FAO provides countries with technical support to conduct nutrition assessments, in particular to build the evidence base required for countries to achieve commitments made at the Second International Conference on Nutrition (ICN2) and under the 2016-2025 UN Decade of Action on Nutrition. Such concrete evidence can only derive from precise and valid measures of what people eat and drink. There is a wide range of dietary assessment methods available to measure food and nutrient intakes (expressed as energy insufficiency, diet quality and food patterns etc.) in diet and nutrition surveys, in impact surveys, and in monitoring and evaluation. Differenct indicators can be selected according to a study's objectives, sample population, costs and required precision. In low capacity settings, a number of other issues should be considered (e.g. availability of food composition tables, cultural and community specific issues, such as intra-household distribution of foods and eating from shared plates, etc.). This manual aims to signpost for the users the best way to measure food and nutrient intakes and to enhance their understanding of the key features, strengths and limitations of various methods. It also highlights a number of common methodological considerations involved in the selection process. Target audience comprises of individuals (policy-makers, programme managers, educators, health professionals including dietitians and nutritionists, field workers and researchers) involved in national surveys, programme planning and monitoring and evaluation in low capacity settings, as well as those in charge of knowledge brokering for policy-making.

This book presents a comprehensive discussion of classical ideas, core topics, currents and detailed theoretical underpinnings in medical sociology. It is a globally renowned source and reference for those interested in social dimensions of health and illness. The presentation is enriched with explanatory and illustrative styles. The design and illustration of details will shift the minds of the readers from mere classroom discourse to societal context (the space of health issues), to consider the implications of those ideas in a way that could guide health interventions. The elemental strengths are the sociological illustrations from African context, rooted in deep cultural interpretations necessitated because Africa bears a greater brunt of health problems. More so, the classical and current epistemological and theoretical discourse presented in this book are indicative of core themes in medical sociology in particular, but cut across a multidisciplinary realm including health social sciences (e.g., medical anthropology, health psychology, medical demography, medical geography and health economics) and health studies (medicine, public health, epidemiology, bioethics and medical humanities) in general. Therefore, apart from the book’s relevance as a teaching text of medical sociology for academics, it is also meant for students at various levels and all health professionals who require a deeper understanding of social dimensions of health and illness (with illustrations from the African context) and sociological contributions to health studies in general.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

Remarkable achievements in parasitic disease research, both basic and translational, have occurred over the last ten years, and we have incorporated the majority of these into the 6th edition of Parasitic Diseases. We have added over 1,000 new references to document these advances. Innovative work in the laboratory has provided the clinician/research scientist with a much clearer understanding of the mechanisms of pathogenesis. The number of recently discovered interleukins and their cellular networks has completely re-ordered our comprehension of how parasites and our defense system works to produce protection against infection/reinfection, or in some cases, how it becomes subverted by the offending pathogen to enable it to endure inside us for long periods of time. A plethora of molecular-based diagnostic tests have found their way into the routine of the parasitology diagnostic laboratory, improving the ease at which the offending pathogen can be rapidly identifed. Newer drugs, many with less harmful side-effects than the ones they replaced, have come on the market that make controlling parasite populations at the community level possible without the risk of harming the very ones we wish to help.

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