IMPORTANT: This deposit contains a range of supplementary material related to the deposit of the SIPHER Synthetic Population for Individuals, 2019-2021 (https://doi.org/10.5255/UKDA-SN-9277-1). See the shared readme file for a detailed description describing this deposit. Please note that this deposit does not contain the SIPHER Synthetic Population dataset, or any other Understanding Society survey datasets. The lack of a centralised and comprehensive register-based system in Great Britain limits opportunities for studying the interaction of aspects such as health, employment, benefit payments, or housing quality at the level of individuals and households. At the same time, the data that exist, is typically strictly controlled and only available in safe haven environments under a “create-and-destroy” model. In particular when testing policy options via simulation models where results are required swiftly, these limitations can present major hurdles to coproduction and collaborative work connecting researchers, policymakers, and key stakeholders. In some cases, survey data can provide a suitable alternative to the lack of readily available administrative data. However, survey data does typically not allow for a small-area perspective. Although special license area-level linkages of survey data can offer more detailed spatial information, the data’s coverage and statistical power might be too low for meaningful analysis. Through a linkage with the UK Household Longitudinal Study (Understanding Society, SN 6614, wave k), the SIPHER Synthetic Population allows for the creation of a survey-based full-scale synthetic population for all of Great Britain. By drawing on data reflecting “real” survey respondents, the dataset represents over 50 million synthetic (i.e. “not real”) individuals. As a digital twin of the adult population in Great Britain, the SIPHER Synthetic population provides a novel source of microdata for understanding “status quo” and modelling “what if” scenarios (e.g., via static/dynamic microsimulation model), as well as other exploratory analyses where a granular geographical resolution is required As the SIPHER Synthetic Population is the outcome of a statistical creation process, all results obtained from this dataset should always be treated as “model output” - including basic descriptive statistics. Here, the SIPHER Synthetic Population should not replace the underlying Understanding Society survey data for standard statistical analyses (e.g., standard regression analysis, longitudinal multi-wave analysis). Please see the respective User Guide provided for this dataset for further information on creation and validation. This research was conducted as part of the Systems Science in Public Health and Health Economics Research - SIPHER Consortium and we thank the whole team for valuable input and discussions that have informed this work.**THE PROBLEM:** There is strong evidence that the social and economic conditions in which we grow, live, work and age determine our health to a much larger degree than lifestyle choices. These social determinants of health, such as income, good quality homes, education, or work, are not distributed equally in society, which leads to health inequalities. However, we know very little about how specific policies influence the social conditions to prevent ill health and reduce health inequalities. Also, most social determinants of health are the responsibility of policy sectors other than health, which means policymakers need to promote health in ALL their policies if they are to have a big impact on health. SIPHER will provide new scientific evidence and methods to support such a shift from health policy to healthy public policy. **OUR POLICY FOCUS:** We are working with four policy partner organisations at local, regional, and national level to tackle their above-average chronic disease burden and persistent health inequalities: Sheffield City Council, Greater Manchester Combined Authority, the Scottish Government and Public Health Scotland. We will focus on three jointly agreed policy priorities for good health: - Inclusive Economies - Public Mental Health - Providing affordable, good quality housing **OUR COMPLEX SYSTEMS SCIENCE APPROACH:** Each of the above policy areas is a complex political system with many competing priorities, where policy choices in one sector (e.g., housing) can have large unintended effects in others (e.g., poverty). There is often no correct solution because compromises between different outcomes require value judgements. This means that to assess the true benefits and costs of a policy in relation to health, policy effects and their interdependencies need to be assessed across a wide range of possible outcomes.

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