基层医疗中抑郁管理的资源配置：一个简单基于数据的筛选模型。
Resource allocation for depression management in general practice: A simple data-based filter model.
机构信息
Health Behaviour Research Collaborative, School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia.
Priority Research Centre for Health Behaviour, University of Newcastle, Callaghan, NSW, Australia.
出版信息
PLoS One. 2021 Feb 19;16(2):e0246728. doi: 10.1371/journal.pone.0246728. eCollection 2021.
BACKGROUND
This study aimed to illustrate the potential utility of a simple filter model in understanding the patient outcome and cost-effectiveness implications for depression interventions in primary care.
METHODS
Modelling of hypothetical intervention scenarios during different stages of the treatment pathway was conducted.
RESULTS
Three scenarios were developed for depression related to increasing detection, treatment response and treatment uptake. The incremental costs, incremental number of successes (i.e., depression remission) and the incremental costs-effectiveness ratio (ICER) were calculated. In the modelled scenarios, increasing provider treatment response resulted in the greatest number of incremental successes above baseline, however, it was also associated with the greatest ICER. Increasing detection rates was associated with the second greatest increase to incremental successes above baseline and had the lowest ICER.
CONCLUSIONS
The authors recommend utility of the filter model to guide the identification of areas where policy stakeholders and/or researchers should invest their efforts in depression management.
背景
本研究旨在阐明一种简单的筛选模型在理解初级保健中抑郁干预的患者结局和成本效益影响方面的潜在效用。
方法
对治疗途径不同阶段的干预情景进行建模。
结果
针对与提高检出率、治疗反应和治疗参与度相关的抑郁，开发了三种情景。计算了增量成本、增量成功数（即抑郁缓解）和增量成本效益比（ICER）。在模型情景中，提高提供者的治疗反应导致了比基线时更高的增量成功数，但也伴随着更高的 ICER。提高检出率与基线时增量成功的第二大增长有关，且具有最低的 ICER。
结论
作者建议使用筛选模型来指导政策利益相关者和/或研究人员确定在哪些领域应该投入精力来管理抑郁。
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