Epidemiology has been defined as the study of the distribution and determinants of health states or events in defined populations and its application to the control of health problems. Psychiatric epidemiology has continued to develop and apply these core principles in relation to mental health and mental disorders. This long-awaited second edition of Practical Psychiatric Epidemiology covers all of the considerable new developments in psychiatric epidemiology that have occurred since the first edition was published. It includes new content on key topics such as life course epidemiology, gene/environment interactions, bioethics, patient and public involvement in research, mixed methods research, new statistical methods, case registers, policy, and implementation. Looking to the future of this rapidly evolving scientific discipline and how it will to respond to the emerging opportunities and challenges posed by 'big data', new technologies, open science and globalisation, this new edition will continue to serve as an invaluable reference for clinicians in practice and in training. It will also be of interest to researchers in mental health and people studying or teaching psychiatric epidemiology at undergraduate or postgraduate level.

As the editor, I feel extremely happy to present to the readers such a rich collection of chapters authored/co-authored by a large number of experts from around the world covering the broad field of guided wave optics and optoelectronics. Most of the chapters are state-of-the-art on respective topics or areas that are emerging. Several authors narrated technological challenges in a lucid manner, which was possible because of individual expertise of the authors in their own subject specialties. I have no doubt that this book will be useful to graduate students, teachers, researchers, and practicing engineers and technologists and that they would love to have it on their book shelves for ready reference at any time.

In March 1946, scientists began to track thousands of children born in one cold week. No one imagined that this would become the longest-running study of human development in the world, growing to encompass five generations of children. Today, they are some of the best-studied people on the planet, and the simple act of observing human life has changed the way we are born, schooled, parent and die. This is the tale of these studies and the remarkable discoveries that have come from them. Touching people across the globe, they are one of the world's best-kept secrets.

Although it is becoming increasingly more common for clinicians to use genomic data in their practices for disease prevention, diagnosis, and treatment, the process of integrating genomic data into the practice of medicine has been a slow and challenging one. Some of the major barriers impeding the incorporation of new genomic technology into clinical practice are: the difficulty of changing routine medical practices to account for the use of genetic testing, the limited knowledge of patients and providers about genomic medicine, assessing sufficient evidence to support the use of genetic tests, privacy and data security issues, and uncertainty about reimbursement. The field of implementation science may be able to provide insights concerning efficient ways to incorporate genomic applications into routine clinical practice. The focus of implementation science studies is to identify integration bottlenecks and optimal approaches for a given setting and ultimately to promote the up-take of research findings. To explore the potential of implementation science to improve the integration of genomics into medicine, the National Academies of Sciences, Engineering, and Medicine held a workshop in Washington, DC, in November 2015. Participants explored the challenges and opportunities of integrating genomic advances into the clinic through the lens of implementation science. This report summarizes the presentations and discussions from the workshop.

POPULATION HEALTH SCIENCE formalizes an emerging discipline at the crossroads of social and medical sciences, demography, and economics--an emerging approach to population studies that represents a seismic shift in how traditional health sciences measure and observe health events. Bringing together theories and methods from diverse fields, this text provides grounding in the factors that shape population health. The overall approach is one of consequentialist science: designing creative studies that identify causal factors in health with multidisciplinary rigor. Distilled into nine foundational principles, this book guides readers through population science studies that strategically incorporate: · macrosocial factors · multilevel, lifecourse, and systems theories · prevention science fundamentals · return on investment · equity and efficiency Harnessing the power of scientific inquiry and codifying the knowledge base for a burgeoning field, POPULATION HEALTH SCIENCE arms readers with tools to shift the curve of population health.

Whilst the ‘health sciences’ are a broad and diverse area, and includes public health, primary care, health psychology, psychiatry and epidemiology, the research methods and data analysis skills required to analyse them are very similar. Moreover, the ability to appraise and conduct research is emphasised within the health sciences – and students are expected increasingly to do both. Introduction to Research Methods and Data Analysis in the Health Sciences presents a balanced blend of quantitative research methods, and the most widely used techniques for collecting and analysing data in the health sciences. Highly practical in nature, the book guides you, step-by-step, through the research process, and covers both the consumption and the production of research and data analysis. Divided into the three strands that run throughout quantitative health science research – critical numbers, critical appraisal of existing research, and conducting new research – this accessible textbook introduces: Descriptive statistics Measures of association for categorical and continuous outcomes Confounding, effect modification, mediation and causal inference Critical appraisal Searching the literature Randomised controlled trials Cohort studies Case-control studies Research ethics and data management Dissemination and publication Linear regression for continuous outcomes Logistic regression for categorical outcomes. A dedicated companion website offers additional teaching and learning resources for students and lecturers, including screenshots, R programming code, and extensive self-assessment material linked to the book’s exercises and activities. Clear and accessible with a comprehensive coverage to equip the reader with an understanding of the research process and the practical skills they need to collect and analyse data, it is essential reading for all undergraduate and postgraduate students in the health and medical sciences.