Physician Financial Incentives for Use of Outpatient Intravenous Antimicrobial Therapy: An Interrupted Time Series Analysis.

BACKGROUND: In 2011, policymakers in British Columbia introduced a fee-for-service payment to incentivize infectious diseases physicians to supervise outpatient parenteral antimicrobial therapy (OPAT). Whether this policy increased use of OPAT remains uncertain. METHODS: We conducted a retrospective cohort study using population-based administrative data over a 14-year period (2004-2018). We focused on infections that required intravenous antimicrobials for ≥10 days (eg, osteomyelitis, joint infection, endocarditis) and used the monthly proportion of index hospitalizations with a length of stay shorter than the guideline-recommended "usual duration of intravenous antimicrobials" (LOS < UDIVA) as a surrogate for population-level OPAT use. We used interrupted time series analysis to determine whether policy introduction increased the proportion of hospitalizations with LOS < UDIVA. RESULTS: We identified 18 513 eligible hospitalizations. In the pre-policy period, 82.3% of hospitalizations exhibited LOS < UDIVA. Introduction of the incentive was not associated with a change in the proportion of hospitalizations with LOS < UDIVA, suggesting that the policy intervention did not increase OPAT use (step change, -0.06%; 95% confidence interval [CI], -2.69% to 2.58%; P = .97 and slope change, -0.001% per month; 95% CI, -.056% to .055%; P = .98). CONCLUSIONS: The introduction of a financial incentive for physicians did not appear to increase OPAT use. Policymakers should consider modifying the incentive design or addressing organizational barriers to expanded OPAT use.

Combining parametric and nonparametric models to estimate treatment effects in observational studies.

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.