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The discipline of politics examines the debates, ideas and institutions that surround community organisation and collective decision making about resources. In this chapter, the contribution of politics to health studies is examined. The first part of the chapter considers how politics is defined and how this underpins the various strands of political science. It also examines some of the key concepts of political study: power, ideology, democracy, government and the state. It also explores what contribution politics has and can make to health studies. The second part considers some of the theoretical and methodological approaches within politics. It looks at political ideologies, how they offer competing definitions of politics, varied views of the social and political world, and divergent views on health and health improvement. A case study explores how recent changes in many contemporary societies, associated in particular with neo-liberal economic policies, have led to a greater emphasis on freedom through choice. Individuals are called upon to take a greater role in self-care and risk management in relation to their bodies. At the same time the development of large multi-national companies has given rise to a system of production whereby their size and dominance have provided them with an ability to structure the food market.

RepeAT includes 103 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT.

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group. Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest matching clinical history. Results: For conditions investigated, the nearest method performed well in comparison with standard logistic regression. Conclusions: Results indicate that it may be possible to use histories to identify ‘similar’ patients and thus to modulate future likelihoods of a condition occurring.