4 results
Improving the generalizability of infant psychological research: The ManyBabies model
- Ingmar Visser, Christina Bergmann, Krista Byers-Heinlein, Rodrigo Dal Ben, Wlodzislaw Duch, Samuel Forbes, Laura Franchin, Michael C. Frank, Alessandra Geraci, J. Kiley Hamlin, Zsuzsa Kaldy, Louisa Kulke, Catherine Laverty, Casey Lew-Williams, Victoria Mateu, Julien Mayor, David Moreau, Iris Nomikou, Tobias Schuwerk, Elizabeth A. Simpson, Leher Singh, Melanie Soderstrom, Jessica Sullivan, Marion I. van den Heuvel, Gert Westermann, Yuki Yamada, Lorijn Zaadnoordijk, Martin Zettersten
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- Journal:
- Behavioral and Brain Sciences / Volume 45 / 2022
- Published online by Cambridge University Press:
- 10 February 2022, e35
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- Article
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Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.
Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium
- Renato Polimanti, Roseann E. Peterson, Jue-Sheng Ong, Stuart MacGregor, Alexis C. Edwards, Toni-Kim Clarke, Josef Frank, Zachary Gerring, Nathan A. Gillespie, Penelope A. Lind, Hermine H. Maes, Nicholas G. Martin, Hamdi Mbarek, Sarah E. Medland, Fabian Streit, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Arpana Agrawal, Howard J. Edenberg, Kenneth S. Kendler, Cathryn M. Lewis, Patrick F. Sullivan, Naomi R. Wray, Joel Gelernter, Eske M. Derks
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- Journal:
- Psychological Medicine / Volume 49 / Issue 7 / May 2019
- Published online by Cambridge University Press:
- 01 April 2019, pp. 1218-1226
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Background
Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.
MethodsLinkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).
ResultsPositive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.
ConclusionThis study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.
Contributors
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- By Giustino Albanese, Andrew Amaranto, Brandon H. Backlund, Alexander Baxter, Abraham Berger, Mark Bernstein, Marian E. Betz, Omar Bholat, Suzanne Bigelow, Carl Bonnett, Elizabeth Borock, Christopher B. Colwell, Alasdair Conn, Moira Davenport, David Dreitlein, Aaron Eberhardt, Ugo A. Ezenkwele, Diana Felton, Spiros G. Frangos, John E. Frank, Jonathan S. Gates, Lewis Goldfrank, Pinchas Halpern, Jean Hammel, Kristin E. Harkin, Jason S. Haukoos, E. Parker Hays, Aaron Hexdall, James F. Holmes, Debra Houry, Jennifer Isenhour, Andy Jagoda, John L. Kendall, Erica Kreisman, Nancy Kwon, Eric Legome, Matthew R. Levine, Phillip D. Levy, Charles Little, Marion Machado, Heather Mahoney, Vincent J. Markovchick, Nancy Martin, John Marx, Julie Mayglothling, Ron Medzon, Maurizio A. Miglietta, Elizabeth L. Mitchell, Ernest Moore, Maria E. Moreira, Sassan Naderi, Salvatore Pardo, Sajan Patel, David Peak, Christine Preblick, Niels K. Rathlev, Charles Ray, Phillip L. Rice, Carlo L. Rosen, Peter Rosen, Livia Santiago-Rosado, Tamara A. Scerpella, David Schwartz, Fred Severyn, Kaushal Shah, Lee W. Shockley, Mari Siegel, Matthew Simons, Michael Stern, D. Matthew Sullivan, Carrie D. Tibbles, Knox H. Todd, Shawn Ulrich, Neil Waldman, Kurt Whitaker, Stephen J. Wolf, Daniel Zlogar
- Edited by Eric Legome, Lee W. Shockley
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- Book:
- Trauma
- Published online:
- 07 September 2011
- Print publication:
- 16 June 2011, pp ix-xiv
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4 - Evaluation and public policy
- from Part I - INTRODUCTION
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- By Frank J. Sullivan, Department of Health and Human Services
- Edited by Helle Charlotte Knudsen, University of Copenhagen, Graham Thornicroft, Institute of Psychiatry, London
- Foreword by Norman Sartorius
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- Book:
- Mental Health Service Evaluation
- Published online:
- 05 August 2016
- Print publication:
- 14 March 1996, pp 50-64
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Summary
Introduction
Sound public policy should rest on sound information, and analysis of that information from a policy perspective. A statement of the obvious? Not if you ask policy-makers about the availability of useful information! In fact, determinations of emphases for research and evaluation generally are not made with an eye to the interests and needs of policy-makers. On the contrary, there is some degree of intellectual resistance to examining a research and evaluation agenda according to criteria relating to the needs of public policy. But public resources for mental health depend to a great extent on how well the evaluation and research community interacts with the public policy community.
The aim of this chapter is to describe the information needs and interests of the public policy community, and the importance of the political process in maintaining and enhancing mental health programmes. The role outcome data-reflecting a full array of measures —is discussed. The complexity of organising systems of care in the context of both multiple levels of government and multiple sources of support for services is described, along with several examples of State planning activities. The chapter concludes with a discussion of advocacy in the political process with a focus on mental health consumers’ and families’ interests, and the role of the research and evaluation communities.
Information for policy-makers
To meet the interests and needs of policy-makers, data must be timely, pertinent to the policy-level decisions, and analysed and presented in the context of other relevant information so that their implications are clear both to the policy-makers and to the policymakers’ constituents. The policy-maker will want to understand the extent to which results are replicable, statistically robust, and relevant to the issue at hand. But neither a policy-maker nor a politician is in a position to engage in a detailed analysis of the technical aspects of information used to support a recommended course of action. Hence, communication must be so clear that the facts, and their implications, can be accepted at face value by decision-makers and the constituencies they must respond to. Thus, information should also address the points that advocacy and other groups that impinge on public policy are likely to raise.