ArticleJMIR public health and surveillance2017
Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance.
Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Diagnostic Code Ambiguity and Misclassification of Adults With Spinal Muscular Atrophy: Single-Center Chart Review.JMIR medical informatics · 2026Article
- Current Challenges of Using Patient-Level Claims and Electronic Health Record Data for the Longitudinal Evaluation of Duchenne Muscular Dystrophy Outcomes.Advances in therapy · 2024Article
- Development and electronic health record validation of an algorithm for identifying patients with Duchenne muscular dystrophy in US administrative claims.Journal of managed care & specialty pharmacy · 2023Article
- Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database.Pediatric blood & cancer · 2023Article
- Magnitude of the Potential Screening Gap for Fabry Disease in Manitoba: A Population-Based Retrospective Cohort Study.Canadian journal of kidney health and disease · 2023Article
- Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses.Journal of autism and developmental disorders · 2022Review
- Direct costs of adhering to selected Duchenne muscular dystrophy Care Considerations: Estimates from a midwestern state.Muscle & nerve · 2022Article
- Characterizing demographics, comorbidities, and costs of care among populations with Duchenne muscular dystrophy with Medicaid and commercial coverage.Journal of managed care & specialty pharmacy · 2021Article
- Assessment of the Accuracy of Identification of Selected Disabilities and Conditions in Hospital Discharge Data for Pregnant Women.Epidemiology (Cambridge, Mass.) · 2020Article
- Muscular Dystrophy Surveillance, Tracking, and Research Network pilot: Population-based surveillance of major muscular dystrophies at four U.S. sites, 2007-2011.Birth defects research · 2018Article
- Utilizing Combined Claims and Clinical Datasets for Research Among Potential Cases of Rare Diseases.International journal of healthcare information systems and informatics : official publication of the Information Resources Management Association · 2018Article
- Capture-recapture methodology to study rare conditions using surveillance data for fragile X syndrome and muscular dystrophy.Orphanet journal of rare diseases · 2017Article
- Identifying Algorithms to Improve the Accuracy of Unverified Diagnosis Codes for Birth Defects.Public health reports (Washington, D.C. : 1974)Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundAdministrative 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.
objectiveThe 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).
methodsWe 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.
resultsPassive 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.
conclusionsAccuracy 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.
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