This paper is in the following e-collection/theme issue:

Surveillance Systems (466)

Published on in Vol 3, No 1 (2017): Jan-Mar

Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance

Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance

Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance

Authors of this article:

Michael G Smith1 Author Orcid Image ;   Julie Royer2 Author Orcid Image ;   Joshua R Mann3 Author Orcid Image ;   Suzanne McDermott4 Author Orcid Image
Article Authors Cited by (13) Tweetations (2) Metrics

Journals

  1. Do T, Street N, Donnelly J, Adams M, Cunniff C, Fox D, Weinert R, Oleszek J, Romitti P, Westfield C, Bolen J. 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 2018;110(19):1404 View
  2. Schwartz N, Crane D, Doody D, Schiff M, Mueller B. Assessment of the Accuracy of Identification of Selected Disabilities and Conditions in Hospital Discharge Data for Pregnant Women. Epidemiology 2020;31(5):687 View
  3. Salemi J, Rutkowski R, Tanner J, Matas J, Kirby R. Identifying Algorithms to Improve the Accuracy of Unverified Diagnosis Codes for Birth Defects. Public Health Reports® 2018;133(3):303 View
  4. Smith M, Royer J, Mann J, McDermott S, Valdez R. Capture-recapture methodology to study rare conditions using surveillance data for fragile X syndrome and muscular dystrophy. Orphanet Journal of Rare Diseases 2017;12(1) View
  5. Pham Nguyen T, Bravo L, Gonzalez-Alegre P, Willis A. Geographic Barriers Drive Disparities in Specialty Center Access for Older Adults with Huntington’s Disease. Journal of Huntington's Disease 2022;11(1):81 View
  6. Klimchak A, Szabo S, Qian C, Popoff E, Iannaccone S, Gooch K. Characterizing demographics, comorbidities, and costs of care among populations with Duchenne muscular dystrophy with Medicaid and commercial coverage. Journal of Managed Care & Specialty Pharmacy 2021;27(10):1426 View
  7. Conway K, Grosse S, Ouyang L, Street N, Romitti P. Direct costs of adhering to selected Duchenne muscular dystrophy Care Considerations: Estimates from a midwestern state. Muscle & Nerve 2022;65(5):574 View
  8. Grosse S, Nichols P, Nyarko K, Maenner M, Danielson M, Shea L. Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses. Journal of Autism and Developmental Disorders 2022;52(9):4150 View
  9. Cao L, Huang Y, Wu C, Getz K, Miller T, Ruiz J, Fisher B, Seif A, Aplenc R, Li Y. Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database. Pediatric Blood & Cancer 2023;70(5) View
  10. Whitlock R, Nour-Mohammadi M, Curtis S, Komenda P, Bohm C, Collister D, Tangri N, Rigatto C. Magnitude of the Potential Screening Gap for Fabry Disease in Manitoba: A Population-Based Retrospective Cohort Study. Canadian Journal of Kidney Health and Disease 2023;10 View
  11. Schrader R, Posner N, Dorling P, Senerchia C, Chen Y, Beaverson K, Seare J, Garnier N, Walker V, Alvir J, Mahn M, Merla V, Zhang Y, Landis C, Buikema A. 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 2023;29(9):1033 View
  12. Gooch K, Audhya I, Ricchetti-Masterson K, Szabo S. Current Challenges of Using Patient-Level Claims and Electronic Health Record Data for the Longitudinal Evaluation of Duchenne Muscular Dystrophy Outcomes. Advances in Therapy 2024;41(9):3615 View

Books/Policy Documents

  1. Bennett K, Mann J, Ouyang L. Data Analytics in Medicine. View