减少老年妇女乳腺癌过度筛查的信息在医疗最小化者和最大化者之间有不同的反应吗?

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Nancy L Schoenborn, Sarah E Gollust, Rebekah H Nagler, Mara A Schonberg, Cynthia M Boyd, Qian-Li Xue, Yaldah M Nader, Craig E Pollack
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引用次数: 0

摘要

短信策略有望减少乳腺癌的过度筛查。然而,目前尚不清楚它们是否会在医疗最大化者和医疗最小化者之间产生不同的影响,前者更愿意为自己的健康采取行动,后者更愿意观望。方法在一项随机对照调查实验中,我们对3041名65岁以上的女性进行了2次连续调查,这些女性来自一个基于美国人群的在线小组,我们将参与者随机分为3组:1)没有信息,2)单一暴露于筛查性戒烟信息,或3)2次暴露于筛查性戒烟信息。我们以7分制评估了对假设患者停止筛查的支持度和自己停止筛查的意愿,其中较高的值表示更强的支持度和停止筛查的意愿。我们通过医学最大化偏好和适度分析进行分层分析。结果40.7% (n = 1238)的女性是医学最大化者;与医学最小化者相比,他们在所有组中对筛查戒烟的支持度和意愿都较低。两种信息暴露增加了对药物最大化者筛查戒烟的支持,平均得分为3.68(95%可信区间[CI] 3.51-3.85),而无信息暴露者(平均得分2.20,95% CI 2.00-2.39, P < 0.001)。筛选意向也出现了类似的模式。线性回归模型显示,医疗最大化偏好没有差异信息效应。结论:医学最大化者虽然不太可能支持停止筛查,但仍然对旨在减少乳腺癌过度筛查的信息策略有反应。目前尚不清楚关于停止乳腺癌筛查的理由的信息是否会在医疗最大化者和医疗最小化者之间产生不同的影响,前者在涉及到自己的健康时更愿意采取行动,后者更愿意观望。在一项对3041名老年妇女进行的两波随机对照调查实验中,我们发现,尽管与医疗最小化者相比,医疗最大化者不太可能支持筛查停止,但仍然对信息干预有反应,并且最大化者和最小化者之间的干预效果相似。与最小化者相比,医学最大化者在阅读信息后报告的担忧和烦恼程度更高,但绝对担忧和烦恼程度较低。我们的研究结果表明,即使是在极不情愿的人群中,短信也可以成为减少过度筛查的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does Messaging for Reducing Breast Cancer Overscreening in Older Women Have Differential Responses among Medical Minimizers and Maximizers?

BackgroundMessaging strategies hold promise to reduce breast cancer overscreening. However, it is not known whether they may have differential effects among medical maximizers who prefer to take action about their health versus medical minimizers who prefer to wait and see.MethodsIn a randomized controlled survey experiment that included 2 sequential surveys with 3,041 women aged 65+ y from a US population-based online panel, we randomized participants to 1) no messages, 2) single exposure to a screening cessation message, or 3) 2 exposures over time to the screening cessation message. We assessed support for stopping screening in a hypothetical patient and intention to stop screening oneself on 7-point scales, where higher values indicated stronger support and intentions to stop screening. We conducted stratified analyses by medical-maximizing preference and moderation analysis.ResultsOf the women, 40.7% (n = 1,238) were medical maximizers; they had lower support and intention for screening cessation in all groups compared with the medical minimizers. Two message exposures increased support for screening cessation among medical maximizers, with a mean score of 3.68 (95% confidence interval [CI] 3.51-3.85) compared with no message (mean score 2.20, 95% CI 2.00-2.39, P < 0.001). A similar pattern was seen for screening intention. Linear regression models showed no differential messaging effect by medical-maximizing preference.ConclusionsMedical maximizers, although less likely to support screening cessation, were nonetheless responsive to messaging strategies designed to reduce breast cancer overscreening.HighlightsIt is not known if a message on rationales for stopping breast cancer screening would have differential effects among medical maximizers who prefer to take action when it comes to their health versus medical minimizers who prefer to wait and see.In a 2-wave randomized controlled survey experiment with 3,041 older women, we found that medical maximizers, although less likely to support screening cessation compared with medical minimizers, were nonetheless responsive to the messaging intervention, and the magnitude of the intervention effect was similar between maximizers and minimizers.Medical maximizers reported higher levels of worry and annoyance after reading the message compared with the minimizers, but the absolute levels of worry and annoyance were low.Our findings suggest that messaging can be a useful tool for reducing overscreening even in a highly reluctant population.

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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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