Center for Psychotherapy Research, Heidelberg University Hospital, Heidelberg, Germany.
Institute of Psychology, Heidelberg University, Heidelberg, Germany.
Epidemiol Psychiatr Sci. 2024 Apr 2;33:e19. doi: 10.1017/S204579602400012X.
Depressive disorders are ranked as the single leading cause of disability worldwide. Despite immense efforts, there is no evidence of a global reduction in the disease burden in recent decades. The aim of the study was to determine the public health impact of the current service system (status quo), to quantify its effects on the depression-related disease burden and to identify the most promising strategies for improving healthcare for depression on the population level.
A Markov model was developed to quantify the impact of current services for depression (including prevention, treatment and aftercare interventions) on the total disease burden and to investigate the potential of alternative scenarios (e.g., improved reach or improved treatment effectiveness). Parameter settings were derived from epidemiological information and treatment data from the literature. Based on the model parameters, 10,000,000 individual lives were simulated for each of the models, based on monthly transition rates between dichotomous health states (healthy vs. diseased). Outcome (depression-related disease burden) was operationalized as the proportion of months spent in depression.
The current healthcare system alleviates about 9.5% (95% confidence interval [CI]: 9.2%-9.7%) of the total disease burden related to depression. Chronic cases cause the majority (83.2%) of depression-related burden. From a public health perspective, improving the reach of services holds the largest potential: Maximum dissemination of prevention (26.9%; CI: 26.7%-27.1%) and treatment (26.5%; CI: 26.3%-26.7%) would result in significant improvements on the population level.
The results confirm an urgent need for action in healthcare for depression. Extending the reach of services is not only more promising but also probably more achievable than increasing their effectiveness. Currently, the system fails to address the prevention and treatment of chronic cases. The large proportion of the disease burden associated with chronic courses highlights the need for improved treatment policies and clinical strategies for this group (e.g., disease management and adaptive or personalized interventions). The model complements the existing literature by providing a new perspective on the depression-related disease burden and the complex interactions between healthcare services and the lifetime course.
抑郁障碍是全球范围内导致残疾的单一主要原因。尽管付出了巨大努力,但近几十年来,全球疾病负担并没有减少的迹象。本研究的目的是确定当前服务系统(现状)的公共卫生影响,量化其对与抑郁相关的疾病负担的影响,并确定改善人群抑郁保健的最有前途的策略。
开发了一个马尔可夫模型,以量化当前抑郁症服务(包括预防、治疗和康复干预)对总疾病负担的影响,并研究替代方案(例如,改善服务范围或提高治疗效果)的潜力。参数设置源自流行病学信息和文献中的治疗数据。基于模型参数,针对每种模型,对 1000 万个人的生命进行了模拟,依据的是健康和患病两种状态之间的月度转移率。结果(与抑郁相关的疾病负担)的操作化定义为处于抑郁状态的月数比例。
当前的医疗保健系统缓解了约 9.5%(95%置信区间[CI]:9.2%-9.7%)与抑郁相关的总疾病负担。慢性病例导致了与抑郁相关负担的大部分(83.2%)。从公共卫生的角度来看,改善服务范围具有最大的潜力:最大程度地普及预防(26.9%;CI:26.7%-27.1%)和治疗(26.5%;CI:26.3%-26.7%)将在人群层面上取得显著改善。
结果证实,迫切需要在抑郁症的医疗保健方面采取行动。扩展服务范围不仅更有希望,而且可能比提高其效果更容易实现。目前,该系统未能解决慢性病例的预防和治疗问题。与慢性病程相关的疾病负担比例很大,突出了需要改善该人群的治疗政策和临床策略(例如,疾病管理和适应性或个性化干预)。该模型通过提供与抑郁相关的疾病负担和医疗保健服务与终身病程之间的复杂相互作用的新视角,补充了现有文献。