by discussing the use of assets as such a proxy. The first part of this thesis is relatively long, as compared to the second. However, this is justified, due to its purpose. The goal of the first part of the dissertation is to participate to the research agenda on poverty. It attempts to measure it in a ?non traditional?12 way. 11 There is a consensus in the economic literature that income is more suitable to measure wealth or welfare in developed countries while consumption is more adequate for developing ones due to various reasons such as irregularity of incomes for informal sector, seasonality, prices, recall periods, trustworthy, etc. (see Deaton 1998 for detail). 12 i.e. a non monetary way. The main rationale for this first part therefore is thus to find a new, non monetary measure to characterize in best, life conditions, welfare and then the poor. This measure is referred to as the ?assets index?. Indeed, as the majority of developing countries are engaged more and more in fighting poverty, inequality and deprivation, more and more information on the state of poverty13 is needed. If in almost all these countries, many household surveys have been implemented to collect information on socioeconomic indicators, the major indicator that is needed to analyze poverty (namely income or consumption data) is unfortunately not often collected due to various reasons (time, cost, periodicity, etc.). Even, if they were collected, the quality of the data is often poor. Therefore, economists tend to rely more on other indicators to compensate for the absence of monetary measures. One of the indicators often used are the assets owned by households. The question arose then how to use these assets to characterize the poor in this context? How to weight each of them? In a first attempt, many economists built a simple linear index by assigning arbitrary weights to the assets14. In a seminal paper, Filmer and Pritchett (2001) propose to construct the so-called ?assets index? which could be used as a proxy for consumption or income. It is commonly agreed that their methodology follows a ?scientific? approach, thus is more credible. In their case, they use a Principal Component Analysis (PCA, henceforth) to build their assets index. Since, many other economists have followed in their footsteps which we label in our dissertation, the ?material? poverty approach (as opposed to the monetary one) since it is based on materials (goods and assets) owned by the households or individuals. Because of the importance of the subject (poverty) and because the method is pretty new and original, this first part of our thesis is as said quite long as compared to the second one and has two papers which focus mainly on poverty and inequality issues and their connections with economic growth. In this part, we start by presenting a methodology of measuring non monetary (material) poverty, when a consumption or income data is not available. We show how one can obtain robust results using assets or wealth variables. Once the method is clearly 13 And more generally welfare. 14 For example a television is given a weight of 100, a radio 50, and so on. But this is clearly not a `scientific? way to proceed, as there is no rational ground in giving such weights. tested and validated, it is then confronted to real data. We show that the index shares basically the same properties with monetary metrics and roughly scales households in the same way as does the consumption or income variables. We discuss the advantages and also the limitations of using the assets index. The important thing to bear in mind is that, once it is obtained, it could be used to rank the observational units by wealth or welfare level. - The first chapter defines in a first section poverty and how it is usually measured (by the income metric approach). We discuss the limitations of the use of income/expenditure and what could be alternative measures. We then propose in section 2 the assets metric as a proxy for poverty measurement and test the material poverty approach on international datasets collected by the DHS program. We explore the material poverty and inequality nexus in the world and how Sub-Saharan Africa (SSA)15 compares with other regions. We show, using that index and DHS data, that poverty, at least from an assets point of view, was also decreasing in SSA as well as in other regions of the world. This result contrasts with other findings such as Ravallion and Chen (2001) or Sala-i-Martin (2002) that show that, while other regions of the world are experiencing a decline in their (monetary) poverty rates, SSA is lagging behind, with rates starting to rise over the last decade. Therefore, two different measures of welfare could yield opposite results and messages in terms of policies to implement to combat poverty. Moreover, the method we use not only allows observing changes over time for each country, but also provides a natural ranking among countries (from the poorest to the richest). In this chapter, aside the measure of welfare and poverty, we also discuss in a final section the impact of demographic transition on economic growth and therefore on poverty. Indeed, demographic transition is a new phenomenon that is occurring in developing countries, especially African ones. Its negligence could lead to underestimating poverty measures (both material and monetary) by underestimating real economic growth rates. We show that changes in the composition and the size of households put an extra-pressure on the development process. While traditional authors have not considered the impact of these 15 SSA countries are Benin, Burkina Faso, Central African Republic, Cameroon, Chad, Comoros, Republic of Congo, Côte d?Ivoire, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Togo, Uganda, Zambia and Zimbabwe. The ?rest of the world? is represented by Armenia, Bangladesh, Bolivia, Brazil, Colombia, Dominican Republic, Egypt, Guatemala, Haiti, Honduras, India, Indonesia, Kazakhstan, Kyrgyz Republic, Moldova, Morocco, Nepal, Nicaragua, Pakistan, Paraguay, Peru, Philippines, Turkey, Uzbekistan, Vietnam and Yemen. changes, we show that taking this into account implies higher economic growth rates than those actually observed or forecasted. - Once the assets index approach is established and tested on international data, the question arose how it performs as compared to the monetary metric. Indeed, if monetary measures remain the reference, then our assets index should share some common properties with them. The second chapter assesses the trends in material poverty in Ghana from the assets perspective using the Core Welfare Indicators Questionnaires Surveys (CWIQ). It then compared these trends with the monetary poverty over roughly the same period. We show that the assets index could be used and yields the same consistent results as using other welfare variable (such as income, consumption or expenditure). Therefore, using two consecutive CWIQ surveys, we find that material poverty in Ghana has decreased roughly by the same magnitude as monetary one, as found in other studies by other authors such as Coulombe and McKay (2007) using Ghanaian GLSS16 consumption data. Thus, this chapter could thus be viewed as providing the proof that the material and the monetary approaches could be equivalent. The second part of our dissertation seeks how to define and measure health and inequality in health. While the definition of health is not obvious, we propose to measure it with child mortality rates. Our main rationale in doing so is that low child mortality generates, ceteris paribus, higher life expectancy17, thus is an adequate measure of a population?s health. This may not be true in areas devastated by wars, famines, and HIV and other pandemics where child mortality could be high (in this case, the best measure should be life expectancy by age groups). Also, the reader should bear in mind that in fact, child mortality could be itself is a good indicator for measuring the (success of the) economic development level of a society as a whole (Sen, 1995), mainly because in developing countries, child mortality is highly correlated to factors linked to the level of development such as access to safe water, sanitation, vaccination coverage, access to health care, etc. - In the third chapter, we focus on measuring overall population?s health. For this, we estimate child mortality in SSA and compare it to the rest of the world. We explore the 16 Ghana Living Standard Surveys. 17 By construction, life expectancy at birth is highly correlated and sensitive to child mortality (it is based on child mortality rates for various cohorts). Lower child mortality rates lead to higher life expectancy and vice versa. determinants of child mortality using mainly a Weibull model and DHS data with socioeconomic variables18 as one of our major covariates. The use of the assets index information is to see how these quintiles behave in a multivariate regression framework of child mortality (i.e. how they affect child mortality). We find, among others, that mother?s education and access to health care and sanitation are one of the strongest predictors for child survival. Controlling for education and other factors, family?s wealth and the area of residency do not really matter for child survival in SSA, contrasting with results found elsewhere. - The fourth and last chapter answers the ultimate goal of this dissertation, that is, the scope of health inequalities in the developing world, particularly in SSA. It uses the factor analysis (FA) method of Chapter 1 to rank household according to their economic gradient status19 and then studies inequalities in various health indicators in relation with these groups. The intention is to analyze inequality rates between rich and poor for various health variables. In this chapter, we concentrate solely on inequality issues in health and health-related infrastructures and services. Mainly, we analyze inequality in access to sanitation infrastructures (water and electricity20) and various health status and access to health indicators (such as child death, child anthropometry, medically assisted delivery and vaccination coverage) using a Gini and Marginal Gini Income Elasticity approach (GIE and MGIE, henceforth) on one hand, and the Concentration Index (CI) approach on the other. Results show that, while almost all countries have made great efforts in improving coverage in, and access to, these indicators, almost all the gains have been captured by the better-offs of the society, especially in SSA. We extend the analysis to compare GIE estimates to those of CI and find consistent results yielding quite similar messages. 18 Quintiles groups derived from an assets index. 19 By grouping usually households in 5 quintiles from poorer to richer ones. 20 On the rationale of using electricity, see footnote 7 above. 3. Results and Policy Implications. As said above, the major goal in conducting this thesis research is to analyze inequality in health status, health care and health-related services using DHS data. To reach our objective, we follow two intermediate steps: - For assets poverty, results show that assets poverty and inequality are decreasing in every region of the world, including Sub-Saharan Africa. This tends to support our hypothesis that, contrary to common beliefs, African households use assets and building ownerships as saving tools and buffer to economic shocks. The first paper also shows however that the demographic transition actually occurring in developing countries could impede on economic growth and trigger a bullet on policies aiming at combating poverty. - Our third paper shows that child mortality is decreasing in all parts the world. However, the 1990s and early 2000s have been a lost decade for the African continent where many countries have witnessed an increase in rates that is mostly attributable among other factors to the economic and financial turmoils of the 1990s and early 2000s and the HIV epidemic. Our hypothesis is that these phenomena have destabilized the organization of the health care system, cut its funding and hampered its performance. High levels of health inequality can also be part of the puzzle. Coming back to the particular case of HIV/AIDS, the reader should observe that it affects more and more the less poor so that it can also lead to a decline in assets inequality (richer people are dying) along with an increase in child mortality and thus explain in great part our paradox. This setback (the rise in mortality over recent periods despite poverty reduction) will make impossible for these countries to reach the millennium development goals, at least for child mortality. The conclusion to this is that African population?s health has been stagnant over the period 1990-2005. Regression analysis reveals no strong correlation between our measure of welfare (assets index) and child mortality. More important are mothers? education and access to health care and sanitation services. - Finally, our inequality estimates show that they are quite high for all indicators considered. For ill-health indicators (child malnutrition and death), rates are excessively concentrated in poor and rural groups. Concerning access to health care services, rich and urban groups tend to be more favoured than poor and urban ones. But the high level of inequality tends to be reducing at the margin over time, as the poor have increasing access. Finally for access to sanitation services, results show that while the majority of countries have made substantial efforts to increase coverage on the first two periods, the rich and urban classes have benefited more and inequality (which is at high levels) tends to rise at the margin over time, especially for the poor. More preoccupying is the fact that rates are falling between 1995-2000 and 2000-2005, probably because of the privatization of these services and the new costs they impose on households. Overall, inequality in all variables considered is more pronounced in SSA than the rest of the world (expect for death and malnutrition). The sub-continent is still disadvantaged in terms of access to services or ill-health. Where to go from here? In the African sub-continent, we have the following picture: a decreasing (material) poverty and inequality but coupled with a stagnant child mortality situation, a stagnant or increasing malnutrition. This is mostly due to high levels of, and an increasing inequality at the margin in access to sanitation and electricity services coupled with a decreasing access to these services. Thus, despite the fact that we observe a decreasing inequality at the margin in access to health care (even though the average level of inequality is still high) the missing link in health-related services coupled with an overall high inequality in these two types of services hugely impact child health and survival. Therefore, as access to health care services and health-related sanitation services is essential to child survival, our findings call for vigorous policies to promote access of the poor groups and rural areas to these services. African Governments should continue to favour access of the poor to health care and reverse the inequality trends in access to water, sanitation and electricity. This is vital for the health of the population and for the development of Africa. Funding can come from various sources: the Government Budget, International Assistance but also from households themselves (since the first part of our thesis has demonstrated that they are getting richer (and various surveys show that they are willing to pay for quality health care), an adequate fees policy could benefit to the health care system). Measures should be put in place to strengthen the performance of the health system and to mitigate the negative effects of macroeconomic imbalances, economic crises and HIV/AIDS. Only on these conditions the Sub-Continent could hope to eradicate poverty and promote health for all. 4. Contribution of this Thesis. This thesis seeks to analyze empirically the inequality in health and access to health in SSA and how this region compared to the rest of the world. To do so, it develops a new method to characterize poor households and to analyze assets-based poverty, when the monetary measure is unavailable. Such a method is indeed necessary as almost all developing countries have collected many surveys that lack the consumption or income information. Once a poverty measure and a correct measure of health have been found, and their core determinants clearly established, we then proceed to the health inequality analysis, along with its determinants, using two methodologies: the traditional CI and the more recent GIE approaches. These approaches have been the mostly used to explore the inequality in health and access to health these last years. Though already studied in the literature, and sometimes applied on DHS or some groups of DHS datasets, our dissertation differs in its purpose and scope and its large scale. No paper to our knowledge used the totally to-date freely available DHS datasets to study poverty and inequality topics and provide basic statistics. Our main contribution is to shed a new light on the welfare-inequality-health nexus in Africa, how it evolves over time and how it compares to other regions around the world, using all available information. It also put numbers on various important socioeconomic indicators such as poverty, inequality, child health and mortality, access to health-related infrastructures, etc., for developing countries, especially African ones. As we sometimes lack these important information, this thesis proves finally to be a very useful exercise. ; Cette thèse part d'un postulat simple : « l'amélioration du niveau de vie s'accompagne de l'amélioration de l'état de santé générale d'une population » et teste sa validité dans le contexte de l'Afrique au Sud du Sahara (ASS). Si cette hypothèse se vérifie en général dans le contexte de l'ASS en ce qui concerne le niveau (plus le pays est riche, plus sa population est en bonne santé), il l'est moins en ce qui concerne les dynamiques, du moins à court et moyen terme. Notamment, les pays qui connaissent une amélioration tendancielle de bien-être matériel ne connaissent pas forcément une amélioration de la santé de leurs populations. Ceci constitue un paradoxe qui viendrait invalider notre postulat. En écartant tout effet de retard ou de rattrapage qui pourrait l'expliquer car nous travaillons sur une période de 15 ans réparties en 3 sous-périodes (1990-1995, 1995-2000 et 2000-2005), nous expliquons ce paradoxe, toutes choses égales par ailleurs, par deux canaux principaux qui peuvent interagir : - la performance du système de santé et - l'inégalité en santé. Si le premier est plus évident mais aussi plus difficile à prouver empiriquement du fait du manque de données sur des séries longues, ou du fait que ces données sont trop agrégées et éparses, le second canal est testable avec des bases de données adéquates qui, elles, sont disponibles au niveau microéconomique (ménages). Les bases de données que nous avons privilégiées sont les Enquêtes Démographiques et de Santé (EDS) du fait de leur comparabilité dans l'espace et le temps (mêmes noms de variables standardisées, même méthodologie d'enquête, mêmes modules, etc.). Ces atouts sont d'autant plus importants que les comparaisons de pauvreté et de bien-être basées sur les enquêtes de revenus ou de consommation butent sur de sérieux problèmes à savoir la comparabilité de ces enquêtes (méthodologies différentes, périodes de rappel différents, prix souvent non collectés de la même manière, etc.). Pour montrer ces effets de l'inégalité de santé sur les niveaux et les tendances de la santé des populations et la pauvreté et le bien-être, nous avons axé notre recherche autour de 3 axes principaux : 1- Comment mesurer le niveau de richesse et donc le bien-être des ménages en l'absence d'information sur la consommation et le revenu ? Les chapitres 1 et 2 de notre thèse se penchent sur cette question. Nous avons privilégié, à l'instar de plus en plus d'économistes, l'utilisation des biens des ménages et les méthodes de l'analyse factorielle et d'analyse en composantes principales pour construire un indice de richesse. Cet indice de richesse est pris comme un substitut du revenu ou de la consommation et sert donc de proxy pour la mesure du bien-être. Bien qu'il comporte quelques lacunes (notamment le fait qu'il ne concerne que les biens matériels et durables du ménage alors que la consommation ou le revenu sont des concepts plus globaux de bien-être, il ne prend pas en compte les préférences des ménages, il ne comporte aucune notion de valeur car le prix n'est pas pris en compte, de telle façon qu'une petite télévision en noir blanc vieille de vingt ans est mise au même niveau qu'un grand écran plasma flambant neuf, etc.), il n'en demeure pas moins que d'un côté, avec les EDS, il n'y a pas moyen de faire autrement en l'état actuel des choses, mais aussi et surtout parce que ces données permettent d'éviter les problèmes évoqués plus haut, notamment celui de la comparabilité des données pour faire de la comparaison spatiale et inter-temporelle des données en matière de pauvreté. Dans le premier chapitre, en nous basant sur cet indice et une ligne de pauvreté définie a priori à 60% pour la première observation dans notre échantillon (Benin, 1996), et en utilisant les données EDS et une analyse en composantes principales (ACP), nous avons pu mesurer la tendance de la pauvreté dite « matérielle » (en opposition à la pauvreté monétaire, basée sur la métrique monétaire). Cette méthode qui est privilégiée par des auteurs comme Sahn et Stifel est d'autant plus intéressante qu'elle donne non seulement les tendances de la pauvreté dans chaque pays, mais elle permet aussi une classification naturelle de ces pays par ordre de grandeur de pauvreté. Cependant, dans la mesure où les biens des ménages et la dépenses de consommation sont disponibles, l'analyste devrait estimer les deux types de pauvreté (matérielle via l'indice de richesse et monétaire via le revenu ou la consommation) car les études montrent souvent que les biens matériels et la consommation ou le revenu ne sont pas très bien corrélés, et donc le choix de l'indicateur de bien-être est crucial en termes de politiques économique et de santé. En effet, si l'indicateur sous-estime le vrai niveau de pauvreté ou d'inégalité (ou les surestime), les dépenses publiques qui en résultent peuvent être plus ou moins surévaluées, de même que les réponses apportées se révéler inadéquates. Donc dans la mesure du possible, il conviendrait de se pencher sur la question du choix de l'indicateur. Les résultats de notre méthodologie montrent que l'ASS reste la région la plus pauvre du monde en termes de possession d'actifs. La région orientale de l'ASS est la plus pauvre au monde (75%) suivie de l'Asie du Sud (64%), le Sud de l'ASS (61%), l'Afrique Centrale (57%), l'Afrique de l'Ouest (55%), l'Asie de l'Ouest (40%), l'Asie du Sud-Est (19%), l'Amérique Latine (18%), les Caraïbes (17%), l'Afrique du Nord (6%), l'Asie Centrale (2%) et l'Europe de l'Est (1%). Notre analyse nous montre que la pauvreté baisse dans l'ensemble des pays Africains au Sud du Sahara (sauf la Zambie), à l'instar des autres pays du monde dans l'échantillon. En effet, en considérant les trends, nous voyons que la moyenne de l'ASS passe de 63% de pauvreté matérielle entre 1990-1995 à 62% en 1995-2000 et 58% entre 2000 et 2005. La baisse est modeste et lente mais non négligeable et surtout, elle est en accélération sur les 2 dernières périodes. Mais elle demeure toutefois beaucoup plus marquée dans le reste du monde. Concomitamment à la baisse de la pauvreté, nous observons aussi une baisse de l'inégalité. Nous terminons ce chapitre par une réflexion sur l'effet de la transition démographique sur la croissance économique et la pauvreté en ASS et dans les autres pays en développement. En effet, la chute de la fertilité et de la mortalité couplées à un exode rural font que le nombre de famille se démultiplie du fait de la transition vers des tailles plus réduites. Ceci impose plus de contraintes (et donc peut avoir un impact négatif) sur la croissance économique et risque de sous-estimer le niveau réel de pauvreté. Il convient, une fois que la pauvreté matérielle et ses tendances ont été bien calculées avec les biens durables (et la transition économique prise si possible en compte), de tester la validité de cette méthode en la confrontant avec les résultats issus de l'analyse monétaire de la pauvreté. Les EDS ne comportant pas données d'information sur la consommation, nous nous sommes tournés vers une autre source de données. Dans le chapitre 2, nous avons testé la robustesse de notre méthode dans le cas particulier du Ghana, en utilisant les enquêtes du Questionnaire Unifié sur les Indicateurs de Base de Bienêtre (QUIBB), et en confrontant les résultats issus de la méthode ACP avec ceux issus de la méthode traditionnelle monétaire et trouvons grosso modo les mêmes résultats (10% de baisse avec la méthode monétaire traditionnelle et 7% avec notre méthode sur la période 1997- 2003). Ceci valide donc le fait que la méthode que nous proposons (à savoir, mesurer le bienêtre et la pauvreté par les biens durables des ménages) est tout aussi valide que la méthode plus traditionnelle utilisant des métriques monétaires. Une analyse fine dans le cas du Ghana montre que la baisse de la pauvreté est due à une croissance économique particulièrement pro-pauvre mais aussi à des dynamiques intra et intersectorielles (réallocation des gens des secteurs moins productifs vers ceux plus productifs) et aussi une forte migration des campagnes vers les villes. Nos simulations montrent que les migrants ruraux ont aussi bénéficié de cette croissance dans les villes où ils trouvent plus d'opportunités. 2- Une fois établie que la pauvreté est en recul en ASS, nous avons voulu mesurer la tendance de la santé de sa population (approximée par les taux de mortalité infantile et infanto-juvénile). Nous discutons dans le chapitre 3 de trois méthodes pour estimer et comparer les taux de mortalité des enfants : - la méthode des cohortes fictives (sur laquelle l'équipe de l'EDS se base pour estimer les taux « officiels » de mortalité), - la méthode non paramétrique (Kaplan et Meier) que privilégient un certain nombre d'économistes et - la méthode paramétrique (Weibull) de plus en plus utilisée pour sa souplesse et sa robustesse. Les deux premières méthodes ont tendance à sous-estimer le vrai niveau de mortalité et de ce fait nous avons privilégié le Weibull. De plus, avec cette dernière, nous pouvons évaluer l'effet de chaque variable spécifique (comme l'éducation ou l'accès à l'eau) sur le niveau de mortalité. Une étude des déterminants de cette mortalité montre qu'outre l'effet attendu de l'éducation des mères, l'accès aux infrastructures de santé (soins médicaux et surtout prénataux durant et lors de l'accouchement) et sanitaires (accès aux toilettes et dans une moindre mesure à l'eau potable) en sont les principaux facteurs. L'effet de richesse joue peu en ASS (mais pas dans le reste du monde), une fois que nous contrôlons pour le lieu de résidence (urbain) et le niveau d'éducation. Ce résultat nous surprend quelque peu, même s'il a été trouvé dans d'autres études. Ensuite, nous avons calculé la mortalité prédite des enfants. De toutes les régions du monde, l'ASS a le niveau de mortalité le plus élevé (par exemple en moyenne 107 décès pour la mortalité infantile contre 51 pour le reste du monde, soit plus du double). Ce résultat était toutefois attendu. Par contre nous avons été quelque peu surpris en ce qui concerne les tendances. Le constat est que sur les 15 ans, la mortalité des enfants a très peu ou pas du tout baissé dans le sous-continent africain (et est même en augmentation dans certains pays, alors qu'ils enregistrent une baisse de la pauvreté matérielle sur la même période). En moyenne, considérant les enfants de moins d'un an, les taux sont passés de 95%o à 89.5%o pour remonter à 91.5%o pour les 3 périodes 1990-1195, 1995-2000 et 2000-2005. Ainsi sur 15 ans, la mortalité infantile n'a baissé que de 3 points et demie en moyenne et surtout, elle remonte sur la période 1995-2005. Un examen des taux de malnutrition des enfants confirme ces tendances. On pourrait dire que ces résultats sont plutôt encourageants et normaux si on fait une analyse d'ensemble du sous-continent. En effet pour l'ensemble de l'ASS, cette légère baisse semble en conformité avec la baisse de 5 points des taux de pauvreté matérielle (63% en 1990-1995 à 58% en 2000-2005). Mais l'ordre de grandeur est faible en termes de magnitude, et surtout si compare au reste du monde où on observe une baisse de la mortalité beaucoup plus conséquente. Mais c'est l'arbre qui cache la forêt. Une analyse plus fine par pays montre en effet une situation plus contrastée. Notre postulat de départ nous dit que sur une période suffisamment longue, une amélioration de bien-être s'accompagne d'une amélioration de la santé. Or on constate que certains pays qui connaissent une baisse de la pauvreté matérielle connaissent également une recrudescence de la mortalité des enfants. Pour une même année, ce résultat peut être normal, traduisant un simple décalage pour que l'amélioration de bien-être se traduise par un meilleur état de santé de la population. Mais à moyen terme (période de 5 ans), nous observons la même absence d'effet. Nous sommes donc face à un paradoxe qu'il nous faut comprendre et tenter d'expliquer. Une des pistes pour comprendre ces résultats est d'analyser la performance des systèmes de santé en Afrique. Les facteurs qui expliquent notamment cette performance sont : des facteurs « classiques » comme la performance économique des périodes passées, les montants et l'allocation des dépenses de santé, l'organisation des systèmes de santé, la baisse de la fourniture de services de soins de santé (vaccination, assistance à la naissance, soins prénataux, soins curatifs, .), la malnutrition, le SIDA, les guerres, la fuite des cerveaux notamment du personnel médical, etc., à côté de facteurs plus « subtils » ou ténus car moins saisissables comme les crises financières des années 1990s qui ont plombé certaines des économies de la sous-région, la qualité des soins, la corruption et les dessous-de-table, l'instabilité de la croissance économique (même si elle est positive), etc. La seconde voie que nous examinons pour expliquer le manque de résultat en santé dans certains pays concerne l'inégalité en santé et ceci fait l'objet de notre dernier chapitre. 3- Expliquer l'absence de lien entre santé et pauvreté dans certains pays de l'ASS : l'effet de l'inégalité en santé. Dans le chapitre 4, nous émettons l'hypothèse que le fort niveau d'inégalité dans l'accès aux services de santé et d'assainissement couplé à la faible performance du système de santé (avec en toile de fond l'impact du Sida) peuvent servir à expliquer en partie notre paradoxe. Nous considérons deux types de services : - soins de santé (vaccination, assistance médicale à la naissance et traitement médical de la diarrhée) et - hygiène et assainissement (accès à l'eau potable et à l'électricité, accès aux toilettes propres). Le choix de ces services est motivé par le fait que le modèle Weibull dans le chapitre 3 nous montre que toutes choses égales par ailleurs, ils sont cruciaux pour la survie des enfants, en particulier en Afrique. Les niveaux d'accès montrent une baisse tendancielle des taux pour les services de santé (surtout pour la vaccination) et une légère augmentation de l'accès à l'électricité et dans une moindre mesure à l'eau potable. L'accès aux toilettes propres demeure un luxe réservé à une petite fraction de la population. Pour les calculs d'inégalité, nous considérons deux indicateurs: - l'indice de concentration (pour mesurer le niveau moyen d'inégalité) - et l'élasticité-revenu du Gini (inégalité « à la marge » quand le revenu d'un individu ou d'un groupe augmente d'un point de pourcentage). Globalement, les pays d'ASS ont un niveau d'inégalité beaucoup plus élevé comme on s'y attendait par rapport au reste du monde. Pour les tendances, nous remarquons que l'inégalité marginale s'accroît pour les services d'assainissement (eau, toilette et électricité), mais qu'elle diminue pour les soins de santé. En ce qui concerne l'inégalité moyenne, elle indique une disproportion dans l'accès des classes riches par rapport à celles pauvres. Même si les groupes pauvres « rattrapent » ceux riches dans la provision de certains services, cela se fait de façon trop lente. De fait, le haut niveau d'inégalité couplé à une recrudescence de cette inégalité à la marge pour certains services tendent à annihiler les effets positifs de la croissance économique et de la réduction de la pauvreté et maintiendraient la mortalité, la malnutrition et la morbidité des enfants en Afrique à des niveaux relativement élevés et plus particulièrement concentrées dans les groupes les plus pauvres. Tout ceci appelle à des politiques économiques, sociales et sanitaires pour renverser fortement les tendances de la mortalité des enfants. En particulier, nos résultats suggèrent qu'il faudrait que les pays Africains puissent entre autres : - accroître les services de soins de santé, notamment les soins préventifs comme les services essentiels à la santé de l'enfant dès sa naissance (vaccination, services prénataux et assistance à la naissance), les soins curatifs et les campagnes de sensibilisation. - renverser la tendance baissière dans la provision des services sanitaires (eau, électricité, environnement et assainissement, prise en charge des déchets, etc.). - améliorer la nutrition et l'environnement immédiat de ces enfants et les comportements des ménages (espacement des naissances, éducation des mères en matière de santé, etc.). - plus généralement comme le montrent d'autres études, il faudrait aussi améliorer la performance globale de leur système de santé en empêchant la fuite des cerveaux, en allouant un budget suffisant à la santé, en organisant mieux les différents organes, de même que les ciblages des politiques de santé, en empêchant la corruption, en améliorant la qualité (accueil, propreté des centres de soins, etc.), en équipant les centres en médicaments, vaccins, moyens de transport et de communication, etc. Intégrer si possible les systèmes plus traditionnels de soins (comme les matrones et les guérisseurs) et le secteur privé, de même qu'une meilleure organisation du système pharmaceutique. Ces politiques constituent un tout et doivent être mise en oeuvre rapidement, ou renforcées le cas échéant. A cette seule condition les pays Africains pourraient espérer rattraper leur retard dans les Objectifs du Millénaire.
BASE
Publisher's version (útgefin grein). ; Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10−8), breast and ovarian cancer (rg = 0.24, p = 7 × 10−5), breast and lung cancer (rg = 0.18, p =1.5 × 10−6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10−4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis. ; The authors in this manuscript were working on behalf of BCAC, CCFR, CIMBA, CORECT, GECCO, OCAC, PRACTICAL, CRUK, BPC3, CAPS, PEGASUS, TRICL-ILCCO, ABCTB, APCB, BCFR, CONSIT TEAM, EMBRACE, GC-HBOC, GEMO, HEBON, kConFab/AOCS Mod SQuaD, and SWE-BRCA. The breast cancer genome-wide association analyses: BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community's Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l'Économie, Science et Innovation du Québec through Genome Québec and the PSR-SIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978), and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112—the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia), and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) is generously supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The ACP study is funded by the Breast Cancer Research Trust, UK. The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, and BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. For BIGGS, ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. BOCS is supported by funds from Cancer Research UK (C8620/A8372/A15106) and the Institute of Cancer Research (UK). BOCS acknowledges NHS funding to the Royal Marsden/Institute of Cancer Research NIHR Specialist Cancer Biomedical Research Centre. The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CAMA study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). Sample collection and processing was funded in part by grants from the National Cancer Institute (NCI R01CA120120 and K24CA169004). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The CNIO-BCS was supported by the Instituto de Salud Carlos III, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070). COLBCCC is supported by the German Cancer Research Center (DKFZ), Heidelberg, Germany. D.T. was in part supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. H.A.C eceives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). FHRISK is funded from NIHR grant PGfAR 0707-10031. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: Rita K. Schmutzler, Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, and 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0, and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GEPARSIXTO study was conducted by the German Breast Group GmbH. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). GLACIER was supported by Breast Cancer Now, CRUK and Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, and by the Rudolf-Bartling Foundation. The HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, and the Sigrid Juselius Foundation. The HERPACC was supported by MEXT Kakenhi (No. 170150181 and 26253041) from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan, by National Cancer Center Research and Development Fund, and "Practical Research for Innovative Cancer Control (15ck0106177h0001)" from Japan Agency for Medical Research and development, AMED, and Cancer Bio Bank Aichi. The HMBCS was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. The HUBCS was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), and by the Russian Foundation for Basic Research and the Federal Agency for Scientific Organizations for support the Bioresource collections and RFBR grants 14-04-97088, 17-29-06014, and 17-44-020498. ICICLE was supported by Breast Cancer Now, CRUK, and Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust and King's College London. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (A.L.F.) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (E.V.O.) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01-1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, The Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia (NHMRC; 400413, 400281, 199600). G.C.-T. and P.W. are supported by the NHMRC. RB was a Cancer Institute NSW Clinical Research Fellow. The KOHBRA study was partially supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 1020350; 1420190). LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018, 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP) which is supported under subcontract by the California Department of Health. The CSP is also part of the National Cancer Institute's Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. L.M.B.C. is supported by the 'Stichting tegen Kanker'. D.L. is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov" and supported by the German Academic Exchange Program, DAAD. The MARIE study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects "5 × 1000"). The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839, and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286, and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. MYBRCA is funded by research grants from the Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Malaysia. MYMAMMO is supported by research grants from Yayasan Sime Darby LPGA Tournament and Malaysian Ministry of Higher Education (RP046B-15HTM). The NBCS has been supported by the Research Council of Norway grant 193387/V50 (to A.-L. Børresen-Dale and V.N. Kristensen) and grant 193387/H10 (to A.-L. Børresen-Dale and V.N. Kristensen), South Eastern Norway Health Authority (grant 39346 to A.-L. Børresen-Dale and 27208 to V.N. Kristensen) and the Norwegian Cancer Society (to A.-L. Børresen-Dale and 419616 - 71248 - PR-2006-0282 to V.N. Kristensen). It has received funding from the K.G. Jebsen Centre for Breast Cancer Research (2012-2015). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, and U01 CA179715), and the North Carolina University Cancer Research Fund. The NGOBCS was supported by Grants-in-Aid for the Third Term Comprehensive Ten-Year Strategy for Cancer Control from the Ministry of Health, Labor and Welfare of Japan, and for Scientific Research on Priority Areas, 17015049 and for Scientific Research on Innovative Areas, 221S0001, from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support Foundation, and the special Governmental EVO funds for Oulu University Hospital-based research activities. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956, and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, UMCA182910, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The scientific development and funding of this project were, in part, supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network U19 CA148065. The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. The SCCS is supported by a grant from the National Institutes of Health (R01 CA092447). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. SEBCS was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347). SGBCC is funded by the NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant and the NMRC Clinician Scientist Award. Additional controls were recruited by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC), which was funded by the Biomedical Research Council, grant number: 05/1/21/19/425. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. The TBCS was funded by The National Cancer Institute, Thailand. The TNBCC was supported by a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. The TWBCS is supported by the Taiwan Biobank project of the Institute of Biomedical Sciences, Academia Sinica, Taiwan. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The US3SS study was supported by Massachusetts (K.M.E., R01CA47305), Wisconsin (P.A.N., R01 CA47147) and New Hampshire (L.T.-E., R01CA69664) centers, and Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WAABCS study was supported by grants from the National Cancer Institute of the National Institutes of Health (R01 CA89085 and P50 CA125183 and the D43 TW009112 grant), Susan G. Komen (SAC110026), the Dr. Ralph and Marian Falk Medical Research Trust, and the Avon Foundation for Women. The WHI program is funded by the National Heart, Lung, and Blood Institute, the US National Institutes of Health and the US Department of Health and Human Services (HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C). This work was also funded by NCI U19 CA148065-01. D.G.E. is supported by the all Manchester NIHR Biomedical research center Manchester (IS-BRC-1215-20007). HUNBOCS, Hungarian Breast and Ovarian Cancer Study was supported by Hungarian Research Grant KTIA-OTKA CK-80745, NKFI_OTKA K-112228. C.I. received support from the Nontherapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI P30-CA-51008) and the Jess and Mildred Fisher Center for Hereditary Cancer and Clinical Genomics Research. K.M. is supported by CRUK C18281/A19169. City of Hope Clinical Cancer Community Research Network and the Hereditary Cancer Research Registry, supported in part by Award Number RC4CA153828 (PI: J Weitzel) from the National Cancer Institute and the office of the Directory, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The colorectal cancer genome-wide association analyses: Colorectal Transdisciplinary Study (CORECT): The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the CORECT Consortium, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the CORECT Consortium. We are incredibly grateful for the contributions of Dr. Brian Henderson and Dr. Roger Green over the course of this study and acknowledge them in memoriam. We are also grateful for support from Daniel and Maryann Fong. ColoCare: we thank the many investigators and staff who made this research possible in ColoCare Seattle and ColoCare Heidelberg. ColoCare was initiated and developed at the Fred Hutchinson Cancer Research Center by Drs. Ulrich and Grady. CCFR: the Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and financial support from the U.S. National Cancer Institute, without which this important registry would not exist. Galeon: GALEON wishes to thank the Department of Surgery of University Hospital of Santiago (CHUS), Sara Miranda Ponte, Carmen M Redondo, and the staff of the Department of Pathology and Biobank of CHUS, Instituto de Investigación Sanitaria de Santiago (IDIS), Instituto de Investigación Sanitaria Galicia Sur (IISGS), SERGAS, Vigo, Spain, and Programa Grupos Emergentes, Cancer Genetics Unit, CHUVI Vigo Hospital, Instituto de Salud Carlos III, Spain. MCCS: this study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited participants and continue to work on follow-up. We would also like to express our gratitude to the many thousands of Melbourne residents who took part in the study and provided blood samples. SEARCH: We acknowledge the contributions of Mitul Shah, Val Rhenius, Sue Irvine, Craig Luccarini, Patricia Harrington, Don Conroy, Rebecca Mayes, and Caroline Baynes. The Swedish low-risk colorectal cancer study: we thank Berith Wejderot and the Swedish low-risk colorectal cancer study group. Genetics & Epidemiology of Colorectal Cancer Consortium (GECCO): we thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. ASTERISK: we are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank 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. DACHS: we thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance. HPFS, NHS and PHS: we 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, and PHS 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 and Haiyan Zhang who assisted in programming for the PHS. We 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: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A., W.Y. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. We assume full responsibility for analyses and interpretation of these data. PLCO: we 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, Wen Shao and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions for making this study possible. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. PMH: we thank the study participants and staff of the Hormones and Colon Cancer study. WHI: we thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short20List.pdf. CORECT: The CORECT Study was supported by the National Cancer Institute, National Institutes of Health (NCI/NIH), U.S. Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350; P01 CA196569; and R01 CA201407) and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678). The ATBC Study was supported by the US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184, U01 CA206110, 2P30CA015704-40 (Gilliland)), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative. Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): funding for GECCO was provided by the National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (grant numbers U01 CA137088, R01 CA059045, and U01 CA164930). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704. The Colon Cancer Family Registry (CFR) Illumina GWAS was supported by funding from the National Cancer Institute, National Institutes of Health (grant numbers U01 CA122839, R01 CA143247). The Colon CFR/CORECT Affymetrix Axiom GWAS and OncoArray GWAS were supported by funding from National Cancer Institute, National Institutes of Health (grant number U19 CA148107 to S.G.). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by the National Cancer Institute, National Institutes of Health (grant number UM1 CA167551) and through cooperative agreements with the following Colon CFR centers: Australasian Colorectal Cancer Family Registry (NCI/NIH grant numbers U01 CA074778 and U01/U24 CA097735), USC Consortium Colorectal Cancer Family Registry (NCI/NIH grant numbers U01/U24 CA074799), Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (NCI/NIH grant number U01/U24 CA074800), Ontario Familial Colorectal Cancer Registry (NCI/NIH grant number U01/U24 CA074783), Seattle Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074794), and University of Hawaii Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074806), Additional support for case ascertainment was provided from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute to Fred Hutchinson Cancer Research Center (Control Nos. N01-CN-67009 and N01-PC-35142, and Contract No. HHSN2612013000121), the Hawai'i Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No. HHSN26120100037C, and the California Department of Public Health (contracts HHSN261201000035C awarded to the University of Southern California, and the following state cancer registries: A.Z., C.O., M.N., N.C., N.H., and by the Victoria Cancer Registry and Ontario Cancer Registry. ESTHER/VERDI was supported by grants from the Baden–Württemberg Ministry of Science, Research and Arts and the German Cancer Aid. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. GALEON: FIS Intrasalud (PI13/01136). The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MSKCC: the work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the National Institutes of Health (grant numbers R01 CA189184, P30 CA076292), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (grant number P30 CA076292). SEARCH: Cancer Research UK (C490/A16561). The Spanish study was supported by Instituto de Salud Carlos III, co-funded by FEDER funds –a way to build Europe– (grants PI14-613 and PI09-1286), Catalan Government DURSI (grant 2014SGR647), and Junta de Castilla y León (grant LE22A10-2). The Swedish Low-risk Colorectal Cancer Study: the study was supported by grants from the Swedish research council; K2015-55 × -22674-01-4, K2008-55 × -20157-03-3, K2006-72 × -20157-01-2 and the Stockholm County Council (ALF project). CIDR genotyping for the Oncoarray was conducted under contract 268201200008I (to K.D.), through grant 101HG007491-01 (to C.I.A.). The Norris Cotton Cancer Center - P30CA023108, The Quantitative Biology Research Institute - P20GM103534, and the Coordinating Center for Screen Detected Lesions - U01CA196386 also supported efforts of C.I.A. This work was also supported by the National Cancer Institute (grant numbers U01 CA1817700, R01 CA144040). ASTERISK: a Hospital Clinical Research Program (PHRC) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC). COLO2&3: National Institutes of Health (grant number R01 CA060987). DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1, and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A, and 01ER1505B). DALS: National Institutes of Health (grant number R01 CA048998 to M.L.S). HPFS is supported by National Institutes of Health (grant numbers P01 CA055075, UM1 CA167552, R01 137178, and P50 CA127003), NHS by the National Institutes of Health (grant numbers UM1 CA186107, R01 CA137178, P01 CA087969, and P50 CA127003), NHSII by the National Institutes of Health (grant numbers R01 050385CA and UM1 CA176726), and PHS by the National Institutes of Health (grant number R01 CA042182). MEC: National Institutes of Health (grant numbers R37 CA054281, P01 CA033619, and R01 CA063464). OFCCR: National Institutes of Health, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (grant number U01 CA074783); see Colon CFR section above. As subset of ARCTIC, OFCCR is supported by a GL2 grant from the Ontario Research Fund, the Canadian Institutes of Health Research, and the Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society Research Institute. T.J.H. and B.W.Z. are recipients of Senior Investigator Awards from the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation. PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Additionally, a subset of control samples was genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS, Colon CGEMS pancreatic cancer scan (PanScan), and the Lung Cancer and Smoking study. 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. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, 23 and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. PMH: National Institutes of Health (grant number R01 CA076366). VITAL: National Institutes of Health (grant number K05-CA154337). WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. The head and neck cancer genome-wide association analyses: The study was supported by NIH/NCI: P50 CA097190, and P30 CA047904, Canadian Cancer Society Research Institute (no. 020214) and Cancer Care Ontario Research Chair to R.H. The Princess Margaret Hospital Head and Neck Cancer Translational Research Program is funded by the Wharton family, Joe's Team, Gordon Tozer, Bruce Galloway and the Elia family. Geoffrey Liu was supported by the Posluns Family Fund and the Lusi Wong Family Fund at the Princess Margaret Foundation, and the Alan B. Brown Chair in Molecular Genomics. This publication presents data from Head and Neck 5000 (H&N5000). H&N5000 was a component of independent research funded by the UK National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Human papillomavirus (HPV) in H&N5000 serology was supported by a Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant number: C18281/A19169). National Cancer Institute (R01-CA90731); National Institute of Environmental Health Sciences (P30ES10126). The authors thank all the members of the GENCAPO team/The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). CPS-II recruitment and maintenance is supported with intramural research funding from the American Cancer Society. Genotyping performed at the Center for Inherited Disease Research (CIDR) was funded through the U.S. National Institute of Dental and Craniofacial Research (NIDCR) grant 1 × 01HG007780-0. The University of Pittsburgh head and neck cancer case-control study is supported by National Institutes of Health grants P50 CA097190 and P30 CA047904. The Carolina Head and Neck Cancer Study (CHANCE) was supported by the National Cancer Institute (R01-CA90731). The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). The authors thank all the members of the GENCAPO team. The HN5000 study was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034), the views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Toronto study was funded by the Canadian Cancer Society Research Institute (020214) and the National Cancer Institute (U19-CA148127) and the Cancer Care Ontario Research Chair. The alcohol-related cancers and genetic susceptibility study in Europe (ARCAGE) was funded by the European Commission's 5th Framework Program (QLK1-2001-00182), the Italian Association for Cancer Research, Compagnia di San Paolo/FIRMS, Region Piemonte, and Padova University (CPDA057222). The Rome Study was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) IG 2011 10491 and IG2013 14220 to S.B., and Fondazione Veronesi to S.B. The IARC Latin American study was funded by the European Commission INCO-DC programme (IC18-CT97-0222), with additional funding from Fondo para la Investigacion Cientifica y Tecnologica (Argentina) and the Fundação de Amparo à Pesquisa do Estado de São Paulo (01/01768-2). We thank Leticia Fernandez, Instituto Nacional de Oncologia y Radiobiologia, La Habana, Cuba and Sergio and Rosalina Koifman, for their efforts with the IARC Latin America study São Paulo center. The IARC Central Europe study was supported by European Commission's INCO-COPERNICUS Program (IC15- CT98-0332), NIH/National Cancer Institute grant CA92039, and the World Cancer Research Foundation grant WCRF 99A28. The IARC Oral Cancer Multicenter study was funded by grant S06 96 202489 05F02 from Europe against Cancer; grants FIS 97/0024, FIS 97/0662, and BAE 01/5013 from Fondo de Investigaciones Sanitarias, Spain; the UICC Yamagiwa-Yoshida Memorial International Cancer Study; the National Cancer Institute of Canada; Associazione Italiana per la Ricerca sul Cancro; and the Pan-American Health Organization. Coordination of the EPIC study is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The lung cancer genome-wide association analyses: Transdisciplinary Research for Cancer in Lung (TRICL) of the International Lung Cancer Consortium (ILCCO) was supported by (U19-CA148127, CA148127S1, U19CA203654, and Cancer Prevention Research Institute of Texas RR170048). The ILCCO data harmonization is supported by Cancer Care Ontario Research Chair of Population Studies to R. H. and Lunenfeld-Tanenbaum Research Institute, Sinai Health System. The TRICL-ILCCO OncoArray was supported by in-kind genotyping by the Centre for Inherited Disease Research (26820120008i-0-26800068-1). The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN. The work performed in the CARET study was supported by the National Institute of Health/National Cancer Institute: UM1 CA167462 (PI: Goodman), National Institute of Health UO1-CA6367307 (PIs Omen, Goodman); National Institute of Health R01 CA111703 (PI Chen), National Institute of Health 5R01 CA151989-01A1(PI Doherty). The Liverpool Lung project is supported by the Roy Castle Lung Cancer Foundation. The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578, CA074386. The Multi-ethnic Cohort Study was partially supported by NIH Grants CA164973, CA033619, CA63464, and CA148127. The work performed in MSH-PMH study was supported by The Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. NJLCS was funded by the State Key Program of National Natural Science of China (81230067), the National Key Basic Research Program Grant (2011CB503805), the Major Program of the National Natural Science Foundation of China (81390543). The Norway study was supported by Norwegian Cancer Society, Norwegian Research Council. The Shanghai Cohort Study (SCS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The Singapore Chinese Health Study (SCHS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The work in TLC study has been supported in part the James & Esther King Biomedical Research Program (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997), and by a Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (grant number P30-CA76292). The Vanderbilt Lung Cancer Study—BioVU dataset used for the analyses described was obtained from Vanderbilt University Medical Center's BioVU, which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. Dr. Aldrich was supported by NIH/National Cancer Institute K07CA172294 (PI: Aldrich) and Dr. Bush was supported by NHGRI/NIH U01HG004798 (PI: Crawford). The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The NELCS study: Grant Number P20RR018787 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). The Kentucky Lung Cancer Research Initiative was supported by the Department of Defense [Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program] under award number: 10153006 (W81XWH-11-1-0781). Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense. This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The M.D. Anderson Cancer Center study was supported in part by grants from the NIH (P50 CA070907, R01 CA176568) (to X.W.), Cancer Prevention & Research Institute of Texas (RP130502) (to X.W.), and The University of Texas MD Anderson Cancer Center institutional support for the Center for Translational and Public Health Genomics. The deCODE study of smoking and nicotine dependence was funded in part by a grant from NIDA (R01- DA017932). The study in Lodz center was partially funded by Nofer Institute of Occupational Medicine, under task NIOM 10.13: Predictors of mortality from non-small cell lung cancer—field study. Genetic sharing analysis was funded by NIH grant CA194393. The research undertaken by M.D.T., L.V.W., and M.S.A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.D.T. holds a Medical Research Council Senior Clinical Fellowship (G0902313). The work to assemble the FTND GWAS meta-analysis was supported by the National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA) grant number R01 DA035825 (Principal Investigator [PI]: DBH). The study populations included COGEND (dbGaP phs000092.v1.p1 and phs000404.v1.p1), COPDGene (dbGaP phs000179.v3.p2), deCODE Genetics, EAGLE (dbGaP phs000093.vs.p2), and SAGE. dbGaP phs000092.v1.p1). See Hancock et al. Transl Psychiatry 2015 (PMCID: PMC4930126) for the full listing of funding sources and other acknowledgments. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT)study was funded by the Sheffield Hospitals Charity, Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. The ovarian cancer genome-wide association analysis: The Ovarian Cancer Association Consortium (OCAC) is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium that was funded by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The OCAC OncoArray genotyping project was funded through grants from the U.S. National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.), and R01-CA058598 (M.T.G.); Canadian Institutes of Health Research (MOP-86727 (L.E.K.) and the Ovarian Cancer Research Fund (A.B.). The COGS project was funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175) and through a grant from the U.S. National Institutes of Health (R01-CA122443 (E.L.G)). Funding for individual studies: AAS: National Institutes of Health (RO1-CA142081); AOV: The Canadian Institutes for Health Research (MOP-86727); AUS: The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia (199600, 400413 and 400281), Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania and Cancer Foundation of Western Australia (Multi-State Applications 191, 211, and 182). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; BAV: ELAN Funds of the University of Erlangen-Nuremberg; BEL: National Kankerplan; BGS: Breast Cancer Now, Institute of Cancer Research; BVU: Vanderbilt CTSA grant from the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) (ULTR000445); CAM: National Institutes of Health Research Cambridge Biomedical Research Centre and Cancer Research UK Cambridge Cancer Centre; CHA: Innovative Research Team in University (PCSIRT) in China (IRT1076); CNI: Instituto de Salud Carlos III (PI12/01319); Ministerio de Economía y Competitividad (SAF2012); COE: Department of Defense (W81XWH-11-2-0131); CON: National Institutes of Health (R01-CA063678, R01-CA074850; and R01-CA080742); DKE: Ovarian Cancer Research Fund; DOV: National Institutes of Health R01-CA112523 and R01-CA87538; EMC: Dutch Cancer Society (EMC 2014-6699); EPC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom); GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center (DKFZ); GRC: This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)—Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund; GRR: Roswell Park Cancer Institute Alliance Foundation, P30 CA016056; HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424, and N01-PC-67001); HJO: Intramural funding; Rudolf-Bartling Foundation; HMO: Intramural funding; Rudolf-Bartling Foundation; HOC: Helsinki University Research Fund; HOP: Department of Defense (DAMD17-02-1-0669) and NCI (K07-CA080668, R01-CA95023, P50-CA159981 MO1-RR000056 R01-CA126841); HUO: Intramural funding; Rudolf-Bartling Foundation; JGO: JSPS KAKENHI grant; JPN: Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare; KRA: This study (Ko-EVE) was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 0920010); LAX: American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124; LUN: ERC-2011-AdG 294576-risk factors cancer, Swedish Cancer Society, Swedish Research Council, Beta Kamprad Foundation; MAC: National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; Fraternal Order of Eagles; MAL: Funding for this study was provided by research grant R01- CA61107 from the National Cancer Institute, Bethesda, MD, research grant 94 222 52 from the Danish Cancer Society, Copenhagen, Denmark; and the Mermaid I project; MAS: Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation; MAY: National Institutes of Health (R01-CA122443, P30-CA15083, and P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; MCC: Cancer Council Victoria, National Health and Medical Research Council of Australia (NHMRC) grants number 209057, 251533, 396414, and 504715; MDA: DOD Ovarian Cancer Research Program (W81XWH-07-0449); MEC: NIH (CA54281, CA164973, CA63464); MOF: Moffitt Cancer Center, Merck Pharmaceuticals, the state of Florida, Hillsborough County, and the city of Tampa; NCO: National Institutes of Health (R01-CA76016) and the Department of Defense (DAMD17-02-1-0666); NEC: National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NHS: UM1 CA186107, P01 CA87969, R01 CA49449, R01-CA67262, UM1 CA176726; NJO: National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; If Sara Olson and/or Irene Orlow is a co-author, please add NCI CCSG award (P30-CA008748) to the funding sources; NOR: Helse Vest, The Norwegian Cancer Society, The Research Council of Norway; NTH: Radboud University Medical Centre; OPL: National Health and Medical Research Council (NHMRC) of Australia (APP1025142) and Brisbane Women's Club; ORE: OHSU Foundation; OVA: This work was supported by Canadian Institutes of Health Research grant (MOP-86727) and by NIH/NCI 1 R01CA160669-01A1; PLC: Intramural Research Program of the National Cancer Institute; POC: Pomeranian Medical University; POL: Intramural Research Program of the National Cancer Institute; PVD: Canadian Cancer Society and Cancer Research Society GRePEC Program; RBH: National Health and Medical Research Council of Australia; RMH: Cancer Research UK, Royal Marsden Hospital; RPC: National Institute of Health (P50-CA159981, R01-CA126841); SEA: Cancer Research UK (C490/A10119 C490/A10124); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; SIS: NIH, National Institute of Environmental Health Sciences, Z01-ES044005 and Z01-ES049033; SMC: The bbSwedish Research Council-SIMPLER infrastructure; the Swedish Cancer Foundation; SON: National Health Research and Development Program, Health Canada, grant 6613-1415-53; SRO: Cancer Research UK (C536/A13086, C536/A6689) and Imperial Experimental Cancer Research Centre (C1312/A15589); STA: NIH grants U01 CA71966 and U01 CA69417; SWE: Swedish Cancer foundation, WeCanCureCancer and VårKampMotCancer foundation; SWH: NIH (NCI) grant R37-CA070867; TBO: National Institutes of Health (R01-CA106414-A2), American Cancer Society (CRTG-00-196-01-CCE), Department of Defense (DAMD17-98-1-8659), Celma Mastery Ovarian Cancer Foundation; TOR: NIH grants R01-CA063678 and R01 CA063682; UCI: NIH R01-CA058860 and the Lon V Smith Foundation grant LVS39420; UHN: Princess Margaret Cancer Centre Foundation-Bridge for the Cure; UKO: The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre; UKR: Cancer Research UK (C490/A6187), UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; USC: P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389V-20170, 2II0200); VAN: BC Cancer Foundation, VGH & UBC Hospital Foundation; VTL: NIH K05-CA154337; WMH: National Health and Medical Research Council of Australia, Enabling Grants ID 310670 & ID 628903. Cancer Institute NSW Grants 12/RIG/1-17 & 15/RIG/1-16; WOC: National Science Centren (N N301 5645 40). The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academia Reserve. The prostate cancer genome-wide association analyses: we pay tribute to Brian Henderson, who was a driving force behind the OncoArray project, for his vision and leadership, and who sadly passed away before seeing its fruition. We also thank the individuals who participated in these studies enabling this work. The ELLIPSE/PRACTICAL (http//:practical.icr.ac.uk) prostate cancer consortium and his collaborating partners were supported by multiple funding mechanisms enabling this current work. ELLIPSE/PRACTICAL Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) (U19 CA148537 for ELucidating Loci Involved in Prostate Cancer SuscEptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I). Additional analytical support was provided by NIH NCI U01 CA188392 (F.R.S.). Funding for the iCOGS infrastructure came from the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, and C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112; the GAME-ON initiative), the Department of Defense (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. This work was supported by the Canadian Institutes of Health Research, European Commission's Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692, C16913/A6135, C5047/A21332 and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative grant: No. 1 U19 CA148537-01 (the GAME-ON initiative). We also thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, and The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The Prostate Cancer Program of Cancer Council Victoria also acknowledge grant support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, and 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, and Tattersall's. E.A.O., D.M.K., and E.M.K. acknowledge the Intramural Program of the National Human Genome Research Institute for their support. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233 to D.J.H., U01-CA98710 to S.M.G., U01-CA98216 to E.R., and U01-CA98758 to B.E.H., and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). CAPS GWAS study was supported by the Swedish Cancer Foundation (grant no 09-0677, 11-484, 12-823), the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant no K2010-70 × -20430-04-3, 2014-2269). The Hannover Prostate Cancer Study was supported by the Lower Saxonian Cancer Society. PEGASUS was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. RAPPER was supported by the NIHR Manchester Biomedical Research Center, Cancer Research UK (C147/A25254, C1094/A18504) and the EU's 7th Framework Programme Grant/Agreement no 60186. Overall: this research has been conducted using the UK Biobank Resource (application number 16549). NHS is supported by UM1 CA186107 (NHS cohort infrastructure grant), P01 CA87969, and R01 CA49449. NHSII is supported by UM1 CA176726 (NHSII cohort infrastructure grant), and R01-CA67262. A.L.K. is supported by R01 MH107649. We would like to thank the participants and staff of the NHS and NHSII 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. ; Peer Reviewed
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