Department of Population Health Sciences, Duke University, Durham, NC, USA; Duke Clinical Research Institute, Duke University, Durham, NC, USA; Signature Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore.
Department of Population Health Sciences, Duke University, Durham, NC, USA; Duke Clinical Research Institute, Duke University, Durham, NC, USA.
Soc Sci Med. 2024 May;348:116850. doi: 10.1016/j.socscimed.2024.116850. Epub 2024 Apr 9.
Discrete Choice Experiments (DCEs) are widely employed survey-based methods to assess preferences for healthcare services and products. While they offer an experimental way to represent health-related decisions, the stylized representation of scenarios in DCEs may overlook contextual factors that could influence decision-making. The aim of this paper was to evaluate the predictive validity of preferences elicited through a DCE in decisions likely influenced by a hot-cold empathy gap, and compare it to another commonly used method, a direct-elicitation question. We focused on preferences for pain-relief modalities, especially for an epidural during childbirth - a context where direct-elicitation questions have shown a preference for or intention to have a natural birth (representing the "cold" state), yet individuals often opt for an epidural during labor (representing the "hot" state). Leveraging a unique dataset collected from 248 individuals, we incorporated both the stated preferences collected through a survey administered upon hospital admission for childbirth and the actual pain-relief modality usage data documented in medical records. The DCE allowed for the evaluation of scenarios outside of those expected by respondents to simulate decision-making during childbirth. When we compared the predicted epidural use with the actual epidural use during labor, we observed a choice concordance of 71-60%, depending on the model specification. The concordance rate between the predicted and actual choices increased to 77-76% when accounting for the initial use of other ineffective modalities. In contrast, the direct-elicitation choices, relying solely on respondents' baseline expectations, yielded a lower concordance rate of 58% with actual epidural use. These findings highlight the flexibility of the DCE method in simulating complex decision contexts, including those involving hot-cold empathy gaps. The DCE proves valuable in assessing nuanced preferences, providing a more accurate representation of the decision-making processes in healthcare scenarios.
离散选择实验（DCE）是一种广泛应用于评估医疗服务和产品偏好的基于调查的方法。虽然它们提供了一种实验性的方法来表示与健康相关的决策，但 DCE 中情景的程式化表示可能忽略了可能影响决策的情境因素。本文的目的是评估通过 DCE 得出的偏好在可能受到冷热共情差距影响的决策中的预测有效性，并将其与另一种常用方法，即直接 elicitation 问题进行比较。我们专注于疼痛缓解方式的偏好，特别是在分娩期间使用硬膜外麻醉的情况下——在这种情况下，直接 elicitation 问题显示出对自然分娩的偏好或意图（代表“冷”状态），但在分娩过程中，个体通常选择硬膜外麻醉（代表“热”状态）。利用从 248 名个体收集的独特数据集，我们将通过在医院分娩时进行的调查收集的陈述偏好以及在医疗记录中记录的实际疼痛缓解方式使用数据纳入其中。DCE 允许评估超出受访者预期的情景，以模拟分娩期间的决策。当我们将预测的硬膜外麻醉使用率与实际分娩期间的硬膜外麻醉使用率进行比较时，我们观察到模型规范的选择一致性在 71-60%之间。当考虑到初始使用其他无效方式时，预测和实际选择之间的一致性率增加到 77-76%。相比之下，仅依赖于受访者基线预期的直接 elicitation 选择产生的实际硬膜外麻醉使用率的一致性率较低，为 58%。这些发现强调了 DCE 方法在模拟复杂决策情境（包括涉及冷热共情差距的情境）方面的灵活性。DCE 在评估细微偏好方面具有价值，为医疗场景中的决策过程提供了更准确的表示。
