OBJECTIVES: To estimate the diagnosed incidence and prevalence of Duchenne muscular dystrophy (DMD) in the US. METHODS: Diagnosed incidence of DMD was estimated for the US via a system dynamics model based on a triangular distribution of incidence rates identified from a systematic review of the available literature. Diagnosed prevalent cases of DMD were calculated using an incidence to prevalence model and age-specific Kaplan-Meiersurvival data stratified by birth cohort to account for improvements in treatment practices. To calculate the number of diagnosed incident and prevalent cases of DMD in 2019, the incidence rate estimate was multiplied by the United States Census Bureau projections for the number of male births starting in 1971. The number of patients still alive in 2019 constituted the diagnosed prevalent population, which was subsequently divided by the projected 2019 US male population to yield the diagnosed prevalence rate. RESULTS: The diagnosed incidence of DMD was estimated to be 17.24 (95% CI: 14.3, 20.8) per 100,000 live male births, corresponding to approximately 362 incident cases in 2019. The diagnosed prevalence of DMD was found to be 6.09 (95% CI: 5.06, 7.37) per 100,000 male population, corresponding to approximately 10,015 prevalent cases in 2019. The majority (64.5%) of DMD patients are under the age of 20 and fewer than 2% live to be between 40-45 years of age. The prevalence of DMD among male population 45 years of age or younger is 10.0 (95% CI: 8.3, 12.1) per 100,000. CONCLUSIONS: This is the first examination of the prevalence of DMD in the US. While the incidence of DMD in the US has remained stable, the prevalence of the disease has increased in the past decades due to improvements in life expectancy. Additional advancements in treatments may similarly lead to greater longevity of life, thereby further increasing prevalence.
Conference/Value in Health Info
2019-05, ISPOR 2019, New Orleans, LA, USA
Value in Health, Volume 22, Issue S1 (2019 May)
Epidemiology & Public Health, Methodological & Statistical Research
Modeling & Simulation
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