Using an Agent-based Model to Examine Deimplementation of Breast Cancer Screening.
Using an Agent-based Model to Examine Deimplementation of Breast Cancer Screening.
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DOI：
10.1097/mlr.0000000000001442
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发表时间：
2021-01
期刊：
影响因子：
3
通讯作者：
Pollack CE
中科院分区：
文献类型：
--
作者：
Nowak SA;Parker AM;Radhakrishnan A;Schoenborn N;Pollack CE
研究提供者社交网络和患者经历对取消乳腺癌筛查的潜在影响。我们构建了乳腺癌社交网络基于代理的模型 (BC-SAM)，这是一种基于代理的模型，描述了 10,000 名 40 岁及以上女性的乳腺癌筛查决策、发病率和进展情况，以及其提供者在 30 年间的筛查建议。该模型具有患者和提供者模块，每个模块都包含社交网络的影响。患者和提供者在一个网络中连接，该网络代表患者与患者的同伴连接、提供者与提供者的同伴连接、提供者和他们治疗的患者之间的连接以及患者和提供者之间的朋友/家人关系。我们使用 2016 年进行的美国 CanSNET 全国初级保健医生调查的数据来校准模型中提供者的决策。首先，假设提供者对 50 至 74 岁女性的筛查建议保持不变，但他们对年轻（<50 岁）和老年（75 岁以上）女性的筛查建议减少，我们观察到由于溢出效应，50-74 岁女性的预测筛查率有所下降。其次，年轻和老年女性的筛查率对医疗服务提供者建议的变化反应缓慢； 30 岁以上老年女性的医疗服务提供者推荐量下降了 78%，导致该群体的患者筛查量估计下降了 23%。第三，随着时间的推移，提供者对未经筛查的患者、朋友和家人的经验略有增加筛查建议（7 个百分点）。最后，我们发现提供者的同伴效应可以对人群筛查率产生重大影响，并可以巩固现有的做法。将癌症筛查建模为一个复杂的社会系统，展示了一系列潜在影响，并可能有助于确定未来旨在减少过度筛查的干预措施。
To examine the potential impact of provider social networks and experiences with patients on de-implementation of breast cancer screening. We constructed the Breast Cancer-Social network Agent-based Model (BC-SAM), an agent-based model, which depicts breast cancer screening decisions, incidence, and progression among 10,000 women ages 40 and over and the screening recommendations of their providers over a thirty-year period. The model has patient and provider modules that each incorporate social network influences. Patients and providers were connected in a network, which represented patient-patient peer connections, provider-provider peer connections, connections between providers and patients they treat, and friend/family relationships between patients and providers. We calibrated provider decisions in the model using data from the CanSNET national survey of primary care physicians in the United States, which we fielded in 2016. First, assuming that providers’ screening recommendations for women ages 50 to 74 remain unchanged but their recommendations for screening among younger (<50 years-old) and older (75+ years-old) women decrease, we observed a decline in predicted screening rates for women ages 50-74 due to spillover effects. Second, screening rates for younger and older women were slow to respond to changes in provider recommendations; a 78 percent decline in provider recommendations to older women over 30 years resulted in an estimated 23 percent decline in patient screening in that group. Third, providers’ experiences with unscreened patients, friends, and family members modestly increased screening recommendations over time (7 percentage points). Finally, we found that provider peer effects can have a substantial impact on population screening rates and can entrench existing practices. Modeling cancer screening as a complex social system demonstrates a range of potential effects and may help target future interventions designed to reduce over-screening.