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  • Author or Editor: Fabrice Smieliauskas x
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Ricky H. Wong, Fabrice Smieliauskas, I-Wen Pan and Sandi K. Lam

OBJECT

Neurosurgery studies traditionally have evaluated the effects of interventions on health care outcomes by studying overall changes in measured outcomes over time. Yet, this type of linear analysis is limited due to lack of consideration of the trend’s effects both pre- and postintervention and the potential for confounding influences. The aim of this study was to illustrate interrupted time-series analysis (ITSA) as applied to an example in the neurosurgical literature and highlight ITSA’s potential for future applications.

METHODS

The methods used in previous neurosurgical studies were analyzed and then compared with the methodology of ITSA.

RESULTS

The ITSA method was identified in the neurosurgical literature as an important technique for isolating the effect of an intervention (such as a policy change or a quality and safety initiative) on a health outcome independent of other factors driving trends in the outcome. The authors determined that ITSA allows for analysis of the intervention’s immediate impact on outcome level and on subsequent trends and enables a more careful measure of the causal effects of interventions on health care outcomes.

CONCLUSIONS

ITSA represents a significant improvement over traditional observational study designs in quantifying the impact of an intervention. ITSA is a useful statistical procedure to understand, consider, and implement as the field of neurosurgery evolves in sophistication in big-data analytics, economics, and health services research.

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Christopher D. Witiw, Fabrice Smieliauskas, Sandra A. Ham and Vincent C. Traynelis

OBJECTIVE

Cervical disc replacement (CDR) has emerged as an alternative to anterior cervical discectomy and fusion (ACDF) for the management of cervical spondylotic pathology. While much is known about the efficacy of CDR within the constraints of a well-controlled, experimental setting, little is known about general utilization. The authors present an analysis of temporal and geographic trends in “real-world” utilization of CDR among those enrolled in private insurance plans in the US.

METHODS

Eligible subjects were identified from the IBM MarketScan Databases between 2009 and 2017. Individuals 18 years and older, undergoing a single-level CDR or ACDF for cervical radiculopathy and/or myelopathy, were identified. US Census divisions were used to classify the region where surgery was performed. Two-level mixed-effects regression modeling was used to study regional differences in proportional utilization of CDR, while controlling for confounding by regional case-mix differences.

RESULTS

A total of 47,387 subjects met the inclusion criteria; 3553 underwent CDR and 43,834 underwent ACDF. At a national level, the utilization of single-level CDR rose from 5.6 cases for every 100 ACDFs performed in 2009 to 28.8 cases per 100 ACDFs in 2017. The most substantial increases occurred from 2013 onward. The region of highest utilization was the Mountain region (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming), where 14.3 CDRs were performed for every 100 ACDFs (averaged over the 9-year period of study). This is in contrast to the East South Central region (Alabama, Kentucky, Mississippi, and Tennessee), where only 2.1 CDRs were performed for every 100 ACDFs. Patient factors that significantly increased the odds of undergoing a CDR were age younger than 40 years (OR 15.9 [95% CI 10.0–25.5]; p < 0.001), no clinical evidence of myelopathy/myeloradiculopathy (OR 1.5 [95% CI 1.4–1.7]; p < 0.001), and a Charlson Comorbidity Index score of 0 (OR 2.7 [95% CI 1.7–4.2]; p < 0.001). After controlling for these factors, significant differences in utilization rates remained between regions (chi-square test = 830.4; p < 0.001).

CONCLUSIONS

This US national level study lends insight into the rate of uptake and geographic differences in utilization of the single-level CDR procedure. Further study will be needed to ascertain specific factors that predict adoption of this technology to explain observed geographic discrepancies.