Development and validation of a case-finding algorithm for neck and back pain in the Canadian Armed Forces using health administrative data

Abstract

Introduction: In military organizations, neck and back pain are a leading cause of clinical encounters, medical evacuations out of theatres of operations, and involuntary release from service. However, tools to efficiently and accurately study these conditions in Canadian Armed Forces (CAF) personnel are lacking, and little is known about their distribution across the Canadian military. Methods: We reviewed the medical charts of 691 randomly sampled CAF personnel, and determined whether these subjects had suffered from neck or back pain at any point during the 2016 calendar year. We then developed an algorithm to identify neck or back pain patients, using large clinical and administrative databases. The algorithm was then validated by comparing its output to the results of our medical chart review. Results: Of the 691 randomly sampled subjects, 190 (27%) had experienced neck or back pain at some point during the 2016 calendar year, 43% of whom had experienced chronic pain (i.e. pain lasting for at least 90 consecutive days). Our final algorithm correctly identified 65% of all patients with past-year pain, and 80% of patients with past-year chronic pain. Overall, the algorithm’s measures of diagnostic accuracy were as follows: 65% sensitivity, 97% specificity, 91% positive predictive value, and 88% negative predictive value. Discussion: We have developed an algorithm that can be used to identify neck and back pain in CAF personnel efficiently. This algorithm is a novel research and surveillance tool that could be used to provide the epidemiological data needed to guide future intervention and prevention efforts.