TB Modelling Group, TB Centre, Centre for the Mathematical Modelling of Infectious Disease, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Am J Epidemiol. 2019 Jun 1;188(6):1155-1164. doi: 10.1093/aje/kwz038.
Mathematical models are increasingly being used to compare strategies for tuberculosis (TB) control and inform policy decisions. Models often do not consider financial and other constraints on implementation and may overestimate the impact that can be achieved. We developed a pragmatic approach for incorporating resource constraints into mathematical models of TB. Using a TB transmission model calibrated for South Africa, we estimated the epidemiologic impact and resource requirements (financial, human resource (HR), and diagnostic) of 9 case-finding interventions. We compared the model-estimated resources with scenarios of future resource availability and estimated the impact of interventions under these constraints. Without constraints, symptom screening in public health clinics and among persons receiving care for human immunodeficiency virus infection was predicted to lead to larger reductions in TB incidence (9.5% (2.5th-97.5th percentile range (PR), 8.6-12.2) and 14.5% (2.5th-97.5th PR, 12.2-16.3), respectively) than improved adherence to diagnostic guidelines (2.7%; 2.5th-97.5th PR, 1.6-4.1). However, symptom screening required large increases in resources, exceeding future HR capacity. Even under our most optimistic HR scenario, the reduction in TB incidence from clinic symptom screening was 0.2%-0.9%-less than that of improved adherence to diagnostic guidelines. Ignoring resource constraints may result in incorrect conclusions about an intervention's impact and may lead to suboptimal policy decisions. Models used for decision-making should consider resource constraints.
数学模型越来越多地被用于比较结核病（TB）控制策略，并为决策提供信息。这些模型通常不考虑实施的财务和其他限制因素，并且可能高估了可以实现的影响。我们开发了一种将资源限制纳入结核病数学模型的实用方法。我们使用针对南非进行校准的结核病传播模型，估计了 9 种病例发现干预措施的流行病学影响和资源需求（财务、人力资源（HR）和诊断）。我们将模型估计的资源与未来资源可用性的情景进行了比较，并估计了在这些约束下干预措施的影响。在没有限制的情况下，预测在公共卫生诊所和接受人类免疫缺陷病毒（HIV）感染治疗的人群中进行症状筛查，将导致结核病发病率更大幅度下降（分别为 9.5%（2.5-97.5%分位数范围（PR），8.6-12.2）和 14.5%（2.5-97.5% PR，12.2-16.3）），而改善诊断指南的依从性仅导致 2.7%的下降（2.5-97.5% PR，1.6-4.1）。然而，症状筛查需要大量增加资源，超过未来 HR 能力。即使在我们最乐观的 HR 情景下，从诊所症状筛查中减少结核病发病率也比改善诊断指南的依从性少 0.2%-0.9%。忽略资源限制可能会导致对干预措施影响的错误结论，并可能导致决策不佳。用于决策的模型应考虑资源限制。
