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Using patient-level decision modeling to improve use of treatments for alcohol use disorder

Using patient-level decision modeling to improve use of treatments for alcohol use disorder
使用患者层面的决策模型来改善酒精使用障碍治疗的使用
批准号:
10712474
负责人:
Phuc Le
金额:
$68.37万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-15 至 2028-06-30

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中文摘要
翻译
项目摘要 酒精使用障碍(AUD),以前是酒精滥用和依赖,是一个重大的公共卫生问题。 在美国,过度饮酒导致9.3万人死亡和270万岁的潜在寿命损失, 每年耗资2500亿美元。2012-13年,美国成年人的12个月和终生AUD患病率为 分别为14%和29%,比十年前增长了40%以上。新冠肺炎以来 在大流行期间,酒精消费甚至增加得更多。过度饮酒会影响各种 身体器官,导致许多健康后果。幸运的是,AUD比一般情况下更容易治疗。 相信。认知行为疗法、简短干预和动机增强疗法是最多的 被广泛研究的行为干预措施也同样有效,而戒酒互助会(AA)是 大多数人寻求互助团体。药物治疗可以与行为干预相结合 增加成功。然而,AUD的治疗没有得到充分利用。患者和患者之间的误解 医生认为治疗无效是使用的一个重要障碍。另一种普遍的观点是 治疗的成功需要完全戒酒。事实上,从澳元病中恢复可以包括大量饮酒, AUD的治疗是有效的。将治疗效果转化为长期的健康益处可能 帮助说服医生提供治疗,并鼓励患者使用它。然而,随机对照 单靠试验(RCT)不能提供这种信息,因为它们不能衡量长期的健康结果, 他们也不会一对一地比较所有的治疗方法,这使得在其中进行选择变得困难。模拟法 建模提供了比较处理的综合方法,但现有的AUD模型中没有一个是 旨在评估所有治疗方法。我们设计了一个描述治疗选项的决策辅助工具(DA),但它确实如此 尚不包括由于在开发时缺乏此类数据而产生的长期惠益估计。项目 目的:开发、验证和应用计算机模拟模型,以便为决策者、医生、 以及患者对AUD治疗的终生受益。目标1:开发和验证微观模拟模型 导致饮酒行为改变的酒精相关并发症的自然病史和 随时间推移的AUD状态。目的2:评估AUD治疗的比较效果。目标3:成立公司 患者和提供者的反馈,以评估增强现有DA的清晰度、内容和可接受性 具有比较有效性的数据。影响:我们的模型将更好地理解预期 并为决策建模和治疗选择提供了一种创新的方法。 通过将长期有效性数据纳入现有的DA,我们的研究将为医生和 患者拥有有价值的证据和支持选择最佳治疗的工具,从而帮助关闭 治疗差距。最终,我们的项目将解决NIAAA的研究任务,同时提高 数千万患有澳元的美国成年人的生活。
英文摘要
Project Summary Alcohol use disorder (AUD), previously alcohol abuse and dependence, is a significant public health problem. In the US, excessive alcohol use is responsible for 93,000 deaths and 2.7 million years of potential life lost, at a cost of $250 billion annually. In 2012-13, the 12-month and life-time prevalence of AUD in US adults was 14% and 29%, respectively, increasing by more than 40% from a decade earlier. Since the Covid-19 pandemic, alcohol consumption has increased even more. Excessive alcohol consumption affects various bodily organs, leading to many health consequences. Fortunately, AUD is more treatable than commonly believed. Cognitive behavioral therapy, brief interventions, and motivational enhancement therapy are the most widely studied behavioral interventions and are similarly efficacious, while Alcoholics Anonymous (AA) is the most sought out mutual support group. Pharmacotherapy can be combined with behavioral interventions to increase success. However, treatments for AUD are underutilized. The misconception among patients and physicians that treatment is ineffective is an important obstacle to use. Another is the common view that treatment success requires total abstinence. In reality, recovery from AUD can include some heavy drinking, and AUD treatment can be effective. Expressing treatment effectiveness into long-term health benefits could help convince physicians to offer treatment and encourage patients to use it. However, randomized controlled trials (RCTs) alone cannot provide this information because they do not measure long-term health outcomes, nor do they compare all treatments head-to-head, making it difficult to choose among them. Simulation modeling offers a comprehensive approach to comparing treatments, but none of existing AUD models were designed to assess all treatments. We designed a decision aid (DA) describing treatment options, but it does not yet include estimates of long-term benefits due to lack of such data at the time of development. Project objective: To develop, validate, and apply a computer simulation model to inform policy makers, physicians, and patients of the lifetime benefit of treatments for AUD. Aim 1: Develop and validate a microsimulation model of the natural history of alcohol-associated complications accounting for the change in drinking behaviors and AUD status over time. Aim 2: Assess the comparative effectiveness of AUD treatments. Aim 3: Incorporate patient and provider feedback to assess the clarity, content, and acceptability of augmenting an existing DA with comparative effectiveness data. Impact: Our model will offer a better understanding of the expected benefit of AUD treatments and provide an innovative approach to decision modeling and treatment selection. By incorporating long-term effectiveness data into an existing DA, our study will provide physicians and patients with valuable evidence and a tool to support selection of optimal treatments, thereby helping to close the treatment gap. Ultimately, our project will address NIAAA’s research missions while improving the quality of life for tens of millions of US adults suffering from AUD.
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