Centre for Surveillance and Applied Research, Health Promotion and Chronic Disease Prevention Branch, Public Health Agency of Canada, 785 Carling Ave, Ottawa, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
Health Res Policy Syst. 2023 Jun 13;21(1):50. doi: 10.1186/s12961-023-00995-7.
Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field.
We searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting.
We identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability.
The review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level.
抑郁是一种由个体和系统层面的动态过程驱动的疾病。系统动力学 (SD) 模型是捕捉这种复杂性的有用工具，可以预测未来抑郁的流行程度，并了解干预措施和政策的潜在影响。SD 模型已被用于对传染病和慢性病进行建模，但很少应用于心理健康领域。本范围综述旨在确定基于人群的抑郁 SD 模型，并报告其建模策略以及对政策和决策的应用，以推动这一新兴领域的研究。
我们从 2021 年 10 月 20 日起，在 MEDLINE、Embase、PsychInfo、Scopus、MedXriv 和系统动力学学会的摘要中搜索了有关人群水平抑郁 SD 模型的文章，以确定研究人群水平 SD 模型的文章。我们提取了有关模型目的、SD 模型要素、结果和干预措施的数据，并评估了报告质量。
我们确定了 1899 条记录，发现了四项符合纳入标准的研究。这些研究使用 SD 模型评估了各种系统层面的过程和干预措施，包括加拿大抗抑郁药使用对人群水平抑郁的影响；美国回忆错误对终生抑郁估计的影响；美国有和没有抑郁的成年人吸烟相关结果；以及津巴布韦抑郁发病率和咨询率增加对抑郁的影响。这些研究包括了抑郁严重程度、复发和缓解的各种存量和流量，但所有模型都包括了抑郁的发病率和复发流量。所有模型都存在反馈回路。三项研究提供了足够的可复制信息。
该综述强调了 SD 模型在对人群水平抑郁的动态进行建模和为政策和决策提供信息方面的有用性。这些结果可以帮助指导未来在人群水平上应用 SD 模型来治疗抑郁症。
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