Can We Change Health Care Costs in Patients With Complex Back Pain?: Results From a 5-year Before and After Study

A before and after study cohort study.

The aim of this study was to examine changes in health care costs after multidisciplinary spine care in patients with complex chronic back pain (CBP), to analyze the predictive value of patient and disease characteristics on health care costs, and to study the potential impact of biases concerning the use of real world data.

Due to high direct and indirect societal costs of back pain there is a need for interventions that can assist in reducing the economic burden on patients and society.

All patients referred to a university-based spine center insured at a major health care insurer in the Netherlands were invited. Personal and disease-related data were collected at baseline. Health care costs were retrieved from the health care insurer from 2 years before to 2 years after intervention. Repeated measures analysis of variances were calculated to study changes in health care costs after intervention. Multivariable regression analyses and cluster robust fixed effect models were applied to predict characteristics on health care costs. To study regression to the mean, a fixed effect model was calculated comparing 2 years before and 2 years post-intervention.

In total 428,158 declarations during 4.6 years were filed by 997 participants (128,666 considered CBP-related). CBP-related costs significantly increased during the intervention period and reduced 2 years after the intervention. Total health care costs kept rising. The intervention was associated with a 21% to 34% (P < 0.01) reduction in costs depending on the model used. Reduction in costs was related to being male and lower body mass index.

This study suggests that reduction in CBP-related health care utilization in patients with complex CBP can be achieved after a multidisciplinary spine intervention. The results are robust to controlling for background characteristics and are unlikely to be fully driven by regression to the mean.

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