Assessment of Surgical Antibiotic Prophylaxis Compliance in Pediatrics: A Pre-post Quasi-experimental Study.

OBJECTIVE: Data from rigorous evaluations of the impact of interventions on improving surgical antibiotic prophylaxis (SAP) compliance in pediatrics are lacking. Our objective was to assess the impact of a multifaceted intervention on improving pediatric SAP compliance in a hospital without an ongoing antimicrobial stewardship program. STUDY DESIGN: A multidisciplinary team at the Montreal Children's Hospital performed a series of interventions designed to improve pediatric SAP compliance in June 2015. A retrospective, quasi-experimental study was performed to assess SAP compliance before and following the interventions. Our study included patients under 18 years old undergoing surgery between April and September in 2013 (preintervention) and in 2016 (postintervention). A 10-week washout period was included to rigorously assess the persistence of compliance without ongoing interventions. SAP, when indicated, was qualified as noncompliant, partially compliant (adequate agent and timing) or totally compliant (adequate agent, dose, timing, readministration, duration). RESULTS: A total of 982 surgical cases requiring SAP were included in our primary analysis. The composite partial and total compliance increased from 51.4% to 55.8% [adjusted odds ratio 1.3; 95% confidence interval: 1.0-1.8; P = 0.06]. Although improvements in correct dose and readministration were significant, there was no significant improvement in correct timing, agent selection or duration. CONCLUSION: Our study demonstrated that overall SAP compliance did not significantly improve following a washout period, illustrating the importance of ongoing surveillance and feedback from an antimicrobial stewardship program. Our strict approach in evaluating the timing criterion may also explain the lack of a significant impact on SAP compliance.

Oral Anticoagulant Prescription Trends, Profile Use, and Determinants of Adherence in Patients with Atrial Fibrillation.

BACKGROUND AND PURPOSE: Data on oral anticoagulant (OAC) uptake and pattern of use are limited. Real-life data in patients with atrial fibrillation (AF) are important for understanding patient exposure. A cohort study of new OAC users was built to assess trends of drug use from 2011 to 2017, persistence rate, switching rate, adherence level, and predictors of adherence. METHODS: We built a cohort using the Régie d'Assurance Maladie du Québec (RAMQ) and Med-Echo administrative databases of new adult OAC users within 1 year following hospitalization with a diagnosis of AF. New users of OAC were defined as having no OAC claims in the year before cohort entry. We assessed trends of OAC use; persistence rate, defined as a gap between refills of no longer than two times the duration of the previous prescriptions; and adherence level, defined as the proportion of days covered (PDC) over a 1-year period following initiation. Predictors of nonadherence (PDC less than 80%) were analyzed using logistic regression models. RESULTS: The cohort consisted of 33,311 incident OAC users. Of total OAC claims, the proportions of warfarin claims decreased from 77.9% in 2011 to 12.7% in 2017, with direct oral anticoagulants (DOACs) accounting for 87.3% of claims, of which apixaban and rivaroxaban accounted for 60.1% and 23.4%, respectively, by the end of 2017. One year after OAC initiation, persistence rates ranged from 53% with warfarin to 77% with a high dose of apixaban. Approximately 75% of incident OAC users were considered "adherent" (PDC 80% or more), with a mean PDC of 95.6-98.1%, compared with "nonadherent," with a mean PDC varying between 43.1% and 50.7%. Older age, female sex, higher CHA2 DS2 -VASc score (to predict thromboembolic risk in AF), prior stroke, and treatment with chronic cardiovascular disease drugs were associated with high adherence levels. CONCLUSION: The clinical uptake of DOACs increased over time, accounting for 87.3% of prescriptions in 2017. In our study, 25% of new OAC users presented a low adherence level. Adherence to OACs remains a significant challenge in patients with AF.

Methods for the assessment of selection bias in drug safety during pregnancy studies using electronic medical data.

Electronic health data are routinely used for population drug studies. Due to the ethical dilemma in carrying out experimental drug studies on pregnant women, the effects of medication usage during pregnancy on fetal and maternal outcomes are largely evaluated using this data collection medium. One major limitation in this type of study is the delayed inclusion of pregnancies in the cohort. For example, in the province of Quebec, Canada, a major pregnancy cohort only captured pregnancies after 20 weeks gestation. The purpose of this study was to demonstrate three methods that can be used to assess the extent of selection bias due to the delayed inclusion of pregnancies. We use causal directed acyclic graphs to explain the source of this selection bias. In an example involving a cohort of pregnant asthmatic women reconstructed from the linkage of administrative health databases from the province of Quebec, we use numerical derivations, a simulation study and a sensitivity analysis to investigate the potential for bias and loss of power due to the delayed inclusion. We find that this selection bias can be partially mitigated by controlling for variables related to (spontaneous or therapeutic) abortion and the outcome of interest. The three proposed methods allow for the pre and post hoc ascertainment of the bias. While delayed pregnancy inclusion selection bias (which includes "live birth bias") can produce substantial bias in pregnancy drug studies, all three methods are effective at producing estimates of the size of the bias.

PHIRST Trial - pharmacist consults: prioritization of HIV-patients with a referral screening tool.

The role of pharmacists in HIV outpatient clinics has greatly increased in the past decades. Given the limited resources of the health system, the prioritization of pharmacist consults is now a main concern. This study aimed to create a scoring system allowing for standardized prioritization of pharmacist consults for patients living with HIV. Data was retrospectively collected from 200 HIV patients attending the Chronic Viral Illness Service at the McGill University Health Center. An expert panel consisting of four pharmacists working in the field of HIV prioritized each patient individually, after which a consensus was established and was considered as the gold standard. In order to create a scoring system, two different methods (Delphi, statistical) were used to assign a weight to each characteristic considered to be important in patient prioritization. A third method (equal weight to each characteristic) was also evaluated. The total score per patient for each method was then compared to the expert consensus in order to establish the score cut-offs to indicate the appropriate categories of delay in which to see the patient. All three systems failed to accurately prioritize patients into urgency categories ("less than 48 h", "less than 1 month", "less than 3 months", "no consult required") according to expert pharmacist consensus. The presence of high level interactions between patient characteristics, the limited number of patients and the low prevalence of some characteristics were hypothesized as the main causes for the results. Creating a prioritization tool for pharmacy consults in HIV outpatient clinics is a complex task and developing a decision tree algorithm may be a more appropriate approach in the future to take into account the importance of combinations of patient characteristic.

Effect Estimation in Point-Exposure Studies with Binary Outcomes and High-Dimensional Covariate Data - A Comparison of Targeted Maximum Likelihood Estimation and Inverse Probability of Treatment Weighting.

Inverse probability of treatment weighting (IPW) and targeted maximum likelihood estimation (TMLE) are relatively new methods proposed for estimating marginal causal effects. TMLE is doubly robust, yielding consistent estimators even under misspecification of either the treatment or the outcome model. While IPW methods are known to be sensitive to near violations of the practical positivity assumption (e. g., in the case of data sparsity), the consequences of this violation in the TMLE framework for binary outcomes have been less widely investigated. As near practical positivity violations are particularly likely in high-dimensional covariate settings, a better understanding of the performance of TMLE is of particular interest for pharmcoepidemiological studies using large databases. Using plasmode and Monte-Carlo simulation studies, we evaluated the performance of TMLE compared to that of IPW estimators based on a point-exposure cohort study of the marginal causal effect of post-myocardial infarction statin use on the 1-year risk of all-cause mortality from the Clinical Practice Research Datalink. A variety of treatment model specifications were considered, inducing different degrees of near practical non-positivity. Our simulation study showed that the performance of the TMLE and IPW estimators were comparable when the dimension of the fitted treatment model was small to moderate; however, they differed when a large number of covariates was considered. When a rich outcome model was included in the TMLE, estimators were unbiased. In some cases, we found irregular bias and large standard errors with both methods even with a correctly specified high-dimensional treatment model. The IPW estimator showed a slightly better root MSE with high-dimensional treatment model specifications in our simulation setting. In conclusion, for estimation of the marginal expectation of the outcome under a fixed treatment, TMLE and IPW estimators employing the same treatment model specification may perform differently due to differential sensitivity to practical positivity violations; however, TMLE, being doubly robust, shows improved performance with richer specifications of the outcome model. Although TMLE is appealing for its double robustness property, such violations in a high-dimensional covariate setting are problematic for both methods.

Mediation Analysis for Health Disparities Research.

Social epidemiologists often seek to determine the mechanisms that underlie health disparities. This work is typically based on mediation procedures that may not be justified with exposures of common interest in social epidemiology. In this analysis, we explored the consequences of using standard approaches, referred to as the difference and generalized product methods, when mediator-outcome confounders are associated with the exposure. We compared these with inverse probability-weighted marginal structural models, the structural transformation method, doubly robust g-estimation of a structural nested model, and doubly robust targeted minimum loss-based estimation. We used data on 900,726 births from 2003 to 2007 in the Penn Moms study, conducted in Pennsylvania, to assess the extent to which breastfeeding prior to hospital discharge explained the racial disparity in infant mortality. Overall, for every 1,000 births, 3.36 more infant deaths occurred among non-Hispanic black women relative to all other women (95% confidence interval: 2.78, 3.93). Using the difference and generalized product methods to assess the disparity that would remain if everyone breastfed prior to discharge suggested a complete elimination of the disparity (risk difference = -0.87 per 1,000 births; 95% confidence interval: -1.39, -0.35). In contrast, doubly robust methods suggested a reduction in the disparity to 2.45 (95% confidence interval: 2.20, 2.71) more infant deaths per 1,000 births among non-Hispanic black women. Standard approaches for mediation analysis in health disparities research can yield misleading results.