Multifactorial Rare Diseases: Can Uncertainty Analysis Bring Added Value to the Search for Risk Factors and Etiopathogenesis?

Abstract

Uncertainty analysis is the process of identifying limitations in knowledge and evaluating their implications for scientific conclusions. Uncertainty analysis is a stable component of risk assessment and is increasingly used in decision making on complex health issues. Uncertainties should be identified in a structured way and prioritized according to their likely impact on the outcome of scientific conclusions. Uncertainty is inherent to the rare diseases (RD) area, where research and healthcare have to cope with knowledge gaps due to the rarity of the conditions; yet a systematic approach toward uncertainties is not usually undertaken. The uncertainty issue is particularly relevant to multifactorial RD, whose etiopathogenesis involves environmental factors and genetic predisposition. Three case studies are presented: the newly recognized acute multisystem inflammatory syndrome in children and adolescents associated with SARS-CoV-2 infection; the assessment of risk factors for neural tube defects; and the genotype–phenotype correlation in familial Mediterranean fever. Each case study proposes the initial identification of the main epistemic and sampling uncertainties and their impacts. Uncertainty analysis in RD may present aspects similar to those encountered when conducting risk assessment in data-poor scenarios; therefore, approaches such as expert knowledge elicitation may be considered. The RD community has a main strength in managing uncertainty, as it proactively develops stakeholder involvement, data sharing and open science. The open science approaches can be profitably integrated by structured uncertainty analysis, especially when dealing with multifactorial RD involving environmental and genetic risk factors.

Sources of variation in estimates of Duchenne and Becker muscular dystrophy prevalence in the United States

“…We therefore include articles that discussed possible sources of bias in their surveillance system or data even if they did not estimate the magnitude of the bias. The placement of the information within the article and 1 The dependent variable was number of Duchenne and Becker muscular dystrophy cases, with the logarithm of stratum population used as an offset variable 2 Average number of reporting sources for each patient in stratum 3 Proportion of cases diagnosed by genetic testing in the index case or a family member 4 Proportion of patients within stratum that were treated at a muscular dystrophy clinic the depth of detail varied greatly among studies. This variability made the use of structured abstraction or a data extraction tool impossible.…”

“…Structured uncertainty analysis can be an important tool for assessing such differences. Taruscio and Mantovani recently demonstrated the value of uncertainty analysis to identify gaps in our knowledge of the epidemiology of rare diseases and assess their impact [ 3 ]. They categorize the sources of uncertainty into epistemic (uncertainty due to lack of knowledge), sampling uncertainty (uncertainty associated with data and disparate methods), and variability (uncertainty due to heterogeneity within a population).…”