University of Massachusetts-Amherst, Amherst, MA, USA.
World Health Organization, Geneva, Switzerland.
Med Decis Making. 2020 Apr;40(3):364-378. doi: 10.1177/0272989X20910724. Epub 2020 Mar 11.
Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.
中低收入国家（LMICs）的乳腺癌死亡率与发病率之比高于高收入国家（HICs），原因是诊断较晚。乳腺 X 线筛查被推荐用于早期诊断，但 LMICs 的基础设施能力远远低于采用当前筛查指南所需的水平。当前的指南是从 HICs 推断出来的，因为有限的数据限制了针对 LMICs 的模型开发，因此，针对基础设施能力的特定筛查计划的经济分析是不可用的。我们应用了一种新的马尔可夫过程方法来开发癌症进展模型和马尔可夫决策过程模型，以确定在每人进行不同次数的终生筛查（基础设施能力的代表）下的最佳筛查计划。我们以中低收入国家秘鲁为案例研究，并以高收入国家美国进行验证。实施 2 次、5 次、10 次和 15 次终生筛查将分别需要大约 55 个、135 个、280 个和 405 个乳腺 X 线机，并且将分别为每 1000 名女性节省 31 个、62 个、95 个和 112 个生命年。当前的指南建议进行 15 次终生筛查，但秘鲁全国只有 55 台乳腺 X 线机。在这种能力下，最佳策略是在 50 岁和 56 岁时进行 2 次终生筛查。随着基础设施扩大以适应 5 次和 10 次终生筛查，分别在 44-61 岁和 41-64 岁之间进行筛查将产生最佳效果。我们在美国的结果与其他模型和当前指南一致。我们模型的范围仅限于对国家级指南的分析。我们没有对全国范围内的异质性进行建模。在不同基础设施能力下，针对特定国家的最佳筛查计划可以系统地指导癌症控制计划的制定和卫生投资的规划。
