Modeling delay to diagnosis for Amyotrophic lateral sclerosis: Under reporting and incidence estimates

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Abstract

Background: This paper provides a strategy to obtain a reliable estimate of the incidence rate for Amyotrophic lateral sclerosis based on data from the National Registry of Rare Diseases (NRRD). In fact, unobserved cases may be due to the fact that " a long time" may intercour between the suspect of having the disease (onset) and the date the disease is diagnosed. Potential factors that may influence the probability of experiencing the event (diagnosis) conditionally on the onset (suspected) are investigated. Since we are treating rare diseases, the role of social and economic factors is not that obvious; latent as well as observed factors may influence the delay to diagnosis.Methods: We use a semiparametric estimator based on the distribution of delay to diagnosis to account for potential underreporting. In particular, we propose to adopt an Horvitz-Thompson based estimator to correct the incidence figure that can be derived for the period 2007-2009 from the NRRD, Italy.Results: The incidence estimates obtained by adopting the proposed approach are about 1 case per 100000 inhabitants and despite they let recovering a good part of underreporting, they are still far from ALS incidence international ranges between 1.5 and 2.5. However, by looking only at northern Italy, the incidence estimates we can derive are coherent with those known internationally.Conclusions: These results confirm the existence of substantial differences in reporting accuracy, and point out where the system of data collection must be improved. In particular, when reliable individual characteristics will be available, they could be employed to refine the proposed estimator. © 2012 Rocchetti et al.; licensee BioMed Central Ltd.

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Rocchetti, I., Taruscio, D., & Pierannunzio, D. (2012). Modeling delay to diagnosis for Amyotrophic lateral sclerosis: Under reporting and incidence estimates. BMC Neurology, 12. https://doi.org/10.1186/1471-2377-12-160

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