title: Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance.
abstract: BACKGROUND
  Administrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. Identifying rare but catastrophic conditions is a challenge since approaches for maximizing valid case detection are not firmly established.
  
  
  OBJECTIVE
  The purpose of our study was to explore a number of algorithms in which International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and other administrative variables could be used to identify cases of muscular dystrophy (MD).
  
  
  METHODS
  We used active surveillance to identify possible cases of MD in medical practices in neurology, genetics, and orthopedics in 5 urban South Carolina counties and to identify the cases that had diagnostic support (ie, true cases). We then developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Cases of all types of MD and those with Duchenne or Becker MD (DBMD) that were common to both surveillance systems were examined to identify the most specific administrative variables for ascertainment of true cases.
  
  
  RESULTS
  Passive statewide surveillance identified 3235 possible cases with MD in the state, and active surveillance identified 2057 possible cases in 5 actively surveilled counties that included 2 large metropolitan areas where many people seek medical care. There were 537 common cases found in both the active and passive systems, and 260 (48.4%) were confirmed by active surveillance to be true cases. Of the 260 confirmed cases, 70 (26.9%) were recorded as DBMD.
  
  
  CONCLUSIONS
  Accuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used.
authors.affiliation: Bureau of Maternal and Child HealthDivision of Research and PlanningSouth Carolina Department of Health and Environmental ControlColumbia, SCUnited States.
authors.forename: Michael G
authors.identifier: 0000-0002-8297-2203
authors.initials: MG
authors.lastname: Smith
authors.affiliation: South Carolina Budget and Control BoardRevenue and Fiscal Affairs OfficeColumbia, SCUnited States.
authors.forename: Julie
authors.identifier: 0000-0001-5323-1986
authors.initials: J
authors.lastname: Royer
authors.affiliation: Department of Preventive MedicineUniversity of Mississippi Medical CenterJackson, MSUnited States.
authors.forename: Joshua R
authors.identifier: 0000-0002-3716-3642
authors.initials: JR
authors.lastname: Mann
authors.affiliation: Department of Epidemiology and BiostatisticsUniversity of South CarolinaColumbia, SCUnited States.
authors.forename: Suzanne
authors.identifier: 0000-0003-1517-5033
authors.initials: S
authors.lastname: McDermott
keywords: administrative records; algorithm; muscular dystrophy