CWD Algorithm | Overview
The Children with Disabilities Algorithm (CWDA) is a claims-based algorithm for identifying International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) codes with a high likelihood of indicating children with disabilities (CWD). Funded through the Pediatric Quality Measures Program (PQMP) by the federal Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare & Medicaid Services (CMS), the Center of Excellence for Pediatric Quality Measurement (CEPQM) developed and triangulated the algorithm to allow for the identification of CWD to assess care experience and quality and to compare care delivered to CWD to that of children without disabilities. CWDA includes a set of 669 ICD-9-CM codes that are indicative of disability in children. CEPQM has tested CWDA in inpatient and outpatient datasets.
CWD are a distinct subset of children with long-term functional impairments who have specific needs within the health care system. Current studies based on nationally-representative parent surveys indicate that the prevalence of CWD in the United States is 5 to 8 percent and rising. Federal spending on Medicaid-insured CWD as well as parents’ out-of-pockets expenses on health care services are also increasing. Studies show that CWD use certain healthcare services up to eight times more frequently than children without disabilities, but little is known about the quality of the care CWD receive. CWDA is an important first step toward being able to rigorously assess care quality for CWD by being able to identify CWD in large databases.
CWDA Core Team
- Alyna Chien, MD, MS (Measure Co-Leader; Boston Children’s Hospital and Harvard Medical School)
- Karen Kuhlthau, PhD (Measure Co-Leader; Massachusetts General Hospital and Harvard Medical School)
- Jessica Quinn, MS (Boston Children’s Hospital)
- Mark Schuster, MD, PhD (Kaiser Permanente, Boston Children’s Hospital, and Harvard Medical School)
- Sara Toomey, MD, MPH, MPhil, MSc (Boston Children’s Hospital and Harvard Medical School)