Identifying neck and back pain in administrative data: defining the right cohort.

Sinnott PL, Siroka AM, Shane AC, Trafton JA, Wagner TH. Identifying neck and back pain in administrative data: defining the right cohort. Spine (Philadelphia, Pa. : 1986). 2012 May 1; 37(10):860-74.

Related HSR&D Project(s)

• IIR 09-062 – Musculoskeletal Spine Pain in VA: Description and Guideline Adherence

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Abstract:

STUDY DESIGN: We reviewed existing methods for identifying patients with neck and back pain in administrative data. We compared these methods using data from the Department of Veterans Affairs. OBJECTIVE: To answer the following questions: (1) what diagnosis codes should be used to identify patients with neck pain and back pain in administrative data; (2) because the majority of complaints are characterized as nonspecific or mechanical, what diagnosis codes should be used to identify patients with nonspecific or mechanical problems in administrative data; and (3) what procedure and surgical codes should be used to identify patients who have undergone a surgical procedure on the neck or back. SUMMARY OF BACKGROUND DATA: Musculoskeletal neck and back pain are pervasive problems, associated with chronic pain, disability, and high rates of health care utilization. Administrative data have been widely used in formative research, which has largely relied on the original work of Volinn, Cherkin, Deyo, and Einstadter and the Back Pain Patient Outcomes Assessment Team first published in 1992. Significant variation in reports of incidence, prevalence, and morbidity associated with these problems may be due to nonstandard or conflicting methods to define study cohorts. METHODS: A literature review produced 7 methods for identifying neck and back pain in administrative data. These code lists were used to search Veterans Health Administration data for patients with back and neck problems, and to further categorize each case by spinal segment involved, as nonspecific/mechanical and as surgical or not. RESULTS: There is considerable overlap in most algorithms. However, gaps persist. CONCLUSION: Gaps are evident in existing methods and a new framework to identify patients with neck pain and back pain in administrative data is proposed.