Benjamin Smart

This book, inspired by public health policymaking during the Covid-19 pandemic, proposes an innovative capacities-based approach to understanding health at all levels of biological complexity - including at the level of the population. This capacities approach to health is applied to various issues within the philosophy of public health, including the goal of public health, evidence-based public health, the metaphysics of epidemics, public health ethics, and decolonizing public health. The author advocates for greater attention to be paid to the contextual conditions in which public health policies are to be implemented, for a public health principlism grounded by the enhancement of emergent capacities of populations and those of their members, and for the elimination of contributory injustice in medicine and public health policy.

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested. Causation in Population Health Informatics and Data Science provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

Disease is everywhere. Everyone experiences disease, everyone knows somebody who is, or has been diseased, and disease-related stories hit the headlines on a regular basis. Many important issues in the philosophy of disease, however, have received remarkably little attention from philosophical thinkers. This book examines a number of important debates in the philosophy of medicine, including 'what is disease?', and the roles and viability of concepts of causation, in clinical medicine and epidemiology. Where much of the existing literature targets conceptual analyses of health and disease, this book provides the reader with an insight into these debates, and develops plausible alternative accounts. The author explores a range of related subjects, discussing a host of interesting philosophical questions within clinical medicine, pathology and epidemiology. In the second part of the book, the author examines the concepts of causation employed by clinicians and pathologists,how one should classify diseases, and whether the epidemiologist's models for inferring the causes of disease are all they're cracked up to be.