Larner College of Medicine, University of Vermont, Burlington, VT.
RAND Corporation, Pittsburgh, PA.
Med Care. 2021 Jan;59(1):e1-e8. doi: 10.1097/MLR.0000000000001442.
The objective of this study was to examine the potential impact of provider social networks and experiences with patients on deimplementation of breast cancer screening.
We constructed the Breast Cancer-Social network Agent-based Model (BC-SAM), 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 30-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 50-74 remain unchanged but their recommendations for screening among younger (below 50 y old) and older (75+ y 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% decline in provider recommendations to older women over 30 years resulted in an estimated 23% 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 overscreening.
本研究旨在探讨提供者的社交网络和患者体验对乳腺癌筛查实施的潜在影响。
我们构建了乳腺癌-社交网络基于代理的模型（BC-SAM），该模型描述了 10000 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 年内的筛查建议。该模型有患者和提供者两个模块，每个模块都包含社交网络的影响。患者和提供者在网络中相互连接，该网络代表了患者-患者同伴之间的联系、提供者-提供者同伴之间的联系、提供者与他们治疗的患者之间的联系，以及患者和提供者之间的朋友/家庭关系。我们使用 2016 年在美国开展的全国初级保健医生 CanSNET 调查的数据来校准模型中的提供者决策。
首先，假设提供者对 50-74 岁女性的筛查建议保持不变，但对较年轻（<50 岁）和较年长（75 岁及以上）女性的筛查建议减少，我们观察到由于溢出效应，预计 50-74 岁女性的筛查率下降。其次，年轻和年长女性的筛查率对提供者建议的变化反应缓慢；30 年内，提供者对 75 岁以上女性的建议减少 78%，导致该组患者的筛查估计减少 23%。第三，提供者对未筛查患者、朋友和家庭成员的经验随着时间的推移适度增加了筛查建议（增加了 7 个百分点）。最后，我们发现提供者之间的影响可以对人群筛查率产生重大影响，并可能使现有做法根深蒂固。
将癌症筛查建模为一个复杂的社会系统，展示了一系列潜在的影响，并可能有助于针对未来旨在减少过度筛查的干预措施。
