BACKGROUND Repeated, data-driven optimisation processes have been applied in many fields to rapidly transform the performance of products, processes and interventions. While such processes may similarly be employed to enhance the impact of public health initiatives, optimisation has not been defined in the context of public health and there has been little exploration of its key concepts. METHODS We used a modified, three-round Delphi study with an international group of researchers, public health policy-makers and practitioners to (1) generate a consensus-based definition of optimisation in the context of public health and (2i) describe key considerations for optimisation in that context. A pre-workshop literature review and elicitation of participant views regarding optimisation in public health (round 1) were followed by a daylong workshop and facilitated face-to-face group discussions to refine the definition and generate key considerations (round 2); finally, post-workshop discussions were undertaken to refine and finalise the findings (round 3). A thematic analysis was performed at each round. Study findings reflect an iterative consultation process with study participants. RESULTS Thirty of 33 invited individuals (91%) participated in the study. Participants reached consensus on the following definition of optimisation in public health: "A deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints". A range of optimisation considerations were explored. Optimisation was considered most suitable when existing public health initiatives are not sufficiently effective, meaningful improvements from an optimisation process are anticipated, quality data to assess impacts are routinely available, and there are stable and ongoing resources to support it. Participants believed optimisation could be applied to improve the impacts of an intervention, an implementation strategy or both, on outcomes valued by stakeholders or end users. While optimisation processes were thought to be facilitated by an understanding of the mechanisms of an intervention or implementation strategy, no agreement was reached regarding the best approach to inform decisions about modifications to improve impact. CONCLUSIONS The study findings provide a strong basis for future research to explore the potential impact of optimisation in the field of public health.

中文翻译：

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优化：定义和探索增强公共卫生举措影响力的概念。

背景技术重复的、数据驱动的优化过程已经应用于许多领域，以快速改变产品、过程和干预措施的性能。虽然此类过程可以类似地用于增强公共卫生举措的影响，但优化尚未在公共卫生背景下定义，并且对其关键概念的探索也很少。方法 我们与国际研究人员、公共卫生政策制定者和从业者小组一起使用了一项经过修改的三轮德尔菲研究，以 (1) 在公共卫生背景下生成基于共识的优化定义，并 (2i) 描述关键考虑因素在这种情况下进行优化。研讨会前进行了文献回顾并征求了参与者关于公共卫生优化的意见（第一轮），随后举行了为期一天的研讨会，并促进了面对面的小组讨论，以完善定义并产生关键考虑因素（第二轮）；最后，进行了研讨会后讨论，以完善和最终确定调查结果（第三轮）。每轮都进行主题分析。研究结果反映了与研究参与者的反复协商过程。结果 33 名受邀者中有 30 名 (91%) 参与了这项研究。与会者就公共卫生优化的以下定义达成共识：“一个深思熟虑的、迭代的、数据驱动的过程，旨在改进健康干预措施和/或其实施，以在资源限制内满足利益相关者定义的公共卫生影响”。探索了一系列优化考虑因素。当现有的公共卫生举措不够有效、预计优化过程会产生有意义的改进、通常可以获得评估影响的质量数据并且有稳定且持续的资源来支持时，优化被认为是最合适的。参与者认为，可以应用优化来改善干预措施、实施策略或两者对利益相关者或最终用户重视的结果的影响。虽然优化过程被认为可以通过了解干预或实施策略的机制来促进，但对于通知有关修改以提高影响的决策的最佳方法尚未达成一致。结论 研究结果为未来研究探索优化在公共卫生领域的潜在影响提供了坚实的基础。