Disparities in selective referral for cancer surgeries: implications for the current healthcare delivery system

Abstract

Objectives: Among considerable efforts to improve quality of surgical care, expedited measures such as a selective referral to high-volume institutions have been advocated. Our objective was to examine whether racial, insurance and/or socioeconomic disparities exist in the use of high-volume hospitals for complex surgical oncological procedures within the USA.

Design, setting and participants: Patients undergoing colectomy, cystectomy, oesophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy or prostatectomy were identified retrospectively, using the Nationwide Inpatient Sample, between years 1999 and 2009. This resulted in a weighted estimate of 2 508 916 patients.

Primary outcome measures: Distribution of patients according to race, insurance and income characteristics was examined according to low-volume and high-volume hospitals (highest 20% of patients according to the procedure-specific mean annual volume). Generalised linear regression models for prediction of access to high-volume hospitals were performed.

Results: Insurance providers and county income levels varied differently according to patients' race. Most Caucasians resided in wealthier counties, regardless of insurance types (private/Medicare), while most African Americans resided in less wealthy counties (≤$24 999), despite being privately insured. In general, Caucasians, privately insured, and those residing in wealthier counties (≥$45 000) were more likely to receive surgery at high-volume hospitals, even after adjustment for all other patient-specific characteristics. Depending on the procedure, some disparities were more prominent, but the overall trend suggests a collinear effect for race, insurance type and county income levels.

Conclusions: Prevailing disparities exist according to several patient and sociodemographic characteristics for utilisation of high-volume hospitals. Efforts should be made to directly reduce such disparities and ensure equal healthcare delivery.

Keywords: Health Services Administration & Management.

Figure 1

Procedure-specific multivariable generalised linear regression models predicting the rate of high-volume hospital utilisation focusing on the effect of race (A), insurance status (B) and ZIP code income (C) modulated as OR. Referent category for all comparisons was Caucasian race, private insurance and income <$25 000, respectively. Error bars represent 95% CIs.