Models for estimating projections for disease prevalence and burden: a systematic review focusing on chronic obstructive pulmonary disease.

ObjectiveEpidemiological 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).MethodsTwo 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.ResultsWe 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.ConclusionsThe field of chronic disease modelling is burgeoning. As a result, policy makers need to understand how to interpret epidemiological models and their data sources.