McLean Susannah, Barbour Victoria, Wild Sarah, Simpson Colin, Sheikh Aziz
PhD Student, Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh, UK
Research Fellow, Allergy and Respiratory Research Group, Centre for Population Health Sciences, The University of Edinburgh, UK.
J Health Serv Res Policy. 2015 Oct;20(4):246-53. doi: 10.1177/1355819615579232. Epub 2015 Apr 2.
Epidemiological models for estimating the prevalence and burden of disease inform health policy and service planning decisions. Our aim was to describe the challenges in evaluating such models using the example of epidemiological models for chronic obstructive pulmonary disease (COPD).
Two reviewers searched Medline, Embase, CAB Abstracts and World Health Organization (WHO) Databases from 1980 to November 2013 for epidemiological models of COPD prevalence and burden. Two reviewers extracted data and assessed the quality of the studies. We then undertook a descriptive and narrative synthesis of data.
We identified 22 models employing a variety of techniques to calculate the prevalence and/or burden of COPD. Models calculated prevalence and/or mortality or other facet of disease burden using demographics and risk factors or trends, Markov-type modelling and microsimulation modelling. The six models which scored highly on the quality framework were: the Peabody model, which generated estimates of COPD prevalence; the WHO DISMOD II model which produced burden estimates in terms of disability adjusted life years with COPD and life years lost to COPD; the Atsou model which gave the life expectancy gains of individual smokers who quit smoking and associated costs; two Dutch COPD models which produced estimates of mortality and health care costs related to COPD; and the Pichon-Riviere model which gave the costs and cost effectiveness of smoking quit programmes.
The field of chronic disease modelling is burgeoning. As a result, policy makers need to understand how to interpret epidemiological models and their data sources.
用于估计疾病患病率和负担的流行病学模型为卫生政策和服务规划决策提供依据。我们的目的是以慢性阻塞性肺疾病（COPD）的流行病学模型为例，描述评估此类模型时所面临的挑战。
两名评审人员检索了1980年至2013年11月期间的Medline、Embase、CAB文摘和世界卫生组织（WHO）数据库，以查找COPD患病率和负担的流行病学模型。两名评审人员提取数据并评估研究质量。然后我们对数据进行了描述性和叙述性综合分析。
我们识别出22个采用多种技术来计算COPD患病率和/或负担的模型。这些模型利用人口统计学和风险因素或趋势、马尔可夫型建模和微观模拟建模来计算患病率和/或死亡率或疾病负担的其他方面。在质量框架中得分较高的六个模型分别是：生成COPD患病率估计值的皮博迪模型；根据慢性阻塞性肺病的伤残调整生命年和因慢性阻塞性肺病而损失的生命年得出负担估计值的世界卫生组织疾病负担模型II；给出戒烟的个体吸烟者预期寿命增加情况及相关成本的阿苏模型；两个得出与慢性阻塞性肺病相关的死亡率和医疗保健成本估计值的荷兰慢性阻塞性肺病模型；以及给出戒烟计划成本和成本效益的皮雄 - 里维耶模型。
慢性病建模领域正在蓬勃发展。因此，政策制定者需要了解如何解读流行病学模型及其数据来源。
