Search

Background: Direct estimates of rare disease prevalence from public health surveillance may only be available in a few catchment areas. Understanding variation among observed prevalence can inform estimates of prevalence in other locations. The Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) conducts population-based surveillance of major muscular dystrophies in selected areas of the United States. We identified sources of variation in prevalence estimates of Duchenne and Becker muscular dystrophy (DBMD) within MD STARnet from published literature and a survey of MD STARnet investigators, then developed a logic model of the relationships between the sources of variation and estimated prevalence. Results: The 17 identified sources of variability fell into four categories: (1) inherent in surveillance systems, (2) particular to rare diseases, (3) particular to medical-records-based surveillance, and (4) resulting from extrapolation. For the sources of uncertainty measured by MD STARnet, we estimated each source's contribution to the total variance in DBMD prevalence. Based on the logic model we fit a multivariable Poisson regression model to 96 age–site–race/ethnicity strata. Age accounted for 74% of the variation between strata, surveillance site for 6%, race/ethnicity for 3%, and 17% remained unexplained. Conclusion: Variation in estimates derived from a non-random sample of states or counties may not be explained by demographic differences alone. Applying these estimates to other populations requires caution. [ABSTRACT FROM AUTHOR]

Background: Rare conditions can be catastrophic for families and the implications for public health can be substantial. Our study compared basic surveillance through active medical record review with a linked administrative data file to assess the number of cases of two rare conditions, fragile X syndrome (FXS) and muscular dystrophy (MD) in a population., Methods: Two methods of data collection were used to collect information from five counties comprising two standard metropolitan statistical areas of South Carolina. The passive system relied mostly on health claims data using ICD-9 CM diagnostic codes. The active system relied on a nurse abstracting records from a list of all licensed physicians with specialties in neurology, orthopedics, and genetics., Results: There were 141 FXS cases and 348 MD cases that met the case definitions using active surveillance. Additional cases were found for both conditions but they were determined to not be true cases. After linking the actively collected MD and FXS cases to passive datasets, we found that the estimated total numbers of cases were similar to using capture-recapture analysis; the positive predictive values for cases identified in the passive system were 56.6% for MD and 75.7% for FXS., Conclusions: Applying capture-recapture methods to passively collected surveillance data for rare health conditions produced an estimate of the number of true cases that was similar to that obtained through active data collection.