Trends and disparities in deep brain stimulation utilization in the United States: a Nationwide Inpatient Sample analysis from 1993 to 2017
Bibliographic record
Abstract
Background: Deep brain stimulation (DBS) is an approved treatment option for Parkinson's Disease (PD), essential tremor (ET), dystonia, obsessive-compulsive disorder and epilepsy in the United States. There are disparities in access to DBS, and clear understanding of the contextual factors driving them is important. Previous studies aimed at understanding these factors have been limited by single indications or small cohort sizes. The aim of this study is to provide an updated and comprehensive analysis of DBS utilization for multiple indications to better understand the factors driving disparities in access. Methods: The United States based National Inpatient Sample (NIS) database was utilized to analyze the surgical volume and trends of procedures based on indication, using relevant ICD codes. Predictors of DBS use were analyzed using a logistic regression model. DBS-implanted patients in each indication were compared based on the patient-, hospital-, and outcome-related variables. Findings: Our analysis of 104,356 DBS discharges from 1993 to 2017 revealed that the most frequent indications for DBS were PD (67%), ET (24%), and dystonia (4%). Although the number of DBS procedures has consistently increased over the years, radiofrequency ablation utilization has significantly decreased to only a few patients per year since 2003. Negative predictors for DBS utilization in PD and ET cohorts included age increase and female sex, while African American status was a negative predictor across all cohorts. Significant differences in patient-, hospital-, and outcome-related variables between DBS indications were also determined. Interpretation: Demographic and socioeconomic-based disparities in DBS use are evident. Although racial disparities are present across all indications, other disparities such as age, sex, wealth, and insurance status are only relevant in certain indications. Funding: This work was supported by Alan & Susan Hudson Cornerstone Chair in Neurosurgery at University Health Network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".