Surveillance of Muscular Dystrophies

forecasted

The purpose of this NOFO is to conduct longitudinal population-based surveillance of select muscular dystrophies [Duchenne MD (DMD), Becker MD (BMD), myotonic dystrophy (DM), facioscapulohumeral muscular dystrophy (FSHD), limb-girdle MD (LGMD), Congenital MD (CMD), Emery-Dreifuss MD (EDMD), and distal MD] to describe key health outcomes and health inequities, with the goal of identifying opportunities to improve the health of all individuals living with muscular dystrophies. The population included in this surveillance activity can be an entire state or a region within a state, with the applicant utilizing multiple data sources to generate an accurate and complete population-based cohort. Individuals with muscular dystrophy should be identified and followed longitudinally through sources such as neuromuscular, specialty care and other outpatient clinics; administrative data (e.g., vital records, Medicaid/Medicare, hospital discharge); or other sources available to funding recipients. This project will involve three components. Component A (5-7 awards) includes the core surveillance and dissemination activities. Component B (1 award) includes enhanced activities of abstractor training and data quality improvement. Component C (2 awards) is an optional component; awards will be subject to availability of funding.The total estimated funding for Component A is $14,000,000 for 4 years. The total estimated funding for Component B is $400,000 for 4 years. The total estimated funding for Component C is $800,000 over 4 years.Component A objectives include: (1) estimate prevalence of select muscular dystrophies, along with mortality and survival; (2) describe disease progression and co-morbidities; (3) describe healthcare utilization before and after the publication of standards of care, (4) describe access to care and new treatments/health equity; and (5) describe risk factors and protective factors. Component B objectives include: (1) train and provide ongoing training to abstractors in standardized collection of data from medical records and (2) create and implement a plan to periodically assess data quality of abstracted records. The objectives of Component C will be to either (1) develop and evaluate novel approaches to case-finding or longitudinal data collection (e.g., machine learning algorithms, natural language processing automated data extraction/integration), or (2) describe the use of MD-specific ICD-10-CM codes by source.

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