Detection of high-grade glioma clusters: Spatial case-control study using EMR data from 2010-2021.

Abstract

e14016 Background: High-grade gliomas (HGGs), like Glioblastomas (GBMs), are the most common malignant intracranial tumors with dismal prognosis. Rural areas have higher HGG related mortality and lesser research advancements. The objective of this study was to identify zip codes with disproportionate risk of HGGs compared to other brain malignancies. Methods: Patients’ electronic health data for this case-control study was extracted from West Virginia University Hospital Systems, 2010-2021. HGG cases were defined using diagnoses codes ICD10-C71 and ICD9-191 (excluding low grade gliomas). The control group was defined by using diagnoses codes ICD10-D32, C79.3, C71 (excluding HGG cases) and ICD9-225.2, 225.4, 198.3, 191 (excluding HGG cases). Controls were exact matched four controls to every case based upon patient five-year age categories and gender. Clustering was assessed globally through difference in Ripley’s-K between cases and controls; and locally using scan statistics. Results: The locations of HGGs and other brain tumors were clustered in the northern and northeastern regions of WV. Global cluster analyses detected statistically significant dispersion of HGG cases compared to our control group. Local cluster analysis identified multi-zip code cluster of HGG patients in northeastern WV between 2010-2021. The average relative risk of HGG compared to other brain malignancies was 1.25, indicating a 25% increase in risk of HGGs compared to other brain cancers within this region. While the local cluster was not statistically significant, the zip code level estimates of relative risk within the cluster ranged from 0.00 to 5.01. This indicated that certain zip codes within the cluster had five times the risk of HGGs compared to non-HGG brain malignancies relative to zip codes outside of the cluster. Conclusions: Findings indicate community level disparities in risk of HGGs relative to other brain malignancies. Additionally, results suggest that HGG cases are seen across the state at a more dispersed pattern than for non-HGG related brain tumors. These overall spatial trends could potentially be attributable to differences in clinical practice, access to care, or social determinants of health throughout WV. Spatial multivariable modeling techniques are needed to identify the specific neighborhood level factors associated with disproportionate patterns of risk identified for WV HGG patients.