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

In today’s #apaperaday, Prof. Aartsma-Rus reads and comments on the paper titled: Sources of variation in estimates of Duchenne and Becker muscular dystrophy prevalence in the United States

The last TREAT-NMD TACT themed paper is not about clinical and preclinical studies but on sources of variation for prevalence of Duchenne and Becker by Whitehead et al in Orphanet Journal on Rare Diseases. Doi 10.1186/s13023-023-02662-0. Knowing the prevalence of a disease is important for rare disease trial planning. If a disease is more or less prevalent in specific countries for instance that has consequences for trial planning.

Prevalence calculations are done through public healthcare surveillance. From these findings in catchment areas extrapolations & generalizations are made. For rare diseases this can lead to uncertainty due to differences in prevalence between catchment area and other populations.

Extrapolating from a small sample size risks over- or underestimating prevalence or incidence. E.g. the 1 in 3600 incidence for Duchenne is widely cited (sometimes as 1 in 3500) from a small sample size. Larger newborn screening efforts consistently come to 1 in 5000.

Back to the study: Here authors from the MD Star NEt, which collects information on dystrophinopathy patients in the USA, looked for sources of variability and uncertainty with regards to dystrophinopathy prevalence. Authors performed a literature review and a survey.

For a complete list of factors affecting variability I refer to the paper. Highlighted factors: misclassification of patients and migration. Specifically for rare diseases small numbers and regional differences in incidence cause uncertainty.

For working with medical records: incomplete data & extrapolation to areas with differences in demographics. Eg Duchenne patients generally pass away in the 2nd to 4th decade of life. If a catching area mainly has senior people the prevalence will not be correct when extrapolated from an area with younger people, nor will it be correct when extrapolated from the senior area to an area with younger population.

Age & ethnicity are the main factors influencing uncertainty & variation. You can make correction models to address this. However there is also an unknown component of 17%. Authors discuss knowing source of variability will help but if part is unknown there will be uncertainty.