Antiemetic Prophylaxis as a Marker of Health Care Disparities in the National Anesthesia Clinical Outcomes Registry

Abstract

Background: US health care disparities persist despite repeated countermeasures. Research identified race, ethnicity, gender, and socioeconomic status as factors, mediated through individual provider and/or systemic biases; little research exists in anesthesiology. We investigated antiemetic prophylaxis as a surrogate marker for anesthesia quality by individual providers because antiemetics are universally available, indicated contingent on patient characteristics (gender, age, etc), but independent of comorbidities and not yet impacted by regulatory or financial constraints. We hypothesized that socioeconomic indicators (measured as insurance status or median income in the patients' home zip code area) are associated with the utilization of antiemetic prophylaxis (as a marker of anesthesia quality).

Methods: We tested our hypothesis in several subsets of electronic anesthesia records from the National Anesthesia Clinical Outcomes Registry (NACOR), fitting frequentist and novel Bayesian multilevel logistic regression models.

Results: NACOR contained 12 million cases in 2013. Six institutions reported on antiemetic prophylaxis for 441,645 anesthesia cases. Only 173,133 cases included details on insurance information. Even fewer (n = 92,683) contained complete data on procedure codes and provider identifiers. Bivariate analysis, multivariable logistic regression, and our Bayesian hierarchical model all showed a large and statistically significant association between socioeconomic markers and antiemetic prophylaxis (ondansetron and dexamethasone). For Medicaid versus commercially insured patients, the odds ratio of receiving the antiemetic ondansetron is 0.85 in our Bayesian hierarchical mixed regression model, with a 95% Bayesian credible interval of 0.81-0.89 with similar inferences in classical (frequentist) regression models.

Conclusions: Our analyses of NACOR anesthesia records raise concerns that patients with lower socioeconomic status may receive inferior anesthesia care provided by individual anesthesiologists, as indicated by less antiemetics administered. Effects persisted after we controlled for important patient characteristics and for procedure and provider influences. Findings were robust to sensitivity analyses. Our results challenge the notion that anesthesia providers do not contribute to health care disparities.

Conflict of interest statement

Conflicts of Interest: None

Figures

Figure 1

Missing data in electronic anesthesia records lead to a trade-off between selection bias and confounding bias in research using large databases like NACOR. Inferences based on the analysis of the complete dataset or the larger enriched datasets will more likely be generalizable, but lack of control for confounders (age and gender) may lead to bias. As we control for confounding with increasingly complex models, adding more variables, the dataset becomes smaller due to missing data: We can only include records with complete data in the analysis. Any increase in validity with advanced modelling may come at the expense of generalizability due to selection bias: The few institutions uploading all variables of interest may not represent typical anesthesia practice.

Figure 2