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. However, no policymaker has knowledge of the whole system and future economic and political developments are uncertain. Ongoing monitoring of expected and unexpected effects of policies and other system changes is crucial so failing policies can be revised or dropped. We are using systems modelling, which has been developed to understand and make projections of what might happen in complex systems given different plausible assumptions about future developments. Our models are underpinned by the best available data and prior research in each policy area. Our new evidence about likely policy effects across a wide range of outcomes will help policy partners decide between alternative policies, depending on how important different outcomes are to them (e.g., improving health or economic growth). We are developing a decision support tool that can visualise the forecasts, identify policies that achieve the desired balance between competing outcomes and update recommendations when new information emerges. Whilst new to public health policy, these methods are well-established in engineering and climate science. We are: 1. Developing an in-depth understanding of policy processes and evidence needs using a novel combination of qualitative methods including interviews, system mapping, ethnographic research, and documentary analysis. 2. Developing and applying iterative literature search and review strategies suitable for supporting complex systems research. 3. Building a secure data infrastructure, creating synthetic populations with relevant attributes for policy experiments, and developing a system monitoring function to inform adaptive policymaking. 4. Modelling the dynamics and feedback effects of higher-order causal processes, e.g., the relationships between unemployment, poverty, and mortality. 5. Modelling the impacts of events and policy change on the characteristics of individuals and households, showing how policy impacts differ across geographic areas and societal groups. 6. Providing insight into how people value different policy outcomes, and translate the multiplicity of outcomes that arise from a whole-systems perspective into two common well-being measures needed for economic evaluation. 7. Using distributed, robust multi-objective optimization to develop a cross-sector decision support tool that identifies strategies that perform well across key policy outcomes and for different assumptions about future developments. 8. Assessing SIPHER’s scientific contribution through monitoring of real-world impact and seeking regular multi-perspective feedback from scientists, topic experts and community representatives. SIPHER's MAIN OUTCOME: We will provide policymakers with a new methodology that allows them to estimate the health-related costs and benefits of policies that are implemented outside the health sector. This will be useful to our partners, and others, who want to assess how scarce public sector resources can be spent to maximise the health and wellbeing benefits from all their activities.

Please note that this deposit does not contain the main dataset. The main dataset is available via the UK Data Service (https://doi.org/10.5255/UKDA-SN-9277-1). Please see the respective User Guide provided for this dataset for further information on the rationale for creation, methodology, quality control and intended applications. The SIPHER Synthetic Population is a digital twin of the adult population aged 16 years and older in Great Britain. It reflects more than 50 million synthetic individuals - all of which are represented through “real” individuals covered in the Understanding Society survey. The dataset is a large-scale, two-variable file including the variables “pidp” and “synthetic_zone”. The dataset shared is intended for linkage with Understanding Society survey data files such as “k_indresp” and “k_hhresp” using the survey’s person identifier variable (“pidp”). Please see the respective User Guide provided for this dataset for further information on linkages and intended applications.