目的:在《平价医疗法案》(ACA)的规定中,扩大医疗补助可以说是增加医疗服务的最大贡献者。十多年来,研究人员调查了医疗补助扩张如何影响癌症预后。在同一个十年里,统计理论阐明了基于国家的政策研究如何因无效的推断而受到损害。在回顾文献以确定基于州的癌症注册中心医疗补助扩展研究的推断策略之后,这项研究旨在评估推断决策如何改变医疗补助扩大对分期的影响的解释,治疗,和癌症患者的死亡率。
方法:癌症病例数据(2000-2019年)来自监测,流行病学,最终结果(SEER)计划。病例包括所有癌症部位的合并,合并前10个癌症部位,和三种筛查癌症(结直肠癌,女性乳房,女性子宫颈)。
方法:差异设计估计了医疗补助扩大与四个二元结果之间的关联:遥远阶段,诊断后>1个月开始治疗,没有手术建议,和死亡。比较了三种推理技术:(1)传统的,(2)集群,和(3)野生集群引导。
方法:通过SEER*Stat访问数据。
结果:通过传统推断估计标准误差将表明医疗补助扩大与延迟开始治疗和手术建议有关。传统和集群推断还表明,医疗补助扩大降低了死亡率。使用WildClusterBootstrap技术进行推理从未拒绝过无效假设。
结论:这项研究重申了明确推断的重要性。未来基于状态,癌症政策研究可以通过纳入新兴技术来改进。这些发现在解释之前的SEER研究报告医疗补助扩大对癌症结局的显著影响时,值得谨慎。特别是没有明确定义他们的推理策略的研究。
OBJECTIVE: Among the provisions within the Affordable Care Act (ACA), expanding Medicaid was arguably the greatest contributor to increasing access to care. For over a decade, researchers have investigated how Medicaid expansion impacted cancer outcomes. Over this same decade, statistical theory illuminated how state-based policy research could be compromised by invalid inference. After reviewing the literature to identify the inference strategies of state-based cancer registry Medicaid expansion research, this study aimed to assess how inference decisions could change the interpretation of Medicaid expansion\'s impact on staging, treatment, and mortality in cancer patients.
METHODS: Cancer case data (2000-2019) was obtained from the Surveillance, Epidemiology, End Results (SEER) programme. Cases included all cancer sites combined, top 10 cancer sites combined, and three screening amenable cancers (colorectal, female breast, female cervical).
METHODS: A Difference-in-Differences design estimated the association between Medicaid expansion and four binary outcomes: distant stage, initiating treatment >1 month after diagnosis, no surgery recommendation, and death. Three inference techniques were compared: (1) traditional, (2) cluster, and (3) Wild Cluster
Bootstrap.
METHODS: Data was accessed via SEER*Stat.
RESULTS: Estimating standard errors via traditional inference would suggest that Medicaid expansion was associated with delayed treatment initiation and surgery recommendations. Traditional and clustered inference also suggested that Medicaid expansion reduced mortality. Inference using Wild Cluster
Bootstrap techniques never rejected the null hypotheses.
CONCLUSIONS: This study reiterates the importance of explicit inference. Future state-based, cancer policy research can be improved by incorporating emerging techniques. These findings warrant caution when interpreting prior SEER research reporting significant effects of Medicaid expansion on cancer outcomes, especially studies that did not explicitly define their inference strategy.