Vineis Paolo
Imperial College London, London, United Kingdom.
Department of Computation & Data Science, Italian Institute of Technology, Genoa, Italy.
Front Public Health. 2020 May 12;8:160. doi: 10.3389/fpubh.2020.00160. eCollection 2020.
Here I compare two types of evidence that have recently emerged from the literature. This Commentary is a contribution to the Frontiers Research Topic on social disparities in aging, and aims to draw attention to the novel connections that link social disparities, the biological capital of individuals, and policy strategies. The biological capital (as defined in the paper), accrued since conception by individuals, in turn affects their social, cultural, and economic capitals, and thus creates a positive feedback loop. In a large network funded by the European Commission, Lifepath, we have shown that the determinants of health inequalities start in early life and cumulate throughout the life-course. For example, exposure to adverse childhood experiences (ACEs) influences the likelihood of later in life health effects, including poor aging. In this paper I compare two types of evidence that have recently emerged from the literature. One addresses the role of early vs. late exposures to risk factors for aging and mortality, including ACEs, using e.g., microsimulation models. The second type of evidence, provided in a recent document of the WHO European Regional Office, is based on the analysis of five broad determinants of health inequalities and eight different macroeconomic policies to tackle such inequalities. Six of the policies, if enacted, have the potential to reduce inequalities in the short term by increasing public expenditure on housing and community amenities, increasing expenditure on labor market policies, reducing income inequality, increasing social protection expenditure, reducing unemployment, and/or reducing out-of-pocket payments for health. Both of these lines of evidence suggest that there are ample opportunities for policy interventions. I also discuss the need for analytical methods to bridge the two types of analyses (biomedical and macroeconomic), i.e., fill the gap between analyses based on individual determinants of health inequalities and those based on societal determinants, to help create more effective policy-making. Also, I propose that before launching large projects to reduce health inequalities, well-designed experiments must be conducted to test their efficacy. These experiments, though, are challenging when addressing social policies, in consideration of ethical constraints and timescales.
在此,我比较了文献中最近出现的两种证据类型。本评论文章是对衰老领域社会差异前沿研究主题的贡献,旨在引起人们对连接社会差异、个体生物资本和政策策略的新联系的关注。个体自受孕起积累的生物资本(如本文所定义),反过来又会影响其社会、文化和经济资本,从而形成一个正反馈循环。在由欧盟委员会资助的大型网络“生命轨迹”(Lifepath)中,我们已经表明,健康不平等的决定因素始于生命早期,并在整个生命历程中不断累积。例如,童年时期暴露于不良经历(ACEs)会影响日后出现健康问题的可能性,包括衰老不佳。在本文中,我比较了文献中最近出现的两种证据类型。一种使用例如微观模拟模型,探讨早期与晚期暴露于衰老和死亡风险因素(包括ACEs)的作用。世界卫生组织欧洲区域办事处最近的一份文件提供的第二类证据,基于对健康不平等的五个广泛决定因素和八项解决此类不平等的不同宏观经济政策的分析。如果实施其中六项政策,有可能通过增加住房和社区设施的公共支出、增加劳动力市场政策支出、减少收入不平等、增加社会保护支出、降低失业率和/或减少医疗自付费用,在短期内减少不平等。这两类证据都表明,政策干预有充足的机会。我还讨论了需要分析方法来衔接这两种分析类型(生物医学和宏观经济),即填补基于健康不平等个体决定因素的分析与基于社会决定因素的分析之间的差距,以帮助制定更有效的政策。此外,我建议在启动大型减少健康不平等项目之前,必须进行精心设计的实验来测试其效果。然而,考虑到伦理限制和时间尺度,在涉及社会政策时,这些实验具有挑战性。