Is group-based physiotherapy a cost-effective intervention compared to usual one-on-one physiotherapy care in the management of musculoskeletal disorders in active military personnel? An economic evaluation alongside a pragmatic randomized clinical trial

Abstract: Objective: To conduct a cost-utility analysis of a group physiotherapy intervention, compared to usual care, for musculoskeletal disorders in Canadian military personnel. Design: Economic evaluation alongside a pragmatic randomized clinical trial. Methods: One hundred and twenty military members presenting with shoulder, knee, ankle, or low back pain were randomized to receive either usual one-on-one physiotherapy care or a group intervention. Cumulative health care costs were prospectively collected over 26 weeks from the perspective of the Canadian Armed Forces. The clinical outcome of the cost-utility analysis was the quality-adjusted life-year (QALY) estimated by the ED-5Q-5L (European Quality of Life 5 Dimensions 5 Level Version) at baseline, 6, 12, and 26 weeks. The incremental cost-effectiveness ratio (ICER) was estimated by the cost difference between interventions (in 2023 Canadian dollars [CAD$]) divided by the effect difference. Results: The mean QALY gain was 0.011 in the group intervention, and 0.010 in the usual care. The average cost for a patient was CAD $532 in the group intervention and CAD $599 in the usual care. The ICER (-$67 000/QALY) indicated that the group intervention was cost-effective, as it costs less than usual care while providing comparable effectiveness. Conclusion: Group interventions were cost-effective compared to usual care for treating musculoskeletal disorders in military personnel.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.