A comprehensive framework to estimate the frequency, duration and risk factors for diagnostic delays using simulation-based methods

The incidence of diagnostic delays is unknown for many diseases and particular healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or inapplicable to various diseases or settings. In this paper we propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We start by providing a conceptual model of the disease-diagnostic, data-generating process. We then propose a simulation-based method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach is specifically designed to identify missed diagnostic opportunities based on signs and symptoms that occur prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different simulation algorithms are described for implementing this approach. We summarize estimation procedures that may be used to parameterize the simulation. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke and evaluate the estimated frequency and duration of diagnostic delays for these diseases. Our approach can be customized to fit a range of disease and we summarize how the choice of simulation algorithm may impact the resulting estimates.