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首页> 外文期刊>The European journal of health economics: HEPAC : health economics in prevention and care >Moving beyond a limited follow-up in cost-effectiveness analyses of behavioral interventions.
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Moving beyond a limited follow-up in cost-effectiveness analyses of behavioral interventions.

机译:超越对行为干预的成本效益分析的有限跟进。

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摘要

Cost-effectiveness analyses of behavioral interventions typically use a dichotomous outcome criterion. However, achieving behavioral change is a complex process involving several steps towards a change in behavior. Delayed effects may occur after an intervention period ends, which can lead to underestimation of these interventions. To account for such delayed effects, intermediate outcomes of behavioral change may be used in cost-effectiveness analyses. The aim of this study is to model cognitive parameters of behavioral change into a cost-effectiveness model of a behavioral intervention.The cost-effectiveness analysis (CEA) of an existing dataset from an RCT in which an high-intensity smoking cessation intervention was compared with a medium-intensity intervention, was re-analyzed by modeling the stages of change of the Transtheoretical Model of behavioral change. Probabilities were obtained from the dataset and literature and a sensitivity analysis was performed.In the original CEA over the first 12?months, the high-intensity intervention dominated in approximately 58% of the cases. After modeling the cognitive parameters to a future 2nd?year of follow-up, this was the case in approximately 79%.This study showed that modeling of future behavioral change in CEA of a behavioral intervention further strengthened the results of the standard CEA. Ultimately, modeling future behavioral change could have important consequences for health policy development in general and the adoption of behavioral interventions in particular.
机译:行为干预的成本效益分析通常使用二分结果标准。但是,实现行为改变是一个复杂的过程,涉及多个行为改变步骤。干预期结束后可能会出现延迟效果,这可能导致低估这些干预措施。为了解决此类延迟影响,可以在成本效益分析中使用行为改变的中间结果。这项研究的目的是将行为变化的认知参数建模为行为干预的成本效益模型.RCT现有数据集的成本效益分析(CEA),其中比较了高强度戒烟干预措施通过对行为变化的跨理论模型的变化阶段进行建模,可以重新分析中等强度的干预措施。从数据集和文献中获得概率,并进行敏感性分析。在最初的CEA的前12个月中,高强度干预占了大约58%。在对未来第二年的随访进行认知参数建模之后,大约有79%是这种情况。这项研究表明,行为干预对CEA未来行为变化的建模进一步增强了标准CEA的结果。最终,对未来的行为改变进行建模可能会对总体卫生政策制定,尤其是对行为干预措施的采用产生重要影响。

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