OBJECTIVES: Overestimation of real-world prevalence in rare diseases can be a significant issue for policy makers and the pharmaceutical industry. This work explores reasons for the overestimation and develops an outline methodology for the application of drag factors when calculating prevalence based on reported incidence. Two rare epileptic encephalopathies Dravet syndrome (DS) and Lennox-Gastaut syndrome(LGS) were used as illustrative examples. METHODS: For both rare diseases, a targeted literature review without restriction on publication date was performed to (1) identify all reports of incidence, prevalence and mortality rates and (2) develop a detailed description of how diagnostic practice has evolved over time. The themes considered included time from syndrome identification, developments in disease definition/diagnostic criteria and inclusion (or lack of) in clinical guidelines, availability of any or improved therapies and the development of diagnostic tools. A conceptual model was developed to calculate prevalence based on reported incidence (traditional approach) versus adjusted incidence (according to factors that cause a diagnostic drag). RESULTS: For DS patients, calculated prevalence based on diagnostic incidence data matches the real-world prevalence for patients under 18 years old, but overestimates the 18 years and older real-world prevalent population. In the case of LGS, the available epidemiological data are heterogeneous and we were not able to reliably compare calculated with real-world prevalence. The proposed conceptual model incorporating diagnostic drag reflects predicted real-world prevalence of adults with DS in Sweden (104 individuals) more accurately than traditional calculated prevalence (191 for the conceptual model vs 257 for the traditional model). CONCLUSIONS: Methodological challenges in measuring epidemiology, coupled with advances in rare disease discovery may cause discrepancies between real-world and calculated prevalence. Care should be taken with calculated prevalence figures to not overstate the real-world prevalence in rare diseases.