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

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.

A pragmatic trial to improve adherence with scheduled appointments in an inner-city pain clinic by human phone calls in the patient's preferred language.

STUDY OBJECTIVE: We investigated if human reminder phone calls in the patient's preferred language increase adherence with scheduled appointments in an inner-city chronic pain clinic. We hypothesized that language and cultural incongruence is the underlying mechanism to explain poor attendance at clinic appointments in underserved Hispanic populations. DESIGN: Pragmatic randomized controlled clinical trial SETTING: Innercity academic chronic pain clinic with a diverse, predominantly African-American and Hispanic population PATIENTS: All (n=963) adult patients with a scheduled first appointment between October 2014 and October 2015 at the Montefiore Pain Center in the Bronx, New York were enrolled. INTERVENTIONS: Patients were randomized to receive a human reminder call in their preferred language before their appointment, or no contact. MEASUREMENTS: We recorded patients' demographic characteristics and as primary outcome attendance as scheduled, failure to attend and/or cancellation calls. We fit Bayesian and classical multinomial logistic regression models to test if the intervention improved adherence with scheduled appointments. MAIN RESULTS: Among the 953 predominantly African American and Hispanic/Latino patients, 475 patients were randomly selected to receive a language-congruent, human reminder call, while 478 were assigned to receive no prior contact, (after we excluded 10 patients, scheduled for repeat appointments). In the experimental group, 275 patients adhered to their scheduled appointment, while 84 cancelled and 116 failed to attend. In the control group, 249 patients adhered to their scheduled appointment, 31 cancelled and 198 failed to attend. Human phone reminders in the preferred language increased adherence (RR 1.89, CI95% [1.42, 1.42], (p<0.01). The intervention seemed particularly effective in Hispanic patients, supporting our hypothesis of cultural congruence as possible underlying mechanism. CONCLUSIONS: Human reminder phone calls prior in the patient's preferred language increased adherence with scheduled appointments. The intervention facilitated access to much needed care in an ethnically diverse, resource poor population, presumably by overcoming language barriers.

Stan: A Probabilistic Programming Language.

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

Integrating risk context into risk assessments: the Risk Context Scale.

The context in which offenders are released is an important component of conducting risk assessments. A sample of 257 supervised male parolees were followed in the community (M = 870 days) after an initial risk assessment. Drawing on community-based information, the purpose of this study was to evaluate the recently developed Risk Context Scale. Four domains from the Risk Context Scale include Resource Engagement, Social Network Presence, Integration of Care, and Social Stability. Using mediation analysis, an initial static risk probability was altered up to 26% by accounting for risk context. Implications of the present results include a broader explanation of recidivism, offering intervention strategies, and further individualizing risk assessments.