Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States.
Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia.
BMC Public Health. 2023 Aug 2;23(1):1470. doi: 10.1186/s12889-023-16022-x.
Cervical cancer (CC) is globally ranked fourth in terms of incidence and mortality among women. Vaccination against Human Papillomavirus (HPV) and screening programs can significantly reduce CC mortality rates. Hence, executing cost-effective public health policies for prevention and surveillance is crucial. However, defining policies that make the best use of the available resources is not easy, as it requires predicting the long-term costs and results of interventions on a changing population. Since the simpler task of predicting the results of public health policies is difficult, devising those that make the best usage of available resources is an arduous challenge for decision-makers.
This paper proposes a fine-grained epidemiological simulation model based on differential equations, to effectively predict the costs and effectiveness of CC public health policies that include vaccination and screening. The model represents population dynamics, HPV transmission within the population, likelihood of infection clearance, virus-induced appearance of precancerous lesions and eventually CC, as well as immunity gained with vaccination and early detection with screening.
We offer a compartmentalized modeling approach that separates population, epidemics, and intervention concerns. We instantiate models with actual data from a Colombian case study and analyze their results to show how our modeling approach can support CEA studies. Moreover, we implement models in an open-source software tool to simultaneously define and evaluate multiple policies. With the support of the tool, we analyze 54 policies within a 30-year time horizon and use as a comparator the CC policy that has been used until recently. We identify 8 dominant policies, the best one with an ICER of 6.3 million COP (Colombian Pesos) per averted DALY. We also validate the modeling approach against the available population and HPV epidemic data. The effects of uncertainty in the values of key parameters (discount rate, sensitivity of screening tests) is evaluated through one-way sensitivity analysis.
Our modeling approach can provide valuable support for healthcare decision-makers. The implementation into an automated tool allows customizing the analysis with country-specific data, flexibly defining public health policies to be evaluated, and conducting disaggregate analyses of their cost and effectiveness.
宫颈癌（CC）是全球女性中发病率和死亡率排名第四的癌症。接种人乳头瘤病毒（HPV）疫苗和筛查计划可以显著降低 CC 的死亡率。因此，执行具有成本效益的预防和监测公共卫生政策至关重要。然而，定义能够充分利用现有资源的政策并不容易，因为这需要预测干预措施对不断变化的人群的长期成本和结果。由于预测公共卫生政策结果的简单任务很困难，因此为决策者设计最佳利用可用资源的政策是一项艰巨的挑战。
本文提出了一种基于微分方程的精细流行病学模拟模型，以有效预测包括疫苗接种和筛查在内的 CC 公共卫生政策的成本和效果。该模型代表了人口动态、人群中 HPV 的传播、感染清除的可能性、病毒引起的癌前病变的出现以及最终的 CC，以及疫苗接种获得的免疫力和筛查的早期检测。
我们提供了一种分离人群、流行病和干预关注点的分域建模方法。我们使用来自哥伦比亚案例研究的实际数据实例化模型，并分析其结果，以展示我们的建模方法如何支持 CEA 研究。此外，我们在开源软件工具中实现模型，以同时定义和评估多种政策。在该工具的支持下，我们在 30 年的时间范围内分析了 54 项政策，并将最近使用的 CC 政策作为比较基准。我们确定了 8 种主导政策，其中最好的一种政策每避免一个残疾调整生命年（DALY）的成本效益比（ICER）为 630 万哥伦比亚比索（COP）。我们还针对可用的人口和 HPV 流行数据对建模方法进行了验证。通过单向敏感性分析评估了关键参数（贴现率、筛查试验灵敏度）值不确定性的影响。
我们的建模方法可以为医疗保健决策者提供有价值的支持。将其实现到自动化工具中可以使分析具有国家特定数据，灵活地定义要评估的公共卫生政策，并对其成本和效果进行细分分析。
