University of Sheffield, Sheffield, UK.
University of Cambridge, Cambridge, UK.
BMC Public Health. 2024 Oct 12;24(1):2794. doi: 10.1186/s12889-024-20225-1.
It is challenging to predict long-term outcomes of interventions without understanding how they work. Health economic models of public health interventions often do not incorporate the many determinants of individual and population behaviours that influence long term effectiveness. The aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to: (a) develop a toolbox of methods for incorporating the influences on behaviour into public health economic models (PHEM-B); and (b) set out a research agenda for health economic modellers and behavioural/ social scientists to further advance methods to better inform public health policy decisions.
A core multidisciplinary group developed a preliminary toolbox from a published review of the literature and tested this conceptually using a case study of a diabetes prevention simulation. The core group was augmented by a much wider group that covered a broader range of multidisciplinary expertise. We used a consensus method to gain agreement of the PHEM-B toolbox. This included a one-day workshop and subsequent reviews of the toolbox.
The PHEM-B toolbox sets out 12 methods which can be used in different combinations to incorporate influences on behaviours into public health economic models: collaborations between modellers and behavioural scientists, literature reviewing, application of the Behaviour Change Intervention Ontology, systems mapping, agent-based modelling, differential equation modelling, social network analysis, geographical information systems, discrete event simulation, theory-informed statistical and econometric analyses, expert elicitation, and qualitative research/process tracing. For each method, we provide a description with key references, an expert consensus on the circumstances when they could be used, and the resources required.
This is the first attempt to rigorously and coherently propose methods to incorporate the influences on behaviour into health economic models of public health interventions. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Changing behaviour and maintaining that behaviour change could have different influences; thus, there could be benefits in modelling these separately. Future research is needed to develop, collaboratively with behavioural scientists, a suite of more robust health economic models of health-related behaviours, reported transparently, including coding, which would allow model reuse and adaptation.
如果不了解干预措施的作用机制，就难以预测其长期效果。公共卫生干预措施的健康经济模型通常不纳入影响长期效果的个体和人群行为的众多决定因素。本文旨在借鉴心理学、社会学、行为经济学、复杂性科学和健康经济学，以：（a）开发一套将行为影响纳入公共卫生经济模型的方法工具（PHEM-B）；（b）为健康经济建模者和行为/社会科学家制定研究议程，以进一步推进更好地为公共卫生决策提供信息的方法。
一个核心的多学科小组从文献综述中开发了一个初步的工具包，并使用糖尿病预防模拟的案例研究对这一概念进行了测试。核心小组由涵盖更广泛多学科专业知识的更广泛的小组扩充。我们使用共识方法来达成对 PHEM-B 工具包的一致意见。这包括为期一天的研讨会和对工具包的后续审查。
PHEM-B 工具包提出了 12 种方法，可以以不同的组合方式将行为影响纳入公共卫生经济模型：建模者和行为科学家之间的合作、文献综述、应用行为改变干预本体论、系统映射、基于代理的建模、微分方程建模、社会网络分析、地理信息系统、离散事件模拟、基于理论的统计和计量经济学分析、专家意见征集以及定性研究/过程追踪。对于每种方法，我们提供了描述、关键参考文献、在何种情况下可以使用的专家共识以及所需资源。
这是首次尝试严格而一致地提出将行为影响纳入公共卫生干预健康经济模型的方法。并非总是需要或可行的对行为影响进行明确建模，但了解关键影响因素至关重要。改变行为和维持行为改变可能有不同的影响；因此，分别对这些进行建模可能会有好处。未来的研究需要与行为科学家合作，开发一套更稳健的、透明报告的、包括编码的健康相关行为健康经济模型套件，以便模型能够重复使用和改编。
