Abstract
BackgroundEstimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection.MethodsWe developed a four-part compartment model for undiagnosed cases of irreversible chronic diseases with a preclinical state that precedes the diagnosis. Applicability of the model is tested in a simulation study of a hypothetical chronic disease and using diabetes data from the Health and Retirement Study (HRS).ResultsA two dimensional system of partial differential equations forms the basis for estimating incidence of the undiagnosed and diagnosed disease states from the prevalence of the associated states. In the simulation study we reach very good agreement between the estimates and the true values. Application to the HRS data demonstrates practical relevance of the methods.DiscussionWe have demonstrated the applicability of the modeling framework in a simulation study and in the analysis of the Health and Retirement Study. The model provides insight into the epidemiology of undiagnosed chronic diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0094-y) contains supplementary material, which is available to authorized users.
Highlights
Estimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection
In the case of diabetes, population surveys have shown that 24 % to 75 % of prevalent cases across different countries and settings have not been diagnosed and the diagnosis lag has been estimated as ranging from three to seven years [4, 5]
The governing equations Analogously to Brinks and Landwehr, [14], we look for the numbers N0(t, a), N1(t, a) and N2(t, a) of healthy, Fig. 1 Chronic disease model with four states
Summary
Estimation of incidence of the state of undiagnosed chronic disease provides a crucial missing link for the monitoring of chronic disease epidemics and determining the degree to which changes in prevalence are affected or biased by detection. Most major causes of chronic morbidity and mortality, including diabetes, cancer, osteoporosis, cardiovascular disease, and dementia, pass through undiagnosed stages, at which clinically defined and recognized thresholds for a particular disease have been met, but diagnosis has not occurred due to either lack of awareness, symptoms, or access to care [1,2,3]. In the United States, for example, trends in diabetes incidence at a national level are assessed using self-reports of diagnosed cases [10]; this means that the degree to which recent diabetes trends have been influenced by shifting awareness or detection of existing cases, as opposed to the rate of occurrence of new cases of disease, is unclear
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