Population Health Metrics
A generic model for the assessment of disease epidemiology: the computational basis of DisMod II
Jan J Barendregt*1, Gerrit J van Oortmarssen1, Theo Vos2 and Christopher JL Murray3
Address: 1Department of Public Health, Erasmus MC, Rotterdam, Netherlands, 2Victorian Government Department of Human Services, and Department ofEpidemiology and Preventive Medicine, Monash University, Melbourne, Australia and 3Global Programme on Evidence for Health Policy, World Health Organization, Geneva, Switzerland Email: Jan J Barendregt* - email@example.com; Gerrit J van Oortmarssen - firstname.lastname@example.org; Theo Vos - email@example.com; Christopher JL Murray - firstname.lastname@example.org * Corresponding authorPublished: 14 April 2003 Population Health Metrics 2003, 1:4 This article is available from: http://www.pophealthmetrics.com/content/1/1/4
Received: 19 March 2003 Accepted: 14 April 2003
© 2003 Barendregt et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preservedalong with the article's original URL.
Epidemiology as an empirical science has developed sophisticated methods to measure the causes and patterns of disease in populations. Nevertheless, for many diseases in many countries only partial data are available. When the partial data are insufficient, but data collection is not an option, it is possible to supplement the data by exploitingthe causal relations between the various variables that describe a disease process. We present a simple generic disease model with incidence, one prevalent state, and case fatality and remission. We derive a set of equations that describes this disease process and allows calculation of the complete epidemiology of a disease given a minimum of three input variables. We give the example of asthmawith age-specific prevalence, remission, and mortality as inputs. Outputs are incidence and case fatality, among others. The set of equations is embedded in a software package called 'DisMod II', which is made available to the public domain by the World Health Organization.
Assessment of the epidemiology of a disease is often very hard. Data on incidence, prevalence and diseasespecific mortality are frequently incomplete, not very reliable, or altogether lacking. The solution of choice is gathering good data, but this is time-consuming, often difficult, and always costly. When primary data collection is no real option, as in a burden of disease study where the goal is a comprehensive overview of the epidemiology of a large number of diseases, additional methods of assessingdisease epidemiology are needed. Additional information can be derived from the logical relations between the variables that describe a disease. By
definition, a prevalent case must have been incident at some earlier time and age. Also, it is impossible to die or recover from a disease without having had the disease, however brief. These logical relations can be expressed as a formal model of ageneric disease process. Such a formal disease model allows calculation of a complete and internally consistent description of disease epidemiology from partial data. For the Global Burden of Disease 1990 study a generic formal disease model was implemented as a computer model called 'DisMod' [1,2]. In that study and in subsequent country studies, DisMod has been used extensively to supplementmissing data and force consistency on data that
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were available. DisMod is based on a set of differential equations that describe age specific incidence, remission, case fatality, and 'all other causes' mortality. With total mortality and three transition...
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