Race, insurance status and treating facility type associated with completeness of medical record in patients with breast cancer.

Abstract

143 Background: The National Cancer Database (NCDB) is a large registry that collates real-world medical record data from millions of patients in the United States. A previous published study using the NCDB found that gaps in the medical record were associated with worse overall survival outcomes. We investigated cases of breast cancer in this registry to understand which factors were predictive of records with missing data. Methods: We screened for missing data in 54 clinical parameters documented by the NCDB pertaining to the diagnosis, workup, management and survival of patients with breast cancer diagnosed between 2004 and 2017. We performed univariate statistics to describe gaps in the dataset, followed by multivariate logistic regression modeling to identify factors associated lack of completeness of the medical record – defined as the presence of > 3 missing variables. Results: A total of n = 2,981,732 patients were included in this analysis. The median number of missing variables per record was 3 (5.6% of clinical parameters surveyed). 52.1% of records had ≤ 3 variables missing, while 47.9% had > 3 variables missing. Predictors of a record with missing data in > 3 variables were: age, race, insurance status and facility type . Regarding race, we found that records of Asian patients were less likely to have missing data as compared to records of White patients (OR 0.75, 95% CI: 0.74-0.76, p < 0.001). Conversely, there was no difference in completeness of the medical record between Black and White patients (OR 0.99, 95% CI: 0.99-1.01, p = 0.890). Patients with private insurance (OR 0.77, 95% CI 0.76-0.79, p < 0.001), or Medicaid (OR 0.65, 95% CI 0.64-0.67, p < 0.001) or Medicare (OR 0.66, 95% CI 0.64-0.67, p < 0.001) were also less likely to have missing data compared to uninsured patients, with patients on private insurance being the least likely to have incomplete records. Finally, patient records from academic programs (OR 0.91, 95% CI 0.90-0.92, p < 0.001) were less likely to contain > 3 missing variables compared to records from patients treated at community cancer programs. Conclusions: Despite high fidelity of NCDB data, social determinants of health including insurance status and treating facility type, were associated with differences in the completeness of the medical record. Improvements in documentation and data quality are necessary to optimize use of real-world data in cancer registries. Further research is needed to determine how these differences could be independently associated with inferior outcomes.