System-science Informed Public Health and Economic Research for Non-communicable Disease Prevention (the SIPHER Consortium)

非传染性疾病预防的系统科学知情公共卫生和经济研究（SIPHER 联盟）

• 批准号: MR/S037578/1
• 负责人: Petra Sylvia Meier
• 金额: $ 634.61万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS037578%2F1
• 关键词: System; science; Informed; Public; Health

THE PROBLEMThere 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 FOCUSWe will work with three policy partners 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 and Scottish Government. We will focus on four jointly agreed policy priorities for good health:- Creating a fairer economy- Promoting mental wellbeing- Providing affordable, good quality housing- Preventing long-term effects of difficult childhoods.OUR COMPLEX SYSTEMS SCIENCE APPROACHEach 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 propose to use complex 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 will be 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 will develop support tools 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 will1. Work with policy partners to understand the policy systems and evidence needs2. Bring together existing data and evidence on each policy system (e.g. links between policies and outcomes, interdependencies between outcomes)3. Explore citizens' preferences for prioritising when not all outcomes can be achieved4. Link policies and their health and non-health effects in computer models to analyse benefits and costs over time5. Build an interactive tool to help policy decision-making, inform advocacy action and support political debate.SIPHER's MAIN OUTCOMEWe 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.

有强有力的证据表明，我们成长、生活、工作和衰老的社会和经济条件比生活方式的选择更能决定我们的健康。这些健康的社会决定因素，如收入、优质住房、教育或工作，在社会中分布不均，导致健康不平等。然而，我们对具体政策如何影响社会条件以防止健康不良和减少健康不平等知之甚少。此外，健康的大多数社会决定因素是“卫生”以外的政策部门的责任，这意味着决策者需要在所有政策中促进健康，才能对健康产生重大影响。SIPHER将提供新的科学证据和方法来支持从“健康政策”到“健康公共政策”的转变。我们的政策重点我们将与三个政策伙伴在地方、地区和国家层面合作，以解决他们高于平均水平的慢性病负担和持续的健康不平等问题：谢菲尔德市理事会、大曼彻斯特联合政府和苏格兰政府。我们将专注于四个共同商定的政策优先事项，以促进良好的健康：-创造一个更公平的经济-促进心理健康-提供负担得起的优质住房-防止困难的童年的长期影响。我们的复杂系统科学方法上述政策领域中的每一个都是一个复杂的政治系统，有许多相互竞争的优先事项，其中一个部门（例如住房）的政策选择可能会对其他部门（例如贫困）产生巨大的意外影响。通常没有“正确”的解决方案，因为不同结果之间的妥协需要价值判断。这意味着，要评估一项政策在卫生方面的真正效益和成本，就需要在广泛的可能结果中评估政策效果及其相互依存关系。然而，没有一个政策制定者对整个系统有了解，未来的经济和政治发展是不确定的。持续监测政策和其他系统变化的预期和意外影响是至关重要的，因此失败的政策可以修改或dropped.We建议使用复杂系统建模，这已经发展到了解和预测复杂系统中可能发生的事情给出不同的合理假设对未来的发展。我们的模型将以每个政策领域的最佳可用数据和先前研究为基础。我们的新证据表明，政策可能对各种结果产生影响，这将有助于政策合作伙伴根据不同结果对他们的重要性（例如改善健康或经济增长）在替代政策之间做出决定。我们将开发支持工具，使预测可视化，确定在竞争结果之间实现理想平衡的政策，并在出现新信息时更新建议。虽然这些方法对公共卫生政策来说是新的，但在工程和气候科学中已经得到了很好的应用。与政策合作伙伴合作，了解政策系统和证据需求2。汇集每个政策系统的现有数据和证据（例如政策与成果之间的联系、成果之间的相互依存关系）3.探索公民在并非所有结果都可以优先考虑时的优先选择4。在计算机模型中将政策及其健康和非健康影响联系起来，以分析一段时间内的收益和成本5。建立一个互动工具，以帮助政策决策，为宣传行动提供信息，并支持政治辩论。SIPHER的主要成果我们将为决策者提供一种新的方法，使他们能够估计在卫生部门以外实施的政策的卫生相关成本和收益。这将有助于我们的合作伙伴和其他人，他们希望评估如何使用稀缺的公共部门资源，以最大限度地提高其所有活动的健康和福祉效益。

项目成果

Visualising the spread of COVID-19 across England

可视化 COVID-19 在英格兰的传播

• DOI: 10.3351/ppp.2020.4894678366
• 发表时间: 2020
• 期刊: People, Place and Policy Online
• 作者: Angus C

Dealing with uncertainty in agent-based models for short-term predictions

• DOI: 10.1098/rsos.191074
• 发表时间: 2019-08
• 期刊: Royal Society Open Science
• 影响因子: 3.5
• 作者: L. Kieu;Nicolas Malleson;A. Heppenstall

Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.

在因果关系框架和相关挑战中，调整了“无法解释的残差”模型中的时间不变和时变的混杂因素。

• DOI: 10.1177/0962280218756158
• 发表时间: 2019-05
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Arnold KF;Ellison G;Gadd SC;Textor J;Tennant P;Heppenstall A;Gilthorpe MS
• 通讯作者: Gilthorpe MS

Methodological Issues of Spatial Agent-Based Models

基于空间代理的模型的方法论问题

• DOI: 10.18564/jasss.4174
• 发表时间: 2020
• 期刊: Journal of Artificial Societies and Social Simulation
• 作者: Manson, Steven;An, Li;Clarke, Keith C.;Heppenstall, Alison;Koch, Jennifer;Krzyzanowski, Brittany;Morgan, Fraser;O'Sullivan, David;Runck, Bryan C;Shook, Eric
• 通讯作者: Shook, Eric

Guest editorial for spatial agent-based models: current practices and future trends

基于空间代理的模型的客座社论：当前实践和未来趋势

• DOI: 10.1007/s10707-019-00349-y
• 发表时间: 2019
• 期刊: GeoInformatica
• 影响因子: 2
• 作者: Heppenstall A