The South Carolina Muscular Dystrophy (MD) Surveillance System (SC MDSS) is a collaboration between the state health department and the state's flagship university and supports each institution's mission to assure and improve the health of South Carolinians. This collaboration has a two-fold purpose: (1) to maintain a dynamic combined active and passive surveillance system for MD, spinal muscular atrophy (SMA) and related conditions, and (2) to develop and manage a data system that allows us to conduct ongoing analyses of health care utilization, costs, and community participation of people with these conditions. The SC MDSS has three specific aims to support this purpose. These include: Specific Aim 1: Conduct population-based surveillance for muscular dystrophies and neuromuscular disorders using methods based on those from the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet). Specific Aim 2: Analyze surveillance and administrative data to describe health care utilization and costs for each MD type, SMA, and other disorders Specific Aim 3: Develop statistical models to determine associations between specific treatment (medical, pharmaceutical, and related therapies) and specific outcomes (use of mobility aides, residence in home, progression of disability status, etc.) for each type of MD and neuromuscular disorder. To accomplish these aims, the SC MDSS will conduct active surveillance for MD and SMA through medical records abstraction in physician offices, hospitals, and clinics identified through Medicaid, State Health Plan, and physician licensure datasets. Furthermore, the SC MDSS will conduct passive surveillance for MD and SMA through the following linked datasets: hospital discharge data, emergency department data, Medicaid data, state health plan data, vital records data, and birth defects surveillance data. Appropriate epidemiologic and statistical methods will be used to accomplish the aims through the data collected. Health services research frameworks and innovative research methods, such as small area analysis, will be utilized.