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A framework for describing the impact of antidepressant medications on population health status.

机译：描述抗抑郁药对人群健康状况影响的框架。

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BACKGROUND: In the absence of strategies for primary prevention, public health initiatives for major depression have generally focused on secondary and tertiary strategies such as case-finding, public and professional education and disease management. Much emphasis has been placed on low reported rates of antidepressant utilization. In principle, increased rates of treatment utilization should lead to improved mental health status at the population level. However, methods for relating antidepressant utilization to population health status have not been described. METHODS: An incidence-prevalence model was developed using data from a Canadian national survey, supplemented by parameter estimates from literature reviews. The lifetime sick-day proportion (LSP) was used to approximate point prevalence. RESULTS: Mathematical simulations using this model produced reasonable approximations of point prevalence for major depression. The model suggests that an improved rate of treatment utilization may not, in itself, lead to substantially reduced prevalence. Reducing the rate of relapse in those with highly recurrent disorders, which can be accomplished by long-term antidepressant treatment, is predicted to have a more substantial impact on population health status. CONCLUSIONS: The model presented here offers a framework for describing the impact of antidepressant treatment on population health status. Mathematical models may assist with decision-making and priority setting in the public health sphere, as illustrated by the model presented here, which challenges some commonly held assumptions.
机译：背景：在缺乏一级预防策略的情况下，针对重度抑郁症的公共卫生措施通常集中于二级和三级策略，例如病例发现，公共和专业教育以及疾病管理。人们已将重点放在报告的抗抑郁药利用率低上。原则上，治疗利用率的提高应导致人口水平上精神健康状况的改善。但是，尚未描述将抗抑郁药的利用与人群健康状况相关的方法。方法：使用加拿大国家调查的数据开发了发病率流行模型，并补充了文献综述的参数估计值。终生患病日比例（LSP）用于估计点患病率。结果：使用该模型的数学模拟得出了重度抑郁的流行点的合理近似值。该模型表明，治疗利用率的提高本身并不会导致患病率的大幅降低。可以通过长期抗抑郁药治疗降低高度复发性疾病的复发率，预计将对人口健康状况产生更大的影响。结论：这里提出的模型提供了一个框架，用于描述抗抑郁治疗对人群健康状况的影响。如此处介绍的模型所示，数学模型可能有助于公共卫生领域的决策和优先级设置，这挑战了一些通常的假设。

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