London School of Hygiene & Tropical Medicine, Department of Global Health and Development, 15-17 Tavistock Place, London, WC1H 9SH, United Kingdom.
London School of Hygiene & Tropical Medicine, Department of Global Health and Development, 15-17 Tavistock Place, London, WC1H 9SH, United Kingdom.
Epidemics. 2021 Jun;35:100450. doi: 10.1016/j.epidem.2021.100450. Epub 2021 Mar 13.
Priority setting for infectious disease control is increasingly concerned with physical input constraints and other real-world restrictions on implementation and on the decision process. These health system constraints determine the 'feasibility' of interventions and hence impact. However, considering them within mathematical models places additional demands on model structure and relies on data availability. This review aims to provide an overview of published methods for considering constraints in mathematical models of infectious disease. We systematically searched the literature to identify studies employing dynamic transmission models to assess interventions in any infectious disease and geographical area that included non-financial constraints to implementation. Information was extracted on the types of constraints considered and how these were identified and characterised, as well as on the model structures and techniques for incorporating the constraints. A total of 36 studies were retained for analysis. While most dynamic transmission models identified were deterministic compartmental models, stochastic models and agent-based simulations were also successfully used for assessing the effects of non-financial constraints on priority setting. Studies aimed to assess reductions in intervention coverage (and programme costs) as a result of constraints preventing successful roll-out and scale-up, and/or to calculate costs and resources needed to relax these constraints and achieve desired coverage levels. We identified three approaches for incorporating constraints within the analyses: (i) estimation within the disease transmission model; (ii) linking disease transmission and health system models; (iii) optimising under constraints (other than the budget). The review highlighted the viability of expanding model-based priority setting to consider health system constraints. We show strengths and limitations in current approaches to identify and quantify locally-relevant constraints, ranging from simple assumptions to structured elicitation and operational models. Overall, there is a clear need for transparency in the way feasibility is defined as a decision criteria for its systematic operationalisation within models.
传染病控制的优先排序越来越关注物理投入限制以及对实施和决策过程的其他实际限制。这些卫生系统的限制决定了干预措施的“可行性”，从而影响其效果。然而，在数学模型中考虑这些限制会对模型结构提出额外的要求，并依赖于数据的可用性。本综述旨在提供传染病数学模型中考虑限制的已发表方法概述。我们系统地搜索了文献，以确定采用动态传播模型评估任何传染病和地理区域干预措施的研究，这些研究包括实施的非财务限制。提取了所考虑的限制类型以及如何识别和描述这些限制的信息，以及纳入限制的模型结构和技术。共保留了 36 项研究进行分析。虽然确定的大多数动态传播模型都是确定性的房室模型，但也成功地使用随机模型和基于代理的模拟来评估非财务限制对优先排序的影响。这些研究旨在评估由于阻止成功推出和扩大规模的限制而导致干预措施覆盖率（和项目成本）降低，或者计算需要多少成本和资源来放宽这些限制并实现所需的覆盖率水平。我们确定了在分析中纳入限制的三种方法：（i）在疾病传播模型中进行估计；（ii）将疾病传播和卫生系统模型联系起来；（iii）在约束下进行优化（除预算外）。该综述强调了将基于模型的优先排序扩展到考虑卫生系统限制的可行性。我们展示了当前识别和量化与本地相关的限制的方法的优势和局限性，范围从简单的假设到结构化的启发式和运营模型。总体而言，需要明确界定可行性作为模型中系统操作的决策标准的方式。
